1 A FORMAL METHODS APPROACH TO THE ANALYSIS OF MODE CONFUSION

(完整版)《英语教学法》unit_1_language_and_learning Unit 1: Language and LearningIntroduction to English Language Teaching MethodologyEnglish language teaching methodology is a vital aspect of language education. It plays a crucial role in enabling learners to develop proficiency in a second language. In this unit, we will explore the various approaches, methods, and techniques used in English language teaching. Understanding these methods is essential for language teachers to effectively plan and deliver instruction to their students.1. The role of language in teaching and learningLanguage is not solely a means of communication; it is also a tool for learning. It allows individuals to acquire knowledge, express ideas, and interact with others. In the context of language teaching, a comprehensive understanding of language is important. Teachers must consider the four language skills: listening, speaking, reading, and writing, along with grammar, vocabulary, and pronunciation. By incorporating these elements, teachers can promote language acquisition and development in their students.2. The communicative approachThe communicative approach is widely acknowledged as an effective language teaching method. It focuses on real-life communication and the purposeful use of language. In this approach, learners engage in meaningful tasks that require them to use English to express themselves authentically.The communicative approach encourages learners to develop their fluency and accuracy by providing opportunities for interaction and authentic communication.3. The lexical approachThe lexical approach emphasizes the importance of teaching vocabulary and collocations in language learning. It recognizes that grammar and vocabulary are interconnected and that learners must acquire both to communicate effectively. Teachers following the lexical approach prioritize teaching high-frequency and useful vocabulary, as well as the collocations and phrases associated with them. By developing a strong lexical repertoire, learners can enhance their language proficiency and understanding.4. Task-based language teachingTask-based language teaching (TBLT) is a learner-centered approach that focuses on the completion of meaningful tasks. In TBLT, learners are presented with a task that requires the use of language to accomplish a specific goal. These tasks can be simulations of real-life situations or problem-solving activities. By engaging in these tasks, learners develop their language skills while also achieving the task objective. TBLT promotes both language acquisition and the development of critical thinking and problem-solving skills.5. Technology-enhanced language teachingTechnology has revolutionized language teaching and learning. It provides teachers and learners with access to an array of digital resources and tools. Technology-enhanced language teaching encompasses the use ofeducational software, online platforms, multimedia materials, and interactive activities. It enhances learner engagement and provides opportunities for independent learning. Integration of technology in language teaching opens up new possibilities for personalized and adaptive instruction.ConclusionEnglish language teaching methods continually evolve to meet the needs of diverse learners. It is essential for language teachers to keep abreast of current approaches and techniques to maximize instructional effectiveness. The approaches discussed in this unit, including the communicative approach, lexical approach, task-based language teaching, and technology-enhanced language teaching, provide teachers with valuable frameworks to deliver comprehensive and engaging language instruction. By applying these methods, language teachers can foster language learning and promote language proficiency development in their students.(Word count: 585)。

【含答案解析】人教版高三英语学术研究方法创新不断练习题40题1. In scientific research, when we want to ______ the data from different experiments, we need to use a unified standard.A. analyzeB. synthesizeC. summarizeD. generalize答案解析：B。

在学术研究中，“synthesize”有综合、整合的意思，这里说要将不同实验的数据用统一标准进行整合，所以选B。

选项A“analyze”侧重于分析，通常是对单个数据或一组数据进行剖析，这里强调的是不同实验数据的综合，所以A不符合。

选项C“summarize”主要是总结，一般是概括主要内容，不是对数据进行统一标准的操作，C不合适。

选项D“generalize”是概括、归纳，更多是从个别到一般的归纳，与统一标准整合数据的语境不符。

2. The ______ of this research is to find a new method to treat the disease.A. aimB. objectC. targetD. destination答案解析：A。

“aim”表示目的、目标，在学术研究中，研究的目的是找到治疗疾病的新方法，这是最常用的表达。

选项B“object”更多表示物体、对象，与研究目的的语境不符。

选项C“target”侧重于目标、指标，通常用于具体的数值或对象作为目标，这里表达研究目的用“aim”更合适。

选项D“destination”主要指目的地，用于学术研究目的的表达不恰当。

3. When conducting a literature review, we should ______ relevant papers from various sources.A. collectB. gatherC. assembleD. accumulate答案解析：A。

高三英语学术文章单选题50题1. In the scientific research paper, the term "hypothesis" is closest in meaning to _.A. theoryB. experimentC. conclusionD. assumption答案：D。

解析：“hypothesis”的意思是假设，假定。

“assumption”也表示假定，假设，在学术语境中，当提出一个假设来进行研究时，这两个词意思相近。

“theory”指理论，是经过大量研究和论证后的成果；“experiment”是实验，是验证假设或理论的手段；“conclusion”是结论，是研究之后得出的结果，所以选D。

2. The historical article mentioned "feudal system", which refers to _.A. democratic systemB. hierarchical social systemC. capitalist systemD. modern political system答案：B。

解析：“feudal system”是封建制度，它是一种等级森严的社会制度。

“democratic system”是民主制度；“capitalist system”是资本主义制度；“modern political system”是现代政治制度，与封建制度完全不同概念，所以选B。

3. In a literary review, "metaphor" is a figure of speech that _.A. gives human qualities to non - human thingsB. compares two different things without using "like" or "as"C. uses exaggeration to emphasize a pointD. repeats the same sound at the beginning of words答案：B。

高一英语学术用语单选题50题1. In the scientific research, the term "hypothesis" refers to an educated guess. Which of the following words has a similar meaning?A. TheoryB. AssumptionC. FactD. Conclusion答案：B。

解析：“hypothesis”意思是假设，是基于一定知识的推测。

“Theory”理论，是经过验证的一套思想体系，与假设不同；“Assumption”也是假设、假定的意思，和“hypothesis”相似；“Fact”事实，是客观存在的情况，与假设相反；“Conclusion”结论，是研究等结束后的总结，与假设概念不同，所以这里选B。

2. The academic paper emphasizes the "significance" of this discovery. What does "significance" mean here?A. SizeB. ImportanceC. AmountD. Length答案：B。

解析：“significance”在这里的意思是意义、重要性。

“Size”指大小；“Importance”是重要性，与“significance”意思相符；“Amount”指数量；“Length”指长度，根据语境应选B。

3. When reading an academic article, we often come across the word"methodology". Which word is closest in meaning?A. TechniqueB. ResultC. ProblemD. Subject答案：A。

FormalMethodsFormal MethodsAnalysis of complex systems to ensure correctness and reduce costThe complexity of software that will be embedded in new aircraft and spacecraft has outpaced the capabilities of our current verification and certification methods. Software performs safety- and mission-critical functions on these platforms, and correct operation is essential. Verification and certification based on manual reviews, process constraints, and testing are proving too expensive for even current products, let alone advanced software-based systems. Traditional methods cannot verify the correctness of applications such as adaptive control for upset recovery of aircraft, intelligent control of space craft, and control software for advanced military and unmanned aircraft (UAVs) operating in commercial airspace. Unless safety-critical embedded software can be developed and verified with less cost and effort –while still satisfying the highest reliability requirements –these new capabilities may never reach the market. Honeywell has recognized this challenge and has an active research program in advanced software development and verification tools and methodologies. Over the last 5 years, Formal Methods has emerged as a key component in the development and verification of the next generation of safetycritical systems. multi-threaded system where concurrency is an issue, formal analysis can explore all possible interleavings and event orderings.. This level of coverage is impossible to achieve through testing.initial configuration error state states reachable by test statesreachable by formal analysis Formal Methods such as model checking examine more system behaviors for safety violations than testing aloneAnalytical approach to complexity. The analytical nature of Formal Methods is better suited for verification of complex behaviors than testing alone. Provably correct abstractions can be used to bound the behavioral space of systems with adaptive or non-deterministic behaviors. Formal Methods can also be used to perform “what-if” analyses to study the effects of proposed system changes. Though the basic techniques have been under development world-wide for over two decades, they have just reached the maturity at which, in combination with increased processor speeds and cheaper memory, they can be used to address realworld systems.Formal MethodsFormal Methods is the use of ideas and techniques from mathematics and formal logic to specify and reason about computing systems to increase design assurance and eliminate defects. Formal Methods tools allow comprehensive analysis of requirements and design and complete exploration of system behavior, including fault conditions. Formal Methods provides a disciplined approach to analyzing complex safetycritical systems. The benefits of using Formal Methods include: Product-focused measure of correctness. The use of Formal Methods provides an objective measure of the correctness of a system, as opposed to current process quality measures. Early detection of defects. Formal Methods can be applied to the earliest design artifacts, thereby leading to earlier detection and elimination of design defects and associated late cycle rework. Guarantees of correctness. Unlike testing, formal analysis tools such as modelcheckers consider all possible execution paths through the system. If there is any way to reach a fault condition, a model checker will find it. In aHoneywell’s ExperienceHoneywell has developed a wide array of capabilities in the application of Formal Methods to safety-critical systems. We can draw upon our expertise with many different Formal Methods technologies to choose the right tools and level of abstraction for each verification task. Honeywell’s strength lies in our ability to apply this expertise to real systems based on our deep understanding of the aerospace domain, requirements for safety-critical systems, and actual development processes. Examples of how we have applied existing Formal Methods tools and developed new ones include: Source code: Source code is frequently the only complete design artifact available for verification. Therefore, analysis of source code is an important capability. We have found explicit-state model checkers to be the best tools for verifying source code. We are currently developing automated tools for generating verification models from source code and are usingthis approach to verify the time partitioning guarantees in Honeywell’s Deos? real-time operating system. Deos is a key element of the Primus Epic avionics suite and was implemented in C++, incorporating many advanced featuresProcess 1 ApplicationThread ThreadProcess 2Thread ThreadProcess 3Thread Threadown self-check validity flag. The output of the algorithm is a single sensor output and a validity flag computed from the inputs. We have verified that the algorithm computes the correct output and is tolerant to sensor faults, noise transients, and small differences in sensor measurements.Sensor outputSignal inputSignal inputValidAPI Deos HAL Hardware CPU Kernel PAL Platform Hardware Platform RegistryWorlddataSensor SensorvoteroutputValidFault handling properties of redundant sensor voter design verified by model checkingThread Thread ThreadAPI callsKernel APIinterruptsPlatforminvoke kernel methodsDeos source code translated for automatic analysis and verification of time partitioning propertycontext switchKernel classes and methodsread/write timersuch as dynamic creation and deletion of processes and threads, slack time reclamation, and aperiodic interrupts. While no test case can directly check system level properties like time partitioning, we have been able to verify this property using theSPIN model checker. High integrity communication protocols: When high-level requirements documents are available, critical properties of high-integrity communication protocols can be analyzed using Formal Methods. In our verification of the synchronization protocol of the ASCB-D bus used in Primus Epic, we derived the model from textual design specifications. The verification proved that the protocol achieves synchronization of the timing frames within the required 200 msec start-up period, irrespective of the component start-up order, various bus faults, or clock drift. Control flow diagrams: Control flow diagrams in Simulink? are a common design representation in avionics control systems. We have found that symbolic model checkers capturing the synchronous transition structure of these designs are best suited for their verification. We are working on tools to automatically generate models from block diagrams such as a triplex sensor voter design. This redundancy management algorithm monitors three independent sensors, each with its Real-time scheduling: The MetaH Architectural Description Language was developed by Honeywell for specifying realtime embedded systems. The specifications include information about configurations of tasks, their message and event connections, information about how these objects are mapped onto a specified hardware architecture, and information about timing behaviors and requirements, and partitioning and safety behaviors and requirements. We developed hybrid verification tools for real-time, faulttolerant, high-assurance software and hardware architectures specified in the MetaH language. Dense time linear hybrid automata models are generated automatically through instrumentation of the source code. The models result from the execution of the instrumented code during testing.Properties analyzed using this approach include schedulability and deadline satisfaction. We used this approach to analyze the portion of the MetaH real-time executive that implements uni-processor task scheduling, time partitioning, and error handling. Nine defects were discovered in the course of the verification. Of these, three defects were almost impossible to detect through testing because multiple, carefully-timed events were required to produce erroneous behavior.For further information about Formal Methods and verification of complex systems contact:Murali Rangarajan murali.rangarajan@/doc/dd17413823.htm l, (612) 951-7540Honeywell Laboratories 3660 Technology Drive Minneapolis, MN 55418。

今天要来跟大家分享分享西班牙语邮件的基本形式以及注意事项~ 虽说在日常生活中，微信，微博，qq等聊天工具的使用频率也许更高，但是联系客户，跟老师申请tutoría，投递简历，或是写投诉信等等，信件依然是必不可少的。

电子邮件的出现极大缩短了投递速度，同书信一样，电子邮件也有一套格式，但跟书信大同小异。

如果邮件格式都不注意，该正式的时候用很不正式的口吻，语法错漏百出，那收件人可能就觉得写信人并没有很当回事了。

所以了解了解邮件格式还是有必要的~首先我们来看一下，要开始写信的时候要注意些什么：打开写邮件的窗口我们会先看到这四项内容Para: (填收件人Receptor)Cc: (抄送direcciones vistas)Cco: （密送direcciones ocultas)Asunto: （主题）1. 主题（Asunto)主题填写十分重要，因为要是没有主题，或者主题很花哨冗长，收件人也许会觉得是广告或者病毒垃圾邮件，连点开都不想。

所以大家一定要三思而后行。

2. 称谓（Saludo）如果是一封正式的邮件，比如是投简历等，那就要用正式（formal）的格式。

Formales正式的邮件称谓我们可以用:Estimado Sr. Pérez: (西班牙语信件称谓后面是冒号不是逗号，西班牙语信件称谓后面是冒号不是逗号，西班牙语信件称谓后面是冒号不是逗号。

重要的事情说三遍！虽然有些西班牙人受英语影响有时也写错)Ilmo. Sr. Alcalde: (尊敬的市长先生。

这是Ilustrísimo señor Alcalde的简写，也就是说，如果不是简写，señor的s不用大写)Estimado/a alumno/a: 这个非常常用，表示"亲爱的同学们”，当然我们可以直接说Estimado + 某男士，Estimada + 某女士。

但是千万注意，不要偷懒把Estimado/a 写成Estimad@, 这是非常不规范的！）如果我们并不知道收件人是谁，只知道是某个办公室，那我们可以用下面三种方式：Muy señor mío:或者A la atención del responsable del Departamento de...:（上面这种表达可以简写为A/A. Responsable del Departamento de...:A quien corresponda: （这个就是英语里面的To whom it may concern)Informales 不正式的称谓有以下这些例子（不正式的场合可以是写给家里人，朋友等）: Queridos todos: 亲爱的大家¡Hola, Jesús!Hola, amor.¿Quétal, Ana?上面这几种情况是完整句子做称谓就不用冒号结尾了，用相应的标点符号，但是无论如何都不可以是逗号！3. 主体（cuerpo del mensaje)几点建议：*简洁breve*明了claro段落别太长Párrafos cortos重要的放前面说poner lo más importante al principio一开始直奔主题：Me pongo en contacto con usted para saber si...为了了解......我跟您取得联系En relación con ... 关于......如果实在没办法，邮件很长，那尽量分点说，加小标题Si es muy largo, poner títulos a los apartados注意语法！Respetar las reglas de ortografía habituales不要滥用大写来强调，因为用多了反而人家不知道哪里是重点No abusar de la mayúscula 4. 祝语&落款（Despedida）同称呼一样，这也分正式不正式两种Formales:正式A la espera de su respuesta, se despide atentamente, （这个逗号可有可无，看大家习惯）或者直接Atentamente,或者Un saludo, / Saludos,...然后过行写名字Informales: 不正式¡Hasta pronto!Un beso,Un abrazo,...然后过行写名字等把邮件写完，检查检查有没有语法错误，就可以发送啦！大家在不同场合，不管是书面还是口头，都要注意使用不同的语体，这在西语中叫做registro。

Logic as a Formal MethodAntony GaltonDepartment of Computer ScienceUniversity of ExeterExeter EX44PTJune25,1997IntroductionThe aim of this article is to present,in outline,a representative selection of the ways in which formal logic has been of service to computer science.Logic offers so many possibilities of application,and there are so many diverse groups of researchers developing logic-based applications,that it will be impossible in the space available to do justice to the wholefield.Indeed it will be impossible even to mention everything that is going on,let alone say anything about it.Therefore I confine myself to a few areas which I believe,taken together,give a fair impression of the promise that formal logic holds as a tool for computer scientists.I assume in this article that the reader has a working knowledge of the classical Propositional and Predicate Calculi:a lightning sketch of these systems can be found in my article‘Classical Logic:a Crash Course for Beginners’which appears earlier in this issue;for more details,the reader is urged to consult a textbook such as my Logic for Information Technology (Wiley,1990).1Applications of Classical LogicFirst-order logic has a number of virtues which make it a valuable tool in Computer Science.The first of these is that it is an artificial language,totally under our control,with none of the maverick and unpredictable ambiguities that pervade ordinary language.Once one hasfixed the domain of the interpretation,and the denotation of the constants,predicates and function symbols,the meaning of every formula is therebyfixed too,in a completely unambiguous way.It is therefore a good medium in which to codify precisely the facts and rules pertaining to the sorts of domains we are interested in when thinking computationally—which can mean either the domains we want our computations to be about,or the domain of computation itself,for example if we want to reason about the behaviour of programs.Second,and no less important,is the fact that the language of the Predicate Calculus comes with a ready-made inferential apparatus,enabling us not just to express the facts pertaining to our chosen domain but also to reason with them in a way that is guaranteed to be logically correct.And finally,a virtue that is often claimed for the Predicate Calculus is that it is universal in that it does not prejudge its possible domains of application.It is not entirely clear to me how far this claim is really correct,but there certainly seem to be potential application areas which pose considerable difficulty for the Predicate Calculus,for example reasoning about mass terms such as‘water’or‘gold’,where it is only with the greatest artificiality that we can conceptualise the domain in terms of a set of discrete individuals.For computer science,however,this limitation is not serious,since in this discipline we1do almost always think of things in just the discrete kind of way that is required for a direct application of the Predicate Calculus.1.1Program specificationMyfirst example of such an application concerns the range of activities to which we may apply the term specification.Writing programs is only a meaningful activity so long as one starts off with some idea,however vague,of how one wants one’s program to behave.In small-scale‘recreational’programming,and in some areas of Artificial Intelligence,the idea may only ever be formulated in the vaguest way,programmers relying on their ability to recognize intuitively when they have come up with something interesting that conforms to their original idea.In a commercial or industrial context, however,it has long been recognized that a systematic codification of the desired program behaviour is an absolute prerequisite for responsible programming.How systematic should one be?According to the Formal Methods school,nothing less than a rigorous specification infirst-order logic or some comparable formalism will do,for without such a specification,it is not only impossible to be sure that the program behaves as desired,it is even left indeterminate what the desired program behaviour is in thefirst place.Suppose,to take a simple example,that one wanted to develop a library information system which users can consult in order tofind out about what books,periodicals,etc.,the library possesses, and also about their own use of the library—who has a given book on loan,when it is due back, and so on.The domain of the program consists of a number of different types of object:library items such as books and periodicals,catalogue numbers assigned to these,locations where items are physically stored in the library,and individual borrowers,who may have various different statuses (e.g.,in a university library,undergraduate,postgraduate,staff),their addresses,and so on.There are innumerable constraints that must be satisfied,for example that a book cannot be on loan to more than one user at a time,that each copy of every book has a catalogue number that identifies it uniquely,that each borrower has a unique status which determines the normal period of loan for items borrowed, that certain items may have restricted loan periods,and so on.Somehow thefinished information system must behave in such a way that these constraints are all satisfied.How is the programmer to ensure that this is achieved?Of course the answer is by being systematic,and from what we have already said it should be clear that a very good way of being systematic is to express all the relevant constraints in logical form.Thus,for example,thefirst constraint mentioned above might come out looking something like this:The complete set of such constraints will constitute a formal specification of the information system, laying down criteria for what is to count as a correct implementation of the system.There are several things that can be done with such a specification.Ideally,we would like to be able to do the following: Use the specification to prove that the implementation is correct—i.e.,that it does not violate any of the constraints1;Even better,by systematically transforming the specification,to produce a working program from it automatically,in such a way that the correct behaviour of the program is guaranteed.By logical inference from the specification to determine what further properties any correct implementation of the specification must possess.It is generally found that logic alone is not enough;the logic,which is as we have remarked highly general,has to be embedded in procedures that are more specifically tailored to the computational context.What is important,though,is that we can effect a clean separation between the specification of what we are trying to achieve—that is,how thefinished program should behave—and the imple-mentational details,the procedures by which the desired behaviour is to be realised.The specification should thus be presented as a set of declarations of what is required,rather than in terms of algorithms, so the declarative nature of logic suits it to that purpose.Formal specification methods such as Z and VDM generally contain a substantial core of pure logic,although as already hinted they contain much else besides.1.2Program verificationGiven a formal specification and an actual program,how can one tell whether the latter is correct in relation to the former,that it actually behaves as specified?Formal methods of program verification are designed to allow one to check just this.In general terms,the behaviour of a program,or of a self-contained module within a program,can be specified by1.the class of inputs it is to accept;2.the class of outputs it is to deliver;3.the required relation between the input and the output.For example,a program to compute the quotient and remainder when one natural number2is divided by another can be specified as follows:1.Input:two natural numbers,where0.2.Output:two natural numbers.3.Input-Output Relation:and.An example procedure,in Pascal,isprocedure quot_rem(x,y:integer;var q,r:integer);beginq:=0;r:=x;while r>=y dobeginr:=r-y;q:=q+1endend.We assume here that the input condition,that0and0,is satisfied before the procedure is called.A logical system specifically designed for program verification was developed by C.A.R.Hoare over twenty years ago(Hoare,1969).It makes use offirst-order logic together with a special form of program logic in which to express propositions of the form‘if before the execution of a piece of code,the values of the program variables satisfy the formula,then after execution of they will satisfy the formula’.The notation used to express this is.Here is known as a precondition and as a postcondition.In general one wants the precondition to be as weak as possible,to cover the greatest possible range of initial states(hence the weakest precondition rules of Dijkstra,1976), and the postcondition to be as strong as possible,so that one gets as detailed a picture of the output state as possible.For example,the formula:1states that if we execute the instruction x:=y+1when,then in the resulting state we will have1and(note that the initial value of is irrelevant,so it is not mentioned in the precondition).In general,Hoare laid down as an axiom(the Axiom of Assigment)the rule:Here is the statement obtained from by replacing each occurrence of‘’by‘’—so,in the example above we have‘1’1i.e.,‘11’,which is equivalent to what we had before since11is equivalent to and for any formula,is equivalent to.In addition to the axiom of assignment,Hoare used a number of rules of inference,includingtwo Rules of Consequence:(Cons1)If,and implies,then;(Cons2)If implies,and thenwhich enable one to weaken the postcondition or strengthen the precondition,a Rule of Composition:(Comp)If1and2then1;2which enables pre-and post-conditions to be stated for compound instructions built up by concatenating simpler ones,anda Rule of Iteration:(it)If then while B do Swhich enables us to handle while-loops.One can now prove that the Pascal procedure above meets the specification for the quotient-remainder computation.Specifically,the relationbut the precondition here is equivalent to,so we have:;:making this formula an invariant for the while-loop of the procedure.We now have,from the rule of iteration,:;:;but since is implied by0,and implies,the rules of consequence give us:;:It is not hard to see how to get from here to the full correctness proof.Note that we several times made use of equivalences and implications between formulae;of course in a fully formal proof we should have to prove these equivalences,and that is wherefirst-order logic comes in.The whole proof makes use of both ordinary standard logic and Hoare’s special logic of programs.This illustrates the point made above thatfirst-order logic achieves its greatest power when used in conjunction with other formalisms.For further details of methods such as those mentioned here,see for example Dijkstra (1976)or Backhouse(1986).1.3Program synthesisIn the quotient-remainder example above we gave the specification and the program independently of one another,and then showed how to prove that the program is correct with respect to the specification. In actual programming practice,though,it is obviously desirable that the construction of the program should be guided by the specification,so that it is not as it were just a matter of luck that the program should happen to satisfy it;formal specification methods such as Z and VDM are designed to facilitate this(see Jones,1986,for VDM,and Diller,1990for Z).Ideally,we would like a systematic method for deriving the program from the specification,and the goal of a number of researchers is to make the transformation of specification to program so systematic that it can be actually automated,so that the activity of programming is in effect reduced to that of composing specifications.This goal defines the area of Automated Program Synthesis(APS).The approach to APS that makes greatest use of logic is the so-called Deductive Approach, pioneered by Manna and Waldinger(1980).The central idea behind the deductive approach is that a formal specification of the formGiven inputs12satisfying the formula12find outputs12such that the formula11holds5can be written as a Specification Theorem11111which has to be true in order for there to exist a program meeting the specification(for if the required outputs do not even exist,then obviously no program can deliver them).To synthesize the required program we attempt to prove the specification theorem;our proof must be constructive,i.e.,we prove that something exists by showing how to construct it.The program itself then emerges as a by-product of the proof.Here I shall discuss a variant of the deductive approach which uses the Constructive Matching methodology of Fraˇn ov´a(1988).I shall illustrate this method by showing how it can be used to synthesize a quotient-remainder program.The specification theorem in this case isNote that we assume the domain is the set of natural numbers.The proof of the theorem uses mathematical induction,using as the induction variable3.The base case is therefore when0,and the theorem reduces in this case to00Constructive matching requires us to match0with;given the axioms for addition and multiplication(in3above),there are only two ways of doing this:either by putting0or by putting0.The second conflicts with the condition0in the antecedent,so we are left with thefirst,for which we must now check that holds;since0and0,it does.This gives us the construction‘if0,then put0and0’,which in turn gives us thefirst part of the synthesized programif x=0thenbeginq:=0;r:=0endelse...For the‘else’part,we use the fact that if0then for some(where denotes the successor function).In this case the specification theorem becomesand we may make use in addition of the induction hypothesis4,which isConstructive matching requires us to match with.Without even using the induction hypothesis,we can immediately write down two‘trivial’solutions,namely0and0.The second conflicts with the condition0,but thefirst is acceptable so long as,i.e.,.This gives us the next part of the synthesized program:...if x=s(a)and s(a)<y thenbeginq:=0;r:=s(a)endelse...which may be simplified to...if x<y thenbeginq:=0;r:=xendelse...We must now consider the case where,the‘non-trivial’solutions.For these we must take into account the induction hypothesis.For given,this guarantees us the existence of values1and1 such that11and1.To match with,then,we must try to match11 with.The axioms for addition and multiplication give us the following possibilities:1.0112.1103.114.11Since0and11,thefirst possibility implies0,which duplicates the trivial solution already found.Of the remaining cases,numbers2and4lead nowhere since we can’t match with either11or1.This leaves number3,which can be simplified to11.This solution is acceptable so long as the condition is satisfied,i.e.,1.The axioms for‘’(not given here)break this down into11.The induction hypothesis gives us1,so we are left with1as a condition for this solution to be acceptable.To convert this into the next part of the algorithm,all we need is to note that1and1 are the values returned by the quotient-remainder procedure when called with inputs and,so we have...beginquot_rem(x-1,y,q1,r1);if r1+1<>y thenbeginq:=q1;r:=r1+1endelse...(Here we represent by1since.)Finally we are left with the‘failure case’,in which1.This will occur when is a multiple of,though we don’t need to know this in order to execute the proof.For this case we have to prove a‘missing lemma’,namely that the induction hypothesis implies117(this is got by substituting1for in the earlier formula).We must match with1.But substituting1for in the induction hypothesis gives us111,which implies(via the addition and multiplication axioms)11.So we are really trying to match11 with1,which we can do by putting10.This enables us to complete the algorithm asbeginq:=q1+1;r:=0endPutting it all together,then,we have succeeded in synthesizing the following recursive procedure for computing quotient and remainder:procedure quot_rem(x,y:integer;var q,r:integer);beginif x=0thenbeginq:=0;r:=0endelse if x<y thenbeginq:=0;r:=xendelsebeginquot_rem(x-1,y,q1,r1);if r1+1<>y thenbeginq:=q1;r:=r1+1endelse beginq:=q1+1;r:=0endendend.This program may not be maximally efficient;it may be necessary to transform it in some way to improve it in this respect.Nonetheless it is impressive that by an almost purely mechanical procedure we have been able to synthesize a program from the specification at all!It is guaranteed to be correct so long as the condition0is satisfied.The goal of APS is to take techniques like this and refine them so that they can be applied practically to much more complicated cases,the sort of cases that one is likely to encounter in‘real life’.It has to be said that we are as yet nowhere near to achieving this goal;nonetheless I think that the enterprise is a good illustration of the power and insight that purely mechanical operations on logical formalisms can provide.81.4Logic ProgrammingThe drive towards automated synthesis opens up the prospect of programming by writing specifi-cations:that is,instead of writing a program,the programmer writes a specification which is then automatically converted into a program.In a sense this is already something we are doing all the time,for what else is a program written in a high-level programming language but a specification for the low-level code into which it is compiled?However,there is another sense in which it is quite misleading to regard a Pascal program,say,as a specification,and that is that it already consists of a sequence of instructions which the computer is to follow in order to derive the output from the input rather than a bare statement of the relations that are to hold between the input and the output. Thus Pascal,and the majority of other widely-used programming languages which resemble it in this respect,is an imperative language,whereas ideally a specification language should be purely declar-ative,i.e.,it should enable one to describe conditions on the input-output relation independently of any particular procedure for realizing them.If,therefore,we are ever to program by writing specifications,we require our programming languages to be declarative,just like our specification languages.A program written in such a language could then be regarded as an executable specification.There are two main classes of declarative programming languages in existence,the functional languages,of which Lisp is a rather impure example,and Miranda a purer one,and the logic programming languages,of which so far only Prolog has come to be widely used,although a recently developed language called G¨o del seem to be gaining in popularity.In a functional language the input-outputrelation is specified as a function whose values for a given set of inputs is the corresponding set of outputs.The language thus consists of a set of expressions denoting primitive functions and operations for constructing new functions from old,and the task of implementing it is that of specifying algorithms for evaluating the complex functional expressions built up in this way.Several different logical systems may be used to provide the foundation for functional languages,for example the-calculus or Martin-L¨o f’s intuitionistic type theory.On the latter,see section2.1.Turning now to logic programming,the central idea here is that a statement of the form‘if then’can be regarded either declaratively,as asserting something which may or may not be true, or procedurally,as telling you that if you want to know whether is true,you should tryfinding out whether is true.Suppose,for example,we have the following set of rules and facts:1.If it is sunny then it is warm.2.If it is daytime and there are no clouds then it is sunny.3.It is daytime.4.There are no clouds.and suppose we are asked‘Is it warm?’.From1we know that it is warm if it is sunny,so that in effect we have replaced the original question by a new question,‘Is it sunny?’.From2we know that it is sunny if it is daytime and there are no clouds,so now we can replace our question by the two questions‘Is it daytime?’and‘Are there no clouds?’.From3and4we know that the answer to each of these questions is‘yes’;this gets transmitted back up the chain of questions to give us an answer ‘yes’to the original question‘Is it warm?’.This example could be translated directly into Prolog,and the resulting code might look like: warm:-sunny.sunny:-daytime,no_clouds.9daytime.no_clouds.With this program loaded,we can then pose the query?-warm.to the Prolog interpreter,which will then set in train a sequence of moves which amount to a proof that the statements in the program logically imply the statement warm.This example makes use of no resources beyond the Propositional Calculus,and in fact only involves a restricted subset of formulae known as Horn clauses.A Horn clause in the Propositional Calculus is a formula of the form12where the and are atomic formulae(i.e.,single schematic letters).This corresponds to the Prolog clauseH:-B1,B2,...,Bn.The logic of Horn clauses has a sound and complete proof theory which uses a single inference rule, called resolution,which says that given the clauses12and12if for some then we may infer the clause11121Logic programming would be of little use if it did not go beyond the Propositional Calculus,but in fact the ideas presented above can be extended in a rather natural way to the Predicate Calculus.In this setting,a Horn clause is now defined as a formula having the form1212where each and is an atom,that is a formula obtained byfilling in the argument places of a simple predicate by constants,variables,or complex terms derived from these using function symbols, and12are all the variables that occur in any of the or.To extend the resolution procedure to Horn clauses of this kind,we make use of a pattern-matching algorithm called the unification algorithm which enables us to determine whether two atoms have a common instance.For example,given the two clauses and,we can observe that the atoms involving can be unified by substituting for and for. We can therefore resolve the two clauses to give.Prolog notation dispenses with quantifiers since Horn clauses do not contain existential quantifiers and all the universal quantifiers are placed in a string at the head of the clause.Thus the two examples above would be written asr(Y):-p(X),q(g(X),Y).q(Z,f(Z)):-s(Z).10Note Prolog’s odd convention of writing predicates,function symbols and constants beginning with lower case letters and variables with upper case.To illustrate the use of Prolog as an executable specification language,we shall consider some simple list-processing tasks.Prolog notates a list in the form[X|Xs],where X is thefirst element of the list and Xs is tail of the list,i.e.,the list consisting of the remaining elements.An individual list with known members may be written out in full as,e.g.,[monday,tuesday,wednesday,thursday,friday,saturday,sunday]. The empty list is written as[].Suppose we want to specify the relation in which a list stands to that list which contains the same elements but in the reverse order.If we know that the reverse of a list12is11,then we know that the reverse of the list 12,obtained by adding an element to the beginning of,will be the listobtained by adding to the end of.We also know that the reverse of the empty list is itself.This gives us two Prolog clausesreverse([],[]).reverse([H|L],S):-reverse(L,R),add_to_end(H,R,S).We must also specify the relation:we know that the result of adding an element to the end of the empty list is the one-element list;also,if the result of adding to the end of a list 12is the list12,then the result of adding to the end of the list 12will be12.The required Prolog clauses are thus add_to_end(E,[],[E]).add_to_end(E,[H|L],[H|M]):-add_to_end(E,L,M).The four clauses we have written down,which express in a purely declarative way the basic facts about the and relations,can now be used to answer a query such as?-reverse([a,b,c,d,e],X).to which the Prolog interpreter will duly come up with the answerX=[e,d,c,b,a].It would be idle to pretend that Prolog fulfils all the requirements of an executable specification language:manifestly it does not,as even its most enthusiastic devotees will admit.The way the Prolog interpreter works is sensitive to the ordering of atoms within a clause and to the ordering of clauses within a program;programs which are equivalent from a declarative point of view can turn out to have quite different behaviour in practice—it can often happen,for example,that by changing the order of the clauses one can turn a program which always terminates into a program that never does.On the other hand,merely swapping around clauses in a correct program will never generate a program that is incorrect in the sense of delivering wrong answers,the worst that can happen is that instead of delivering correct answers it fails to deliver anything.But Prolog has in addition a number of non-logical,i.e.,purely procedural features,most notably the infamous‘cut’operator(written‘!’), which gives the programmer control over which parts of the search space are examined,and as a result can result in incorrect programs unless one is very careful to observe the procedural niceties in one’s programming.Another source of possible error is the(again infamous)‘negation by failure’operator (not)which was introduced in an attempt to circumvent the limitation to Horn clauses by means of a procedural definition of negation.(For more on this,see section2.4on non-monotonic reasoning.)11Thus Prolog is not perfect,nor was it ever claimed to be.For all its faults,though,it has proved to be a very congenial medium in which to encode certain kinds of programming tasks,notably those which essentially involve recursion(such as our list-processing examples above)and whichfigure prominently in Artificial Intelligence.It also serves as a pointer to what might be achieved once the problems have been ironed out.For further details on Prolog,see Sterling and Shapiro(1986),and for Logic Programming generally,Hogger(1990).2Beyond Classical LogicAlthough classical logic,i.e.,thefirst-order Predicate Calculus,can be,when appropriately handled, a formidable tool for representing and reasoning about almost any domain,it is not the last word in formal logic.During the present century logicians,mathematicians,philosophers and computer scientists have studied a wide range of alternative formalisms designed for specific applications which do not appear to be easily handled by classical logic.In this section we shall briefly review a number of these formalisms,with particular emphasis on those that have attracted the attention of computer scientists.There are broadly speaking two ways of devising a non-classical logic:one can either take the language of classical logic unaltered,but reinterpret the logical constants(i.e.,the connectives and the quantifiers)so that the class of formulae that count as logically true or inferences that count as valid is altered;or one can alter the language itself by introducing new logical constants.2.1Intuitionistic LogicIntuitionistic Logic belongs to thefirst of these two categories,in that it does not extend the syntax of classical logic,but reinterprets the connectives so that they are no longer truth-functional.Intuitionists are very much concerned with the grounds one might have for asserting a proposition:it is no good just saying that a formula is true,one’s conviction of its truth must be grounded in some concrete intuition.As a consequence of this,the intuitionist is disinclined to accept as valid such classical theorems as the Law of the Excluded Middle or the Law of Double Negation. In the former case,the intuitionist would say that one is only warranted in asserting a formula of the form if either one is warranted in asserting or one is warranted in asserting—and clearly there are cases where one is not warranted either in asserting or.In mathematics,for example,one may not be able to prove either a proposition or its negation;this is currently the case with Goldbach’s conjecture that every even number greater than2is the sum of two primes.If we represent this conjecture by,then the classical logician will be quite happy to assert that, and will be prepared to use this assertion as a premiss in a proof;the intuitionist however will not be prepared to assert this until a proof of either or is available.In the case of the Law of Double Negation,the intuitionist interprets to mean that one has well-grounded reasons for denying; so says that the supposition that one has a proof that is false is untenable,and the intuitionist will not accept that this amounts to a proof of,since it is quite possible that neither nor can be proved.Intuitionists demand a similar grounding for the existence of objects.One is only warranted in saying that something exists if one has a constructive means to exhibit it.This places limitations on the conditions under which an intuitionist is prepared to accept a formula of the form.For example,whereas classical mathematicians will accept as a proof of the existence of transcendental (i.e.,non-algebraic)numbers the fact that the class of real numbers is of higher cardinality than the12。

A Formal Approach to Testing L USTRE SpecificationsIoannis ParissisLaboratoire Logiciels, Systèmes et Réseaux - Institut IMAG BP 72 - 38402Saint Martin d’Hères Cedex - FranceIoannis.Parissis@imag.frAbstractL USTRE is a synchronous declarative language designed to specify and to implement reactive software. One of its main advantages is that it can be used as a temporal logic to express software invariant properties. The satisfaction of the latter can be proven by model-checking, using L ESAR, a verification tool designed for L USTRE programs. In this paper, we address two important problems related to this verification process. First, developing the specifica-tions of a synchronous software is a difficult and error-prone task. Before attempting to formally prove their satis-faction, one should validate them. We propose random automatic animation as a means to validate such formal specifications. Second, due to the often huge required memory and time amounts, proof may not be applicable, in which case the specification work is wasted. To cope with this problem, we propose testing techniques which reuse the software specifications to formally test the software.1. IntroductionA synchronous software satisfies the synchrony hypothesis: every reaction of the software to its inputs is theoretically instantaneous. In fact, this requirement is met if the external environment remains invariant during soft-ware reactions [1].The main advantage of the synchronous approach is that the software implementation is simple and can, in some cases, be easily translated into finite abstract models. Model-checking can be applied on these models to prove the satisfaction by the software of some important proper-ties (typically safety properties).This work is particularly concerned with L USTRE[2], a declarative data-flow language developed at the IMAG institute in the middle of the past decade. A prover,L ESAR [3], has also been developed to automatically check the satisfaction of safety properties by L USTRE programs han-dling boolean variables.L ESAR uses L USTRE as an executable specification language (to describe the model) as well as a linear temporal logic of the past (to express the safety properties) [4]. An important drawback of this for-mal proof technique is that it may require prohibitive memory and time amounts, due to the big number of states of the program abstract model which must be exhaustively checked.We suggest that testing can bring a solution to this prob-lem and we propose a set of testing techniques designed to deal with L USTRE specifications. These techniques use the same theoretical framework and require the same software specification as formal proof. As a result, the specification effort spent during the latter, is not wasted if model-check-ing fails to terminate. Furthermore, the aim of testing is not to prove the software correctness but to adequately chose inputs checking some of its behaviors. Hence, valuable information about the software correctness can be obtained even after a small number of executions.Formal proof has another drawback : the specification involved in the proof process can be incomplete or false. If proof fails, it is difficult to decide if this failure is due to a software defect or to a fault in the expression of the proper-ties. Moreover, a successful proof only ensures that the software satisfies a specification but does not provide any information on the correctness of the latter.Our approach to this problem is to provide tools making the software designer able to animate the specifications and to observe the resulting behaviors. This observation could be a valuable means to detect defects in the specifi-cation before attempting to prove the software.As for formal proof, we assume that the software and its specifications use only boolean variables. Although this may appear as a severe restriction, many reactive software meet this requirement.The paper is structured in two main sections. Section 2 is a short overview of L USTRE, illustrated on an elevator control software, a well known example which has often been used [5] [6] for similar purposes. The formal proof process is also outlined. In section 3 we present the testing and animation techniques.2.L USTRE and the synchronous approach2.1. Overview of the L USTRE languageThe presentation of L USTRE is restricted to the elementsnecessary for understanding the paper (see [2] for details).A L USTRE program behaves as an infinite loop con-trolled by a clock : at every tick of the clock, the program reads its inputs and issues new values for its outputs. Thus,time is assumed to be isomorphic to the set of natural num-bers or, in other words, time is considered as a sequence of discrete ticks of the clock.AL USTRE program is structured into nodes,which are subprograms describing a relation between their input and output variables. This relation is expressed by an unor-dered set of equations, possibly involving local variables.An equation X =E , where E is a L USTRE expression states that the variable X is always equal to E . A variable is intended to be a function of time : it denotes the sequence of values it takes at the different clock instants. Expres-sions are made of variables, constants, arithmetic, boolean and conditional operators and only two specific operators :the “previous” operator and the “followed-by” operator:•If E is a L USTRE expression denoting the sequence of values (e 0, .., e n , ..), then pre E denotes the sequence (nil, e 0, .., e n , ..)where nil is an undefined value. In short, at instant t ,pre E denotes the value of E at t-1.•If E and F are expressions of the same type, denoting the sequences (e 0, .., e n , ..) and (f 0, .., f n , ..), then E ->F denotes the sequence (e 0, f 1, .., f n , ..). In other words,E ->F takes the value of E at the first tick of the clock and the value of F at every other tick.For instance, the node edge (figure 1), returns true whenever its parameter raises from false to true .L USTRE is a data-flow language : nodes can be viewed as operator nets (see figure 1). At every tick of the clock,the input data “cross” the net causing the new outputs to be computed.2.2. Specification of a software in L USTREThe main application area of synchronous programming is safety-critical software. For such software, three kind of specifications are needed [7]:node edge(X:bool )returns (EDGE:bool );letEDGE =X ->(X and not pre X);telFigure 1 :A L USTRE node and its operator netXEDGE•The functional specification of the software is a L USTRE node computing the software outputs from the software inputs. This node is deterministic : a given input sequence will always cause the software to issue the same output sequence.•The software environment specification is a set of invari-ant properties providing a nondeterministic description of the valid software inputs.•The safety properties are invariant temporal logic for-mulae stating that some dangerous behaviors will never occur.The environment specification and the safety properties are expressed as L USTRE temporal operators [8]. In L US-TRE , a node N P is associated to a temporal operator P , such as P is true if and only if N P returns a true value [3]. For instance, two widely used temporal operators [9] are always A from B to C and once A from B to C . They require A to continuously hold (resp. to hold at least once) between two subsequent occurrences of B and C . For instance,always A from B to C is implemented by the node Always_from_to(A, B, C) :node Always_from_to_(A, B, C :bool )returns (ok :bool );letok = Once_since(C,B) or Always_since(A,B);telThe node Once_since(A, B) (resp.Always_since(A, B))returns a true value if and only if its first input has been once (resp. continuously) true since the last time its second input was true :node Once_since_(A, B :bool )returns (ok :bool );letok =if B then A else (true -> (A or pre (ok)));telnode Always_since_(A, B :bool )returns (ok :bool );letok =if Never(B)then trueelse if B then A else A and pre (ok);telThe node Never(A) returns a true value if and only if A has never been true in the past :node Never(A :bool )returns (ok :bool );letok =not A -> (not A and pre (ok));tel2.3. A software exampleInformal description.We consider a lift controller, i.e. a reactive software designed to control an elevator system,for a four floor building. The system is composed of a sin-gle elevator car equipped with motorized doors, whichrides up and down inside the shaft (see figure 2).Two sensors inform the software about the current state of the doors by issuing two boolean signals,doors_open and doors_closed .Three sensors are installed at each floor, emitting the signals above_floor ,at_floor and below_floor according to the car position. In particular, the signal at_floor[floornb]is true when the elevator lies on the floor floornb .A passenger at floor floornb can call the elevator by pressing the upper button to go up or the lower button to go d o w n : t h e s i g n a l s u p _c a l l [fl o o r n b ]a n d down_call[floornb] will then be sent to the software.In the car there is a panel with four buttons, one for each floor. When the button corresponding to floornb is pressed,the signal request[floornb]is sent to the software.Panel and floor buttons contain lightbulbs turned on if t h e s o f t w a r e o u t p u t s l i g h t _p a n e l [fl o o r n b ],light_up[floornb] and light_down[floornb] are true.The software must turn on or off the lightbulbs and send the appropriate commands to the lift motor (stop , to stop the car,raise or lower , to make the car move) and to the door motor (open_the_doors ,close_the_doors ) according to scheduling constraints, not reported here.Specification of the software environment.The lift con-troller environment is fully characterized by the signals issued by the buttons and the sensors. The signals above_floor ,at_floor ,below_floor ,up_call ,down_call and request are 4-element boolean arrays 1 while doors_open and doors_closed are single boolean variables. Operators can be defined on arrays as recursive nodes 2.1. Arrays are just a syntactic facility : they are expanded into as many variables as they have elements.Figure 2 :The elevator systemopen_the_doors raise lowerstop2.The software will never attempt to open the doors whenthe car moves:Implies(open_the_doors, OR(4, at_floor)).The next safety feature aims at ensuring that a passen-ger will never be locked in the car. The corresponding soft-ware safe behavior is formulated as follows:3.The software must issue a door opening signal betweenthe time the car reaches a floor and the time it leaves it:Once_from_to(open_the_doors, edge(OR(4, at_floor)),edge(not OR(4, at_floor))).4.The software will not issue a raise or a lower signal afterthe car has reached a floor unless the doors have beenopen at least once in the meantime:Once_from_to(doors_open, edge(OR(4, at_floor)),raise or lower).5.Finally, we can specify that the commands raise,lowerand stop or open_the_doors and close_the_doors cannotbe simultaneously active1:#(raise, lower, stop)and#(open_the_doors, close_the_doors).Functional specification.It is beyond the scope of thispaper to report the L USTRE implementation of the elevatorcontrol software here. A small part of it is presented in sec-tion 3.3.2.4. Formal proof : outlineFormal proof of L USTRE programs is addressed asfollows:Given a program P, a specification S of the software safetyproperties and a specification E of the software environ-ment, does P satisfy S assuming that E holds ?Since P, E and S are expressed in L USTRE, the proofprocess is simplified : it consists in including S and E in Pand in checking that S is satisfied in every reachable stateof this new program, say P’, which is viewed as a finitestate machine M=(Q, I, O, q init, a, s, t). The latter is defined as follows (V X denoting the set{0, 1}|X|):•Q is the finite set of program states,•q init∈Q is the initial state•I is the set of input variables•O is the set of output variables•a:Q x V I→{0, 1} is the assertion function defining the states and inputs satisfying the environment constraints,•s:Q x V I→V O is the function computing the value of the safety properties,•t:Q x V I→Q is the transition function.L ESAR checks the above machine using “forward” or“backward” verification. Forward verification consists inexhaustively computing the reachable states Q r of themachine by executing all the valid transitions, starting 1. The operator# applies to a list of boolean expressions and returns a true value if at most one of the expressions is true.from the initial state q init while checking that the function s(q, e) is true for every state q∈Q r and for every input e∈I for which t(q, e) is defined.The second (“backward”) approach computes the states which can lead to a state vio-lating the safety properties. For this, the prover computes the successive predecessors of such property violating states. When all the predecessors have been computed, one must check that q init is not one of them. Both methods use the assertion function to identify and to ignore the states which are reachable only by executing invalid transitions. The visited states are stored (in order to avoid to visit them again). Their number can be very big and may prevent the proof from terminating, even when symbolic techniques and binary decision diagrams (B DD) [11] are used.3. Animation of specifications and testing3.1. OverviewUnlike formal proof, testing aims at uncovering faults in a software. In the particular case of synchronous soft-ware, faults must be sought in each of the three specifica-tions (functional, safety and environment specification). In the following, we use the term “testing” for the techniques focusing on the implementation of the software while “ani-mation of specifications” denotes the techniques concerned with the specification of the software environment and of the software safety properties.The first technique that we have studied is random test-ing. Although this kind of testing is usually easy to imple-ment, there is some difficulties when synchronous software have to be tested. This is due to the fact that the randomly generated test data must satisfy the environment specifica-tion. According to this technique, the software environ-ment specification is automatically analyzed in order to generate valid random test data or, in other words, to ran-domly simulate the software environment.Such a random environment simulation can be useful for several reasons:•The software will never have to process inputs corre-sponding to invalid environment behaviors and, hence, no failures due to such improper data will be observed.•Random simulation animates the specification of the software environment and allows the software designer to gain confidence in this specification.•Random simulation can be used to assess the software reliability, especially if it is combined with operational profiles [12].The random environment simulation defines a first test data selection criterion, since test data are chosen accord-ing to their consistency with the environment specification. This criterion is the weakest one can define for synchro-nous software: every other criterion contains at most the same test data. Hence, random environment simulation isequivalent to exhaustive testing for this class of software 1.Next, we have defined more specific techniques taking into account the specification of the software safety prop-erties. This can be done in two different ways :•First, by animating the safety properties in order to get confidence in their correction.•Second, by testing the software using the safety proper-ties to guide the random test data selection. Among all the possible environment behaviors those for which vio-lations of the safety properties are more likely to occur are chosen. This new test data selection criterion,safety testing [13], is more appropriate to the particular prob-lem of uncovering violations of the safety properties.All the above techniques perform a random generation of values according to constraints (which can be the con-formity to the environment specification or to the safety properties or their ability to cause a violation of the safety properties). For this reason, they are presented in section 3.2 as constrained random generation techniques.We have also addressed (see section 3.3), the problem of testing L USTRE programs using structured-based testing criteria. The more usual criteria reported in the related lit-erature can be easily adapted to operator nets associated with L USTRE programs.3.2. Constrained random generation techniquesTest nodes, safe nodes.The description of the environ-ment specification and of the safety properties is made by means of special L USTRE nodes, called test nodes and safe nodes . These nodes (see figure 3) are respectively com-piled into simulators of the software environment or of the safety properties. The nodes of figure 3 are related to a reactive software the input and output variables of which are respectively i 1, ..., i m and o 1, ... o l . The environment and safety operators are not part of the standard L USTRE language. They are specific to the use of L USTRE to write1. This equivalence is theoretic. Exhaustive testing requires all the infi-nite valid sequences of input values to be processed.Figure 3 :Test node and safe node syntaxsafenode N (i 1, ..., i m :bool )returns (o 1, ... o l :bool );var k 1, ..., k r :bool ;letsafety (S 1, S 2, ..., S q );k 1 = ... ; ... k r = ... ;tel ;testnode N (o 1, ... o l :bool )returns (i 1, ..., i m :bool );var l 1, ..., l n :bool ;letenvironment (E 1, E 2, ..., E p );safety (S 1, S 2, ..., S q );l 1 = ... ; ... l n = ... ;tel ;test or safe nodes. The expression list of the environment operator is composed of all the assumptions (E j )j=1,p made on the environment. The safety operator is used to list the safety properties (S k )k=1,q of the reactive software under test.E j and S k are L USTRE boolean expressions involving input and output variables as well as local variables (l 1, ...,l n and k 1, ..., k r ). Unlike L USTRE common nodes, test and safe nodes do not contain equations defining their output variables. The values of these variables are randomly com-puted, as explained in the next sections. Thus, the seman-tics of test and safe nodes is different from usual L USTRE semantics since it must deal with nondeterminism. We sup-pose, besides, that in these nodes the pre operator can only apply to variables (we can always obtain a node meeting this requirement by introducing additional local variables).Formal definition of generators .The following defini-tions are inspired from [14] and have been adapted to deal with the particular problem of test data generation.Definition 1 :An I/O machine is a 5-tuple M = (Q, q init , A,B, t) where•Q is a finite set of states •q init ∈Q is the initial state •A is a set of input variables •B is a set of output variables•t :Q x V A x V B →Q is the transition (possibly partial)function.Definition 2 :An I/O machine M = (Q, q init , A, B, t) is reactive iff ∀q ∈Q ∀a ∈V A ∃b ∈V B ∃q’∈Q t(q, a, b) = q’.This definition says that a reactive machine is never blocked (i.e. in every state, whatever the input is, a new output can be computed to enable a transition).Definition 3 :A machine associated with a test (resp. safe)node N is an I/O machine M = (Q, q init , O, I, t) (resp.M =(Q, q init , I, O, t)) where•O is the set of the software output variables (hence, if N is a test node,O is the set of its input variables whereas if N is a safe node,O is the set of its output variables).•I is the set of the software input variables (that is, the output variables of the test node N or the input variables of the safe node N ).•The set of states Q is defined as follows :A state variable is associated with every distinct expres-sion pre (x) occurring in the node N. A state is a particu-lar value of the state variables while Q is the set of all the possible states. For the definition of the initial state q init an additional state variable is introduced the value of which is initially true and becomes false after the first transition.•The transition function is a vector of boolean functions t j : Q x V O x V I →{0, 1}. Each t j is associated with a particular state variable v j and computes the new value of v j , for a given state and a given value of the input andoutput variables.The transition function of such a machine is total since every function t j is total (at this stage, we do not require any compatibility of the transition function with the envi-ronment or the safety properties).Let’s note that using a vector of boolean functions to represent the transition function reduces the memory amount needed for the storage of M: it is easier to repre-sent several simple boolean functions than a complex tran-sition function computing the successor of every state. Definition 4 :Let M = (Q, q init, O, I, t) be an I/O machine associated with a test node and let f:Q x V I→{0, 1} be a boolean function. The machine M constrained by f is a machine M f = (Q, q init, O, I, t f) the transition function t f ofwhich is such as for any state q and any input a:if f(q, a) = 1∧∃q’∈Q∃b∈V O t(q, a, b) = q’ then t f (q, a, b) = q’else t f (q, a, b) is undefined.Similarly, if M = (Q, q init, I, O, t) is an I/O machine associated with a safe node and f:Q x V I x V O→{0, 1} a boolean function,M f = (Q, q init, I, O, t f) is a machine the transition function t f of which is such as for any state q, any input a and any output b:if f(q, a, b) = 1∧∃q’∈Q t(q, a, b) = q’ then t f (q, a, b) = q’else t f (q, a, b) is undefined.The transition function of M f is defined only for states and inputs which make f true.Note that, according to this definition, the satisfaction of the environment properties at a given instant t cannot depend on the values of the software outputs at this same instant. This means that the software input (say i(t)) must be issued before the computation by the software of the resulting software output (o(t)). If the satisfaction of the environment properties depends on o(t), the environment may be unable to compute a valid input for instant t (“cau-sality paradox” [14]).Definition 5 :Let M = (Q, q init, A, B, t) be an I/O machine. Let post: Q -> 2Q be the function defined as follows: post(q) = {q’ |∃(a,b)∈V A x V B, t(q, a, b) = q’}The set of reachable states of M is recursively computable as the image of q init under the transitive closure of post (i.e.a state is reachable if it can be reached by a sequence of successive transitions starting from the initial state). Moreover,M a =(Q a, q init, A, B, t a) is the accessible com-ponent of M iff Q a is the set of reachable states of Q and t a is the restriction of the function t to the set Q a. Definition 6 :Let M = (Q, q init, O, I, t) (resp.M = (Q, q init, I, O, t)) be an I/O machine associated with a test (resp. safe) node N and let f:Q x V I→{0, 1} (resp.f:Q x V I x V O →{0, 1}) be a boolean function.f is a generating func-tion(with respect to M) iff the accessible component of M f is reactive.Informally, a function f is generating if the machine constrained by f is always able to emit a new software input satisfying f.Definition 7 :Let M = (Q, q init, O, I, t) (resp.M = (Q, q init, I, O, t)) be a machine associated with a test (resp. safe) node N and let f:Q x V I→{0, 1} (resp.f:Q x V I x V O →{0, 1}) be a boolean function. The pair G = (M, f) is a gen-erator iff f is a generating function w.r.t M.If f is not gener-ating,G is called a weak generator.Consider, for instance, the two test nodes N1 and N2 given in figure 4. The input-output machine M associated with them has 2 state variables, one associated with the ini-tial state (say init) and one associated with the expression pre i. Thus, the transition function is defined as follows:•t(init, o, i) = 0(this means that the initial state will be never reached again)•t(pre i, o, i) = i(this means that the state variable pre i in the next state will be equal to i in the present state).The boolean function associated with the environment operator of the test node N1 is f1=pre i or o. This function is not generating: when pre i= 0, there is no value of the input variable i for which f1 = 1. On the contrary, the func-tion f2 =pre i or i associated with the environment opera-tor of the node N2 is generating: for any value of the state variable pre i, the input value i = 1forces f2 to1. Hence, (M, f2) is a generator and(M, f1) is a weak generator.The generation process.Let T be a test node, let M = (Q, q init, O, I, t) be the machine associated with T and let f E:Q x V I→{0, 1} be the boolean function representing the con-junction of the environment assumptions E i of T. We assume that f E is a generating function w.r.t.M since the software environment should never be blocked. Thus, the environment simulator is a generator G E = (M, f E) on which is applied the standard generation algorithm pre-sented in figure 5.The random function applied to a finite set X returns a randomly selected element in X. The function f E is generat-ing, so the input i will always be set to a value for which f E is true.A similar algorithm is available for a safe node, the testnode N2(o:bool)returns(i:bool);var l:bool;letenvironment(l or i);l =true ->pre i;tel;testnode N1(o:bool)returns(i:bool);var l:bool;letenvironment(l or o);l =true->pre i;tel;Figure 4 :Examples of test nodesassociated machine of which is M = (Q, q init , I, O, t),assuming that f S :Q x V I x V O →{0, 1} is the conjunction of the safety properties.Note that there is no need to explicitly build M f E to use the above algorithm. Indeed, the transition function t is called only with arguments satisfying f E . Therefore, only states and transitions of M f E are involved.If the function f E is not generating, we consider that there is an error in the environment specification. How-ever, it is possible to transform the function f E into a gener-ating function by computing the set of states of M leading inevitably to the violation of f E , as shown in [14]. But the symbolic computation of the least fixpoint on which is based this transformation may be impractical in some cases.Finally, according to the definition 6, to determine whether f E is generating we must prove that the accessible component of M f E is reactive. The accessible component (i.e. the reachable states) of an I/O machine is a least fix-point and therefore, when the computation of that fixpoint is possible, we can restrict the function f E to the domain of the reachable states. It is then possible to determine if f E is generating. When the reachable states cannot be computed,the constrained random generation can, nonetheless, be performed. To this purpose, we should modify the standard generation algorithm in order to detect blocking situations.Safety testing.According to safety testing, test data are selected in order to facilitate the detection of safety prop-erty violations. The following definition aims at character-izing such test data :Definition 8 :Let M = (Q, q init , O, I, t) be a machine asso-ciated with a test node N , let f P :Q x V O x V I →{0, 1} be a boolean function. An input i ∈V I (adequately) tests the function f P on state q ∈Q iff ∃o ∈V O f P (q, o, i) = 0.Hence, if f P is the boolean function associated with the software safety properties, input data for which the safety properties are true (regardless of the value of state and out-put variables) cannot adequately test these properties.Let T be a test node, let M = (Q, q init , O, I, t) thevar q ∈Q; i ∈V I ; o ∈V O ;beginq <- q init ;o <- any element of V O ;do foreveri <- random({i’∈V I | f E (q, i’) = 1});write(i);read(o);q <- t(q, i, o);enddo ;end .Figure 5 :Standard generation algorithmmachine associated with T and let G E = (M, f E ) be the associated generator. Let f P be the boolean function associ-ated with the conjunction of the safety properties S j of T and let f S :Q x V I →{0, 1} defined as follows :∀q ∈Q ∀i ∈V I (f S (q,i) = 1⇔∃o ∈V O f P (q, o, i) = 0).Let’s consider the weak generator G S = (M, f S ). Since f S is not necessarily generating, we cannot apply the standard generation algorithm. However, a generation process can be defined using both the weak generator G S and the gener-ator G E . The safety testing algorithm given in figure 6 will generate a new value satisfying both functions f S and f E every time it is possible. When the satisfaction of f S is impossible, it will generate a value satisfying only f E (such a value always exists, because f E is generating). In other words, the generated test data will always correspond to valid environment behaviors and, when it is possible, will adequately test the safety properties.Another problem related to this kind of test data genera-tion is illustrated by the following example. Consider the test node N1 given in figure 4 and assume that the corre-sponding software under test must satisfy the following two safety properties :1.not i or o 2.pre i or oAccording to the definition 8, to adequately test the first property, the safety testing process will always set i to true .As a result,pre i will always be true and the second prop-erty will never be tested.Thus, choosing data adequately testing one safety prop-erty may prevent us from testing some other property . A solution to this problem is to successively apply the safety testing algorithm to each safety property S i (that is,f P will successively be set to S 1,S 2, and so on) assuming that every single safety property is not concerned with the problem mentioned above.Implementation issues.All the boolean functions used in the generators and in the test data generation process are represented by usual ordered binary decision diagramsFigure 6 :Safety testing algorithmvar q ∈Q; i ∈V I ; o ∈V O ;beginq <- q init ;o <- any element of V O ;do foreverif ∃x ∈V I (f S (q, x) = 1 ∧f E (q, x) = 1)theni <- random({i’∈V I | (f S (q, i’)∧f E (q,i’) = 1)});elsei <- random({i’∈V I |f E (q,i’) = 1});write(i);read(o);q<- t(q, i, o);enddo ;end .。

导语：Java是一门面向对象编程语言，不仅吸收了C++语言的各种优点，还摒弃了C++里难以理解的多继承、指针等概念，因此Java语言具有功能强大和简单易用两个特征。

下面小编为大家带来java英文，供各位阅读和参考。

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高三英语艺术批评方法科学严谨单选题30题1.In art criticism, a masterpiece is often described as having ______ beauty.A.intenseB.intensiveC.intendingD.intention答案：A。

“intense”表示强烈的、极度的；“intensive”主要指集中的、密集的；“intending”是动词“intend”的现在分词形式，表示打算；“intention”是名词，表示意图。

在艺术批评中，杰作通常被描述为具有强烈的美，所以选A。

2.When evaluating a painting, critics might use the term ______ to describe its color palette.A.vividB.virtualC.vitalD.vicious答案：A。

“vivid”表示鲜艳的、生动的；“virtual”意为虚拟的；“vital”是至关重要的；“vicious”表示恶毒的。

在评价一幅画时，评论家可能会用“生动的”来描述它的调色板，所以选A。

3.Art criticism often requires a ______ understanding of different art forms.A.profoundB.profuseC.proficientD.progressive答案：A。

“profound”表示深刻的；“profuse”意为大量的；“proficient”是熟练的；“progressive”表示进步的。

艺术批评通常需要对不同艺术形式有深刻的理解，所以选A。

4.The critic praised the sculpture for its ______ craftsmanship.A.exquisiteB.exhaustiveC.excessiveD.exclusive答案：A。

高三英语学术研究方法创新不断练习题40题答案解析1. In academic research, we need to be accurate and _____.A.preciseB.vagueC.casualD.sloppy答案解析：A。

选项A“precise”意为“精确的、准确的”，与题干中的“accurate”意思相近且符合学术研究需要精准的语境。

选项B“vague”是“模糊的”；选项C“casual”是“随意的”；选项D“sloppy”是“草率的”，都不符合学术研究的要求。

2. The research findings should be reliable and _____.A.dubiousB.incredibleC.trustworthyD.suspicious答案解析：C。

选项C“trustworthy”意为“值得信赖的”，与“reliable”相近，符合研究结果应可靠可信的语境。

选项A“dubious”是“可疑的”；选项B“incredible”是“难以置信的”；选项D“suspicious”是“怀疑的”，都不符合要求。

3. Academic research requires a lot of patience and _____.A.hasteB.impatienceC.diligenceziness答案解析：C。

选项C“diligence”意为“勤奋”，学术研究需要耐心和勤奋。

选项A“haste”是“匆忙”；选项B“impatience”是“不耐烦”；选项D“laziness”是“懒惰”，都与学术研究的要求相悖。

4. We should use valid methods and reliable data in academic research to ensure the ____ of the results.A.accuracyB.inaccuracyC.uncertaintyD.doubt答案解析：A。

2023 高考英语模拟试卷考生请注意：1．答题前请将考场、试室号、座位号、考生号、姓名写在试卷密封线内，不得在试卷上作任何标记。

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第一部分（共 20 小题，每小题 1.5 分，满分 30 分）1.All children should get access to a high-quality education their race, zip code or family income.B．regardless ofA．depending onC．due to D．apart from2.Any information of the oral test paper are regarded as strictly before it is open.B.analyticalA.conventionalC．controversial D．confidential3．I think Ana her packing since she started getting things ready early this morning.A．finishes B．has finishedC．had finished D．would finish4.Zhang Xuan, university student from Shanghai, learns art in her spare time.A．an; / B．a; the C．a; / D．an; the5.You will have to stay at home all day you finish all your homework.A．if B．unless C．whether D．because6.Our E nglish t eacher i s c onsiderate，helpful，and w arm-hearted，but s ometimes s he be angry at our silly mistakes.B.mustA.shouldC．can D．shall7.Lucia impressed her peer students with her musical talent, as well as several foreign languages .A．on her own B．under her control C．in her charge D．at her command8.Kate was very sad over loss of the photos she had shot in China, this was a memory she especially treasured.A.if B．whenC．as D．which9.It is not how much money you will give us but that you are present at the ceremony really matters. A．which B．it C．what D．that10．---Sorry to have broken your glass.--- . You didn’t mean to, did you?A．No problem B．Forget itC．All right D．Don’t say so11.An old lady came to the bus stop only the bus had gone.A．to run ; to find B．running;to find C．and ran ; finding D．running; finding12.That was not the first time he us. I think it's high time we strong actions against him. A．betrayed, take B．had betrayed, tookC．has betrayed, took D．has betrayed, take13.–Let’s take a coffee break, shall we?–I wish I , but I have a really tight schedule.A．can B．shall C．could D．should14．I would persuade her to make room for you it be necessary.B．mightA．couldC．should D．would15.—Jenny, how was your trip to Beijing?—Oh, I missed it. I wish I my vacation there.A．am spending B．will spend C．have spent D．had spent16.Abraham set himself up in front of his daughter, hands her hair, and was close to tears, reluctant to tear himself from her.A．to toy with B．toyed with C．toying with D．being toyed with17.It was not until she got home Jennifer realized she had lost her keys.B．thatA．whenC．where D．before18.At the meeting, the headmaster said that the government should prevent such things as hurt children again. A．happening B．happen C．to happen D happened19.The main issue at the APEC meeting was a climate-change plan by Australia’s Howard and backed by Bush.B．put offA．put outC．put away D．put forward20.Experience is a hard teacher because she the test first, the lesson afterwards.A．gives B．has given C．was giving D．would give第二部分阅读理解（满分40 分）阅读下列短文，从每题所给的A、B、C、D 四个选项中，选出最佳选项。

一种新科学方法英文*Abstract:*The scientific method has been the backbone of experimental research for centuries. However, in recent years, there has been a growing need for a new scientific method that can keep up with the rapid advancements in technology and expanding knowledge. This article proposes a new approach to scientific inquiry that takes into account the complexities of the modern world.IntroductionSince its inception, the scientific method has provided a systematic way to acquire knowledge, test hypotheses, and make accurate predictions. However, with the advent of new technologies and the increasing complexity of scientific questions, the traditional scientific method may no longer be sufficient. This article aims to introduce a new scientific method that can better cater to the demands of the modern scientific community.The Limitations of the Traditional Scientific MethodThe traditional scientific method, often referred to as the "hypothesis-driven" approach, is a linear process that involves making observations, formulating a hypothesis, conducting experiments, analyzing data, and drawing conclusions. While this method has been immensely successful in advancing scientific knowledge, it has itslimitations.One significant limitation is the exclusion of complex real-world systems. Traditional experiments are often conducted in highly controlled environments, which fail to capture the complex interactions and interdependencies that exist in nature. Additionally, the traditional scientific method tends to favor reductionism, dissecting complex problems into simpler components, and focusing only on one variable at a time. This reductionist approach may work in some cases, but it fails to address the holistic nature of interconnected systems found in nature. Introducing the Holistic Scientific MethodThe proposed holistic scientific method recognizes the limitations of the traditional approach and aims to bridge the gap between reductionism and the complexity of real-world systems. This method combines elements from other scientific approaches, such as systems thinking, network analysis, and computational modeling, to provide a more comprehensive understanding of complex systems.The holistic scientific method employs a multidisciplinary approach, integrating knowledge from various fields. Instead of starting with a single hypothesis, this method begins with a "conceptual framework" that represents the system under investigation. The conceptual framework takes into account the interdependencies, feedback loops, and emergent properties of the system. This framework is then used toguide the collection of data, design experiments, and analyze results.A key aspect of this method is the use of computational modeling and simulation techniques. These tools allow researchers to simulate the behavior of complex systems under different conditions, making it possible to explore scenarios that would be challenging or unethical to study in real life. The holistic scientific method also emphasizes the importance of collecting large datasets and utilizing advanced data analysis techniques, such as machine learning, to extract meaningful patterns and insights.Advantages and Potential ApplicationsThe holistic scientific method offers several advantages over the traditional approach. By considering the complexity and interconnectedness of natural systems, this method allows researchers to study phenomena that were previously difficult to tackle. It provides a more realistic representation of the real world, enabling the development of more accurate models and predictions.Moreover, the holistic scientific method can be applied to a wide range of disciplines, such as biology, ecology, economics, and social sciences. It can help understand complex biological systems, analyze social networks, predict economic fluctuations, and optimize resource allocation.ConclusionIn conclusion, the holistic scientific method aims to address the limitations of the traditional scientific method by incorporating interdisciplinary knowledge, computational modeling, and large datasets. This method provides a more comprehensive understanding of complex systems and enables researchers to tackle the challenges of the modern scientific landscape. By embracing the holistic scientific method, scientists can advance our knowledge and find solutions to the intricate problems we face in the 21st century.*Keywords: scientific method, holistic approach, complex systems, computational modeling, data analysis.*。

A Formal Approach to Designing Secure Software ArchitecturesHuiqun Yu,Xudong He,Yi Deng,Lian MoSchool of Computer ScienceFlorida International UniversityMiami,FL33199,USAyhq hex deng lmo01@AbstractSoftware architecture plays a central role in developing software systems that provide basic functionality and satisfy critical properties such as reliability and security.How-ever,little has been done to formally model software ar-chitectures and to systematically enforce required proper-ties.We aim to propose a formal approach to designing secure software architectures.We use the Software Archi-tecture Model(SAM),a general software architecture model combining Petri nets and temporal logic,as the underlying formalism.Architecture design consists of the functionality part and the security part.Guidelines are proposed to de-sign functionality of software architectures at both element level and composition level.Software security is enforced by stepwise refinement.1IntroductionSoftware security has emerged as a foremost concern for modern information enterprise.Several well-known secu-rity system architectures and models,including CORBA, EJB,and DCOM,are cornerstones for designing scalable andflexible security systems.Despite these advances,how-ever,how to analyze the design of security systems to en-sure its consistency and integrity is still a largely open prob-lem.There is lack of rigorous and systematic ways in the literature to assess and assure critical properties in archi-tectural composition of security systems.Although formal verification of security protocols has received increasing attention in recent years[3,8],these techniques are nor-mally based on abstract computation models and are not concerned with composition or architecture of security sys-tems.Many of these formal models or techniques are de-veloped for a single security model and do not scale well.Supported in part by the NSF under grants HRD-0317692and CCR-0226763,and by NASA under grant NAG2-1440.To address the problem,we propose a formal approach to designing secure software architectures based on SAM [9].In SAM,a software architecture is defined by a hierar-chical set of compositions,each of which consists of a set of components,a set of connectors and a set of constraints to be satisfied by the interacting components.Our research objectives are to provide a formal architec-tural model for security systems,and a formal method for security enforcement and analysis.2Research DescriptionSecurity system architecture design in SAM includes two parts.One is the functionality part,which deals with the overall structure of the software architecture,including the components,connectors,their hierarchical relationships,as well as the interfaces.The other is the security part,which handles security requirement modeling,specification,and enforcement.There are two distinct levels of software architecture for functionality design in SAM,i.e.element level and compo-sition level.In SAM,each element(either a component or a connector)is specified by a tuple.is a prop-erty specification,written in temporal logic[7],that speci-fies the required properties of the element and is a behav-ior model,defined by a Predicate Transition net(PrT net) [4],that defines the behavior of the element.and can be view as the specification and the implementation,respec-tively,as in many other software architecture models such as Wright[1].Therefore,to model an element is essentially to write and.To define an element constraint,we can either directly formulate the given user requirements or carry out a cause and effect analysis by viewing input ports as causes and output ports as effects.The general procedure to develop includes the following steps.e all the input and output ports as places of;2.Identify a list of events directly from the user require-ments or through Use Case analysis[2];3.Represent each event with a simple PrT net;4.Merge all the PrT nets together through shared placesto obtain;5.Apply the transformation technique[6]to makemore structured and or meaningful.For composition level design,SAM supports both top-down and bottom-up system development approaches.The top-down approach is used to develop a software architec-ture specification by decomposing a system specification into specifications of components and connectors and by refining a higher-level component into a set of related sub-components and connectors at a low level.The bottom-up approach is used to develop a software architecture specifi-cation by composing existing specifications of components and connectors and by abstracting a set of related compo-nents and connectors into a higher-level component.Often both the top-down approach and the bottom-up approach have to be used together to develop a software architecture specification.Security models are used to precisely describe the secu-rity relevant features of information systems.These models can be broadly categorized into access control models and informationflow models.Access control models describe the protection features of information access.The compo-nents of an access control model includea set of subjects,a set of objects,an access matrix that maintains the protection state of the system,anda set of rules for changing the protection state of thesystem.Informationflow models deal with informationflows that can be used to check whether any illegalflows can occur.In addition to those components for access control models,classes(or levels)for entities in informationflow models are necessary.We develop a formal way to embed the protection fea-tures of a software system in PrT nets.Each place in a PrT net represents a subject.The token contains interested at-tributes of its corresponding subject.Access matrix can be derived from the markings of places.The access control rules are coded as transitions and taken as operators on Petri nets.These rules provide a disciplined way to construct Petri net models,in which security policies are proved to be correctly enforced.Three correctness criteria of SAM models are identified in[5],which include element correctness,composition cor-rectness,and refinement correctness.Basically,to ensure the correctness of a SAM model is to show that all the con-straints are satisfied by the corresponding behavior models. There are broadly two approaches to correctness.One ap-proach is an ad hoc design followed by verification.Several verification techniques have been established in SAM,in-cluding reachability tree technique,deductive proof tech-nique,and structural induction technique[5];The other approach is correctness by construction,which involves a series of correctness-preserving transformations from one specification to another,and thefinal specification will meet certain required properties.Our security enforcement tech-nique follows the latter approach.Our future research interests include multi-policy en-forcement,modularity in policy representation,composi-tion,design,and analysis tools.References[1]R.Allen and D.Garlan.A formal basis for architecturalconnection.ACM Transactions on Software Engineer-ing and Methodology,6(3):213–249,1997.[2]G.Booch,J.Rumbaugh,and I.Jacobson.The Uni-fied Modeling Language User Guide.Addison-Wesley Longman,Inc.,1999.[3]M.Burrows,M.Abadi,and R.Needham.A logic of au-thentication.ACM Transactions on Computer Systems (TOCS),8(1):18–36,1990.[4]X.He.A formal definition of hierarchical predicatetransition nets.In Proceedings of the17th International Conference on Application and Theory of Petri Nets, LNCS1091,pages212–229.Springer-Verlag,1996. [5]X.He and Y.Deng.A framework for developing andanalyzing software architecture specifications in SAM.The Computer Journal,45(1):111–128,2002.[6]X.He and J.A.N.Lee.A methodology for construct-ing predicate transition net specifications.Software-Practice and Experience,21(8):845–875,1991.[7]Z.Manna and A.Pnueli.The Temporal Logic of Reac-tive and Concurrent Systems:Specification.Springer-Verlag,1992.[8]S.Schneider.Verification authentication protocols inCSP.IEEE Transactions on Software Engineering, 24(9):741–758,1998.[9]J.Wang,X.He,and Y.Deng.Introducing software ar-chitecture specification and analysis in SAM through an rmation and Software Technology, 41:451–467,1999.。

SectionⅡLesson2HowDoWeLikeTeachers’Feedback?&Lesson3SoClose,YetSoFar一、单词拼写1.Mr.Li is busy these days because he has many problems to(处理) in his work.2.She has a special (偏好) for physical sports when she is free from work.3.When she was caught stealing,the woman felt much (羞愧的).4.Much to my surprise,the boy worked out this problem in a different (方式).5.With the help of my (笔记本电脑),I can update my blog whenever and wherever I am.6.Mike is more (内敛的) than most of his colleagues in his office.7.Only by working hard can you make your dream come into(现实).8.You must get all of your work finished by the (最后期限) which falls on March 5.9.Don’t (抱怨) about your problems as they may be troubled by the same problems.二、单句语法填空1.The little boy cried with the (intend) of getting his grandpa to buy him sweets.2.Do you have any special (prefer) in your spare time?3.This medicine sounds very (effect),but it doesn’t really work much.4.Do you know that woman (dress) in red over there?5.We shall have (far) discussion about this problem at the meeting.6.I wish you (stay) home yesterday.You only made things worse.7.With time going by,the girl has got used to (live) by herself now.8.It is a fact that Mike is always the first person (get) to the office.9.Mrs.Smith does almost all the housework his husband does nothing of it at all.10.Johnson found rather difficult for him to fit into the group.三、完成句子1.All the students, (包括这对双胞胎),are going on a field trip.2.The government (禁止出版) such bad books.3.She reached the top of the hill and(停下来休息) on a big rock by the side of the path.4.The would-be father stood outside the operation room,(焦急等待着) the coming baby.5.He wrote a silly letter, (使得整个局面更复杂).6.The result of their research(提出进一步的问题) about the issue.7.The house fell down (他们还没来得及跑出来).8.By no means (你应该把我的教科书给他).四、阅读理解AWe’ve all been there:in a lift,in line at the bank or on an airplane,surrounded by people who are,like us,deeply focused on their smartphones or,worse,struggling with the uncomfortable silence.What’s the problem?It’s possible that we all have compromised conversational intelligence.It’s more likely that none of us start a conversation because it’s a wkward and challenging,or we think it’s annoying and unnecessary.But neong strangers,consider that small talk is worth the trouble.Experts say it’s an invaluable social practice that results in big benefits.“Dismissing small talk as unimportant is easy,but we can’t forget that deep relationships wouldn’t even exist if it weren’t for casual conversation.Small talk is the grease(润滑油) for social communication,”says Bernardo Carducci,director of the Shyness Research Institute at Indiana University Southeast.“A lmost every great love story and each big business deal begins with small talk,” he eall talk is learning how to connect with others,not just communicate with them.”In a study,Elizabeth Dunn,associate professor of psychology at UBC,invited people on their way into a coffee shop.One group was asked to seek out an interaction(互动) with its waiter;the other,to speak only when necessary.The results showed that those who chatted with their server reported significantly higher positive feelings and a better coff ee shop experience.“It’s not that talking to the waiter is better than talking to your husband,” says Dunn.“But interactions with peripheral(外围的) members of our social network matter for our well-being also.”Dunn believes that people who reach out to strangers feel a significantly greater sense of belonging,a bond withothers.Carducci believes developing such a sense of belonging starts with small talk.“Small talk is the basis of good manners,” he says.1.What phenomenon is described in the first paragraph?A.Addiction to smartphones.B.Inappropriate behaviours in public places.C.Absence of communication among strangers.D.Impatience with slow service.2.What is important for successful small talk according to Carducci?A.Showing good manners.B.Relating to other people.C.Focusing on a topic.D.Making business deals.3.What does the coffee shop study suggest about small talk?A.It improves family relationships.B.It raises people’s confidence.C.It matters as much as a formal talk.D.It makes people feel good.4.What is the best title for the teaking Small TalkC.Benefits of Small TalkD.Uncomfortable SilenceBArriving in Sydney on his own from India,myhusband,Rashid,stayed in a hotel for a short time while looking for a house for me and our children.During the first week of his stay,he went out one day to do some shopping.He came back in the late afternoon to discover that his suitcase was gone.He was extremely worried as the suitcase had all his important papers,including his passport.He reported the case to the police and then sat there,lost and lonely in a strange city,thinking of the terrible troubles of getting all the paperwork organised again from a distant country while trying to settle down in a new one.Later in the evening,the phone rang.It was a stranger.He was trying to pronounce my husband’s name and was asking him a lot of questions.Then he said they had found a pile of papers in their trash can that had been left out on the footpath.My husband rushed to their home to find a kind family holding all his papers and documents.Their young daughter had gone to the trash can and found a pile of unfamiliar papers.Her parents had carefully sorted them out,although they had found mainly foreign addresses on most of the documents.At last they had seen ahalf-written letter in the pile in which my husband had given his new telephone number to a friend.That family not only restored the important documents to us that day but also restored our faith and trust in people.We still remember their kindness and often send a warm wish their way.5.What did Rashid plan to do after his arrival in Sydney?A.Go shopping.B.Find a house.C.Join his family.D.Take a vacation.6.The girl’s parents got Rashid’s phone number from .A.a friend of his familyB.a Sydney policemanC.a letter in his papersD.a stranger in Sydney7.What does the underlined word “restored” in the last paragraph mean?A.Showed.B.Sent out.C.Delivered.D.Gave back.8.Which of the following can be the best title for the te India to AustraliaB.Living in a New CountryC.Turning Trash into TreasureD.In Search of New Friends五、七选五阅读理解HowtoCommunicatewithaDeafPersonCommunicating with a deaf person doesn’t have to be as difficult as it might seem.The trick is to bepatient,straightforward,and to remember that deaf people communicate visually.Method1:StartingYourConversation1 You can do this by moving into the person’s field of vision and waving from a polite distance,or by tapping the person gently on the shoulder.Position yourself carefully.Make sure that the light in the room is shining directly onto your face,and that you’re not standing with your back to a light. 2Find out how the person prefers to communicate.Some deaf people are better lip-readers than others.Some deaf people may prefer to write back and forth or to use an interpreter.Man interactions between the deaf and the hearing require a combination of these methods. 3Method2:CommunicatingThroughLip-readingKeep your sentences simple and use plain language. 4 The more compleore likely your deaf companion is to miss something.When someone else is speaking,don’t turn away from the deaf person in your group. 5 You don’t have to look at the deaf person while someone else is talking,but try to make sure your face is visible.A.Get the person’s attention.B.It’s important not to talk too quickly.C.Or,they’ll miss parts of the conversation.D.If so,it’ll make them feel left out of the conversation.E.Stand directly in front of the person,at a normal distance.F.The best way to know which methods are most effective is to ask.G.Try not to be too difficult when using your words in the beginning.答案：一、1.handle 2.preference 3.ashamed ptop6.reserved7.reality8.deadlineplain二、1.intention 2.preference 3.effective4.dressed 5.further6.had stayed7.living8.to get9.while 10.it三、1.including the twins/the twins included2.forbids publishing3.stopped to rest4.waiting anaking the whole situation more complicated 6.raised further questions 7.before they could run out8.should you give him my textbook(s)四、1.C 段落大意题。

高一英语学术写作技巧单选题50题1. In academic writing, it is important to use precise _____.A. wordsB. phrasesC. sentencesD. paragraphs答案：A。

在学术写作中，使用精确的词汇很重要。

选项B“phrases”是短语；选项C“sentences”是句子；选项D“paragraphs”是段落。

相比之下，精确性更多体现在词汇上。

2. Academic papers often require _____ language.A. formalB. informalC. casualD. slang答案：A。

学术论文通常需要正式的语言。

选项B“informal”是非正式的；选项C“casual”是随意的；选项D“slang”是俚语，都不适合学术写作。

3. When writing an academic essay, you should avoid using _____ expressions.A. vividB. colorfulC. vagueD. clear答案：C。

写学术论文时，应避免使用模糊的表达。

选项A“vivid”是生动的；选项B“colorful”是丰富多彩的；选项D“clear”是清晰的，都不是应该避免的。

4. In academic writing, _____ words can make your argument more persuasive.A. weakB. strongC. simpleD. common答案：B。

在学术写作中，有力的词汇能使你的论点更具说服力。

选项A“weak”是软弱的；选项C“simple”是简单的；选项D“common”是常见的，都不如有力的词汇效果好。

5. Academic reports should be written in _____ style.A. narrativeB. descriptiveC. argumentativeD. poetic答案：C。

高一英语学术用语单选题50题1. In an academic paper, when you present an idea that you will test, you call it a ____.A. resultB. hypothesisC. factD. theory答案：B。

解析：在学术论文中，当提出一个将要进行测试的想法时，这个想法被称为假设，“hypothesis”就是假设的意思。

A选项“result”是结果，是经过测试等得出的最终成果，不是刚开始提出的待测试的想法。

C选项“fact”是事实，是已经被证实的情况，不是待测试的想法。

D选项“theory”是理论，是经过大量研究和论证形成的一套体系，也不是刚开始提出的待测试的想法。

2. At the end of an academic research, we usually draw a ____.A. decisionB. conclusionC. choiceD. judgment答案：B。

解析：在学术研究结束时，通常会得出一个结论，“conclusion”有结论的意思。

A选项“decision”更多指做决定，侧重于在不同选项之间做出抉择，与学术研究得出结果的语境不符。

C选项“choice”是选择，与学术研究得出最终结果的含义不同。

D选项“judgment”更多指判断、评判，不是学术研究最后得出的结论的意思。

3. When we want to add more information in an academic paper, we can use the phrase ____.A. in factB. in generalC. in additionD. in short答案：C。

解析：在学术论文中，当想要添加更多信息时，常用“in addition”这个短语。

A选项“in fact”是事实上，用于强调事实情况，不是用于添加信息。