Improving performance of case-based classification using context-based relevance
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大学英语四级考试2024年6月真题(第一套)Part I Writing (30 minutes) Directions: Suppose your university is seeking students'opinions on whether university libraries should be open to the public.You are now to write an essay to express your view.You will have 30 minutes for the task.You should write at least 120 words but no more than 180 words.Part ⅡListening Comprehension (25 minutes) Section ADirections: In this section,you will hear three news reports.At the end ofeach news report,you will hear two or three questions.Both the news report and the questions will bespoken only once.Afier you hear a question,you must choose the best answer from the four choices marked A),B),C)and D).Then mark the corresponding letter on Answer Sheet 1 with a single line through the centre.Questions 1 and 2 are based on the news report you have just heard.1.A)Due to a fire alarm in their apartments.B)Because of the smoke and heat damage2.A)Investigating the cause of the incident.B)Helping search for the suspect of the crime. C)Due to the water used to extinguish the flames.D)Becauseof the collapse of the three-story building.C)Rescuing the businessmen trapped in the building.D)Checking town records for the property developer.Questions 3 and 4 are based on the news report you have just heard.3.A)It plays a less important role in one's health than nutrient intake.B)It impacts people's health to a lesser degree than sun exposure.C)It is associated with people's mental health conditionsD)It is linked with older adults'symptoms ofdepression4.A)It was indefinite C)It was straightforward.B)It was systematic. D)It was insignificant. Questions 5 to 7 are based on the news report you have just heard.5.A)It has helped solve several murder cases.B)It has become a star police dog in Beijing6.A)To speed up investigation into criminal cases.B)To test the feasibility of cloning technology.7.A)Cloning is too complicated a processB)The technology is yet to be accepted C)It has surpassed its mother in performance.D)It has done better than naturally born dogs.C)To cut down training expensesD)To reduce their training time.C)Cloning is ethically controversial.D)The technology is too expensive.Section BDirections: In this section,you will hear two long conversations.At the end ofeach conversation,you will hearfour questions.Both the conversation and the questions will bespoken only once.After you hear a question,you must choose the best answer from the four choices marked A),B),C)and D).Then mark the corresponding letter on Answer Sheet I with a single line through the centre.Questions 8 to 11 are based on the conversation you have just heard.8.A)He read it somewhere online.B)He heard about it from a coworker.9.A)His publications.B)His first book.10.A)Collect a lot more data.B)Relax a bit less often.11.A)Find out the show's most interesting episodesB)Watch the series together with the woman. C)He read an article reviewing it.D)He watched a TV series based on it.C)His addressD)His name.C)Clarify many new conceptsD)Read more reference books.C)Get an e-Copy of the book to read.D)Check to see when the show starts.Questions 12 to 15 are based on the conversation you have just heard.12.A)To check the prices of his farm produce.B)To ask the way to the Newcastle City Hall.13.A)Bakers.B)Vendors14.A)The issuing of certificates to vendors.B)The completion of the baking task.15.A)The closing date of submission. C)To inquire about the vegetarian food festival.D)To seekthe man's help with her work on the farm.C)Vegetarians.D)OrganisersC)The festival they are organising.D)The deadline for application.C)The details of the ceremonyB)The website of his company. D)The organiser'saddressSection CDirections: In this section,you will hearthre passages.At the end ofeach passage,you will hear three or four questions.Both the passage and the questions will be spoken only once.Afteryou hear a question,you must choose the bestanswer from the four choices marked A),B),C)and D).Then mark the corresponding letter on Answer Sheet I with a single line through the centreQuestions 16 to 18 are based on the passage you havejust heard.16.A)Most scenic sites have been closed.B)Access to official campsites is limited17.A)It is strongly opposed by nearby residentsB)It leads to much waste of public money18.A)Look for open land in ScotlandB)Leave no trace of their camping C)Health experts advise going outdoors.D)People have more time during the summer.C)It has caused environmental concernsD)It has created conflicts among campers.C)Avoid getting close to wilderness.D)Ask for permission from authorities.Questions 19 to 21 are based on the passage you have just heard.19.A)They outcompete mythical creatures.B)They usually mind their own business. C)They truly exist in the AmazonregionD)They resemble alarmingly large snakes20.A)Scar tissue from dolphins'fighting.B)Skin infection from water pollution.21.A)It has been shrinking at an astonishing pace. C)Unhealed wounds from snake bites.D)Swimming along in seasonal floods.B)It has been placed under international protection.C)It has been appealing to both freshwater and sea dolphinsD)It has been abandoned as a battleground for male dolphins.Questions 22 to 25 are based on the passageyou have just heard.22.A)About 58%of young adults call parental support the new normal.B)Most adult children enjoy increasing sources of financial supportC)A full 70%of the young adults cannot afford to buy a car by themselves.D)Most early adults cannot sustain their lifestyles without parental support23.A)It renders them dependent. C)It makes them mentally immature.B)It causes them to lose dignity. D)It hinders them from getting ahead.24.A)It challenges one's willpower C)It calls for due assistance.B)It results from education. D)It defines adulthood.25.A)Current lifestyles C)College loansB)Poor budgeting. D)Emergency expensesPart ⅢReading Comprehension (40 minutes) Section ADirections: In this section,there is a passage with ten blanks.You are required to select one word for each blank from a list of choices given in a word bank following the passage.Read the passage through carefully before making your choices.Each choice in the bank is identified by a letter:Please mark the corresponding letter for each item on Answer Sheet 2 with a single line through the centre.You may not use any of the words in the bank more than once.It's well known that physical exercise is beneficial not just to physical health but also to mental health.Yet whereas most countries have 26 evidence-backed guidelines on the type and intensity of exercise 27 for various physical health benefits,such guidelines do not yet exist for exercise and mood. This is 28 due to a lack of necessary evidence.However,a new systematic review brings us usefully up- to-date on the current findings in this area.Before 29 into some of the key take-aways,an important 30 made in the review is between aerobic exercise and anaerobic.The former 31 such things as walking,jogging and cycling and meansexercising in such a way that your body is able to use oxygen to burn fat for energy.In contrast,anaerobic exercise—such as lifting heavy weights—is of such 32 intensity that your body does not have time to use oxygen to create energy and so instead it breaksdown glucose(葡萄糖)in your blood or muscles.Beginning first with the influence of exercise intensity on the mood benefits of aerobic exercise,the researchers,led by John Chan at Shenzhen University,found 33 resultsfrom 19 relevant studies.Somefavoured higher intensity,others low,while seven studies found that intensity made no 34 _to mood benefits.In relation to the intensity of anaerobic exercise,however,the results were far clearer—the optimum (最佳选择)for improving mood is 35 intensity,perhaps because low intensity is too dull while high intensity is too unpleasantSection BDirections: In this section,you are going to read a passage with ten statements attached to it.Each statement contains information given in one of the paragraphs.Identify the paragraph from which the information is derived.You may choose a paragraph more than once.Each paragraph is marked with a letter.Answer the questions by markingthe corresponding letter on Answer Sheet 2.Why DoAmericans Work So Much?A)How will we all keep busy when we only have to work 15 hours a week?That was the question that worriedthe British economist John Maynard Keynes when he wrote his short essay“Economic Possibilities for Our Grandchildren”in1930.Over the next century,he predicted,the economy would become so productive that people would barely need to work at all.For a while,it looked like Keynes was right.In 1930 the average working week was 47 hours in the United States.But by 1970,the number of hours Americans worked on average had fallen to slightly less than 39.B)But then something changed.Instead of continuing to decline,the duration of the working weekremained stable.It has stayed at just below 40 hours for nearly five decades.So what happened?Why are people working just as much today as in 1970?C)There would be no mystery in this if Keynes had been wrong about the power of technology to increase theeconomy's productivity,which he thought would lead to a standard of living “between four and eight times as high as it is today.”But Keynes got that right:Technology has made the ec onomy massively moreproductive. According to Benjamin M.Friedman,an economistat Harvard,the U.S.economy is right ontrack to reach Keynes's eight-fold(八倍)multiple by2029.That is a century after the last data Keynes wouldhave had access to.D)In a new paper,Friedman tries to figure out why that increased productivity has not translated into increasedleisure time.Perhaps people just never feel materially satisfied,always wanting more money to buy the nextnew thing.This is a theory that appeals to many economists.“This argument is,at best,far from sufficient,”he writes.If that were the case,why did the duration of the working week decline in the first place?E)Another theory Friedman considers is that,in an era of ever fewer settings that provide effectiveopportunitiesfor personal connections and relationships,people may place more value on the socializing that happens at work.There is support for this theory.Many people today consider colleagues as friends.But Friedman argues that the evidence for this theory is far from conclusive.Many workers report that they would like to spend more time with family,rather than at work.Furthermore,this theory cannot explain the change in trend in the U.S.working week in the 1970s.F)A third possibility proves more convincing for Friedman.That is:American inequality means that the gainsof increasing productivity are not widely shared by everyone.In other words,most Americans are too poor to work less.Unlike the other two explanations Friedman considers,this one fits chronologically(按年代).Inequality declined in America during the period following World War II,along with the duration of the working week.But since the early 1970s it has risen dramatically.G)Keynes's prediction of a shorter working week rests on the idea that the standard of living would continuerising for everyone.But Friedman says that this is not what has happened.Although Keynes's eight-fold figure holds up for the economy as a whole,it is not at all the case for the median(中位数的)American worker.For them,output by 2029 is likely to be around 3.5 times what it was when Keynes was writing.This is a bit below his four-to eight-fold predicted rangeH)This can be seen in the median worker's income over this time period,complete with a shift in 1973 that fitsin precisely with when the working week stopped shrinking.According to Friedman,between 1947 and 1973 the average hourly wage for normal workers (those who were not in management roles)in private industries other than agriculture nearly doubled in terms of what their money could buy.But by 2013 the average hourly wage for ordinary workers had fallen 5 percent from the 1973 level in terms of actual purchasing power.Thus,though American incomes may have gone up since 1973,the amount that American workers can actually buy with their money has gone down.For most Americans,then,the magic of increasing productivity stopped working around 1973.Thus,they had to keep working just as much in order to maintain their standard of livingI)What Keynes predicted was a very optimistic version of what economists call technological unemployment.This is the idea that less labor will be necessary because machines can do somuch.In Keynes's vision,the resulting unemployment would be distributed more or less evenly across society in the form of increased leisure.But Friedman says that,for Americans,reality is much darker.Americans now have a labor market in which millions of people—those with fewer skills and less education—are seeking whatever poorly paid work they can get.This is confirmed by a recent poll that found that,for half of hourly workers,their top concern is not that they work too much but that they work too little.This is most likely not because they like their jobs so much.Rather,we can assume it is because they need the money.J)This explanation leaves an important question.If the very rich—the workers who have reaped above-average gains from the increased productivity since Keynes's time—can afford to work less,why do they continue to work so much?(Indeed,research has shown that the highest earners in America tend to work the most.)Friedman believes that for many top earners,work is a labor of love.They are doing work they care about and are interested in,and doing more of it is not necessarily a burden.For them,it may even be a pleasure.Thesetop earners derive meaning from their jobs and work is an important part of how they think of themselves.And,of course,they are compensated for it at a level that makes it worth their while.K)Friedman concludes that the prosperity(繁荣)Keynes predicted is here.After all,the economy as a whole has grown even more brilliantly than he expected.But for most Americans,that prosperity is nowhere to beseen.And,as a result,neither are those shorter working weeks.36.Some people view socializing at the workplace as a chance to develop personal relationships.37.As ordinary American workers'average hourly pay had decreased despite increasing productivity,they hadto work just as manyhours as before to keep their living standards.38.American workers'average weekly workingtime has not changed for nearly half a century.39.Friedman believes inequality in the rgely explains why increasing productivity has not resulted inreduced working hours.40.Many economists assume people's thirst for material things has prevented them from enjoying moreleisure time.41.An economist'sprediction about a shorter average working week seemed to be correct for a time in the 20thcentury.42.In the bor market,the primary concern of people with less schooling and fewer skills is to secure anyemployment even if it is low-paid.43.Keynes was right in predicting that technology would make the economy much more productive.44.Many of the highest earners have a keen interest in and love for what they are doing45.According to Keynes,there would be a shorter working week with everyone's standard of living continuingtorise.Section CDirections:There are 2 passages in this section.Eachpassage is followed by some questions or unfinished statements.For each of them there are four choices marked A),B),C)and D).You should decide on the best choice and mark the corresponding letter on Answer Sheet 2 with a single line through the centre.Passage OneQuestions 46 to 50 are based on the following passage.Lao Zi once said,“Care about what other people think and you will always be thei r prisoner.”People-pleasing,or seeking self-worth through others'approval,is unproductive and an exhausting way to go through life.Why do we allow what others think of us to have so much power over how we feel about ourselves?If it's true that you can't please all people all of the time,wouldn't it make sense to stop trying?Unfortunately,sense often isn't driving our behavior.For social beings who desire love and belonging, wanting to be liked,and caring about the effect we have on others,is healthy and allows us to make connections.However,where we get into trouble is when our self-worth is dependent upon whether we win someone's approval or not.This need to be liked can be traced back to when we were children and werecompletely dependent on others to take care of us:Small children are not just learning how to walk and communicate,they are alsotrying to learn how the world works.We learn about who we are and what is expected of us based on interactions with others so,to a four-year-old,if Mommy or Daddy doesn't like him or her,there is the danger that they will abandon them.We need to understand that when we desperately want someone to approveof us,it's being driven by thatlittle kid part of us that is still terrified of abandonment.As you become more capable of providing yourself with the approval you seek,your need for external validation will start to vanish,leaving you stronger,more confident,and yes,happier in your life.Imagine howmuch time we lose each moment we restrainour authentic selves in an effort to be liked.If we base our worth on the opinions of others,we cheat ourselves of the power to shape our experiences and embrace life not only for others but also for ourselves,becauseultimately,there is no difference.So embrace the cliché(老话)and loveyourself as it's highly doubtful that you'll regret it.46.What can we conclude from Lao Zi's quotation?A)We should seethrough otherpeople's attempt to make a prisonerof us.B)We can never really please other people even if we try as hard as we can.C)We can never be truly free if taking to heart others'opinion of us.D)We should care about other people's view as much as they care about our own.47.What will happen if we base our self-worth on other people's approval?A)Our desire to be loved will be fulfilled. C)Our identity as social beings will be affected.B)Our life will be unfruitful and exhausting D)Our sense of self will be sharpened and enhanced.48.What may account for our need to be liked or approved of?A)Our desperate longing for interactions with others.C)Our knowledge about the pain of abandonment.B)Our understanding of the workings of the world. D)Our early childhood fear of being deserted.49.What can we do when we become better able to provide ourselves with the desired approval?A)Enjoy a happier life.C)Receive more external validation.B)Exercise self-restraint.D)Strengthen our power of imagination.50.What does the author advise us to do in the last paragraph?A)Embrace life for ourselves and for others. C)See our experiencesas assets.B)Base our worth on others'opinions. D)Love ourselves as we arePassage TwoQuestions 51 to 55 are based on the following passage.Some people have said aging is more a slide into forgetfulness than a journey towards wisdom.However,a growing body of research suggests that late-in-life learning is possible.In reality,education does an aging brain good.Throughout life,people's brains constantly renovate themselves.In the late 1960s,British brain scientist Geoffrey Raisman spied growth in damaged brain regions ofrats through an electron microscope; their brains were forging new connections.This meant brains may change every time a person learns something new.Of course,that doesn't mean the brain isn't affected by the effects of time.Just as height usually declines over the years,so does brain volume:Humans lose about 4 percent every decade starting in their 40s.But that reduction doesn't necessarily make people think slower;as long as we are alive and functioning,we can alter our brains with new information and experiences.In fact,scientists now suspect accumulating novel experiences,facts,and skills can keep people's minds more flexible.New pathways can strengthen our ever-changing mental structure,even as the brain shrinks.Conventional fixes like word puzzles and brain-training apps can contribute to mental durability.Even something as simple as taking a different route to the grocery store or going somewhere new on vacation can keep the brain healthy.A desire for new life challenges can further boost brainpower.Research about aging adults who take on new enterprises shows improved function and memory as well as a reduced risk of mental disease.Openness—a characteristic defined by curiosity and a desire for knowledge—may also help folks pass brain tests.Some folks are born with this take-in-the-world atitude,but those who aren't as genetically gifted aren't necessarily out of luck.While genes can encourage an interest in doing new things,a 2012 study in the journal Psychology and Aging found completing reasoning tasks like puzzles and number games can enhance that desire for novel experiences,which can,in turn,refresh the brain.That's why brain scientist Richard Kennedy says “It's not that old dogs can't learn new tricks.It's that maybe old dogs don't realize why they should.”51.What do some people think of aging adults?A)Their wisdom grows as time goes by. C)They can benefit from late-in-life learning.B)Their memory gradually deteriorates D)They are likely to have mental health issues.52.What can we conclude from Geoffrey Raisman's finding?A)Brain damage seriously hinders one'slearning. C)Brains can refresh and improve with learning.B)Brain power weakens slower than we imagine D)Brains forge connections under new conditions53.What is one thing that helps maintain the health of our brain even as it shrinks?A)Doing daily routines by conventional means. C)Imitating old dogs'way of learning new tricksB)Avoiding worrying about our mental durability. D)Approaching everyday tasks in novel ways.54.What does the author say can contribute to the improvement of brain function?A)Being curious and desiring knowledge. C)Rising to life's challenges and avoiding risks.B)Being eager to pass brain tests at an old age. D)Boosting immunity to serious mental diseases55.What is the finding of the 2012 study in the journal Psychology and Aging?A)Wishing to solve puzzles enhances one's reasoning power.B)Playingnumber games unexpectedly stimulates one's memory.C)Desiring new experiences can help to renovate thebrain.D)Learning new tricks shouldnot beconfined to old dogs only.Part IV Translation (30 minutes) Directions: For this part,you are allowed 30 minutes to translate a passage from Chinese into English.You should write your answer on AnswerSheet 2.四合院(siheyuan) 是中国一种传统的住宅建筑,其特点是房屋建造在一个院子的四周,将院子合围在中间。
如何提高象棋业绩英语作文How to Improve Chess Performance。
Introduction:Chess is a strategic board game that requires critical thinking, planning, and decision-making skills. To improve chess performance, players need to focus on various aspects such as studying tactics, analyzing games, practicing regularly, and adopting a growth mindset. In this essay, we will explore the different strategies and techniques that can be employed to enhance chess performance.Body:1. Study Tactics:One of the most important aspects of chess is understanding and applying tactics. Tactics involve combinations and sequences of moves that can help gain anadvantage or win the game. To improve chess performance, players should dedicate time to studying tactics, solving puzzles, and practicing different tactical patterns. This not only enhances their ability to recognize tactical opportunities but also improves their calculation and visualization skills.2. Analyze Games:Analyzing games, especially those played by strong players, is an effective way to improve chess performance. By studying the moves and strategies employed by experienced players, one can gain valuable insights into the game. It helps players understand different opening variations, middle game plans, and endgame techniques. Additionally, analyzing one's own games allows for self-reflection and identification of weaknesses, which can be further worked upon.3. Practice Regularly:Consistent practice is crucial for improving chessperformance. Regularly playing games against opponents of varying skill levels helps in developing a better understanding of different positions and strategies. It also helps in improving decision-making skills under time pressure. Additionally, participating in chess tournaments and competitions provides an opportunity to test one's skills and gain experience in a competitive environment.4. Study Endgames:Endgames often play a decisive role in determining the outcome of a chess game. Therefore, it is essential to study and practice different endgame positions. By learning various endgame techniques, such as pawn promotion, king and pawn endgames, and rook endgames, players can improve their ability to convert advantages into victories. Understanding the fundamental principles of endgames, such as king activity and pawn structure, is crucial for making accurate decisions and avoiding unnecessary mistakes.5. Develop a Growth Mindset:Having a growth mindset is essential for continuous improvement in chess. Embracing challenges, learning from failures, and seeking feedback are characteristics of a growth mindset. Chess players should view losses as opportunities for learning and identify areas that need improvement. By adopting a growth mindset, players can remain motivated, resilient, and open to new strategies and ideas.Conclusion:Improving chess performance requires a combination of strategic thinking, practice, and continuous learning. By studying tactics, analyzing games, practicing regularly, studying endgames, and adopting a growth mindset, chess players can enhance their skills and achieve better results. It is important to remember that improvement takes time and effort, and consistent dedication to these strategies will lead to progress in chess performance.。
海军工程大学学报JOURNAL OF NAVAL UNIVERSITY OF ENGINEERING第32卷第5期2020年10月Vol32 No5Oct 2020DOI :10.7495/j.issn.1009-3486.2020.05.008基于PBC 模式的装备修理定价模型研究林名驰12,张澄宜3,訾书宇1,魏 华1(1.海军工程大学管理工程与装备经济系,武汉430033; 2.海军工程大学兵器工程学院,武汉430033;3.海军指挥学院战略战役系,南京210016/摘要:针对装备修理费用高、传统修理定价模式激励效果欠佳的问题,建立了基于性能的合同(performance based contracts ,PBC )模式下改进装备可靠性的模型。
不同于传统的维修保障模式,PBC 模式下军方购买的是长期的装备维修服务而并非单次的维修活动,这种模式被证明能够提高装备性能并降低维修费用。
该模型将 承制单位的利润与装备可靠性挂钩,通过定价机制设计在满足参与约束和激励相容约束的基础上,激励承制单位选择使军方年均支付最小化的可靠性,求得对应的价格作为修理价格。
结果表明:使用该定价模型,会促进 承制单位改进装备可靠性,从而获得更高的收益。
关键词:定价模型;可靠性;基于性能的合同;装备维修中图分类号:F270文献标志码:A文章编号:1009 —3486(2020)05 — 0038 — 06Equipment maintenance pricing model based onperformance-based contractsLIN Ming-chi 12, ZHANG Cheng-yi 3 , ZI Shu-yu 1, WEI Hua 1(1.Dept . of Management Engineering and Equipment Economics , Naval Univ. of Engineering , Wuhan430033,China ; 2Co l egeof WeaponryEngineering ,NavalUniv ofEngineering ,Wuhan430033,China ; 3Dept ofStrategyandCampaign ,NavalCommandCo l ege ,Nanjing210016,China )Abstract : Inviewofthehighcostofequipmentrepairandthepoorincentivee f ectofthetraditionalrepairpricing model ,a modelforimprovingequipmentreliabilityundertheperformance-basedcon- tracts (PBC ) wasestablished Unlikethetraditionalmaintenancesupportmethod ,customersinPBCpurchase long-term equipment maintenanceservicesratherthanasingle maintenanceactivity Thiskindofcontracthasbeenproventoimproveequipmentperformanceandreduce maintenancecosts The model links the profit of supplier to the equipment reliability . According to the design of the pii-cing mechanism , on the basis of satisfying the participation constraint and incentive compatibility con-straint ,themodelmotivatessupplierstochoosethereliabilitythatminimizestheaverageannualpay- mentofcustomers ,andthecorrespondingrepairpriceisobtainedastherepairprice Thecasestudy provesthatthesupplierwi l improvetheequipmentreliabilitytoobtainhigherreturnsKeywords : pricing model ;reliability ; performance-basedcontracts ; equipmentmaintenance在装备修理定价中,传统的定价模式采用基方的要求修理装备以使其恢复到一定的技术状于时间和材料(T &M)的合同,承制单位根据军 态,而双方根据修理活动中消耗的资源进行定收稿日期:2019-04-06;修回日期:2019-06-14。
软考高级架构师系统设计40题1. In a system design, which of the following is the most important consideration for scalability?A. Hardware performanceB. Software architectureC. Network bandwidthD. User interface design答案:B。
解析:软件架构对于系统的可扩展性至关重要。
硬件性能在一定程度上影响,但不是最关键的。
网络带宽主要影响数据传输,对可扩展性的直接影响较小。
用户界面设计与系统的可扩展性关系不大。
2. When designing a system, which principle should be followed to ensure high availability?A. RedundancyB. Minimization of componentsC. Simple architectureD. Low cost答案:A。
解析:冗余是确保高可用性的重要原则。
减少组件可能会降低复杂性,但不一定能保证高可用性。
简单架构有助于理解和维护,但不是保证高可用性的关键。
低成本通常不是高可用性设计的首要考虑因素。
3. Which of the following is a key factor in determining theperformance of a system?A. The number of usersB. The algorithm usedC. The color scheme of the interfaceD. The brand of the hardware答案:B。
解析:算法的优劣直接决定了系统的性能。
用户数量会影响系统负载,但不是决定性能的根本因素。
界面的颜色方案与性能无关。
硬件品牌对性能有一定影响,但算法的影响更为关键。
第1篇Date: March 15, 2023Time: 2:00 PM - 4:30 PMLocation: Conference Room B, Education BuildingFacilitator: Dr. Emily ZhangParticipants:- Mr. Wang, Head of the English Department- Ms. Li, Senior English Teacher- Ms. Wu, Junior English Teacher- Mr. Zhang, Language Lab Coordinator- Ms. Chen, Curriculum Developer- Ms. Yang, Educational TechnologistIntroduction:The English Teaching and Research Class was organized to discuss recent trends in English language teaching, share innovative teaching methods, and explore the integration of technology in the classroom. The focus was on enhancing student engagement and improving language proficiency.Agenda:1. Opening Remarks by Dr. Emily Zhang2. Current Trends in English Language Teaching3. Innovative Teaching Methods4. Technology Integration in the Classroom5. Student Engagement and Assessment6. Group Discussions and Sharing7. Conclusion and Next Steps1. Opening Remarks by Dr. Emily ZhangDr. Zhang welcomed the participants and emphasized the importance of continuous professional development in the field of English language teaching. She highlighted the need to adapt to the changing educational landscape and explore new ways to engage students.2. Current Trends in English Language TeachingMs. Li presented the latest trends in English language teaching, which included:- Flipped Classroom: Where students learn new concepts outside of the classroom and then apply them in class discussions.- Project-Based Learning: Encouraging students to work on real-world projects that require them to use English in practical situations.- Gamification: Incorporating games and interactive elements to make learning more enjoyable and motivating.3. Innovative Teaching MethodsMr. Wang shared some innovative teaching methods that have been successful in his classroom:- Collaborative Learning: Pairing students with different language abilities to work on tasks together, promoting peer learning.- Language Learning Journals: Encouraging students to reflect on their learning experiences and set personal goals.- Storytelling: Using storytelling to make language learning more relatable and memorable.4. Technology Integration in the ClassroomMs. Wu discussed the effective use of technology in the classroom:- Interactive Whiteboards: Facilitating dynamic lessons and engaging students visually.- Online Learning Platforms: Providing students with access toadditional resources and allowing for flexible learning.- Educational Apps: Utilizing apps for vocabulary building, grammar practice, and pronunciation exercises.Mr. Zhang, the Language Lab Coordinator, demonstrated the use of language learning software and explained how it could be integrated into regular lessons.5. Student Engagement and AssessmentMs. Chen focused on strategies to enhance student engagement and assessment methods:- Differentiated Instruction: Tailoring lessons to meet the individual needs of students.- Formative Assessment: Regularly assessing student progress to inform teaching and learning.- Reflective Feedback: Providing students with constructive feedbackthat encourages growth and improvement.6. Group Discussions and SharingThe participants were divided into small groups to discuss the following topics:- How to effectively incorporate technology into the classroom.- Best practices for assessing speaking and listening skills.- Strategies for engaging reluctant learners.Each group shared their findings and received feedback from the other groups.7. Conclusion and Next StepsDr. Zhang summarized the key points discussed during the session and highlighted the importance of collaboration and ongoing professional development. The following next steps were agreed upon:- Develop a plan for integrating technology into the curriculum.- Implement a pilot program for differentiated instruction.- Organize a workshop on formative assessment techniques.Participants' Feedback:- Mr. Wang: "The session was very informative. I learned a lot about new teaching methods and technology integration."- Ms. Li: "I appreciate the opportunity to share ideas with my colleagues. It's always great to see what others are doing and learn from their experiences."- Ms. Wu: "The group discussions were very helpful. I feel more confident about using technology in my lessons."- Mr. Zhang: "The session was well-organized and the facilitator did a great job. I look forward to the next workshop."Conclusion:The English Teaching and Research Class was a successful event that provided valuable insights into the latest trends and innovative practices in English language teaching. The participants left with new ideas and strategies to enhance their teaching and improve student learning outcomes.第2篇Date: March 15, 2023Time: 2:00 PM - 5:00 PMLocation: School of English, Greenfield UniversityParticipants:- Dr. Jane Smith, Head of English Department- Mr. John Doe, Senior English Teacher- Ms. Emily Johnson, Junior English Teacher- Ms. Lisa Lee, Head of Teaching and Learning Unit- Ms. Sarah Wang, Assistant Professor of Linguistics- Five English teachers from different schoolsAgenda:1. Introduction of the seminar2. Sharing of best practices in English teaching3. Discussion on challenges and solutions in English language learning4. Group activities and brainstorming5. Feedback and recommendations6. Closure and future plans1. Introduction of the seminarDr. Jane Smith opened the seminar by welcoming all participants and highlighting the importance of continuous professional development in the field of English language teaching. She emphasized that the aim of the seminar was to foster a collaborative environment where teachers could share their experiences, learn from each other, and explore innovative teaching methods.2. Sharing of best practices in English teachingMr. John Doe began the session by sharing his experience inincorporating technology into his English classes. He discussed how the use of interactive whiteboards and educational apps has enhanced student engagement and improved learning outcomes. Mr. Doe also highlighted the importance of project-based learning and how it encourages students to apply their language skills in real-life contexts.Ms. Emily Johnson followed with a presentation on the benefits offlipped classrooms. She explained how this approach allows students to access instructional content outside of the classroom, freeing up class time for interactive activities and discussions. Ms. Johnson shared specific strategies for creating engaging video lessons and encouraged teachers to use a variety of multimedia resources.3. Discussion on challenges and solutions in English language learningThe group then engaged in a lively discussion on the challenges they face in teaching English. Common concerns included:- Lack of resources: Many teachers mentioned the limited availability of educational materials, particularly in rural or underprivileged areas.- Student motivation: Teachers discussed the importance of creating a supportive and engaging learning environment to keep students motivated.- Assessment methods: There was a consensus that traditional testing methods often do not accurately reflect students' language proficiency.To address these challenges, the group proposed several solutions:- Resource sharing: Teachers agreed to create a shared database of educational resources to help each other access materials more easily.- Innovative teaching methods: The group brainstormed ideas foractivities that could boost student motivation, such as role-playing, debates, and collaborative projects.- Alternative assessment methods: Teachers discussed the possibility of using portfolios, presentations, and performance-based assessments to better evaluate student learning.4. Group activities and brainstormingThe seminar continued with a series of group activities designed to encourage collaboration and creativity. Each group was tasked with identifying a specific challenge in English language teaching and brainstorming innovative solutions. The groups shared their ideas with the larger group, leading to a rich exchange of ideas and best practices.5. Feedback and recommendationsMs. Lisa Lee facilitated a feedback session where participants provided constructive criticism and suggestions for improvement. Key recommendations included:- Regular seminars and workshops: Teachers requested more opportunities for professional development and networking.- Collaboration with experts: The group suggested inviting linguists, educational psychologists, and other experts to share their insights.- Policy support: Teachers called for better resources and support from the administration to improve the quality of English language education.6. Closure and future plansDr. Jane Smith concluded the seminar by summarizing the key points discussed and expressing her gratitude to all participants for their contributions. She announced that a follow-up meeting would be scheduled to discuss the implementation of the recommendations and to plan future seminars.Conclusion:The English Teaching and Research Class Seminar was a successful event that provided a valuable opportunity for teachers to share their experiences, learn from each other, and explore innovative approaches to English language teaching. The collaborative spirit and enthusiasm of the participants bode well for the future of English language education in our schools.第3篇Date: [Insert Date]Time: [Insert Time]Location: [Insert Location]Participants: [List of Participants]---I. IntroductionThe English Research and Development class commenced with a brief introduction by the instructor, highlighting the objectives of the session. The focus was to discuss innovative teaching methods, explore current educational trends, and share practical strategies for enhancing English language learning in the classroom.II. Opening RemarksInstructor: Good morning, everyone. Today's session is designed tofoster a collaborative environment where we can share our experiences and insights into English language teaching. Our goal is to explore new methods and techniques that can be implemented in our classrooms to improve student engagement and learning outcomes.III. Discussion TopicsA. Technology Integration1. Introduction to Technology in English Language Teaching- Participant A: I agree that technology plays a crucial role in modern English language teaching. Tools like interactive whiteboards, educational apps, and online platforms can make learning more engaging and interactive.- Participant B: Absolutely, but it's important to use technology appropriately. It should complement traditional teaching methods rather than replace them.2. Case Study: Flipped Classroom- Participant C: I've tried the flipped classroom approach and found it very effective. Students watch videos at home, and class time is usedfor activities and discussions.- Instructor: That's a great example. The flipped classroom model allows for more personalized learning and can help students develop independent learning skills.B. Student-Centered Learning1. Understanding Student-Centered Pedagogy- Participant D: Student-centered learning is all about empowering students to take control of their learning. It's not just about the teacher delivering information.- Instructor: Precisely. This approach encourages critical thinking and problem-solving skills.2. Group Work and Collaborative Learning- Participant E: Collaborative learning is a fantastic way to foster teamwork and communication skills. I've seen students really shine when they work together on projects.- Instructor: It's also important to provide clear guidelines and structures for group work to ensure that it's productive and inclusive.C. Assessment and Feedback1. Traditional vs. Alternative Assessment Methods- Participant F: Traditional tests and quizzes are still important, but we should also consider alternative assessment methods like presentations, portfolios, and reflective essays.- Instructor: Absolutely. Alternative assessments can provide a more holistic view of a student's abilities and achievements.2. Providing Constructive Feedback- Participant G: Feedback is key to student progress. It should be specific, timely, and focused on both strengths and areas for improvement.- Instructor: I couldn't agree more. Constructive feedback can motivate students and help them understand how to improve.IV. Breakout SessionsParticipants were divided into small groups to discuss specific topicsin more depth:Group 1: Technology Integration- Discussion Points: Best practices for integrating technology, challenges faced, and successful case studies.Group 2: Student-Centered Learning- Discussion Points: Strategies for implementing student-centered learning, challenges, and benefits observed.Group 3: Assessment and Feedback- Discussion Points: Alternative assessment methods, providing effective feedback, and evaluating student progress.V. Group ReportsEach group presented their findings to the larger group:Group 1 Report:- Summary: The group highlighted various technological tools and resources that can be used in English language teaching, includingdigital storytelling, gamification, and virtual reality.Group 2 Report:- Summary: The group emphasized the importance of creating a supportive learning environment where students feel empowered to take risks and learn from their mistakes.Group 3 Report:- Summary: The group discussed different types of alternative assessments and the importance of providing constructive feedback that encourages student growth.VI. Conclusion and Next StepsInstructor: Thank you, everyone, for your insightful contributions. It's clear that there are many innovative approaches to English language teaching that we can implement in our classrooms. Let's continue to share our experiences and support each other in our professional development.Next Steps:- Participants will create an action plan outlining specific strategies and techniques they plan to implement in their teaching practice.- A follow-up session will be scheduled in [insert time frame] to discuss progress and share experiences.VII. AdjournmentThe English Research and Development class concluded with a sense of excitement and motivation. Participants left with a wealth of new ideas and a renewed commitment to enhancing their teaching practices.---End of Record。
MANAGEMENT RESPONSE ON THE 2005 ANNUAL EVALUATION REVIEWOn 2 August 2005, the Director General, Operations Evaluation Department, received the following response from the Managing Director General on behalf of Management:CommentsA. Overall1. Management appreciates that OED has undertaken an extensiveconsultation and productive interactions with relevant ADB departments andoffices in finalizing the 2005 Annual Evaluation Review. Management notes thatthis year’s OED annual review has changed in character by reporting on“development results” instead of “evaluation activities” as in the past. This Reportis a succinct reflection of OED’s strengthened role in implementing Managing forDevelopment Results (MfDR) effectively. ADB has been making considerableefforts to mainstream MfDR in its operations, particularly through its results-based Country Strategies and Programs (CSPs) and the Project PerformanceManagement System (PPMS).2. Management agrees with most of the conclusions and recommendations,some of which are very important. ADB’s ongoing and future operations willsupport the implementation of major recommendations.B. Specific Comments on Recommendations3. Development results. While welcoming OED’s reinforced focus ondevelopment results, given the multi-faceted nature of the MfDR agenda, thisReport does not adequately cover many of the important elements ofdevelopment results which will be the key to ADB's success in this area—e.g.,ADB’s own and developing member countries’ (DMCs) capacity for MfDR; use ofMfDR to manage performance of sectoral and policy work; and so on.4. Improving performance in poorly performing sectors and regions.As recommended by the Report, ADB will make efforts to improve performanceand not to repeat the same mistakes in the problem sectors and regions. ADBhas modified its quality assurance mechanisms following the recommendationsof the independent panel for the assessment of the impact of the 2002reorganization and has also introduced an action plan for the ProjectPerformance Management System in 2004. These recent measures, as well asthe MfDR initiative, will help improve ADB’s overall development results,including those in such sectors and regions with relatively poor performance.5. Improving selectivity and focus based on past results. ADB needs toimprove selectivity and focus in its operations. Management, through the2006–2008 planning directions, has emphasized the need for selectivity andprioritization in ADB’s future assistance to its DMCs. In improving the selectivityand focus in ADB operations, we will take into account the recommendationsmade by the Report.6.Strengthening Project Economic Analysis. The Report recommended that a task force of ADB economists be formed to undertake long-term forecasts at the country level. ADB’s Economics and Research Department (ERD) had extensive discussions with OED on the issues of project economic analysis as determinants of economic viability and country-level long-term forecasts in project analysis. ERD reiterated that the major element of the process should be the focus of attention in improving quality at entry, and that the link between country-level forecasts and project economics is weak.7.Addressing potential risks posed by bunching. The yearend bunching is caused by various factors such as ADB’s project processing cycle and annual lending targets. The Report suggests several actions to be taken by ADB to reduce yearend bunching in project processing. Currently, ADB is studying measures to reduce the chronic bunching problem, with its operational departments strengthening the project processing schedules monitoring and the project readiness filtering.8.Evaluation of ADB’s Energy Policy. We support OED’s proposed evaluation of ADB’s Energy Sector Policy under its 2006–2008 work program.OED’srecommendations. Management appreciates OED’s 9. Improvingrecommendations to improve the strategic impact of its evaluations. Management will coordinate with OED and ADB’s operational departments to establish more efficient mechanisms and practices in this regard. We propose that OED prepare a more focused program each year so that the conclusions of the annual review of evaluation findings can be drawn from a more structured and comprehensive review of ADB operations in various sectors and areas.10. We suggest that OED consider introducing a system of evaluating results-based CSPs. Such evaluation work would desirably cover both (i) the methodology and procedures ADB is introducing to design and craft results-based CSPs, and (ii) the mid-term reviews of results-based CSPs and the completion reports for results-based CSPs. We also suggest that OED consider a special evaluation study of ADB's MfDR agenda around 2006.。
Praise is a powerful tool that can significantly impact an individuals selfesteem, motivation,and overall wellbeing.Here are some of the key values of praise in English composition:1.Boosting Confidence:When someone receives praise for their work or efforts,it can greatly enhance their confidence.This can be particularly important for students or individuals who are learning new skills or facing challenges.2.Encouraging Growth:Praise can motivate individuals to continue improving and developing their abilities.It serves as a positive reinforcement that encourages further effort and progress.3.Fostering Positive Relationships:Expressing praise can strengthen relationships between peers,teachers and students,or managers and employees.It shows appreciation and respect,which are essential for building trust and camaraderie.4.Enhancing Creativity:When creativity is praised,it can inspire individuals to think outside the box and come up with innovative ideas.This is especially valuable in fields that require original thought and problemsolving skills.5.Improving Performance:Praise can lead to better performance in various areas, including academics,sports,and professional settings.It can act as a catalyst for individuals to push themselves to achieve more.6.Promoting a Positive Environment:An environment where praise is given freely can create a positive atmosphere.This can lead to increased morale and a more enjoyable experience for everyone involved.7.Recognizing Effort:Praise is not just for the end results but also for the effort put in. Recognizing the hard work behind achievements can be incredibly motivating and validating.8.Building Resilience:When individuals are praised for overcoming obstacles or for their perseverance,it can help build resilience.This can be particularly important in the face of setbacks or failures.9.Cultivating a Growth Mindset:Praise can help individuals develop a growth mindset, where they view challenges as opportunities for learning and growth rather than as insurmountable barriers.10.Inspiring Others:When people see others being praised for their achievements,it can inspire them to strive for similar recognition.This can create a culture of excellence and ambition.In conclusion,the value of praise in English composition cannot be overstated.It is a fundamental aspect of human interaction that can lead to personal growth,improved performance,and a more harmonious environment.。
摘要有机太阳能电池因其重量轻、制备工艺简单、柔韧性好及易实现大面积加工等优势被认为是最有前途的绿色能源技术之一。
该领域亟待解决的问题是如何提高有机太阳能电池的能量转换效率,而优化活性层与界面层是提高有机太阳能电池性能的关键。
为此,本论文为提高有机太阳能电池的能量转换效率,研究了高结晶性小分子对聚合物/富勒烯有机太阳能电池活性层形貌的影响、萘酰亚胺类聚合物受体材料的分子结构与光伏性能的关系、SnO2电子传输层厚度和形貌对器件光伏性能的影响。
主要分为以下三个部分:(1)高结晶性小分子调控聚合物/富勒烯有机太阳能电池活性层形貌本文将高结晶度的小分子(ITDCN/ITDCF)引入到PBDB-T:PC71BM活性层中,制备了三元共混的有机太阳能电池。
在不同共混比例下(ITDCN/ITDCF相对于PBDB-T的质量比,由5%逐渐增加到30%),三元混合器件最优质量比例分别为PBDB-T:ITDCN:PC71BM=0.85:0.15:1、PBDB-T:ITDCF:PC71BM=0.80:0.20:1的器件获得了更高更平衡的电子、空穴迁移率(电子/空穴迁移率的比值分别为1.09和1.14)。
性能最优的ITDCN三元光伏器件的J SC为12.78 mA·cm-2、V OC为0.83 V、FF为63.02%、PCE为6.68%,比相应的二元器件的效率提高了24%。
原子力显微镜(AFM)和透射电子显微镜(TEM)表明,添加适量的具有高度结晶性的小分子能够有效调控聚合物/富勒烯体系有机太阳能电池活性层的薄膜形貌,从而达到纳米级的相分离,有利于激子的解离和电荷的运输,提高其光电转换效率。
(2)萘酰亚胺类聚合物受体材料的合成及其光伏性能研究在众多的聚合物受体材料中,萘酰亚胺(NDI)类聚合物因较大的共轭骨架与强吸电子能力而具有较高的迁移率、较大的极性、较低电子云密度与较好的稳定性,因而表现出非常优秀的光伏性能。
为提高HOMO,减小其带隙,增加吸收,本文用甲氧基取代硒吩为给电子单元合成了聚合物(PNDIS-HD-OMe)。
International Journal of Intelligence Science, 2013, 3, 1-4doi:10.4236/ijis.2013.31001 Published Online January 2013 (/journal/ijis)Improving Rule Base Quality to Enhance ProductionSystems PerformanceNabil ArmanDepartment of Computer Science, Palestine Polytechnic University, Hebron, PalestineReceived September 1,2012; revised October 3, 2012; accepted October 12,2012ABSTRACTProduction systems have a special value since they are used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial intelligence. Therefore, it is of high importance to con- sider different techniques to improve their performance. In this research, rule base is the component of the production system that we aim to focus on. This work therefore seeks to investigate this component and its relationship with other components and demonstrate how the improvement of its quality has a great impact on the performance of the produc- tion system as a whole. In this paper, the improvement of rule base quality is accomplished in two steps. The first step involves re-writing the rules having conjunctions of literals and producing a new set of equivalent rules in which long inference chains can be obtained easily. The second step involves augmenting the rule base with inference short-cut rules devised from the long inference chains. These inference short-cut rules have a great impact on the performance of the production system. Finally, simulations are performed on randomly generated rule bases with different sizes and goals to be proved. The simulations demonstrate that the suggested enhancements are very beneficial in improving the performance of production systems.Keywords: Production System; Rule Base Quality; Inference Short-Cut Rules; Inference Chains1. IntroductionA production system, in the context of artificial intelli- gence, is a model of computation that has proved par- ticularly important in different sub domains such as search algorithms implementation and modeling human problem-solving [1]. A schematic diagram of a produc- tion system is presented in Figure 1. As shown, a pro- duction system is defined by three major components, namely the set of production rules (a rule base), a work- ing memory containing a description of the current state of the world in a reasoning process, and the recognize-act cycle that represents the control structure for a produc- tion system.Production systems are intensively used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial in- telligence. Thus, improving their performance is an issue of great importance. The improvement can be focused on either improving the representation and access of facts in the working memory or improving the rule base quality. In this paper, rule base quality is the component that we focus on. This work therefore seeks to demonstrate how the enhancement of the rule base quality has a great im- pact on the performance of the production system as a whole.In this paper, the enhancement of rule base quality is performed by firstly re-writing the production system rules having conjunctions of literals and producing a new set of equivalent rules in which long inference chains can be devised. Secondly, augmenting the rule base with in- ference short-cut rules obtained from the long inference chains. These inference short-cut rules improve the per-formance of the production system. Finally, several simulations are performed on a set of random rule bases with different sizes and goals to be proved. These simu- lations demonstrate that these enhancements are very useful in improving the performance of the production system.2. Related WorkThe performance of a production system has attracted a large amount of research efforts. Improving performance of production systems by restructuring facts, where the focus was on the set of facts in the production system, was presented in [2]. A genetic algorithm for produc- tion systems optimization was presented in [3]. The algo- rithm finds an ordering of condition elements in the rules of a production system that results in a (near) optimal production system with respect to execution time. TryingN. ARMAN. 2Figure 1. A production system.to find such an ordering can be complicated since there is often a large number of ways to order condition elements in the rules of a production system. In addition, using heuristics to order condition elements, in many cases, conflict with each other. The improvements presented in [2,3] are considered dynamic improvements that are em-ployed during production system execution and thus in-curred additional overhead that may slow down the pro-duction system execution. This affects the performance of a production system negatively.The quality of rule bases has been a subject of a large amount of research efforts in the context of different ap- plication domains including information distribution systems, active databases, expert systems to name just a few [4,5]. In [4], the focus was on generating a set of rules that are fault-free and has a minimum cost in case the cost of applying different rules is known and of great importance. In [5], the focus was on dynamic rule bases where rules’ status changes during the operation of the rule-based system. However, these improvements did not include the issue of the rule-based system execution per se.Another technique for improving the quality of a rule base was presented in [6]. This technique used a number of heuristics to suggest certain corrections of the rule base faults found in the rule base. Again, this technique did not consider the issue of rule-based system execution.A declarative verification approach for improving the quality of rule-based applications was presented in [7]. This approach presented a particular way to control and to improve the rule quality by means of rule verification using a declarative approach. Although the approach is flexible and easy to maintain, it did not take into account the issue of rule-based system execution.A state-based knowledge representation approach, in which domain-specific knowledge is expressed by com- binations of the relevant objects’ states were presented in [8]. Using this technique, a method for detecting logical inconsistencies was developed, which can deal with the demands of various domain-specific situations through reducing part of restrictions in existing methods. How- ever, this approach did not affect the execution of the system.In this paper, the focus is on the quality of the rule base rather than the ordering of the facts in the fact base or the ordering of the condition elements in the rules of a production system or the rule base verification. This can be performed before the production system starts execu- tion as a preprocessing step and thus speeds up the pro- duction system execution without incurring additional overhead during the production system execution. Therefore, this is considered a static improvement that is performed before a production system starts execution. 3. Rule Base Quality Enhancement of a Production System PerformanceThe performance of a production system depends largely on the rules in its rule base. The structure of rules in a production system and their interrelationships determine how the search space is explored. In large production systems, many rules are used and these rules will be tried in order based on a conflict resolution strategy. A number of conflict resolution strategies have been used including refraction, recency, priority, and specificity. In this paper, priority is used to order the rules for possible firing during the production system execution.To improve the performance of a production system, one should consider enhancing the quality of its rule base by handling all possible problems and anomalies associ- ated with rule bases including inconsistency, contradic- tion, circularity, and redundancy [9]. This should be han- dled before the production system starts execution and can be performed using well-known verification tools and techniques [5,9]. An inconsistency exists in the rule base of a production system if a condition of one rule is mutu- ally exclusive to the consequent or action of such rule (or a chain of rules). A contradiction exists if two rules con- clude different actions from the same condition. A redun- dancy exists if two rules conclude the same action from the same rule condition. A subsumption exists if two rules conclude the same action, but one has additional con- straints in the rule condition, which may or may not be necessary (a specific kind of redundancy). A circularity exists if the rule base contains a cycle that makes the production system to enter an endless loop by keeping the insertion of the same facts to the fact base of the produc- tion system without making any progress towards the solution or goal of the problem.In this paper, the focus is on the issues that affect the production system performance during its execution. A restructuring of the rules based on their ations/consequent makes it easier to apply the major improvement of theN. ARMAN. 3quality of the rule base. Re-writing the rules having a conjunction of literals is of high importance since it fa- cilitates the process of checking other types of anomalies and to apply other improvements. For example, a rule of the form:12n p q q q →∧∧∧Λcan be re-written as a set of rules of the form:12,,n p q p q p q →→→Λwhere the conjunction of literals 12n is used to devise the set of new rules obtained by having the left-hand side p of the original rule to be the left-hand side of all new rules and taking each literal q i as the right-hand side of the new rules. This makes it easier to detect long chains in the rule base of the production system. In large rule bases, long chains may result when one rule triggers a second rule, and the second rule triggers a third rule, and so on. In this case, a number of new rules represent-ing a form of inference short-cuts can be added to the production system rule base. The original rule base of the production system is augmented with these inference short cut rules. These rules can be assigned a higher prior-ity to guarantee their applications first during the infer-ence process of the production system. For example, a rule base of the production system having a set of rules of the form:q q q ∧∧∧Λ122311,,,j j n p p p p p p p p +-→→→→ΛΛn can be augmented with a number of inference short-cut rules depending on the selected value of j representing the inference chain length. For example, if j is selected with a value of 3, then the rule base is augmented with the rules:142533,,,j j n p p p p p p p p +-→→→→ΛΛnAgain, these inference short-cut rules are assigned higher priorities than rules with the same conditions to guarantee their applications first and thus rule base ordering can be based on that.Based on the improvements presented above, our ap- proach can be presented in the following algorithm:Rule Base Quality Enhacement (Rule Base (RB ), Aug-mented Rule Base (ARB ), Inference Chain Length (J )) {Use a Verification Tool to Handle Anomalies (Incon-sistencies , Contradictions , Redundancies , and Circulari-ties ) in RBRe-write rules having actions /consequents consisting of conjunction of literals in the modified RBAugment the modified RB with a number of inference short-cut rules based on the value of J and devise ARB }4. Results and Performance EvaluationTo determine the performance of our approach, several simulations were performed for random rule bases with 50, 100, 150, and 200 rules and proving/answering a setof 30 randomly generated goals. The same 30 randomly generated goals were answered using the original rule base without the enhancements suggested. The time taken to prove these goals was determined for both tech- niques and the times were plotted for different sizes of the rule bases as presented in Figure 2. As shown in Figure 2, our approach for proving the goals outperforms the traditional approach.In addition, a rule base of 200 rules was tested for varying number of inference chain length of values 2, 3, 4 and 5 and proving/answering a set of 30 randomly generated goals. The same 30 randomly generated goals were answered using the original rule base without in-troducing the inference short-cut rules. The time taken to prove these goals was determined for both techniques and the times were plotted for different sizes of the rule bases as presented in Figure 3. As shown in Figure 3, our approach for proving the goals outperforms the tradi-tional approach since our approach benefits from the inference short cuts and the re-ordering of the rule bases based on new inference short-cut rules’ priorities.5. Discussion and ConclusionIn this paper, we proposed an approach for enhancing theFigure 2. Performance evaluation of our approach using rule base size.Figure 3. Performance evaluation of our approach using inference chain length.N. ARMAN. 4quality of a rule base component of a production system which is of high importance to improve the performance of a production system. Production systems have a spe- cial value since they are used in state-space searching algorithms and expert systems in addition to their use as a model for problem solving in artificial intelligence. The simulations demonstrated that the improvement of rule base quality, in terms of augmenting the rule base with inference short-cut rules obtained after replacing rules with conjunctions of literals by a new set of equivalent rules in which long inference chains can be obtained eas-ily, has a great impact on the performance of the produc- tion system as a whole.6. AcknowledgementsThe author thanks the reviewers for suggestions to im-prove the paper.REFERENCES[1]G. Luger, “Artificial Intelligence: Structures and Strate-gies for Complex Problem Solving,” 6th Edition, Addi-son-Wesley, Boston, 2009, pp. 200-201.[2]W. Mustafa, “Improving Performance of Production Sys-tems by Restructuring Facts,” Abhath Al-Yarmouk Jour-nal of Natural Sciences and Engineering, Vol. 11, No. 1A, 2002, pp. 105-120.[3]W. Mustafa, “Optimization of Production Systems UsingGenetic Algorithms,” International Journal of Computa- tional Intelligence and Applications, Vol. 3, No. 3, 2003,pp. 233-248. doi:10.1142/S1469026803000987[4]N. Arman, “Generating Minimum-Cost Fault-Free RuleBases Using Minimum Spanning Trees,” InternationalJournal of Computing and Information Sciences, Vol. 4, No. 3, 2006, pp. 114-118.[5]N. Arman, “Fault Detection in Dynamic Rule Bases Us-ing Spanning Trees and Disjoint Sets,” The InternationalArab Journal of Information Technology, Vol. 4, No. 1, 2007, pp. 67-72.[6]N. Arman, D. Richards and D. Rine, “Structural and Syn-tactic Fault Correction Algorithms in Rule-Based Sys- tems,” International Journal of Computing and Informa-tion Sciences, Vol. 2, No. 1, 2004, pp. 1-12.[7]S. 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International Journal of Artificial Intelligence Tools.Special Issue of IEEE ITCAI-96Best Papers.6:(3&4),1997.In Press.IMPROVING PERFORMANCE OF CASE-BASED CLASSIFICATIONUSING CONTEXT-BASED RELEV ANCEIGOR JURISICADepartment of Computer ScienceUniversity of TorontoToronto,Ontario M5S1A4juris@cs.utoronto.ca(1–416)978-7589JANICE GLASGOWDepartment of Computing and Information SciencesQueen’s UniversityKingston,Ontario K7L3N6janice@qucis.queensu.ca(1–613)545-6058Received(1May,1997)Revised(19June,1997)AbstractClassification involves associating instances with particular classes by maximizing intra-class similarities and minimizing inter-class similarities.Thus,the way similarityamong instances is measured is crucial for the success of the system.In case-basedreasoning,it is assumed that similar problems have similar solutions.The case-basedapproach to classification is founded on retrieving cases from the case base that aresimilar to a given problem,and associating the problem with the class containing themost similar cases.Similarity-based retrieval tools can advantageously be used in buildingflexible retrieval and classification systems.Case-based classification uses previously classifiedinstances to label unknown instances with proper classes.Classification accuracy isaffected by the retrieval process–the more relevant the instances used for classification,the greater the accuracy.The paper presents a novel approach to case-based classification.The algorithm is based on a notion of similarity assessment and was developed for supportingflexibleretrieval of relevant information.Case similarity is assessed with respect to a givencontext that defines constraints for matching.Context relaxation and restriction is usedfor controlling the classification accuracy.The validity of the proposed approach istested on real-world domains,and the system’s performance,in terms of accuracy andscalability,is compared to that of other machine learning algorithms.Keywords:Case-Based Reasoning;Classification;Context-Based Similarity;Rele-vance Assessment.11IntroductionClassification involves associating instances with classes:based on the object’s description, a classification system determines whether a given object belongs to a specified class.In general,this process consists offirst generating a set of categories and then classifying given objects into the created categories.For the purpose of this paper,it is assumed that the categories have been determined a priori from a prescribed domain theory[44].V arious techniques have been utilized for the classification task,for example neural networks[43],genetic algorithms[13],inductive and instance-based learning[1,46,42] and case-based reasoning[36,4].Individual approaches are compared to one another based on the method they deploy,the accuracy they achieve,and the complexity of the used algorithm.Most systems extract classification rules from training examples during a learning process.Then,they use these rules for the classification of unseen instances. Case-based systems,however,store whole cases during the learning process and assess the similarity between a given problem and a stored case in a case base,in order to determine an appropriate class for the problem.Evaluating a classifier’s performance is not a straightforward process.Individual sys-tems are usually tested on different problem domains,and because of differences in domain complexities,obtained performance measures cannot be compared directly.While perfor-mance is highly domain dependent,it is possible to derive evaluation techniques that allow for a meaningful performance comparison of different algorithms[2,32].This paper presents a novel,case-based classification scheme,called3,and evaluates it with respect to several real-world domains.The proposed algorithm is based on a notion of relevance assessment[20]and on a modified nearest-neighbor matching[47].Its modifications include:Grouping attributes into categories of different priorities so that different preferences and constraints can be used for individual categories during query relaxation;Using an explicit context,a form of bias represented as a set of constraints,during similarity assessment;andUsing an efficient query relaxation algorithm based on incremental context modifi-cations.3(pronounced tah-tree)is different from other case-based approaches since instead of using predefined retrieval strategies,case retrieval is custom-tailored and dynamically changed for a particular domain and specific application.Thisflexibility is accomplished by using context in similarity-based retrieval,deploying the Telos[34]representation language which treats objects and attributes uniformly.Flexibility is needed to support the following aspects of3:Depending on the resources available the system allows for changing the classification accuracy.Based on a given task,the system allows for changing the retrieval objective in the following ways:(i).recall-oriented retrieval guarantees that all relevant cases are retrieved,althoughsome irrelevant ones may also be included,and(ii).precision-oriented retrieval assures that only relevant cases are be retrieved, although some may be missed.The system allows for imprecise query specification,which enables the retrieval of cases even if the query is not specified completely.Moreover,similar cases are retrieved in addition to exact matches.Depending on the result of retrieval,the system allows for manual or automatic query modifications through context restriction or relaxation,as well as for query-by-example and query-by-reformulation.These features are desirable for supporting a variety of different domains and tasks since the task requirements may change over time and individual users may have different preferences.In addition,the classification process must be efficient,i.e.,the system must scale up as more cases are acquired.The remainder of the paper is organized as follows:Section2describes the classification problem and presents various approaches to the problem.Section3introduces an approach to context-based relevance which is used to define similarity-based retrieval and case-based classification.Context manipulation is used for controlling the classification accuracy. Section4presents performance evaluation results and shows how controlling the relevance of retrieved cases can affect classification accuracy.Section5presents concluding remarks. 2ClassificationThis section defines the classification task,discusses traditional approaches to the problem and presents a case-based approach to classification.2.1Classification problem definitionIn a classification system,there is usually afixed set of classes to which a given object can be assigned.Classification systems are generally trainedfirst,i.e.,already classified objects are presented,and the system induces knowledge from previous examples.For example,neural networks change connection weights,decision tree algorithms generate rules and case-based systems remember individual instances.The goal of such systems is to learn a correct classification for given examples.Obviously,the system must be able to generalize from these examples,so that novel examples are classified correctly with satisfactory accuracy.Generally,objects in a classification system are represented as a collection of properties –attributes or features.In real-world domains,objects may or may not be represented properly and/or error-free:some attribute values might be missing or imprecise,or there3may be irrelevant attributes present and relevant attributes missing.Such domains are called imperfect.In conventional data analysis,objects are clustered into classes based on a distance or similarity measure[44].The similarity between two objects is represented as a number–the value of a similarity function applied to symbolic descriptions of objects.Thus,the similarity measure is context free.Note that such methods fail to capture the properties of a cluster as a whole and are not derivable from properties of individual entities[44].Learning algorithms can be divided into two approaches:supervised and unsupervised. Supervised learning algorithms use inductive learning.The learning program is given examples of the form and it is supposed to learn a function,such thatfor all.Typically,is a composite description of some object,situation or event andis a simpler description.In classification,represents a problem and corresponds to a solution or a class.Thus,supervised learning involves learning a function that assigns each into an appropriate class.The learning goal is tofind a function that captures general patterns in the training data,so that can be applied to predict values for a new, previously unseen,values.Unsupervised learning covers clustering and discovery.Here,the learning program is given a collection of values and asked to look for regularities.How regularities are represented varies.Most of the programs search for some form of clusters of values. Discovery programs look for more complex relationships among the values.Supervised learning algorithms for classification can be described as follows[42]:Given a sequence of training examples,whereis a pair consisting of an object description and a proper classification,the classification system must learn the mapping,whereis the space of objects descriptions andis the space of possible classes.Case-based classification systems classify instances based on their similarity to stored cases:Given a new problem(a partial case),the system retrieves a set of casesfrom a case base,,and classifies the new problem based on the case solutions in.The main task of such a system is to identify the similarity of a problem to cases in a case base.Since the stored cases are used for classifying the new case,the more similar they are to the problem,the more accurate the classification can be.Similar cases are ordered based on their closeness to the problem description.Then,if not all retrieved cases belong to the same class,the class for the problem is determined using this partial order,preferring closer matches.2.2Overview of some existing approaches to classificationTraditional approaches to classification involve generating a set of rules,based on induction from training examples.The requirement for such algorithms is that created rules correctly4classify known,i.e.,training,examples and perform well on novel(i.e.,test)examples[37]. Next we describe some relevant classification systems,as well as systems that use case-based reasoning or similarity-based retrieval.Classification systems are relevant due to the fact that we apply case-based reasoning to a classification problem and similarity-based retrieval algorithms are relevant because this type of retrieval is the basis of our approach.ID3[38]is a classification system based on a decision tree ing the induction from training examples,ID3generates a decision tree–a classification rule that examines the values of some attributes of an object in order to assign it into a proper class.AC[44]is a classification algorithm based on attribute-based conjunctive conceptual clustering–a way of grouping objects into conceptually simple ing this method, a set of objects forms a class only if it can be described by a conjunctive concept involving relations on object attributes.AUTOCLASS II[9]is an induction algorithm used to discover classes from databases based on Bayesian statistical techniques.The system determines the number of classes, their probabilistic descriptions and the probability that each object is a member of a given class.This allows for making each and every attribute potentially significant as well as assigning objects to different classes.The system also allows for identifying hierarchies of attributes,selecting common attributes and distinguishing attributes between classes.IB1[2,5]is a nearest-neighbor,instance-based learning system used for classification and for control tasks.Based on a problem description,IB1retrieves the-nearest neighbors and uses them to supply a solution to a given problem.Since all attributes are used during retrieval,the system’s performance decreases quickly with the number of irrelevant attributes.Such a performance degradation of IB1can be solved either by data pre-processing that would remove all irrelevant attributes or by the use of an intelligent,selective partial matching algorithm.The latter approach is similar to concepts in machine learning[35],i.e.,the system considers only attributes during retrieval.There are various methods used to decide and Ortega[35]presents an evaluation of system performance for various.In addition to deciding the size of,the approach described in this paper allows for specifying which attributes to exclude and for posing additional constraints on attribute values,if desired.CB1[18]is a PAC(Probably Approximately Correct)learning,case-based classification algorithm designed for general purpose learning.Even though it is relatively inefficient, considering the sample complexity,it is an interesting step towards a computational learning theory for CBR systems.PROTOS[36]is a a case-based system used for heuristic classification in the clinical audiology domain.The system uses exemplar-based learning[30,6,7,3],a method espe-cially appropriate for domains lacking a strong domain theory.In this system,classification is combined with explanation.Explanation is used for justifying case classification and for determining similarity between two cases.An incremental retrieval system,I-CBR,is presented in[11].For this case-based reasoning paradigm,the user specifies a case skeleton and attempts to retrieve all cases similar to it.I-CBR partitions case attributes into two groups:the ones that are known immediately and the ones that need some extra effort to specify.During the retrieval5process,the user specifies known attributes and the system returns a pool of possible case candidates.From the set of unknown attributes the most discriminating one is selected and the user is queried for its value.Thus,the initial pool of retrieved cases is processed to decrease its size to eliminate less-similar cases which increases classification accuracy.The most appealing feature of the similarity-based retrieval approach proposed in this paper is that it allows for retrieving relevant information even without a complete and precise query or a perfect match.Such retrieval is an essential part of a case-based classification system.After retrieving relevant cases,i.e.,cases that are similar to a given problem,the case-based classification system can adapt previous solutions and use them to predict the class for a current case.3The3Case-Based Classification SystemIn this section we present our proposed case-based classification theory,usingflexible relevance assessment[20,24]–a fundamental component of the3case-based reasoning system.We define relevance in terms of context and similarity,and we also show how context modifications can be used to dynamically control the accuracy of the classification process.Although it has been acknowledged that context plays a central role in determining similarity,previous works on similarity measures for case-based reasoning generally assume that context is implicit in the case representation,or is acquired through machine learning techniques[8].We define context as a parameter of a similarity relation and demonstrate the monotonicity of case retrieval.We show how this property can be used for controlling the classification accuracy.Definition3.1(A case)A case,,is a representation of a real world experience or en-tity in a particular representation scheme.A case will be represented as afinite set of attribute/value pairs(descriptors):where is an attribute/value pair.Using the information about the usefulness of individual attributes and information about their properties,attributes are grouped into two or more Telos-style categories[34].This enhancement of case representations will be used during the retrieval process to increase the accuracy of classification andflexibility of retrieval,and to improve system performance. In classification tasks,each case has at least two categories:problem description and class. The problem description characterizes the problem and the class gives a solution to a given problem.We shall explain the functionality of the proposed system using an example from a simulation of a servo system that involves a servo amplifier,a motor,a lead screw/nut and a sliding carriage.A servo system is an automatically operating device for the regulation of a system’s variable(s)actuated by the difference between the actual and a desired value6of such a variable.The servo domain covers a non-linear phenomenon–predicting the rise time of a servo-mechanism in terms of two gain settings and two choices of mechanical linkages.The task in a given servo domain is to compute the rise time of a specific servo-mechanism.Thus,the problem is described by the servo characteristics that include motor, screw,pgain and vgain.The solution,i.e.,the class,is a rise time or the time required for the system to respond to a step change in a position set point(more details are presented in Section4.2).Here,the case is a collection offive attribute/value pairs,as illustrated in Table1.Table1:A servo domain case example.MOTOR:ESCREW:DPGAIN:4VGAIN:5RISETable2:A servo domain context example.MOTOR:{D,E}SCREW:{C,D,E}PGAIN:{4,5}VGAIN:{5}RISE(iii).First,a knowledge discovery algorithm is used to locate relevant attributes and at-tribute values.Then,a user forms a context for retrieval using this information.This approach is suitable for novice users.(iv).The user submits an ad hoc query,reviews the resulting solution,and then iteratively modifies and resubmits the query(retrieval by reformulation).This approach is best suited for repository browsing.Using the context,the input problem and cases in a case base are interpreted and their similarity is assessed.Definition3.4(Similarity)A case is similar to a case with respect to a given context ,denoted:,if and only if both and satisfy context:Table3:A servo domain case example.MOTOR:CSCREW:CPGAIN:4VGAIN:5RISEThe complexity of this task is characterized by the number of required comparisons which is,in the worst case,proportional to the size of the context and the size of a given case base:i.e.,.Case base organization may improve the efficiency of performing the task by limiting the search space of the case base.Using the presented theory,we define a case-based classification algorithm,CBC,as part of the3case-based classification system as follows[26]:Definition3.6(Case-based classification)The input to a classification problem comprises a case base,a context and a problem(also called an input case,i.e.,a case without a solution or appropriate class).An output is a class for a problem,defined as follows:where denotes is the class for caseUsing the definitions of similarity(Definition3.4)and CBC(Definition3.6),it can be shown that by manipulating the context it is possible to control the quantity and quality of retrieved cases.Thus,context serves as a form of bias.Since CBC is based on the idea that similar past cases can be used to determine the class for a given problem,context manipulation allows for changing the classification for the problem.It should be noted that the context cannot be arbitrarily chosen.For a correct answer,it must include attributes and constraints that would allow for organizing cases into clusters based on classification,i.e., it must include attributes predictive of a correct class.During the classification process,the following three situations may occur:(i).No relevant cases are retrieved–the system cannot classify a given problem. (ii).All relevant cases belong to the same class–the input problem is assigned to the class suggested by retrieved cases.(iii).Retrieved cases belong to multiple classes–the system assesses the relevance of individual cases to produce a partial order and assigns the input problem into the class of the most relevant case.If this is not possible,due to equally relevant cases belonging to different classes,the majority class is selected.If this step fails as well,i.e.,there are at least two groups of equally relevant cases belonging to differentclasses,then the system returns all possible classifications with an“Undecided”answer.3.2Context relaxation and restriction.Explicitly defined context allows for controlling the closeness of retrieved cases.If too many or too few relevant cases are retrieved using the initial context,then the system automatically transforms the context or a user may modify it manually.There are two possible transformations:relaxation–to allow for retrieving more cases and restriction–to allow for retrieving fewer cases.These context transformations are a foundation for supporting iterative retrieval and browsing.Based on the introduced theory,this section discusses specific examples of relaxation and restriction transformations,with an emphasis on automatic methods.Definition3.7(Relaxation)A context is a relaxation of a context,denoted, if and only if the set of attributes for is a subset of the set of attributes for and for all attributes in,the set of constraints in is a subset of the constraints in.As well, contexts and are not equal.Definition3.8(Restriction)A context is a restriction of a context,denoted, if and only if is a relaxation of.As pointed out by Gaasterland in[14],the relaxation technique can advantageously be used for returning answers to a specific query as well as for returning related answers. Without such automatic relaxation,users would need to submit alternative queries.The restriction technique works analogously,but is used mainly for controlling the amount of returned information,keeping it to manageable levels for the user.Since the search for relaxed or restricted contexts can be infinite,there must be a mechanism for controlling it, either by user intervention via user preferences or by other means.Context relaxation and restriction are used during retrieval to control the quantity and quality,or closeness,of cases considered relevant.Thus,when modifying the context,the system may return an approximate answer quickly or may spend more resources getting an accurate answer.An approximate answer can be iteratively improved,so that the change between an approximate and an accurate answer is continuous.Theorem3.1If is a relaxation of then all cases that satisfy also satisfy: Corollary3.1If is a restriction of then the set of cases that satisfy is a subset of the set of cases that satisfy.Considering Definition3.7,there are two possible implementations of context relaxation –reduction and generalization.Reduction removes constraints by reducing the number of attributes required to match:given matching,the required number of attributes is reduced from to,where.Generalization is a context transformation that relaxes the context by enlarging the set of allowable values for an attribute.This technique has been used in[10,15]for relaxation of database queries.Consider the example presented in Table3.Applying reduction to thefirst category, attributes motor and screw,changes the requirement that both attributes must satisfy the constraint to the requirement that if either of the two attributes satisfies the requirement then the whole category would satisfy it.Considering the example presented in Table2,the two cases would match if the con-straint on the motor attribute is relaxed to include value,i.e.,motor:{C,D,E}.Analogically,considering Definition3.8there are two possible implementations of context restriction–expansion and specialization.Expansion is a context transformation that strengthens constraints by enlarging the number of attributes required to match:given matching,the required number of attributes is increased from to,where .Thus,expansion is the inverse operation to reduction.Specializationstrengthens constraints by removing values from a constraint set for an attribute.This may lead to a decreased number of cases that satisfy the resulting context.3.3Monotonicity of context-based similarityMonotonicity is an important property of aflexible retrieval system and can advantageously be used in case-based classification–as more constraints are put in the query,fewer cases satisfy it.It should be noted that the resulting set of retrieved cases is equal to or subset of the original set of cases.We will show that the retrieval function defined using context-based similarity is monotonic with an inverse monotonicity–more constraints imply less retrieved cases and vice versa.Theorem3.2(Monotonicity)The retrieval function based on context-based similarity as-sessment is monotonic.More precisely,given a case base and contexts,:A graphical illustration for Theorem3.2is presented in Figure1.Assume a graphical matching function,where a case satisfies a given context if and only if the whole context overlaps with a case.Both example A and exampleB use the same rectangles a,b and c. In example A,all three rectangles are similar with respect to context.However,in the example B,when the context is more restrictive(graphically larger,i.e.,),only rectangles a and c are similar–rectangle b does not qualify since it matches only part of a given context.Figure1:Monotonicity.The usefulness of the monotonicity feature during the retrieval process was reported in[41,33,20].On the one hand,if not enough cases have been retrieved from the case base,the initial context may be relaxed to retrieve additional“less similar”cases.On the other hand,if too many cases are retrieved,the initial context may be restricted to lower the number of retrieved cases,as depicted in Figure1.Restricting the context increases the similarity between cases since more attributes are required to match.43Performance EvaluationThe goal of the performance evaluation presented here is to show that the theoretical basis for the proposed approach to case-based classification is both effective and efficient.We are interested in two aspects of system performance:accuracy of classification and scalability. Classification accuracy is the most important measure of system functionality.We use this measure to support the claim of usability of the presented theory.Many times neglected, yet important,is the dependency of the system on the case base size and the problem complexity.Performance evaluation must be conducted with a special care on the used data set and on the selected measures[2].It is not only important to know what to measure when evaluating a system,but also to know how to interpret the results.There are many accepted benchmarks used in differentfields and there are numerous evaluations available.Yet,many times wrong conclusions are made or even worse,useless measures are considered.System performance should be determined with respect to task and time.Thefirst characteristic reveals if the system can perform the specified task(e.g.,retrieve all relevant cases from the case base).Usually,task-performance of a classification system is measured by evaluating its accuracy,i.e.,percentage of correct classifications.The second character-istic measures how long it takes for the system to perform the specified task.In addition, scalability measures task/time performance dependability on the case base size.It should be noted,that accuracy is not only system dependent but is also strongly domain dependent[2].Kononenko and Bratko[32]present a fair evaluation criterion to measure classification accuracy.The motivation for the study is that a simple comparison of classification accuracy might be misleading.For example,80%accuracy in a perfect domain with2classes can be achieved trivially whereas40%accuracy in a domain with26 classes(and possible missing information)might be hard to achieve.Moreover,accuracy13。