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11Visualizing document content using language structure

11Visualizing document content using language structure
11Visualizing document content using language structure

Eurographics/IEEE-VGTC Symposium on Visualization2009

H.-C.Hege,I.Hotz,and T.Munzner

(Guest Editors)

Volume28(2009),Number3

DocuBurst:Visualizing Document Content

using Language Structure

Christopher Collins1,Sheelagh Carpendale2,and Gerald Penn1

1University of Toronto,Toronto,Canada;2University of Calgary,Calgary,Canada

Abstract

Textual data is at the forefront of information management problems today.One response has been the development

of visualizations of text data.These visualizations,commonly based on simple attributes such as relative word

frequency,have become increasingly popular tools.We extend this direction,presenting the?rst visualization of

document content which combines word frequency with the human-created structure in lexical databases to create

a visualization that also re?ects semantic content.DocuBurst is a radial,space-?lling layout of hyponymy(the

IS-A relation),overlaid with occurrence counts of words in a document of interest to provide visual summaries

at varying levels of granularity.Interactive document analysis is supported with geometric and semantic zoom,

selectable focus on individual words,and linked access to source text.

Categories and Subject Descriptors(according to ACM CCS):Document And Text Processing[I.7.1]:Document and

Text Editing—Document Management;Computer Graphics[I.3.6]:Methodology and Techniques—Interaction

Techniques;Information Storage and Retrieval[H.3.7]:Digital Libraries—User Issues

1.Introduction

‘What is this document about?’is a common question when navigating large document databases.In a physical library, visitors can browse shelves of books related to their inter-est,casually opening those with relevant titles,thumbing through tables of contents,glancing at some pages,and de-ciding whether this volume deserves further attention.In a digital library(or catalogue search of a traditional library) we gain the ability to coalesce documents which may be lo-cated in several areas of a physical library into a single list-ing of potentially interesting documents.However,the ex-perience is generally quite sterile:people are presented with lists of titles,authors,and perhaps images of book covers.In feature-rich interfaces,page previews and tables of contents may be browsable.If the library contents are e-books,users may even open the entire text,but will have to page through the text slowly,as interfaces are often designed to present a page or two at a time(to dissuade copying).Our goal in this work is to bring some of the visceral exploratory expe-rience to digital libraries,to provide interactive summaries of texts which are comparative at a glance,can serve as de-cision support when selecting texts of interest,and provide entry points to explore speci?c passages.

Prompted by the ever increasing volume and open access to digital text,developing overviews of document content has been an active research area in information visualiza-tion for many years.However,reported works do not make use of existing richly studied linguistic structures,relying

instead on simple statistical properties of documents(e.g., [AC07]),or analytic methods such as latent semantic analy-sis(e.g.,[DFJGR05]),which can produce unintuitive word associations.The resulting visualizations provide detail on content without a consistent view that can be compared across documents.In DocuBurst,we provide a complement to these works:a visualization of document content based on the human-annotated IS-A noun and verb hierarchies of WordNet[Fel98]which can provide both uniquely-and consistently-shaped glyph representations of documents,de-signed for cross-document comparison(see Figure1).

2.Related Work

2.1.Document Content Visualization

Visualizations of document content take two common forms: synoptic visualizations for quick overviews and visualiza-tions specialized for discovering patterns within and be-tween documents.Specialization in the type of document

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Collins et al./

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Figure1:DocuBurst of a science textbook rooted at{idea}.

A search query for words starting with‘pl’has been per-formed.Nodes matching the query are highlighted in gold. used as input further divides the reported research:books and long documents,historical documents,multilingual texts,and computer-mediated communication archives such as emails,instant messages,and threaded discussions.In this space,DocuBurst focuses on long texts,such as books, and provides a visualization that is simultaneously synoptic, comparative,and allows for deeper intra-document analysis of occurrence patterns.In the remainder of this section we will review signi?cant examples in each of these categories, describing how their feature sets compare.

Synoptic visualizations of text most often use a selected subset of the language to create a glyph based on word oc-currence counts.Glyphs are then combined in small mul-tiples visualizations to perform comparisons.Glyph tech-niques include Starstruck[HWMT98],which creates glyphs by arranging lines of varying length in a circular pattern, and Gist Icons[DFJGR05],which builds on this idea by drawing a smoothed contour around this pattern.The vo-cabularies used are either restricted user-selected term sets (Starstruck)or automatically selected and potentially unre-lated(Gist Icons).Subtle shape perturbations can be hard to compare in side by side radial glyphs.Other synoptic visu-alizations of document content use space-?lling techniques to provide an overview of the vocabulary a single document, such as TextArc[Pal02]and tag clouds(e.g.,[Fei08]).These techniques provide a quick overview of document content.

A second category of document visualizations are those created to reveal patterns within texts.FeatureLens [DZG?07]suggests repeated phrases that may be of inter-Figure2:Comparison of features available(Y),possible with a trivial extension(P),or not possible(N)in document visualizations.*denotes cases that only visualize a selected subset of words;+denotes a coordinated visualization. est,and visualizes selections,while Arc Diagrams[Wat02] provide an overview of repetition throughout a document. DocuBurst also provides views of the distribution of selected words.Other pattern-based visualizations focus on distribu-tions of signi?cant features in a document such as emotion (e.g.,extensions[OBK?08]of TileBars[Hea95]),or hand-annotated properties(e.g.,Compus[FD00]).The Word Tree [WV08]is an example of a pattern-based visualization fo-cused on repetition in context.While Word Trees provide a unique view on repetition,overall word frequencies and full document overviews are not visible.

To compare document visualizations,we order a list of the types of features document visualizations have provided from most unique to DocuBurst to the most common in other visualizations:

semantic indicate word meaning

cluster generalize by clustering words into concepts overview provide quick overviews of an entire text

zoom support varying the semantic or graphical detail freq reveal frequency of individual words

compare compare multiple documents

search search for speci?c words/phrases

read drill-down to original text

pattern reveal patterns within or between texts features reveal extracted features(e.g.,emotion) suggest suggest interesting focus words/phrases phrases can show multi-word phrases

all words can show all parts of speech

Figure2summarizes how these features relate to DocuBurst and other well known text visualizations.Note that only DocuBurst provides some re?ection of seman-tics through integrated word de?nitions and the use of a semantically-de?ned linguistic structure.Only DocuBurst and Gist Icons provide word clustering into higher concepts, however in Gist Icons the groups are only one level deep

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and based on statistical measures whose meaning may not be readily apparent to a reader.Note that all visualizations that provide overviews of entire text suffer from screen real estate issues with large texts.

2.2.Graph Drawing

Radial graph-drawing techniques have been previously re-ported and serve as the basis of this work.Of particular in-terest are the semi-circular radial space-?lling(RSF)hier-archies of Information Slices[AH98]and the focus+con-text interaction techniques of the fully circular Starburst vi-sualization[SZ00].The InterRing[YWR02]visualization expands on the interaction techniques for RSF trees,sup-porting brushing and interactive radial distortion.TreeJux-taposer[MGT?03]illustrates methods for interacting with very large trees,where nodes may be assigned very few pix-els.We adapt techniques such as tracing the path from a node of interest to the root and performing interactive accordion expansion from this work.

3.Background on WordNet

Despite the growing dependence on statistical methods, many Natural Language Processing(NLP)techniques still rely heavily on human-constructed lexical resources such as WordNet[Fel98].WordNet is a lexical database composed of words,collocations,synsets,glosses,and edges.Words are literally words as in common usage.A collocation is a set of words such as“information visualization”which are frequently collocated and can be considered a unit with a particular de?nition.For the purposes of this paper,we will use words to refer to both words and collocations—they are treated equally in the visualization.Sets of synonymous words and collocations are called synsets.Glosses are short de?nitions that the words in a synset share,thus they are de?nitions of synsets.An edge in WordNet represents a con-nection between synsets.

Synsets are the most important data unit in WordNet. Throughout this paper,we will refer to words in single quotes(e.g‘thought’),and synsets using a bracketed set notation(e.g.{thought,idea}).A word may be a member of multiple synsets,one for each sense of that word.Word senses are ranked,either by order of familiarity(a subjec-tive judgement by the lexicographer)or,in some cases,by using a synset-tagged reference corpus to provide numerical relative frequencies.

Synsets in WordNet are connected by many types of edges,depending on the part of speech(noun,verb,etc.). WordNet contains28different types of relations,but the most widely used part of WordNet is the hyponymy(IS-A) partial order.An example of hyponymy is{lawyer,attorney} IS-A{professional,professional person}.When traversing this graph,we remove any cycles(they are very rare)by taking a depth-?rst spanning tree at the user-selected root. In this work we focus on the noun hyponymy relationships in English WordNet(v2.1),rooted under the synset{entity} having73,736nodes(synsets)and75,110edges,and a max-imum depth of14.Verb hyponymy is also supported—that hierarchy is smaller and takes a more shallow,bushier form. In addition,there is no single“root”verb.The visualizations produced can be generalized to any partial order of a lexicon.

3.1.WordNet Visualization

Many interfaces for WordNet exist,the most popular of which is the text-based WordNet Search which is part of the publicly available WordNet package.With the excep-tion of the work of Kamps[KM02],the existing interfaces for WordNet either provide for drill-down textual or graph-ical interaction with the data starting at a single synset of interest or provide path-tracing between two synsets e.g., [Alc04,Thi05].We do not know of any visualization of WordNet that uses the graph structure to enhance a visual-ization of other data such as document content.

4.DocuBurst Visualization

The combined structure of WordNet hyponymy and docu-ment lexical content is visualized using a radial space-?lling tree layout implemented with prefuse[HCL05].Traversing the tree from center to periphery follows a semantic path of increasing speci?city using the IS-A relation.In WordNet, synset members are ordered according to their polysemy count,which WordNet researchers call familiarity.Since more familiar words come?rst,we chose the?rst word in a synset as the node https://www.doczj.com/doc/4213886788.html,bel fonts are maximized,ro-tated to?t within the node,and overlap is minimized.

4.1.Linguistic Processing and Scoring

In order to populate a hyponymy hierarchy with word counts,several pre-processing steps are necessary.Starting with raw text,we subdivide the text into tiles based on the pre-existing structure,such as section headings.If no structure is detectable,we break the text into roughly co-herent topic segments using a segmenter[Cho00].For each tile,we label parts of speech(NOUN,VERB,etc.)[Bri93]. Nouns and verbs are then extracted and stemmed(e.g.,books →book,going→go)using a morphological processor [Did03].Punctuation is omitted.If short word sequences, noted in WordNet,are found in the document,the words are combined into a collocation,and treated as a single word. Next we look up in which WordNet synsets the(word, part-of-speech)pairs occur.Because pairs usually occur in multiple synsets,we do not perform word sense disambigua-tion.Instead,we divide the word count amongst the available synsets.If WordNet supplies relative sense frequency infor-mation for a word,we use this to distribute the count.Oth-erwise,we distribute the count weighted linearly by sense rank.This results in weighted occurrence counts that are not

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(a)Node ?ll transparency proportional to number of occurrences of words in subtree (cumulative view).Node size based on leaf

count.(b)Node ?ll transparency proportional to number of occurrences of words in a single synset (single node view).Node size based on leaf

count.

(c)Single node view,node size pro-portional to occurrence count total for subtree rooted at that node.

Figure 3:DocuBurst of a science textbook rooted at ‘thought’;node hue distinguishes the synsets containing ‘thought’.

integers,but the overall results more accurately re?ect doc-ument content.By dividing the counts,we dilute the contri-bution of highly ambiguous terms.The full text of tiles and their associated (word,part-of-speech,count )triples are then read into the data structure of the visualization.4.2.Visual Encoding Node Size

Within the radial tree,angular width can be proportional to the number of leaves in the subtree rooted at that node (leaf count )or proportional to the sum of word counts for synsets in the subtree rooted at that node (occurrence count ).The leaf count view is dependent on WordNet and so is consistent across documents.The word count view maximizes screen space for synsets whose words actually occur in the docu-ment of interest,thus the shape,as well as node colouring,will differ across documents.Depth in the hyponymy tree determines on which concentric ring a node appears.The width of each annulus is maximized to allow for all visible graph elements to ?t within the display space.Node Colour

It is possible to look at multiple senses of a word in one view.Views rooted at a single word contain a uniquely coloured subtree for each synset (sense)containing that word.In con-trast,trees rooted at a single synset use a single hue.Since luminance variation in the green region of the spectrum is the most readily perceived,it is the ?rst colour choice [Sto03,30].Gray is used for nodes with zero occurrence counts,since their presence provides a visual reminder of what words are not used.

Transparency is used to visualize relative word or synset

count.Similar to the concept of value,transparency pro-vides a range of light to dark colour gradations,thus of-fering ordered [Ber83]and pre-attentive [War04]visuals.Highly opaque nodes have many occurrences;almost trans-parent nodes have few occurrences.Word senses that are more prominent in the document stand out against the more transparent context.

Two ways to visualize word occurrence are provided:single-node and cumulative.In the single-node visualization,only synset nodes whose word members occur in the docu-ment are coloured.In the cumulative view,counts are propa-gated up to the root of the tree.In both views,transparency is normalized so maximum counts achieve full opacity.When multiple documents are visualized,the cross-document max-imum is used to set the scale.These modes support a grad-ual re?nement of focus.The cumulative,or subtree,view uses the association of words into synsets and synsets into a hyponymy tree to aggregate counts for related concepts.Similar to the TreeJuxtaposer techniques for visualizing dif-ferences embedded deep in a large tree [MGT ?03],by high-lighting the entire subtree containing the node,salient small nodes can be more easily located,even if hidden from view by a ?lter.The single-node view reveals precise concepts in the document and supports the selection of synsets whose word members appear in the document being analyzed.In addition,for a fully expanded graph,the single node view may highlight nodes that are otherwise too small to notice.The subtree and cumulative views are compared in Figure 3.While transparency is an effective visual method for dis-tinguishing large differences and trends,it is impossible to read exact data values using it.To facilitate the exact read-ing of synset occurrence counts for the selected text tiles,we provide a dynamic legend (see Figure 4).

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(a)

(b)(c)

Figure4:DocuBurst of a general science textbook rooted at{animal}.Single-node colouring and occurrence count sizing were used with zero-occurrence synsets hidden.Mouse hover point is revealed by blue trace-to-root colouring.(a)Synset{cattle, cows,kine,oxen}highlighted.(b)Synset{world,human race,humanity,mankind,man,...}highlighted.(c)Detail of the

dynamic legend,showing,from top to bottom,no selection,selection from(a),and

(b).

Figure5:DocuBurst of a general science textbook rooted at{energy}.At right,mouse wheel interaction was used to shrink the angular width of the subtree rooted at{radiation} and expand the subtree under{electricity}exposing the pre-viously illegible node{signal}.

5.Interaction

A root node can be a word,in which case its immediate chil-dren are the synsets containing that word.Alternatively the visualization can be rooted at a synset.Root nodes in the vi-sualization are selectable by searching for either a word or synset of interest.Once a root is chosen,the visualization is populated with all its hyponyms.

As there are more than70,000English noun synsets in WordNet,techniques to abstract and?lter the data are im-portant.First,we provide a highlight search function which visually highlights nodes whose label matches any of the given search terms.Highlight nodes have a gold background and border,and a darker font colour,drawing attention to even the smallest of search results.The transparency of the highlight(gold)background is attenuated to the word occur-rence counts so as to not disrupt this data-carrying value and to provide for stronger pop-out of search results with high occurrence counts.

Second,we implement a generalized?sheye view[Fur86] that collapses all subtrees which are more than a user-speci?ed distance from the central root node.Changing this distance-based?lter allows for a semantic zoom,creating vi-sual summaries of varying speci?city.The presence of non-zero word occurrence counts within collapsed subtrees is in-dicated by using the cumulative colouring,in which counts are propagated to the root.Optionally,all highlight nodes can be exempted from the distance?lter(by increasing their a priori importance in the DOI function),effectively ab-stracting the graph to all synsets within a given distance from the root or highlight nodes(see Figure1).

Double clicking on a node of interest restricts the visual-ization to the hyponyms of the node’s synset;double right-clicking reverses this action by reloading the graph at the parent of the clicked node,thus providing bi-directional data navigation through the hyponymy relation.To create more space for the details of the children of a given synset,the angular width of a node and its subtree can be manually in-creased using the mouse wheel.This increase provides a ra-dial detail-in-context view which causes the node’s siblings to be correspondingly compressed.Changes to a node’s an-gular width affect its children equally and its siblings in an inverse manner(see Figure5).

The visualization can be based on selected subsections of the document.The initial view is based on all text tiles in the document,but a selection can limit the tiles from which counts are drawn.Unrestricted visual pan and geo-metric zoom of the display space are also supported,as well as a zoom-to-?t control to reset the pan and zoom to a best-?t for the currently visible tree.Rendering is dependent on the zoom factor:node borders are not rendered when the nodes are very small,and labels are not rendered when they would not be legible.All highlighting,navigation,and emphasis in-teractions are provided in real time.

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(a)Linked visualizations for

details-on-demand.

(b)The details window showing the concordance lines for the selected synset.

Figure 6:A search for ‘electricity’reveals synsets containing that word (gold).On selecting a node,the distribution of the word is revealed in the tile browser.Selecting a tile reveals the text with occurrences of the selected synset highlighted.

6.Drill Down

The full text can be read through accessing the text tiles at the bottom of the interface.To navigate the text,we use a linked visualization:the text tile browser.Rectangles repre-senting the text tiles appears in a linear,vertical array to the right of the DocuBurst.A ?sheye distortion [Bed00]facil-itates navigation and selection of text tiles within this list.Clicking any tile brings it into view.Furthermore,this vi-sualization can be used to see occurrence patterns in the document.By clicking nodes in the DocuBurst visualiza-tion,synsets and entire subtrees can be selected.Text tiles in which selected synsets appear show as varying intensity of orange in the text tile browser,depending on number of oc-currences in the tile.Occurrences of those synsets and words are also highlighted in the full text window.

Concordance lines are a standard linguistic analysis tool

in which all occurrences of a word of interest are extracted and displayed with their left and right N (usually 5)context words.Concordance lines for selections are extracted and shown in the concordance window.Patterns of orange in the tile browser can indicate how localized concepts are in the document.For example,in Figure 6,we see that {electricity }appears more frequently toward the end of the document.We can use the tile browser and full text window to quickly ?nd occurrences of the terms of interest in context.By clicking the text tile rectangles in the tile browser,we ?nd,in the tile detail window,that there is a chapter on ‘electricity’at the end of the book.

7.Example:Document Comparison

DocuBurst can be used to compare multiple documents.Trees rooted at the same synset but coloured based on differ-c

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(a)First2008

debate(b)Third2008debate

Figure7:DocuBursts rooted at{skill worker}reveal the traditional US focus on military of?cers and veterans was eclipsed in the third US Presidential debate by discussions of plumbers.

ent texts will reveal relative frequency differences between them.While the other examples in this paper were visualiza-tion of a high school general science text book,we also can apply the technique to other forms of electronic text.In Fig-ure7we applied DocuBurst to the transcripts of two2008 US presidential debates.Note that to ensure comparability when viewing multiple documents,colour scaling is based on the maximum count for visible nodes across all docu-ments.

A high level view of the debates rooted at{person}re-vealed strong colour for the{leader}and{serviceman,mil-itary personnel,military man}subtrees.Drill down revealed {senator}is a descendant of{leader}(both participants were senators).Attention to military issues and veterans is also expected given current con?icts.Examining the third debate showed an additional region of colour under the {craftsman}subtree.Further investigation,by switching to the occurrence count size function,revealed a dramatic shift in concentration within the{skilled worker}https://www.doczj.com/doc/4213886788.html,-itary people became relatively less important compared to craftspeople—speci?cally,plumbers.This is the effect of Senator McCain’s focus on“Joe the Plumber”in the third debate,and was the genesis point of this phrase which dom-inated the remainder of the campaign.

8.Challenges and Future Work

Re?ecting on this work has suggested several interesting op-portunities for future research in both the data and visual-ization realms.From a data perspective,the original goal of viewing what parts of an entire language are included in a document merits further research.As with all text visual-izations,it is necessary to view a subset of language due to limited display space and computational resources with ex-tremely large data.Views rooted at{entity}and covering all English nouns appear cluttered and interaction is slow.It is commonly held that WordNet sense-divisions are too?ne-grained for many computational applications;investigation into other ways to abstract WordNet may help alleviate this problem.Additionally the use of uneven tree cut models to abstract the DocuBurst in a dynamic way may clarify the view.For example,if the subtree under a node has only one leaf with a non-zero count,we may omit that subtree. Finding a place to begin exploration is a challenge with the current implementation.Providing hints for which synsets may be of interest as visualization roots for a partic-ular document or set of documents may assist an analyst to ?nd views of interest.Methods which may be useful include suggesting synsets with a high fraction of non-zero leaves below them,or when the cumulative count divided by the number of leaves is high,indicating an area of unusual con-centration.

Word sense disambiguation is an area of active research in computational linguistics that could bene?t DocuBurst. Currently,an occurrence of a word is divided among all synsets in which it appears.Thus‘river bank’will augment the count for{bank,savings bank,depository?nancial in-stitution}.Incorporating word sense disambiguation into the preprocessing step would greatly enhance the value of the visualization.For example,in the general science textbook used for the examples in this paper,‘man’occurs quite often in the sense of‘people’(“man invented the wheel”).How-ever,these occurrences are also counted towards the biolog-ical{hominid}sense of‘man’,resulting in the incorrectly strong appearance of the{primate}subtree.Additionally, we currently use word occurrence count as the only avail-able word scoring function.Other scoring functions,such as the Dunning Log-likelihood ratio[Dun93],could be used to highlight important or unusual words in a document.Other text features,such as hapax legomena(words which occur only once)could be used with the WordNet-based layout to provide special-purpose summaries of content.

Visually,the use of transparency to indicate word occur-rence is useful for the intuitive mapping between data and vi-sual appearance.However,it also introduces the possibility of misleading illusions.For instance,siblings in DocuBurst are unordered;furthermore,non-sibling nodes may be adja-cent.By chance,unrelated nodes that both have high occur-rence counts can appear as a large swath of strong colour. Gestalt perception may lead viewers to impart signi?cance to this coincidence.Stronger node borders would distinguish these regions,but node borders become obstructive on small nodes.Finding an experimentally validated solution to this design trade-off could impact space-?lling visualizations in general.

9.Conclusion

DocuBurst provides an overview visualization of document content which is based on a human-centered view of lan-guage whereas previous works were based on far simpler, derivative statistical analyses.The visual design is grounded in established research on human abilities in colour percep-tion.Semantic and geometric zooming,?ltering,search,and

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details-on-demand provide a visual document summary,re-vealing what subset of language is covered by a document, and how those terms are distributed.

Initially motivated by the current lack of a digital equiva-lent of?ipping through a book,this work leads well into an investigation of the DocuBurst technique to view the differ-ences between two or more documents,which may be useful for plagiarism detection,document categorization,and au-thorship attribution.Existing digital library interfaces could be enhanced with arrays of DocuBurst icons,allowing com-parison against one another or a baseline reference corpus to portray content in more pleasing and information-rich ways. Acknowledgements

Thanks to Ravin Balakrishnan for advice and guidance. Funding for this research was provided by NSERC,iCore, SMART Technologies,and NECTAR.

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员工离职管理规定及流程

员工离职管理规定及流程 第一条为规范公司离职管理,维护正常的人才流动秩序,特制定本规范。离职分为辞职、辞退、合同期满解除劳动关系、自动离职、协议解除劳动合同、退休等。 第二条本规范适用公司全体员工 第三条办理流程 一、辞职 1、辞职申请 有辞职意向的员工,应于辞职前至少30天向部门负责人提出辞职申请(试用期的员工须提前至少3天)。 部门负责人必须于接到辞职申请2日内与辞职员工积极沟通,对绩效良好的员工努力挽留,探讨改善其工作环境、条件和待遇的可能性。 确认辞职的员工应本人携带经部门负责人签字认可的辞职申请,到综合管理部领取并填写《员工辞职审批表》(附件1),需进行离职谈话的员工,普通岗位员工由其直接领导、部门负责人与辞职人进行离职谈话;重要岗位及公司管理岗位员工离职由部门负责人及综合管理部负责人、分管领导进行离职谈话,谈话内容详见《员工离职面谈表》(附件2),如有必要,可请其他人员协助进行谈话。 离职谈话清单应详细记录,经员工和谈话人共同签字,并分存公司和员工档案。 辞职员工因故不能亲临公司会谈,应通过电话交谈。 2、辞职审核 离职员工填写《员工辞职审批表》,经部门负责人和分管领导批准后交综合管理部办理后续审批手续。综合管理部

须于接到《员工辞职审批表》5个工作日内办理完辞职审批 工作。 3、辞职办理 员工辞职经审批后,由综合管理部通知办理如下手续: ①综合管理部与离职员工解除《劳动合同》,双方签订 《云南省用人单位解除劳动合同协议书》和《云南省 用人单位解除(终止)劳动合同证明书》,并经劳动 用工登记机关盖章生效; ②综合管理部封存辞职员工社会保险,更新人事资料; ③综合管理部于公司薪酬核算日按照公司《薪酬管理规 定》进行薪资核算,并由计划财务部进行现金核发; ④通知辞职员工所在部门,当离职人员再次拜访公司时,需按照公司会客规定办理。 4、辞职交接 员工辞职申请获准,所在部门应安排其他人员接替其工作和职责,辞职员工于离职前1个工作日办理辞职交接手续,填写《员工离职手续交接表》(附表3),交接内容及各部门的职责如下: ①辞职员工所在部门:工作、工具、办公用品、资料等移交,并由部门负责人负责监交,如交接清单较多可另附清单; ②物业管理部:负责办理宿舍退宿等手续,由经办人及部门负责人签字确认; ③信息部:对辞职员工的工号、网络账户等进行删除或注销,由经办人及部门负责人签字确认;

集团员工离职管理制度(范例)

集团员工离职管理规定 1.目标: 1.1 离职管理是为了规范离职员工的多种结算活动,交接工作,以利于工作的延续性。 1.2 离职手续的完整可以保护员工免于陷入离职纠纷。 1.3 与离职人员的面谈可提供管理方面的改进信息,帮助提高公司管理水平。 2.适用范围:本制度适用于总公司及各分(子)公司员工的离职管理。 3.离职流程 3.1 离职申请 3.1.1 离职申请提出人分别为:员工(个人辞职) 3.1.2 员工提出离职的,试用期员工需在离职前级主管提出离职申请;正式员工需在离职前至少级主管提出离职申请。3.1.3 未按规定通知期提前通知公司的离职员工,、员工直接上级主管(公司辞退)。 7 日填写《员工离职申请表》,向其直接上 30 日填写《员工离职申请表》,向其直接上 须向公司支付相应天数的工资(计算方式: 当地最低工资标准十24 X相应天数)作为补偿金后方能提前办理离职手续。 3.1.4 未办理离职手续擅自脱岗的员工,公司将根据相关制度,视该员工严重违纪(旷工三天按自动离职处理),由其直接上级按照辞退为其办理离职手续。公司将依据上款之规定,向其追偿相应的补偿金。 3.2 离职申请提出后, 按流程办理申请审批,具体审批权限划分如下: 3.2.1 总公司员工: 3.2.1.1 经理级(含)以上员工: 由部门负责人签署意见,总公司人力资源经理审核,总公司总裁审批; 3.2.1.2 ;经理级以下员工: 由直接上级签署意见,部门负责人审批,总公司人力资源经理负责会签。 3.2.2 分子公司员工: 3.2.2.1 分公司负责人: 由总公司人力资源经理审核, 总公司总裁审批; 3.2.2.2 经理级(含)以上员工:由直接上级签署意见,分公司人力资源经理审核,分子公司负责人审批,总公司人力资源经理负责会签; 3.2.2.3 经理级(含)以上员工:由直接上级签署意见,分公司人力资源经理审核,分子公 司负责人审批,总公司人力资源经理负责会签; 注:1. 会签人对手续办理的合规性和合理性负责,没有决定权,但有否决权。 2. 没有人力资源经理的分子公司可由代理人事负责人代理职责,下同。 3.3 离职手续办理离职手续办理具体分工如下:

员工离职管理办法(超全)

员工离职管理办法 目录 1.目的 (2) 2.范围 (2) 3.名词解释 (2) 4.职责 (2) 5.管理制度 (2) 5.1离职审批流程 (2) 5.1.1员工主动离职审批流程 (2) 5.1.2员工被动离职流程 (3) 5.2离职交接流程 (4) 5.3离职分析 (6) 5.4退休及返聘流程、政策 (6) 6.注意事项 (6) 7.附件 (7)

1.目的 为规范公司的离职管理流程,保证公司各项工作的正常运行,降低员工离职给公司带来的风险,同时在离职面谈的过程中挽留优秀员工,了解离职员工的真实想法,以助改善公司的经营管理,特制订本办法。 2.范围 3.名词解释 3.1离职:指员工主动申请辞职或者被动辞退等原因,办理所有物品、财务、信息及工作 等各项交接手续,离开工作岗位,与公司解除或者终止劳动关系。 3.2离职流程:指员工离职所需要办理的各项手续。 3.3离职面谈:员工在提出离职申请后,由人力资源部相关人员进行面谈,对优秀员工予 以挽留,了解员工离职的真正原因,以制订有利于公司发展的人力资源策略和经营管理方式。 4.职责 4.1离职人员所属部门:负责系统内部离职审批流程跟踪、离职交接手续的指导办理工作, 通常由部门人事员负责。 4.2人力资源部:负责离职面谈、离职审批、离职交接和离职分析工作,保证离职交接程 序完整无误。 5.管理制度 5.1离职审批流程 5.1.1员工主动离职审批流程 5.1.1.1员工提出离职:员工如有离职意向,应至少提前一个月向人力资源部及所 在部门负责人处提出书面辞职申请,试用期员工提前三天向人力资源部及 所在部门负责人处提出书面辞职申请《员工离职申请》。员工主动离职,书 面申请应包括如下内容:姓名,所在部门,离职原因,个人原因辞职要求 写明“本人主动提出辞职”,辞职书要求有日期及本人签字。 5.1.1.2部门负责人进行离职面谈:部门负责人接到员工辞职的第一时间和辞职员 工充分沟通,了解离职原因,确定是否挽留。

显示桌面快捷方式为什么不见了怎么办怎么找回来

显示桌面快捷方式为什么不见了怎么办怎么找回来? 法一: 一般情况下,右击任务栏空白处,在弹出菜单上点“工具栏”-快速启动,使快速启动前打上对勾,任务栏上就会辟出快速启动栏,里面就有“显示桌面”的图标(一个方块上面支笔的图标式样),点一下,就能切换到桌面。注:可以按“win+d”快捷键实现快速切换到桌面。(win键位于Ctrl与Alt 之间) 方法二: 也可以点击“开始→运行”,在弹出的“运行”对话框中输入“REGSVR32 /n /i:u shell32 ”(不含双引号。注:32后面有个空格),然后回车,片刻后会弹出“shell32中的DllInstall成功”提示对话框,这样“显示桌面”按钮就可以完美归来了。 方法三: 可以自己做一个。 打开记事本,输入以下内容: [Shell] Command=2 IconFile=explorer.exe,3 [Taskbar] Command=ToggleDesktop 其中,第三行代码代表的是图标的位置,数字“3”显示的是,而当把数字“3”换成“4”,刷新,图标会变成;当数字换成“6”时,图标变成了回收站的图标,如图。虽然图标的式样变了,但是同样是“显示桌面”的功能。因此,更改显示桌面图标的方法就是这样。其实,只要在“IconFile=”后输

入你所中意的图标的路径就可以了。 然后点“文件”——>“另存为”,在文件类型中选择"所有文件",在文件名中打上“显示桌面.scf”(不包括双引号)就成了。 接下来,用鼠标把保存好的文件拖到快速启动栏里就OK了。为了以后便于使用,还可以将该图标保存到以下路径:C:\Documents and Settings\Administrator\Application Data\Microsoft\Internet Explorer\Quick Launch。至此,“显示桌面”图标的新建工作,搞定! 方法四: 如果觉得这个麻烦,还有一个简单的方法,从另一台电脑上复制“显示桌面”快捷方式。首先到另一台电脑上找到这个快捷方式,按住ctrl 把这个图标拖到电脑桌面上,然后把这个“显示桌面”复制到存储盘或者联网传给需要的电脑,传到后再用鼠标拖动到快速启动栏即可。 方法五: 或者运行“regedit”打开注册表,找到下面键值:HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\ Policies\System,在右边的窗口中有一个DOWRD值:“NoDispCPL”,将其值设为“0”或者删除即可。 在完成此操作后,有些电脑可能需要重启后才能生效

员工离职管理制度办法

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辞职是指员工因本人原因离开研究院或部门领导认为其不适应现任工作岗位而劝其离开公司,并与公司解除工作关系。 (二)辞退 辞退是根据国家有关规定、公司相关的规章制度或协议,决定终止与员工的聘用关系的行为。 (三)自动离职 自动离职是指对未提出辞职申请或未办理正常辞职手续即离开公司七天以上的行为视为自动离职。 第三章离职基本制度 第七条辞职的基本制度。辞职人员应按研究院规定提前30日向部门领导和院人力资源部递交书面辞职申请。 员工有下列情形之一的,不准或暂时不准辞职。 ①研究院重要工作尚未处理完毕,须由该员工继续处理,或该员工辞职将对工作造成较大损失或不良影响的。 ②由研究院出资培训、进修或由研究院负担学费参加业余学习,服务期限未满的。 ③正在接受研究院审查,在经济或其他问题上未做结论的。 ④其他原因暂不宜辞职的。

显示桌面图标不见了怎么办

显示桌面图标不见了怎么办 篇一:电脑桌面图标消失了怎么办 电脑桌面图标消失了怎么办? 1、电脑桌面图标消失了 Windows XP有一个功能,就是把桌面上的图标隐藏!所以可能是无意中改动了这个选项的设定,只要在桌面展开右键,选择“依次排列图标”-“显示桌面图标”就可以了:) 2、桌面上的IE图标竟然不见了 在桌面单击右键,选“属性→桌面→自定义桌面”,在桌面图标处看到“Internet Explorer”项了吧,选择上它桌面就可以恢复IE 图标了。 3、控制音量的小喇叭图标不见了 出现这种情况,只要打开[控制面板],选择[声音、语言和音频设备]这一项,再选择[声音和音频设备],然后再打开窗口的[音量]选项,在[将音量图标放入任务栏]选项前面打上钩,单击[确定],图标便会出现了。(如图) 4我的输入法图标哪儿去了?怎么办? 如果你用的是Windows XP中,输入法图标也会莫名其妙地丢失,但在控制面板中却没有“输入法”,这时可以按以下方法尝试:方法1:在任务栏单击鼠标右键,弹出快捷菜单,把鼠标移动到”工具栏”上,会弹出子菜单,看看其中的“语言栏”有没有被选中,

如果没有选中,单击选中“语言栏”,一般会显示输入法图标。 方法2:依次单击“开始→设置→控制面板”,打开控制面板,在控制面板中单击“日期、时间、语言和区域设置”,单击“语言和区域设置”,弹出“语言和区域设置”对话框,单击“语言”标签,在“文字服务和输入语言”下单击“详细信息”按钮,弹出“文字服务和输入语言”对话框,单击“高级”标签,在“系统配置”下,把“关闭高级文字服务”前面的对号取消(看到下面的注释了没有,选中”会关闭语言栏”),单击“确定”按钮,输入法图标就回来了。 方法3:点“开始→运行”,键入“msconfig”,单击“确定”或回车,运行“系统配置实用程序”,在“启动”里把“”选中,单击“确定”,然后注销或重新启动应该就可以了。这是因为控制Alternative User Input Text Processor (TIP)和Microsoft Office 语言条,提供语音识别、手写识别、键盘、翻译和其它用户输入技术的支持。这个程序没有启动也会造成输入法图标不显示。 5、“网络连接”窗口中我的“本地连接”和“ADSL”图标不见了 这可能是你的Network Connections服务被禁用了原因造成的,查看方法如下:在“运行”窗口中输入“”,然后在打开的“服务”窗口右侧服务列表中找到名称为“Network Connections”的服务,看看其状态是否为“已启动”。假如不是,那么就需要将其设置为“已启动”。 6、任务栏按钮忽然消失了

员工离职管理办法

员工离职管理办法 第一章总则 第一条为加强公司员工离职管理,规范离职办理程序,特制定本办法。 第二条员工离职分为辞职、解雇两种方式。 第三条本规定适用于公司全体员工。 第二章辞职 第四条员工辞职,试用期员工应提前5个工作日,正式员工应提前30天填写《离职申请表》,向其主管提出辞职请求。 第五条合同期满后,员工不愿意续签劳动合同时,应提前30天填写《离职申请表》,向其主管提出辞职请求。 第三章解雇 第六条解雇员工根据其原因可分为自动离职、辞退、开除和劳动合同终止4种方式。 第七条不按规定办理解除、终止劳动合同手续而自行离职的员工,按自动离职处理。 第八条员工若出现以下情形,公司可以视情节予以辞退或开除处分,必要时将追究其法律责任: 1、严重违反公司规章制度的; 2、严重失职或有其他不良行为,对公司利益或声誉造成损害的; 3、提供虚假入职资料或有其他对公司有严重欺骗或欺诈行为的; 4、不能胜任工作,经培训或调整工作岗位,仍不能胜任的; 5、劳动合同中约定或公司规定的其他情况。 第九条劳动合同期满,公司不愿意与员工续签时,应提前通知员工终止劳动合同。 第五章离职手续办理 第十条员工离职审批权限,参见《公司人力资源管理权限表》。

第十一条员工离职申请获得批准后,人力管理部门应及时通知集团相关部门关闭其信息系统权限。同时离职员工需按《离职交接单》的要求办理交接手 续,经人事管理部门审核确认后方为离职完毕,并自动解除劳动合同。第十二条直接主管应指派承接人负责离职人员工作的移交;同时直接主管负有监交责任。 第十三条离职交接事项包括但不仅限于以下方面: 1、各种公用印章、戳记; 2、现金、财务借款、票据、帐册、凭证; 3、各种图书、规章制度、文书、档案等资料; 4、办公系统上未尽事项、重要经营资料; 5、电脑系统、硬件的完整性以及各系统帐户的密码; 6、待办或未了的与本部门及其他相关部门的业务; 7、系统管理员离职时还必须与集团相关部门办理相关手续交接。交接 时应包括: (1)电脑资料(书籍、光盘) (2)服务器超级用户密码 (3)各应用系统(含服务器)的完整性。 8、个别岗位需经过特别培训方能上岗的,离职时的工作交接内容必须 包括离职者对交接人的培训,如系统管理员等; 9、各种与工作相关的社会关系及客户关系资源。 第十四条公司总经理助理及以上人员离职需经集团审计部进行离任审计。 第十五条工作交接双方应认真办理交接事项,如因离职工作交接疏忽给公司带来损失,追究交接当事人及其直接上级责任。若因此使公司遭受损失,则 由责任人负责追讨或承担赔偿责任。 第十六条离职手续未办理清楚者,担保人应承担相应的责任。员工办理离职手续时,当事人应当配合人事部门联络担保人。 第六章离职结算 第十七条离职员工工资结算至考勤截止日(事实离职日),如有迟到、旷工等考勤异常行为仍需按照考勤制度的相关规定进行处理。 第十八条员工离职,若存在以下事项,应承担相应的赔偿费用: 1、员工若即辞即离,需要承担1个月全额薪资的即辞即离费用; 2、员工违反与公司签订的相关协议,应按协议承担赔偿责任;

最全员工离职管理制度及流程

员工离职管理制度 第一章总则 第一条为维护员工正常流动秩序,规范员工离职行为,特制定本制度。 第二条本制度中“公司”指“淮南市信谊投资管理有限公司”。 第三条本制度所称“高层管理人员”是指“公司总经理、常务副总、副总经理”,“中层管理人员”指“公司各部门经理”,“基层员工”是指除以上人员之外的公司其他正式员工。 第四条员工离职包括劳动合同(以下简称合同)终止和合同解除两种情况。 第二章合同终止 第五条合同终止是指公司与员工在合同期满或约定的合同终止条件成立时终止合同关系的行为,主要指合同到期不续签的情况。 第六条合同到期公司提出不再续签合同时,由公司行政事业部在合同到期两周前以书面形式通知员工(《劳动合同终止通知单》见附件一)办理合同终止的相关手续;员工提出不再续签合同时,应在接到行政事业部关于续签合同通知(《续签劳动合同审批表》见附件二)的一周内以书面形式反馈(《员工离职申请单》附件四)。合同另有约定的,按约定条款办理。 第七条行政事业部须在合同到期前一个月内完成员工合同续签意见审批(《续签劳动合同审批表》见附件二)。 第三章合同解除 第八条合同解除指合同订立后,尚未完全履行以前,由于某种原因导致合同一方或双方提前终止劳动关系的行为。公司所指合同解除包括试用期合同解除、辞职、擅自离职、辞退、除名。 第九条试用期合同解除 (一)员工试用期表现不佳未能达到岗位说明书要求,由用人部门与行政事业部、

员工本人在试用期满前一周内进行沟通,沟通后不能达成一致的,由行政事业部在试用期结束前以书面形式通知员工(《劳动合同解除通知单》见附件三)进行试用期合同解除手续的办理。 (二)员工本人试用期提出合同解除,应提前三天以书面形式(《员工离职申请单》附件四)告知用人部门,办妥相关手续后方可离职。 第十条辞职 (一)员工在合同期内辞职,应提前三十天(劳动合同另有约定的按约定执行)向其直接上级提出,并提交《员工离职申请单》(见附件四)按本制度第十六条规定程序审批后,由公司行政事业部办理相关手续。 (二)员工合同期内提出辞职,公司应在一周内做出同意、不同意或暂缓待审议的答复;做出同意辞职答复的应明确离职时间、交接要求等。 (三)辞职员工所在部门及行政事业部应主动了解员工辞职原因,做好辞职沟通工作,对绩效良好的员工应引导挽留,探讨改善其工作环境、条件和待遇的可能性,沟通记录并已书面形式留存。(《离职员工沟通记录》见附件八) 第十一条擅自离职 (一)擅自离职是指员工未递交辞职申请就擅自离岗、或员工已递交辞职申请但未经批准就擅自离岗、或员工辞职申请虽获批准但未履行完毕离职手续就擅自离岗。 (二)擅自离职按《员工奖惩管理制度》作除名处理并承担违约责任,造成经济损失的,公司有权提出赔偿要求。 第十二条辞退、除名 (一)员工因各种原因不能胜任岗位工作,经过培训或者调整工作岗位,仍不能胜任工作的;或符合《员工奖惩管理制度》中规定的辞退情况,以及《劳动合同法》规定的可以解除劳动合同的情况,公司可作辞退处理。 (二)员工符合《员工奖惩管理制度》规定的须作除名处理的情形,公司可作辞退处理。 (三)对员工作辞退、除名处理由员工所在部门向行政事业部提交《员工辞退、除名申请单》见附件五),按本制度第十六条规定程序审批后以书面形式通知员工本人。

桌面图标丢失了怎么恢复

点击“开始→运行”,在弹出的“运行”对话框中输入“REGSVR32 /n /i:u shell32”(不含双引号,否则会很没面子),然后回车,片刻后会弹出“shell32中的DllInstall成功”对话框,“显示桌面”按钮即可恢复,这也是最简单的办法了。 显示桌面图标丢了怎么恢复 “快速启动”实际上就是一个目录,您可以找到它,然后在其中添加自己想要的快捷方式。比如:您的系统安装在C盘,并且您使用“name”作为用户名登录的。那么“快速启动”的目录应该在 C:\Documents and Settings\name\Application Data\microsoft\internet explorer\qui ck launch 如果“显示桌面”图标丢了,请新建一个文本文件,键入下面的内容,然后将其存为“显示桌面.scf”文件名就可以了。 [Shell] Command=2 IconFile=explorer.exe,3 [Taskbar] Command=ToggleDesktop 我的电脑由于安装了两个杀毒软件,导致了桌面的图标全部不见了,如何恢复? ★桌面-点击鼠标右键-点击排列图标-点击显示桌面图标 ★在桌面上右键点击→“属性”→桌面项→左下有个“自定义桌面”进入设置,把你需要的桌面项目打上勾,然后确定就行了。 ★先按ctrl+alt+del三键打开任务管理器,(如果打不开任务管理器,你最好重做系统)。在任务管理器中点“文件”--“新建任务”。在弹出的创建新任务选项卡中的运行中输入:Explorer.exe ,然后点确定。 或按CTRL+AIT+DEL,选任务管理器,点击文件——新建任务——浏览,选C:\windows下的explorer.exe运行。 ★在任务栏上点右键,属性,点工具栏,选中快速启动 如果不行,打开“记事本”程序,在其中输入如下内容: [Shell] Command=2 IconFile=explorer.exe,3 [Taskbar] Command=ToggleDesktop 然后把这个文件保存为:“Show Desktop.scf”,必须确认文件名和双引号中的一样。然后把保持的Show Desktop.scf文件复制到:“C:\Documents and Settings\用户名\Application Data\ Microsoft\Internet Explorer\Quick Launch”目录下。其中你需要把“用户名”替换成你需要恢复“显示桌面”按钮的用户名。 ▲要是还不行的话,可能是你系统文件被破坏,桌面启动项丢失,建议从新安装系统!

员工离职管理规定办法范本

工作行为规范系列 员工离职管理规定办法(标准、完整、实用、可修改)

编号:FS-QG-38175员工离职管理规定办法 Regulations on employee turnover management 说明:为规范化、制度化和统一化作业行为,使人员管理工作有章可循,提高工作效率和责任感、归属感,特此编写。 员工离职管理规定【1】 1目的 规范公司离职员工的多种结算活动和交接工作,以利于工作的延续性;保护员工和公司免于离职纠纷;与离职人员的面谈可提供管理方面的改进信息,帮助提高公司管理水平。 2适用范围 适用于公司所有员工的离职工作。 3关键词 3.1辞职:在劳动合同期限内,员工自动提出与公司解除劳动关系。 3.2擅自离职:合同期内,员工擅自离开工作岗位、或员工已递交辞职申请但未经批准就擅自离岗、或员工辞职申请虽获批准但未履行完毕离职手续就擅自离岗。

3.3劳动合同到期:劳动合同期限已满,员工或公司一方提出不再续签合同。 3.4辞退:员工因各种原因不能胜任岗位工作,经过培训或调整工作岗位,仍不能胜任工作的;或因违反公司相关规章制度,由公司提出与其解除劳动关系。 3.5开除:员工严重违反公司规章制度或国家法律法规,由公司提出与其解除劳动关系。 4离职审批权限 4.1总监级及其以上员工:收到员工离职申请的3个工作日内,由其直接上司签署意见并报送分管副总审核后,报送人力资源部,人力资源总监在3个工作日内会签完毕后报送总经理审批,总经理在3个工作日内将审批结果反馈人力资源部。 4.2总监级以下员工:收到员工离职申请的3个工作日内,员工所在部门负责人或直接上司在2个工作日内签署意见,报送人力资源部,人力资源总监在3个工作日内审核并提出意见,报送人力资源分管副总复核,人力资源分管副总在2个工作日内复核完毕后,报送总经理审批,总经理在3

win7显示桌面的快捷方式做法

首先讲下原理,代码和XP是一样,不同就是在Win7下不能直接拖入任务栏,不然达不到XP中的效果。我们随便找个程序的快捷方式,然后修改它的图标及链接位置,接下来只记锁定到任务栏就可以了。 1.随便找个程序的快捷方式,比如我找的是Media Player,在图标上右击 选择【属性】—【更改图标】,将【查找此文件中的图标】下的路径删除并回车,这时就可以显示所有图标了,然后选择显示桌面的图标,接着右击选择“锁定到任务栏”。

2.在桌面上新建记事本,把下面的代码复制进去,然后点【另存为】,保存 类型为:所有文件,文件名为:显示桌面.scf。然后将该文件放到一个你认为不碍眼的地方,比如我放到C盘根目录,路径为C:\显示桌面.scf。 [Shell] Command=2 IconFile=%SystemRoot%system32SHELL32.dll,34 [Taskbar] Command=ToggleDesktop 3.打开【计算机】—【组织】—【文件夹和搜索选项】—【查看】—【隐藏 文件和文件】选项,然后选择【显示隐藏的文件、文件夹和驱动器】,设置完后打开该路径C:\用户\你的用户名 \AppData\Roaming\Microsoft\Internet Explorer\Quick Launch\User Pinned\TaskBar。这时我们可以看到被我们改过的Media Player图标,这时我们在图标上右击选择【属性】,将【目标】改成C:\显示桌面.scf,

当然如果你放在了别的地方,就换成你的路径。 4.4、OK!Win7中也有了XP中的“显示桌面”图标了,是不是更方便点了 呢。

员工离职管理规定完整版

员工离职管理规定 HEN system office room 【HEN16H-HENS2AHENS8Q8-HENH1688】

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用品。薪资结算:员工离职,薪资结算至停职日(不含),交接时间不计薪资。 2.试用期辞职的员工凡不满15天的不予发放工资,超过15天的按实际发放。 员工辞退管理细则 一、目的 为加强公司劳动纪律,提高员工队伍素质,增强公司活力,促进本公司的发展,特制定本细则。 二、辞退原则 公司对违纪员工,原则上经劝告、批评、教育或行政处分后仍不改者,有辞退的权利。 三、辞退形式 辞退的形式包括辞退、开除。 四、辞退条件 符合下列条件之一的员工,部门主管可提出辞退建议。 1.试用期未满,被证明不符合录用条件或能力较差,表现不佳,不能保质保量完成工作任务的。 2.工作态度差,工作缺乏责任心和主动性的 3.严重违反劳动纪律或公司规章制度的。 5.不服从公司的正常岗位调动,在规定的到岗时间不予报道的. 6.严重失职、营私舞弊、贪污腐化或有其他严重不良行为,对公司利益或声誉造成损害的。 7.滋事干扰公司的管理和业务活动的。 8.对公司存在欺骗行为。 9.散布谣言,致使同事、主管或公司蒙受损失的。 10.泄露商业或技术秘密,使公司蒙受损失的。 11.其他情形。 五、辞退员工的操作流程 1.部门主管根据公司规定的辞退条件,实事求是地对照员工的现实能力、表现或某特定的事实提出辞职建议,填写“员工辞退建议及评审报告单”(以下简称“报告单”)。

公司员工离职管理办法

公司员工离职管理办法 **加美木业有限公司员工离职管理办法 一、范围 本办法规定了员工劳动关系解除的类型、条件及有关规定。 本办法适用于本公司全体员工。 二、引用标准 2.1《中华人民共和国劳动法》( 2007年6 月29日第十届全国人民代表大会常务委员会第二十八次会议通过,中华人民共和国主席令第65号公布) 2.2 国家劳动和社会保障部发布的相关配套法规 三、职责 3.1人力资源部是员工离职管理的归口部门,负责办理员工离职审核及面谈、离职手续,核算员工离职前的工资、福利等项目。

3.2各部门经理负责审核员工离职事项,确定员工最后工作日以及办理工作交接手续。 3.3财务部负责结清员工借款。 四、工作程序 4.1 离职类别及条件 4.1.1辞职:合同期内,员工可以提出辞职、解除劳动合同。 ⑴试用期内员工辞职应提前3天书面向公司提出,试用期满后须提前30天书面申请。 ⑵一般情况下公司应同意员工辞职,但有以下情形除外: a. 给公司造成经济损失,尚未处理完毕的; b. 被有关国家机关依法审查尚未结案的; c. 在劳动合同或与劳动合同有关联的专项协议中约定有必须服务期,且必须服务期未满的。

4.1.2资谴:符合下列条件之一的,公司可提前30天书面提出与员工解除劳动合同: ⑴员工患病或非因工负伤,在规定的医疗期满后,不能从事原工作,也不能从事公司另行安排的适当工作; ⑵员工不能胜任工作,经培训或调整工作岗位后仍不能胜任工作的; ⑶合同订立时所依据的客观情况发生重大变化,致使合同无法履行,经双方协商不能就变更合同达成协议的; ⑷因经营业务发生变化,包括但不限于机构调整、岗位撤并等,致使员工的岗位消失的; ⑸公司因生产、经营、管理需要或因员工的工作能力、工作表现及体力、健康状况,调整员工的工作岗位、工作部门或工作地点,员工无正当理由不接受工作调整的。 4.1.3协商解除 公司可以根据《劳动法》第36条规定,随机提出解除劳动合同,但须经过与员工协商一致才能解除。

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