Query Generation

Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on possibilistic networks are limited by the size of the possibility distributions. Generally, these approaches are based on possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG (Direct Acyclic Graph) into a secondary structure, known as the junction trees (JT). This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of normalization in the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.

We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.

A considerable interest has been given to Multiword Expression (MWEs) identification and treatment. The identification of MWEs affects the quality of results of different tasks heavily used in natural language processing (NLP) such as parsing and generation. Different approaches for MWEs identification have been applied such as statistical methods which employed as an inexpensive and language independent way of finding co-occurrence patterns. Another approach relays on linguistic methods for identification, which employ information such as part of speech (POS) filters and lexical alignment between languages is also used and produced more targeted candidate lists. This paper presents a framework for extracting Arabic MWEs (nominal or verbal MWEs) for bi-gram using hybrid approach. The proposed approach starts with applying statistical method and then utilizes linguistic rules in order to enhance the results by extracting only patterns that match relevant language rule. The proposed hybrid approach outperforms other traditional approaches.

Discharge summaries serve a variety of aims, ranging from clinical care to legal purposes. They are also important tools in patient empowerment, but a patient's comprehension of the information is often suboptimal. Continuing in the tradition of focusing on automated approaches to increasing patient comprehension, The CLEFeHealth2014 lab tasked participants to visualize the information in discharge summaries while also providing connections to additional online information. Participants were provided with six cases containing a discharge summary, patient profile and information needs. Of fifty registrations, only the FLPolytech team completed all requirements related to the task. They augmented the discharge summary by linking to external resources, inserting structure related to timing of the information need (past, present future), enriching the content, i.e., with definitions, and providing meta-information, e.g., how to make future appointments. Four panellists evaluated the submission. Overall, they were positive about the enhancements, but all agreed that additional visualization could further improve the provided solution.

Discharge summaries serve a variety of aims, ranging from clinical care to legal purposes. They are also important tools in patient empowerment, but a patient's comprehension of the information is often suboptimal. Continuing in the tradition of focusing on automated approaches to increasing patient comprehension, The CLEFeHealth2014 lab tasked participants to visualize the information in discharge summaries while also providing connections to additional online information. Participants were provided with six cases containing a discharge summary, patient profile and information needs. Of fifty registrations, only the FLPolytech team completed all requirements related to the task. They augmented the discharge summary by linking to external resources, inserting structure related to timing of the information need (past, present future), enriching the content, i.e., with definitions, and providing meta-information, e.g., how to make future appointments. Four panellists evaluated the submission. Overall, they were positive about the enhancements, but all agreed that additional visualization could further improve the provided solution.

Online information is growing enormously day by day with the blessing of World Wide Web. Search engines often provide users with abundant collection of articles; in particular, news articles which are retrieved from different news sources reporting on the same event. In this work, we aim to produce high quality multi document news summaries by taking into account the generic components of a news story within a specific domain. We also present an effective method, named Genetic-Case Base Reasoning, to identify cross-document relations from un-annotated texts. Following that, we propose a new sentence scoring model based on fuzzy reasoning over the identified cross-document relations. The experimental findings show that the proposed approach performed better that the conventional graph based and cluster based approach.

In this research work, we examine one of the most applied networking website, namely the Facebook, for conducting courses as a replacement of valuable classical electronic learning platforms. At the initial stage of the Internet community, users of the Internet used email as the main communication mean. Even though email is still the indispensable approach of communication in a appropriate but offline mode, other services were presented, such as many Instant Messaging (IM) software applications like ICQ, Skype, Viber, WhatsApp, Instagram, Musical.ly and Messenger, which let people connect in a real-time mode. Nevertheless, the communication between people was further improved to the next phase, when Facebook came to reality as a social networking homepage that supports many features. People do not only link with others, but also establish all kinds of connections between them. Facebook offers rich functionalities for forming associations. The framework of Facebook delivers without charge software that were provided by traditional electronic learning. This research work looks at how people apply Facebook for teaching and learning, together with recommendations provided.

In this paper, we study the social networking website, Facebook, for conducting courses as a replacement of high-cost classical electronic learning platforms. At the early stage of the Internet community, users of the Interned used email as the main communication mean. Although email is still the essential approach of communication in a suitable but offline mode, other services were introduced, such as many Instant Messaging (IM) software applications like ICQ, Skype, Viber, WhatsApp and MSN, which enable people to connect in a real-time mode. However, the communication between people was further improved to the next phase, when Facebook came to reality as a social networking homepage that wires many features. People do not only link with others, but also establish all kinds of connections between them. Facebook offers rich functions for forming associations. The framework of Facebook actually delivers without charge software that were provided by traditional electronic learning. This paper looks at how people apply Facebook for teaching and learning, together with recommendations provided.

We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.

This article describes an ongoing research which intends to develop a plagiarism detection system for Arabic documents. We developed different heuristics to generate effective queries for document retrieval from the Web. The performance of those heuristics was empirically evaluated against a sizeable corpus in terms of precision, recall and fmeasure. We found that a systematic combination of different heuristics greatly improves the performance of the document retrieval system.

Energy efficient building design and construction calls for extensive collaboration between different subfields of the Architecture, Engineering and Construction (AEC) community. Performing building design and construction engineering raises challenges on data integration and software interoperability. Using Building Information Modeling (BIM) data hub to host and integrate building models is a promising solution to address those challenges, which can ease building design information management. However, the partial model query mechanism of current BIM data hub collaboration model has several limitations, which prevents designers and engineers to take advantage of BIM. To address this problem, we propose a general and effective approach to generate query code based on a Model View Definition (MVD). This approach is demonstrated through a software prototype called QueryGenerator. By demonstrating a case study using multi-zone air flow analysis, we show how our approach and tool can h...

In this research work, we examine one of the most applied networking website, namely the Facebook, for conducting courses as a replacement of valuable classical electronic learning platforms. At the initial stage of the Internet community, users of the Internet used email as the main communication mean. Even though email is still the indispensable approach of communication in a appropriate but offline mode, other services were presented, such as many Instant Messaging (IM) software applications like ICQ, Skype, Viber, WhatsApp, Instagram, Musical.ly and Messenger, which let people connect in a real-time mode. Nevertheless, the communication between people was further improved to the next phase, when Facebook came to reality as a social networking homepage that supports many features. People do not only link with others, but also establish all kinds of connections between them. Facebook offers rich functionalities for forming associations. The framework of Facebook delivers without charge software that were provided by traditional electronic learning. This research work looks at how people apply Facebook for teaching and learning, together with recommendations provided.

In this paper, we study the social networking website, Facebook, for conducting courses as a replacement of high-cost classical electronic learning platforms. At the early stage of the Internet community, users of the Interned used email as the main communication mean. Although email is still the essential approach of communication in a suitable but offline mode, other services were introduced, such as many Instant Messaging (IM) software applications like ICQ, Skype, Viber, WhatsApp and MSN, which enable people to connect in a real-time mode. However, the communication between people was further improved to the next phase, when Facebook came to reality as a social networking homepage that wires many features. People do not only link with others, but also establish all kinds of connections between them. Facebook offers rich functions for forming associations. The framework of Facebook actually delivers without charge software that were provided by traditional electronic learning. This paper looks at how people apply Facebook for teaching and learning, together with recommendations provided.

A considerable interest has been given to Multiword Expression (MWEs) identification and treatment. The identification of MWEs affects the quality of results of different tasks heavily used in natural language processing (NLP) such as parsing and generation. Different approaches for MWEs identification have been applied such as statistical methods which employed as an inexpensive and language independent way of finding co-occurrence patterns. Another approach relays on linguistic methods for identification, which employ information such as part of speech (POS) filters and lexical alignment between languages is also used and produced more targeted candidate lists. This paper presents a framework for extracting Arabic MWEs (nominal or verbal MWEs) for bi-gram using hybrid approach. The proposed approach starts with applying statistical method and then utilizes linguistic rules in order to enhance the results by extracting only patterns that match relevant language rule. The proposed hybrid approach outperforms other traditional approaches.

Document keywords are associated to documents as summarized versions of the documents' content. Considering that the number of documents is quickly growing every day, the availability of these keywords is very important. Although, usually keywords are ...

In this paper we describe our participation to the CLEF 2018 Consumer Health Search Task, sub task IRTask1. This track aims to evaluate and advance search technologies aimed at supporting consumers to find health advice online. Our solution addressed this challenge by extending the Entity Query Feature Expansion model (EQFE), a knowledge base (KB) query expansion method. In previous work we showed that Wikipedia, UMLS and CHV can be effective as basis for CHS query expansions within the EQFE model. To obtain the query expansion terms, first, we mapped entity mentions to KB entities by performing exact matching. After mapping, we used the Title of the mapped KB entities as the source for expansion terms. For our first three expanded query sets, we expanded the original queries sourcing expansion terms from each of Wikipedia, the UMLS, and the CHV. For our fourth expanded query set, we combined expansion terms from Wikipedia and CHV.

Nowadays, social medias are very popular among their users. One of the most well-known social networks is Twitter. It is a micro-blog that enables its users to send short messages called tweets. A tweet is a 140 characters long message that is rarely self-cont, hence additional information are necessary to allow better readability of the tweet. This new task has attracted a great deal of attention recently. Given a tweet, the aim of tweet contextualization is to produce an informative and coherent paragraph, called a context, from a set of documents in response to topics treated by the tweet. In this paper, we propose a new approach of Tweet Contextualization based on combining automatic summarization techniques and sentence aggregation. The main idea of our proposed method is to select relevant, informative and semantically related sentences that best describe themes expressed by the tweet, and then build a concise context.

Multi Sentence Compression (MSC) is of great value to many real world applications, such as guided microblog summarization, opinion summarization and newswire summarization. Recently, word graph-based approaches have been proposed and become popular in MSC. Their key assumption is that redundancy among a set of related sentences provides a reliable way to generate informative and grammatical sentences. In this paper, we propose an effective approach to enhance the word graph-based MSC and tackle the issue that most of the state-of-the-art MSC approaches are confronted with: i.e., improving both informativity and grammaticality at the same time. Our approach consists of three main components: (1) a merging method based on Multiword Expressions (MWE); (2) a mapping strategy based on synonymy between words; (3) a re-ranking step to identify the best compression candidates generated using a POS-based language model (POS-LM). We demonstrate the effectiveness of this novel approach using ...

This article describes an ongoing research which intends to develop a plagiarism detection system for Arabic documents. We developed different heuristics to generate effective queries for document retrieval from the Web. The performance of those heuristics was empirically evaluated against a sizeable corpus in terms of precision, recall and fmeasure. We found that a systematic combination of different heuristics greatly improves the performance of the document retrieval system.

Nowadays, social medias are very popular among their users. One of the most well-known social networks is Twitter. It is a micro-blog that enables its users to send short messages called tweets. A tweet is a 140 characters long message that is rarely self-cont, hence additional information are necessary to allow better readability of the tweet. This new task has attracted a great deal of attention recently. Given a tweet, the aim of tweet contextualization is to produce an informative and coherent paragraph, called a context, from a set of documents in response to topics treated by the tweet. In this paper, we propose a new approach of Tweet Contextualization based on combining automatic summarization techniques and sentence aggregation. The main idea of our proposed method is to select relevant, informative and semantically related sentences that best describe themes expressed by the tweet, and then build a concise context.

To evaluate and improve medical information retrieval, benchmarking data sets need to be created. Few benchmarks have been focusing on patients’ information needs. There is a need for additional benchmarks to enable research into effective retrieval methods. In this paper we describe the manual creation of patient queries and investigate their automatic generation. This work is conducted in the framework of a medical evaluation campaign, which aims to evaluate and improve technologies to help patients and laypeople access eHealth data. To this end, the campaign is composed of different tasks, including a medical information retrieval (IR) task. Within this IR task, a web crawl of medically related documents, as well as patient queries are provided to participants. The queries are built to represent the potential information needs patients may have while reading their medical report. We start by describing typical types of patients’ information needs. We then describe how these queri...

Plagiarism is becoming more of a problem in academics. It's made worse by the ease with which a wide range of resources can be found on the internet, as well as the ease with which they can be copied and pasted. It is academic theft since the perpetrator has "taken" and presented the work of others as his or her own. Manual detection of plagiarism by a human being is difficult, imprecise, and time-consuming because it is difficult for anyone to compare their work to current data. Plagiarism is a big problem in higher education, and it can happen on any topic. Plagiarism detection has been studied in many scientific articles, and methods for recognition have been created utilizing the Plagiarism analysis, Authorship identification, and Near-duplicate detection (PAN) Dataset 2009-2011. Verbatim plagiarism, according to the researchers, plagiarism is simply copying and pasting. They then moved on to smart plagiarism, which is more challenging to spot since it might include text change, taking ideas from other academics, and translation into a more difficult-to-manage language. Other studies have found that plagiarism can obscure the scientific content of publications by swapping words, removing or adding material, or reordering or changing the original articles. This article discusses the comparative study of plagiarism detection techniques.

Abstract. This paper describes the participation of MRIM team in Task 3: Patient-Centered Information Retrieval-IRTask 1: Ad-hoc search of CLEF eHealth Evaluation lab 2016. The aim of this task is to evaluate the effectiveness of information retrieval systems when searching for health content on the web. Our submission investigates the effectiveness of word embedding for query expansion in the health domain. We experiment two variants of query expansion method using word embedding. Our first run is a baseline system with default stopping and stemming. The other two runs expand the queries using two different word embedding sources. Our three runs are conducted on Terrier platform using Dirichlet language model.

An Automatic Summary generation process creates a shortened version of the text using a Digital programming Technology, with the aim of holding the most advanced important points of the original text. In a Common Law system, previous judgments were referred to the current case arguments as well as decision making. Thus there is a need to view the previous judgments and to grasp and analyze the important points present in the legal judgments. Text Summarization technique helps the legal experts to read the key points present in a judgment just by reading the Head note generated by the system. Such techniques save the time as well as the manpower. In this paper, an automatic Legal Judgment Summarization system was implemented and tested by Fuzzy Logic, Classification and Segmentation techniques among that based on the experimental study Fuzzy Logic and Conditional Random Field Algorithm produces a meaningful summary.

Nowadays, social medias are very popular among their users. One of the most well-known social networks is Twitter. It is a micro-blog that enables its users to send short messages called tweets. A tweet is a 140 characters long message that is rarely self-cont, hence additional information are necessary to allow better readability of the tweet. This new task has attracted a great deal of attention recently. Given a tweet, the aim of tweet contextualization is to produce an informative and coherent paragraph, called a context, from a set of documents in response to topics treated by the tweet. In this paper, we propose a new approach of Tweet Contextualization based on combining automatic summarization techniques and sentence aggregation. The main idea of our proposed method is to select relevant, informative and semantically related sentences that best describe themes expressed by the tweet, and then build a concise context.

Plagiarism in students' source codes constitutes an important drawback for the educational process. In addition, plagiarism detection in source codes is time consuming and tiresome task. Therefore, many approaches for plagiarism detection have been proposed. Most of the aforementioned approaches receive as input a set of source files and calculate a similarity between each pair of the input set. However, the tutor often needs to detect the clusters of plagiarism, i.e. clusters of students' assignments such as all assignments in a cluster derive from a common original. In this paper, we propose a novel plagiarism detection algorithm that receives as input a set of source codes and calculates the clusters of plagiarism. Experimental results show the efficiency of our approach and encourage us to further research.

This paper details the collection, systems and evaluation methods used in the IR Task of the CLEF 2016 eHealth Evaluation Lab. This task investigates the e↵ectiveness of web search engines in providing access to medical information for common people that have no or little medical knowledge. The task aims to foster advances in the development of search technologies for consumer health search by providing resources and evaluation methods to test and validate search systems. The problem considered in this year’s task was to retrieve web pages to support the information needs of health consumers that are faced by a medical condition and that want to seek relevant health information online through a search engine. As part of the evaluation exercise, we gathered 300 queries users posed with respect to 50 search task scenarios. The scenarios were developed from real cases of people seeking health information through posting requests of help on a web forum. The presence of query variations ...

Discharge summaries serve a variety of aims, ranging from clinical care to legal purposes. They are also important tools in patient empowerment, but a patient's comprehension of the information is often suboptimal. Continuing in the tradition of focusing on automated approaches to increasing patient comprehension, The CLEFeHealth2014 lab tasked participants to visualize the information in discharge summaries while also providing connections to additional online information. Participants were provided with six cases containing a discharge summary, patient profile and information needs. Of fifty registrations, only the FLPolytech team completed all requirements related to the task. They augmented the discharge summary by linking to external resources, inserting structure related to timing of the information need (past, present future), enriching the content, i.e., with definitions, and providing meta-information, e.g., how to make future appointments. Four panellists evaluated the submission. Overall, they were positive about the enhancements, but all agreed that additional visualization could further improve the provided solution.

Plagiarism detection is gaining importance due to requirements for integrity in Research works especially when it comes
to Cross-lingual plagiarism. In this paper, we have researched a new approach for Cross-Lingual sentence level plagiarism
detection. The proposed approach works as initially the suspicious document is translated to a particular language and candidate
document is retrieved for further processing. Pre-processing techniques will be selected using NB Classifier for better efficiency
which will be used on document which includes POS tagging, Chunking, Stop-word removal, Lemmatization, Syntactic-semantic
rule. Then Machine Learning classification algorithms such as Support Vector Machine (SVM), Naïve Bayes Classifier (NBC),
Decision Tree (DT) are used to check plagiarism. The Evaluation and Analysis are performed using F-score, Accuracy, Precision
and Recall. Our aim in this paper is to find the plagiarism and improve the accuracy of plagiarism detection between two
languages by using concepts of natural language processing.

Many applications require the affiliation of sentences (which includes text summarization, answering questions, producing natural language, analyzing natural language, and text clustering). The similarity of terms may be improved using the algorithm. Estimating the relationship among phrases at some point of the NLP data interpretation remains a notably critical problem. This paper suggests the concept of accomplice similar sentences. First, define the relations that could be used for text clustering. This could be identified by various algorithms like support vector machine, and other algorithms Secondly, terms are correlated and their similarities are recognized. Finally, similar sentences are clustered.

In this paper we introduce the MatchDetectReveal(MDR) system, which is capable of identifying overlapping and plagiarised documents. Each component of the system is briefly described. The matching-engine component uses a modified suffix tree representation, which is able to identify the exact overlapping chunks and its performance is also presented.

An Automatic Summary generation process creates a shortened version of the text using a Digital programming Technology, with the aim of holding the most advanced important points of the original text. In a Common Law system, previous judgments were referred to the current case arguments as well as decision making. Thus there is a need to view the previous judgments and to grasp and analyze the important points present in the legal judgments. Text Summarization technique helps the legal experts to read the key points present in a judgment just by reading the Head note generated by the system. Such techniques save the time as well as the manpower. In this paper, an automatic Legal Judgment Summarization system was implemented and tested by Fuzzy Logic, Classification and Segmentation techniques among that based on the experimental study Fuzzy Logic and Conditional Random Field Algorithm produces a meaningful summary.

The recruitment of new personnel is one of the most essential business processes which affect the quality of human capital within any company. It is highly essential for the companies to ensure the recruitment of right talent to maintain a competitive edge over the others in the market. However IT companies often face a problem while recruiting new people for their ongoing projects due to lack of a proper framework that defines a criteria for the selection process. In this paper we aim to develop a framework that would allow any project manager to take the right decision for selecting new talent by correlating performance parameters with the other domain-specific attributes of the candidates. Also, another important motivation behind this project is to check the validity of the selection procedure often followed by various big companies in both public and private sectors which focus only on academic scores, GPA/grades of students from colleges and other academic backgrounds. We test if such a decision will produce optimal results in the industry or is there a need for change that offers a more holistic approach to recruitment of new talent in the software companies. The scope of this work extends beyond the IT domain and a similar procedure can be adopted to develop a recruitment framework in other fields as well. Data-mining techniques provide useful information from the historical projects depending on which the hiring-manager can make decisions for recruiting high-quality workforce. This study aims to bridge this hiatus by developing a data-mining framework based on an ensemble-learning technique to refocus on the criteria for personnel selection. The results from this research clearly demonstrated that there is a need to refocus on the selection-criteria for quality objectives.

Plagiarism has become an infamous problem in the global academic community. Detecting plagiarism in Arabic documents is particularly a challenging task due to the complexity of the structure of the language. This paper introduces a framework and methods for detection of plagiarism in Arabic documents, which is based on a logical representation of a document as paragraphs, sentences, and words.

We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.

This article describes an ongoing research which intends to develop a plagiarism detection system for Arabic documents. We developed different heuristics to generate effective queries for document retrieval from the Web. The performance of those heuristics was empirically evaluated against a sizeable corpus in terms of precision, recall and fmeasure. We found that a systematic combination of different heuristics greatly improves the performance of the document retrieval system.

This paper discusses a new metric that has been applied to verify the quality in translation between sentence pairs in parallel corpora of Arabic-English. This metric combines two techniques, one based on sentence length and the other based on compression code length. Experiments on sample test parallel Arabic-English corpora indicate the combination of these two techniques improves accuracy of the identification of satisfactory and unsatisfactory sentence pairs compared to sentence length and compression code length alone. The new method proposed in this research is effective at filtering noise and reducing mis-translations resulting in greatly improved quality.

The shift in human computer interaction from desktop computing to mobile interaction highly influences the needs for new designed interfaces. In this paper, we address the issue of searching for information on mobile devices, an area also known as Mobile Information Retrieval. In particular, we propose to summarize as much as possible the information retrieved by any search engine to allow universal access to information.

Plagiarismisdescribedasthereuseofsomeoneelse’spreviousideas,workorevenwordswithoutsufficientattributiontothesource.Thispaperpresentsamethodtodetectexternalplagiarismusingtheintegrationofsemanticrelationsbetweenwordsandtheirsyntacticcomposition.Theproblemwiththeavailablemethodsisthattheyfailtocapturethemeaningincomparisonbetweenasourcedocumentsentenceandasuspi-ciousdocumentsentence,whentwosentenceshavesamesurfacetext(thewordsarethesame)ortheyareaparaphraseofeachother.Thereforeitcausesinaccurateorunnecessarymatchingresults.However,thismethodcanimprovetheperformanceofplagiarismdetectionbecauseitisabletoavoidselectingthesourcetextsentencewhosesimilaritywithsuspicioustextsentenceishighbutitsmeaningisdifferent.Itisexe-cutedbycomputingthesemanticandsyntacticsimilarityofthesentence-to-sentence.Besides,theproposedmethodexpandsthewordsinsentencestotackletheproblemofinformationlimit.Itbridgesthelexicalgapsforsemanticallysimilarcontextsthatareexpressedinadifferentwording.Thismethodisalsocapabletoidentifyvariouskindsofplagiarismsuchastheexactcopiedtext,paraphrasing,transformationofsentencesandchangingofwordstructureinthesentences.Asaresult,theexperimentalresultshavedisplayedthattheproposedmethodisabletoimprovetheperformancecomparedwiththeparticipatingsystemsinPAN-PC-11.TheexperimentalresultsalsodisplayedthattheproposedmethoddemonstratesbetterperformanceascomparedtootherexistingtechniquesonPAN-PC-10andPAN-PC-11datasets.

Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on possibilistic networks are limited by the size of the possibility distributions. Generally, these approaches are based on possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG (Direct Acyclic Graph) into a secondary structure, known as the junction trees (JT). This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of normalization in the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.

Motivated by the problem of computing investment portfolio weightings we investigate various methods of clustering as alternatives to traditional mean-variance approaches. Such methods can have significant benefits from a practical point of view since they remove the need to invert a sample covariance matrix, which can suffer from estimation error and will almost certainly be non-stationary. The general idea is to find groups of assets which share similar return characteristics over time and treat each group as a single composite asset. We then apply inverse volatility weightings to these new composite assets. In the course of our investigation we devise a
method of clustering based on triangular potentials and we present associated theoretical results as well as various examples based on synthetic data.

Min-based qualitative possibilistic networks are one of the effective tools for a compact representation of decision problems under uncertainty. The exact approaches for computing decision based on possibilistic networks are limited by the size of the possibility distributions. Generally, these approaches are based on possibilistic propagation algorithms. An important step in the computation of the decision is the transformation of the DAG into a secondary structure, known as the junction trees. This transformation is known to be costly and represents a difficult problem. We propose in this paper a new approximate approach for the computation of decision under uncertainty within possibilistic networks. The computing of the optimal optimistic decision no longer goes through the junction tree construction step. Instead, it is performed by calculating the degree of normalization in the moral graph resulting from the merging of the possibilistic network codifying knowledge of the agent and that codifying its preferences.

This paper stresses on the need of using Knowledge Management (KM) in the higher education institutions of Saudi Arabia. The paper is based on the literature review and personal experience of the author in the education sector.
The paper aims at highlighting the importance of KM for the educational institutions particularly for developing countries. It also reviews the readiness of Saudi Arabia for KM application by illustrating different development initiatives taken by the Saudi government in different sectors. However, the literature also identifies many barriers on the way.
Keeping the importance of KM for the higher education institutions in view, this paper proposes to adopt the model of Library Knowledge Management Center (LKMC) with needed modifications for Universities of Saudi Arabia. This LKMC model was proposed by Parker, Nitse, and Flowers (2005) for the small business corporate for providing the Knowledge Management (KM) and Competitive Intelligence (CI) services. The paper discusses different components of the LKMC model and their relevance to the education sector.

This paper discusses a new metric that has been applied to verify the quality in translation between sentence pairs in parallel corpora of Arabic-English. This metric combines two techniques, one based on sentence length and the other based on compression code length. Experiments on sample test parallel Arabic-English corpora indicate the combination of these two techniques improves accuracy of the identification of satisfactory and unsatisfactory sentence pairs compared to sentence length and compression code length alone. The new method proposed in this research is effective at filtering noise and reducing mis-translations resulting in greatly improved quality.

In this paper, we study the social networking website, Facebook, for conducting courses as a replacement of high-cost classical electronic learning platforms. At the early stage of the Internet community, users of the Interned used email as the main communication mean. Although email is still the essential approach of communication in a suitable but offline mode, other services were introduced, such as many Instant Messaging (IM) software applications like ICQ, Skype, Viber, WhatsApp and MSN, which enable people to connect in a real-time mode. However, the communication between people was further improved to the next phase, when Facebook came to reality as a social networking homepage that wires many features. People do not only link with others, but also establish all kinds of connections between them. Facebook offers rich functions for forming associations. The framework of Facebook actually delivers without charge software that were provided by traditional electronic learning. This paper looks at how people apply Facebook for teaching and learning, together with recommendations provided.

In this paper, implementations of three Hough Transform based fingerprint alignment algorithms are analyzed with respect to time complexity on Java Card environment. Three algorithms are: Local Match Based Approach (LMBA), Discretized Rotation Based Approach (DRBA), and All Possible to Match Based Approach (APMBA). The aim of this paper is to present the complexity and implementations of existing work of one of the mostly used method of fingerprint alignment, in order that the complexity can be simplified or find the best algorithm with efficient complexity and implementation that can be easily implemented on Java Card environment for match on card. Efficiency involves the accuracy of the implementation, time taken to perform fingerprint alignment, memory required by the implementation and instruction operations required and used.

We are developing a web-based plagiarism detection system to detect plagiarism in written Arabic documents. This paper describes the proposed framework of our plagiarism detection system. The proposed plagiarism detection framework comprises of two main components, one global and the other local. The global component is heuristics-based, in which a potentially plagiarized given document is used to construct a set of representative queries by using different best performing heuristics. These queries are then submitted to Google via Google's search API to retrieve candidate source documents from the Web. The local component carries out detailed similarity computations by combining different similarity computation techniques to check which parts of the given document are plagiarised and from which source documents retrieved from the Web. Since this is an ongoing research project, the quality of overall system is not evaluated yet.

Clustering is an extensively studied data mining problem in the text domains. The difficulty finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In text mining, clustering the sentence is one of the processes and used within general text mining tasks. Several clustering methods and algorithms are used for clustering the documents at sentence level. In this article, the sentence level based clustering algorithm is discussed as a survey. The main goal of this survey is to present an overview of the sentence level clustering techniques. This demonstration of these techniques is used to obtain the efficient scheme for clustering for sentence level text. We can obtain the more efficient method or we may propose the new technique to overcome the problems in these existing approaches. This survey article is intended to provide easy accessibility to the main ideas for non-experts.

Abstract—The nature of Arabic language structure exposes the need for fuzzy or vague concept to reveal dishonest practices in Arabic documents. In this paper, we present a statement-based plagiarism detection approach in Arabic scripts using fuzzy-set IR model. The degree of similarity is calculated and compared to a threshold value to judge whether two statements are the same or different. Our corpus collection has been built in which all stopwords were removed and non-stop words were stemmed for typical Arabic IR.

Abstract Research in Information Retrieval has traditionally focused on serving the best results for a single query. Real users, however, often begin an interaction with a search engine with a sufficiently under-specified information need that they will need to reformulate before they find either the one thing or every thing they are looking for. We define a session as the sequence of queries and interactions that a user performs in service of an information need.