Text Summarization is the process of generating a concise and meaningful summary of a text. To better help identify relevant information and consume relevant information faster, automatic text summarizing methods are needed to address the growing amount of text data available online. Text Summarization techniques are classified into abstractive and extractive summarization. The extractive summarization technique focuses on important information like sentences or phrases which are extracted from a given text file or original document and stack them together to create a summary. In this study, we review and compare the performance of three extraction-based summarization techniques which are Conceptual method, Text Rank and Sentence Scoring. Furthermore, we evaluate the quality of summarization by comparing individual methods on unsummarized text with their corresponding human made gold standard summaries.

Currently, during runtime, programs are mostly uncontrollable objects. They are very vulnerable to either transient or permanent, external, or internal contaminations of the program state. Here one finds a new method that combines a theoretical approach for goal-oriented software fault tolerance with SGX technology. Our approach consists to protect program critical information, by using enclaves in SGX security technology, in addition to checking regularly their preservation. Each module in the software is split into two parts: trusted codes protecting any operation on critical information, and an untrusted code for the remaining operations. The program contains two kinds of recovery routines: first, recovering against endless loops, and second, a goal-oriented recovery by repeating each module call until consensus (i.e. two consecutive executions of the module give the same output). Applied to several programs, it enabled different transient faults recovery successfully with minor time complexity overhead. The method proposes a new fault-tolerant program structure. Designers should use such kind of approach in a systematic way to avoid software failure caused by several types of transient faults. By protecting the code that preserves critical information, we reinforce the initial fault tolerance of the programs. This solution is convenient for critical applications requiring high security.

The Internet is the main source of information nowadays. Consequently, end users need to be knowledgeable about how to use search engines in order to locate relevant information in a reasonable time with minimal effort. On the other hand, search engines must provide different and alternative ways to represent the search results to facilitate the user access to the information especially for the visually impaired (VI) users. Our research aim is to produce a new representational model for the search engine results targeting VI users. The result of this study will be a functional prototype that summarizes the search results as main ideas that are identified as concepts. Formal Concept Analysis (FCA) defines a concept as the maximum number of objects that are sharing the maximum number of features or attributes. Concepts are discovered by analyzing data patterns for the text of the study. The outcome of the first step of summarization concepts as keywords is used to minimize the number of listed websites and URLs that match the user selection of the multi-level tree of concepts. This scenario of summarization can give the user different directions for the shortest path to reach the target information with the minimum amount of time and effort required. The purpose of these directions can be either to proceed with reading the whole document in detail, or to continue the search for finding other related documents that match the user's inquiry. Experiments run on an iterative testing basis until VI users find proper results that satisfy their needs for the search context. User observations and interpretations based on the experiments are used for the user evaluation. This study will guide us for designing a new model for summarizing search results based on the FCA algorithm to the VI end users, and with a new representation interface based on the discovered concepts' weights.

The ranked retrieval model has rapidly become the de-facto way for search query processing in web databases. Despite the extensive efforts on designing better ranking mechanisms, in practice, many such databases fail to address the diverse and sometimes contradicting preferences of users. In this paper, we present QR2, a third-party service that uses nothing but the public search interface of a web database and enables the on-the-fly processing of queries with any user-specified ranking functions, no matter if the ranking function is supported by the database or not.

Text categorization is an important research field that finds many applications nowadays. It is usually performed in two steps: feature extraction and classification. In the feature extraction step, discriminating keywords are extracted in order to distinguish between different categories of documents. In the classification step, the extracted keywords are fed to a classifier in order to detect the category of each document. In this paper, we use the hyper rectangle method which represents the corpus of documents using a binary relation in which the documents correspond to objects and words to attributes. The hyper rectangle method extracts a tree of keywords such that most discriminative keywords are at the top levels and less discriminative keywords are in the deep levels. We are particularly interested to study different proposed weighting metrics that yield different orderings of keywords. We study how these weighting metrics impact the categorization performance. For the classification step we used both a logistic regression and random forests classifiers. We tested our method on both the 20 newsgroups dataset as well as the Reuters R8 dataset. Our method achieves high performance on both datasets which compete very well with state-of-the-art methods.

We propose to demonstrate HDBExpDetector, a system for analysts to discover bursty events from a thirdparty web database by using nothing but the existing search interface of the web database to monitor and detect sudden changes to aggregates that satisfy certain user-defined conditions. Video https://youtu.be/7Pwd8o1h5CY.

Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of the existing e-marketplaces are performed to improve the end-users experience through a Mobile App. The main goal of this study is to find a solution that is capable of integrating multiple heterogeneous hidden data sources and unify the received responses into one single, structured and homogeneous source. Furthermore, the user can easily choose the desired product or reformulate the query through the interface. The proposed Android Mobile App is based on the multi-level conceptual analysis and modeling discipline, in which, data are analyzed in a way that helps in discovering the main concepts of any unknown domain captured from the hidden web. These concepts discovered through information extraction are then structured into a tree-based interface for easy navigation and query reformulation. The application has been evaluated through substantial experiments and compared to other existing mobile applications. The results showed that analyzing the query results and re-structuring the output before displaying to the end-user in a conceptual multilevel mechanism are reasonably effective in terms of number of clicks, time taken and number of navigation screens. Based on the proposed intelligent application, the interface is minimized to only two navigation screens, and the time needed to browse products from multiple marketplaces is kept reasonable in order to reach the target product.

Authentication of Hadiths (sayings of Prophet Muhammad) is very important field for religious scholars as well as historians. Authenticity verification is traditionally conducted by studying how trustworthy is each person in the narration chain. In this study, we propose a novel approach completely based on the content of each Hadith. For each category of hadiths (authentic and non-authentic), we create a binary relation in which the hadiths correspond to the objects of the relation and the words correspond to its attributes. Keywords for each category are then obtained in a hierarchical ordering of importance using the hyper rectangular decomposition. Classification is done by feeding the extracted keywords to a logistic regression classifier. The method has been validated on a database of about 1600 hadiths. Results show that classification accuracy increases with the number of annotators who agreed on the authenticity of each hadith. These findings suggest that our method successfully extracts relevant keywords and can be combined with other traditional methods.

Knowledge discovery from data is a challenging problem that has significant importance in many different fields such as biology, economics and social sciences. Real-world data is incomplete and ambiguous; moreover, its rapid increase in size complicates the analysis process. Therefore, data reduction techniques that consider data uncertainty are highly required. In this paper, our objective is to conceptually reduce uncertain data without losing information. Two reduction methods are proposed that are mainly rooted in formal concept analysis theory. The first method is targeting approximate data reduction; it uses the result of Baixeries et al. for detecting functional dependencies by transforming an instance of a database into an approximate formal context. The second method is based on fuzzy data reduction that employs the algorithm of Elloumi et al. in fuzzy data reduction using Lukasiewicz logic. These reduction methods have been compared to three other machine learning based reduction algorithms through a classification case study of breast cancer data. Classification accuracy, root mean square error and reduced data size have been reported to show that reduced training sets using our methods result in very accurate classifiers with minimal data size. Moreover, the reduced data has the advantage of decreasing communication time and memory space.

The rapid advances in wireless communication and sensor technologies facilitate the development of viable mobile-Health applications that boost opportunity for ubiquitous real-time healthcare monitoring without constraining patients' activities. However, remote healthcare monitoring requires continuous sensing for different analog signals which results in generating large volumes of data that needs to be processed, recorded, and transmitted. Thus, developing efficient in-network data reduction techniques is substantial in such applications. In this paper, we propose an in-network approach for data reduction, which is based on fuzzy formal concept analysis. The goal is to reduce the amount of data that is transmitted, by keeping the minimal-representative data for each class of patients. Using such an approach, the sender can effectively reconfigure its transmission settings by varying the target precision level while maintaining the required application classification accuracy. Our results show the excellent performance of the proposed scheme in terms of data reduction gain and classification accuracy, and the advantages that it exhibits with respect to state-of-the-art techniques.

The Islamic websites play an important role in disseminating Islamic knowledge and information about Islamic ruling. Their number and the content they provide is continuously increasing which require in-depth investigations in content evaluation automation. In this paper, we are proposing the use of conceptual reasoning for detecting inconsistencies in case of Fatwas evaluation. Inconsistencies are detected from propositional logic point-of-view based on Truth table binary relation.

ConProve is an automated prover for propositional logic. It takes, as an input, a set of propositional formulas and proves whether a goal holds or not. ConProve converts each formula to its corresponding Truth Table Binary Relation (TTBR) considered also as a formal context (FC). The objects in FC correspond to all possible formulas interpretations (in terms of their truth value assignments), and the properties in FC correspond to the terms. When the function the 'BuildContext' function, ConProve starts the new goal proving. Firstly, it adds the goal negation to the set of formulas and constructs the formal contexts (FCs) relating formulas to terms. Secondly, it makes the FCs grouping and deduces, based on the conceptual reasoning, if the goal holds. The tool offers a user-friendly interface allowing the editing of the set of formulas as well as the visualization of the reasoning steps. Besides the tool, the paper illustrates the importance of the conceptual reasoning in deriving new conclusions as well as in discovering new, possibly implications by applying the extended Galois Connection.

Trend analysis is a research field with a large number of applications ranging from monitoring potential rivals to analyzing interests of a certain category of people. Applying trend analysis to the Islamic domain makes it possible to have a general idea about topics discussed by Muslims all over the world. It helps both scholars and social science researchers understanding the needs and the interest domains of each Muslim society. In this paper, we present a trend analysis method based on hyper-concepts. Hyper-concepts make it possible to decompose any corpus into non-overlapping rectangular relations and to highlight the most representative attributes or keywords. We illustrate the effectiveness of our method in identifying relevant keywords related to the Islamic context and we show how to use our method for identifying trending topics with respect to time.

Numerous web databases, e.g., amazon.com, eBay.com, are "hidden" behind (i.e., accessible only through) their restrictive search and browsing interfaces. This demonstration showcases HDBTracker, a web-based system that reveals and tracks (the changes of) user-specified aggregate queries over such hidden web databases, especially those that are frequently updated, by issuing a small number of search queries through the public web interfaces of these databases. The ability to track and monitor aggregates has applications over a wide variety of domains - e.g., government agencies can track COUNT of openings at online job hunting websites to understand key economic indicators, while businesses can track the AVG price of a product over a basket of e-commerce websites to understand the competitive landscape and/or material costs. A key technique used in HDBTracker is RS-ESTIMATOR, the first algorithm that can efficiently monitor changes to aggregate query answers over a hidden web database.

The prediction of gender from handwriting is a very interesting research field. However, no standard benchmark is available for researchers in this field. The aim of this competition is to gather researchers and compare recent advances in gender prediction from handwriting. This competition has been hosted on Kaggle, it has attracted 194 teams from both academia and industry. This paper gives details on this competition, including the dataset used, the evaluation procedure and description of participating methods and their performances.

Arabic writer identification is a very active research field. However, no standard benchmark is available for researchers in this field. The aim of this competition is to gather researchers and compare recent advances in Arabic writer identification. This competition was hosted by Kaggle, it has attracted thirty participants from both academia and industry. This paper gives details on this competition, including the evaluation procedure, description of participating methods and their performances.

In the scope of Financial Watch project, several targeted events have been required by contacted users in banking and investment domains. Financial news are classified with respect of the list of desired events. In this paper, a conceptual approach for indexing short English news in the financial domain is presented. By using a supervised original learning approach, a categorization method is proposed. Experimentation of the method on a sample of news showed that in almost all cases, occurrences of the right events in each document have been recognized, with respect to a corpus on the financial domain.

Translation memories play a vital role in Computer-Aided Translation (CAT). Approaches to align sentences from parallel corpora are generally used to construct translation memories. In this paper, we present, on the one hand, a hybrid approach to align sentences combining different information sources (bilingual lexicon, sentence length and sentence position, semantic similarity), and on the other hand, a Computer-Aided Translation prototype based on a cross-language information retrieval approach. This approach consists in building a database of source and target sentences augmented with grammatical information of words and considering the sentence to translate as a "query" to that database. The Computer-Aided Translation prototype has been experimented on a set of examples and the experimental results we obtained are encouraging and outperform the current CAT tools.

This paper discusses how a program can be represented by a binary relation, R, and how to decompose the latter into a set of rectangular relations. Next, we present our methodology based on relational operators and dependence relations, to show how we can use these rectangles to obtain more interesting ones that describe the entire behavior of every variable in the program. The notion of lattice of maximal rectangles is effective in that it permits to have a particular representation of the program which shows all the different parts that constitute the original program. By looking at this lattice structure, we find that the set of the leaves of this lattice, which represent "pertinent" rectangles associated to output variables, gives a minimal program covering.