This paper describes a novel algorithm that enumerates a set of Boolean variables from a 3-SAT instance such that for any truth assignments, it will be reduced to a 2-SAT instance or an empty formula or a formula with one or more empty clauses. We showed that the set of Boolean variables of interest can be found by formulating the given 3-SAT instance into an instance of set covering problem.

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.

Formal Concept Analysis (FCA) is emerging in Data Science because of its generality, simplicity, and powerful mathematical foundation. It enabled a uniform data clustering methods into structured space of formal concepts. Several FCA based machine learning (ML) methods gave competitive results compared to classical methods. In another side, ensemble approach proved to be effective by aggregating different basic ML methods. Randomness improved other ML approaches. In this paper, we propose a new conceptual ML method by using random conceptual decomposition. This method integrated and experimented in the context of ensemble learning methods, gave encouraging good results, in general.

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.

Could we detect anomalies during the run-time of a program by learning from the analysis of its previous traces for normally completed executions? In this paper we create a featured data set from program traces at run time, either during its regular life, or during its testing phase. This data set represents execution traces of relevant variables including inputs, outputs, intermediate variables, and invariant checks. During a learning mining step, we start from exhaustive random training input sets and map program traces to a minimal set of conceptual patterns. We employ formal concept analysis to do this in an incremental way, and without losing dependencies between data set features. This set of patterns becomes a reference for checking the normality of future program executions as it captures invariant functional dependencies between the variables that need to be preserved during execution. During the learning step, we consider enough input classes corresponding to the different patterns by using random input selection until reaching stability of the set of patterns (i.e. the set is almost no longer changing, and only negligible new patterns are not reducible to it). Experimental results show that the generated patterns are significant in representing normal program executions. They also enable the detection of different executable code contamination at early stages. The proposed method is general and modular. If applied systematically, it enhances software resilience against abnormal and unpredictable events.

There are limited studies that are addressing the challenges of visually impaired (VI) users when viewing search results on a search engine interface by using a screen reader. This study investigates the effect of providing an overview of search results to VI users. We present a novel interactive search engine interface called InteractSE to support VI users during the results exploration stage in order to improve their interactive experience and web search efficiency. An overview of the search results is generated using an unsupervised machine learning approach to present the discovered concepts via a formal concept analysis that is domain-independent. These concepts are arranged in a multi-level tree following a hierarchical order and covering all retrieved documents that share maximal features. The InteractSE interface was evaluated by 16 legally blind users and compared with the Google search engine interface for complex search tasks. The evaluation results were obtained based on both quantitative (as task completion time) and qualitative (as participants’ feedback) measures. These results are promising and indicate that InteractSE enhances the search efficiency and consequently advances user experience. Our observations and analysis of the user interactions and feedback yielded design suggestions to support VI users when exploring and interacting with search results.

Cultural heritage takes an important part in defining the identity and the history of a civilization or a nation. Valuing and preserving this heritage is thus a top priority for governments and heritage institutions. Through this paper, we present an image completion (inpainting) approach adapted for the curation and the completion of damaged artwork. Our approach uses a set of machine learning techniques such as Generative Adversarial Networks which are among the most powerful generative models that can be trained to generate realistic data samples. As we are focusing mostly on visual cultural heritage, the pipeline of our framework has many optimizations such as the use of clustering to optimize the training of the generative part to ensure a better performance across a variety of cultural data categories. The experimental results of our framework are promising and were validated on a dataset of paintings.

Many techniques for automated program repair involve syntactic program transformations. Applying combinations of such transformations on faulty code yields fix candidates whose correctness must be determined. Exploring these combinations leads to an explosion on the number of generated fix candidates that severely limits the applicability of such fault repair techniques. This explosion is most times tamed by not considering fix candidates exhaustively, and by disabling intra-statement modifications. In this article we present a technique for program repair that considers an ample set of intra-statement syntactic operations, and explores fix candidates exhaustively up to a provided bound. The suitability of the technique, implemented in our tool Stryker, is supported by a novel mechanism to detect and prune infeasible fix candidates. This allows Stryker to repair programs with several bugs, whose fixes require multiple modifications. We evaluate our technique on a benchmark of faulty Java container classes, which Stryker is able to repair, pruning significant parts of the space of generated candidates when more than one bug is present in the code.

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 increase in biomedical data has given rise to the need for developing data sampling techniques. With the emergence of big data and the rise of popularity of data science, sampling or reduction techniques have been assistive to significantly hasten the data analytics process. Intuitively, without sampling techniques, it would be difficult to efficiently extract useful patterns from a large dataset. However, by using sampling techniques, data analysis can effectively be performed on huge datasets, to produce a relatively small portion of data, which extracts the most representative objects from the original dataset. However, to reach effective conclusions and predictions, the samples should preserve the data behavior. In this paper, we propose a unique data sampling technique which exploits the notion of formal concept analysis. Machine learning experiments are performed on the resulting sample to evaluate quality, and the performance of our method is compared with another sampling technique proposed in the literature. The results demonstrate the effectiveness and competitiveness of the proposed approach in terms of sample size and quality, as determined by accuracy and the F1-measure.

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the network edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart healthcare systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make autonomous decisions for smart health applications. In particular, we present and implement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low computational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient's conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%. (C) 2018 Elsevier Ltd. All rights reserved.

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.

The aim of anomaly detection is to find patterns or data points that are not confirming the expected behavior inside the dataset. Techniques from a variety of disciplines like machine learning, statistics, information theory and data mining are used to solve this problem. The form of input data from stock market is a non-linear complex time series. Hence, the statistical methods in this case will be ineffective. Using the behavior of similar time series for detecting anomalies in Qatar stock exchange and American stock market index (Standard & Poor (S&P)) is the main goal of this paper. Supervised learning techniques were used extensively in detecting stock market manipulation. The problem of supervised learning techniques is that they require substantial effort in labeling the data. Having dynamic nature of anomalous behavior causes another problem. In this research, we investigate the use of unsupervised learning for detecting stock market manipulation and we introduce a new preprocessing step for improving the recall of the anomaly detection system without hurting the precision. The Contextual Anomaly Detector (CAD) that is based on unsupervised technique is used to find anomalies by looking at time series that have similar behaviors. The use of our new preprocessing steps with CAD improved the recall significantly compared to other studies.

Online news media provides aggregated news and stories from different sources all over the world and up-to-date news coverage. The main goal of this study is to find a solution that is considered as a homogeneous source for the news and to represent the news in a new conceptual framework. Furthermore, the user can easily and quickly find different updated news in a fast way through the designed interface. The Mobile App implementation is based on modeling the multi-level conceptual analysis frame. Discovering main concepts of any domain is captured from the hidden unstructured data that are analyzed by the proposed solution. Concepts are discovered through analyzing data patterns to be structured into a tree-based interface for easy navigation for the end user. Our final experiment results show that analyzing the news before displaying to the end-user and restructuring the final output in a conceptual multilevel structure produces a new display frame for the end user to find the related information of interest.

Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure.

Faults are an important concept in the study of system dependability, and most approaches to dependability can be characterized by the way in which they deal with faults (e.g., fault avoidance, fault removal, fault tolerance, fault forecasting). In their seminal work on modeling dependable computing, Laprie et al. define a fault as the adjudged or hypothesized cause of an error. In this paper, we propose a more formal definition of a fault in the context of software products and discuss some of its implications.

Most modern search engines feature keyword based search interfaces. These interfaces are usually found on websites belonging to enterprises or governments or sites related to news articles, blogs and social media that contain a large corpus of documents. These collections of documents are not easily indexed by web search engines, and are considered as hidden web databases. These databases provide opportunities for data analysis for many third-parties through their keyword search interfaces. A significant amount of research has already been carried out on analyzing and extracting aggregate information about these hidden document corpora. But most of these research focus on the high level big-picture information of the database. Not enough focus has been done on extracting analytical information which is specific to individual queries. This paper focuses on that analysis gap and takes ideas from other existing research to formulate a query cardinality estimation technique i.e. the count of documents matching a query in the document corpus of a search engine. We experimentally assess the effectiveness of our method by building a search engine on the Reuters-21578 document corpus. For a given keyword the corresponding documents' count is estimated only by sending search queries using the interface.