Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Annotation and Search: Bridging the Gap between Text, Knowledge and Language

In recent years, the ever-increasing quantities of entities in large knowledge bases on the Web, such as DBpedia, Freebase and YAGO, pose new challenges but at the same time open up new opportunities for intelligent information access. These knowledge bases (KBs) have become valuable resources in many research areas, such as natural language processing (NLP) and information retrieval (IR). Recently, almost every major commercial Web search engine has incorporated entities into their search process, including Google’s Knowledge Graph, Yahoo! ’s Web of Objects and Microsoft’s Satori Graph/Bing Snapshots. The goal is to bridge the semantic gap between natural language text and formalized knowledge. Within the context of globalization, multilingual and cross-lingual access to information has emerged as an issue of major interest. Nowadays, more and more people from different countries are connecting to the Internet, in particular the Web, and many users can understand more than one language. While the diversity of languages on the Web has been growing, for most people there is still very little content in their native language. As a consequence of the ability to understand more than one language, users are also interested in Web content in other languages than their mother tongue. There is an impending need for technologies that can help in overcoming the language barrier for multilingual and cross-lingual information access. In this thesis, we face the overall research question of how to allow for semantic-aware and cross-lingual processing of Web documents and user queries by leveraging knowledge bases. With the goal of addressing this complex problem, we provide the following solutions: (1) semantic annotation for addressing the semantic gap between Web documents and knowledge; (2) semantic search for coping with the semantic gap between keyword queries and knowledge; (3) the exploitation of cross-lingual semantics for overcoming the language barrier between natural language expressions (i.e., keyword queries and Web documents) and knowledge for enabling cross-lingual semantic annotation and search. We evaluated these solutions and the results showed advances beyond the state-of-the-art. In addition, we implemented a framework of cross-lingual semantic annotation and search, which has been widely used for cross-lingual processing of media content in the context of our research projects.
Underwater Wireless Sensor Networks: A Review of Recent Issues and Challenges

Underwater Wireless Sensor Networks (UWSNs) contain several components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection tasks. These networks are used interactively between different nodes and ground-based stations. Presently, UWSNs face issues and challenges regarding limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and power constraints. In the last few decades, research community provided different methodologies to overcome these issues and challenges; however, some of them are still open for research due to variable characteristics of underwater environment. In this paper, a survey of UWSN regarding underwater communication channel, environmental factors, localization, media access control, routing protocols, and effect of packet size on communication is conducted. We compared presently available methodologies and discussed their pros and cons to highlight new directions of research for further improvement in underwater sensor networks.
A comparative assessment of the difficulty of authorship attribution in Greek and in English

Authorship attribution is an important problem in text classification, with many applications and a substantial body of research activity. Among the research findings are that many different methods will work, including a number of methods that are superficially language‐independent (such as an analysis of the most common “words” or “character n‐grams” in a document). Since all languages have words (and all written languages have characters), this method could (in theory) work on any language. However, it is not clear that the methods that work best on, for example English, would also work best on other languages. It is not even clear that the same level of performance is achievable in different languages, even under identical conditions. Unfortunately, it is very difficult to achieve “identical conditions” in practice. A new corpus, developed by George Mikros, provides very tight controls not only for author but also for topic, thus enabling a direct comparison of performance levels between the two languages Greek and English. We compare a number of different methods head‐to‐head on this corpus, and show that, overall, performance on English is higher than performance on Greek, often highly significantly so.
Neither a Bazaar nor a cathedral: The interplay between structure and agency in Wikipedia's role system

Roles provide a key coordination mechanism in peer‐production. Whereas one stream in the literature has focused on the structural responsibilities associated with roles, another has stressed the emergent nature of work. To date, these streams have proceeded largely in parallel. In seeking to enhance our understanding of the tension between structure and agency in peer‐production, we investigated the interplay between structural and emergent roles. Our study explored the breadth of structural roles in Wikipedia (English version) and their linkage to various forms of activities. Our analyses show that despite the latitude in selecting their mode of participation, participants' structural and emergent roles are tightly coupled. Our discussion highlights that: (a) participants often stay close to the “production ground floor” despite the assignment into structural roles; and (b) there are typical modifications in activity patterns associated with role‐assignment, namely: functional specialization, multispecialization, defunctionalization, changes in communication patterns, management of identity, and role definition. We contribute to theory of coordination and roles, as well as provide some practical implications.
qSCMS: Post-quantum certificate provisioning process for V2X

Security and privacy are paramount in the field of intelligent transportation systems (ITS). This motivates many proposals aiming to create a Vehicular Public Key Infrastructure (VPKI) for managing vehicles’ certificates. Among them, the Security Credential Management System (SCMS) is one of the leading contenders for standardization in the US. SCMS provides a wide array security features, which include (but are not limited to) data authentication, vehicle privacy and revocation of misbehaving vehicles. In addition, the key provisioning process in SCMS is realized via the so-called butterfly key expansion, which issues arbitrarily large batches of pseudonym certificates in response to a single client request. Although promising, this process is based on classical elliptic curve cryptography (ECC), which is known to be susceptible to quantum attacks. Aiming to address this issue, in this work we propose a post-quantum butterfly key expansion process. The proposed protocol relies on lattice-based cryptography, which leads to competitive key, ciphertext and signature sizes. Moreover, it provides low bandwidth utilization when compared with other lattice-based schemes, and, like the original SCMS, addresses the security and functionality requirements of vehicular communication.
Subversion in Practice: How to Efficiently Undermine Signatures

Algorithm substitution attack (ASA) on signatures can have severe consequences as the authentication services of numerous systems and applications rely on signature schemes. In this paper, we present a highly efficient ASA on the widely-used digital signature algorithm (DSA). Compared with the generic ASAs on signature schemes proposed in the literature, our attack provides fast and undetectable subversion, which can extract the user’s private signing key by collecting the maximum three signatures arbitrarily. Moreover, our ASA is proven to be robust against state reset. We implemented the proposed ASA by replacing the original DSA in Libgcrypt (a popular cryptographic library used in many applications) with our subverted DSA. Our experiment shows that the user’s private key can readily be recovered once the subverted DSA is used to sign messages. In our implementation, various measures have been considered to significantly reduce the possibility of detection through comparing the running time of the original DSA and the subverted one (i.e. timing analysis). To our knowledge, this is the first implementation of the ASA in practice, which shows that the ASA is a real threat rather than only theoretical speculation.
Assessing impact of class change by mining class associations

Data mining plays vital role in data analysis and also encompasses immense potential of mining software engineering data to manage design and maintenance issues. Change impact assessment is one of the crucial issues in software maintenance. In Object Oriented (OO) software system, classes are the core components and changes to the classes are always inevitable. So, OO software system must support the expected changes. In this paper, to assess impact of change in the class, we have proposed changeability measures by mining associations among the classes. These measures estimate a) change propagation by identifying its ripple effect; b) change impact set of the classes; c) changeability rank of the classes and d) class change cost. Further, we have performed the empirically study and evaluation to analysis our results. Our results indicate that by mining associations among the classes, the development team can effectively estimate the probable impact of the class change. These measures can be very helpful to perform changes to the classes while maintaining the software system.
Connectivity Analysis in Vehicular Networks with Slight Traffic Interference

This paper analyzes the network connectivity in vehicular networks with slight traffic interference, i.e., small-scale traffic accident. In this paper, we develop an analytical model for highway scenarios or sparse urban scenarios, where the slight traffic interference can make the vehicles slow down rather than block the traffic flow. When a traffic accident occurs, it is necessary to inform the nearby vehicles to slow down in order to reduce congestion at the accident location. Once they pass by, they can return to the normal value. Consequently, we can divide an entire road into two or three subsections, which helps us to analyze the connectivity performance. In addition, we analyze the impact of several key parameters on connectivity probability, including vehicle arrival rate, vehicle communication range, length of road, vehicle normal speed, and safe speed. All analytical results are verified through Monte Carlo simulation experiments. The simulation results are very close to analytical results, which means that the analytical results are accurate and the analytical model we propose is effective.
An efficient user privacy preserving multi-server private information retrieval: an efficient privacy preserving information storage and retrieval framework

The thirst of acquiring and sharing the knowledge has been increased exponentially in these days due to the availability of the Internet at the finger tips. As a consequence, the need of privacy at various levels and various contexts has been comprehensively studied by various cryptographers. One of the user privacy preserving concepts called Private Information Retrieval (PIR) also provides user privacy at various levels. Several years of efforts on PIR have succeeded to provide almost practical communication efficient privacy preserving solutions.
SURAKSHIT: a smartphone-based application for 'localized' GIS data aggregation in absence of internet

Dissemination of information after a large-scale disaster is always a difficult task and hinders the possibilities of proper decision making. Relief work and rescuing survivors take a major hit without a proper map showcasing the current scenario of the disaster-hit terrain. All due to the lack of traditional communication channels after a disaster. However, we want to change this with the proposition of Android-based mobile application SURAKSHIT that prepares 'localized' crisis map through 'offline' crowdsourcing of circumstantial data and distribution of the same in a smooth manner using a delay-tolerant network. The information gap between the first responders and local survivors can thus be greatly reduced with proper knowledge about the affected area collected by the volunteers. Multimedia data accumulated with the application can further assist the rescue forces in planning rescue-relief operations better.
Distributed α-fair transmit power adaptation based congestion control in VANET

Major problem of IEEE 802.11 based Vehicular Ad-hoc Network (VANET) is traffic congestion. The traffic congestion occurs due to unnecessary bandwidth usages, high vehicle density, excess increase in transmission power and immediate topology changes in a vehicular ad-hoc network which leads to excessive packet loss and lowers the safety of the applications. Under such conditions, all the transmitted packets from the source may not be delivered to the destination. Vehicles unaware of the traffic congestion increase the difficulty of it by eventually joining it. Many congestion control techniques have been proposed, but still, the problem arises. In this paper, we propose Distributed α-Fair Transmit Power Adaptation Based Congestion Control in Vehicular Ad-hoc Network to discover and reduce traffic congestion using the transmit power control and optimum node selection for cooperative VANET in the framework of the utility function optimization. The proposed system has better performance as compared to DFAV, DV-CAST, and UV-CAST regarding packet reception probability, average packet delivery ratio, and average end-to-end packet delivery delay in Vehicular Ad-hoc Network (VANET).
Functional description of sequence constraints and synthesis of combinatorial objects. (Description fonctionnelle de contraintes sur des séquences et synthèse d'objets combinatoires)

Contrary to the standard approach consisting in introducing ad hoc constraints and designing dedicated algorithms for handling their combinatorial aspect, this thesis takes another point of view. On the one hand, it focusses on describing a family of sequence constraints in a compositional way by multiple layers of functions. On the other hand, it addresses the combinatorial aspect of both a single constraint and a conjunction of such constraints by synthesising compositional combinatorial objects, namely bounds, linear inequalities, non-linear constraints and finite automata. These objects are obtained in a systematic way and are not instance-specific: they are parameterised by one or several constraints, by the number of variables in a considered sequence of variables, and by the initial domains of the variables. When synthesising such objects we draw full benefit both from the declarative view of such constraints, based on regular expressions, and from the operational view, based on finite transducers and register automata.There are many advantages of synthesising combinatorial objects rather than designing dedicated algorithms: 1) parameterised formulae can be applied in the context of several resolution techniques such as constraint programming or linear programming, whereas algorithms are typically tailored to a specific technique; 2) combinatorial objects can be combined together to provide better performance in practice; 3) finally, the quantities computed by some formulae cannot just be used in an optimisation setting, but also in the context of data mining.
Multiple attribute group decision-making method with single-valued neutrosophic interval number information

ABSTRACT To describe decision makers’ judgments for an evaluated object more comprehensively in the decision-making process, this paper firstly defines the concept of a single-valued neutrosophic interval number (SVNIN), which is composed of both its interval number (interval judgment) and its single-valued neutrosophic number (SVNN) (confident degree). Then we present the basic operational laws of SVNINs and the score function of SVNIN based on the attitude index and confident level of decision makers to compare SVNINs. Next, we propose a single-valued neutrosophic interval number weighted arithmetic averaging (SVNINWAA) operator and a single-valued neutrosophic interval number weighted geometric averaging (SVNINWGA) operator to aggregate SVNIN information. Further, a multiple attribute group decision-making (MDGDM) method using the proposed SVNINWAA or SVNINWGA operator is established under an SVNIN environment. Finally, an illustrative example regarding the selection problem of global suppliers is presented to show the application and effectiveness of the developed method.
Exponential stability of impulsive discrete-time systems with infinite delays

This paper investigates the exponential stability of impulsive discrete-time systems with infinite delays. Some sufficient conditions are obtained to guarantee the exponential stability of the considered systems by employing Lyapunov functions together with Razumikhin technique. Finally, three numerical examples are given to illustrate the effectiveness and superiority of the obtained results. It is the first time that the exponential stability of impulsive discrete-time systems with infinite delays is studied, the obtained results complement some recent works.
A Quality of Experience Management Framework for Mobile Users

Voice transmission is no longer the main usage of mobile phones. Data transmissions, in particular Internet access, are very common actions that we might perform with these devices. However, the spectacular growth of the mobile data demand in 5G mobile communication systems leads to a reduction of the resources assigned to each device. Therefore, to avoid situations in which the Quality of Experience (QoE) would be negatively affected, an automated system for degradation detection of video streaming is proposed. This approach is named QoE Management for Mobile Users (QoEMU). QoEMU is composed of several modules to perform a real-time analysis of the network traffic, select a mitigation action according to the information of the traffic and to some predefined policies, and apply these actions. In order to perform such tasks, the best Key Performance Indicators (KPIs) for a given set of video traces are selected. A QoE Model is trained to define a global QoE for the set of traces. When an alert regarding degradation in the quality appears, a proper mitigation plan is activated to mitigate this situation. The performance of QoEMU has been evaluated over a degradation situation experiments with different video users.
Controller synthesis for switched positive linear systems by output feedback

ABSTRACT The output-feedback controller synthesis issue for a class of continuous-time switched linear systems via average dwell time switching with constrained controls and output is addressed. First, we focus on the output-feedback stabilisation by decomposing control gain matrix. Second, the stabilisation problem with sign-restricted controls while imposing positivity and stability on closed-loop system is solved, then the results extend to asymmetrically bounded controls and constrained outputs. All the derived conditions are formulated as linear programming. Finally, numerical examples are given to show the applicability of the proposed methods.
An automatic localization of optic disc in low resolution retinal images by modified directional matched filter

An automatic optic disc localization in retinal images used to screen eye related diseases like diabetic retinopathy. Many techniques are available to detect Optic Disc (OD) in high-resolution retinal images. Unfortunately, there are no efficient methods available to detect OD in low-resolution retinal images. The objective of this research paper is to develop an automated method for localization of Optic Disc in low resolution retinal images. This paper proposes a modified directional matched filter parameters of the retinal blood vessels to localize the center of optic disc. The proposed method was implemented in MATLAB and evaluated both normal and abnormal low resolution retinal images using the subset of Optic Nerve Head Segmentation Dataset (ONHSD) and the success percentage was found to be an average of 96.96% with 23seconds.
AnyPyTools: A Python package for reproducible research with the AnyBody Modeling System

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Majority vote and monopolies in social networks

Occasionaly, every society needs to reach a decision among its members. For this, it may use a voting mechanism, i.e., collect the votes of the group members and output a decision that best expresses the group's will. To make up their minds, individuals often discuss the issue with friends before taking their votes, thus mutually affecting each other's votes. Individuals are also, to some extent, influenced by the opinions of key figures in their culture, such as politicians, publicists, etc., commonly considered as the "elite" of the society. This work studies the "power of the elite": to what extent can the elite of a social network influence the rest of society to accept its opinion, and thus become a monopoly. We present an empirical study of local majority voting in social networks, where the elite forms a coalition against all other (common) nodes. The results, obtained on several social networks, indicate that an elite of size [MATH HERE] (where m is the number of connections) has disproportionate power, relative to its size, with respect to the rest of society: it wins the majority voting and remains stable over time.