Miracle-dz/UniFAR-FTUs huggingface dataset:
A Naïve Algorithmic Approach for Detection of Users' with Unusual Behavior in online Social Networks

Unusual users' behavior in online social networks can be detected by analyzing the activities of particular individuals or a group of individuals who make frequent changes in their order of communication and that considerably differ from rest of the group. This type of behavior can be detected by examining the structure of the network and comparing it with the presumed model of the normal network. The regions of the network where architecture is different from the normal network structure can be stated as outliers, exceptions, abnormalities etc. Today, with the convulsive use of the online social networking such as Facebook, Flickr, LinkedIn etc., there has been a gigantic hike in the availability of online social network data, and also the exponential growth in the number of users on online social networks is a severe issue. Here an analysis of Facebook Egonet dataset is carried out which presents various aspects of social network analysis like graph structure, density, degree distribution, modularity etc. Then, on the basis of in degree and out degree of nodes an algorithm is presented which detects the users' with abnormal behavior and the algorithm is tested on the Facebook Egonet dataset.
The distribution of downtoning gestures: A pilot study

This paper deals with the distribution of downtoning cospeech gestures in German. On the basis of three types of video recordings (sports reports, talk shows, and parliamentary debates), it is investigated (1) how a number of downtoning gestures are distributed over these three types of settings, (2) to what extent their distribution differs from that of verbal downtoners, and (3) which factors may have influenced the distribution of the gestures.
Multicore Scheduling of Real-Time Irregular Parallel Algorithms in Linux

With sequential machines approaching their physical bounds, parallel computers are rapidly becoming pervasive in most areas of modern technology. To realize the full potential of parallel platforms, applications must split onto concurrent parts that can be assigned to different processors and execute in parallel. Parallel programming models abstract the myriad of parallel computer specifications to simplify the development of concurrent applications, allowing programmers to decompose their code onto concurrent tasks, and leaving it to the runtime system to schedule these tasks for parallel execution. The resulting parallelism is often input-dependent and irregular, requiring that the mapping of tasks to processors be performed at runtime in response to dynamic changes of the workload. Motivated by the promises of performance scalability and cost effectiveness, real-time researchers are now beginning to exploit the benefits of parallel processing, with ground-breaking scheduling heuristics to improve the efficiency of time-sensitive concurrent applications. Realtime developments are switching to open scenarios, where real-time tasks of variable and unpredictable size share the available processing resources with other applications, making it essential to utilize as much of the available processing capacity as possible. The p-CSWS algorithm employs bandwidth isolation, capacity-sharing and work-stealing to exploit the intra-task parallelism of hard and soft real-time executions on parallel platforms. This thesis proposes an implementation of the p-CSWS scheduler for the Linux kernel, to evaluate its applicability to real scenarios and bring Linux one step closer to becoming a viable open real-time platform. To the best of our knowledge we are the first to employ scheduling heuristics to exploit dynamic parallelism of real-time tasks on the Linux kernel. Through extensive tests, we show that.
Find top-k representative skyline points by using grid data structure

To analyze a database more intensively, we need some representative records, such as, the cheapest one, the most popular one, the most convenient one and so on. Skyline query and its variant are popular functions to find such representative objects from a numerical database. However, a skyline query often retrieves too many points to analyze intensively especially for high-dimensional dataset. Therefore, we put our focus on selecting top- k points that represents a database. We select the k points so that many points are dominated by the k points. This paper proposes an efficient grid-based algorithm to find such top- k representative skyline points. We conducted a set of experiments to show the effectiveness and the scalability of the propose algorithm.
Collaborative knowledge, data and control generation for real time information and control system

This invited lecture presents the experience that Nottingham Trent University has gathered in the area of pervasive computing where collaborative knowledge generation is a requirement and the possibility of collaborative control exists. In this respect the participants in a real-life process (e.g. traffic movements) not only provide measurements based on their own experiences, but together with the other participants generate new knowledge which in turn is solid base for generating the actual demand responsive control. INTRODUCTION The real time wireless communication test bed in Nottingham. From March 2002 until October 2005 I have been involved in a British Department of Trade and Industry (DTI) funded project called “Traffimatics”. The aim of this project was to show how off-the-shelf wireless components can be used for car-to-car and car-to-roadside communication. Nottingham was chosen to be the place to deploy the research test bed required for the project. It has been decided to deploy 6 different wireless nodes across the Nottingham City Center, so that a car can be driven in a closed loop and be always in the range of one or more access points for car-toroadside infrastructure communication. Figure 1 below gives the locations of these 6 access points superimposed on the map of Nottingham. The minimum distance between the points is 250 m. and the maximum distance between the points is 650 m. The overall distance of the closed loop is 4.5 km. The type of devices used in the test bed is 802.11b, which limits the speed to 10mb/s. The devices used are found to be most prone to interferences which in Nottingham City Centre proved to be major obstacle for deploying wireless devices. Only one of the 6 points is directly connected to the Internet, while all others must rely on multi-hopping service to get Internet data. Each nod combines two separate devices – Access Point and Wireless Bridge. The access point accepts all local requests for connection and through the bridge gets access to the previous access point in the chain. Some of the location where there is a node infrastructure deployed are shown on the Figure 2 (in a traffic lights) and Figure 3 (street lights pole). The deployment proved that a variety of hosting devices can be used providing Figure 1
Metaheurística ILS para la solución del problema de ruteo de vehículos con flota propia y subcontratada VRPPC

Vehicle Routing Problem with Private Fleet and Common Carriers (VRPPC) is a variant of Capacitated Vehicle Routing Problem (CVRP) that searches for lowest cost routes while serving a complete set of clients. VRPPC is most prominent when demand is higher than the number of owned vehicles or due to private fleet unavailability which leads to outsourcing operation of some or all routes. This problem statement is considered relatively new based on the literature review made in this work. The scope of this work is the always changing transport industry, where demand can reach significant peaks that are impossible to serve with a private fleet. A literature review on the problem is made with the objective of verifying the state of the art and inputs on the subject. In addition to the above, gaps where contributions can be provided are identified. This vehicle routing problem is defined as an NP-complete combinatorial optimization problem due to its computational complexity. For these types of problems, there are two approaches to find their solution: generation of exact optimization algorithms or generation of approximate optimization algorithms. An approximate optimization algorithm is developed in this work, named Iterated Local Search (ILS), which implements Variable Neighborhood Search (VNS). Procurement of an initial solution is considered in the first stage based on modified savings heuristic; this is a basis for the global performance of the algorithm. In the second stage, ILS metaheuristics is applied based on concepts of intensification and diversification. These concepts are applied through VNS heuristics and an appropriate perturbation scheme respectively. A final verification on the performance of the developed algorithm is implemented using as reference literature examples. Results from the proposed methodology based on ILS metaheuristics are verified with results obtained from exact methods to validate the effectiveness of the proposed method. Subsequently, instances of great mathematical complexity are used. Results obtained are not answered in specialized literature and are defined as new BKS (Best Known Solution) for the problem under study.
A new family of NDA carrier phase detectors for coherent M-PSK receivers

A new family of NDA (non data aided) carrier phase detectors for M-PSK coherent receivers operating in AWGN channels is defined. The behavior of the phase detectors is explored using stochastic theory, and it is found that the phase detectors' gain curves are independent of AGC performance and signal-levels. Furthermore, these self-normalizing properties allow the performance requirements of the AGC circuit to be relaxed considerably. Analysis of the self-noise of the phase detectors is presented, and their closed-loop phase error variance is calculated as well as simulated and found to be comparable to that of DD (decision directed) detectors. The phase detectors are shown also to possess a simple hardware realization, which allows for their straightforward and efficient implementation within an FPGA or ASIC. Finally, an adaptive scheme is outlined, one which aims to produce a phase detector that (along with the previously attained AGC independence) is independent of the SNR and M.
Développement de nouvelles technologies pour la formation aux gestes médico-chirurgicaux

La plupart des formations medicales visent a developper chez les stagiaires une articulation des savoirs theoriques et pratiques. La formation in situ est indispensable pour eprouver la theorie, developper la dexterite manuelle, et les capacites de prise de decisions inherentes a ce type d’activites. Mais la formation actuelle sur le terrain peut comporter des risques pour le patient. Se pose alors la question recurrente en milieu medical d’un lieu de formation qui permettrait la pratique des gestes a risque, sans risque pour le patient et qui presenterait les caracteristiques necessaires pour developper chez les stagiaires des savoirs operatoires et transferables aux situations reelles. Les systemes d’entrainement par simulation peuvent etre une reponse a ce probleme de formation, a condition qu’ils integrent une reflexion sur les enjeux de la formation et des developpements techniques permettant leur realisation. Nous presentons dans cet article, comment de tels simulateurs doivent etre concus couplant une partie numerique, un dispositif physique et un composant pedagogique.
Automatic Procedure to Extract Signature Pentapeptides from the Protein Sequence Database

A method is described for extracting signature pentapeptides that are conserved and exclusively found in a group of homologous proteins. The BLAST algorithm is used to count the frequency of occurrences of pentapeptide patterns allowing limited substitutions, as well as to perform homology search. For those pentapeptides that appear in a given sequence we examine the frequency of occurrences of these pentapeptides and related ones in homologous sequences which are ordered according to the homology score. By comparing against the frequency in the entire database, we can extract uniquely conserved pentapeptides and at the same time perform a grouping of homologous sequences. Thus, our procedure can automatically identify, if any, pentapeptides that are strongly tied with the group. Possibility of using our pentapeptide word dictionary to infer protein function is
Lexicon-free recognition strategies for online handwritten Tamil Words

In this thesis, we address some of the challenges involved in developing a robust writerindependent, lexicon-free system to recognize online Tamil words. Tamil, being a Dravidian language, is morphologically rich and also agglutinative and thus does not have a finite lexicon. For example, a single verb root can easily lead to hundreds of words after morphological changes and agglutination. Further, adoption of a lexicon-free recognition approach can be applied to form-filling applications, wherein the lexicon can become cumbersome (if not impossible) to capture all possible names. Under such circumstances, one must necessarily explore the possibility of segmenting a Tamil word to its individual symbols. Modern day Tamil alphabet comprises 23 consonants and 11 vowels forming a total combination of 313 characters/aksharas. A minimal set of 155 distinct symbols have been derived to recognize these characters. A corpus of isolated Tamil symbols (IWFHR database) is used for deriving the various statistics proposed in this work. To address the challenges of segmentation and recognition (the primary focus of the thesis), Tamil words are collected using a custom application running on a tablet PC. A set of 10000 words (comprising 53246 symbols) have been collected from high school students and used for the experiments in this thesis. We refer to this database as the ‘MILE word database’. In the first part of the work, a feedback based word segmentation mechanism has been proposed. Initially, the Tamil word is segmented based on a bounding box overlap criterion. This dominant overlap criterion segmentation (DOCS) generates a set of
Neural network-based adaptive robust control for a class of uncertain systems with measurement noise

In this paper, neural networks (NNs) and adaptive robust control (ARC) design philosophy are integrated to design performance-oriented control laws for a class of uncertain systems whose output is corrupted by external disturbances. The derived adaptive-robust control schemes not only guarantee all the signals are bounded in the closed loop, but also make the system preserve certain prescribed properties. The cone-bounded assumption on the uncertain dynamics is removed via neural networks. The feedback information is the state with measurement noise.
Wikitology: Wikipedia as an ontology

Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. We are investigating the use of Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits of the approach are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood by reading the associated pages. We have demonstrated the use of Wikitology to improve the performance of an information retrieval system and as a source of evidence in a intra-document entity co-reference task.
Survey of Possibilities for the Automated Configuration of Large IP Networks

This memo discusses the steps required to bring a large number of devices into service in IP networks in an automated fashion. The goal of this document is to list known solutions where they exist, to point out approaches proven to be problematic, and to identify gaps that require further specifications. Status of this Memo This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79 . Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current InternetDrafts is at http://datatracker.ietf. org/drafts/current/ . Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress. " This Internet-Draft will expire on July 27, 2013.
Contribution à l'étude de l'intonation française prononcée par les chinois en vue de l'apprentissage

Les chinois, influences par le systeme phonetique et phonologique de leur langue maternelle, presentent dans la prononciation du francais des caracteristiques specifiques. L'analyse des reactions des auditeurs phoneticiens francophones et non francophones a mis en lumiere les proprietes contrastives entre le chinois et le francais du point de vue de la hauteur. Notre etude portant sur la realisation intonative dans la lecture de phrases et d'un texte par des francais et des chinois a fait apparaitre que les points de difficulte majeure dans la realisation de l'intonation francaise par les chinois s'expriment essentiellement par la forme du contour melodique a l'interieur du mot prosodique d'une part, et par la notion de degre de dependance d'autre part. Dans la realisation des francais le contour melodique est realise par une pente descendante qui designe la descente graduelle d'une syllabe a l'autre, tandis que dans celle des chinois, il est caracterise par un plateau presentant une neutralisation de la frequence fondamentale a l'interieur du mot prosodique. En ce qui concerne la notion de degre de cohesion, nous avons en effet mis en evidence deux comportements tonals qui peuvent s'analyser et se differencier au moyen du critere de plus ou moins grande dependance : alors qu'en chinois les tons peuvent s'affranchir de la dependance.
Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

The increased use of social media in Arab regions has attracted spammers seeking new victims. Spammers use accounts on Twitter to distribute adult content in Arabic-language tweets, yet this content is prohibited in these countries due to Arabic cultural norms. These spammers succeed in sending targeted spam by exploiting vulnerabilities in content-filtering and internet censorship systems, primarily by using misspelled words to bypass content filters. In this paper we propose an Arabic word correction method to address this vulnerability. Using our approach, we achieve a predictive accuracy of 96.5% for detecting abusive accounts with Arabic tweets.
Automated SKOS Vocabulary Design for the Biopharmaceutical Industry

ABSTRACT Ensuring quick and consistent access to large collections of unstructured documents is one of the biggest challenges facing knowledge-intensive organizations. Designing specific vocabularies to index and retrieve documents is often deemed too expensive, full-text search being preferred despite its known limitations. However, the process of creating controlled vocabularies can be partly automated thanks to natural language processing and machine learning techniques. With a case study from the biopharmaceutical industry, we demonstrate how small organizations can use an automated workflow in order to create a controlled vocabulary to index unstructured documents in a semantically meaningful way.
System Analysis and Design Based on Methods of FSM and Petri Net

A real-time multitask system for the space aircraft docking simulation is presented and the system composition,the functions requirement and the system structure are introduced.Based on the methods of finite state machine(FSM)and Petri net,the single task and the multitask models are built respectively.According to these models,the system design is accomplished.By applying the fork and the resources sharing prototypes,the synchronization and the mutex functions can be realized.In actual application,the engineering and modularization methods are used and the system performances are good as a result.Experimental results show that the methods for the system analysis and design are rational and feasible.
EARS 2019: The 2nd International Workshop on ExplainAble Recommendation and Search

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also interpretability of the models or explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The workshop focuses on the research and application of explainable recommendation, search, and a broader scope of IR tasks. It will gather researchers as well as practitioners in the field for discussions, idea communications, and research promotions. It will also generate insightful debates about the recent regulations regarding AI interpretability, to a broader community including but not limited to IR, machine learning, AI, Data Science, and beyond.
Health Cards for Consumer Health Search

This paper investigates the impact of health cards in consumer health search (CHS) - people seeking health advice online. Health cards are a concise presentations of a health concept shown along side search results to specific health queries; they have the potential to convey health information in easily digestible form for the general public. However, little evidence exists on how effective health cards actually are for users when searching health advice online, and whether their effectiveness is limited to specific health search intents. To understand the impact of health cards on CHS, we conducted a laboratory study to observe users completing CHS tasks using two search interface variants: one just with result snippets and one containing both result snippets and health cards. Our study makes the following contributions: (1) it reveals how and when health cards are beneficial to users in completing consumer health search tasks, and (2) it identifies the features of health cards that helped users in completing their tasks. This is the first study that thoroughly investigates the effectiveness of health cards in supporting consumer health search.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation

Generating qualitative responses has always been a challenge for human-computer dialogue systems. Existing dialogue systems generally derive from either retrieval-based or generative-based approaches, both of which have their own pros and cons. Despite the natural idea of an ensemble model of the two, existing ensemble methods only focused on leveraging one approach to enhance another, we argue however that they can be further mutually enhanced with a proper training strategy. In this paper, we propose ensembleGAN, an adversarial learning framework for enhancing a retrieval-generation ensemble model in open-domain conversation scenario. It consists of a language-model-like generator, a ranker generator, and one ranker discriminator. Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts. The experimental results on a large short-text conversation data demonstrate the effectiveness of the ensembleGAN by the amelioration on both human and automatic evaluation metrics.