Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects and applications of video games and virtual environments

Research has shown that video game players perform better on a multitude of visual attention tasks than non-video game players. However, more recent research questions these findings, showing limited or no benefit of video game playing. The aim of this study was to further investigate this discrepancy by considering multiple attentional processes, including alerting, orienting, executive control, searching/filtering and visual short-term memory. Three different attention tasks were selected to measure these processes: The Attention Network Task (ANT), the preview search task, and the enumeration task. A total of eighty participants completed the tasks and filled out a questionnaire about their past video game experience. For the analysis, participants were divided into three groups depending on whether they played rarely/never, occasionally, or frequently. The groups were equivalent in terms of other control measures, such as personality. Those playing rarely/never had a stronger alerting effect in the ANT than those playing occasionally or frequently. All three groups had the same preview benefit in the preview search task. In the enumeration task, no difference was found between the three groups in the deflection points. Those who played occasionally or frequently were also further grouped into action and non-action players. Action video game players had a higher orienting effect than non-action players. We suggest that the conflicting
Retrieving Comparative Arguments using Deep Language Models

In this paper, we present a submission to the Touché lab’s Task 2 on Argument Retrieval for Comparative Questions. Our team Katana employs approaches based on pre-trained deep language model architecture ColBERT [1]. This BERT-based architecture is adapted to the text ranking task by learning to represent both queries and documents as vectors and measuring the similarity between them. We use a model trained on a question-answering dataset MSMARCO, with the proposed weights and weights pre-trained by us. We also customize ColBERT for the comparative retrieval domain by fine-tuning the model on the data from the previous years’ Touché competitions. The proposed experiments verify the usefulness of the transfer learning from a large pre-trained ranking language models to the problem of arguments extraction for comparative topics. Ours solutions rank third in both relevance, quality, and stance prediction evaluations.
SINAI at CLEF 2022: Leveraging biomedical transformers to detect and normalize disease mentions

This paper presents the system developed by SINAI team for Disease Text Mining and Indexing Shared Task at CLEF 2022 BioASQ workshop. This task brings the community effort to development of automatic disease mention detection and semantic indexing systems for electronic health records written in Spanish. Our proposal is based on a deep learning RoBERTa architecture model fine-tuned for the named entity recognition task, which achieved 0.75 micro-average F1-score during the official evaluation. For the entity linking task, we introduce a system based on a combination of term matching and embedding similarity calculation which best micro-average F1-score is 0.41.
Knowledge-based Contexts for Historical Named Entity Recognition & Linking

This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the Identifying Historical People, Places, and other Entities (HIPE) evaluation campaign of CLEF 2022 in both tasks: named entity recognition and classification (NERC), coarse-and fine-grained, and entity linking (EL) in historical newspapers and classical commentaries. For both tasks, we developed models based on our previous models, which ranked first at CLEF-hipe-2020 . The NERC model is a Transformer-based architecture and the EL model is a BiLSTM-based architecture. For NERC, our main contribution is two-fold: (1) data-wise improvement – we propose a knowledge-based strategy to provide related context information to the NERC model; (2) model-wise improvement – we adapt the NERC model to the task of detecting coarse-and fine-grained entities in non-standard text via adapters and we include the knowledge-based contexts as context jokers. Our approaches ranked first on 84.6% of the leaderboards we participated in for NERC and 85.7% of them for EL.
Nonparametric pixel-wise background modelling and segmentation to detect moving object with RGB-D camera

IV NOMENCLATURE . V SYMBOLS . V ACRONYMS / ABBREVIATIONS . VI TABLE OF CONTENT . VIII LIST OF FIGURES . XI LIST OF TABLES . XIII CHAPTER 1: INTRODUCTION . 1 1.1 MOTIVATION . 1 1.2 CONTRIBUTIONS . 3 1.3 PUBLICATIONS . 5 1.4 THESIS STRUCTURE . 5 CHAPTER 2: BACKGROUND . 7 2.
Style Change Detection Based On Bert And Conv1d

For the style change detection task of the 2022 PAN competition, the goal of the task is to identify the text positions of author switches in a given multi-author document. There are three subtasks in it. Task 1 is to cut the text into two authors' texts at the paragraph level; task 2 is to find the location of all writing style changes for texts written by two or more authors; task 3 is for texts written by two or more authors to find the locations of all writing style changes, where style changes now occur not only between paragraphs but also at the sentence level. This paper proposes a method based on the Bert pre-training model and one-dimensional convolution to process the task. This method regards judging the number of authors and the location of text changes as a binary classification task based on the similarity of two texts. For the prediction of the three tasks accomplished by the proposed method, the f1 scores of task 1, task 2, and task 3 in the test set are 0.74, 0.41, and 0.63, respectively.
Profiling irony and stereotype spreaders on Twitter based on term frequency in tweets

The use of stereotypes, irony, mocking and scornful language is prevalent on social media platforms such as Twitter. Identification or profiling of users who are involved in the spread of such content is beneficial for monitoring its spread. In our work, we study the problem of profiling irony and stereotype spreaders on Twitter as a part of the PAN shared task in CLEF 2022. We experiment with machine learning models applied on a TF-IDF representation of user tweets, and find Random Forest to be the best working one.
Intelligent perception in virtual sensor networks and space robotics

Intelligent Perception in Virtual Sensor Networks and Space Robotics Faisal Zubair Qureshi Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2007 Intelligent perception is a fundamental requirement of systems that exhibit sophisticated autonomous operation in complex dynamic worlds. It combines low-level, bottom-up, datadriven vision with high-level, top-down, knowledge-based processes. This thesis develops two embodied, task-oriented vision systems that exhibit autonomous, intelligent, goal-driven behavior through intelligent perception. In Part I of the thesis, we develop a prototype surveillance system featuring a visual sensor network comprising wide field-of-view (FOV) passive cameras and pan/tilt/zoom active cameras. Novel multicamera control strategies enable the camera nodes to collaborate both in tracking pedestrians of interest that move across the FOVs of different cameras and in acquiring close-up videos of pedestrians as they travel across extended areas. Impediments to deploying and experimenting with appropriately extensive camera networks in large, busy public spaces would make our research more or less infeasible in the real world. However, a unique centerpiece of our approach is the virtual vision paradigm, in which we employ a visually and behaviorally realistic simulator in the design and evaluation of our surveillance systems. In particular, we employ a virtual train station populated by autonomous, lifelike virtual pedestrians, wherein easily reconfigurable virtual cameras generate synthetic video feeds that emulate those acquired by real surveillance cameras monitoring public spaces. In Part II of the thesis, we develop a cognitively-controlled vision system that combines
BERT Sentence Embeddings in different Machine Learning and Deep Learning Models for Author Profiling applied to Irony and Stereotype Spreaders on Twitter

Irony detection is an interesting problem with different fields of application, both for managing social media and for studying people’s behavior and opinions. This paper focuses on classifying Twitter author profiles as ironic or non-ironic based on their tweets. For this purpose, different types of feature extraction are applied to each author’s tweets, from classical techniques to the most recent ones that are part of the state of the art. Furthermore, once the feature extraction has been performed, classical Machine Learning techniques such as SVM, Random Forest, and Logistic Regression are applied, up to more recent methods such as artificial neural networks with Self Attention mechanisms. Finally, a discussion is opened on the methods used and what each technique can contribute to solving this task. As it happens in most of the tasks where Embedding techniques are applied in natural language, new frontiers of study, analysis, and application are opened. Therefore, this study provides different brushstrokes for the development of robust systems for the detection of ironic and non-ironic authors.
UniNE at PAN-CLEF 2022: Profiling Irony and Stereotype Spreaders on Twitter

This work proposes to solve the problem of profiling irony and stereotype spreaders on twitter using a random forest model with features obtained using chi square feature scoring. The task is to determine whether the author of a given Twitter feed in English spreads irony and stereotypes. The training sample contains timelines of authors sharing irony and stereotypes towards, for instance, women or the LGTB community. Transforming this question into binary classification problem which requires us to classify authors as ironic or not. Evaluation with 300 chi2 features shows an overall performance of ACC = 0.9722.
Text_Minor at CheckThat!-2022: Fake News Article Detection Using RoBERT

Disinformation detection is emerging as an important research challenge due to the rise of disinformation on digital platforms. Several methods have been proposed in the literature to counter the spread of disinformation over digital platforms. However, most of these studies are based on social media, evidence claim verification, and incongruent news article detection. Earlier studies on fake news article detection are based on the stance detection approach over synthetically generated fake news datasets. This paper presents our RoBERT based proposed model submitted to checkThat! task 3 CLEF-2022. We conducted our experiment on the fake news dataset provided by the organizers of task 3 CLEF-2022.
ur-iw-hnt at CheckThat!-2022: Cross-lingual Text Summarization for Fake News Detection

We describe our submission to the CLEF CheckThat! 2022 challenge. We contributed to Tasks 3A and 3B – multiclass fake news classification in English and German, respectively. Our approach incorporates extractive and abstractive summarization techniques by utilizing fine-tuned DistilBART and T5-3B. For cross-linguality, we use automatic machine translation to improve model inference. Our approved run for Task 3B was the official winner according to both F1 and Accuracy, with a fair margin to the second place. For Task 3A, we describe a wide range of models that we experimented with. While only one submission per team was permitted, we also describe the non-submitted setup that tops the leaderboard performance in this task.
Kommunikation mit Automaten

Diese Arbeit befasst sich mit den begrifflichen Grundlagen einer Theorie der Kommunikation. Die Aufgabe dieser Theorie soll es sein, moglichst viele Erscheinungen bei der Informationsubertragung und Informationswandlung in einheitlicher und exakter Weise zu beschreiben. The theory of automata is shown not capable of representing the actual physical flow of information in the solution of a recursive problem. The argument proceeds as follows: 1. We assume the following postulates: a) there exists an upper bound on the speed of signals; b) there exists an upper bound on the density with which information can be stored. 2. Automata of fixed, finite size can recognize, at best, only iteratively defined classes of input sequences. (See Kleene (11) and Copi, Elgot, and Wright (8). ) 3. Recursively defined classes of input sequences that cannot be defined iteratively can be recognized only by automata of unbounded size. 4. In order for an automaton to solve a (soluble) recursive problem, the possibility must be granted that it can be extended unboundedly in whatever way might be required. 5. Automata (as actual hardware) formulated in accordance with automata theory will, after a finite number of extensions, conflict with at least one of the postulates named above. Suitable conceptual structures for an exact theory of communication are then discussed, and a theory of communication proposed. All of the really useful results of automata theory may be expressed by means of these new concepts. Moreover, the results retain their usefulness and the new nrocedure has definite advantages over the older ones. The proposed representation differs from each of the presently known theories concerning information on at least one of the following essential points: 1. The existence of a metric is assumed for either space nor time nor for other physical magnitudes. 2. Time is introduced as a strictly local relation between states. 3. The objects of the theory are discrete, and they are combined and produced only by means of strictly finite techniques. The following conclusions drawn from the results of this work may be cited as of some practical interest: 1. The tolerance requirements for the response characteristics of computer components can be substantially weakened if the computer is suitably structured. 2. It is possible to design computers structurally in such a way that they are asynchronous, all parts operating in parallel, and can be extended arbitrarily without interrupting their computation. 3. For complicated organizational processes of any given sort the theory yields a means of representation that with equal rigor and simplicity accomplishes more than the theory of synchronous automata.
Millimetre-wave T-shaped MIMO antenna with defected ground structures for 5G cellular networks

In this study, a millimetre-wave (MMW) antenna is presented for the fifth-generation (5G) wireless multiple-input multiple-output (MIMO) applications, in order to offer numerous advantages including compactness, planar geometry, high bandwidth, and high gain performance. The concept of defected ground structures has been deployed for the first time in MIMO antenna design at MMW spectrum to fulfil 5G requirements of high bandwidth with compactness and low design complexity. The top surface of antenna comprises of a coplanar waveguide-fed T-shaped radiating patch element, while the bottom part is designed to constitute a partial ground loaded with two iterations of symmetrical split-ring slots at an optimised distance. Experimental results depict a wide bandwidth of 25.1-37.5 GHz, as well as a peak gain of 10.6 dBi at 36 GHz. Moreover, numerically evaluated efficiency of >80% is observed over the entire intended bandwidth of operation. For the demonstration, four-element MIMO antenna array of the proposed antenna element is also fabricated and tested, in order to validate high isolation between the adjacent elements, which makes the proposed antenna a potential candidate to be integrated in cellular phones and base stations for 5G MIMO systems and services.
A Dynamic Colored Traveling Salesman Problem With Varying Edge Weights

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. In it, each city has one to multiple colors and allows a salesman in the same color to visit exactly once. This work presents for the first time a CTSP whose edge weights among the cities change over time. It can be applied to dynamic routing problems arising in logistic distribution systems with various goods accessibilities to different types of vehicles. A non-linear integer mathematical program is constructed. A Variable Neighborhood Search (VNS) algorithm with a direct-route encoding and random initialization is then presented to solve it. To increase the convergence rate and population diversity of VNS, it is further combined with two-stage greedy initialization and an appropriate population immigrant scheme to perform the population search in a dynamic environment. Then, off-line performance evaluation and Wilcoxon test of the proposed algorithms are performed. The results show that their solution quality is improved by 30% ~ 60% over the basic VNS’s. They can track the environmental changes of CTSP-VEW more rapidly and effectively. In the end, a case study with a practical scenario is conducted.
Sensitivity analysis of RANS simulations for flow and dispersion in the generic European city centre Michelstadt

Assessment of the predictive capabilities of CFD simulations requires validation against measured data. In atmospheric science wind tunnel data are the best choice for validating statistically steady RANS simulations, due to steady inflow conditions and small uncertainty because of repeatability. In the current work therefore a sensitivity analysis of different physical and numerical settings is performed for RANS simulations of flow and dispersion within the generic European city centre Michelstadt, for which velocity and concentration data from wind tunnel measurements are available in the CEDVAL-LES database. Examined physical parameters comprise the turbulence model, wall roughness and wall functions, source flow rate, Reynolds number and approach flow modelling. As numerical parameter scalar and multidimensional slope limiting are compared. The employed computational domain includes the detailed source geometry and optionally the last four rows of roughness elements of the flow processing unit. These elements have approximately the same height as the buildings, leading to a very high hydrodynamic roughness height of 1.53 m in full scale, which is impossible to take into account with a reasonable mesh on the ground. Nevertheless simulations are also performed by replacing the explicit roughness elements by a rough ground with the maximum possible hydrodynamic roughness length. The domain is meshed with a total of seven grids, three tetrahedral meshes with increasing resolution from 6.7 to 13 to 27 million cells. From these meshes three polyhedral meshes are created within Fluent. In addition one unstructured mesh made entirely of hexahedra is used, with approximately 23 million cells. RANS simulations are performed with the commercial software Fluent, version 12 and 13. For the Reynolds stresses the standard and the realizable k-epsilon model are used. In the simulations of passive dispersion the simple gradient diffusion model with turbulent Schmidt number of 0.7 is employed for the turbulent scalar fluxes.
Learning the Optimal Controller Placement in Mobile Software-Defined Networks

We formulate and study the problem of online learning of the optimal controller selection policy in mobile Software-Defined Networks, where the controller-switch round-trip-time (RTT) delays are unknown and time-varying. Static optimization approaches are not helpful, since delays vary significantly (and sometimes, arbitrarily) from one slot to another, and only RTT delays from the current active controller can be easily measured. First, we model the sequence of RTT delays across time as a stationary random process so that the value at each time slot is a sample from an unknown probability distribution with unknown mean. This approach is applicable in relatively static network settings, where stationarity can be assumed. We cast the problem as a stochastic multiarmed bandit, where the arms are the different controller choices, and we fit different bandit algorithms to that setting, such as: the Lowest Confidence Bound (LCB) algorithm by modifying the known Upper Confidence Bound (UCB) one, the LCB-tuned one, and the Boltzmann exploration one. The first two are known to achieve sublinear regret, while the last one turns out to be very efficient. In a second approach, the random process of RTTs is non-stationary and thus cannot be characterized statistically. This scenario is applicable in cases of arbitrary mobility and other dynamics that affect RTT delays in an unpredictable, adversarial manner. We pose the problem as an adversarial bandit that can be solved with the EXP3 algorithm which achieves sublinear regret. We argue that all approaches can be implemented in an SDN environment with lightweight messaging. We also compare the performance of these algorithms for different problem settings and hyper-parameters that reflect the efficiency of the learning process. Numerical evaluation shows that Boltzmann exploration achieves the best performance.
What is a (Digital) Identity Wallet? A Systematic Literature Review

Identity management is crucial for any electronic service that needs to authenticate its users. Different identity-management models have been introduced and rolled out on a large scale during the past decades. Key distinguishing criteria of these models are the storage location of users' identity data and the degree of involvement of central entities such as identity providers, which can potentially track user behavior. Growing privacy awareness has led to a renaissance of user-centric identity-management models during the past few years. In this context, especially the concept of wallets applied to the digital identity domain has recently attracted attention, putting users into direct control of their identity data. Various approaches and solutions relying on this concept have been introduced recently. However, no generally accepted definitions of the concept “digital identity wallet” and of its related features and implementations exist so far, leading to considerable confusion in this domain. This paper addresses this issue by providing a systematic literature review on wallets applied to the digital identity domain to identify, analyze, and compare existing definitions, features, and capabilities of such solutions. By means of two research questions, this paper thereby contributes to a better understanding of identity wallets and the various recent developments in this domain.
A Novel Cost-Effective Dual-Colored LED Lighting in Household Applications

This article deals with the design and implementation of a light-emitting diode (LED) driver with a novel cost-effective color-changing solution in low-power household lighting applications. This color-changing concept is implemented by resetting the mains power switch, which is best suited for the retrofitted drive. This LED driver is built using a nonisolated Cuk converter that improves power quality (PQ) performances in low-power lighting designs. A comparative study has been made through simulations of four different operating modes to select the best-suited combination for the lowest harmonic distortions and improved power quality performances. To improve the reliability and efficiency of the driver, a film capacitor is used as the coupling element, which has a high level of ripple current handling capability with a significantly lower dissipation factor than the electrolytic capacitor. A 24 W hardware prototype of this converter with the color-changing concept has been developed to validate its performances over a wide range of ac mains operations.