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It is our pleasure to welcome you to this AIS Transactions on Human Computer Interaction special issue on artificial intelligence (AI) fairness, trust, and ethics. This special issue received research papers that unpacked the potential, challenges, impacts, and theoretical implications of AI. This special issue contains four papers that integrate research across diverse fields of study, such as social science, computer science, engineering, design, values, and other diverse topics related to AI fairness, trust, and ethics broadly conceptualized. This issue contains three of the four papers (along with a regular paper of the journal). The fourth or last paper of this special issue is forthcoming in March 2021. We hope that you enjoy these papers and, like us, look forward to similar research published in AIS Transactions on Human Computer Interaction. [ABSTRACT FROM AUTHOR]

Learning Management System (LMS) has been a widely-used learning media so a study is required to know the trend of its development. The present study aimed to analyze the types of documents, languages, contributing countries, top affiliates, sponsorship funding, top productive authors, research citations, subject areas, top source titles, trend mapping visualization, and top-cited 100 publications, and review some publications on LMS research during 1991-2021 using bibliometric analysis. The metadata were obtained by Scopus database and analyzed by VOSViewer within 2.689 documents. The bibliometric analysis results showed that LMS research had conference papers as the most widely published document type and English was the most commonly used language. The country with the most publications was the United States of America. National Natural Science Foundation of China became the top funding sponsor. The top affiliate was Bina Nusantara University. The most productive authors were Sabine Graf. Top cited author achieved by Fred D. Davis, and the top subject areas were Computer Science. Then, Lecture Notes In Computer Science Including Subseries Lecture Notes In Artificial Intelligence and Lecture Notes In Bioinformatics became the title of the top source. Trends of LMS research in 1991-2021 were: 1) related to E-learning; 2) implementation of learning activities and student-teacher cases; 3) technology integration in learning; 4) distance learning; 5) technology education; 6) online learning environment; and 7) interactive learning environment. [ABSTRACT FROM AUTHOR]

Analyzing human behaviors using computer-based approaches is a new dimension of digital humanity. It integrates computing with healthcare, psychology, and social media. Like other healthcare and medical-related problems, anxiety and depressive disorders are common in Pakistan. In the existing era, this disease is not only affecting people of all ages but the impact of depression is also witnessed on social media communications. On social media, anyone could not guess about the mental conditions of a user through his/ her tweets or comments. Especially, the prediction of depressed unknown users through his behavior on social media is a hard task. Computer science and the medical domain can eliminate such healthcare-related issues. In this paper, a system has been proposed to detect and analyze the behavior of a user on social media. The proposed system comprises of an algorithm that performs sentimental analysis of users’ tweets and comments and a web-based platform to share best practices and success stories of users who have recovered from the disease. The system has been tested on real-time data obtained from tweeter using Application Programming Interface (API). Top trends have been focused to obtain the data and the proposed system successfully detected the users under depression. After working on the huge dataset, the proposed system will be a good contribution to humanity. [ABSTRACT FROM AUTHOR]

Internet has been source of knowledge for decades. The pool of information cannot be sustained in absence of the network of networks. Internet has many useful applications in commercial, social and educational areas. In today's scenario, e-learning is also one of the useful applications in the world of Internet. The medium of e-learning has achieved advancement in various fields such as adaptive e-learning systems. The branch of computer science with psycholinguistics has done tremendous job in providing technical solutions to learners. However, learning disorders on the platform of e-learning still require lots of research. Therefore, this paper provides a personalized assessment model for alphabet learning with learning objects for children's who face dyslexia. The cognitive inclination of dyslexic learner has been determined using assessment model. This paper studies the cognitive potential of dyslexic learner and has built a personalized e-learning platform to alleviate their alphabetical problems. [ABSTRACT FROM AUTHOR]

This paper aims at identifying the key role of "Smart Energy and Power Systems Modelling", within the context of Energy Informatics. The main objective is to describe how the specific subject of "Smart Energy and Power Systems Modelling" can give a key contribution within the novel domain of Energy Informatics, by successfully linking and integrating the different disciplines involved. First the paper will present how and where the specific subject of "Smart Energy and Power Systems Modelling" can position itself within the broad Energy Informatics domain. Afterwards the paper will explain how Cyber-Physical Systems (CPS) and Information and Communication Technologies (IoT), coupled with "Smart Energy and Power Systems Modelling", can enhance the Energy Informatics domain. In addition, the main challenges and opportunities for Energy Informatics specialists will be outlined, with regard to the interdisciplinary approach that characterises this field. [ABSTRACT FROM AUTHOR]

To date, all stakeholders are working intensively on policy design for artificial intelligence. All initiatives center around the requirement that AI algorithms should be fair. But what exactly does it mean? And how can algorithmic fairness be translated to legal and policy terms? These are the main questions that this paper aims to explore. Each discipline approaches those questions differently. While computer scientists may favor one notion of fairness over others across the board, this paper argues in favor of a case-by-case analysis and application of the relevant fairness notion. The paper discusses the legal limitations of the computer science (CS) notions of fairness and suggests a typology of matching each CS notions to its corresponding legal mechanism. The paper concludes that fairness is contextual. The fact that each notion, or group of notions, correspond with a different legal mechanism, makes them suitable for a certain policy domain more than others. Thus, throughout the paper, examples for possible applicability of the CS notions to some policy domains will be introduced. In addition, the paper will highlight for both developers and policymakers the practical steps that need to be taken in order to better address algorithmic fairness. In some instances, notions of fairness that seem, on their face, unproductive from a technical perspective, could in fact be quite helpful from a legal perspective. In other instances, desirable notions in the eye of computer scientists could be challenging to implement in the legal regime, due to the need to determine complex moral and legal questions. Thus, as the article emphasizes, a one-size-fits-all solution is not applicable for algorithmic fairness. Rather, an approach that demonstrates a deep understanding of the specific context that a certain algorithm is operating in can guarantee a fairer outcome. [ABSTRACT FROM AUTHOR]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

Situational cognition can help students to construct their knowledge to a great extent. In order to solve the problem of lack of situational cognition in College English teaching, this paper studies the validity of students' situational cognition in College English teaching based on the method of image block gain optimization. First, this paper analyses the general situation of situational cognition capacity in College English teaching in China at present, and puts forward the function of device image in constructing situational cognitive competence in teaching. Then, it divides device image into blocks under pseudo-haze conditions, and proposes the optimization method of block gain. Finally, on the basis of block gain, it makes an empirical test of situational cognitive competence in College English teaching. The empirical results show that image block gain optimization can effectively improve the construction of situational cognition capacity in College English teaching. With the help of this study, some new and useful ideas can be traced for the development of computer science and college English teaching, and also stimulate the further improvement of College English education in China. [ABSTRACT FROM AUTHOR]

As an international scientific journal, Studies in Informatics and Control (SIC) covers the field of Information Technology (IT) and topics related to research areas, as well as important applications in IT, with particular emphasis on Advanced Automatic Control, Modeling and Optimization. SIC has greatly contributed to the areas where it involves since its first online publication way back in 1992. This paper sets out to analyze the structure and the underlying trend of the journal by making use of bibliometric methods. Firstly, the classical indicators are provided to illustrate the performance of the journal. This current study performs an in-depth analysis of the most productive and the most influential authors, institutions, and countries/regions, as well as the most cited research works published in SIC. Secondly, the visualization tools VoS viewer and CiteSpace are used to create scientific maps that may explain the structure of the journal in an intuitionistic way. In the science mapping, the co-citation maps, and the co-authorship networks of various items (such as authors, institutions, and countries/regions) are conducted. Also, the bursts detection of these items are derived. The co-occurrence of keywords and their bursts detection and timeline review are shown, respectively. Finally, some conclusions are given. This paper provides a comprehensive and visual understanding of this well-regarded scientific journal. [ABSTRACT FROM AUTHOR]

International Journal of Computers Communications & Control (IJCCC) is an international journal in the fields of automation control systems and computer science. According to Web of Science (WoS), the first document of IJCCC was published in 2006. In this paper, we study the research trends of publications in IJCCC by performing bibliometric analysis from 2006 to 2019. 982 publications are selected from WoS after data preprocessing by VOS viewer and CiteSpace. Firstly, fundamental information of publications is explored including the type, the annual trend and the most cited publications in IJCCC. Secondly, characteristics of countries/regions, institutions and authors are presented in terms of evaluation indicators. Next, landscape analysis is conducted to show the development of IJCCC at level of countries/regions, institutions, authors and references, such as co-authorship analysis, bibliographic coupling analysis, co-citation and burst detection analysis, cooccurrence and timeline view analysis. Based on which, discussions about current challenges and possible research trends of IJCCC are provided. Finally, some main findings are summarized. This paper offers a valuable reference for scholars to understand the research trends of IJCCC and grasp hot topics related to relative fields. [ABSTRACT FROM AUTHOR]

The text is a foreword for a special issue of the Journal of Information Science & Engineering on Combinatorial Mathematics and Computational Theory. It explains that these fields have a significant impact on computer science, mathematics, and science in general. Combinatorial Mathematics involves the study of discrete objects and has applications in various areas, while Computational Theory focuses on studying algorithms and their efficiency. The text also mentions a symposium that was held to promote academic exchanges and advance research in these fields. The special issue includes four papers on topics such as opinion optimization, local diagnosis, enumerating furthest pairs, and determining the 2-tuple total domination number of a graph. The authors express gratitude to the reviewers and authors for their contributions to the special issue. [Extracted from the article]

Mental workload is considered to be strongly linked to human performance, and the ability to measure it accurately is key for balancing human health and work. In this study, brain signals were elicited by mental arithmetic tasks of varying difficulty to stimulate different levels of mental workload. In addition, a finite impulse response (FIR) filter, independent component analysis (ICA), and multiple artifact rejection algorithms (MARAs) were used to filter event-related potentials (ERPs). Then, the data consisting of ERPs, subjective ratings of mental workload, and task performance, were analyzed through the use of variance and Spearman's correlation during a simulated computer task. We found that participants responded faster and performed better in the easy task condition, followed by the medium and high-difficulty conditions, which verifies the validity of the ERP filtering. Moreover, larger P2 and P3 waveforms were evoked as the task difficulty increased, and a higher task difficulty elicited a more enhanced N300. Correlation analysis revealed a negative relationship between the amplitude of P3 and the subjective ratings, and a positive relationship between the P3 amplitude and accuracy. The results presented in this paper demonstrate that a combination of FIR, ICA, and MARA methods can filter ERPs in the non-invasive real-time measurement of workload. Additionally, frontocentral P2, N3, and parietal P3 components showed differences between genders. The proposed measurement of mental workload can be useful for real-time identification of mental states and can be applied to human-computer interaction in the future. [ABSTRACT FROM AUTHOR]

The primary goal of cloth simulation is to express object behavior in a realistic manner and achieve real-time performance by following the fundamental concept of physic. In general, the mass-spring system is applied to real-time cloth simulation with three types of springs. However, hard spring cloth simulation using the mass-spring system requires a small integration time-step in order to use a large stiffness coefficient. Furthermore, to obtain stable behavior, constraint enforcement is used instead of maintenance of the force of each spring. Constraint force computation involves a large sparse linear solving operation. Due to the large computation, we implement a cloth simulation using adaptive constraint activation and deactivation techniques that involve the mass-spring system and constraint enforcement method to prevent excessive elongation of cloth. At the same time, when the length of the spring is stretched or compressed over a defined threshold, adaptive constraint activation and deactivation method deactivates the spring and generate the implicit constraint. Traditional method that uses a serial process of the Central Processing Unit (CPU) to solve the system in every frame cannot handle the complex structure of cloth model in real-time. Our simulation utilizes the Graphic Processing Unit (GPU) parallel processing with compute shader in OpenGL Shading Language (GLSL) to solve the system effectively. In this paper, we design and implement parallel method for cloth simulation, and experiment on the performance and behavior comparison of the mass-spring system, constraint enforcement, and adaptive constraint activation and deactivation techniques the using GPU-based parallel method. [ABSTRACT FROM AUTHOR]

In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression. [ABSTRACT FROM AUTHOR]

The Journal of Computer Science & Technology's October 2023 issue features 10 papers covering various topics such as human-computer interaction, software engineering, machine and deep learning, open data, gender issues in computer science, and smart cities. The papers were written by 31 authors from 14 different universities in Argentina, Chile, Ecuador, Mexico, and Spain. The issue also includes an overview of a Ph.D. thesis. The editor expresses gratitude to the editorial staff, authors, reviewers, and collaborating universities. [Extracted from the article]

The existence of fundus diseases not only endangers people's vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance.. [ABSTRACT FROM AUTHOR]

Unemployment status is considered one of the divisive economic issues. This paper aims to examine the factors such as age, gender, geographical area, educational level, college major and work experience that affect unemployment status in Lebanon. As well as predicting college majors that reduce unemployment in Lebanon. Using the survey method to collect data. This study found that individuals with higher educational levels and higher work experience are set to have higher chances to be employed. Another finding is that males have more opportunities to get hired. With respect to the geographical area, citizens that live in the capital Beirut, have more chances to enter the labor market. Individuals between the age of 24 and 37 have higher chances to get recruited. By studying the college majors, this study found that individuals that have business management, computer science, art, health, nursing, nutrition and psychology as a college major could guarantee a job opportunity more than individuals with other majors. The findings would help students in choosing a college major that helps them find a job opportunity after graduating. [ABSTRACT FROM AUTHOR]

In all engineering discipline in universities, introductory programming is an essential part of the curriculum. However, many students find great difficulty with the learning of programming without instructor assistance and it becomes a barrier to their further studies of computer science and other disciplines. Intelligent Programming Tutors (IPTs) assist students to overcome difficulties in solving programming exercises, when they don't know how to proceed or they unable to find an error in their current state. Such a student may benefit from hints, guiding her toward a more complete and/or more correct version and allowing her to continue working on her own (Aleven et al., 2016). However, traditional IPTs take much time to create the expert knowledge because in computer programming tasks, the space of possible solutions is too large (Piech et al., 2015b). New development methods have emerged in recent years, which provide hints without an expert model. These techniques are based on data-driven approaches and/or program repair techniques and provide hints in the form of edits (such as inserting or deleting a piece of code). Students can apply these hints in their current state to change it into a more correct and/or more complete state. This paper aims to provide a best understanding of these new techniques and presents a comparative study between different approaches of automatic hint generation by presenting the forces and weaknesses of each approach. [ABSTRACT FROM AUTHOR]

Diagrams continue to play an important role in different areas of computer science. In this paper, we discuss how the cognitive processing of such diagrams can be conceptually understood. To this end, we refer to the CogniDia framework, a recent framework that describes the consecutive phases of cognitive processing. We discuss experimental results for the four categories of the framework. We conclude with an outlook on future research. [ABSTRACT FROM AUTHOR]

With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition. [ABSTRACT FROM AUTHOR]

Copyright of Revista Electrónica Interuniversitaria De Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social choice functions (SCFs) map the preferences of a group of agents over some set of alternatives to a non-empty subset of alternatives. The Gibbard-Satterthwaite theorem has shown that only extremely restrictive SCFs are strategyproof when there are more than two alternatives. For set-valued SCFs, or so-called social choice correspondences, the situation is less clear. There are miscellaneous—mostly negative—results using a variety of strategyproofness notions and additional requirements. The simple and intuitive notion of Kelly-strategyproofness has turned out to be particularly compelling because it is weak enough to still allow for positive results. For example, the Pareto rule is strategyproof even when preferences are weak, and a number of attractive SCFs (such as the top cycle, the uncovered set, and the essential set) are strategyproof for strict preferences. In this paper, we show that, for weak preferences, only indecisive SCFs can satisfy strategyproofness. In particular, (i) every strategyproof rank-based SCF violates Pareto-optimality, (ii) every strategyproof support-based SCF (which generalize Fishburn’s C2 SCFs) that satisfies Pareto-optimality returns at least one most preferred alternative of every voter, and (iii) every strategyproof non-imposing SCF returns the Condorcet loser in at least one profile. We also discuss the consequences of these results for randomized social choice. [ABSTRACT FROM AUTHOR]

Time-series data arises in many real-world applications (e.g., mobile health) and deep neural networks (DNNs) have shown great success in solving them. Despite their success, little is known about their robustness to adversarial attacks. In this paper, we propose a novel adversarial framework referred to as Time-Series Attacks via STATistical Features (TSA-STAT). To address the unique challenges of time-series domain, TSA-STAT employs constraints on statistical features of the time-series data to construct adversarial examples. Optimized polynomial transformations are used to create attacks that are more effective (in terms of successfully fooling DNNs) than those based on additive perturbations. We also provide certified bounds on the norm of the statistical features for constructing adversarial examples. Our experiments on diverse real-world benchmark datasets show the effectiveness of TSA-STAT in fooling DNNs for time-series domain and in improving their robustness. [ABSTRACT FROM AUTHOR]

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert’s payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We also compare the behavioral patterns in this experiment to the equivalent patterns in similar experiments where the communication is based on the numerical values of the reviews rather than the reviews’ text, and observe substantial differences which can be explained through an equilibrium analysis of the game. We consider a number of modeling approaches for our verbal communication setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied. Further analysis of the hand-crafted textual features allows us to make initial observations about the aspects of text that drive decision making in our setup.1 [ABSTRACT FROM AUTHOR]

Predicting the results of matches in sport is a challenging and interesting task. In this paper, we review a selection of studies from 1996 to 2019 that used machine learning for predicting match results in team sport. Considering both invasion sports and striking/fielding sports, we discuss commonly applied machine learning algorithms, as well as common approaches related to data and evaluation. Our study considers accuracies that have been achieved across different sports, and explores whether evidence exists to support the notion that outcomes of some sports may be inherently more difficult to predict. We also uncover common themes of future research directions and propose recommendations for future researchers. Although there remains a lack of benchmark datasets (apart from in soccer), and the differences between sports, datasets and features makes between-study comparisons difficult, as we discuss, it is possible to evaluate accuracy performance in other ways. Artificial Neural Networks were commonly applied in early studies, however, our findings suggest that a range of models should instead be compared. Selecting and engineering an appropriate feature set appears to be more important than having a large number of instances. For feature selection, we see potential for greater inter-disciplinary collaboration between sport performance analysis, a sub-discipline of sport science, and machine learning. [ABSTRACT FROM AUTHOR]

This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction. [ABSTRACT FROM AUTHOR]

The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in this area. Similar to previous works like Fish et al. (WWW ’19) and Tsang et al. (IJCAI ’19), we study the maximin criterion for (group) fairness. In contrast to their work however, we model the problem in such a way that, when choosing the seed sets, probabilistic strategies are possible rather than only deterministic ones. We introduce two different variants of this probabilistic problem, one that entails probabilistic strategies over nodes (node-based problem) and a second one that entails probabilistic strategies over sets of nodes (set-based problem). After analyzing the relation between the two probabilistic problems, we show that, while the original deterministic maximin problem was inapproximable, both probabilistic variants permit approximation algorithms that achieve a constant multiplicative factor of 1 − 1/e minus an additive arbitrarily small error that is due to the simulation of the information spread. For the node-based problem, the approximation is achieved by observing that a polynomial-sized linear program approximates the problem well. For the set-based problem, we show that a multiplicative-weight routine can yield the approximation result. For an experimental study, we provide implementations of multiplicative-weight routines for both the set-based and the node-based problems and compare the achieved fairness values to existing methods. Maybe non-surprisingly, we show that the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This indicates that studying fairness via randomization is a worthwhile path to follow. Interestingly and maybe more surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies computed by our routines, dominate over the fairness achieved by previous methods on many of the instances tested. [ABSTRACT FROM AUTHOR]

In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions, aiming to minimize a given objective function. Many MAPF solvers were introduced in the past decade for optimizing two specific objective functions: sum-of-costs and makespan. Two prominent categories of solvers can be distinguished: search-based solvers and compilation-based solvers. Search-based solvers were developed and tested for the sum-of-costs objective, while the most prominent compilation-based solvers that are built around Boolean satisfiability (SAT) were designed for the makespan objective. Very little is known on the performance and relevance of solvers from the compilation-based approach on the sum-of-costs objective. In this paper, we start to close the gap between these cost functions in the compilation-based approach. Our main contribution is a new SAT-based MAPF solver called MDD-SAT, that is directly aimed to optimally solve the MAPF problem under the sum-of-costs objective function. Using both a lower bound on the sum-of-costs and an upper bound on the makespan, MDD-SAT is able to generate a reasonable number of Boolean variables in our SAT encoding. We then further improve the encoding by borrowing ideas from ICTS, a search-based solver. In addition, we show that concepts applicable in search-based solvers like ICTS and ICBS are applicable in the SAT-based approach as well. Specifically, we integrate independence detection, a generic technique for decomposing an MAPF instance into independent subproblems, into our SAT-based approach, and we design a relaxation of our optimal SAT-based solver that results in a bounded suboptimal SAT-based solver. Experimental evaluation on several domains shows that there are many scenarios where our SAT-based methods outperform state-of-the-art sum-of-costs search-based solvers, such as variants of the ICTS and ICBS algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that comprises four elements: faithfulness (degree of factual consistency with the source), focus (precision of summary content relative to the reference), coverage (recall of summary content relative to the reference), and inter-sentential coherence (document fluency between adjacent sentences). We construct a novel dataset for focus, coverage, and inter-sentential coherence, and develop automatic methods for evaluating each of the four dimensions of FFCI based on cross-comparison of evaluation metrics and model-based evaluation methods, including question answering (QA) approaches, semantic textual similarity (STS), next-sentence prediction (NSP), and scores derived from 19 pre-trained language models. We then apply the developed metrics in evaluating a broad range of summarization models across two datasets, with some surprising findings. [ABSTRACT FROM AUTHOR]

The problem of lifting a preference order on a set of objects to a preference order on a family of subsets of this set is a fundamental problem with a wide variety of applications in AI. The process is often guided by axioms postulating properties the lifted order should have. Well-known impossibility results by Kannai and Peleg and by Barberà and Pattanaik tell us that some desirable axioms – namely dominance and (strict) independence – are not jointly satisfiable for any linear order on the objects if all non-empty sets of objects are to be ordered. On the other hand, if not all non-empty sets of objects are to be ordered, the axioms are jointly satisfiable for all linear orders on the objects for some families of sets. Such families are very important for applications as they allow for the use of lifted orders, for example, in combinatorial voting. In this paper, we determine the computational complexity of recognizing such families. We show that it is Πp 2 -complete to decide for a given family of subsets whether dominance and independence or dominance and strict independence are jointly satisfiable for all linear orders on the objects if the lifted order needs to be total. Furthermore, we show that the problem remains coNP-complete if the lifted order can be incomplete. Additionally, we show that the complexity of these problems can increase exponentially if the family of sets is not given explicitly but via a succinct domain restriction. Finally, we show that it is NP-complete to decide for a family of subsets whether dominance and independence or dominance and strict independence are jointly satisfiable for at least one linear order on the objects. [ABSTRACT FROM AUTHOR]

This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occurring with spoken utterances. Several fields have made important contributions to this approach to modeling or mimicking the process of learning language: Machine Learning, Natural Language and Speech Processing, Computer Vision and Cognitive Science. The current paper brings together these contributions in order to provide a useful introduction and overview for practitioners in all these areas. We discuss the central research questions addressed, the timeline of developments, and the datasets which enabled much of this work. We then summarize the main modeling architectures and offer an exhaustive overview of the evaluation metrics and analysis techniques. [ABSTRACT FROM AUTHOR]

State-of-the-art multilingual machine translation relies on a shared encoder-decoder. In this paper, we propose an alternative approach based on language-specific encoder-decoders, which can be easily extended to new languages by learning their corresponding modules. To establish a common interlingua representation, we simultaneously train N initial languages. Our experiments show that the proposed approach improves over the shared encoder-decoder for the initial languages and when adding new languages, without the need to retrain the remaining modules. All in all, our work closes the gap between shared and language-specific encoder-decoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings. [ABSTRACT FROM AUTHOR]

The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic AT L, hence AT L∗, under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we investigate a notion of bounded recall under incomplete information. We present a novel three-valued semantics for AT L∗ in this setting and analyse the corresponding model checking problem. We show that the three-valued semantics here introduced is an approximation of the classic two-valued semantics, then give a sound, albeit partial, algorithm for model checking two-valued perfect recall via its approximation as three-valued bounded recall. Finally, we extend MCMAS, an open-source model checker for AT L and other agent specifications, to incorporate bounded recall; we illustrate its use and present experimental results. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) methods usually treat reward functions as black boxes. As such, these methods must extensively interact with the environment in order to discover rewards and optimal policies. In most RL applications, however, users have to program the reward function and, hence, there is the opportunity to make the reward function visible – to show the reward function’s code to the RL agent so it can exploit the function’s internal structure to learn optimal policies in a more sample efficient manner. In this paper, we show how to accomplish this idea in two steps. First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure. We then describe different methodologies to exploit this structure to support learning, including automated reward shaping, task decomposition, and counterfactual reasoning with off-policy learning. Experiments on tabular and continuous domains, across different tasks and RL agents, show the benefits of exploiting reward structure with respect to sample efficiency and the quality of resultant policies. Finally, by virtue of being a form of finite state machine, reward machines have the expressive power of a regular language and as such support loops, sequences and conditionals, as well as the expression of temporally extended properties typical of linear temporal logic and non-Markovian reward specification. [ABSTRACT FROM AUTHOR]

Copyright of Educacao e Pesquisa is the property of Faculdade de Educacao da Universidade de Sao Paulo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This systematic literature review on the issues faced by female computer science undergraduates sought to examine the reported reasons for female evasion from computer science major. A full range of indexed journals was surveyed using the ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, Web of Science, and Springer databases. Of the 818 articles retrieved from the digital libraries, only 24 papers were selected for data extraction. The several issues cited as reasons for female evasion from computer science undergraduate courses were divided into six major categories and described. Initiatives that have been implemented to minimize the dropout rate among undergraduate computer science female students were also addressed. Pointing out the main issues faced by female computer science students and identifying the limitations of the initiatives taken to solve them is the first step for future work, proposing good ways around them and outlining specific solutions for the classroom, making education professionals and even classmates aware of these problem. Attention to these issues may pique the researchers' interest, while pursuing a graduate STEM degree, in working to make the experience of female undergraduate students more positive, thus decreasing their chances of evasion. Moreover, based on the results of this research, it is possible to make theory-based academic, managerial and administrative decisions concerning gender issues. [ABSTRACT FROM AUTHOR]

Abstract Photo-identification is a commonly used non-invasive technique that has been profitably employed in biological studies throughout the years. It starts from the assumption that a single individual can be recognized in multiple photos captured at different times by exploiting its unique representative and visible physical qualities such as marks, notches or any other definite feature. Hence, photo-identification is performed to infer knowledge about wild species' spatial and temporal distributions as well as population dynamics, thus providing valuable information especially when the species being investigated is ranked as data deficient. Furthermore, the technological improvements of the last decades and the large availability of devices with powerful computing capabilities are driving the research towards a common goal of enriching bio-ecological studies with innovative computer science approaches. In this scenario, computer vision plays a fundamental role, as it can successfully assist researchers in the analysis of large amounts of data. The aim of this paper is, in fact, to effectively provide a computer vision approach for the photo-identification of the Risso's dolphin, exploiting specific visual cues with a feature-based approach relying on SIFT and SURF feature detectors. The experiments have been conducted on image data acquired in the Gulf of Taranto from 2013 to 2017, conducting a comparative analysis of the performance of both SIFT and SURF, as well as a comparison with the state-of-the-art software DARWIN, and they proved the effectiveness of the proposed approach and suggested its application would be suitable to large scale studies. In conclusion, this paper shows an innovative computer vision application for the identification of unknown Risso's dolphin individuals that relies on a feature-based automated approach. The results suggest that the proposed approach can efficiently assist researchers during the photo-identification task of large amounts of data collected in such a challenging domain. Highlights • Computer vision is strategic for automatic photo-identification of Risso's dolphin. • SIFT based approach is robust and accurate (90%) for Risso's dolphin photo-ID. • This approach can efficiently assist researchers in large scale studies. • Increased knowledge of Risso's dolphin as data deficient species [ABSTRACT FROM AUTHOR]

This paper presents a new descriptor, named Histograms of Prominent Edge Directions (HPED), for the recognition of facial expressions in a person-independent environment. In this paper, we raise the issue of sampling error in generating the code-histogram from spatial regions of the face image, as observed in the existing descriptors. HPED describes facial appearance changes based on the statistical distribution of the top two prominent edge directions (i.e., primary and secondary direction) captured over small spatial regions of the face. Compared to existing descriptors, HPED uses a smaller number of code-bins to describe the spatial regions, which helps avoid sampling error despite having fewer samples while preserving the valuable spatial information. In contrast to the existing Histogram of Oriented Gradients (HOG) that uses the histogram of the primary edge direction (i.e., gradient orientation) only, we additionally consider the histogram of the secondary edge direction, which provides more meaningful shape information related to the local texture. Experiments on popular facial expression datasets demonstrate the superior performance of the proposed HPED against existing descriptors in a person-independent environment. [ABSTRACT FROM AUTHOR]

There is a need to assess the merit and utility of existing cybersecurity curricula. This paper seeks to take a deeper dive into the question of whether a college degree is necessary in the cybersecurity field with an exploration into some examples of US-based undergraduate and graduate cybersecurity degree programs. The authors review the industry standard IT security certifications and governing bodies to compare and contrast the domains and common bodies of knowledge across the various sponsoring organizations. The authors provide their recommendations for building and enhancing undergraduate cybersecurity curricula, in particular new programs should not be siloed within the computer science and information security departments. Faculty should intentionally look to include disciplines outside of the traditional areas of study such as business, psychology, and legal and ethical studies. [ABSTRACT FROM AUTHOR]

The subtitle recognition under multimodal data fusion in this paper aims to recognize text lines from image and audio data. Most existing multimodal fusion methods tend to be associated with pre-fusion as well as post-fusion, which is not reasonable and difficult to interpret. We believe that fusing images and audio before the decision layer, i.e., intermediate fusion, to take advantage of the complementary multimodal data, will benefit text line recognition. To this end, we propose: (i) a novel cyclic autoencoder based on convolutional neural network. The feature dimensions of the two modal data are aligned under the premise of stabilizing the compressed image features, thus the high-dimensional features of different modal data are fused at the shallow level of the model. (ii) A residual attention mechanism that helps us improve the performance of the recognition. Regions of interest in the image are enhanced and regions of disinterest are weakened, thus we can extract the features of the text regions without further increasing the depth of the model (iii) a fully convolutional network for video subtitle recognition. We choose DenseNet-121 as the backbone network for feature extraction, which effectively enabling the recognition of video subtitles in complex backgrounds. The experiments are performed on our custom datasets, and the automatic and manual evaluation results show that our method reaches the state-of-the-art. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) is a pandemic disease that is currently affecting over 200 countries around the world and impacting billions of people. The first step to mitigate and control its spread is to identify and isolate the infected people. But, because of the lack of reverse transcription polymerase chain reaction (RT-CPR) tests, it is important to discover suspected COVID-19 cases as early as possible, such as by scan analysis and chest X-ray by radiologists. However, chest X-ray analysis is relatively time-consuming since it requires more than 15 minutes per case. In this paper, an automated novel detection model of COVID-19 cases is proposed to perform real-time detection of COVID-19 cases. The proposed model consists of three main stages: image segmentation using Harris Hawks optimizer, synthetic image augmentation using an enhanced Wasserstein And Auxiliary Classifier Generative Adversarial Network, and image classification using Conventional Neural Network. Raw chest X-ray images datasets are used to train and test the proposed model. Experiments demonstrate that the proposed model is very efficient in the automatic detection of COVID-19 positive cases. It achieved 99.4% accuracy, 99.15% precision, 99.35% recall, 99.25% F-measure, and 98.5% specificity. [ABSTRACT FROM AUTHOR]

Privacy, identity preserving and integrity have become key problems for telecommunication standards. Significant privacy threats are expected in 5G networks considering the large number of devices that will be deployed. As Internet of Things (IoT) and long-term evolution for machine type (LTE-m) are growing very fast with massive data traffic the risk of privacy attacks will be greatly increase. For all the above issues standards' bodies should ensure users' identity and privacy in order to gain the trust of service providers and industries. Against such threats, 5G specifications require a rigid and robust privacy procedure. Many research studies have addressed user privacy in 5G networks. This paper proposes a method to enhance user identity privacy in 5G systems through a scheme to protect the international mobile subscriber identity (IMSI) using a mutable mobile subscriber identity (MMSI) that changes randomly and avoids the exchange of IMSIs. It maintains authentication and key agreement (AKA) structure compatibility with previous mobile generations and improves user equipment (UE) synchronization with home networks. The proposed algorithm adds no computation overhead to UE or the network except a small amount in the home subscriber server (HSS). The proposed pseudonym mutable uses the XOR function to send the MMSI from the HSS to the UE which is reducing the encryption overhead significantly. The proposed solution was verified by ProVerif. [ABSTRACT FROM AUTHOR]

[Display omitted] • DL algorithms unveil hidden patterns in genomics and biomedicine to empower IHS. • DL optimization enables intricate analysis of molecular-level data in biomedicine. • Open-source DL frameworks democratize access to cutting-edge tools for researchers. • Integration of DL in IHS allows for real-time monitoring and prediction of disease progression for proactive interventions. • DL-powered decision support systems aid clinicians in treatment planning and predicting patient response. The advancements in genomics and biomedical technologies have generated vast amounts of biological and physiological data, which present opportunities for understanding human health. Deep learning (DL) and machine learning (ML) are frontiers and interdisciplinary fields of computer science that consider comprehensive computational models and provide integral roles for disease diagnosis and therapy investigation. DL-based algorithms can discover the intrinsic hierarchies in the training data to show great promise for extracting features and learning patterns from complex datasets and performing various analytical tasks. This review comprehensively discusses the wide-ranging DL approaches for intelligent healthcare systems (IHS) in genomics and biomedicine. This paper explores advanced concepts in deep learning (DL) and discusses the workflow of utilizing role-based algorithms in genomics and biomedicine to integrate intelligent healthcare systems (IHS). The aim is to overcome biomedical obstacles like patient disease classification, core biomedical processes, and empowering patient-disease integration. The paper also highlights how DL approaches are well-suited for addressing critical challenges in these domains, offering promising solutions for improved healthcare outcomes. We also provided a concise concept of DL architectures and model optimization in genomics and bioinformatics at the molecular level to deal with biomedicine classification, genomic sequence analysis, protein structure classification, and prediction. Finally, we discussed DL's current challenges and future perspectives in genomics and biomedicine for future directions. [ABSTRACT FROM AUTHOR]

Meadows and Cliff (2012) failed to replicate the results of Deffuant et al. (2002) and concluded that our paper was wrong. In this note, we show that the conclusions of Meadows and Cliff are due to a wrong computation of indicator y, which was not fully specified in our 2002 paper. In particular, Meadows and Cliff compute indicator y before model convergence whereas this indicator should be computed after model convergence. [ABSTRACT FROM AUTHOR]

Directed Acyclic Graphs (DAGs) are directed graphs in which there is no path from a vertex to itself. DAGs are an omnipresent data structure in computer science and the problem of counting the DAGs of a given number of vertices has been solved in the 70's by Robinson. In many applications one needs to construct connected DAGs and to control their number of edges, but the adaptation of Robinson's enumeration to take this into account led to counting formulas based on the inclusion-exclusion principle, inducing a high computational cost for the uniform random sampling of DAGs based on this formula. In the present paper we propose two contributions. First we enumerate a new class of DAGs, enriched with an independent ordering of the children of each vertex, according to their numbers of vertices and edges. We obtain a constructive recursive counting formula for them (i.e. without using the inclusion-exclusion principle) using a new decomposition scheme. Then we show the applicability of our method by proposing a constructive enumeration of Robinson's labelled DAGs, by vertices and edges, based on the same decomposition. As a consequence we are able to derive efficient uniform random samplers for both models. [ABSTRACT FROM AUTHOR]

Copyright of Iberica is the property of Asociacion Europea de Lenguas para Fines Especificos and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Graph theory has been since long time very helpful either in science or in real life. Since it has been introduced, it was considered like a crossroad in science. Many problems are computer science, in logistics, in physics, in electrical engineering, in automatic science were all inspired by graph theory. Moreover, sone algorithms such as the shortest path are omnipresent within our life. That is why nobody could ignore the importance of graph theory. Nowadays, the graph theory, as is, could not answer correctly to our new needs. In my point, for the current graph theory to answer much better to our new problem-solving requirements, the theory must include two more main pillars: the semantic factor and the superimposition factor. In this paper we introduce the all-new graph theory: the semantic superposed graph theory. [ABSTRACT FROM AUTHOR]

In the paper, the important role of tools and distributed IT resources in the dynamic development of astrophysics, astronomy, and space exploration are discussed. New possibilities and approaches to conducting research in the field of astronomy and astrophysics with the use of cloud computing and advanced data visualization techniques are also presented. Finally, some examples of the use of digital sky survey databases in scientific research are shown. [ABSTRACT FROM AUTHOR]

The treatment of concepts as decision functions characteristic for computer science and Artificial Intelligence has its roots in the logical tradition of Gottlob Frege. In its modern incarnation, decision functions, embedded in concrete knowledge representations, describe either crisp or vague assignments of categorization decisions to objects. In order to allow for effective communication between humans and computer systems, these decisions should be either effectively explained by the system or explainable with respect to the system, which in turn depends on the degree of epistemic transparency of the computer system and the conceptual representations it uses. In this, paper we distinguish between two basic types of representation: logic-based and nature-based. With respect to the latter, we identify five contributing factors to their relative epistemic opacity: A. structural complexity, B. learning procedures, C. operating on uncertain data, D. lack of translation procedures for low-level rules and representations, and E. lack of complete knowledge about natural computing systems. We consider the fourth factor (D.) to be the most important source of opacity on the level of computer systems and the fifth (E.) the main contributing factor on the level of human knowledge. [ABSTRACT FROM AUTHOR]

This paper aims at making a review of the artificial intelligence concept, its global scope from the agro-livestock sector perspective and the understanding, approach and application of this concept Romania in early 2021. Artificial intelligence is a computer science sub-field that is materialized by algorithms developed starting from the logical or mathematical models of the cognition, perception and action processes. Globally, large agricultural companies are trying to grasp concepts such as big data, artificial intelligence (AI), machine learning and analysis. These areas have moved rapidly towards the agro-livestock sector too, but most companies have not been prepared to deal thoroughly with these new technologies. It really sounds interesting, but what does it take to take the next steps? The voice of the expert says: "If we really want to have a global impact on food sustainability, production and safety, we need to think about data standards, data sharing, benchmarking and analysis on aggregated data sets. Today, we see farmers who are reluctant to share data with agritech companies that have developed closed systems, which will hinder the evolution of things" (Claudia Roessler, IT specialist, Microsoft). [ABSTRACT FROM AUTHOR]