Reviewerly/exHarmony huggingface dataset:
Looking for Emotions in Evolutionary Art

Although evolutionary art has demonstrated its potential over the past two decades, based primarily on interactive versions of the evolutionary algorithm (IEAs), new implementations of the algorithm continue to be developed and applied both to produce works of art and to study computer-added creative processes. Recently, human artists have been asked to participate more directly in every step included in the algorithm so that we learn from them. Nevertheless, the behavior shown is so complex that retrieving useful information that allows to understand better the creative process they develop is not an easy task. Moreover, emotions have not been analysed previously in this context. Although useful information has already been obtained and conclusions reached regarding the future improvement of EAs devoted to art, the role of emotions as conveyed by artists and understood by the audience is still unexplored. This paper paves the way towards that specific goal, that we summarize as follows: (i) applying harsh constraints on the creative processes so that the number of formal elements to be analyzed is notably reduced, thus allowing an easier analysis and at the same time to draw useful conclusions when evolutionary algorithms are applied to art and creativity; (ii) being able to analyze both, the human emotions shown by artists and those felt by the audience; together with their connections with formal elements present in the artwork. The main goal is to derive new measures that may allow in the future to introduce changes to the fitness functions that allow the artificial evolution of artwork conveying emotions. Finally, the quality of the artwork produced is formally tested by submitting it to an international art competition.
A Comprehensive Review of Moving Object Identification Using Background Subtraction in Dynamic Scenes

Generally, images are divided into the foreground part and the background part. The foreground detection method can distinguish foreground objects, e.g., Static or dynamic from the background. For background detection, there are many modeling methods that can effectively recognize the background part. These methods have many advantages and are used in many applications such as video analysis, in particular video surveillance, shopping centers, road traffic. With an excellent model that is used to recognize background parts, we can get the result to the basic truth precisely enough. In order to recognize the foreground part in an image, we have different modeling methods. Different challenges for these methods are dynamic background and lighting changes. The GMM, AGMM, and VIBE methods were analyzed and their performance in terms of quality and time metrics calculated.Keywords- background subtraction; adaptive gaussian mixture model (AGMM); gaussian mixture model (GMM); VIBE.
A reflection on the design and user acceptance of Tamil talk

Tamil talk is a speech to text application and was designed from a perspective of language and philosophy. This paper takes an indigenous approach in reflecting on the design and user acceptance of Tamil talk. The paper makes use of literature in critically reflecting on the design and the potential user acceptance of the application. It takes a multi-disciplinary approach and explores the influence of factors like language shift, language maintenance and philosophy in the context of user acceptance of speech to text. The application may appeal to a section of the native Tamil speakers as suggested in the literature but there are complex challenges that needs further research. Further research shall be in developing the application that conforms to the conceptual framework and widely test with the native speakers to arrive at a more precise prediction of user acceptance.
Dynamic Adaptation of Genetic Algorithm for Solving Routing Problems on Large Scale Systems

This paper is devoted to the implementation of the dynamic adaptation procedure for the genetic algorithm used for solving large-scale travelling salesman problem. This procedure serves to obtain more profitable solutions by a fixed operating time. In order to evaluate effectiveness of new approach computational experiments were performed on well-known problem instances from TSPLib library. As a result, generated solutions reduce the length of the routing plans in considered problem instances compare to classical genetic heuristics. By that, we show how to use the property of time inconsistency of heuristics to get better solutions. New criteria for estimating the efficiency of heuristics algorithms called experimental level of time consistency is introduced.
Forged Copy-Move Recognition Using Convolutional Neural Network

Due to the extreme robust image editing techniques, digital images are subject to multiple manipulations and decreased costs for digital camera and smart phones. Therefore, image credibility is becoming questionable, specifically when images have strong value, such as news report and insurance claims in a crime court. Therefore, image forensic methods test the integrity of the images by applying various highly technical methods set out in the literature. The present work deals with one important research module is the recognition of forged part that applied on copy move forgery images. Two datasets MICC-F2000 and CoMoFoD are used, these datasets are usually adopted in the field of interest. The module concerned with recognizing which is the source image portion and which is the target one of that already detected. Thus, the two detected tampered parts of the image are recognized the original one from them, the other is then referred as forged or tampered part. The proposed module used the buster net of three neural networks that basically adopted the principle of training by using Convolution Neural Network (CNN) to extract the most important features in the images. The first and second networks are parallel working to detect and identify areas that have been tampered with, and then display them through two masks. While the last network classifier takes a copy of these two catchers to decide which is the source image portion from the two detected ones. The achieved recognition results were about F-score 98.98% even if the forged area is rotated or scaled or both of them. Also, the recognition results of the forged image part was 98% when using images do not contributed in the training phase, which refers to that the proposed module is more confident and reliable.
Towards an Architecture-based Ensemble Methods for Online Social Network Sensitive Data Privacy Protection

&lt;p&gt;&lt;strong&gt;Abstract - &lt;/strong&gt;In 2014, the world woke up to a giant data breach that leveraged users’ personal information that was taken from one of the world’s biggest social network platform. Based on the literature, this was possible because of the Centralised Architectural-based Approach to protecting the privacy of users’ online data. Although the literature is inundated with decentralized approaches, there is none to the best of our knowledge that uses an ensemble of methods and draws on a consensus mechanism to address the challenges caused by the Centralised Architectural-based Approach. This paper presents a decentralized approach that adopts and adapts an ensemble of methods. These methods include cryptographic, hashing, and the plenum byzantine fault tolerance algorithms that present a consensus platform, protocol, and mechanism to use the technology of blockchain in a novel manner as a significant contribution. This paper adopts the descriptive approach in its presentation as the usable implementation of the presented proposal is near completion with issues of computational overhead addressed based on preliminary results that show promise of being able to support agreement up to ≈ 75% in terms of making changes by participants in the chain. &lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
RESTful Web Service as Data Generator for Reporting of Academic Information System

The academic information system in higher education is a system that has an important role in higher education operations.Interoperability between academic information system is needed to bridge the exchange of data and information in heterogeneous environments, where systems are developed using different programming languages, database management systems, operating systems, and network operating systems.This study proposes a model for reporting academic information using the RESTful web service as a data generator to present data that can be consumed for various applications that can be run on desktop applications, web applications, and mobile applications.Based on the test results, the results show that the methods in the RESTful web service can be consumed properly and show the expected performance.
High Performance Optimization Computing Platform

Structural optimization over the past decades matured from an academic theoretical field, to an important tool in the design procedure in various engineering disciplines.Some commercial software applications provide some suites with optimization solutions, but they are focused mostly in the aeronautics, automotive and aerospace industry.High Performance Optimization Computing Platform (HP-OCP) is a software developed by the ISAAR-NTUA and provides a holistic optimization approach for civil engineering structures.More precisely, HP-OCP is a computational suite that has the ability to integrate with several structural analysis and design software and provide optimization solutions.Structural optimization is mainly divided in three groups, sizing (or parametric), shape and topology optimization.All of them are integrated in HP-OCP and the appropriate algorithms are provided in each category.Considering size and shape optimization, the parametric optimization module is developed, in which the design variables of the mathematical formulation can be the dimension of the section properties, the quality of the material, the coordinates of the nodes etc.In this module plenty of derivativebased and derivative-free algorithms are provided like the Projected Quasi-Newton, Constrained Optimization by Linear Approximation, Latin Hypercube Sampling etc. [1].Considering the topology optimization module [2], the SIMP method is applied and the mathematical algorithms that are implemented are the Optimality Criteria and Method of Moving Asymptotes.HP-OCP was developed in C# programming language, making it a powerful suite that can be integrated with any commercial software that provide Application Programming Interface, batch analysis via XML files or any other type of data exchange format.In the current work the integration of HP-OCP with the SAP2000, ETABS and SCIA Engineering software is presented.Several examples considering parametric and topology optimization problems are examined.Remarkable cost reduction is succeeded in real-world structures, validating in this way the usefulness of HP-OCP not only in the research field but also in applied civil engineering problems.
Modelling an Intelligent Schemes for Black Money Versus Poverty Using Fuzzy Cognitive Map

The economy of the world, its association with poverty and black money has been a long untold story that used to have a major contribution in shaping our society but only a little scientific analysis was available in past centuries. In the present decade, the focus has again been diverted to these topics and an in-depth study has been made on various issues that can’t be described exactly by the mathematical models developed so far. The major tool in shaping this science is Fuzzy Cognitive maps. It has been applied to wide areas including decision-making, managing, and study of the different complicated processes where measurements are not available or imprecise. This tool can be used to simulate a very complicated process that is experimentally infeasible. The present study revolves around the fact that using FCM along with the intuition behind the process, highly accurate models can be developed that exactly predicts the future with a very minimal degree of uncertainty. Various concepts related to the economy has been used to develop this model and results are astounding, based on the results solution to the present-day problems has also been proposed.
Práticas de autocuidado em saúde mental em contexto de pandemia

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Analysis of Acceptance And Use of Learning Management System Using The UTAUT Model (Case study: STMIK Logika): Analysis of Acceptance And Use of Learning Management System Using The UTAUT Model (Case study: STMIK Logika)

Web technology is the result of a set or organized activities and procedures which are the development of renewable information system technology where the development of the application uses the Android system model. The development of the website system and its application creates an ease in providing information, especially in the material or teaching material at which the Covid-19 outbreak is currently happening. This learning system indirectly provides information to the public and students, especially in the form of knowledge and information related to knowledge in a particular field of science. STMIK Logika applies the learning system as a medium for online learning in addition to face to face directly. Learning system developed with the aim of improving the quality and effectiveness of learning that offers several facilities such as online assignments, online tests, lecture materials and so on. To find out the behavior in the use of e-learning, the UTAUT (Unified Theory of Acceptance and Use of Technology) model is used. The results of the study are in the form of Performance Expansion (PE), Business Expansion (EE), Social Influence (SI) and Facilitating (FC) variables that have a significant effect on User Acceptance Interest (BI). Overall, all of these predictor variables are able to explain the effect on user acceptance interest. the UTAUT (Unified Theory Of Acceptance And Use Of Technology) model is used. The results of the study are in the form of Performance Expansion (PE), Business Expansion (EE), Social Influence (SI) and Facilitating (FC) variables that have a significant effect on User Acceptance Interest (BI). Overall, all of these predictor variables are able to explain the effect on user acceptance interest. the UTAUT (Unified Theory Of Acceptance And Use Of Technology) model is used. The results of the study are in the form of Performance Expansion (PE), Business Expansion (EE), Social Influence (SI) and Facilitating (FC) variables that have a significant effect on User Acceptance Interest (BI). Overall, all of these predictor variables are able to explain the effect on user acceptance interest.
Analysis of Reading Difficulties for Class III in Elementary Schools

This study aims to determine students who have difficulty reading whether it is difficulty distinguishing letters or difficulty spelling a word in grade III of Elementary School at SD Negeri Plawad VI, East Karawang. This research is a type of qualitative research. The population used in this study was 2 of the 21 total students in class III SD Negeri Plawad VI, East Karawang. This data collection technique through interviews and also the A DANA media demonstration method. Interviews were conducted with 4 respondents represented by the Principal, Teachers, Students and also Parents of Students. The data reduction analysis technique in this study is to select the main things that are in accordance with the focus of the research and the researcher uses to classify, direct and discard the data obtained by the researcher in the form of data from interviews, observations, and documentation from informants. So that it can provide a sharper picture of the results of the observations. The results of this study seem to be very helpful for students who have difficulty reading. After learning to read using the A DANA media according to the schedule applied, these students experienced changes such as easier spelling and smoother reading which was different from before.
Research on Model and Algorithm of User Access Pattern Data Mining

The era of big data has come. Today, all kinds of information and data are showing explosive growth. Internet activities of different scales are struggling to catch up with the pace and pace of the development of "big data. This paper uses time series mining algorithms, and uses Microsoft's data mining tools to model the data sets collected from data halls, so as to discover the user's online behaviour patterns and potential online rules within a certain period of time. we made reasonable suggestions for the scientific management of the campus network. A new clustering method based on the improved Kohonen self-organizing feature mapping neural network is proposed. A Gaussian-shaped membership function has introduced to output several neurons with a degree of membership greater than the threshold, thereby solving the problem of mining users' multiple interests.
Replacing Probability Distributions in Security Games via Hellinger Distance

Security of cryptographic primitives is usually proved by assuming probability distributions. We need to replace them with approximated real distributions in the real-world systems without losing the security level. We demonstrate that the Hellinger distance is useful for this problem, while the statistical distance is mainly used in the cryptographic literature. First, we show that for preserving λ-bit security of a given security game, the closeness of 2^{-λ/2} to the ideal distribution is sufficient for the Hellinger distance, whereas 2^{-λ} is generally required for the statistical distance. The result can be applied to both search and decision primitives through the bit security framework of Micciancio and Walter (Eurocrypt 2018). We also show that the Hellinger distance gives a tighter evaluation of closeness than the max-log distance when the distance is small. Finally, we show that the leftover hash lemma can be strengthened to the Hellinger distance. Namely, a universal family of hash functions gives a strong randomness extractor with optimal entropy loss for the Hellinger distance. Based on the results, a λ-bit entropy loss in randomness extractors is sufficient for preserving λ-bit security. The current understanding based on the statistical distance is that a 2λ-bit entropy loss is necessary.
Authorship Candidates for Arden of Faversham: Kyd, Shakespeare, and Thomas Watson

In the first half of the twentieth century, five scholars working in four different languages ascribed the anonymous 1592 tragedy Arden of Faversham to Thomas Kyd. Since 1963, however, attribution studies of Arden have been dominated by the influence of MacDonald P. Jackson, who has repeatedly rejected Kyd while attributing sections of the play to William Shakespeare. Thanks to Jackson, the play was included in The New Oxford Shakespeare, and a new search for potential coauthors has nominated a wide range of candidates. The New Oxford Shakespeare's general editor, Gary Taylor, has published two essays claiming Thomas Watson as coauthor of Arden, one based on stylometric evidence and another arguing from literary and theatrical history. In this essay, I evaluate the methods Taylor has used to reach this conclusion and reconsider the merits of the evidence he has deployed, arguing against Watson's authorship of Arden and offering a renewed case for viewing Kyd as the play's author.
Sentiment Analysis for Afaan Oromoo Using Combined Convolutional Neural Network and Bidirectional Long Short-Term Memory

Sentiment analysis has become the most popular research topic due to its various application in business, politics, entertainment, however analyzing opinion of people from short text such as Twitter message and single sentence is quite a challenging task due to their informality, misspell and semantic error. In this study, we propose character level multiscale sentiment analysis for Afaan Oromoo using combined Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) approach. Since there is no standardized and sufficient corpus prepared for Afaan Oromoo Natural Language Processing (NLP) task including sentiment analysis so far, we have collected data from two domain, Facebook and Twitter for the experiment. After collecting data, we removed user names, links, none Afaan Oromoo texts, and any unnecessary characters. The cleaned data were annotated manually by 4 different annotators into five class namely, 2 ,1, -2, -1, and 0 which represent very positive, positive, very negative, negative and neutral respectively. This multi-scale sentiment analysis provides a more refined analysis, which is vital for prioritizing and comparison of different opinion. Afterward we performed experiments on the prepared corpus from Facebook and Twitter by applying Convolutional Neural Network, Bidirectional Long Short-Term Memory and combined Convolutional Neural Network-Bidirectional Long Short-Term Memory with character level word embedding. The experimental result validate that the proposed model outperforms both CNN and Bi-LSTM in both Facebook and Twitter dataset. Based on the implemented Facebook dataset we achieved a promising performance accuracy of 93.3%, 91.4%, and 94.1% for CNN, Bi-LSTM and CNN-Bi-LSTM respectively. Consequently, we executed twitter dataset and achieved 92.6%, 90.3%, 93.8% for CNN, Bi-LSTM and CNN-Bi-LSTM respectively. The result suggests the possibility of multi-scale sentiment analysis as well as CNN-Bi-LSTM on Afaan Oromoo. We have also suggested that the accuracy can be improved by building standardized and sufficient amount of data set, which was one of the most difficult and demanding tasks of our work.
EVA 2.0: Emotional and rational multimodal argumentation between virtual agents

Abstract Persuasive argumentation depends on multiple aspects, which include not only the content of the individual arguments, but also the way they are presented. The presentation of arguments is crucial – in particular in the context of dialogical argumentation. However, the effects of different discussion styles on the listener are hard to isolate in human dialogues. In order to demonstrate and investigate various styles of argumentation, we propose a multi-agent system in which different aspects of persuasion can be modelled and investigated separately. Our system utilizes argument structures extracted from text-based reviews for which a minimal bias of the user can be assumed. The persuasive dialogue is modelled as a dialogue game for argumentation that was motivated by the objective to enable both natural and flexible interactions between the agents. In order to support a comparison of factual against affective persuasion approaches, we implemented two fundamentally different strategies for both agents: The logical policy utilizes deep Reinforcement Learning in a multi-agent setup to optimize the strategy with respect to the game formalism and the available argument. In contrast, the emotional policy selects the next move in compliance with an agent emotion that is adapted to user feedback to persuade on an emotional level. The resulting interaction is presented to the user via virtual avatars and can be rated through an intuitive interface.