Reviewerly/exHarmony huggingface dataset:
An Innovative Approach to Teaching Project Resource Leveling

This paper presents an innovative approach using an integrated example for teaching students effective schedule creation and resource management using Linear Programming.This approach is designed to deepen student understanding of the various factors that should be considered for effective project decision making.Successful schedule control requires an understanding of important project elements to include requirements, schedule, budget, assumptions, constraints, and objectives.These elements can compete against each other and makes resource allocation difficult to address without using optimization techniques.Engineering students in introductory project management courses are typically taught how to identify and sequence activities to develop a project schedule using simple example problems.These examples tend to be composed of only a few activities and resources which support development of an initial schedule and often do not address the impact of scope changes that require reallocation of resources and revisions to the schedule.Successfully adjusting resource allocation to address project scope and activity duration changes requires a solid understanding of project crashing.Project crashing involves shortening project duration by reducing the time of one or more activities.Crashing is done by increasing the resources to the project, and involves cost and schedule trade-offs.The integrated example focuses on minimizing the total cost that results from (a) resource allocation, (b) activity dependencies, (c) timing of deliverables using the available spreadsheets.The method has been introduced in both undergraduate and graduate introductory project management classes.Student feedback shows that the method supports concept mastery by demonstrating how to achieve balanced solutions.
BANKS' CORPORATE SOCIAL RESPONSIBILITY (CSR) DISCLOSURE AND THEIR ROLE IN THE BETTERMENT OF SOCIETY IN THE REPUBLIC OF NORTH MACEDONIA

Corporate Social Responsibility (CSR) is of great importance for companies and other stakeholders, which obliges the companies to establish a way of management that will simultaneously take care of the environmental, social, and ethical aspects of factors. If corporate responsibility is the guiding factor for companies, they can easily gain a competitive advantage over others. This paper researches how the banks in the Republic of North Macedonia report on CSR and in what report format CSR data is published. The research is based on the method of content analysis and statistical analysis to determine whether the size and profit of banks affect the CSR Reporting. The sample included in the research is 14 banks that the National Bank of the Republic of North Macedonia has granted a license to operate. The results of this research show that the size of banks has a positive effect on CSR reporting, while on the other hand, profit does not affect it.
Development of Teaching Materials Growth of Motion Learning Development Based on Android Applications

The purpose of this research is the development of teaching materials for motion learning based on android applications. ADDIE is a research model that researchers choose, namely analysis, design, development, implementation and evaluation. The results of the research as a whole are feasible by material experts and media experts on the components of content, language presentation, and graphics, it is obtained that an average percentage of 81.5% has very high criteria. Based on the overall assessment of the ease of using the product, the difficulty of understanding the material, and the difficulty of doing the questions, the average percentage is 84% in the very feasible category. The development of teaching materials for the development of motion learning development based on android applications is the result of this research. Based on the results of the study, it can be concluded that the product developed is considered very feasible.
Combination of MOORA and WASPAS Methods in Selecting the Best Hafidzoh

The combination of the Multi-Objective Optimization method on the basis of Ratio Analysis (MOORA) and the Weight Aggregated Sum Product Assessment (WASPAS) is used by researchers in selecting the best hafidzoh so as to produce effective and efficient research objectives and make it easier for leaders to determine the best hafidzoh. Choosing the best hafidzoh is not an easy matter due to the large number of students and the large number of criteria given. Furthermore, the best students or candidates for hafidzoh are called alternatives, while the things that are assessed are called criteria. Based on the results of interviews and references that the researchers conducted, the criteria for helping the Chairperson of the Dzunnur'ain Simalungun Institute in determining the best hafidzoh were letter makhorijul, tajwid, itqon hifzd, adabul qiroah and Arabic. Meanwhile, the best alternative or candidate for hafidzoh is the total number of students in Lambaga Dzunnur 'ain, which is 30 students. Furthermore, researchers conducted analysis and calculations based on the formula of the MOORA method and the formula of the WASPAS method. The results of the analysis and calculations are that the Moora and WASPAS methods get the same best santri, namely the alternative name Santri 7, while for the second rank the alternatives obtained are santri 16 and santri 25.
Deploying Vaccine Distribution Sites for Improved Accessibility and Equity to Support Pandemic Response

In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We formulate a combinatorial problem that captures these factors and then develop efficient algorithms with theoretical guarantees on both of these aspects. Furthermore, we study the inherent hardness of the problem, and demonstrate strong impossibility results. Finally, we run computational experiments on real-world data to show the efficacy of our methods.
Subject Identification Across Large Expression Variations Using 3D Facial Landmarks

Landmark localization is an important first step towards geometric based vision research including subject identification. Considering this, we propose to use 3D facial landmarks for the task of subject identification, over a range of expressed emotion. Landmarks are detected, using a Temporal Deformable Shape Model and used to train a Support Vector Machine (SVM), Random Forest (RF), and Long Short-term Memory (LSTM) neural network for subject identification. As we are interested in subject identification with large variations in expression, we conducted experiments on 3 emotion-based databases, namely the BU-4DFE, BP4D, and BP4D+ 3D/4D face databases. We show that our proposed method outperforms current state of the art methods for subject identification on BU-4DFE and BP4D. To the best of our knowledge, this is the first work to investigate subject identification on the BP4D+, resulting in a baseline for the community.
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR

Performance degradation of an Automatic Speech Recognition (ASR) system is commonly observed when the test acoustic condition is different from training. Hence, it is essential to make ASR systems robust against various environmental distortions, such as background noises and reverberations. In a multi-stream paradigm, improving robustness takes account of handling a variety of unseen single-stream conditions and inter-stream dynamics. Previously, a practical two-stage training strategy was proposed within multi-stream end-to-end ASR, where Stage-2 formulates the multi-stream model with features from Stage-1 Universal Feature Extractor (UFE). In this paper, as an extension, we introduce a two-stage augmentation scheme focusing on mismatch scenarios: Stage-1 Augmentation aims to address single-stream input varieties with data augmentation techniques; Stage-2 Time Masking applies temporal masks on UFE features of randomly selected streams to simulate diverse stream combinations. During inference, we also present adaptive Connectionist Temporal Classification (CTC) fusion with the help of hierarchical attention mechanisms. Experiments have been conducted on two datasets, DIRHA and AMI, as a multi-stream scenario. Compared with the previous training strategy, substantial improvements are reported with relative word error rate reductions of 29.7-59.3% across several unseen stream combinations.
Digital Forensic Analysis of Wallapop Application on Android Operating System

The use of second hand shopping applications is increasing day by day. Applications can be used for product search, product sale / purchase, product search / search, specific filtering for the searched product, product selection by category, saving searched products, product listing and messaging with people. There may be situations that may create legal problems between users in shopping applications. Defamation, fraud, fraudulent products, sale of prohibited substances are carried to judicial authorities. Wallapop app is a second hand sales app with more than 10 million downloads on google play store. Application were examined by XRY, Praben, Oxygen, Axiom, Ufed and Autopsy Forensic examination programs and no characteristic information about the application could be obtained. In the study, manual forensic analysis of Wallapop application on mobile devices with Android operating system was performed and an examination method was proposed. In the study, manual forensic analysis of Wallapop application on mobile devices with Android operating system is performed and a methodology about the artifacts produced by Wallapop application, their analysis and the relations with each other is presented. The contents of the database files, location information, session information and other data that may be considered as evidence are shown.
What Are Pluriversal Politics and Ontological Designing?. Interview with Arturo Escobar

What is ontological design, and how do we define Pluriversal politics? In this long interview, Arturo Escobar describes the fundamental shifts in contemporary social theory placing life at the center of his scholarly inquiry. He discusses the reappearance of the debate about ontology through the ontological turn in social theory. His statement involves the idea that every design action has implications for the making of life, the kinds of worlds we construct, and how life is produced. Escobar introduces the concept of ontological design and the connected vision of his Pluriversal politics as the emerging areas of critical design studies. These approaches challenge the extractivist ideology of the Global North and its impactful action, which originated the actual crisis of climate, energy, biodiversity, inequality, poverty, and of social and collective meaning linked to the western capitalistic, patriarchal, colonial, supremacist mode of existence that has been spreading throughout the world through globalization and development.
A Salp Swarm-Based Under-Sampling Approach for Medical Imbalanced Data Classification

Data imbalance refers to the unequal distribution of classes within a dataset that directly affects the accuracy of machine learning classification algorithms.Although many resampling techniques have been proposed by researchers, learning from imbalanced data is still considered one of the contemporary challenges.The class imbalanced problem has been complicated as most of the existing techniques don't manage the similarity relationships between minority and majority classes well.In addition, due to the complex relationships among classes, most of the existing techniques do not focus on retaining valuable samples in the majority class(es) properly.In this article, a salp swarm optimization-based under-sampling technique (SSBUT) is proposed to address data class imbalance problems.Utilizing the proposed SSBUT, the similarity relationship among the samples of the majority class is well analyzed, and the samples that do not affect the accuracy of the classification algorithm are eliminated from the majority class.The performance of the proposed SSBUT has been tested on benchmark medical imbalanced datasets and the obtained results have been compared with state-of-the-art under-sampling techniques.The experimental results show that the proposed SSBUT consistently outperformed the state-of-the-art under-sampling techniques in terms of various evaluation criteria.
Diverse Dialogue Generation by Deep Learning Methods Using Loss Function IncorporatingWord Statistics

In recent years, there has been a lot of research on building dialogue systems using deep learning, which can generate relatively fluent response sentences to user utterances. Nevertheless, they tend to produce responses that are not diverse and which are less context-dependent. Assuming that the problem is caused by the Softmax Cross- Entropy (SCE) loss, which treats all words equally without considering the imbalance in the training data, a loss function Inverse Token Frequency (ITF) loss, which multiplies the SCE loss by a weight based on the inverse of the token frequency, was proposed and confirmed the improvement of dialogue diversity. However, in the diversity of sentences, it is necessary to consider not only the information of independent tokens, but also the frequency of incorporating a sequence of tokens. Using frequencies that incorporate a sequence of tokens to compute weights that dynamically change depending on the context, we can better represent the diversity we seek. Therefore, we propose a loss function, Inverse N-gram Frequency (INF) loss, which is weighted based on the inverse of the n-gram frequency of the tokens instead of the frequency of the tokens. In order to confirm the effectiveness of the proposed method on INF loss, we conducted metric-based and human evaluations of sentences automatically generated by models trained on the Japanese and English Twitter datasets. In the metric-based evaluation, Perplexity, BLEU, DIST-N, ROUGE, and length were used as evaluation indices. In the human evaluation, we assessed the coherence and diversity of the response sentences. In the metric-based evaluation, the proposed INF model achieved higher scores in Perplexity, DIST-N, and ROUGE than the previous methods. In the human evaluation, the INF model also showed superior values.
Exploring the Applicability of Blockchain Technology in the Judicial Deposit of Electronic Evidence

In the era of electronic data, the form of evidence in the judicial evidence chain is also increasingly iterative, and at this stage electronic evidence has become a form of evidence that cannot be ignored. The storage, inspection, authentication and analysis of electronic evidence are all possible to be tampered with. The main feature of Blockchain technology is tamper-proof, which realizes the verification needs of electronic evidence itself at the technical level, ensures data authenticity and completes the tasks of electronic evidence in the judicial appraisal process more efficiently.
Applying the language acquisition model to the solution small language processing tasks

The problem of building a computer model of a small language was under solution. The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages. There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. Computer modeling techniques using neural networks, semi-supervised learning and reinforcement learning were involved. The paper provides a review of the literature on modeling the learning of vocabulary, morphology, and grammar of a child's native language. Based on the current understanding of the language acquisition and existing computer models of this process, the architecture of the system of small language processing, which is taught through modeling of ontogenesis, is proposed. The main components of the system and the principles of their interaction are highlighted. The system is based on a module built on the basis of modern dialogical language models and taught in some rich-resources language (e.g., English). During training, an intermediate layer is used which represents statements in some abstract form, for example, in the symbols of formal semantics. The relationship between the formal recording of utterances and their translation into the target low-resource language is learned by modeling the child's acquisition of vocabulary and grammar of the language. One of components stands for the non-linguistic context in which language learning takes place. This article explores the problem of modeling small languages. A detailed substantiation of the relevance of modeling small languages is given: the social significance of the problem is noted, the benefits for linguistics, ethnography, ethnology and cultural anthropology are shown. The ineffectiveness of approaches applied to large languages in conditions of a lack of resources is noted. A model of language learning by means of ontogenesis simulation is proposed, which is based both on the results obtained in the field of computer modeling and on the data of psycholinguistics.
Design and Research of Intelligent Waste Sorting and Disposal System for High-rise Buildings

To solve the problem of high-floor garbage disposal in the city, and help residential residents to quickly and accurately classify, transport, and process the generated garbage. A high-rise building intelligent garbage classification and treatment system has been designed, which includes a garbage classification device, a trolley transport well, With structures such as garbage temporary storage devices, garbage collection trolleys, and garbage transportation trolleys. The system can also intelligently identify garbage and collect it separately. The finite element transient dynamics analysis of the rack and pinion mechanism of the key structure of the waste sorting system is carried out. The result shows that the structure meets the requirements, and finally shows that the designed high-rise building intelligent waste sorting and processing system is reasonable in design.
Art education in Tatarstan in 1917–2018: continuity and perspectives

The article presents the process of art education of decorative and applied profile formation during a century. There is continuity of specialists training of this direction, finding and solving a problem during the soviet period and modern time.
Exploring Lossy Compressibility through Statistical Correlations of Geophysical Datasets

&amp;lt;p&amp;gt;Lossy compression plays a growing role in geophysical and other computer-based simulations where the cost of storing their output data on large-scale systems can span terabytes and even petabytes in some cases. Using error-bounded lossy compression reduces the amount of storage for each simulation; however, there is no known bound for the upper limit on lossy compressibility for a given dataset. Correlation structures in the data, choice of compressor and error bound are factors allowing larger compression ratios and improved quality metrics. Analyzing these three factors provides one direction towards quantifying limits of lossy compressibility. As a first step, we explore statistical methods to characterize correlation structures present in several climate simulations and their relationships, through functional regression models, to compression ratios. In particular, we show results for climate simulations from the Community Earth System Model (CESM) as well as for hurricanes simulations from&amp;amp;#160;Hurricane-ISABEL from IEEE Visualization 2004 contest,&amp;amp;#160;compression ratios of the widely used lossy compressors for scientific data SZ, ZFP and MGARD exhibit a logarithmic dependence to the global and local correlation ranges when combined with information on the variability of the considered fields through the variance or gradient magnitude. Further works will focus on providing a unified characterization of these relationships across compressors and error bounds. This consists of a first step towards evaluating the theoretical limits of lossy compressibility used to eventually predict compression performance and adapt compressors to correlation structures present in the data.&amp;amp;#160;&amp;lt;/p&amp;gt;
Call for Special Issue Papers: Sustainable Solutions for Internet of Things Using Artificial Intelligence and Blockchain in Future Networks

The world is undergoing a thoughtful revolution with the arrival of the intelligent information era. The central realms accompanying smart living such as transportation, entertainment, healthcare and smart cities are projected to improve service quality assuring a high-end user experience. Future mobile networks are projected to foster the future of ubiquitously connected data-intensive intelligent society powered with complete automation by seamless integrating of all sorts of wireless networks spread over the ground, underwater, air and space. This special issue aims to bring together foremost researchers in academia and engineering from various backgrounds to disseminate to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of modern networks in artificial intelligence, internet of things, and blockchain-based applications.
Implementation of an Intelligent Model to Predict Solar Energy in North Morocco

Air pollution is mainly due to the use of fossil energy in industrial and transport activities [1,2].A solution to this problem is to replace fossil fuels by solar energy.This study is about the prediction of solar energy production, in order to decide when and where the switch between the two sources can be made.In this work, different prediction techniques weretested.They were developed with different Machine Learning models, namely, Decision Tree, Random Forest and Neural Networks.The best proposedalgorithmwas implemented in a Web application that shows prediction results, based on environmental variables values.