Reviewerly/exHarmony huggingface dataset:
Comparative analysis of super-resolution reconstructed images for micro-expression recognition

Abstract It is an established fact that the genuineness of facial micro-expression is an effective means for estimating concealed emotions (Li et al. in Micro-expression recognition under low-resolution cases. SciTePress, Science and Technology Publications, Setúbal, 2019). Conventionally, analysis of these expressions has been performed using high resolution images which are ideal cases. However, in a real-world scenario, capturing expressions with high resolution images may not always be possible particularly using low-cost surveillance cameras. Faces captured using such cameras are often very tiny and of poor resolution. Due to the loss of discriminative features these images may not be of much use particularly for identifying certain minute facial details. To make these images useful, enhancing the textural information becomes essential and super-resolution algorithms can be ideal to achieve this. In this work, we utilize algorithms based on deep learning and generative adversarial network for transforming low-resolution micro-expression images into super-resolution images and examine their fitness particularly for micro-expression recognition. The proposed approach is tested on simulated dataset obtained from two popular spontaneous micro-expression datasets namely CASME II and SMIC-VIS; the experimental results demonstrate that the method achieved favourable results with the best recognition performance recorded as 61.63%. The significance of this work is: first, it thoroughly investigates reconstruction performance of several deep learning super-resolution algorithms on simulated low-quality micro-expression images; second, it provides a comprehensive analysis of the results obtained employing these reconstructed images to determine their contribution in addressing image quality issues specifically for micro-expression recognition.
Dietary Application through Image Processing for Calorie Management

“Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is a number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well. Support Vectors are simply the coordinates of individual observation. The SVM classifier is a frontier that best segregates the two classes (hyper-plane/ line).
PEDAGOGICAL SUPPORT OF THE SPIRITUAL AND MORAL FORMATION OF THE FUTURE TEACHER IN THE COURSE OF MASTERING THE SUBJECT «PEDAGOGICS»

Introduction. The article considers a significant problem of the formation of spir­itual values of students of pedagogical universities as a peculiar marker of their spir­itual and moral education.&#x0D; The purpose of the article is to study the methods of pedagogical support of the spiritual and moral formation of the future teacher.&#x0D; Methods. M. Rokich’s test «Value Orientations», the author’s survey methodology-essay «Your Opinion» revealing students’ attitudes to the effectiveness of the forms used in the organization of their educational and cognitive activities.&#x0D; Results. The forms and techniques of the curricular activities (creation and presentation of the «personal pedagogical coat of arms», project-presentation «My favourite teacher», «listen – draw») and extra-curricular activities (diary of the student’s independent work, business and role-playing games, «Pedagogical Club», «Pedagogical Workshop») have been identified as part of the development of the discipline «Pedagogics». Their identification is based on the theoretical analysis and empirical research. The positive effect of the given forms and techniques on the spiritual and moral formation of future teachers is also proved.&#x0D; Conclusion. The proposed and tested methods of pedagogical support of the spiritual and moral formation of the future teacher are an effective resource for the develop­ment of their value-semantic sphere.&#x0D; Practical implications. The results of the study can be used in the professional edu­cation of bachelors - future teachers in the teaching of pedagogical discipline.
Optimization-based method removing highlights with flash/no-flash image

This paper proposes an optimization-based method to remove highlights from specular surfaces without any special device other than a flash, or any limited capturing environment required by existing methods. Accordingly, we formulate an optimization problem to estimate a highlight-free image from the difference image between a flash/no-flash image pair. This optimization will automatically find and recover erroneous pixels (saturated pixels and remaining highlights) in the difference image. The optimization is designed to recover erroneous pixels using the intensities of their neighboring pixels while retaining the other correct pixels to preserve the original image details. We evaluated the proposed method qualitatively and quantitatively through experimentation using datasets of synthetic and real-world scenes. In the experiments, the proposed method outperforms existing state-of-the-art methods.
Cloud services for digital repositories (Library Technology Reports)

"Cloud services for digital repositories (Library Technology Reports)." Journal of the Australian Library and Information Association, 71(2), p. 171
The application of particle swarm optimization algorithm in multi-robot task allocation problem

In contemporary society, with the increasing difficulty of tasks required by robots, multi-robot system has gradually become a research hotspot. As multi-robot task allocation is an important part of multi-robot system cooperation, it has also become a hot topic of research. In this paper, particle swarm optimization is used as the basic optimization tool to study the optimization solution of multi-robot task allocation problem. The multi-robot task allocation problem solved by the improved particle swarm optimization, the three main control parameters of the particle swarm optimization are updated by the linear parameter updating rule. Finally, the MATLAB simulation verification is given. The simulation results show that the algorithm has good adaptability in solving the multi-robot task allocation problem.
Learning Via Compact Data Representation

We present an unsupervised learning methodology derived from compact data encoding and demonstrate how to construct models of polysemy, priming, semantic disambiguation and learning using this theoretical basis. The model is capable of simulating human-like performance on artificial grammar learning.
empirical study to understand the effect of the awareness of Agile and Lean Software Development Life Cycle on factors to reduce the time taken for product development

Every firm today strives to deliver its products on time. Delay in delivery not only affects the organizations goodwill but also can affect its client base. During our extensive literature survey, we found that the small firms frequently are not able to deliver their software on time. To understand this better, we performed a questionnaire survey, which revealed three major factors contributing to the delay. Over engineering, impediment to innovation and lack of essential documentation; were the four factors identified. These four factors were subjected to cross tabulation. As agile and lean development practices are implemented, the time spent on features which are not necessary is reduced, and documentation is also brought down to bare minimum. However, working on new ideas is not encouraged and is not taken in an open-minded setup. This work can be extended to determine the impact of these factors on different phases of software development.
TopSort: A High-Performance Two-Phase Sorting Accelerator Optimized on HBM-based FPGAs

The emergence of high-bandwidth memory (HBM) brings new opportunities to boost the performance of sorting acceleration on FPGAs, which was conventionally bounded by the available off-chip memory bandwidth. However, it is nontrivial for designers to fully utilize this immense bandwidth. First, the existing sorter designs cannot be directly scaled at the increasing rate of available off-chip bandwidth, as the required on-chip resource usage grows at a much faster rate and would bound the sorting performance in turn. Second, designers need an in-depth understanding of HBM’s characteristics to effectively utilize the HBM bandwidth. To tackle these challenges, we present TopSort, a novel two-phase sorting solution optimized for HBMbased FPGAs. TopSort can sort up to 4 GB data using all 32 HBM channels, with an overall sorting performance of 15.6 GB/s. TopSort is 6.7× and 2.2× faster than state-of-the-art CPU and FPGA sorters.
O papel do farmacêutico frente à pandemia de Covid-19 nas farmácias comunitárias: revisão integrativa / The role of the pharmacist in the Covid-19 pandemic in community pharmacies: integrative review

Objective: To analyze the scientific production on the role of the pharmacist in the Covid-19 pandemic in community pharmacies, through an integrative literature review.Method: The search for articles was performed in the following databases: PubMed, Virtual Health Library (VHL), Scientific Electronic Library Online (SciELO) and Google Scholar.The inclusion criteria established were complete publications in English and Portuguese; Articles made available electronically in full in the last two years and as exclusion criteria were excluded: articles not available in full and indexed in duplicate and that do not meet the objective of the
Open Access: The Use of Military Profiles in Romance Fraud Schemes

Romance fraud occurs when offenders use the guise of a genuine relationship to defraud a victim, typically via online communications. Research has focused on the general structure of romance frauds, but few scholars have considered the potential link between victimization and specific narratives employed by offenders. This article explores the reported romance fraud victimizations of a group of Australians and explores similarities and differences in how a military narrative is involved compared to other genres. Limited differences were observed between those frauds using military narratives, highlighting the need for broader consideration of fraud prevention and disruption measures.
Acute Myocardial Infarction: Comparison of morbidity in adults 40 years and older among an underdeveloped country/ Infarto Agudo do Miocárdio: Comparação da morbilidade em adultos com 40 anos ou mais de idade entre um país subdesenvolvido

The interruption of cardiac blood flow by a clot, known as acute myocardial infarction (AMI), results in the death of cells in a specific region of the myocardium and is considered the most serious pathology of coronary syndromes. Besides, the main cause of AMI is atherosclerosis, leading to clot formation and interruption of blood flow. Thereby, the high mortality rate is explained by the increased prevalence of exposure to risk factors. Comparing 2021 with 2020, it is possible to see an increase of approximately 2.16% of cases in adults in the state of Rio Grande do Sul in Brazil.
Supplemental Material: A High-Resolution Multispectral Macro-Imager for Geology and Paleontology

&lt;p&gt;This supplement is intended to show our multispectral setup in more detail and explain how we mitigate chromatic aberration. We include a figure with annotated computer-aided design renderings of our transmitted and reflected light setups, and details for our light emission spectra. The text begins with background on the problem of chromatic aberration, details our experimental setup and blur modeling calculations, and discusses our final results. &lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;&lt;br&gt;&lt;/p&gt;
Managing organization information security systems, conflicts, and integrity for sustainable Africa transformation

The ubiquitous reliance on technological innovations by enterprise organizations for electronic file-sharing networks across all business transactions has further exposed organization system enterprises to security threats and risks. Globally, a positive relationship exists between employees’ adherence to security policy enforcement, enterprise definitions, and effective management of organization security systems. Security measures required to handle threats to the organizations’ data: confidentiality, integrity, and availability are becoming complex, dynamic, psychological, but largely undeveloped, outdated, and non-sustainable in Africa despite the huge cyber-security innovations. This study highlights the gaps created by poor employees’ adherence to security policies and enforcement in managing organization information security systems, conflicts, and integrity, and strategies to close them. The authors explored a narrative review of prior research that revealed significant information on strategies for managing organization information security systems. Peer-reviewed articles within the last five years were extracted from electronic databases, using relevant search keywords Results show that organizational security issues can be prevented or mitigated through effective adherence to security policies, control over policy enforcement, and enterprise definitions. Findings from this study may extend proper security management practices and prevention strategies for Africa’s transformation.
Perancangan Platform MOOC Di Kota Jambi Dengan Fitur Video Live Streaming

In the current covid 19 pandemic, where a lot of business businesses are experiencing difficulties, including course institutions and tutoring. Many tutoring teachers have been sent home. Online Tutoring platforms such as Ruangguru are very popular with the public. The purpose of this research is to create a MOOC Massively Open Online Course platform which is an important innovation in the world of education. By using the live streaming feature, it allows students to learn directly with tutoring teachers directly. Massively Open Online Course/ MOOC provides a way for more independent education. This MOOC is very good if applied in Jambi city which has a limited number of teachers.
Prediction of Software Bugs Using FCM Fused Artificial Neural Network Classifier

The software engineering technique makes it possible to create high-quality software. One of the most significant qualities of good software is that it is bug-free. Bug detection and prevention aims to provide high-quality software that reduces the cost and time required to fix a defect, increases productivity, and allows for complete customer satisfaction. While it will be impossible to eradicate all flaws, it will be able to reduce the number of problems and their negative influence on software quality. This may be accomplished by putting in place a bug prediction mechanism that focuses on increasing software quality by lowering defect density. To extract usable information, soft computing approaches such as data mining and machine learning algorithms can be used. Soft computing approaches have been used to solve the problem of software bug prediction in a number of researches. The ability to predict necessary bugs considerably sooner in the development process is a major incentive for this study. The purpose is to stimulate people's interest in software quality testing and encourage them to employ fuzzy c means clustering (FCM), artificial neural network (ANN), and machine learning approaches to solve challenges in the field of software project success prediction. The FCM fused ANN used for bug prediction model in this study to predict software reliability. With an accuracy of 98.237percent, this research effort exhibits the usefulness and applicability of FCM fused ANN technology.
Digital Skills and Professional Development in Teachers at UGEL 12, Canta - Lima

The objective of this article is to determine the relationship between digital skills and professional development in teachers at UGEL 12, Canta. The study was of type, of correlational-causal design, it is non-experimental and cross-sectional since the phenomena were observed in their natural state and the data collection was carried out in a single moment. The survey technique was used to measure the variables, likewise a questionnaire was used as an instrument for each variable, for the quantitative variable digital skills a 32-item questionnaire was used considering three dimensions; In relation to the professional development variable, a 33-item questionnaire was used, which includes three dimensions: time organization (12 items), planning (9 items) and the virtual resources dimension (15 items). The population was 450 teachers, the inclusion criteria were the teachers who worked throughout the year 2021 in the institutions of UGEL 12, the study sample consisted of 205 teachers; According to the results obtained, a correlation coefficient of rho =.695** was obtained, demonstrating that the relationship between digital skills and professional development of teachers is positive, so it is concluded that there is a significant relationship between digital skills and professional development in teachers at UGEL 12, Canta – Lima.
Aplikasi Kamus Bahasa Indonesia - Rejang Menggunakan Metode Sequential Searching Berbasis Android

One of the technologies that is currently developed very rapidly is Smartphones, where Android is a necessity for people. Preserving regional languages is very necessary in the midst of increasingly rapid technological developments. This study helps people who do not understand the Rejang language. The Rejang language dictionary application is designed to be able to perform an electronic search function for the Rejang language dictionary. To support the system development method, the reseacher uses the waterfall method. As well as data collection techniques with research methods by means of observation, interviews and literature study. The implementation of the program used by the computer in this application system uses the Java programming language on Android studio version 1.0 with SQLite Database which provides facilities to simplify the process of system creation and product implementation.
Pelatihan dan Pendampingan Penggunaan Aplikasi Sistem Upload Penerimaan dan Pengeluaran Dokumen (Suppermen) Berbasis Web di PT PUSRI Palembang

PT PUSRI Palembang is one of the State-Owned Enterprises (BUMN) companies that manage fertilizers in Indonesia. One of the activities in the financial division at PT PUSRI is collecting data on invoices and MPJP. Initially, this activity was still carried out manually which made it difficult for many vendors from outside the city
Polygonal approximation for digital image boundary using firefly algorithm

Polygonal approximation (PA) is a challenging problem and has its wide applications in computer vision, electronic imaging, image classification, image visualization, pattern recognition and image analysis. This paper proposes a stochastic technique-based firefly algorithm (FA) for PA. FA customizes a kind of randomization by searching a set of solutions where PA requires more combinations of approximation to find an optimal solution. The significant feature of the FA is real random sample generation and attractive features. The attractiveness and brightness of the firefly have been used efficiently to solve the approximation problem by detecting the dominant points (DPs) with fewer iterations to produce better results. Thus, this technique achieves the main goal of PA that is a minimum error value with a lesser number of DPs. While compared with other similar algorithms, FA is independent of velocities which are considered as an advantage for this algorithm. Subsequently, the multiswarm nature of FA allows finding multiple optimal solutions concurrently. The experimental results show that the proposed algorithm generates better solutions compared with other algorithms.
Revision by Comparison for Ranking Functions

Revision by Comparison (RbC) is a non-prioritized belief revision mechanism on epistemic states that specifies constraints on the plausibility of an input sentence via a designated reference sentence, allowing for kind of relative belief revision. In this paper, we make the strategy underlying RbC more explicit and transfer the mechanism together with its intuitive strengths to a semi-quantitative framework based on ordinal conditional functions where a more elegant implementation of RbC is possible. We furthermore show that RbC can be realized as an iterated revision by so-called weak conditionals. Finally, we point out relations of RbC to credibility-limited belief revision, illustrating the versatility of RbC for advanced belief revision operations.
Noun2Verb: Probabilistic Frame Semantics for Word Class Conversion

Abstract Humans can flexibly extend word usages across different grammatical classes, a phenomenon known as word class conversion. Noun-to-verb conversion, or denominal verb (e.g., to Google a cheap flight), is one of the most prevalent forms of word class conversion. However, existing natural language processing systems are impoverished in interpreting and generating novel denominal verb usages. Previous work has suggested that novel denominal verb usages are comprehensible if the listener can compute the intended meaning based on shared knowledge with the speaker. Here we explore a computational formalism for this proposal couched in frame semantics. We present a formal framework, Noun2Verb, that simulates the production and comprehension of novel denominal verb usages by modeling shared knowledge of speaker and listener in semantic frames. We evaluate an incremental set of probabilistic models that learn to interpret and generate novel denominal verb usages via paraphrasing. We show that a model where the speaker and listener cooperatively learn the joint distribution over semantic frame elements better explains the empirical denominal verb usages than state-of-the-art language models, evaluated against data from (1) contemporary English in both adult and child speech, (2) contemporary Mandarin Chinese, and (3) the historical development of English. Our work grounds word class conversion in probabilistic frame semantics and bridges the gap between natural language processing systems and humans in lexical creativity.
Privacy-Preserving Techniques in Social Distancing Applications: A Comprehensive Survey

During the world’s challenge to confront the rapidly spreading coronavirus disease (COVID-19) pandemic and the consequent heavy losses and disruption to society, returning to normal life has become a demand. Social distancing, also known as physical distancing, plays a pivotal role in this scenario. Social distancing is a practice to maintain a safe space between a person and others who are not from the same household, preventing the spread of contagious viral diseases. To support this case, several public authorities and governments around the world have proposed social distancing applications (also known as contact-tracing apps). However, the adoption of these applications is arguable because of concerns regarding privacy and user data protection. In this study, we present a comprehensive survey of privacy-preserving techniques for social distancing applications. We provide an extensive background on social distancing applications, including measuring the physical distance between people. We also discuss various privacy-preserving techniques that are used by social distancing applications; specifically, we thoroughly analyze and compare these applications, considering multiple features. Finally, we provide insights and recommendations for designing social distancing applications while reducing the burden of privacy problems.