Reviewerly/exHarmony huggingface dataset:
Fine-grained Multimodal Sentiment Analysis Based on Gating and Attention Mechanism

&lt;p&gt;In recent years, more and more people express their feelings through both images and texts, boosting the growth of multimodal data. Multimodal data contains richer semantics and is more conducive to judging the real emotions of people. To fully learn the features of every single modality and integrate modal information, this paper proposes a fine-grained multimodal sentiment analysis method FCLAG based on gating and attention mechanism. First, the method is carried out from the character level and the word level in the text aspect. CNN is used to extract more fine-grained emotional information from characters, and the attention mechanism is used to improve the expressiveness of the keywords. In terms of images, a gating mechanism is added to control the flow of image information between networks. The images and text vectors represent the original data collectively. Then the bidirectional LSTM is used to complete further learning, which enhances the information interaction capability between the modalities. Finally, put the multimodal feature expression into the classifier. This method is verified on a self-built image and text dataset. The experimental results show that compared with other sentiment classification models, this method has greater improvement in accuracy and F1 score and it can effectively improve the performance of multimodal sentiment analysis.&lt;/p&gt;
Human Facial Disease Diagnosis System Using Dempster Shafer Method

Skin is a very important and sensitive part of the human body. It is directly related to the outside world. In everyday life, the skin plays an important role. One of its functions is to remove waste substances. The lack of knowledge and limited sources of information causes low public awareness of skin health as well as not seeing a doctor. Regular skin checks can overcome the beginning of skin diseases that might trigger other more dangerous diseases. The system in this study is designed to detect and to know the symptoms of facial skin diseases that are felt. This knowledge-based system design was developed to diagnose human facial skin disease using the dempster shafer method. The results can help detecting earlier facial skin diseases and overcoming them.&#x0D; Key words : diagnosis system, facial skin disease, dempster-shafer
On Single-environment Extrapolations in Graph Classification and Regression Tasks

Extrapolation in graph classification/regression remains an underexplored area of an otherwise rapidly developing field. Our work contributes to a growing literature by providing the first systematic counterfactual modeling framework for extrapolations in graph classification/regression tasks. To show that extrapolation from a single training environment is possible, we develop a connection between certain extrapolation tasks on graph sizes and Lovasz's characterization of graph limits. For these extrapolations, standard graph neural networks (GNNs) will fail, while classifiers using induced homomorphism densities succeed, but mostly on unattributed graphs. Generalizing these density features through a GNN subgraph decomposition allows them to also succeed in more complex attributed graph extrapolation tasks. Finally, our experiments validate our theoretical results and showcase some shortcomings of common (interpolation) methods in the literature.
On the approaches of the phenomenon “cultural intelligence” interpretation

On the approaches of the phenomenon "cultural intelligence" interpretation]Cultural intelligence, being a universal category that proves to show the properties of obvious interdisciplinarity, is based on such components as empathy, recognition of the interlocutor's emotional state (which actualizes the relationship between cultural and emotional intelligences), and the ability to demonstrate emotional resonance, which determines the choice of linguistic means taking into account the connotative coloring of structures and predicative complexes; awareness and analysis of what is happening within the communicative situation up to the manifestation of "communicative altruism", when the pragmatic attitudes and communicative interests of the interlocutor are placed above their own.
PEMBELAJARAN TAHSIN TILAWAH AL-QUR’A&gt;N UNTUK PEMBACA PEMULA

It cannot be denied that the verses of the Quran are arranged with Arabic vocabulary. Except for a few words that entered the treasury duw tho acculturation. The Quran recognizes this in its many verses, including those that refute the accusation that the Quran was recommended by ‘Ajam (non-Arab) to the Prophet Allah. Arabic grammar is also very rational and Indonesian but it is quite complicated, especially when compared to. Al-Qur’an as a guide life for every Muslim. As a guide, of course we must always read it and understand its contents. In order to read properly and correctly according to the rules of recitation, it is necessary for us to study the Qur’an, especially learning how to read it. Through this Qur’an recitations we are trying to improve and smooth the reading of the Qur’an. Because reading and reciting letters of the Qur’an properly and correctly they are Muslims. Therefore, the writer of study about how Learning to Resist the Recitations of the Quran, for beginners Readers (Learning of the Quran, Good and Islamic way of being able to read the Quran well and in accordance with the rules that want to express is what the people believe). The approach uses a qualitative approach with data collection techniques in the form of document studies. While the data analysis technique uses content analysis is analyzes the contents of reading material related to this research. The results of the study explained that the recitation of the recitations of the Qur’an is an activity that emphasizes more on the correction or improvement of the reading of the Qur’an including makhorijul letters, letter character, relationship between letters, mad (long and short in reciting verses) and waqaf (start reading and stop at reading) as well as terms in the Qur’an
Many-sorted hybrid modal languages

We continue our investigation into using the hybridization technique on top of the many-sorted polyadic modal logic we have previously defined. By adding different operators and binders, we present in this paper several systems we have developed. We briefly discuss the goals we have achieved by developing each system. We identify a fragment of the full hybrid logic, for which we prove its sound and complete deduction and we show that it is powerful enough to represent both the programs and their semantics in an uniform manner. Finally, we provide a standard translation from full hybrid many-sorted logic to first-order logic.
Review of BlockChain Technology in Supply Chain Scenarios

Information and communication technology has changed how easily the knowledge and access to information is shared across the globe. It has increased the inter-organization communication by many folds but companies that are part of a supply chain are still not inclined to share sensitive information through it because they do not trust the other members of the chain. Blockchain application here becomes the saving grace as it is a decentralized certificate authority which helps to solve all the mentioned problem but companies are generally unaware of its features due to its novel nature and lack of case studies available to reference to. In this paper, different solutions proposed over the years for implementing this technology is inspected and its vulnerabilities are discussed.
Developing restaurant information system to support decision making

Abstract The purpose of this study is to discuss how far the use of information systems and the effectiveness of information systems to assist in decision making in a restaurant, so that the system can be developed according to the needs in decision making. The method used in this research was descriptive method by using several references from several journals and previous studies to describe how information systems were developed for restaurants. This study discusses the display of information systems that can be used by a restaurant in loading transaction data and transaction reports that have been carried out, so that it can help the cashier to calculate the total transactions made by consumers, and assist in making data on income and expenditure, so they can calculate profit and loss at a restaurant. The results of this study indicate that the existence of an information system in a restaurant that contains various income report data, per-item income reports, and income statements, can help restaurant owners in making decisions, because restaurant owners can make decisions based on data which is accurately recorded in an information system.
Attitude of Science Education Students towards the Use of Mobile Learning in Nigeria

This study investigates the Attitude of Science Education Students towards the Use of Mobile Learning. Students Attitude Mobile-Learning Questionnaire was the instrument used in data collection with a five-point Likert scale questionnaire. The reliability of the instrument was established r = 0.89 using Cronbach alpha at 0.05 level of significance. The questionnaire was administered to 511 students from the 4 units in the Science Education Department. The sample was selected using a simple random sampling method. Data were collected and analyzed using descriptive statistics of mean and standard deviations to answer the research questions while inferential statistics of two-way analysis of variance (ANOVA) statistics was used to test the two hypotheses stated at 0.05. The results showed no significant differences. The F-test calculated values were less than the F-critical values. Findings from the study indicated mobile-learning significantly improves studentsâ€™ interest in learning. Also, there was no significant difference in gender attitude towards the use of mobile learning. As a result of these positive results, students could study any course that involves the use of mobile learning. It is therefore recommended that mobile-learning should be encouraged in the teaching and learning process.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games. Combining reward randomization and policy gradient, we derive a new algorithm, Reward-Randomized Policy Gradient (RPG). RPG is able to discover a set of multiple distinctive human-interpretable strategies in challenging temporal trust dilemmas, including grid-world games and a real-world game Agar.io, where multiple equilibria exist but standard multi-agent policy gradient algorithms always converge to a fixed one with a sub-optimal payoff for every player even using state-of-the-art exploration techniques. Furthermore, with the set of diverse strategies from RPG, we can (1) achieve higher payoffs by fine-tuning the best policy from the set; and (2) obtain an adaptive agent by using this set of strategies as its training opponents.
Image Classification using Convolution Neural Network Based Hash Encoding and Particle Swarm Optimization

Image Retrieval (IR) has become one of the main problems facing computer society recently. To increase computing similarities between images, hashing approaches have become the focus of many programmers. Indeed, in the past few years, Deep Learning (DL) has been considered as a backbone for image analysis using Convolutional Neural Networks (CNNs). This paper aims to design and implement a high-performance image classifier that can be used in several applications such as intelligent vehicles, face recognition, marketing, and many others. This work considers experimentation to find the sequential model's best configuration for classifying images. The best performance has been obtained from two layers' architecture; the first layer consists of 128 nodes, and the second layer is composed of 32 nodes, where the accuracy reached up to 0.9012. The proposed classifier has been achieved using CNN and the data extracted from the CIFAR-10 dataset by the inception model, which are called the Transfer Values (TRVs). Indeed, the Particle Swarm Optimization (PSO) algorithm is used to reduce the TRVs. In this respect, the work focus is to reduce the TRVs to obtain high-performance image classifier models. Indeed, the PSO algorithm has been enhanced by using the crossover technique from genetic algorithms. This led to a reduction of the complexity of models in terms of the number of parameters used and the execution time.
Development of Students Social and Value Orientations Towards Training State and Municipal Management

The purpose of this study is to analyze scientific research in the field of training civil servants, as well as to conduct a study on the development of socially significant values of students studying in the direction “State and Municipal Management”. The technology for the development of social and professional value orientations in students (by Rezer T. M., Khokholush M. S.) and the method by M. Rokeach are used to determine the students’ social and value orientations development level and subjects of their personal needs that determine the life guidelines of the future civil servant. The main research results have shown that in recent years various subject areas for the training of public servants, such as the challenges and quality of education; pedagogical conditions and behaviors; professional potential development, traditions and innovations in professional activity have been actively studied. The study has shown that the ranks from 1 to 5 are occupied by the terminal values: “Health”, “Happy family life”, “Love”, “Financially secure life”, “Interesting work”. In the group of the instrumental values, at the ranks from 1 to 5 there were the values: “Good manners”, “Responsibility”, “Honesty”, “High demands”, “Intransigence to shortcomings in oneself and others”.&#x0D; Keywords: students’ social and value orientations, development, state and municipal management, state and municipal employees’ training
CLASSIFIER OF THE KNOWLEDGE AREA AS AN INFORMATION MODEL OF STRUCTURAL SYNTHESIS SYSTEM. PROBLEMS OF ITS CREATION AND DEVELOPMENT

Goal. Application of fundamentally new approaches to solving the problem of structural syn-thesis of ill-defined objects is, on the one hand, a challenge of time that requires reducing the time for development and obtaining high-quality technical solutions, and, on the other hand, the result of research that allows the use of invariant trial and error algorithms for structural synthesis. Solving this problem by the proposed method requires the creation of the set of data and knowledge, the search of which will give the desired result. Formation of this set and control of its correct and actual state is a serious task requiring the development of new approaches, formalization of processes and development of algorithms imposed by these approaches. Methods. The stages of formation of the pointed set are: collection of data on existing technical solutions, their formal description, gene-ralization of these descriptions with the formation of a certain structure, which allows for identifying each existing object unambiguously and creating a new one either by a combination of existing knowledge or by upgrading the known technical solutions. It is proposed to use a classifier as a structure that summarizes the data of a specific area of knowledge. The peculiarity of this classifi-er is that two methods of development are implemented here: hierarchical and faceted. Creation of such a classifier and its correct state is a problem that allows to solve the problem of formation of information support for structural synthesis systems of ill-defined objects of medium and high complexity. Results. The process of creating such a classifier deals with a number of problems, the identification of which is the first step towards the development of high-quality information support. These problems arise at each stage of the domain analysis (analysis of existing versions of objects; generalization of knowledge about the considered objects; addition (extension) of the classifier). The reasons for their occurrence may be violation of the principle of sufficiency in analysis and de-composition, violation of the hierarchy of the structure when generalizing in the process of building a domain model, violation of the signs of mandatory and optional when synthesizing and expand-ing the model, which is based on the classifier. Conclusion. Identifying these problems and studying the process of avoiding and overcoming them is an important task, the solution of which will allow you to for-malize the process of creating a classifier as an information model of a structural synthesis system.
Dynamicity-based Crop-Drop: A Context-based 2D Pose Refreshing Algorithm

2D pose estimation plays an important role in many activity recognition applications. However, pose estimation is computationally expensive. In this paper, we propose DCD (Dynamicity based Crop-Drop), a context-based pose refreshing algorithm with controllable information loss and much better processing speed. The main idea is to utilize the correlation between consecutive frames to understand the dynamicity of each individual pose and decide its pose refreshing rate to minimize pose information loss. Full pose estimation is only applied when needed. Our test results show up to twice the speed as compared to that of OpenPose with acceptable information loss when tested on multi-person, multi-dynamicity activities like sitting idle, practicing thai-qi, and dancing.
Indonesian as the Language of ASEAN During the New Life Behavior Change 2021

Indonesian is studied and used as a language in ASEAN because Indonesian is the lingua franca. In WordPress posts, the Indonesian language ranks third after English and Spanish. However, the attitude of native Indonesian speakers is still negative, so the Indonesian language UN scores (30% do not pass) are lower than the English national examination scores (only 5% do not pass). The purpose of this research is to describe the tendency of using Indonesian in ASEAN. The research data is in the form of information about the use of Indonesian in ASEAN. The data were collected online from members of 5 (international) communities, such as MLI, KSLI (alumni), AMCCA, and lecturers who had assisted students during internships through SEA teachers, SEAMEO, and youth exchanges with partner universities. This research data analysis using the Delphi technique and inter-subjectivity test. The findings are based on the panelist reliability test with a value of 0.7441 greater than 0.70, it is concluded that the model is consistent. Besides, the model reliability test was also carried out, and the result was that the value of r11 = 0.874 was greater than 0.70, so the model was reliable. The tendency in the use of Indonesian in ASEAN carries native speakers of conditioning results (nurture nativism) with the tendency as mentioned above is valid and reliable.
Impact of virtual classes on the university students in the context of COVID-19 quarantine: The case of the PUCP

The objective of the research was to analyze the academic self-effectiveness perceived by the students of the postgraduate courses in administration at the time of COVID-19 to determine its influence on school performance The type of study was a quantitative investigation with a correlational scope which main variables were self-effectiveness and academic performance The study sample consisted of 97 participants distributed in four master's programs and organized by gender, 55% in women and 45% in men The instrument used was the Inventory of Academic Self-Efficacy Expectations (IEAA) prepared and validated by Barraza (2010) with a four-point Likert-type scale: Not sure (0), a little sure (1), sure (2) and very sure (3) The organization and analysis of the information was done using descriptive and inferential statistics The main results were the existence of a high level of academic self-effectiveness in times of the coronavirus, as well as a direct and significant association between the two research variables (r = 0 721, p = 0 00) Statistically significant differences were also found for work experience (F = 4,234, p = 0 00);educational program (F = 5,348, p = 0 00) and semester (F = 3,453, p = 0 00)
Stage-by-stage knowledge distillation

Knowledge Distillation (KD) aims at using a low-capacity model, called student, to learn from a high-capacity one, termed as teacher, such that the performance of student can be improved. Previous KD methods typically train a student by minimizing a task-related loss and the KD loss simultaneously, with the help of a loss weight hyper-parameter to balance these two terms. In this work, we propose to first transfer the backbone knowledge from a teacher to the student, and then only learn the task-head of the student network. Such a training decomposition alleviate the use of loss weight, which can be hard to define. This allows our method to be easily applied to different datasets or tasks with strong stability. Importantly, the decomposition permits the core of our method, Stage-by-Stage Knowledge Distillation (SSKD), which facilitates progressive feature mimicking from teacher to student. Extensive experiments on CIFAR-100 and ImageNet suggest that SSKD significantly narrows down the performance gap between student and teacher, outperforming state-of-the-art approaches. We also demonstrate the generalization ability of SSKD on object detection on COCO dataset. On both tasks SSKD shows significant improvements.
The K-Means Algorithm for Generating Sets of Items in Educational Assessment

In a national-scale educational assessment system, such as the National Examination, the need for several sets of questions that have the same level of difficulty is very required to avoid cheating by students. Therefore, the objective, which is to make a set of questions with the same level of difficulty automatically, is done in this research. It used a machine learning approach, namely K-Means. To achieve this goal, several following procedures need to be implemented. Firstly, we need to create banks of questions to be assigned to students. Then, we build training data by determining the value of each question based on Bloom's Taxonomy, item characters/types, and other parameters. Then, with utilizing K-Means, several cluster centers are obtained to represent the uniformity of the questions in the cluster members. By using several heuristics criteria defined previously, several sets or packages of questions that have the same characteristics and difficulty levels are obtained. From the experiments conducted, the analysis with descriptive (i.e., mean, standard deviation, and data visualization) and inference (i.e., ANOVA) statistics of results are presented showing that questions of each sets have the same characteristics to ensure the fairness of examinations. Moreover, by using this system, the contents of the questions in the generated set do not need to be the same, the package of questions can be generated automatically quickly, and the level of the difficulties can be measured and guaranteed.