Reviewerly/exHarmony huggingface dataset:
Edge Intelligence for Rendering Green Camera-Network-as-a-Service

This work proposes the Green Camera-Network-as-a-Service (G-CNaaS) architecture, which provides on-demand camera networks to multiple end-users simultaneously while utilizing minimal energy. G-CNaaS simultaneously reduces the carbon footprint and eliminates the single application-centric approach of traditional camera networks (TCNs) by enabling each camera to participate in multiple Virtual-Camera-Networks (VCNs) and selecting an optimal set of cameras for each VCN. We couple each camera node in every VCNs with a learning model suitable for the requested application. We assign an intelligent edge device to each VCN to analyze time-sensitive events. We introduce the camera selection factor by leveraging the properties of cameras: 1) field-of-view (FoV); 2) angular-distance; 3) observation range; and 4) residual energy to select the optimal camera set. The results of the extensive simulation of the G-CNaaS architecture show that it excels in performance concerning attributes such as the average lifetime, fair distribution of the work among the camera owners, and cost-effectiveness compared to the TCNs. We observe that the expenditure of a user using the TCN varies by 88.7%, while in the case of G-CNaaS, the expenditure varies by 10.28% with the increase in time from 1–60 months. On the other hand, the average energy consumed increases by 59.88% and 99.5% in the presence of 10 and 20 camera sensor owners.
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups

Deep learning pervades heavy data-driven disciplines in research and development. The Internet of Things and sensor systems, which enable smart environments and services, are settings where deep learning can provide invaluable utility. However, the data in these systems are very often directly or indirectly related to people, which raises privacy concerns. Federated learning (FL) mitigates some of these concerns and empowers deep learning in sensor-driven environments by enabling multiple entities to collaboratively train a machine learning model without sharing their data. Nevertheless, a number of works in the literature propose attacks that can manipulate the model and disclose information about the training data in FL. As a result, there has been a growing belief that FL is highly vulnerable to severe attacks. Although these attacks do indeed highlight security and privacy risks in FL, some of them may not be as effective in production deployment because they are feasible only given special—sometimes impractical—assumptions. In this paper, we investigate this issue by conducting a quantitative analysis of the attacks against FL and their evaluation settings in 48 papers. This analysis is the first of its kind to reveal several research gaps with regard to the types and architectures of target models. Additionally, the quantitative analysis allows us to highlight unrealistic assumptions in some attacks related to the hyper-parameters of the model and data distribution. Furthermore, we identify fallacies in the evaluation of attacks which raise questions about the generalizability of the conclusions. As a remedy, we propose a set of recommendations to promote adequate evaluations.
Language Contents of Modules and Grade 7-10 Students’ Engagement during the COVID 19

Aims: To determine the relationship between modules’ language contents and students learning engagement during the COVID 19&#x0D; Study Design: Descriptive-correlational&#x0D; Place and Duration of Study: This study was conducted in Bato National High School, Sta. Cruz, Davao del Sur from February to June 2021.&#x0D; Methodology: This research used the stratified random sampling. It involved 258 Grades 7-10 students. It utilized adopted questionnaires developed by Vergara (2017), Fredericks et al. (2005) and Moore and Lippman (2005). &#x0D; Results: The level of language contents of the modules in English gained an overall mean of 4.05 (SD=0.94) described as “high.” All components under language contents had rated high as follows: presentation (4.05), communication style (4.07), assessment (4.05) and content (3.92). On the other hand, the level of student engagement gained an overall mean score of 3.96 (SD = 1.06) with a verbal description of “highly engaged.” All components under students engagement had rated high as follows: behavioral engagement (4.19), emotional engagement (4.04), and cognitive engagement (3.65). The tested hypothesis gained an r value of 0.633, and a p-value of 0.00 which is less than 0.01, the alpha level of significance is denoting a strong positive correlation between the level of language contents of the modules and engagement of students. Hence, it determined that there was a significant relationship between language content of the modules and engagement of students.&#x0D; Conclusion: Although all variables in the contents of the modules and engagement are rated high and highly engaged respectively, specific statements which are within the border of often and seldom may be given attention. Moreover, the findings of this research shows significant relationship between language content of modules and students’ engagement may allow the module developers to consider that quality of modules is more relevant than quantity and that meaningful activities promote learners’ engagement.
Simultaneous Real Walking and Asymmetric Input in Virtual Reality with a Smartphone-based Hybrid Interface

Compared to virtual navigation methods like joystick-based teleportation in Virtual Reality (VR), real walking enables more natural and realistic physical behaviors and better overall user experience. This paper presents a smartphone-based hybrid interface that combines a smartphone and a handheld controller, which enables real walking by viewing the physical environment and asymmetric 2D-3D input at the same time. The phone is virtualized in VR and streams a view of the real world to a collocated virtual screen, enabling users to avoid or remove physical obstacles. The touchscreen and the controller provide an asymmetric input choice for users to improve interaction efficiency in VR. We implemented a prototype system and conducted a pilot study to evaluate its usability.
Measurement at Student Service Satisfaction Using Fuzzy Service Quality Method at Indramayu State Polytechnic

Providing satisfaction to students and preventing students from all campus facilities services is very important to improve the quality of higher education. Therefore, this study aims to build a system to measure student satisfaction with campus services using the fuzzy servqual method. The number of samples is 100 students. The measuring instrument used in the study was declared valid with a significance level of 5% or a 95% confidence level. The reliability test with the result of 0.746 means that the measuring instrument used has high mobility. Data processing is done by calculating the value of fuzzification, defuzzification, and calculating the value of GAP between students' perceptions and expectations. From the results of the fuzzy servqual gap ranking with 5 variables, it shows that empathy gets to level 1 with the smallest value of -0.74. Level 2 is an assurance with a value of -0.85; then level 3 is reliability with a gap value of -0.89, the 4th position is the reaction force with a gap value of -0.97, and the 5th position is real with a value of -1.27. The results obtained can help the Indramayu State Polytechnic in improving the performance and quality of service.
Study on the Construction Method of Requirement Knowledge Atlas Based on Graph Neural Network

As a structured semantic knowledge system, knowledge graph mainly uses symbols to present physical concepts in real life, which involves three aspects: triples, entities and the network structure connected with related number structures. Although there is a wide range of research on knowledge graph at present, most of the research on knowledge graph in China is of general type. Therefore, on the basis of clarifying the construction method of required knowledge graph, this paper, aiming at the overall formal representation and construction framework of knowledge graph, makes clear the specific representation mode of pattern layer and data layer from the logical perspective, and then verifies and analyzes the proposed method.
Digital Model for a Multidisciplinary Educational Base for Training a Multi-skilled Specialist

The article investigates information technology competencies in terms of modern market demands. The need for multi-skilled specialists increases every year, while the need for highly profiled specialists not only does not grow, but has a downward trend. The authors believe that the professional training of multi-skilled specialists must have a multidisciplinary educational base with a wide range of competencies. For this a digital model for creating a multidisciplinary educational base for training a multi-skilled specialist is being developed in this article. It creates a great variety of consistent educational paths for training multi-skilled specialists and allows to pick the best ones according to the criteria of cost, efficiency or complexity.
A Method of Using Data Mining and Edge Computing to Calculate the Standing Efficiency of Basketball Games

The continuous improvement of basketball tactics has high requirements for athletes’ position selection. This article proposes an intelligent method for basketball position selection. Massive basketball game data will provide people with richer content. Analyzing massive basketball game data can provide a new method for position efficiency calculation. To solve this problem, we can combine edge computing and data mining technology classification technology to build a basketball game position efficiency calculation model. First of all, we build a basketball game position efficiency calculation architecture through edge computing technology. Secondly, we use random forest algorithm and fuzzy neural network algorithm to analyze relevant basketball game information. The experimental simulation test results verify the superior performance of the basketball game position efficiency calculation model established in this paper. This model can provide help to improve the information level of basketball games.
TA-BiLSTM: An Interpretable Topic-Aware Model for Misleading Information Detection in Mobile Social Networks

As essential information acquisition tools in our lives, mobile social networks have brought us great convenience for communication. However, misleading information such as spam emails, clickbait links, and false health information appears everywhere in mobile social networks. Prior studies have adopted various approaches to detecting this information but ignored global semantic features of the corpus and lacked interpretability. In this paper, we propose a novel end-to-end model called Topic-Aware BiLSTM (TA-BiLSTM) to handle the problems above. We firstly design a neural topic model for mining global semantic patterns, which encodes word relatedness into topic embeddings. Simultaneously, a detection model extracts local hidden states from text content with LSTM layers. Then, the model fuses those global and local representations with the Topic-Aware attention mechanism and performs misleading information detection. Experiments on three real datasets prove that the TA-BiLSTM could generate more coherent topics and improve the detecting performance jointly. Furthermore, case study and visualization demonstrate that the proposed TA-BiLSTM could discover latent topics and help in enhancing interpretability.
CRITICAL PEDAGOGY: THE ROLE OF STUDENT DIGITAL LITERACY IN UNDERSTANDING CRITICAL PEDAGOGY

Technological developments encourage students to adapt and must be able to control technology with digital literacy. Critical pedagogy understanding can be the basis for students in developing pedagogy mindsets and skills. The purpose of this study is to explore how digital literacy influences students' critical pedagogy understanding. The method in this research uses an exploration method with a quantitative approach. The sample in this study is a student at one of the universities in West Java. The instrument used is a questionnaire instrument containing students' digital literacy and a test instrument containing students' critical pedagogical abilities. The instruments used have gone through expert validation. The analysis in this study uses SEM (Structural Equation Modeling) which can provide information about the effect of a variable with a complex model. The results of the analysis show that digital literacy has a significant influence on students' critical pedagogical understanding abilities. The influence is represented by the dimensions of access, evaluate and share. Although the results of the study show a positive value, there needs to be an in-depth study of how these dimensions can have an effect.
Research Progress in the Application of Artificial Neural Networks in Catalyst Optimization

The catalyst can speed up the chemical reaction and increase the selectivity of the target product, playing an important role in the chemical industry. By improving the performance of the catalyst, the economic benefits can be greatly improved. Artificial Neural Network (ANN), as one of the most popular machine learning algorithms, has parallel processing and self-learning capabilities as well as good fault tolerance, and has been widely used in various fields. By optimizing the catalyst through ANN, time and resource consumption can be greatly reduced, and greater economic benefits can be obtained. This article reviews how CNN technology can help people solve highly complex problems and accelerate progress in the catalytic world.
Assessment of the balance of the competence formation process: analysis of the curricula of Russian universities

Introduction. Curricula are the main tool for organizing the educational process in Russian universities. The quality of education and the possible level of formation of the graduate's competencies directly depend on their validity and balance. Going on a competence model of education demanded change in the principles of drawing up of curricula. However, until now, the quality of curricula is assessed according to formal criteria that are weakly related to the competence-based approach. Also, answers to simple questions have not been formulated: how many disciplines can and should form one competence, how many competencies can be formed within one discipline and within one semester, how the presence of gaps in the process of forming competencies affects the final level of their formation. The aim of the work is to propose quantitative characteristics for assessing the balance and consistency of the process of forming competencies based on a statistical analysis of the current curricula of Russian universities. Materials and methods. As a material for the study, curricula were chosen in the specialty "Applied Informatics" of federal state budgetary and autonomous educational organizations (45 institutes and universities in total). Methods of mathematical and statistical analysis were applied: frequency analysis, methods for comparing means, analysis of standard deviations. Research results. On average, each competency forms from 2 to 8 disciplines. The largest number of disciplines in most curricula forms 1, 2 or 3 competencies. There are relatively few disciplines that form more than 4 competencies in the curricula of most universities. It is shown that the lack of continuity and development, the presence of gaps in the process of forming competence can reduce the achieved level of competence to 30% of the possible due to forgetting. The discussion of the results. The presence of deviations from the average indicators may in some cases indicate an insufficient balance of curricula. In the future, it is possible to embed the proposed methodology into an intelligent system for assessing the characteristics of the educational process within the framework of the university's QMS.
A Systematic Review on Techniques of Facemask Detection using Digital Images

The hasty spread of Coronavirus around the entire world prompted a number of red alerts in various areas including human health, humanity and day to day living styles of human beings. During this pandemic situation, face masks have been used worldwide for protecting humans against the disease of Coronavirus. Face masks are the best and most effective known method to control the dissemination of Covid-19. It is really very difficult to correctly monitor whether the individual have worn face mask or not in order to notify the victim in crowded and hustled places. The face mask detection process generally uses a camera to capture live streaming videos of crowd places and converting them into images. The systems for mask detection are developed with a toll-way gate which allows only if the person crossing it has a face mask worn on his/her face or it does not allow the person in. This paper aims to review various techniques proposed for identifying whether the individuals are wearing facemask or not to protect themselves and others from this deadly virus.
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources.

Warning: this paper contains content that may be offensive or upsetting. Commonsense knowledge bases (CSKB) are increasingly used for various natural language processing tasks. Since CSKBs are mostly human-generated and may reflect societal biases, it is important to ensure that such biases are not conflated with the notion of commonsense. Here we focus on two widely used CSKBs, ConceptNet and GenericsKB, and establish the presence of bias in the form of two types of representational harms, overgeneralization of polarized perceptions and representation disparity across different demographic groups in both CSKBs. Next, we find similar representational harms for downstream models that use ConceptNet. Finally, we propose a filtering-based approach for mitigating such harms, and observe that our filtered-based approach can reduce the issues in both resources and models but leads to a performance drop, leaving room for future work to build fairer and stronger commonsense models.
Student-centered online assesment in foreign language classes

In 2020 educational institutions in many countries had to implement online learning due to quarantine restrictions caused by the coronavirus pandemic. The research aims to study the peculiarities of the distance learning technologies used by Ukrainian foreign languages teachers for formative and summative assessment and their impact on students. At the end of 2020, a survey about online resources used for creating different types of tasks for foreign language classes in Ukraine was conducted by the authors of the study, and the main characteristics of the most popular online resources were analyzed. According to the survey, the majority of Ukrainian teachers use such platforms to create tasks for assessment: Kahoot, Google Forms, Quizlet, Classtime, Quizizz, Socrative, Quizalize, Gimkit, Blooket, Liveworksheets, and Wizerme. Some of these resources provide a strong element of competition that makes them perfect for formative assessment, while the others better suit summative assessment as they have a clear interface and don’t include distracting elements. While designing online tasks for the assessment, teachers should also take into consideration the possibilities online resources provide to reduce their students’ anxiety and stress caused by performing test tasks.
Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model

A text retrieval system for language learning returns reading materials at the appropriate difficulty level for the user.The system typically maintains a learner model on the user's vocabulary knowledge, and identifies texts that best fit the model.As the user's language proficiency increases, model updates are necessary to retrieve texts with the corresponding lexical complexity.We investigate an open learner model that allows user modification of its content, and evaluate its effectiveness with respect to the amount of user update effort.We compare this model with the graded approach, in which the system returns texts at the optimal grade.When the user makes at least half of the expected updates to the open learner model, simulation results show that it outperforms the graded approach in retrieving texts that fit user preference for new-word density.
Efficiency of Using Smart Technologies in Teaching Technical Sciences in Higher Educational Institutions

This article presents the problems, solutions and advantages of using SMART technologies in the teaching of technical sciences in higher education institutions. At the same time, the essence of the concept of SMART is highlighted in pedagogical-psychological and methodological terms
Performing Differential and Integral Calculus Using Radial-Gaussian Neural Networks

The paper describes a novel means of performing calculus using networks of radial-Gaussian neurons. The network architecture and training algorithm used for this purpose is described briefly. Once trained, a network can be converted into a form that provides the differential or integral of the learned function, by a simple substitution of the type of activation function used at the hidden neurons. A range of substitute activation funtions, for conversion to first and second order partial differential and integral forms of the network, are derived. Following this, the technique is tested on a selection of calculus operations. The converted networks produced in these experiments provide accurate models of the actual differentials and integrals of the function the origmal networks had been taught. A method of improving the accuracy of results by training the original network beyond the region in which the converted networks operate, is described. The paper concludes by identifying some areas for further development of the technique.
Extracting Material Property Measurement Data from Scientific Articles

Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training datasets.The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods.In this work, we describe a methodology for an automatic property extraction framework using material solubility as the target property.We create an annotated dataset containing tags for solubility-related entities using a combination of regular expressions and manual tagging.We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units.Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual effort.Finally, we perform an analysis to identify the causes of classification errors.
Discussion on MSaaS Architecture and Enabling Technology

Modeling and simulation as a service (MSaaS) is a promising solution to resolve the problems of standardization, interoperability and composability in M&S (modeling and simulation). To provide agile access to diverse and high-quality M&S service, the M&S resource, including data, document, model and algorithm, should be unified described, designed, issued, and managed. Further, the evaluation of QoMSS (quality of M&S service) is an indispensable technique to promote the upgrading and evolution of MSaaS. Finally, modeling and simulation process should be elaborately designed in MSaaS architecture to support the application.