Reviewerly/exHarmony huggingface dataset:
Use of and Satisfaction With Mobile Health Education During the COVID-19 Pandemic in Thailand: Cross-sectional Study

Background RamaCovid is a mobile health (mHealth) education system that provides the Thai population with information about COVID-19 and self-risk assessment. RamaCovid has a chatbot system that provides automatic conversations (available 24 hours per day) and a live chat function that allows users to directly communicate with health professionals (available 4 hours per day in the evening). The system consists of (1) COVID-19 vaccine information, (2) self-care after vaccination, (3) frequently asked questions, (4) self-risk assessment, (5) hospital finding, (6) contact number finding, and (7) live chat with a health professional. Objective This study investigates the use of and satisfaction with the RamaCovid system. Methods Overall, 400 people were recruited via RamaCovid by broadcasting an infographic about the study. Questionnaires collected demographic data, users’ experiences of RamaCovid, and the use of and satisfaction with the system. The questions were answered using a 5-point Likert scale. Descriptive statistics were used to describe the participant characteristics and their use of and satisfaction with the RamaCovid system. The Mann-Whitney U test was performed to examine the difference in use and satisfaction between the adult and older adult groups. Results The participants showed high use of and satisfaction with the RamaCovid system. They used the information to take care of themselves and their family, and they gained information about their COVID-19 risk. The users were satisfied with the system because the information was easy to understand, trustworthy, and up to date. However, the older adult group had lower use of and satisfaction with the system compared to the adult group. Conclusions RamaCovid is an example of the successful implementation of mHealth education. It was an alternative way to work with the call center during the COVID-19 pandemic and increased access to health information and health care services. Providing ongoing updated information, improving the attractiveness of the media information, and the age group difference are important issues for further system development.
Volarian Game: Engaging EFL Learners’ Vocabulary Learning During the Covid-19 Outbreak

This research aims at developing a Volarian game to engage EFL learners to learn vocabulary during the Covid-19 outbreak. This research was conducted with Research and Development (R&amp;D) and tested in Class Action Research (CAR). Then, the researcher distributes pre-motivation and post-motivation questionnaires to know the students’ engagement in learning vocabulary during study at home before and after using Volarian. In the pre-motivation questionnaire total score indicated a point of 1036. The number 1036 is in the strongly disagree and disagree score range which means that student engagement in learning vocabulary during study at home is low. However, in a post-motivation questionnaire result, a total score indicated a point of 1853. The number 1853 is in the agree and strongly agree score range which means that after the student using Volarian can engage them to learn vocabulary during study at home. Accordingly, based on the recapitulation of pre-motivation and post-motivation questionnaires show that the results of field-testing by using CAR (Class Action Research) method prove that there is an engagement of students in learning vocabulary during distance learning before and after using Volarian.
SA-ES: Subspace Activation Evolution Strategy for Black-Box Adversarial Attacks

Deep neural networks are vulnerable to adversarial examples that alter the output significantly with imperceptible change in the input. In our black-box setting, the adversarial attacker can only query the model to predict the value after the softmax layer without accessing the underlying model. Currently, generating adversarial examples with high qualifications in the query-limited setting and investigating the distribution of adversarial examples are two main challenges in the black-box attack. In this paper, we propose a zeroth-order optimization method for the black-box adversarial attack, termed subspace activation evolution strategy (SA-ES). It captures the most promising direction for generating more convincing adversarial examples.Moreover, instead of only searching for one reliable adversarial example for an original input, SA-ES finds a distribution of adversarial examples, such that a sample drawn from this distribution is likely an adversarial example. We conduct comprehensive experiments on various data sets and validate that the proposed algorithm can efficiently find perturbation-sensitive regions of an image and stably explore the distribution of adversarial examples with the limited query, and outperforms the existing methods. In addition, we apply SA-ES to physical reality black-box attacks, which effectively generate simulated physical adversarial examples for the adversarial training model.
A Context-Specific Modularization for Ontology Change Management

Knowledge engineering has a vital role in advancing the semantic web, in which ontologies play a key role in data interoperability and integration. One of the key issues in ontology engineering is how to handle the subsequent updates in the ontologies. A number of concerns need to be considered while working on the ontology change, such as, tracking ontology versions and heterogeneity issues. Ontology change management has been partially addressed by different researchers in overlapping research areas. However, a concrete description of the problem and its related concerns are still not available in the literature. Our work aims to present an overview of ontology change management and its concerns. We point up the need for modularization in ontology change management based on its advantages in the context of ontology reuse from different contextual viewpoints. For this purpose, we propose a protege plugin for reusing OWL modules, and allowing a safe/clean manual integration and reuse of different ontology modules.
A Social Edge-Based IoT Framework Using Reputation-Based Clustering for Enhancing Competitiveness

The introduction of the IoT technology and its pervasive penetration in our daily life implies that IoT smart objects can often participate in social events, and in this case their paradigm of interaction is commonly denoted as social IoT (SIoT). To make reliable transactions into a SIoT scenario, in this article, we introduce a multi-agent SIoT architecture, which integrates a reputation system based on a clustering of the SOs. In our framework, when a SO looks for a resource and detects a reliable partner having that resource, then the two SOs can interact to make a transaction, and at the end of the transaction each of them provides a feedback about the partner to the local reputation system of the edge server, to update the SOs’ reputation scores. Moreover, each edge server sends these reputation scores to the cloud, which updates the associated reputation values of the SOs, deriving from experiences coming from all the edge domains. This architecture provides a given object, moving from an edge domain to another one, with the possibility to have an updated value of its reputation, represented by the value stored in the cloud. We have validated our approach by a campaign of simulations, whose results seem particularly promising.
Faithful Extreme Rescaling via Generative Prior Reciprocated Invertible Representations

This paper presents a Generative prior ReciprocAted Invertible rescaling Network (GRAIN) for generating faithful high-resolution (HR) images from low-resolution (LR) invertible images with an extreme upscaling factor (64×). Previous researches have leveraged the prior knowledge of a pretrained GAN model to generate high-quality upscaling results. However, they fail to produce pixel-accurate results due to the highly ambiguous extreme mapping process. We remedy this problem by introducing a reciprocated invertible image rescaling process, in which high-resolution information can be delicately embedded into an invertible low-resolution image and generative prior for a faithful HR reconstruction. In particular, the invertible LR features not only carry significant HR semantics, but also are trained to predict scale-specific latent codes, yielding a preferable utilization of generative features. On the other hand, the enhanced generative prior is re-injected to the rescaling process, compensating the lost details of the invertible rescaling. Our reciprocal mechanism perfectly integrates the advantages of invertible encoding and generative prior, leading to the first feasible extreme rescaling solution. Extensive experiments demonstrate superior performance against state-of-the-art upscaling methods. Code is available at https://github.com/cszzx/GRAIN.
RESPONSIBILITY AS A UNIVERSAL COMPONENT OF SOFT COMPETENCIES OF VOCATIONAL EDUCATION TEACHERS

The article considers the need to develop specialists’ soft skills that en- sure pre-adaptation to the changing requirements of the labor market. Based on the analy- sis of empirical research and the requirements of educational standards, a list of soft com- petencies of a vocational education teacher is proposed. An assumption is made about the role of responsibility in the structure of soft skills of a modern specialist. The article pre- sents the data of a pilot study of responsibility among teachers of secondary vocational education in Ekaterinburg.
A Quantitative Measurement Method of Code Quality Evaluation Indicators based on Data Mining

Software Code Quality Measurement is a systematic subject with a long history and rich theory. Since the 1960s, a large number of academic research on software code quality measurement methods have been generated. Many scholars have proposed software code quality measurement indicators and measurement methods, and given the measurement trend of evaluation indicators, and proposed a model to measure software quality. However, there are still some blanks in the quantitative evaluation of software code quality evaluation index. The software code quality measurement system is not complete enough to cover all aspects of software code quality from multiple perspectives. In addition, most studies have measured software code quality based on code inspection or code review, which has some limitations. This paper puts forward a software code quality evaluation system based on software network. On this basis, this paper proposes a quantitative measurement method of code quality evaluation indicators based on data mining. It provides a new idea for software quality evaluation or code quality evaluation.
Research and Implementation of Image Dehazing Based on Deep Learning

Image dehazing is an important part of the field of computer vision, and it is also one of the widely used technologies in reality. It has developed rapidly in recent years and has been widely promoted at both hardware and software levels. Dehazing the construction site image for the group of construction workers can ensure the control of the site by the background monitoring during the haze weather, discover the relevant safety hazards on the site in time, and protect the safety and interests of the construction workers. This paper studies and improves the shortcomings of the existing dehazing methods. Compared with the traditional algorithm and the existing deep learning dehazing algorithm, the dehazing effect of the deep learning model used is closer to the clear image in the visual sense. At the same time, a dehazing model based on generative adversarial network is designed. The generator is a convolutional network equipped with a residual short-circuit structure. The discriminator introduces an attention mechanism and performs Fourier transform on the generated image and the real image. The model has certain universality and can be applied to image dehazing in other fields.
Development of Prototype Smart Control Systems to Support IoT and LoRA-Based Smart Farming in Smart Agriculture Applications

Indonesia is an agrarian country; it can be seen from the extent of agricultural land in Indonesia, which has enormous potential. However, managing such an area of land is not optimal if farmers only work on it. The quality of agricultural products depends on the experience and instincts of farmers in understanding the land conditions and crops. Of course, these results are less when compared with the calculated calculations. We are here to help farmers manage their agricultural land from these problems. Iotanic offers products in the form of IoT devices that can monitor and automate agricultural land. With the precision farming method, the monitoring data will be precise, and the execution of agricultural management will become more precise and automated thanks to the resulting data. The manufacture of the product starts by analyzing the device's needs to be produced. These needs are in the form of hardware development needs in the form of IoT devices and software development in the form of applications. This analysis develops the device into a prototype product ready to be tested. These activities will produce IoT products and software to help farmers manage their agricultural land. To be able to produce these devices, production costs and the value of products/services are required funds amounting to 24,998,200.00
Select the Unexpected: A Statistical Heuristic for Story Sifting

Story sifting techniques, which aim to excavate potentially compelling microstories from vast chronicles of storyworld events, present a promising solution to the challenges of interactive emergent narrative. However, current sifting techniques (which rely on large numbers of hand-specified story sifting patterns to identify compelling microstories) are limited by their inability to determine which of many sifting pattern matches are likely to be the most interesting to a human interactor. We present a higher-level story sifting heuristic that addresses this problem by identifying sifting pattern matches that are especially unlikely from a statistical perspective, and illustrate how this heuristic leads to the surfacing of more interesting microstories.
Human-centered artificial intelligence-based ice hockey sports classification system with web 4.0

Abstract Systems with human-centered artificial intelligence are always as good as their ability to consider their users’ context when making decisions. Research on identifying people’s everyday activities has evolved rapidly, but little attention has been paid to recognizing both the activities themselves and the motions they make during those tasks. Automated monitoring, human-to-computer interaction, and sports analysis all benefit from Web 4.0. Every sport has gotten its move, and every move is not known to everyone. In ice hockey, every move cannot be monitored by the referee. Here, Convolution Neural Network-based Real-Time Image Processing Framework (CNN-RTIPF) is introduced to classify every move in Ice Hockey. CNN-RTIPF can reduce the challenges in monitoring the player’s move individually. The image of every move is captured and compared with the trained data in CNN. These real-time captured images are processed using a human-centered artificial intelligence system. They compared images predicted by probability calculation of the trained set of images for effective classification. Simulation analysis shows that the proposed CNN-RTIPF can classify real-time images with improved classification ratio, sensitivity, and error rate. The proposed CNN-RTIPF has been validated based on the optimization parameter for reliability. To improve the algorithm for movement identification and train the system for many other everyday activities, human-centered artificial intelligence-based Web 4.0 will continue to develop.
Developing SPOK Litaracy Board Media (PALIPOK) for Reading of First Grade of Elementary School

Based on the results of observations, namely by knowing the conditions in the field with a total number of students, namely 13 students, the learning media is said to have not trained students' reading skills and students have not experienced, still have difficulty in carrying out learning. For that we need media that can support students' reading skills in learning activities. This study uses the ADDIE model with five stages, namely: analysis, design, development, implementation, and evaluation. The results of the evaluation through the material expert validation test stage 1 were 72.72 in stage 2 of 86.36, while the media validation test was 73.07, the teacher's response was 65.62 and the results of student responses from 13 students who did not enter 2 were 72, 10. This falls into the good category and is good to use as the basis for a conversion table. The results of these efforts indicate that the media is in the appropriate category for use.
Clustering of scientific articles using natural language processing

With the development of the Internet, the number of scientific articles published online has also increased significantly. In recent years there has been a steady increase in the number of articles available online. Despite providing keywords and preparing abstracts, it is often a problem for articles to be properly matched during editorial process - to match the article to the competence of a particular editor. It is also a hassle for researchers who want to be kept informed about the most relevant articles. In paper, we propose the use of natural language processing (NLP) in the process of clustering scientific articles. Our study looks at the impact of clustering on the actual keywords provided by the authors – we analyse both the application of NLP to the abstract itself and to the introduction, followed by clustering using the K–means algorithm. As a result of experiments conducted on more than 1500 scientific articles, we have shown that our proposed approach allows articles to be approximated by their subject matter as a result of the clustering performed. The obtained results show that the best distribution of scientific articles can be obtained using the TF-IDF measure, and the worst - using TF measure.
Common data guided crash injection for cloud systems

Modern distributed systems are designed to tolerate node crashes. However, incorrect crash recovery mechanisms and implementations can still introduce crash recovery bugs, and hurt reliability and availability of cloud systems. In this paper, we present Deminer, a novel crash injection technique that automatically injects node crashes/reboots to effectively expose crash recovery bugs in cloud systems. We observe that, node crashes that interrupt the execution of related operations, which store common data to different places (i.e., different storage paths or nodes), are more likely to trigger crash recovery bugs. Based on this observation, Deminer first tracks the critical data usage in a correct run. Then Deminer identifies related operations and predicts error-prone crash points. Finally, Deminer tests the predicted crash points and checks whether the target system can behave correctly. We have evaluated Deminer on three widely-used cloud systems: ZooKeeper, HBase and HDFS. Deminer has detected 6 crash recovery bugs. A video demonstration of Deminer is available at https://youtu.be/jS6KBcYnTSM.
Review of Most Popular Open-Source Platforms for Developing Blockchains

Because of availability of many blockchain development platforms, the number of blockchain related projects on Github has grown significantly, averaging over 8,000 new projects a year. But selection of the best development framework for a project requires one to compare and contract features of those available platfroms, since there is no report or paper - to the best of our knowledge - that provides a brief introduction to available platforms. In this paper we first provide a table of 15 most popular open-source platforms. In the rest of the paper we summarize features and development supports that available from those blockchain development cores.
A Hybrid Deep Learning Approach to Detect Bangla Social Media Hate Speech

Social media has become an integral part of our day-to-day life. In our activities or posts on social media, the presence of hate speech written in the native language or English has increased significantly. It often leads to the spread of negativity, depression, or even sometimes considered cybercrime. In this paper, a hybrid deep learning approach has been taken to detect Bangla social media hate speech using fastText embedding, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network. A publicly available dataset of 30000 samples has been used, and the proposed hybrid model achieved close to 90% accuracy with significant sensitivity and specificity in Bangla hate speech detection. Several related deep learning approaches were evaluated in this same dataset, but none of them performed better than the proposed model. The hybrid model also showed robustness which made it more suitable for this task.
Graph-based Attribute-aware Unsupervised Person Re-identification with Contrastive learning

This paper is employed on the unsupervised per-son re-identification (Re-ID) task which does not leverage any annotation provided by the target dataset and other datasets. Previous works have investigated the effectiveness of applying self-supervised contrastive learning, which adopts the cluster-based method to generate the pseudo label and split each cluster into multiple proxies by camera ID. This paper applies the Attribute Enhancement Module (AEM), which utilizes Graph Convolutional Network to integrate the correlations between attributes, human body parts features, and the extracted dis-criminative feature. And the experiments are implemented to demonstrate the great performance of the proposed Attribute Enhancement Contrastive Learning (AECL) in camera-agnostic version and camera-aware version on two large-scale datasets, including Market-1501 and DukeMTMC-ReID. Compared with the baseline and the state-of-the-art, the proposed framework achieves competitive results.
Smart Mobile Phone Charging Stations for Public Service as Smart Cities Component

The need to charge a smartphone is because the battery on a smartphone is designed not to have long enough power, especially the need for the smartphone for browsing using a data plan. Mobile phone charging stations in public areas are generally open, and some have also implemented a manual lock on each charging box. Some boxes lose their keys due to someone's carelessness, resulting in the box being unable to be used by others. This research aims to make a prototype mobile charging station equipped with a fingerprint security system. The method used in this research is a case study by making a prototype of a mobile phone box charging station. The security system in the charging station author utilized Arduino Mega connected to the solenoid controlled by fingerprint to open and lock each box. The device successfully registered each user's fingerprint based on the results of creating a prototype of a security system using a fingerprint. The device can read the correct fingerprint to open and close the solenoid in the box. In the case of unrecorded or mismatched fingerprints, the device will issue a warning and refuse to allow access to open and close the box. As a result, this security system is ideal for use in a prototype mobile charging station.