Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN BAHAN AJAR FABEL BERBASIS LINGUISTIK INTELLIGENCE DI KELAS IV SEKOLAH DASAR

Not all students have linguistic intelligence, because they have their own advantages and disadvantages and have their own intelligence. The purpose of this research is for students to master the style of language, both writing and speaking effectively and efficiently. The method used for this research is research and development (Research and Development) research methods used to make a product that is effective and efficient. The results of the media validation test for linguistic-based learning materials involved 3 experts to assess the product, namely 2 lecturers as media experts, 2 lecturers as linguists, and 2 teachers as material experts. The results of media validation obtained a percentage of 89.44% from two media experts, 87.81% from two linguists, and 94.79% from two material experts so that an average of 90.63% was obtained including the "Very Eligible" category. Students' responses to linguistic-based fable teaching materials involved 28 students who obtained a percentage of 9.47% including the "Very Eligible" category.
Factors that Become a Dark Tourist Attraction in the Area of Mount Merapi Yogyakarta

Background: In essence, dark tourism is a type of tourism that diverges because it is conceptually different from the concept of tourism in general. The concept of tourism is a vacation to find entertainment, but the concept of dark tourism is just the opposite, tourists who visit rarely find a pleasant impression. &#x0D; &#x0D; Purpose: This study aims to explain the motivation of tourists to visit dark tourism in the Mount Merapi area of Yogyakarta and describe the factors that become the attraction of dark tourism in the Mount Merapi area of Yogyakarta. &#x0D; &#x0D; Methods: This type of research uses qualitative methods. A research method used to study the state of natural objects, the resource person as a key instrument, the data collection technique is by triangulation, the nature of data analysis is inductive, and research results tend to prioritize benefits, not generalizations. Data retrieval techniques through on-site observation, unstructured interviews, and documentation in the form of references, citations, and images or photos. Data analysis techniques are by reducing data, then the data is arranged systematically, after that verification so that the data can be validated.&#x0D; &#x0D; Results: The results of the study explained that tourists have an interest in visiting dark tourism in the Mount Merapi area of Yogyakarta based on four motivations, namely; curiosity, exploration, self-reflection, and spirituality. Furthermore, six factors were found that became the dark tourist attraction in the Mount Merapi area of Yogyakarta: historical, new attractions, natural conditions, adventure, education, and myths.&#x0D; &#x0D; Conclusion: The results of the research and discussion that have been previously presented can be concluded that the first activity of tourists visiting dark tourism in the Mount Merapi area of Yogyakarta is a sense of curiosity.
Influence of Market Mix Strategy (Marketing Mix) Towards the Increase in the Number of New Students of SDIT Ar-Treatise Seteluk

Background: Education is one of the efforts made to improve the quality of human resources which in essence helps, humans, to be smart, smart and smart. The school is one provides institutions that provide education and learning for children who are not only normal but also have special needs.&#x0D; &#x0D; Objective: To explain the effect of the educational marketing mix at SDIT Ar-Risalah on the increase in the number of students.&#x0D; &#x0D; Method: Based on the title proposed by the researcher, namely "The Effect of Educational Marketing Mix on the Improvement of Students at SDIT Ar-Risalah Seteluk", this study uses quantification research, which is a process where research data in the form of numbers, then analyzed using statistical tests.&#x0D; &#x0D; Results: Education services at SDI Surya Buana and MIN Malang 2 prioritize efforts to fulfill customer satisfaction. Marketing strategies in improving the image are carried out, namely direct, indirect, differentiation, and financing. The impact of marketing strategies and their implications on imaging at SDI Surya Buana and MIN Malang 2.&#x0D; &#x0D; Conclusion: From the results of data analysis and discussion, the author can conclude that: Marketing Mix has a positive and significant effect on the Increase of Students at SDIT Ar-Risalah Seteluk.
Strategic Analysis of Urban Fire Management Systems

A fire is a condition of a building, such as a house, factory, market, or building, burning uncontrollably, causing casualties. Fire is one of the disasters caused by non-natural factors. The purpose of this study is to analyze internal and external environmental factors in the prevention of fire management. As well as designing a strategy formulation for fire prevention. the method used in this study is a SWOT analysis using the QSPM (Quantitative Strategy Planning Matrix). The number of respondents in this study was three people. The results of this study are based on interviews with the fire service which resulted in factors influencing the occurrence of internal and External fires during the fire suppression strategy. The most influential internal factor in terms of strength is establishing an RW-level early handling officer providing counseling to the community. Already has a lot of fleet facilities. Lack of training in new firefighters. External factors in Fire prevention among them. Providing external training to fire officers in the agency/community while in terms of threats, namely weak cooperation with electricity, and water providers. Strategies that can improve at the time of fire prevention in the future. Increase the capacity of building fire prevention to the community and agencies and residents maximize early fire prevention can be done so that the fire is extinguished immediately.
Using natural head movements to continually calibrate EOG signals

Electrooculography (EOG) is the measurement of eye movements using surface electrodes adhered around the eye. EOG systems can be designed to have an unobtrusive form-factor that is ideal for eye tracking in free-living over long durations, but the relationship between voltage and gaze direction requires frequent re-calibration as the skin-electrode impedance and retinal adaptation vary over time. Here we propose a method for automatically calibrating the EOG-gaze relationship by fusing EOG signals with gyroscopic measurements of head movement whenever the vestibulo-ocular reflex (VOR) is active. The fusion is executed as recursive inference on a hidden Markov model that accounts for all rotational degrees-of-freedom and uncertainties simultaneously. This enables continual calibration using natural eye and head movements while minimizing the impact of sensor noise. No external devices like monitors or cameras are needed. On average, our method's gaze estimates deviate by 3.54° from those of an industry-standard desktop video-based eye tracker. Such discrepancy is on par with the latest mobile video eye trackers. Future work is focused on automatically detecting moments of VOR in free-living.
Soft Skills in the Process of Ensuring the Principles of Education with a Life Force

The article analyzes the development of soft skills in the process of ensuring the implementation of the principles of continuous education in the modern transformational conditions of society. Values influence people's goals and, accordingly, the sphere of their professional realization. The emergence of new professions is determined by the needs and values of society. Accordingly, the implementation of continuous education requires the development of not only hard skills but also soft skills. The development of self-awareness and social consciousness in each individual is important. Only plasticity of thought, creativity, and freedom of choice can attract competitive specialists to the market. The trend of wanting to study offline is interesting, but despite the growing virtualization of communication among the younger generation, it gives hope that the choice of profession is conscious and, of course, the person who studies will get good knowledge and skills.
IMROVING STUDENTS WRITING SKILL BY USING COMMUNICATIVE METHOD OF GRADE SEVEN AT DARMA SCHOOL IN MEDAN JOHOR

Writing is an activity to create a note or information on a media using script. The aim of this study to examine the improvement of students' writing skill at Darma Private Middle School in Medan Johor by implementing communicative method. To achieve the objectives of the research, the authors use classroom action reserach methods. This research involved seventh grade students at Darma Private Middle School Medan. The population of this research is fifty five students and the sample of this research is twenty five students. Data were analyzed qualitatively and quantitatively through writing tests. To improves students writing skills in English, proven to improve aspects of writing, vocabulary and grammar. This study figure out that students average on writing improve from the first cycle 61,2 to the second cycle 82,4. It can be as alternative way in teaching writing.
DIGITAL ECO-TEACHING AND ECO-LEARNING IN UNIVERSITY EDUCATION

The rapid development of online technologies and the ongoing changes in the educational system are highly topical issues at the moment. Most educational institutions have come up with new approaches and educational technologies, with a “connectivism approach”, mixed learning and BYOD technology being among them. The authors, who teach foreign languages, set a goal to study how the use of various messengers and applications affects the quality of classes. The authors used a new term - “eco-system”, which means a set of online services united around one topic. In this regard, they spoke about “eco-teaching” and “eco-learning” in higher education. They hypothesized that the use of resources of different online platforms promotes effective learning of a foreign language. To prove their hypothesis, the authors conducted an experiment in Peter the Great St. Petersburg Polytechnic University in the spring term of 2022. The students of the experimental division enjoyed eco-learning using different apps and messengers in their devices. The authors used theoretical (analysis and generalization of methodical literature), empirical (observation, experiment and interviewing) and mathematical (visualization and statistical calculation) research methods. The possibility of “eco-teaching” and “eco-learning” was considered as the IV (independent variable) of the experiment; the students’ academic results were considered as the DV (dependent variable). The obtained data allowed the authors to conclude that the use of the eco-system affected the academic performance of students and increased their satisfaction with the process of learning. Eco-teaching and eco-learning proved to be very promising in higher educational institutions.
Analysis of the Decision System for Selecting Interest in Concentration Using the SMART Method

The information technology study program at the Faculty of Computer Science, Mulia University has two areas of concentration, namely network technology and forensic and security architecture. Students tend to be confused in determining their interest in one of the concentrations so that they are less prepared in choosing concentration courses that suit their talents and interests. This research aims to make it easier for students to classify the types of interests according to their interests. Especially 5 &amp; 6 semester students who are involved in the Information Technology (IT) study program. IT students, have tendencies such as confusion, or doubts in choosing a concentration interest in the study program. Therefore, by conducting this research and realizing its objectives, data collection and data analysis techniques were used and SMART Decision Analysis (Simple Multi Attribute Rating Techniue) was used to produce decisions for students who had the interests and talents of the selected concentrations
Studying the Security Threats of Partially Processed Deep Neural Inference Data in an IoT Device

Partial computation offloading offers lower latency, privacy (by processing data close to the source), and higher performance. In partial offloading, some of the processing is performed on the less capable Internet-of-Things (IoTs), and more complex computation is performed in more capable cloud computers. Though many well-established security protocols are available to address external intrusion, it introduces significant overhead for large data sample. This overhead is becoming a challenging problem with the recent trend and promises of on-device deep neural network (DNN) inference. This paper studies how much prior data distribution knowledge is required by an intruder to retrieve the input from a partially computed vector. We explore simple and complex DNN tasks, which are unknown to the intruder. We show that for simple datasets, Auto-Encoder(AE) and Variational Auto Encoder(VAE) can retrieve the original input image with a 4% accuracy drop on average. However, the same is not valid for complex data distribution.
A Prototype Design for Gamified Blood Donation App Using Blockchain Technology, IPFS and NFTs

Donating blood is a common practice that has a positive effect on the lives of several individuals. Daily, the number of people in urgent need of blood continues to rise substantially. It is often challenging to find a suitable donor in case of emergency. The situation is worse if it is a rare blood group. This paper introduces a blockchain-based decentralised application (DApp) to match recipients with blood donors during an emergency. Personal information such as name, location, blood group, and donation history is stored securely in the blockchain distributed ledger through IPFS. The donors' lack of awareness and motivation are the primary reasons for people not coming forward for blood donations. We propose to gamify the blood donation process by issuing NFT badges as a reward for donors. This paper explains the design of smart contracts for storing donor and recipient data on the ledger and issuing NFTs to donors. The article also briefly discusses the process of designing the DApp.
Selection of the optimal neural network of a Smart building system for object recognition

Recently, artificial intelligence has captured a number of solutions that help us in everyday life. It will not surprise anyone now. It is in every modern smartphone and helps us to search, sort and process information faster. Today, smart home systems have taken a significant place among the developments in the field of information technology. The huge amount of data generated in them and the variety of formats of this data does not allow to create a universal mechanism for their productive processing. Therefore, the integration of neural networks, which is best suited for individual tasks, will provide us with high efficiency of smart home systems with minimal errors in decisions. Year by year, the interest in solving more complex tasks of object recognition is growing, due to automation needs for shaped communication processes in intelligent systems. Therefore, improving the implementation of the recognition of computer image systems is relevant. One of the promising directions for solving this problem is based on the use of artificial neural networks and neurocomputers as the most progressive in relation to the problems of classification of pattern recognition tasks. In our time, a large number of neural network architectures are proposed for application in the recognition of objects. The analysis of the proposed solutions shows that there is still no such model that would be the best among all the resulting performance parameters. Prospects for the improvement of architecture are seen in convolutional neural networks. The advantages of roller networks over multilayers are to use a common weight in the roller coasters, which means that for each pixel of the layer is used the same filter.
VT-GAT: A Novel VPN Encrypted Traffic Classification Model Based on Graph Attention Neural Network

Virtual Private Network (VPN) technology is now widely used in various scenarios such as telecommuting. The importance of VPN traffic identification for network security and management has increased significantly with the development of proxy technology. Unlike other tasks such as application classification, VPN traffic has only one flow problem. In addition, the development of encryption technology brings new challenges to VPN traffic identification. This paper proposes VT-GAT, a VPN traffic graph classification model based on Graph Attention Networks (GAT), to solve the above problems. Compared with existing VPN encrypted traffic classification techniques, VT-GAT solves the problem that previous techniques ignore the graph connectivity information contained in traffic. VT-GAT first constructs traffic behavior graphs by characterizing raw traffic data at packet and flow levels. Then it combines graph neural networks and attention mechanisms to extract behavioral features in the traffic graph data automatically. Extensive experimental results on the Datacon21 dataset show that VT-GAT can achieve over 99 $$\%$$ in all classification metrics. Compared to existing machine learning and deep learning methods, VT-GAT improves F1-Score by about 3.02%–63.55%. In addition, VT-GAT maintains good robustness when the number of classification categories varies. These results demonstrate the usefulness of VT-GAT in the VPN traffic classification.
Perancangan Sistem Informasi Penjualan Dan Persediaan Barang Berbasis Web Pada Toko Wonder Paint

Data management at the WonderPaint paint shop still uses a system that is not yet computerized, which is still recording in the ledger according to its needs at each sale and of course it takes quite a long time to process the data. For inventory, store owners record inventory manually so that it will be difficult to check which items should be purchased to meet inventory or if there is product loss. In addition, in making documents or sales transaction reports only in the form of sheets of paper, the integrity and security of the document is not guaranteed in the event of a complaint. So the author try to research a website's information system that is expected to be able to help administrator or shop owners in recording sales, inventory and help maintain the security of these documents where the admin will be easier to input, and the data will be neater and more efficient. The research method used is waterfall method for the system design. In conclusion, the program is well executed for the store and authors hoped that this system will be very helpful for the store to simplify and speed up their work in processing data
Through an AR Lens

Sensing and Augmented Reality (AR) can benefit a wide range of applications that involve the use of magnifying lenses. Recent developments in AR magnification provide a direct overlay of the magnified scenes in AR. However, instrumentation tasks that require high precision and visual acuity need to selectively magnify a region of interest while maintaining the visual perception of the rest of the environment. In this demo, we present AR-Magnifier, an AR magnification system through feature detection and matching. We propose a general framework based on an edge-computing architecture that can be applied to various types of instrumentation tasks. A pipeline is developed for detecting feature points and computing the homography matching to identify the magnified region of an object. We showcase how selective magnification in AR through sensing can assist the user in complex instrumentation tasks by providing visualization-based guidance.
Design and Deployment of a Multi-Modal Multi-Node Sensor Data Collection Platform

Sensing and data collection platforms are the crucial components of high-quality datasets that can fuel advancements in research. However, such platforms usually are ad-hoc designs and are limited in sensor modalities. In this paper, we discuss our experience designing and deploying a multi-modal multi-node sensor data collection platform that can be utilized for various data collection tasks. The main goal of this platform is to create a modality-rich data collection platform suitable for Internet of Things (IoT) applications with easy reproducibility and deployment, which can accelerate data collection and downstream research tasks.
HideSeeker

Obfuscation technologies have been well established for on-device image privacy protection, including pixelization, blurring, scribbling, sticker-covering, and inpainting. Despite their remarkable resistance to human observation, recent studies find that some of them are vulnerable to attacks by neural network-based recognition methods. In this work, we reveal the risk of privacy re-disclosure post image protection. Given an obfuscation-protected image, the privacy information includes 1) where the obfuscated region is and 2) what the hidden privacy-related objects are. Thus we focus on uncovering categories of privacy-related objects to evaluate the effectiveness of obfuscation technologies. Under severe obfuscation, unfortunately, even powerful object recognition models can hardly infer hidden privacy information.
Federated Learning Biases in Heterogeneous Edge-Devices

Critical machine learning applications (medical image guidance, task prediction, anomaly detection) require large amounts of data that could not be sufficiently supplied from a single entity, so multiple edge devices collaboratively train their collected data. But this raises privacy and overhead concerns. Federated learning (FL) can be a promising solution to enable these applications while preserving data privacy and mitigating communication overhead. However, an FL model originating from edge deployments with heterogeneous resources may be biased towards a set of devices. We observe that existing bias mitigation techniques in FL focus mainly on the bias that originates from label heterogeneity (due to the skewed distribution of data). We argue that sample feature heterogeneity due to different feature representations at devices is a major contributor to bias in FL. In this paper, we present an analysis of the bias that arises from sampling feature heterogeneity, and analyze the potential of existing performance enhancing techniques (normalization) to overcome bias. Our results demonstrate that normalization techniques do not eliminate bias and motivate the need for dedicated bias mitigation techniques in FL.
PENGARUH KECERDASAN VISUAL SPASIAL DAN SELF CONCEPT TERHADAP KEMAMPUAN BERPIKIR KREATIF

This study aims to determine the effect of visual-spatial intelligence and self-concept on creative thinking skills. This research was conducted at Yapermas Junior High School and Taman Siswa Junior High School with a sample of 46 students in class VIII for the 2021/2022 academic year. The research method used is a correlational survey method. The instruments used are description tests (visual spatial intelligence and creative thinking skills) and questionnaires (self concept), all instruments have been validated. Before testing the hypothesis, the data requirements were tested, including: normality test with chi square test, all data were normally distributed, linearity test obtained all data with a linear pattern, and multicollinearity test obtained all data between variables X1 and X2, there was no multicollinearity. Then the hypothesis test is carried out to get the equation persamaan Y=12,58 +0,22 X1+0,75X2 and with a level of 5% it is obtained Fcount (6,99) &gt; Ftable (3,21 ) then H0 is rejected. The results of the study concluded that 1) there was an effect of spatial visual intelligence and self-concept on the ability to think creatively, 2) there was an effect of visual-spatial intelligence on the ability to think creatively, and 3) there was an influence of self-concept on the ability to think creatively.