Reviewerly/exHarmony huggingface dataset:
Upaya Meningkatkan Hasil Belajar Siswa dengan Menggunakan Media Objek Langsung pada Mata Pelajaran Bahasa Indonesia Tingkat Madrasah Ibtidaiyah

Learning Indonesian especially in the ability to write simple essays in class II SDN 2 Sukasenang Sindangkasih Subdistrict, Ciamis District with environmental teaching materials around the school provides opportunities for students to be active, creative, and critical. In general, the results of appropriate studies can significantly meet the needs of teachers and students in which standard learning procedures. The ability of students to write simple essays using direct objects in the first cycle of meeting 1 was 73.51; the second cycle of meeting 1 amounted to 82.5; the second cycle of meeting 2 was 82.96; the third cycle of meeting 1 was 87.6; the second cycle of the second meeting was 91.35. 2) The skills of teachers in teaching in the first cycle of meeting 1 amounted to 79.8; the second cycle of meeting 1 was 80.4; the second cycle of meeting 2 was 81.7; the third cycle of meeting 1 was 85.1; the second cycle of meeting 2 was 89.82. 3) Student learning outcomes in the first cycle of meeting 1 amounted to 64.17; the second cycle of meeting 1 was 67.69; the second cycle of meeting 2 was 71.13; the third cycle of meeting 1 was 78.4; the third cycle of meeting 2 was 81.8. The ability of students to write an essay description using a direct object seems to have an increase even though it is not maximal. Increasing the ability of students to write essays using direct objects can be seen from changes in the average value of each aspect of writing essays in a better direction. This proves that media objects can directly improve student learning outcomes on the theme of safety at home and on the journey Indonesian language is the subject of essay writing at SD Negeri 2 Sukasenang. The results of the analysis indicate that the proposed hypothesis is acceptable
Improvement of the Classification Performance of an Intrusion Detection Model for Rare and Unknown Attack Traffic

How to deal with rare and unknown data in traffic classification has a decisive influence on classification performance. Rare data make it difficult to generate validation datasets to prevent overfitting, and unknown data interferes with learning and degrades the performance of the model. This paper presents a model generation method that accurately classifies rare data and new types of attacks, and does not result in overfitting. First, we use oversampling methods to solve the data imbalance caused by rare data. We separate the test dataset into a training dataset and a validation dataset. A model is created using separate training and validation datasets. Furthermore, the test dataset is used only for evaluating the performance capabilities of classification models, in order to make the test dataset independent of learning. We also use a softmax function that numerically indicates the probability that the model’s predictive results are accurate in detecting new, unknown attacks. Consequently, when applying the proposed method to the NSL_KDD dataset, the accuracy is 91.66%—an improvement of 6–16% compared to existing methods.
Framework for Gender Recognition Using Facial Features by Using Deep Learning

This research work discusses about the proposed Framework for Gender Recognition for using Facial Features by Using Deep Learning over row data. Gender Classification is done in order to predict in different class categories like male and female. Classifiers used are deep learning, CNN and other Multi-classifiers for classifying Gender whether they belong to either male or female. The outcomes of all the classifiers were compared based on accuracy, error, precision, recall and F-Score. It was found that, CNN-a conventional deep learning classifier has yielded good amount of accuracy in classifying Gender datasets. Our proposed framework work for Framework for Gender Recognition Using Facial Features by Using Deep Learning. Our proposed Modified CNN algorithm, to classify huge real time data and gave best results on Real time data compared to BCNN and CNN, Naive Bayes and CNN. We compared three different classifiers namely Naive Bayes, BCNN, CNN with Modified CNN. Modified CNN performed well in classifying the gender dataset by yielding maximum accuracy of 97.33%, followed by Naive Bayes, BCNN, CNN 90.5%, 75.65% with 81.33% accuracies.
A Study of Timing Side-Channel Attacks and Countermeasures on JavaScript and WebAssembly

Side-channel attacks are a group of powerful attacks in hardware security that exploit the deficiencies in the implementation of systems. Timing side-channel attacks are one of the main side-channel attack categories that use the time difference of running an operation in different states. Many powerful attacks can be classified into this type of attack, including cache attacks. The limitation of these attacks is the need to run the spy program on the victim's system. Various studies have tried to overcome this limitation by implementing these attacks remotely on JavaScript and WebAssembly. This paper provides the first comprehensive evaluation of timing side-channel attacks on JavaScript and investigates challenges and countermeasures to overcome these attacks. Moreover, by investigating the countermeasures and their strengths and weaknesses, we introduce a detection-based approach, called Lurking Eyes. Our approach has the least reduction in the performance of JavaScript and WebAssembly. The evaluation results show that the Lurking eyes have an accuracy of 0.998, precision of 0.983, and F-measure of 0.983. Considering these values and no limitations, this method can be introduced as an effective way to counter timing side-channel attacks on JavaScript and WebAssembly. Also, we provide a new accurate timer, named Eagle timer, based on WebAssembly memory for implementing these attacks.
Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Point Clouds

We present Tesla-Rapture, a gesture recognition interface for point clouds generated by mmWave Radars. State of the art gesture recognition models are either too resource consuming or not sufficiently accurate for integration into real-life scenarios using wearable or constrained equipment such as IoT devices (e.g. Raspberry PI), XR hardware (e.g. HoloLens), or smart-phones. To tackle this issue, we developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds. The model outperforms the state of the art on two datasets in terms of accuracy while reducing the computational complexity and, hence, the execution time. In particular, the approach, is able to predict a gesture almost 8 times faster than the most accurate competitor. Our performance evaluation in different scenarios (environments, angles, distances) shows that Tesla generalizes well and improves the accuracy up to 20% in challenging scenarios like a through-wall setting and sensing at extreme angles. Utilizing Tesla, we develop Tesla-Rapture, a real-time implementation using a mmWave Radar on a Raspberry PI 4 and evaluate its accuracy and time-complexity. We also publish the source code, the trained models, and the implementation of the model for embedded devices.
노년 세대를 위한 자서전 제작의 사례 연구 - 자서전 작가의 목소리를 중심으로 -

This study analyzed the experiences of autobiography production for the elderly, focusing on the voice of the autobiography writer. Specifically, by interviewing four writers belonging to a social enterprise specializing in autobiography production, the difficulties experienced during the interview and writing stages of autobiography production were identified, and the value of the autobiography production process according to the writer were explored. At the interview stage, the writer explores and shares the information necessary for autobiography production with the author through questions and answers. The writer faces the author’s “short words” and “long words” in the interview, which hinders obtaining sufficient information for autobiography. Further, in this process, the writer confirms the value of the healing and self-understanding of the interview. Conversely, at the writing stage, the writer produces an autobiography based on the author’s life, which was grasped through interviews. In writing, it is difficult for writers to represent the author’s “truth” as the author''s “voice”. The writer attempts to reproduce the author’s voice by “writing dryly, avoiding lies” or “recording all the words faithfully”. In this process, the writer confirms the value of recognition and communication in writing. Finally, this study suggests four implications for the status of the autobiographic genre, identity of the autobiography writer, procedural knowledge for autobiography production, and application of autobiography production.
New Perspective on Progressive GANs Distillation for One-class Novelty Detection

One-class novelty detection is conducted to identify anomalous instances, with different distributions from the expected normal instances. In this paper, the Generative Adversarial Network based on the Encoder-Decoder-Encoder scheme (EDE-GAN) achieves state-of-the-art performance. The two factors bellow serve the above purpose: 1) The EDE-GAN calculates the distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruction error between images. 2) The model obtains best results when the batch size is set to 1. To illustrate their superiority, we design a new GAN architecture, and compare performances according to different batch sizes. Moreover, with experimentation leads to discovery, our result implies there is also evidence of just how beneficial constraint on the latent space are when engaging in model training. In an attempt to learn compact and fast models, we present a new technology, Progressive Knowledge Distillation with GANs (P-KDGAN), which connects two standard GANs through the designed distillation loss. Two-step progressive learning continuously augments the performance of student GANs with improved results over single-step approach. Our experimental results on CIFAR-10, MNIST, and FMNIST datasets illustrate that P-KDGAN improves the performance of the student GAN by 2.44%, 1.77%, and 1.73% when compressing the computationat ratios of 24.45:1, 311.11:1, and 700:1, respectively.
Cooperative learning and notions of geometry in 5-year-old children in educational preschool institutions

Cooperative learning has become a fundamental teaching-learning strategy for the social construction of knowledge.This methodology is ideal for preschool students in regions such as Latin America and the Caribbean, where the effects oflearning poverty condition socioeconomic development. Therefore, the objective was to assess the incidence of cooperativelearning in the teaching of geometry in 5-year-old students of educational preschool institutions in the three natural regions ofPeru. This research was qualitative and descriptive-correlational. Two data collection instruments were applied to 78 studentswho served as a sample. The Spearman’s Rho was used to analyze the relationship between the variables, which confirmed thepositive relationship between them; therefore, the influence of incorporating this type of learning has an impact on the logicaldevelopment of children. A quality education with optimal learning strategies will lead these students in the future to a goodlevel of performance in academic tests such as PISA.
Analysis System About Muscle-Bone-Joint Symptoms by the Association Rule Method: Analysis System About Muscle-Bone-Joint Symptoms by the Association Rule Method

Muscle, Bone, Joint Problems are health problems in the human body. Problems of muscles, bones, joints felt by almost everyone, both parents, adolescents, and children. Symptoms of muscle, bone, joint problems can be pain in the knee, pain in the shoulder, pain in the neck, pain in the hands, pain in the legs or pain in the back. To categorize the problems of muscles, bones, joints, there are many problems in management reporting to find out the symptoms that often occur in one semester. Based on this it is necessary studies that lead to the creation of a system that can categorize the spread of symptoms in muscles, bones, joints to determine the results of diagnosis based on the pattern of symptoms experienced by patients. So that helps management to find out and get information based on the pattern of symptoms that occur in patients. One alternative to the problem, this study we make a search pattern or association rule association (associative rules) of large-scale data and is very closely related to data mining that can be used to find certain rules that associate data with one other data with a priori algorithm method. The purpose of the method is to search historical data to identify data patterns based on previous characteristics to determine the disease diagnosis. So that the information generated can be used by management to provide appropriate diagnostic information according to the natural pattern of symptoms by the patient.
Investigating Mechanical Engineering Learners’ Satisfaction with a Revised Monozukuri MOOC

Aside from providing instructional materials to the public, developing massive open online courses (MOOCs) can benefit institutions in different ways. Some examples include providing training opportunities for their students aspiring to work in the online learning space, strengthening its brand recognition through courses appealing to enthusiasts, and enabling online linkages with other universities. One such example is the monozukuri MOOC offered by the Tokyo Institute of Technology on edX, which initially presented the Japanese philosophy of making things in the context of a mechanical engineering course. In this paper, we describe the importance of involving a course development team with a diverse background. The monozukuri MOOC and its revision enabled us to showcase an otherwise distinctively Japanese topic (philosophy) as an intersection of various topics of interest to learners with an equally diverse background. The revision resulted in discussing monozukuri in a mechanical engineering lesson and how monozukuri is actively being practiced in the Japanese workplace and academic setting while juxtaposing it to the relatively Western concept of experiential learning. Aside from presenting the course with a broader perspective, the revision had been an exercise for its team members on working in a multicultural environment within a Japanese institution, thus developing their project management and communication skills.
Production of Natural Aid Coagulant for Turbidity Removal in Kaolin Suspension

Water crisis and contamination of water become one of the biggest threats towards the sustainable development of the society where there was lack of clean drinking water since the increased demand of pure clean water. Agricultural activities were the main contributors to water pollution and resulted in high turbidity to nearby river. Hence, the effective water treatment was required which is coagulation and flocculation process, however the usage of conventional chemical coagulant can generate an impact towards human health because of the high volume of sludge residual which it is categorized as schedule waste. This study aims to produce the natural coagulant aid from common fig, characterise the physiochemical properties in natural coagulant and evaluate the turbidity reduction in water treatment. The water extraction of the biopolymer from common fig and the jar test was conducted by adding Fe3+ concentration as coagulant for rapid mixing and biopolymer concentration as aid coagulant for the coagulation and flocculation process.
Scene Graph Generation for Better Image Captioning?

We investigate the incorporation of visual relationships into the task of supervised image caption generation by proposing a model that leverages detected objects and auto-generated visual relationships to describe images in natural language. To do so, we first generate a scene graph from raw image pixels by identifying individual objects and visual relationships between them. This scene graph then serves as input to our graph-to-text model, which generates the final caption. In contrast to previous approaches, our model thus explicitly models the detection of objects and visual relationships in the image. For our experiments we construct a new dataset from the intersection of Visual Genome and MS COCO, consisting of images with both a corresponding gold scene graph and human-authored caption. Our results show that our methods outperform existing state-of-the-art end-to-end models that generate image descriptions directly from raw input pixels when compared in terms of the BLEU and METEOR evaluation metrics.
Digital simulators of the random processes

The proposed universal digital simulators of random processes based on their Markov models are considered as capable of generating sequences of samples of unlimited duration. It is shown that a simple Markov chain allows generating the random numbers with a specified two-dimensional probability distribution of the neighboring values while a doubly connected Markov model makes it possible to get the three-dimensional random numbers. The parameters of the model are determined from either a known probability density or experimental samples of the simulated random process. It is demonstrated that the simulation algorithms do not require complex mathematical transformations and that they can be implemented using a simple element base. To change the properties of the generated random processes one needs to reload the memory device with a pre-formed data array. The block diagrams of the simulators are studied and the probabilistic and correlation characteristics of the generated random processes are determined. It is established that with these simulators a high accuracy of convergence of the probability distributions of the selected model and the histograms of the generated sample sequences is ensured. In the common studies, one can hardly find the results that can surpass by their efficiency the ones that the proposed simulation algorithms demonstrate accounting for their non-problematic hardware implementation (the minimum computational costs) and the simplicity of reconfiguring the Markov model based simulators for generating new random processes. The introduced simulators can be used in the design, development and testing of the multi-purpose electronic equipment, with different meters and the devices for simulating radio paths.
Security Threats to Cloud Services

This research paper looks into the world of how-to of cloud services and the ways through which it is threatened by various elements that regularly disrupt the functioning of these services. We will see how different models are being implemented across different fields. These various models are adopted by different users and organizations according to their needs. As there is almost no service with its specific problems, we delve into the discussion about various threats that loom over this expanded service step by step. These problems may be related to networking or authentication problems or security breach. Then we will look into how these problems are overcome by cloud service experts.
COMBINING OPERATIONAL MODAL ANALYSIS AND GENETIC ALGORITHMS TO UNDERSTAND THE ACTUAL STRUCTURAL BEHAVIOR OF HISTORICAL CONSTRUCTIONS

Evolution of technologies both in the fields of in situ investigations and Finite Element (FE) modeling strongly enhanced the possibility to understand the structural dynamic behavior of masonry historical constructions, allowing a periodic or continuous analysis of their response to environmental, anthropic, and exceptional actions and the formulation of accurate hypotheses about their future behavior, which is fundamental for Cultural Heritage preservation.In order to improve the assessment of the health status of historical buildings, inverse methods re-sorting to dynamic identification techniques are often used to provide experimentally verified data for the accurate calibration of FE models representative of the investigated structures.In this paper, vibration-based identification methods are coupled with an automatic FE model updating procedure to study the dynamic behavior of the Civic Tower of Ostra, Italy, and obtain baseline information for future comparative analyses.The experimental data obtained from two different campaigns of ambient vibration tests are used to update the mechanical characteristics of the detailed FE model of the tower.The updating process, unsolvable via common calibration procedures, is automatically managed through a powerful bio-inspired tool, i.e. the Genetic Algorithm (GA), allowing to closely reproduce the actual behavior of the tower.
Limits for quantum networks with semiconductor entangled photon sources

Semiconductor quantum dots are promising constituents for future quantum communication. Although deterministic, fast, efficient, coherent, and pure emission of entangled photons has been realized, implementing a practical quantum network remains outstanding. Here we explore the limits for sources of polarization-entangled photons from the commonly used biexciton-exciton cascade. We stress the necessity of tuning the exciton fine structure, and explain why the often observed time evolution of photonic entanglement in quantum dots is not applicable for large quantum networks. The consequences of device fabrication, dynamic tuning techniques and statistical effects for practical network applications are investigated. We identify the critical device parameters and present a numerical model for benchmarking the device scalability in order to bring the realization of distributed semiconductor-based quantum networks one step closer to reality.
A comparison of online fraud prevention disclosure in Malaysian and Indonesian Public Universities / Corina Joseph …[et al.]

This study aimed to examine the extent of fraud prevention information disclosure on websites of Malaysian and Indonesian public universities using the Legitimacy Theory (LT). Based on a content analysis of fraud prevention information disclosed on the websites of 20 Malaysian and 86 Indonesian public universities using a newly developed University Fraud Prevention Disclosure Index, it was found that Malaysian public universities on average disclosed 13 items under study compared to just 11 items disclosed by the universities in Indonesia. Overall, the level of disclosure for both countries was low. Malaysian public universities are more vigilant and stronger in the Internal Audit and Bursary aspects, whilst its Indonesian counterparts are better in five aspects: integrity and governance, policy, fraud prevention strategies, fraud response procedure, and raising awareness. Public universities in both countries need to put more effort in disclosing fraud prevention information on their websites. Websites should be used to legitimize societal accountability in the quest to look more transparent in accounting procedures and procurement guidelines. This paper adds to the literature by integrating two elements of sustainable development goals (quality education (SDG No 4) in an effective, accountable, and transparent institution (SDG No 16) using the LT to enhance the understanding of fraud prevention disclosure on universities’ websites.