Reviewerly/exHarmony huggingface dataset:
Penerapan Fungsi Exponential Pada Pembobotan Fungsi Jarak Euclidean Algoritma K-Nearest Neighbor

– k-Nearest Neighbor (k-NN) is one of the popular classification algorithms and is widely used to solve classification cases. This is because the k-NN algorithm has advantages such as being simple, easy to explain, and easy to implement. However, the k-NN algorithm has a lack of classification results that are strongly influenced by the scale of input data and Euclidean which treats attribute data evenly, not according to the relevance of each data attribute. This causes a decrease in the classification results. One way to improve the classification accuracy performance of the k-NN algorithm is the method of weighting its features when measuring the Euclidean distance. The exponential function of the optimized Euclidean distance measurement is applied to the k-NN algorithm as a distance measurement method. Improving the performance of the k-NN method with the Exponential function for weighting features on k-NN will be carried out by experimentation using the Data Mining method. Then the results of the performance of the objective method will be compared with the original k-NN method and the previous k-NN weighting research method. As a result of the closest distance decision, taking the closest distance to k-NN will be determined with a value of k=5. After the experiment, the goal algorithm was compared with the k-NN, Wk-NN, and DWk-NN algorithms. Overall the comparison results obtained an average value of k-NN 85.87%, Wk-NN 86.98%, DWk-NN 88.19% and the k-NN algorithm given the weighting of the Exponential function obtained a value of 90.17%.
Sky Imager Data Reduction Using Autoencoder and Internet of Things Computing

Forecasting the output power of renewable energy sources is of an utmost importance for maintaining the stability of the electricity grid and ensuring uninterrupted service. Internet-of-Things (IoT) devices equipped with sky imagers are often used to estimate solar irradiance whose value is essential for forecasting solar energy. This paper considers distributed sky imagers whose data are transmitted through the Internet to a cloud for possible storage, forecasting, and/or performing other signal processing operations. The data comprise a collection of images, and this makes their streaming to the cloud a burden, i.e., they require a large bandwidth. In an attempt to deal with that, an IoT system is proposed in this paper such that it collects, compresses, transmits, and reconstructs those images with as little bandwidth as possible. More specifically, an IoT device multiplexes the collected images and compresses them using a deep learning algorithm, the encoder of a convolutional autoencoder (CAE) in particular. It, then, transmits the compressed images to a cloud server where they could be decompressed with high-fidelity using the decoder of the CAE. As the core to the proposed system, the performance of the CAE algorithm is evaluated using a publicly available dataset of sky images. Results indicate that the images can be compressed to 2% of their original size (i.e., compression ratio of 98%) and reconstructed again with high fidelity, i.e., average structural similarity index measure (SSIM) of 99%. Hardware implementation of the CAE algorithm has also been attempted using a Raspberry Pi platform representing the IoT device with an average processing speed of 6.6681 seconds per frame. This speed well meets practical requirements given the low frame-rate of sky imagers.
Federated Onboard-Ground Station Computing With Weakly Supervised Cascading Pyramid Attention Network for Satellite Image Analysis

With advances in NanoSat (CubeSat) and high-resolution sensors, the amount of raw data to be analyzed by human supervisors has been explosively increasing for satellite image analysis. To reduce the raw data, the satellite onboard AI processing with low-power COTS (Commercial, Off-The-Shelf) HW has emerged from a real satellite mission. It filters the useless data (e.g. cloudy images) that is worthless to supervisors, achieving efficient satellite-ground station communication. In the application for complex object recognition, however, additional explanation is required for the reliability of the AI prediction due to its low performance. Although various eXplainable AI (XAI) methods for providing human-interpretable explanation have been studied, the pyramid architecture in a deep network leads to the background bias problem which visual explanation only focuses on the background context around the object. Missing the small objects in a tiny region leads to poor explainability although the AI model corrects the object class. To resolve the problems, we propose a novel federated onboard-ground station (FOGS) computing with Cascading Pyramid Attention Network (CPANet) for reliable onboard XAI in object recognition. We present an XAI architecture with a cascading attention mechanism for mitigating the background bias for the onboard processing. By exploiting the localization ability in pyramid feature blocks, we can extract high-quality visual explanation covering the both semantic and small contexts of an object. For enhancing visual explainability of complex satellite images, we also describe a novel computing federation with the ground station and supervisors. In the ground station, active learning-based sample selection and attention refinement scheme with a simple feedback method are conducted to achieve the robustness of explanation and efficient supervisor’s annotation cost, simultaneously. Experiments on various datasets show that the proposed system improves accuracy in object recognition and accurate visual explanation detecting small contexts of objects even in a peripheral region. Then, our attention refinement mechanism demonstrates that the inconsistent explanation can be efficiently resolved only with very simple selection-based feedback.
Context-Aware Sequence Alignment using 4D Skeletal Augmentation

Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality. State-of-the-art methods directly learn image-based embedding space by leveraging powerful deep convolutional neural networks. While being straightforward, their results are far from satisfactory, the aligned videos exhibit severe temporal discontinuity without additional post-processing steps. The recent advancements in human body and hand pose estimation in the wild promise new ways of addressing the task of human action alignment in videos. In this work, based on off-the-shelf human pose estimators, we propose a novel context-aware self-supervised learning architecture to align sequences of actions. We name it CASA. Specifically, CASA employs self-attention and cross-attention mechanisms to incorporate the spatial and temporal context of human actions, which can solve the temporal dis-continuity problem. Moreover, we introduce a self-supervised learning scheme that is empowered by novel 4D augmentation techniques for 3D skeleton representations. We systematically evaluate the key components of our method. Our experiments on three public datasets demonstrate CASA significantly improves phase progress and Kendall's Tau scores over the previous state-of-the-art methods.
VeriMon: A Formally Verified Monitoring Tool

A runtime monitor observes a running system and checks whether the sequence of events the system generates satisfies a given specification. We describe the evolution of VeriMon: an expressive and efficient monitor that has been formally verified using the Isabelle proof assistant.
Which MAPF Model Works Best for Automated Warehousing?

Multi-Agent Path Finding (MAPF) algorithms and their variants can find high-quality collision-free plans for automated warehousing under simplified assumptions about the robot dynamics. However, these simplifying assumptions pose challenging implementational issues as the robots cannot follow the plans precisely. Various robust execution frameworks, such as the Action Dependency Graph (ADG) framework, have been proposed to enable the real-world execution of MAPF plans. Under such a framework, it is unclear how the simplifying assumptions affect the performance of the robots. In this work, we first argue that the ADG framework provides the same robustness guarantees as the single-agent framework (where plans are generated independently for each robot and collisions are avoided through a reservation table), which is widely used in industry. We then improve the efficiency of the ADG framework by integrating it with the Rolling-Horizon Collision-Resolution framework to solve MAPF problems with a persistent stream of online tasks. Using the integrated framework, we compare the standard MAPF model with many of its more complex variants, such as MAPF with rotation, k-robust MAPF, and continuous-time MAPF (taking robot dynamics into account). We examine their effectiveness in improving throughput through realistic simulations of warehouse settings with the Gazebo simulator.
Model for Integrating Knowledge Management System and Quality Management System in Industry 4.0

Some research has highlighted the interrelationship between Quality Management and Knowledge Management for designing methods that help to achieve organizational excellence.Since the adaptability to changes determines the process competitiveness, it is necessary to unify the production of knowledge with 4.0 technologies, resulting in the smart industries.The objective of this paper is to develop a framework to jointly manage aspects of quality management and knowledge management, understanding their interdependence and joint contribution to organizational success.Therefore, we propose a new model for integrating Knowledge Management System and Quality Management System in Industry 4.0, based on a documentary, descriptive and quantitative data analysis carried out through surveys to a population interested in the subject.The proposed model is a response to the business need for tools that facilitate management in the context of Industry 4.0.It allows to focus on the most important variables of quality management and knowledge management and can be applied in any type of organization that has implemented 4.0 technologies.
Exploring First-Year EFL Students’ Problems in Essay Writing

Writing has been considered the most difficult skill to master by EFL university students. It needs more effort and time for mastery. Some challenges in writing can make students confused. This condition has inspired the researchers to conduct the study. The study aims to identify the common errors in first-year students’ essay writing and provide suggestive solutions for the problems encountered by the students. This study was implemented using quantitative and qualitative approaches (mixed method). The researchers analyzed 205 students’ essays to identify the writing errors. After that, they randomly interviewed selected students to find the specific problems and suggestions. The data gathered were then classified and analyzed thematically. Based on the analyzed data, the most frequent error was faulty sentences which was 60.41%, grammar as the second error was 16.88%, the third error was mechanics with 13.1%, and vocabulary and other errors were 5.38% and 4.22%. The result shows that most students got difficulty writing sentences correctly. Therefore, providing appropriate learning materials for students and giving feedback on their writing are suggested.
ENHANCING STUDENTS’ ENGLISH SPEAKING ABILITY THROUGH VRCHAT GAME AS LEARNING MEDIA

Era 5.0 or Society 5.0 is a human-centered technology-based era that aims to create convenient and efficient living circumstances and provide further innovation in the life sustainability area. Era 5.0 is visualized as capable of creating a multinational society, whether in business, social, and educational environments. Thus, English should be noted as a crucial matter needed in communicating and takes part in living within era 5.0 in the future. The current research was conducted because of the limited exploration in the previous study that discusses online game VRChat as a learning medium to enhance students’ English speaking ability. Therefore, this paper aims to fill the gap by investigating and exposing the VRChat game’s value as a learning medium to improve students’ ability to speak English. Mixed method research was applied in the current study to analyze and ensure the VRChat game’s advantages for enhancing students’ speaking ability. Moreover, interviews and testing were used as the instrument in this research. A random sampling of four students who have done taking the FLSP (Foreign Learning for Specific Purposes) course as the current study’s subject. Additionally, the analysis was done using descriptive statistics and hierarchical charts using Nvivo 12 plus. This study showed the use of VRChat games as a learning medium increased students’ ability to speak English, specifically on their accuracy and fluency. This research implies that the VRChat game can support English teaching as the learning media, especially in speaking subjects.
The Effect of Critical Multiliteracy Learning Model on Students' Reading Comprehension

This study aims to determine the influence of the Critical Multiliteracy model on the reading comprehension ability of fifth graders at State Elementary School (SDN) 1 Majasem. The approach used in this research is quantitative. The research design used was a one-group pretest-posttest design. The population was the fifth-grade elementary school at SDN 1 Majasem. The sample of this research is 24 students. The data collection technique in this study used a test in the form of multiple choice questions and a description of 20 questions. The research data were analyzed using normality, homogeneity, and t-test. Based on the study's results, it is known that the multiliteracy learning model has a significant effect on students' reading comprehension skills. This result can be seen from the significance value is 0.013 &lt; 0.05. Thus, the critical multiliteracy learning model positively impacts students' reading comprehension skills.
Characterization of Quality Attributes to Evaluate the User Experience in Augmented Reality

This study proposes a characterization of quality attributes for applications that use augmented reality. This classification is done from the perspective of the user experience. The attribute identification was based on primary studies of the IEEE Xplore, Scopus, and ACM repositories. From an initial set of 1165 papers, 101 documents were selected. The document proposes two categories: objective and subjective. In the objective category 4 subcategories and 40 attributes were found, and in the subjective one 5 subcategories and 54 attributes were found. This is the first time that all these criteria are presented in a single document, which is the input for designing a comprehensive quality assurance tool for user experience in augmented reality.
A Repetitive Watermarking Scheme for Digital Images based on Self-Inverting Permutations

In this work we present a novel watermarking scheme based on the repetitive application of watermarks inside a digital image. Our approach extends the technique proposed initially in [1] and [2] by implementing the corresponding approach repetitively over the wider area of the digital image and embedding the whole watermark in the corresponding cells instead of parts of it. The approach proposed in this work focuses mainly on securing the watermarked image against crop attacks and compression attacks that constitute the most important attacks through the attack vectors. Our approach provides a significant improvement over the computational cost required for the embed procedure of the watermark inside a digital image, achieving an adequately imperceptible and robust watermarking technique. The experimental results exhibited through the evaluation of our proposed model prove its potentials against these types of attacks and ensure that the embedded information will be successful extracted from the watermarked image after the deployment of such attacks.
Language models can prioritize patches for practical program patching

The field of Automated Program Repair (APR) has seen significant growth in the past decade. As the field progressed, the number of templates used by APR tools has grown substantially to increase the number of patches included within the domain each tool finds fixable, thus increasing their fixing capability. However, this heightened potential was not free: new techniques paid by using greater computational resources and time to look over an enlarged repair space. In this paper, we look to curtail this trend by using language models (LMs) to provide guidance about whether a generated patch is natural. By prioritizing patches that generate natural code, which has been demonstrated in prior work to be related to correctness, we can reduce the number of patches that must be inspected to find the first correct patch. We evaluate this prioritization scheme over five APR tools, and find that we can reduce the number of patches that must be inspected in up to 70% of bugs and reduce the total number of patches inspected by up to two-thirds, paving the way for lower-cost program repair.
Temporal-Aware QoS Prediction via Dynamic Graph Neural Collaborative Learning

How to effectively predict missing QoS has become a fundamental research issue for service-oriented downstream tasks. However, most QoS prediction approaches omit high-order implicit invocation correlations and collaborative relationships among users and services. Thus, they are incapable of effectively learning the temporally evolutionary characteristics of user-service invocations from historical QoS records, which significantly affects the performance of QoS prediction. To address the issue, we propose a novel framework for temporal-aware QoS prediction by dynamic graph neural collaborative learning. Dynamic user-service invocation graph and graph convolutional network are combined to model user-service historical temporal interactions and extract latent features of users and services at each time slice, while a multi-layer GRU is applied for mining temporal feature evolution pattern across multiple time slices, leading to temporal-aware QoS prediction. The experimental results indicate that our proposed approach for temporal-aware QoS prediction significantly outperforms state-of-the-art competing methods.
Integrating Digital Literacy Into English As A Foreign Language (EFL) Class: A Cooperative Learning Approach

In this ICT (Information, Communication and Technology) era in which internet service spreads widely over the world enables people to access abundant information more easily. Digital literacy skills are crucially needed to filter reliable and unreliable information. Digital literacy competencies are also essential to filter the massive spread of fake news or hoax and hate speech. In education, digital literacy competencies need to be taught and trained to students to gain information, analyse digital information content critically and to use information in many types of formats from a number of sources obtained through computers. Therefore, digital literacy skills can be integrated in learning. Cooperative learning is a learning approach that allow students take part in group learning activities to analyze, synthesize and reflect thoughtful evaluation of a digital content. This study aims to foster integration of digital literacy competencies into English as a Foreign Language (EFL) class through cooperative learning approach. The subjects of this study were 37 students of 6th semester of TBI-1 (English Education Department) class of the State Islamic University of North Sumatera. This study was conducted using qualitative method. Data collection techniques utilized secondary research and observation. The research findings show that students who are dominated by Generation Z are literate enough in performing digital literacy skills through cooperative learning approach.
A Deep Learning-Based Multidimensional Aesthetic Quality Assessment Method for Mobile Game Images

Mobile games have played an increasingly significant role in people's leisure lives in recent years, thanks to the fast expansion of the gaming industry and the widespread use of mobile devices. The aesthetic quality of game pictures is a very important factor that attracts users' interest. However, evaluating the aesthetic quality of mobile game pictures is difficult since the painting styles of games vary greatly and the evaluation criteria are also diversified. In this article, we propose a multitask deep learning-based method, which is able to predict the aesthetic quality of mobile game images in multiple dimensions. The proposed model consists of two modules, a feature extraction module and a quality regression module. We extract quality-aware features from intermediate layers of the deep convolution neural network and then incorporate them into the final feature representation in the feature extraction module, allowing the model to fully use visual information from low to high levels. The quality regression module uses fully connected layers to map quality-aware features into quality scores across multiple dimensions. The multidimensional aesthetic quality scores are trained using a multitask learning approach, in which quality-aware features are shared across multiple dimensional quality prediction tasks. Finally, several key factors which help the proposed model perform better are analyzed. The experimental results indicate that our proposed method not only achieves the greatest performance on mobile game images, but also is applicable to natural scene images.
Vehicle Fault-Tolerant Robust Power Transmission Line Inspection Planning

This paper concerns fault-tolerant power transmission line inspection planning as a generalization of the multiple traveling salesmen problem. The addressed inspection planning problem is formulated as a single-depot multiple-vehicle scenario, where the inspection vehicles are constrained by the battery budget limiting their inspection time. The inspection vehicle is assumed to be an autonomous multi-copter with a wide range of possible flight speeds influencing battery consumption. The inspection plan is represented by multiple routes for vehicles providing full coverage over inspection target power lines. On an inspection vehicle mission interruption, which might happen at any time during the execution of the inspection plan, the inspection is re-planned using the remaining vehicles and their remaining battery budgets. Robustness is introduced by choosing a suitable cost function for the initial plan that maximizes the time window for successful re-planning. It enables the remaining vehicles to successfully finish all the inspection targets using their respective remaining battery budgets. A combinatorial metaheuristic algorithm with various cost functions is used for planning and fast re-planning during the inspection.