Reviewerly/exHarmony huggingface dataset:
PERAN ORANG TUA TERHADAP MOTIVASI BELAJAR SISWA KELAS V SEKOLAH DASAR

The purpose of this study was to find out how the role of parents on the learning motivation of fifth grade students at SDN 060960 Belawan. This type of research is a qualitative approach with data analysis techniques according to Miles and Huberman. The activities in this data analysis are data reduction, data display and conclusion drawing/verification. The subjects and objects of this data are parents and students of class V SDN 060960 Belawan totaling 26 people consisting of mothers, fathers and students who will be studied using observation, interviews and documentation. Based on the results of research that has been done through interviews, observations and documentation that the role of parents on student learning motivation is giving attention, giving rewards and appreciation in the form of praise and providing learning facilities. It was found that the role of parents on students' learning motivation was not sufficient to carry out its role on student learning motivation, such as: Not paying attention to children, Not providing books and learning facilities for children. It can be concluded that the role of parents on the learning motivation of fifth grade students at SDN 060960 Belawan is not all done well, because there are some parents who are still unable to carry out their role in motivating children's learning, this is evidenced by the results of interviews between researchers and parents of students.
MaskDiffuse: Text-Guided Face Mask Removal Based on Diffusion Models

As masked face images can significantly degrade the performance of face-related tasks, face mask removal remains an important and challenging task. In this paper, we propose a novel learning framework, called MaskDiffuse, to remove face masks based on Denoising Diffusion Probabilistic Model (DDPM). In particular, we leverage CLIP to fill the missing parts by guiding the reverse process of pretrained diffusion model with text prompts. Furthermore, we propose a multi-stage blending strategy to preserve the unmasked areas and a conditional resampling approach to make the generated contents consistent with the unmasked regions. Thus, our method achieves interactive user-controllable and identity-preserving masking removal with high quality. Both qualitative and quantitative experimental results demonstrate the superiority of our method for mask removal over alternative methods.
Aplikasi Media Pembelajaran Iqro’ Berbasis Mobile

The iqro method is a method of reading the Koran that emphasizes direct reading practice. In each volume there are instructions for learning with the intention of making it easier for everyone who learns and teaches the Koran. At present there are still many Muslims who have not been able to read the Qur'an properly, both from children to adults based on the results of an interview with one of the clerics in the Baitul Mustaqim Jimbaran Mosque said children in 5 -10 years, there are only 5 children who study the mosque. Many factors influence the reduction of children's learning interest in learning iqro. From the above problems an application was made that aims to design and build a mobile-based iqro application for children. The use of technology as a medium for learning as a way out to reduce the number of Muslims who have not been able to read the Qur'an. In making the application, the concept of planning and design through the data collection stage, UML (Unified Modeling Language) and then implementing it. the method used is the MDLC (Multimedia Development Life Cycle) method. The results of this study are iqro learning media applications for mobile-based children as a means of learning the Koran. blackbox testing function of the learning media application iqro runs according to what is displayed in the menus in each application.
Introduction for human-centric software engineering

In the evolving landscape of software development, Human-Centric Software Engineering (HCSE) is emerging as a pivotal paradigm, prioritizing human needs and experiences at the core of software engineering processes. This research delves into the fundamental principles of HCSE, its implications on software quality, and the enhanced user satisfaction it promises. Through comprehensive surveys, case study analyses, and user feedback sessions, this study reaffirms the increasing significance of HCSE in modern software development. Despite its evident benefits, the research also sheds light on the organizational barriers hindering its broad adoption. As software systems continue to intertwine with our daily lives, the research underscores the imperative shift from mere functionality to creating holistic, human-centered software experiences.
TAEKWONDO POSE ESTIMATION WITH DEEP LEARNING ARCHITECTURES ON ONE-DIMENSIONAL AND TWO-DIMENSIONAL DATA

Practicing sports is an activity that helps people maintain and improve their health, enhance memory and concentration, reduce anxiety and stress, and train teamwork and leadership ability. With the development of science and technology, artificial intelligence in sports has become increasingly popular with the public and brings many benefits. In particular, many applications help people track and evaluate athletes' achievements in competitions. This study extracts images from Taekwondo videos and generates skeleton data from frames using the Fast Forward Moving Picture Experts Group (FFMPEG) technique using MoveNet. After that, we use deep learning architectures such as Long Short-Term Memory Networks, Convolutional Long Short-Term Memory, and Long-term Recurrent Convolutional Networks to perform the poses classification tasks in Taegeuk in Jang lessons. This work presents two approaches. The first approach uses a sequence skeleton extracted from the image by Movenet. Second, we use sequence images to train using video classification architecture. Finally, we recognize poses in sports lessons using skeleton data to remove noise in the image, such as background and extraneous objects behind the exerciser. As a result, our proposed method has achieved promising performance in pose classification tasks in an introductory Taekwondo lesson.
Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets

Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information, a problem that persists despite user awareness. This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning (ML) models—Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories. Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%. On the other hand, LSTM shows the lowest accuracy of 96%. These findings underscore the potential of ML techniques in enhancing phishing detection systems and bolstering cybersecurity measures against evolving phishing tactics, offering a promising avenue for safeguarding sensitive information and online security.
D3APTS: Design of ECC Based Authentication Protocol and Data Storage for Tactile Internet enabled IoD System With Blockchain

Tactile Internet, a fast and low-latency communication network, plays a crucial role in IoD applications for providing various features like precision control and realistic telepresence. The Internet of Drones (IoD) is a rapidly evolving concept that envisions a network of collaborating and communicating interconnected drones. Drones have diverse applications, including traffic monitoring, medicine, battlefield surveillance, logistics, and agriculture. However, the widespread deployment of drones raises significant security and privacy concerns. In this paper we have proposed secure data delivery authentication protocol for tactile internet enabled IoD system(D3APTS). Also, using blockchain along with IoD for storing data will provide additional characteristics like data immutability. To secure communication among user, ground station server, and drone D3APTS employs one-way hash function and ECC cryptosystem. It resists various attacks like impersonation attacks, capturing attacks, session-key disclosure attacks, especially desynchronisation attacks which is important in case of IoD scenario. D3APTS also provides anonymity and unlinkability features. It is formally verified using the scyther security tool and ROR model. The performance analysis and comparison shows that D3APTS is better in terms of computation, communication overheads, security and functionality features.
Metaverse Communications, Networking, Security, and Applications: Research Issues, State-of-the-Art, and Future Directions

Metaverse is an evolving orchestrator of the next-generation Internet architecture that produces an immersive and self-adapting virtual world in which humans perform activities similar to those in the real world, such as playing sports, doing work, and socializing. It is becoming a reality and is driven by ever-evolving advanced technologies such as extended reality, artificial intelligence, and blockchain. In this context, Metaverse will play an essential role in developing smart cities, which becomes more evident in the post-COVID-19-pandemic metropolitan setting. However, the new paradigm imposes new challenges, such as developing novel privacy and security threats that can emerge in the digital Metaverse ecosystem. Moreover, it requires the convergence of several media types with the capability to quickly process massive amounts of data to keep the residents safe and well-informed, which can raise issues related to scalability and interoperability. In light of this, this research study aims to review the literature on the state of the art of integrating the Metaverse architecture concepts in smart cities. First, this paper presents the theoretical architecture of Metaverse and discusses international companies’ interest in this emerging technology. It also examines the notion of Metaverse relevant to virtual reality, identifies the prevalent threats, and determines the importance of communication infrastructure in information gathering for efficient Metaverse operation. Next, the notion of blockchain technologies is discussed regarding privacy preservation and how it can provide tamper-proof content sharing among Metaverse users. Finally, the application of distributed Metaverse for social good is highlighted. Most importantly, the paper explores the reflections of this cutting-edge technology on the smart city, talks about the role and impact of the Metaverse in the production of urban policies, and eventually identifies the research gaps and the future research directions in this domain.
Integrated Edge Computing and Blockchain: A General Medical Data Sharing Framework

Medical data sharing is crucial to enhance diagnostic efficiency and improve the quality of medical data analysis. However, related endeavors face obstacles due to insufficient collaboration among medical institutions, and traditional cloud-based sharing platforms lead to concerns regarding security and privacy. To overcome these challenges, the paper introduces MSNET, a novel framework that seamlessly combines blockchain and edge computing. Data traceability and access control are ensured by employing blockchain as a security layer. The blockchain stores only data summaries instead of complete medical data, thus enhancing scalability and transaction efficiency. The raw medical data are securely processed on edge servers within each institution, with data standardization and keyword extraction. To facilitate data access and sharing among institutions, smart contracts are designed to promote transparency and data accuracy. Moreover, a supervision mechanism is established to maintain a trusted environment, provide reliable evidence against dubious data-sharing practices, and encourage institutions to share data voluntarily. This novel framework effectively overcomes the limitations of traditional blockchain solutions, offering an efficient and secure method for medical data sharing and thereby fostering collaboration and innovation in the healthcare industry.
Intrinsic Explainability for End-to-End Object Detection

Deep Learning models are automating many daily routine tasks, indicating that in the future, even high-risk tasks will be automated, such as healthcare and automated driving areas. However, due to the complexity of such deep learning models, it is challenging to understand their reasoning. Furthermore, the black box nature of the designed deep learning models may undermine public confidence in critical areas. Current efforts on intrinsically interpretable models focus only on classification tasks, leaving a gap in models for object detection. Therefore, this paper proposes a deep learning model that is intrinsically explainable for the object detection task. The chosen design for such a model is a combination of the well-known Faster-RCNN model with the ProtoPNet model. For the Explainable AI experiments, the chosen performance metric was the similarity score from the ProtoPNet model. Our experiments show that this combination leads to a deep learning model that is able to explain its classifications, with similarity scores, using a visual “bag of words”, which are called prototypes, that are learned during the training process. Furthermore, the adoption of such an explainable method does not seem to hinder the performance of the proposed model, which achieved a mAP of 69% in the KITTI dataset and a mAP of 66% in the GRAZPEDWRI-DX dataset. Moreover, our explanations have shown a high reliability on the similarity score.
Defending Video Recognition Model against Adversarial Perturbations via Defense Patterns

Deep Neural Networks (DNNs) have been widely successful in various domains, but they are vulnerable to adversarial attacks. Recent studies have also demonstrated that video recognition models are susceptible to adversarial perturbations, but the existing defense strategies in the image domain do not transfer well to the video domain due to the lack of considering temporal development and require a high computational cost for training video recognition models. This paper, first, investigates the temporal vulnerability of video recognition models by quantifying the effect of temporal perturbations on the model's performance. Based on these investigations, we propose Defense Patterns (DPs) that can effectively protect video recognition models by adding them to the input video frames. The DPs are generated on top of a pre-trained model, eliminating the need for retraining or fine-tuning, which significantly reduces the computational cost. Experimental results on two benchmark datasets and various action recognition models demonstrate the effectiveness of the proposed method in enhancing the robustness of video recognition models.
Lightweight Learning for Partial and Occluded Person Re-identification

Occluded and partial person re-identification (re-ID) problems have emerged as challenging research topics in the area of computer vision. Existing part-based models, with complex designs, fail to properly tackle these problems. The reasons for their failures are two-fold. Firstly, individual body part appearances are not discriminative enough to distinguish between two closely appearing persons. Secondly, re-identification datasets typically lack detailed human body-part annotations. To address these challenges, we present a lightweight yet accurate solution for partial person re-identification. Our proposed approach consists of two key components, namely, design of a lightweight Unary-Binary projective Dictionary Learning (UBDL) model, and, construction of a similarity matrix for distilling knowledge from the deep Omni-scale network (OSNet) to UBDL. The unary dictionary (UD) pair encodes patches horizontally, ignoring the viewpoints. The binary dictionary (BD) pairs, on the other hand, are learned between two views, giving more weight to less occluded vertical patches for improving the correspondence across the views. We formulate appropriate convex objective functions for unary and binary cases by incorporating the above knowledge similarity matrix. Closed-form solutions are obtained for updating unary and binary dictionary components. Final matching scores are computed by fusing unary and binary matching scores with adaptive weighting of relevant cross-view patches. Extensive experiments and ablation studies on a number of occluded and partial re-identification datasets like Occluded-REID (O-REID), Partial-REID (PREID) and Partial-iLIDS (P-iLIDS), clearly showcase the merits of our proposed solution.
Enhancing bitcoin transaction confirmation prediction: a hybrid model combining neural networks and XGBoost

Abstract With Bitcoin being universally recognized as the most popular cryptocurrency, more Bitcoin transactions are expected to be populated to the Bitcoin blockchain system. As a result, many transactions can encounter different confirmation delays. Concerned about this, it becomes vital to help a user understand (if possible) how long it may take for a transaction to be confirmed in the Bitcoin blockchain. In this work, we address the issue of predicting confirmation time within a block interval rather than pinpointing a specific timestamp. After dividing the future into a set of block intervals (i.e., classes), the prediction of a transaction’s confirmation is treated as a classification problem. To solve it, we propose a framework, Hybrid Confirmation Time Estimation Network ( Hybrid-CTEN ), based on neural networks and XGBoost to predict transaction confirmation time in the Bitcoin blockchain system using three different sources of information: historical transactions in the blockchain, unconfirmed transactions in the mempool, as well as the estimated transaction itself. Finally, experiments on real-world blockchain data demonstrate that, other than XGBoost excelling in the binary classification case (to predict whether a transaction will be confirmed in the next generated block), our proposed framework Hybrid-CTEN outperforms state-of-the-art methods on precision, recall and f1-score on all the multiclass classification cases (4-class, 6-class and 8-class) to predict in which future block interval a transaction will be confirmed.
Multiagent Reinforcement Learning: Rollout and Policy Iteration for POMDP with Application to Multi-Robot Problems

In this paper, we consider the computational and communication challenges of partially observable multiagent sequential decision-making problems. We present algorithms that simultaneously or sequentially optimize the agents' controls by using multistep lookahead, truncated rollout with a known base policy, and a terminal cost function approximation. In particular: 1) We consider multiagent rollout algorithms that dramatically reduce required computation while preserving the key policy improvement property of the standard rollout method. We improve our multiagent rollout policy by incorporating it in an off-line approximate policy iteration scheme, and we apply an additional ‘on-line play’ scheme enhancing off-line approximation architectures. 2) We consider the imperfect communication case and provide various extensions to our rollout methods to deal with this case. 3) We demonstrate the performance of our methods in extensive simulations by applying our method to a challenging partially observable multiagent sequential repair problem (state space size 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">37</sup> and control space size 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">7</sup> ). Our extensive simulations demonstrate that our methods produce better policies for large and complex multiagent problems in comparison with existing methods, including POMCP [1], MADDPG [2] and work well where other methods fail to scale up.
Distributed Constrained Optimization Over Unbalanced Time-Varying Digraphs: A Randomized Constraint Solving Algorithm

Despite the recent development of distributed constrained optimization algorithms in the literature, it is still a challenging issue to construct distributed algorithms to efficiently solve the constrained optimization problem with convergence rate guarantees, especially for the case with general constraints and unbalanced time-varying digraphs. This paper aims to investigate the distributed discrete-time optimization problem over time-varying unbalanced digraphs with general constraints including the non-identical closed convex set constraints, the multiple equality and inequality constraints. Toward this end, a new kind of distributed discrete-time algorithms synthesizing some graph topology-dependent row stochastic and column stochastic weight matrix sequences is proposed and employed. In virtue of a randomized constraint solving method, it is theoretically shown that the proposed algorithm can efficiently deal with the considered distributed optimization problem with a large number of inequality constraints and the inequality constraints that cannot be known in advance. Furthermore, the almost sure convergence of the proposed distributed constrained optimization algorithm is theoretically demonstrated under some mild assumptions. It is exhibited that the designed distributed algorithm converges at a rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${ \emph {O}} (\ln (T+1) /\sqrt{T +1})$</tex-math></inline-formula> , like the centralized counterpart. Finally, numerical simulations are given to verify the effectiveness of the present algorithm.
DRGs grouping Prediction Method based on Semantics Information of Clinical Notes

Efficient grouping of Diagnosis Related Groups (DRGs) plays a crucial role in medical resource management and cost control, ultimately impacting the quality of patient care. To accurately assign patients to appropriate DRGs, it is essential to have precise diagnoses and surgical procedure information. In this study, we propose a novel methodology that leverages semantic analysis of clinical notes to enhance DRGs grouping prediction. We employ the powerful combination of Bidirectional Encoder Representations from Transformers (BERT) and Light Gradient Boosting Machine (LightGBM) algorithms. BERT enables us to capture the contextual semantic information inherent in the text data, while LightGBM provides a robust framework for predicting disease diagnoses and surgical procedures. Through extensive experiments and evaluations, we demonstrate the superior performance of our approach, surpassing traditional methods with an impressive F1-Score of over 91%. Our research not only contributes to natural language text modeling in the medical field but also provides valuable insights for the rational allocation and effective management of medical resources.