Reviewerly/exHarmony huggingface dataset:
Readly - Books Rating Low-Code Platform

This article addresses the development of a digital platform within the OutSystems ecosystem, embracing the principles of Low-Code and No-Code application development. The platform facilitates users in sharing their ideas, opinions, and reviews with a community of fellow book enthusiasts. Users can discover new titles, engage in debates, and foster connections by offering a space for meaningful conversations and suggestions. The platform empowers users to contribute to a shared knowledge base by providing book ratings and reviews, thereby assisting others in their book selection process. The emphasis lies in delivering a seamless, user-friendly experience through a modern and clean interface, further enhanced by personalized book recommendations tailored to individual user preferences. In the context of the rapidly evolving landscape of application development, Low-Code and No-Code approaches have gained significant traction due to their efficiency and accessibility. This article aims to highlight the integration of these approaches within the development of the digital platform. Utilizing the capabilities of the OutSystems ecosystem, which inherently embraces these concepts, the platform's creation becomes a testament to the versatility and benefits of such methodologies. In conclusion, this digital platform not only caters to the needs of book lovers by equipping them with a range of tools, but it also stands as a prime example of the successful implementation of Low-Code and No-Code development strategies. These tools encompass organizing reading lists, active community participation, and receiving customized literary suggestions. The application achieves a fluid and intuitive user experience, further facilitated by incorporating low-code principles. It enables users to manage their reading materials effectively, set goals, track progress, rate and review books, and engage with fellow bibliophiles. This comprehensive solution empowers book aficionados, enriching their digital reading journeys while showcasing the prowess of Low-Code and No-Code application development in creating impactful solutions.
Використання мобільних телефонів у навчанні англійської мови як іноземної

English is considered as the most important foreign language to be taught worldwide. In school, English is taught to children as a foreign language in nearly every foreign country. Because of this matter, English language teaching is of crucial importance nowadays. During different times, different teaching methods were used in language teaching. In recent years, there has been an increasing interest in using mobile phones for teaching English as a foreign language (EFL). Because of the rapid development of technology in recent years, a new era of mobile devices has become popular. These new devices, including smartphones, tablet PCs and MP3 players, are very practical and convenient to use thanks to their wireless nature and easy usage. The mentioned devices are widely used, especially by the younger generation, especially after COVID-19. Mobile-assisted language learning is considered as the new method of language acquisition and teaching. However, teachers and researchers are having an argument about the importance of using mobile technology in language teaching instead of the conventional ways. Mobile technology has many advantages that can be used in a classroom or outside of it. Students can learn anywhere and anytime by using their mobile phones or other devices. Still, many teachers do not pay attention to this fact and they tend to say that technology is only good for entertainment and it does not contribute to the learning process. Many recent studies have been made in the field of using technology in education. The conservative ways of teaching are not necessarily the best ways of helping students acquire a language. They have different needs in the era of portable devices that have to be acknowledged by the teachers too. The purpose of this paper is to review recent research about the importance of mobile-assisted language learning, and to justify the educational benefits of such technologies in language teaching.
ANALISIS MEDIA PUZZLE TERHADAP PENGUASAAN KOSA KATA KELAS I DENGAN MENGGUNAKAN MODEL JIGSAW

The purpose of this study is to find out, describe and analyse the use of a media puzzle game in Jigsaw Model of vocabulary mastery for class one of elementary school students. This research uses Descriptive Qualitative Method. The population of this research is Class 1 of SDN 19 Air Salek which consists of 23 students. Methods of data collection using observations, interviews and documentation. Based on the research proves that the media puzzle game in jigsaw model is worthy of use. It is concluded that the use of media and model rises 18 vocabularies on each students at the average. The previous results are 20 of 52 vocabularies on given semester. Therefore, the use of media puzzle game in jigsaw model is one of the best way to increase the students’ vocabularies.
Auto-Learning-GCN: An Ingenious Framework for Skeleton-Based Action Recognition

The Graph Convolutional Network (GCN) has garnered substantial interest over an extended period owing to its notable efficacy in addressing topological correlations, with particular achievements observed in skeleton-based action recognition. However, it is intriguing to note that the efficacy of the adaptive module within the existing model gradually diminishes as the learning capacity of the adjacency matrix improves. Inspired by this, AL-GCN constructs a comprehensively learnable group adjacency matrix in both spatial and temporal dimensions, thus forming an elegant and efficient GCN-based model. Firstly, the prevalent adaptive module is courageously removed, and the key points of the human body are astutely leveraged as motion compensation to provide dynamic correlation support. Secondly, a similar temporal adjacency matrix group is designed in the temporal domain to capture the global interaction characteristics more effectively. Subsequently, attention modules are purposefully crafted for both the temporal and spatial domains, which provide discriminative information between classes and improve learnability and interpretability. Finally, the Bayesian weight selection algorithm is utilized to achieve efficient and accurate fusion results for multi-stream data. On the NTU-60, NTU-120, and NW-UCLA, AL-GCN outperforms the state-of-the-art method, with up to 7.0% improvement on the challenging UAV-Human, achieving superior performance in multiple settings, all while maintaining a lower computational cost. Related code will be available on Auto-Learning-GCN .
CONTROL SYSTEM BASED ON HAND GESTURE RECOGNITION USING MEMS ACCELEROMETERS

This paper aims to propose a methodology to assist individuals with physical disabilities by utilizing MEMS technology. This technology can be used to help non-verbal people communicate without words or assist those with partial paralysis to control their surroundings. The gesture identification is achieved using MEMS accelerometers. When a gesture is made, the accelerometers register the movement, which is then interpreted by a microcontroller. The microcontroller recognizes the gesture by comparing it to pre-programmed signals and assigns it a unique code. Additionally, there is a control module that includes an RF receiver. The RF receiver can detect the unique code from the user module. The ATMEGA microcontroller used in the control module to trigger specific functions based on the user's preferences and requirements. The proposed model is implemented in real time under various testing conditions.
Methodological tools for managing the innovative development of an educational organization

The article presents a theoretical analysis of the existing tools for implementing controls in the innovative development of an educational organization. The stages of the theory of management of innovation processes, as well as the main approaches are considered. Macro, meso and microfactors of the formation of an individual learning trajectory are highlighted. The main directions of the implementation of the individual educational trajectory are determined. The features of innovative activity in the pedagogical sphere are characterized. The introduction of methodology and methodological tools in the process of introducing innovative processes in the field of education is substantiated.
Illumination Insensitive Monocular Depth Estimation Based on Scene Object Attention and Depth Map Fusion

Monocular depth estimation (MDE) is a crucial but challenging computer vision (CV) task which suffers from lighting sensitivity, blurring of neighboring depth edges, and object omissions. To address these problems, we propose an illumination insensitive monocular depth estimation method based on scene object attention and depth map fusion. Firstly, we design a low-light image selection algorithm, incorporated with the EnlightenGAN model, to improve the image quality of the training dataset and reduce the influence of lighting on depth estimation. Secondly, we develop a scene object attention mechanism (SOAM) to address the issue of incomplete depth information in natural scenes. Thirdly, we design a weighted depth map fusion (WDMF) module to fuse depth maps with various visual granularity and depth information, effectively resolving the problem of blurred depth map edges. Extensive experiments on the KITTI dataset demonstrate that our method effectively reduces the sensitivity of the depth estimation model to light and yields depth maps with more complete scene object contours.
Bi-stream Multiscale Hamhead Networks with Contrastive Learning for Image Forgery Localization

The purpose of image forgery localization task is to localize and segment forgery regions from a forgery image. Traditional forgery localization methods usually rely on specific manipulation traces, while contrastive learning-based methods focus on universal forgery traces. In this study, a general image tampering localization method based on contrastive learning, named Bi-Stream Multi-scale Hamhead Contrast Net (BMHC-Net), was proposed. At first, a multi-scale patch contrastive learning module was proposed, in which rich features of forgery images were projected to a feature space. The projection of rich features and real mask were divided into multi-scale patches, and contrastive learning was utilized to obtain the contrastive loss to locate the forgery regions of the image. Then, inspired by Hamberg network, Hamhead structure was utilized to construct a multi-stage feature fusion module that used attention mechanisms to focus on global information and efficiently fused multi-scale features. Experimental results demonstrate the effectiveness of the proposed method. For example, on IMD2020 dataset, BMHC-Net improved the overall performance in terms of F1-score by 0.1714, in which the contrastive loss module improved the system performance in terms of F1-score by 0.0418.
Student’s perspectives, satisfaction and experiences with online and classroom learning during the COVID-19 pandemic: Findings and implications on blended learning

Objectives: The coronavirus disease 2019 (COVID-19) pandemic disrupted classroom-based learning, necessitating the adoption of online learning in most universities. However, there has been a lack of information on university students’ perspectives regarding online learning during the COVID-19 pandemic. This study assessed the perspectives, satisfaction and experiences with online and classroom learning among human health students at the University of Zambia. Methods: This cross-sectional study was conducted among 737 students at the University of Zambia from October 2022 to April 2023. Data were analysed using Stata version 16.1. Results: Of the 737 participants, 51.6% were female and 56.5% agreed that blended learning should continue even after the COVID-19 pandemic. However, 78.4% of the students believed that group discussions were more suitable in the classroom than online learning. Most students (67.1%) disagreed that they preferred online learning to classroom learning. Furthermore, 77.6% of the students disagreed that online learning gave more satisfaction than classroom learning. Conclusions: This study found that most students recommended the continuation of blended learning after the pandemic. However, they believed that follow-up tutorials and assessments were better undertaken in physical classrooms than online learning. These findings are important in sensitising stakeholders in the education sector and governments to consider blended learning as a teaching strategy in the future. There is a need to develop and implement curricula that offer blended learning to students as well as ensure the students have the necessary facilities and equipment to support such learning.
DEVELOPMENT OF TESTING METHOD FOR ARM MOUNTED ROBOT BASED ON FUKUSHIMA DAIICHI NUCLEAR POWER STATION CASES

In emergency response and decommissioning at Fukushima Daiichi Nuclear Power Station (FDNPS), remote controlled robot is required to work in harmful places instead of human workers. However, remote robot operation is not easy. In order to achieve a mission by remotely robot operation, the robot operator should understand what and how much he/she is capable to do by remote operation with the robot. Thus, a method for evaluation of robot capability is needed. Many of the missions by remote robot operation in FDNPS accomplish main objectives, while, few problems occurred in some missions. The experience from such problems in FDNPS have significant value for practical remote robot operation. The aim of this research is to develop a robot testing method based on cases of FDNPS remote operations with arm mounted robots. We survey FDNPS remote operations with arm mounted robots, and decide to develop a testing method to evaluate the robot capability for approaching a target object over an obstacle by a robot arm. We develop a test field for evaluation the capability and the test procedure to measure the metric to describe the capability using the test field. Moreover, we conduct actual tests and discussions with a team engaged in nuclear emergency response robot maintenance and training to make it more practical.
Target Positioning Accuracy of V2X Sidelink Joint Communication and Sensing

Joint communication and sensing (JCS) is gaining popularity in vehicular scenarios for detecting and localizing nearby objects or other vehicles using vehicle-to-everything (V2X) communications, thus complementing or improving on-board sensors. Despite the numerous benefits, V2X communications, particularly in sidelink, suffer from inter-vehicle interference. In this paper, we evaluate the impact of physical and radio access communication parameters on device-free positioning based on JCS with sidelink V2X. We focus on estimating range, angle, and location in the presence of multi-user interference.
Hybrid Reinforced Medical Report Generation With M-Linear Attention and Repetition Penalty

To reduce doctors’ workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these state-of-the-art methods mainly suffer from three shortcomings: 1) incomprehensive optimization; 2) low-order and unidimensional attention; and 3) repeated generation. In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. Specifically, a hybrid reward with different weights is employed to remedy the limitations of single-metric-based rewards, and a local optimal weight search algorithm is proposed to significantly reduce the complexity of searching the weights of the rewards from exponential to linear. Furthermore, we use m-linear attention modules to learn multidimensional high-order feature interactions and to achieve multimodal reasoning, while a new repetition penalty is proposed to apply penalties to repeated terms adaptively during the model’s training process. Extensive experimental studies on two public benchmark datasets show that HReMRG-MR greatly outperforms the state-of-the-art baselines in terms of all metrics. The effectiveness and necessity of all components in HReMRG-MR are also proved by ablation studies. Additional experiments are further conducted and the results demonstrate that our proposed local optimal weight search algorithm can significantly reduce the search time while maintaining superior medical report generation performances.
SyntaxLineDP: a Line-level Software Defect Prediction Model based on Extended Syntax Information

The existence of software defects greatly restricts the application of software and may bring great economic losses. Defect prediction techniques based on file level or module level can help code reviewers quickly locate defects and fix them. In recent years, researchers have used natural language techniques to explore the defectiveness of code lines by analyzing the semantic information of the code lines. However, the appearance of defects is often associated with the syntactic information of the code. To fully use the syntax information associated with code lines, we propose a code line-level representation considering the coverage of the syntax node. Specifically, we add the syntax node to its corresponding coverage lines. Combining with the BiLSTM model, we propose a new line-level defect prediction model, SyntaxLineDP, which shows good performance achieving 0.72 and 0.62 for AUC and Balanced Accuracy respectively. SyntaxLineDP outperforms the state-of-the-art model and the popular static tools (e.g., ErrorProne, PMD). Specifically, In the within-project evaluation, the SyntaxLineDP model outperforms the start-of-the-art model by 15% and 27% on the Recall@Top20% LOC metric and the Effort@Top20% Recall metric respectively. In the cross-project evaluation, the SyntaxLineDP outperforms the start-of-the-art model by 7% and 42% on the Recall@Top20% LOC metric and the Effort@Top20% Recall metric respectively. Through ablation experiments, we studied how the selection of extended syntax information contributes to the detection and found out some key features that are helpful for the defect prediction task: number of infix expressions of code line, number of blocks of code line, and number of method invocation of the code line.
SAFE: a Standardized Automotive Forensic Engine for Law Enforcement Agencies

Modern automotive infotainment systems offer a substantial source of evidence for digital forensic practitioners. However, due to lack of guidance and supporting validation tools, forensic analysts struggle with both data acquisition, analysis and reporting. There are general digital forensic frameworks and legislative acts that can be applied to automotive forensics. However, processing vehicles may prove challenging due to analysis of proprietary automotive systems and on-site crime scene dynamics, including cross-functional investigation with physical forensic teams. To gain an insight into emerging challenges, the present work surveyed current automotive forensics practices across law enforcement agencies (LEAs) in the EU, NA and AP region. The result of this survey enabled a qualitative evaluation, exposing an overall limited capability along with prevalence of invasive data retrieval methods and lack of standardized investigation trajectories. Based on this evaluation, a predominant set of recommendations were derived and streamlined in SAFE: a standardized automotive forensic engine. SAFE utilizes preliminary information from the crime-scene and presents a best-practice step-by-step investigation guide for front-line vehicle forensic analysts. The engine captures analyst rating on the validity of each investigation trajectory and KNN-based content filtering is employed to improve future recommendations. SAFE, therefore, aims to optimize vehicle forensic processing from initial crime scene to the courtroom.
Visual Explanations: Activation-Based Acute Lymphoblastic Leukemia Cell Classification

The saliency methods are widely used for generating heatmaps that emphasize the important portions of an input image for deep networks on a specific classification task. Interpretability is crucial for deploying deep neural networks in real-world applications. However, the heatmaps produced by current visual explainable methods may contain or visualize particulars differently. To analyze and compare the visualization of different methods, such as Gradient-based, Activation-based, Perturbation-based, and Region-based methods, we empirically evaluated them on the acute lymphoblastic leukemia (cancer cell) classification task using state-of-the-art convolutional neural networks. We also visualized the essential pathological features (salient parts) that are the reasons for the classification results on the classification of normal versus malignant cell (CNMC) dataset.
Goal Space Abstraction in Hierarchical Reinforcement Learning via Set-Based Reachability Analysis

Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning. However, the existing Hierarchical Reinforcement Learning (HRL) approaches relying on symbolic reasoning are often limited as they require a manual goal representation. The challenge in autonomously discovering a symbolic goal representation is that it must preserve critical information, such as the environment dynamics. In this paper, we propose a developmental mechanism for goal discovery via an emergent representation that abstracts (i.e., groups together) sets of environment states that have similar roles in the task. We introduce a Feudal HRL algorithm that concurrently learns both the goal representation and a hierarchical policy. The algorithm uses symbolic reachability analysis for neural networks to approximate the transition relation among sets of states and to refine the goal representation. We evaluate our approach on complex navigation tasks, showing the learned representation is interpretable, transferrable and results in data efficient learning.
Research on the Interface Design of a Situational Awareness-Based Spinal Endoscopic Surgery Robot

Objective: Through the research, practice, and evaluation of the design of a human-machine interaction interface for a situational awareness-based spinal endoscopic surgery robot, this study aims to provide guidance for the design of human-machine interfaces for surgical robots. Methods: Focusing on the SNS interface in spinal endoscopic surgery as the primary subject of research, we identified the process and application scenarios of doctors using this function during surgery through field research and literature review. We then developed two functional frameworks and human-machine interfaces based on augmented reality algorithms. Finally, eight participants took part in an evaluation of the surgical human-machine interface. Feasibility verification was conducted through objective performance evaluation and subjective scale testing (3D-SART and NASA-TLX scales) on information architecture design, prototype output, and rendering design practices. Conclusion: Through the design of a human-machine interaction interface for a spinal endoscopic surgery robot based on augmented reality, the usability of the interface can be improved, reducing the cognitive load of operators.