Reviewerly/exHarmony huggingface dataset:
Research Trend Analysis and Implications on Writing Education for Foreign Students

This study examines the trends in writing-related educational research for foreign students and discusses the results. To achieve this, a comprehensive review of 95 papers published from 2001 to August in 2023 was conducted. Various data analysis techniques were employed, including frequency analysis, centrality analysis, and topic modeling, to extract valuable insights. The keywords obtained from the frequency analysis were “university” (190 times), “purpose” (168 times), “competence” (151 times), “academic” (146 times), “target” (143 times), “course” (139 times), “study abroad” (136 times), “analysis” (129 times), “content” (124 times), and “textbook” (117 times). The result of centrality analysis showed the similar words. The topics extracted through topic modeling were “selection of textbooks suitable for student ability,” “strengthening writing skills using reading materials,” “writing education through cultural understanding,” “utilization of self-reflection and narrative writing,” and “academic writing for academic purposes.” Based on these results, research trends were analyzed and their implications were discussed.
Study on how to use ‘generative AI’ in Writing Education for College students

This study introduces case studies conducted under the premise that college students with relatively poor writing and discussion skills can get help to prepare their writing or discussion with the help of an AI program. The results of the discussion are as follows. First, it was confirmed that AI can be a good guide for learners in all stages of writing, and that better results can be produced through cooperative work. Second, it was confirmed that the emphasis in writing education using AI, albeit in a limited way, should be focused on developing prompt skills. Third, writing education using AI has two limitations for learners. The problem is when it is used without accurate confirmation and verification of the possibility of plagiarism and inaccurate or false information that may be mixed in the result. Therefore, it is urgent to prepare guidelines in terms of writing ethics. In addition, in terms of writing education, learners are awakened to the urgent need for verification and confirmation training.
The Influence of using Audio Visual Media and the Professional Competence of Religious Education Teachers on Learning Motivation

The aim of the research is to determine the influence of using audio-visual media on student learning motivation, the influence of PAK teachers' professional competence on students' learning motivation, and the influence of using audio-visual media and PAK teachers' professional competence together on students' learning motivation. Research with a quantitative descriptive inferential approach. The population of all class X students at SMA Negeri 1 West Sorkam for the 2023/2024 academic year is 300 people. The sample was determined using a random sampling technique of 15%, namely 45 people. The research was analyzed in the following steps: 1) testing the analysis requirements for the relationship between X1 and Y, obtained rcount=0.634&gt;rtabel=0.294, the relationship between 2) Hypothesis testing shows that H0 is rejected and Ha is accepted, namely that there is a positive and significant influence of the use of audio-visual media on the learning motivation of class There is a positive and significant influence between the professional competence of Religious Education teachers on the learning motivation of class.
Evidence pyramid and artificial intelligence: a metamorphosis of clinical research

Abstract We are in the era of disruption. New technologies are all around us and they change our professional and non-professional life very quickly. Evidence-based medicine is changing accordingly. We propose a new version of the evidence pyramid that evolves through the changes of our novel perspective. New technologies are manipulating the pyramid, revealing us unexpected dimensions, and shaping it into a more complex form. From a traditional two-dimensional pyramid, we move on to a three-dimensional one, where the third dimension is represented by the research efforts to go up to the next step. Legal, ethical, educational and cost-effectiveness issues are some of the major current barriers to manage and remove from accessing a secure and daily routine use of these intelligent tools. An additional element, represented by the volume of the step, highlighting the impact in clinical practice of each level of the pyramid, is added creating the fourth new version of evidence pyramid.
Performance Evaluation and Analysis of Deep Learning Autoencoder-Based Wireless Communication System

Recent advancements in deep learning have led to the emergence of autoencoder-based (AE) wireless communication systems, presenting a promising approach to tackle the challenges posed by conventional mathematical models. In this research, a thorough evaluation and analysis of the performance of deep learning AE-based wireless communication systems is conducted. Specifically, our investigation focuses on employing the additive white Gaussian noise (AWGN) channel and explores the impact of varying signal-to-noise ratio (SNR) conditions during the AE training process on system performance. The results show that training the autoencoder under diverse SNR conditions, particularly with an extended number of epochs, surpasses the performance of a fixed-trained autoencoder regarding block error rate (BLER). Additionally, a comparison of BLER between multiple AE models and traditional mathematical representations used in communication systems is conducted. The obtained findings indicate that deep learning AE-based wireless communication systems exhibit promising performance compared to conventional models, underscoring their potential as an effective solution for wireless communication systems.
A Terrain Recognition Method Based on Semantic Segmentation for Field Robot Under Sample Imbalance

Vision-based terrain recognition technology can help the robot understand the surrounding environment and then adjust the control and motion strategy in time. However, most of the existing work focuses on urban environments, and little attention is paid to complex off-road environments. Moreover, the class imbalance in the off-road environment is highly dependent on the specific environment so that the class imbalance problem is more serious, which makes the semantic segmentation of rare classes more challenging. In this study, we propose a visual terrain recognition method based on different weight loss functions for ground mobile robots in the off-road environment with class imbalance. The visual information is input into the improved network for terrain recognition. In order to better identify the rare classes, different weights are assigned to different classes in the loss function of the improved network. After a series of experiments, the results show that our improvement achieves better results than the original model in the off-road environment.
Meningkatkan Hasil Belajar PAKat dan Gotong Royong Melalui Model PBL Fase A SD Negeri 04 Aris

The purpose of this classroom is to determine aspects of learning outcomes and mutual cooperation as well as measure the results of target achievements through the Problem Based Learning model in Class II Phase A of SD Negeri 04 Aris, Capkala District, Bengkayang Regency, West Kalimantan Province. The research subjects of the students were II grade students at SD Negeri 04 Aris, totaling 6 students, consisting of 6 male students. The object of the research is the result of learning and mutual cooperation of students in Catholic Religious and Character lessons on the material “The Birth of Jesus” and “Jesus Presented in the Temple of God”. The data collection techniques are observation, tests and documentation. The results of the research showed that there was an increase in learning outcomes and mutual cooperation for class II students on the material “The Birth of Jesus” and “Jesus was Presented in the Temple of God” by applying the Problem Based Learning model. The increase in learning outcomes and mutual cooperation is evidenced by the average score of learning outcomes in Cycle 1 which was 66,67% and Cycle 2 was 86,67%, the scoreincreased by 20% while the average score of mutual cooperation in Cycle 1 was 61,5% and Cycle 2 is 81,5%, score increases 20%.
A New Complex Deng Entropy with Its Application in Pattern Classification

Complex evidence theory is an effective method for modeling and reasoning uncertain information. But how to handle and measure the uncertainty of complex basic belief assignments is still an open issue. The meaning of this paper is to propose a new complex Deng entropy in complex evidence theory including the information on phase angle and measure of discord and non-specificity. It effectively expresses the uncertainty of complex basic belief assignments and considers more phase information, that is, the physical meaning of the phase angle. When the phase is zero, the number degenerates into a real number, and the new complex Deng entropy degenerates into Deng entropy. And Deng entropy degenerates to Shannon entropy when the space is assumed to be a single subset. We give some numerical examples and discuss them to study the compatibility and effectiveness of the new complex Deng entropy. At last, we apply the proposed new complex Deng entropy to pattern classification. The results further verify that the new complex Deng entropy has better recognition accuracy than the complex-valued Deng entropy on some data sets.
The Construction Process of Meme Meaning among Islamic Youth in Malaysia

Following the progress of technology that is growing today, it can be suggested that the process of delivering information has rapidly developed. Some messages are creatively produced in various forms including words, pictures, or videos. This situation further creates certain meanings in the recipient's thoughts. Hence, this study attempted to explore the process of constructing the meaning of memes among Muslim youth in Malaysia by referring to 30 memes. A total of 15 memes were obtained from Si Rashid's Facebook account and another 15 memes were obtained from the Gags Malaysia account. The meme sample was chosen as it recorded the highest number of likes in the period from 1 May 2018 to 30 July 2018. The qualitative method was applied by interviewing a total of 29 informants through focus group discussion (FGD). The results of this study found several processes involved in the construction of meaning that has taken place, including checking the validity and credibility of the information obtained. In addition, it was found that recipients will also relate to the memes they watched with their personal experiences, religious teachings, and the culture they practice. In addition, the findings of this study also discovered that the recipient would try to reflect on the memes they watched based on current issues that are happening or have happened.
Energy-Efficient Distributed Learning and Sharding Blockchain for Sustainable Metaverse

Nowadays, researchers have started to conceptualize Metaverse with the vision of constituting a fully immersive, hyper spatiotemporal, and persistent interconnected virtualized world. Such network evolution poses sustainability concerns due to its enabling technologies, such as compute-intensive Artificial Intelligence (AI) and energy-consuming blockchain. Combining distributed learning and blockchain shows great potential to solve the energy efficiency issues in Metaverse through secure resource scheduling and decentralization of computing. However, with the expansion of the Metaverse scale, the increased energy consumption and storage of blockchain are still intolerable. Sharding blockchain becomes a feasible solution to efficiently improve energy efficiency and scalability by dividing blockchain into multiple smaller groups called shards. Toward this end, we have proposed a sustainable Metaverse architecture, combining distributed learning and sharding blockchain to tackle the energy efficiency challenges. The proposed sharding mechanism with incentive achieves the parallelization of computing and storage in Metaverse, while guaranteeing the security and activity of distributed learning. Numerical results show that the proposed framework improves energy efficiency and eases pressure on data storage.
Selection of Inverse Kinematics Solution Type for Cooperative Robots and Singularity Avoidance Based on Reinforcement Learning

Abstract In this study, we propose a method to avoid singularity by selecting solution types of inverse kinematics for a six-degree-of-freedom (6-DOF) manipulator based on reinforcement learning. A general 6-DOF manipulator has eight solution types of inverse kinematics for any position/posture of the end-effector. Owing to the complex structure of cooperative robots to prevent pinching during cooperative operations, inverse kinematics is often solved using numerical methods. Because the numerical solution depends on the initial values, it is difficult to select suitable solution types. According to the selection of the solution types, the robot may pass through a singularity, causing some joints to rotate rapidly. To avoid this, solution types must be selected considering the entire motion path of the robot. Therefore, we constructed Deep Q-Learning (DQN), a type of reinforcement learning, to select the solution types that minimize the angular velocity of each joint during the motion path. This was verified by a 6-DOF cooperative robot, where the robot was commanded to take a path through the singularity, and the solution types of inverse kinematics were selected by the DQN. Consequently, singularity was avoided by selecting suitable solution types, and the angular velocity was minimized.
Large-Scale Path Planning in Complex Environments Based on Genetic Algorithm

Abstract Path planning has been a hot research topic in robotics, and it is also a vital functionality for autonomous systems. Generating optimal-quality path plans with the least computing time has always been the goal of researchers. As the time complexity of traditional path planning algorithms grows rapidly with the scale and complexity of the problem, evolutionary algorithms are widely applied due to their capability of giving near-optimal solutions to complex problems. However, evolutionary algorithms can be easily trapped in a local optimum and may converge to infeasible solutions. As the scale of the problem increases, evolutionary algorithms usually cannot find a feasible solution with random exploration, which makes it extremely challenging to solve long-range path-planning problems with these algorithms in environments filled with obstacles. This paper introduces a novel area-based collision assessment method for Genetic Algorithm (GA) that can guide the algorithm to find solutions with a variable number of waypoints in large-scale and obstacle-filled environments. To avoid premature convergence, the mutation process is replaced by a self-improving process to let the algorithm focus the operations on any potential solutions before discarding them in the selection process. The case studies show that the proposed GA-focus algorithm can be applied to different types of large-scale and challenging environments, escape local optimums, and find high-quality solutions.
Enhancing Secure Access and Authorization in Healthcare IoT through an Innovative Framework: Integrating OAuth, DIDs, and VCs

Protecting patient information's confidentiality is paramount considering the widespread use of Internet of Things (IoT) gadgets in medical settings. This study's subjects are decentralized identifiers (DIDs) and verifiable credentials (VCs) in conjunction with an OAuth-based authorization framework, as they are the key to protecting IoT healthcare devices. DIDs enable autonomous authentication and trust formation between IoT devices and other entities. To authorize users and enforce access controls based on verified claims, VCs offer a secure and adaptable solution. Through the proposed framework, medical facilities can improve the privacy and security of their IoT devices while streamlining access control administration. An Smart pill dispenser in a hospital setting is used to illustrate the advantages of this method. The findings demonstrate the value of DIDs, VCs, and OAuth-based delegation in protecting the IoT devices. Improved processes for authorizing and controlling access to IoT devices are possible thanks to the research findings, which also help ensure patient confidentiality in the healthcare sector.
Questions about the (metadata) components of a scientific article.

The conventional procedures for reporting analysis or new results in science is to compose an “article”, augment that perhaps with “supporting information” or “SI”, submit to a journal which undertakes peer review, with revision as necessary for acceptance and finally publication. If errors in the original are later identified, a separate corrigendum can be submitted to the same journal, although this is relatively rare.
A search of some major chemistry publishers for FAIR data records.

In recent years, findable data has become ever more important (the &lt;strong&gt; F &lt;/strong&gt; in FAIR). Here I test that &lt;strong&gt; F &lt;/strong&gt; using the DataCite search service. Firstly an introduction to this service. This is a metadata database about datasets and other research objects.
The &amp;#8220;Accessible&amp;#8221; in FAIR (data).

In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible. The attributes of A[1] include: (meta)data are retrievable by their identifier using a standardized communication protocol. the protocol is open, free and universally implementable. the protocol allows for an authentication and authorization procedure.
Investigating Neural-based Function Name Reassignment from the Perspective of Binary Code Representation

Building a model to reassign descriptive names for binary functions is considerable assistance for reverse engineering. Existing methods proposed for this issue are based on the low-level representation of binary code (e.g., assembly code), and especially the recent approaches employed neural-based models on instruction sequences. However, their performance is still unsatisfactory. Meanwhile, modern decompilers provide lifted representations of binary code, and their effectiveness has not been adequately studied. This paper further explores the issue of function name reassignment from the perspective of binary code representation. Specifically, we present a general and flexible NEural-based function name Reassignment framework NER, which leverages a decompiler to obtain a specific representation and applies the corresponding serialization strategy on it. NER then uses an alternative neural network to make predictions. Three levels of representation are investigated, including assembly code, Intermediate Representation (IR), and pseudo-code. We observe the binary code representations are significant for the final performance. It demonstrates that the pseudo-code is the most effective one. Based on these findings, we leverage the framework to implement a reassignment model NER-pc, which has 25% and 10% F1 score improvements against the state-of-the-art methods. Besides, more experiments are conducted to verify the design of NER and the effectiveness of NER-pc.
NajahniBot: An Intelligent Chatbot aware of Educational Context for Adaptive Learning

The application of natural language to improve learners' interaction with intelligent tutoring systems is demonstrated to be beneficial. In particular, advances in adaptive learning enable a new way of interaction that accelerates insight from existing educational context, thereby contributing to the process of learning. This work aims at researching the application of chatbots in intelligent tutoring system. We propose a modular chatbot architecture for question answering of learning progression, featuring social dialogue (small talk), improved by individual learning preferences. This system has been implemented as a personal agent NajhniBot to assist students in learning a course of algorithmic. Its implementation includes the training of machine learning models and natural language understanding algorithms in a humanlike interface. The effectiveness of the system has been validated through an experiment.
Decentralized Learning of Finite-Memory Policies in Dec-POMDPs

Multi-agent reinforcement learning (MARL) under partial observability is notoriously challenging as the agents only have asymmetric partial observations of the system. In this paper, we study MARL in decentralized partially observable Markov decision processes (Dec-POMDPs) with partial history sharing. In search of decentralized and tractable MARL solutions, we identify the appropriate conditions under which we can adopt the common information approach to naturally extend existing single-agent policy learners to Dec-POMDPs. In particular, under the conditions of bounded local memories and an efficient representation of the common information, we present a MARL algorithm that learns a near-optimal finite-memory policy in Dec-POMDPs. We establish the iteration complexity of the algorithm, which depends only linearly on the number of agents. Simulations on classic Dec-POMDP tasks show that our approach significantly outperforms existing decentralized solutions, and nearly matches the centralized ones that require stronger informational assumptions.
Warehouse Path Planning Using Low-order Bézier Curves with Minimum-Time Optimization*

In this paper, low-order Bézier motion primitives are proposed for motion planning in corridor-constrained spaces, which is illustrated on a warehouse application. The proposed primitives are easily computed for given requirements (initial and final position, orientation and curvature). A new parametrization of the motion primitives is provided which allows an intuitive geometric interpretation. The combined path constructed from these primitives has a minimal number of turns, as third-order Bezier curves are used. At the same time, the combined path has continuous curvature. A path planning approach is proposed that finds the optimal combination of primitives that results in a minimum travel time while satisfying constraints on velocity, acceleration, and free space. To illustrate the applicability of the approach, a comparison with a typical predefined warehouse path and an existing advanced optimisation planner is provided and evaluated.