Reviewerly/exHarmony huggingface dataset:
Prototype-based Decentralized Federated Learning for the Heterogeneous Time-varying IoT Systems

Federated learning (FL) encounters two major obstacles, namely the heterogeneity among Internet of Things (IoT) devices hampers both personalized and generalized performance, and the limited communication resources impede the learning efficiency. This paper proposes a decentralized FL approach based on prototype representation learning (called DeProFL) and time-varying communication topology. The heterogeneity among devices can arise from varying data distributions and model architectures, leading to local or global gradient drift. To mitigate the effects of heterogeneity, we introduce prototype learning to establish consistent representations of samples and models across devices. Rather than directly propagating the large model weights, which requires significant transmission volumes, we propagate prototype representations among local devices to reduce transmission. Given the limited communication resources of IoT and the dynamic wireless environments, we propose a time-varying decentralized FL approach to address these practical constraints. During each global iteration, every device shares its prototype only with adjacent devices for the collaborative learning, aiming to improve system stability and reduce network bandwidth usage. We have theoretically analyzed and verified the convergence of the DeProFL under non-convex conditions. Extensive experiments have been applied to the benchmark datasets, and the results show that DeProFL outperforms state-of-the-art methods.
The Potential for Positronic Machines as Inventors: An Intellectual Property Framework for Artificial General Intelligence

Current United States Intellectual Property (IP) policy only allows patents to be awarded to human beings, largely due to the necessity that an inventor be an individual capable of conception. This prevents Artificial Intelligence (AI) from being recognized as an inventor and awarded IP rights. This article explicates the need for an entirely new IP framework to evaluate Artificial General Intelligence (AGI), a type of AI recently acknowledged by the USPTO as problematic for the U.S. IP system, and its impact on IP laws of the future.
Production Unit Management at SMK Negeri 1 Gelumbang to Respond to Industry 4.0 Challenges

This study intends to describe and assess the production unit management of SMK Negeri 1 Gelumbang, including implementation, monitoring, and evaluation. Descriptive qualitative research is what this study is. procedures for gathering data through observation, interviews, and documentation, followed by methods for analyzing data that include data reduction, data presentation, and conclusion-drawing. According to the study’s overall findings, management has been successfully implemented at SMK Negeri 1 Gelumbang’s production unit. Beginning with the planning, organizing, carrying out, monitoring, and evaluation of what has been accomplished and identifying the issues that are impeding the implementation of the Production Unit. Product marketing from each production unit of knowledge competence is also fairly good, and they are beginning to use digital marketing through social media, including Facebook, Instagram, and Whatsapp, which can be accessed both inside and outside the educational setting.
CT synthesis from CBCT using a sequence-aware contrastive generative network

Computerized tomography (CT) synthesis from cone-beam computerized tomography (CBCT) is a key step in adaptive radiotherapy. It uses a synthetic CT to calculate the dose to correct and adjust the radiotherapy plan in a timely manner. The cycle-consistent adversarial network (Cycle GAN) is commonly used in CT synthesis tasks but it has some defects: (a) the premise of the cycle consistency loss is that the conversion between domains is bijective, but the CBCT and CT conversion does not fully satisfy the bijective relationship, and (b) it does not take advantage of the complementary information between multiple sets of CBCTs for the same patient. To address these problems, we propose a novel framework named the sequence-aware contrastive generative network (SCGN) that introduces an attention sequence fusion module to improve the CBCT quality. In addition, it not only applies contrastive learning to the generative adversarial networks (GANs) to pay more attention to the anatomical structure of CBCT in feature extraction but also uses a new generator to improve the accuracy of the anatomical details. Experimental results on our datasets show that our method significantly outperforms the existing unsupervised CT synthesis methods.
Assessing Current and Emerging Challenges in the Field of Digital Forensics

This paper critically assesses the current and emerging challenges encountered in the field of Digital Forensics (DF) with reference to Cloud Forensics, the Internet of Things (IoT) Forensics, admissibility of digital evidence, lack of standardisation, limitations of tools in the field, as well as the significant problems associated with case backlogs within DF. Following the evaluation, the paper offers a set of recommendations that can be adopted to address or mitigate the stated challenges. To this end, a particular focus will be placed on the analysis of the recent report by His Majesty’s Inspectorate of Constabulary and Fire & Rescue Services (His Majesty’s Inspectorate of Constabulary and Fire and Rescue Services in Digital forensics: an inspection into how well the police and other agencies use digital forensics in their investigations, 2022) and applications of the recommendations discussed therein. This chapter contributes to the existing body of research with the inclusion of the recent recommendations made by the HMICFRS (His Majesty’s Inspectorate of Constabulary and Fire and Rescue Services in Digital forensics: an inspection into how well the police and other agencies use digital forensics in their investigations, 2022) report, making this research relevant in relation to the current and emerging challenges faced in the field of DF.
SISTEM INFORMASI CUTI KARYAWAN BERBASIS WEB PADA PT GIKEN PRECISION INDONESIA

In the advancement of technology in the industrial era 4.0 is characterized by developments in all fields of activity, causing companies and government agencies to compete to keep up with changes by utilizing information technology For companies engaged in manufacturing, especially the PCBA (printed circuit board assemby) department at PT Giken Precision Indonesia is a manufacturing company located in the industrial area of citra buana park batu ampar. One of the needs of employees is a field that must be prioritized, Regarding matters of licensing and leave at a company is one of the company's obligations that must be given, most of the current leave information is still in paper format, one of the things that employees often complain about work is the submission of employee leave. Problems from this employee leave submission system such as the slow search for employee leave data, and the leave submission form is still filled in manually or in writing and there is no database so that data from leave is still stored in archive file cabinets and drawers so that leave information is still less efficient. The problem of applying for employee leave causes many parties to feel a lack of information such as how much data is left on their leave and the leave approval process must meet their respective superiors first, and data processing that is still written using paper format is a waste. Seeing the problems regarding leave.
Innovative AI-driven Automation System Leveraging Advanced Perceptive Technologies to Establish an Ideal Self-Regulating Video Surveillance Model

The primary objective of this research is to develop an innovative and AI-driven automation system that leverages state-of-the-art perceptive technologies for creating an ideal self-regulating video surveillance model. The system will be designed to optimize real-time monitoring and enhance threat detection capabilities through advanced AI algorithms and cutting-edge computer vision techniques. By harnessing machine learning and deep learning methodologies, the model aims to achieve unparalleled accuracy in detecting and analyzing potential security breaches and anomalies. Through continual learning and adaptation, the system seeks to establish a highly efficient and adaptable surveillance framework suitable for various environments, including public spaces, critical infrastructures, and private facilities. The ultimate goal is to revolutionize video surveillance by creating an intelligent, autonomous system that minimizes human intervention, reduces operational costs, and maximizes security effectiveness. The ultimate aim is to revolutionize video surveillance by creating a highly intelligent, self-sufficient system that maximizes security and safety while minimizing human intervention and operational costs.
Revolutionizing Sentiment Analysis through AI

This study explores the paradigm-shifting impact of Artificial Intelligence (AI) on sentiment analysis, systematically evaluating five prominent machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Random Forest, Decision Tree, and Gaussian Naive Bayes (GNB). Through extensive empirical analysis and performance metrics assessment, we discern Random Forest as the most promising and transformative choice for AI-driven sentiment analysis. It consistently exhibits superior accuracy and interpretability across various sentiment analysis tasks, highlighting its potential to revolutionize sentiment analysis within the AI domain. In addition to technical evaluations, we briefly address ethical considerations tied to AI-driven sentiment analysis, including potential biases and transparency issues inherent to machine learning models. Responsible AI practices and mitigation strategies are emphasized to ensure fair and reliable sentiment analysis outcomes. In conclusion, this research advocates for the continued integration of Random Forest in AI-powered sentiment analysis systems, offering valuable insights for decision-making, user experience enhancement, and a more profound comprehension of public sentiment. This study contributes to the ongoing discourse surrounding AI's role in sentiment analysis, serving as a practical reference for researchers and practitioners in the field. Keywords:- KNN,SVC,Random Forest,Decision Tree, and GNB.
Pengembangan E-Modul Menggunakan 3D Pageflip Profesional Pada Materi Puisi Di Kelas X SMK Negeri 2 Gunungsitoli

The purpose of this research is to develop e-modules using 3D Pageflip Professional on Poetry Material in Class X SMK Negeri 2 Gunungsitoli. This type of research is development research. The development model used is the 4D model. The first stage in the 4D model is the define stage carried out by researchers in the field. The second is design (design) carried out after finding the problems that have been defined in the initial stage. This design stage aims to design an e-module media that can be used in learning. The third stage is develop (development) carried out by obtaining a product feasibility value validated by material experts, linguists, and media experts. And the fourth stage of desseminate (dissemination) is the spread of product development, but given the limited costs, it only reaches the development stage. Based on the results of the study, the developed e-module has been tested highly valid by material validators at 95.96%, linguists at 85%, and media experts at 98.75%. Furthermore, the e-module is also very practical to be used by students with a practicality percentage of 86.4%. The e-module was also effective with a percentage of classical completeness of 85.72%.
Sistem Pendukung Keputusan Untuk Pengendalian Bad Stock (BS) Menggunakan Fuzzy Tsukamoto

In Bad Stock (BS) control at PT Sari Roti Semarang, it is crucial to reduce the company's significant losses. However, the selection of rejected and returned bread with poor quality is still prevalent. Based on production data and the amount of bad stock (a collection of returned bread from stores and rejected production), a decision support system for controlling bad stock using Fuzzy Tsukamoto is needed. In this decision support system, three variables are modeled: production, store returns, and production rejects. The production variable consists of two fuzzy sets: increase, remain the same, and decrease. The store returns variable consists of two fuzzy sets: decrease, remain the same, and increase. The production rejects variable consists of three fuzzy sets: few, normal, and many. The membership values are only two values, 0 and 1, while the membership values in the fuzzy sets are closed intervals [0,1]. By combining all these fuzzy sets, three fuzzy rules are obtained, which are then used in the inference stage. In the inference stage, the membership value of the antecedent (α) and the estimated amount of production (z) are found for each rule. The amount of rejected production (Z) is obtained by centered average defuzzification.&#x0D; The research results based on expert validation testing indicate that the obtained values are in the range of 2.51-3.25, which is categorized as valid or good. Meanwhile, the validation testing from users indicates that the obtained value of 3.4 is in the range of 3.26-4.00, which is categorized as highly valid. The effectiveness testing of the system's performance results in an average value of 89.5%.
The development of a wearable device for Sign Language Gesture Recognition (SLGR) by using Conceptual Design Approach (CDA)

In recent years, there has been a significant focus on researching and developing Sign Language Gesture Recognition (SLGR) for people with hearing and speaking impairments. This is especially important in Indonesia, where there are approximately two million people with these disabilities. However, current research and developments on SLGR devices are specifically designed to understand only particular sign language systems, such as Korean Standard Sign Language (KSDSL) and American Sign Language (ASL), each of which has its unique gestures and models. As a result, no device has been developed to recognize the gestures of the Indonesian sign language system, known as Sistem Isyarat Bahasa Indonesia (SIBI). Therefore, this study aims to develop an SLGR device that can recognize and translate SIBI gestures into output images via text and speech. The development of the SLGR device in this study is conducted by using the Conceptual Design Approach (CDA) methodology. Where in this case, previous research on SLGR devices is first observed as a benchmark for comparison. Furthermore, the benchmark is used as the basis for the function, sub-function, and specification of the proposed design. Based on these stages of benchmarking and concept development, the study concluded that the final design of the proposed SLGR device is constructed by using 5 flex and Gyroscope sensors connected wirelessly to the Raspberry microcontroller. The device is equipped with a voice system and LCR RPi as the output system for translation. Based on the combination of these sensors, the device is able to identify any particular gestures that correspond to words and phrases in SIBI and translate them into speech via the designated speaker and text display on the LCD screen. To fully understand the performance of the device, experimental tests are conducted by analyzing the input of 26 alphabets in the SIBI system. As a result, the device demonstrated an average of 92% accuracy to convert sign language into voice and text, which demonstrates the usefulness of the proposed device
AP-NURSE: A Modular Tool to Ease the Life of Patients Suffering from Cognitive Diseases

This paper presents the development and testing of the AP-NURSE monitoring tool aimed to ease the everyday life of patients with Alzheimer’s and Parkinson’s diseases as well as those who take care of such patients. It summarises the activities carried out as part of the international Interreg CE niCE-life project. The team of STU designed, developed, constructed, and tested the AP-NURSE intelligent tool. The paper starts with demographic information on Slovakia. It highlights the fact that population aging is a significant issue, which requires measures to mitigate its consequences at local and European levels. The next part describes the final design of AP-NURSE Home and Care devices and their versions, including details on their hardware and software. A significant part of the paper deals with the testing of AP-NURSE in both laboratory and authentic environments. The laboratory testing was carried out directly at STU. The final testing in an authentic environment was conducted in two social and healthcare centres in Bratislava and in Warsaw. After the activities described in this paper, AP-NURSE reached TRL-6. The AP-NURSE Home platform developed based on the ESP microcontrollers demonstrated the best functionality and has the potential to be used in real practice.
The Use Of The SAMR Model To Improve Students Speaking Ability

Speaking is a God-given human ability to communicate with one another using voices and language that other humans can recognize. The education team hopes that students will be able to employ and master speaking strategies as a result of teaching and learning activities both inside and outside the classroom using the Technology SAMR (Substitution, Augmentation, Modification, Redefinition) Model. The study aims to determine whether there is a significant difference in student’s ability to speak English when using the SAMR Model compared to when using the Manual Model and also to learn about the experiences students have while learning to use the SAMR Model. This study conducts quasi-experimental research in order to collect significant improvement data on the use of the SAMR model to improve students' ability to speak procedure text. Taking the results of the pre-test and post-test scores on the type of presentation test and showing a high level of agreement between researchers and observers, the t-test and ANCOVA test showed a significant change in student learning outcomes, indicating that the SAMR Model follows the requested process and has an impact on student learning.
Special Section Guest Editorial: Introduction to the JMI Special Issue on Advances in Breast Imaging

The editorial introduces the JMI Special Issue on Advances in Breast Imaging.
Bearing-only Circumnavigation Based on Fully Actuated System Approach

This paper investigates the problem of an agent with a second-order integrator dynamic model circumnavigating a static target in a circle orbit with desired distance and speed using bearing-only measurements. In this bearing-only problem, the agent only has access to its own motion information and the relative direction measurements of the target. Existing works only consider the first-order integrator dynamic model. In this paper, we apply the fully actuated system (FAS) approach to design a high-order controller and give a rigorous proof for the convergence of the closed-loop circumnavigation control system. A simulation is performed to verify the feasibility of the proposed method.
Research on Power Marketing Data Sharing Framework Based on Semi-supervised Vertical Federated Learning

As the electric power industry deals with an expanding and diverse data landscape, secure and efficient data usage is crucial for intelligent systems. However, security constraints often lead to data isolation, hindering data sharing and access among departments, companies, and institutions. This results in data silos. To address these challenges, this paper introduces a novel data sharing framework that combines vertical federated learning and semi-supervised learning to overcome power marketing data sharing issues. Vertical federated learning facilitates cross-departmental data modeling while preserving data privacy and improving data analysis accuracy. Semi-supervised learning utilizes unaligned data from federated learning to enhance prediction accuracy and generalization. Empirical results demonstrate that this framework enhances data analysis accuracy and efficiency while maintaining data security and privacy. This approach presents new opportunities and challenges for cooperation and competition within the electric power industry.
Static Analysis for Quantum Software Correctness and Reliability (Keynote)

Software correctness and reliability have been central issues in classical computing for decades. Researchers have developed a wide range of approaches to these problems, but few have been to date for quantum computing. In this talk, I consider two different static analysis approaches to solving quantum software correctness and reliability problems: pattern-based bug detection and entanglement analysis. I will overview how these approaches work and conclude by discussing the challenges they face moving forward.
Recovery Path Planning for Autonomous Underwater Vehicles Using Constrained Bi-RRT*-Smart Algorithms

In this paper, a constrained bi-RRT*-smart algorithm is proposed to solve the recovery path planning problem of an Autonomous Underwater Vehicle (AUV). The RRT*-smart algorithm is taken as a basic strategy for the predefined path-planning problem to generate an optimal path which has the smallest distance and no collisions with the islands. On this basis, a bi-direction technique is fusion with the RRT*-smart algorithm to get a bi- RRT*-smart algorithm. Additionally, two practical constraints are considered to facilitate the feasibility of the proposed method in real applications. For one thing, considering the limited turning ability of the AUV. For another, a local direction constraint is set near the ending point to ensure that the AUV enters the docking station along a reasonable direction. Compared with the previous algorithms, it not only reduces the path cost, but also greatly improves the convergence rate. Simulation results confirm the efficiency of the proposed bi-RRT*- smart algorithm.
A New Topic Modeling Method for Tweets Comparison

Fake news detection is a real problem, especially in social media. The untruth news dispreads very quickly and brings huge damage. This paper is devoted to proposing a topic modeling method that allows for comparing tweets, which is one of the stages of fake news detection. Our method, named Content Weighted Topic (CWT), is based on applying WordNet. Experiments showed that our method is better than the well-known Latent Dirichlet Allocation (LDA) algorithm regarding topic coherence measure. Our CWT method assigns topics for tweets that are more consistent than topics assigned by the LDA algorithm.
NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction

This article studies the challenging two-view 3D reconstruction problem in a rigorous sparse-view configuration, which is suffering from insufficient correspondences in the input image pairs for camera pose estimation. We present a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Neural One-PlanE RANSAC framework</i> (termed NOPE-SAC in short) that exerts excellent capability of neural networks to learn one-plane pose hypotheses from 3D plane correspondences. Building on the top of a Siamese network for plane detection, our NOPE-SAC first generates putative plane correspondences with a coarse initial pose. It then feeds the learned 3D plane correspondences into shared MLPs to estimate the one-plane camera pose hypotheses, which are subsequently reweighed in a RANSAC manner to obtain the final camera pose. Because the neural one-plane pose minimizes the number of plane correspondences for adaptive pose hypotheses generation, it enables stable pose voting and reliable pose refinement with a few of plane correspondences for the sparse-view inputs. In the experiments, we demonstrate that our NOPE-SAC significantly improves the camera pose estimation for the two-view inputs with severe viewpoint changes, setting several new state-of-the-art performances on two challenging benchmarks, i.e., MatterPort3D and ScanNet, for sparse-view 3D reconstruction.