Reviewerly/exHarmony huggingface dataset:
Heterogeneous Data-Aware Federated Learning for Intrusion Detection Systems via Meta-sampling in Artificial Intelligence of Things

Intrusion Detection Systems (IDS) integrated with Machine Learning (ML) techniques have proven to be effective defenses against the increasing cybersecurity attacks in the Artificial Intelligence of Things (AIoT) domain. Privacy concerns have prompted the emergence of Federated Learning (FL) as a promising solution for AIoT intrusion detection. Despite their potential, FL-based IDSs still face challenges related to class-imbalanced data and Non-Independent and Identically Distributed (non-IID) data among AIoT devices. These challenges hinder FL from learning meaningful features from the data, thus impeding the convergence of the learning process. To tackle these issues, this paper proposes a Clustering-enabled Federated Meta-Training (CFMT) framework for AIoT intrusion detection. The proposed CFMT framework effectively addresses the negative impact of imbalanced and non-IID data. Specifically, we design a data-and model-agnostic meta-sampler that adaptively balances local datasets, thereby mitigating the data imbalance problem. Additionally, we propose a dynamic clustering algorithm that selectively eliminates the local models affected by the training state bias caused by non-IID data, thereby addressing the non-IID data issue. Extensive case studies on two real-world datasets demonstrate the superior performance of the proposed CFMT framework compared to existing solutions, including federated non-IID algorithms and federated imbalanced learning algorithms, in terms of IDS performance. Our code and data are available at https://gitee.com/mindspore/models/tree/master/research/cv/HDFL-IDS-Meta.
AN ANALYSIS OF ENGLISH ABSTRACTS WRITTEN BY UNDERGRADUATE STUDENTS OF TANJUNGPURA UNIVERSITY

This research aimed to identify grammatical errors encountered in the undergraduate theses abstracts written by students at Tanjungpura University. This research applied the descriptive method. The data were collected with the purposive sampling technique. The subjects numbered 20 abstracts published in 2021; they were analyzed employing the error analysis technique. The findings showed that the students made errors classified into omission, misinformation, addition, and misordering. The most frequent errors made by the students were omission (52.9%), and the least frequent ones were addition and misordering, each of which occurred (5.88%). Of 20 abstracts, only five were without errors; others contained errors.
Dark-SORT: Multi-person Tracking in Underground Coal Mines Using Adaptive Discrete Weighting

Tracking-by-detection is a popular paradigm for Multi-Object Tracking (MOT), but the problems of unstable tracking and frequent ID transitions still occur due to the low illumination, point light sources, and high dust in the underground coal mine space. In this respect, this paper proposes a Dark-SORT personnel tracking algorithm for downhole environment characteristics. First, a video image enhancement method is designed to enhance the video image quality and improve the localization accuracy of the detector for the dim and unevenly distributed light environment in the well. Second, an Adaptive Discrete-weighted Attention Module (ADAM) is designed, which consists of an Enhanced Discrete Channel Attention (EDCA) module and an Adaptive Discrete Spatial Attention (ADSA) module. EDCA enables the network to capture richer information at different scales by adaptively processing different channels according to their importance and feature scales. The ADSA approach enhances the linkage between different locations within the same region, combines different pooling strategies to highlight important regions, and reduces the focus on overexposed regions. Finally, the OC-SORT tracking algorithm is introduced to solve the error accumulation problem based on the motion model and incorporate the appearance feature information to improve the stability of target tracking. We conducted a comparison test on the self-built dataset MINE-MOT, and the HOTA, MOTA, DetA, AssA, IDF1, AssRe, and FPS metrics of the Dark-SORT tracking algorithm based on the YOLOv7 target detection model were 67.4, 92.6, 80.3, 46.8, 61.7, 65.7 and 23, respectively, which was the best in terms of accuracy and stability of all the models involved in the test.
Chinese sign language recognition based on surface electromyography and motion information

Sign language (SL) has strong structural features. Various gestures and the complex trajectories of hand movements bring challenges to sign language recognition (SLR). Based on the inherent correlation between gesture and trajectory of SL action, SLR is organically divided into gesture-based recognition and gesture-related movement trajectory recognition. One hundred and twenty commonly used Chinese SL words involving 9 gestures and 8 movement trajectories, are selected as research and test objects. The method based on the amplitude state of surface electromyography (sEMG) signal and acceleration signal is used for vocabulary segmentation. The multi-sensor decision fusion method of coupled hidden Markov model is used to complete the recognition of SL vocabulary, and the average recognition rate is 90.41%. Experiments show that the method of sEMG signal and motion information fusion has good practicability in SLR.
Classification and identification of foodborne pathogenic bacteria by Raman spectroscopy based on PCA and LightGBM algorithm

Foodborne pathogens are the most common food safety hazards, and the traditional detection methods of foodborne pathogens are cumbersome, long waiting time and slow efficiency. This paper studies two common foodborne pathogenic bacteria, Brucella and Escherichia coli. LightGBM algorithm combined with Principal Component Analysis (PCA) was used to analyze Raman spectrum sample data to solve the problem of classification and detection of foodborne pathogens. The results show that LightGBM algorithm has excellent detection rate. Compared with traditional machine learning algorithm models such as Decision Tree, Random Forest and XGBoost, LightGBM algorithm has the following advantages of low memory consumption and model training speed fastly, and the model accuracy rate reaches 91.23%.
Seasonal Differences in Rainfall Distribution Within the Bawku Area in the Savanna Belt of Ghana

Empirical evidence suggests that temperatures are continuously rising in the savannah areas of Ghana and impacting negatively on residents’ livelihood activities. However, there is paucity of information on the wet and dry seasons’ rate of wetness or dryness in the driest belt of Ghana. Meanwhile, residents of the area are mainly rained agriculturalists. We employed gauge station rainfall and temperature data from Ghana Meteorological Agency to assess the seasonal rainfall characteristics of the Bawku area using XLSTAT and DrinC software. Results from the rainfall anomalies show persistent dryness (-0.017) in the area during the dry season and continuous wetness in the wet season (0.021). Evapotranspiration was consistently higher in the dry season at a rate of 2.6% (0.26) yearly as well as a high rate of aridity [AI] (0.00≤AI≤0.09) in the dry season and low aridity (0.56 ≤AI ≤1.13) during the wet season. Following the reduction in the amount of rainfall, we can conclude that Bawku area is continuously drying amidst the changing climate. It is recommended that the ministry of agriculture should prioritise the construction of mechanised dams or wells and expand irrigation projects in the area to reduce the climate change effects on the livelihood of the residents especially in the dry season.
Network Source Identification Mechanism for IoT Devices Using Machine Learning Techniques

The rapid progress and evolution of the Internet of Things (IoT) have led to a significant increase in the occurrence of security gaps. Pinpointing the source of network traffic coming from IoT devices can be challenging, but doing so can reduce security risks. This study proposes a network traffic source identification mechanism that leverages machine learning (ML) techniques to accurately determine the source of network traffic. The study utilizes a diverse dataset obtained from a purpose-built IoT/IIoT testbed and employs feature extraction, model development, and evaluation techniques. By utilizing network traffic features, a range of classifiers, including LGBMClassifier (LGBM), CatBoostClassifier (CB), RandomForestClassifier (RF), ExtraTreesClassifier (ET), KneighborsClassifier (KNN), and DecisionTreeClassifier (DT), were trained and evaluated. The results demonstrate exceptional performance across the classifiers, with high accuracy, precision, recall, and F1 scores achieved in identifying the source of network traffic. Among the classifier models, LGBM achieved the best accuracy value of 0.99999857, precision value of 0.99999859, and F1 score of 0.999998803, with CB achieving the best recall of 0.999997875. Some of these results are novel, and others performed better than existing systems. The findings of this study contribute to source identification, ensure the accountability of IoT network users, and provide insights into developing better defenses against security threats in the IoT domain
Hourly Load Curves Disaggregated by Type of Consumer Using A Density-Based Spatial Clustering Technique

This paper discusses the innovative application of a new methodology for acquiring load curves in energy systems such as Azogues Electric Company (EEA). The proposed approach imports the database from Excel and, through an iterative clustering algorithm based on density with noise, generates daily load curves with a breakdown of weekdays and weekends, identifying one curve by type of consumer. Moreover, the groups obtained are validated by means of a Silhouette verification index (IS) identifying bad groupings, which are discarded to obtain results. Per unit value responses are presented through tables with hourly values on weekdays and weekends. The graphical comparison with the previous methodology of real measurements in Excel is also added.
Environment and Benchmarking: Industry 4.0 Sustainable Work Readiness Framework

This paper presents a sustainable work readiness framework for engineering students, incorporating core skills, personal skills, and self-efficacy as mediating variables. The study aims to investigate the impact of core skills and personal skills on engineering students’ work readiness, as well as the mediating influence of self-efficacy on this relationship. The researchers designed and tested a work readiness framework with three focal points: skills, personal skills, and self-efficacy, and their influence on engineering students’ work readiness. The findings indicate that core skills, personal skills, and self-efficacy positively and significantly influence engineering students’ work readiness. Self-efficacy can be explained by the core skills and personal skills variables at 87.8%, while the work readiness variable can be explained by core skills, personal skills, and self-efficacy at 85.4%. This study contributes to the existing literature by proposing a sustainable work readiness framework and measurement instrument that link core skills, personal skills, and self-efficacy as mediating variables influencing work readiness. The results are beneficial not only to Universiti Teknologi PETRONAS, Malaysia, as they directly reflect the work readiness of UTP’s final-year undergraduate engineering students but also to individuals, universities, teaching staff, organizations, human resource specialists, and public administrators. They can use these findings to enhance the development of individual work readiness skills and increase the work readiness of students in their respective universities. The novelty of this study lies in proposing a sustainable work readiness framework tailored to equip engineering students with the skills required and acquired in Industry 4.0.&#x0D; Keywords: student, engineering, PLS-SEM, skills, self-efficacy, sustainable, work readiness
Didactic Model of Project-based teaching of Artificial Intelligence Technologies to Schoolchildren

Domestic research in the field of theory and methodology of teaching artificial intelligence technologies to schoolchildren does not pay sufficient attention to project-based learning as an approach of productive didactics. The basis of the proposed model is the concept of scientific and methodological support for teaching staff in the process of interaction between a pedagogical university and basic schools, implemented through a specialized center that functions as an element of the Unified Federal System of Scientific and Methodological Support for teaching staff and management personnel. Based on a comparative analysis of foreign scientific and methodological literature, a didactic structural model of project-based learning is proposed, which includes seven components: (1) formulating expected learning outcomes, (2) understanding the concept of training materials, (3) developing skills, (4) developing a project topic, (5) submitting a project proposal, (6) completing project objectives, (7) submitting a project report.
Development of Safety Monitoring for an IoT-Enabled Smart Environment

With the increased utilization of reconnaissance mission cameras, a library is proffered in this paper that is employed to try to image preparing victimization programming languages such as Python. This task uses Open CV Library to show the gap between the article and the camera for the eudaimonia checking of the IoT climate. We have added two modules to the reconnaissance mission camera. The first is to understand the items that area unit falling or unit victimization object space detector, once the factor has been recognized, then the client will get a caution message and for that it should have a tendency to victimize Global System for Mobile communication module. Second, there are various prospects to urge an upgrade of the observation camera thanks to deluge or water for the popularity of water precipitation board module and management module that includes an LM393 comparator on the off-chance that the appliance known the precipitation, at the time it sends the message to the consumer. Results gained through screen captures of the examinations show the suitability of the planned plot.
Problems of Working With Computer Graphics Applications in The Educational Process

This paper describes the use of Real Time graphic applications as educational tools, specifically oriented to working with people who have certain learning difficulties. We first focus on identifying the most relevant traits (from a psychological point of view) of those disorders, then we continue by analyzing the advantages of graphics in Real Time in this context, and how they can be used to complement the conventional teaching methods.
VIRTUAL HAND MODEL AND ANIMATION

The human hand is a sophisticated biomechanical system responsible for everyday interactions with the surrounding environment. Like in real life, the hand is considered the primary manipulating mechanism in virtual reality (VR). VR is a
TEACHING METHODS FOR SHARING THE CREATION OF ELECTROACOUSTIC MUSIC WITH INEXPERIENCED PEOPLE

Our lab holds a workshop using computers to create electroacoustic music (EAM) for a variety of inexperienced people. Since EAM is positioned as an extension of contemporary music, which has a large philosophical component, it has not gained as many
Taking a Closer Look at Factor Disentanglement: Dual-Path Variational Autoencoder Learning for Domain Generalization

Domain generalization (DG) aims to train a model with access to a limited number of source domains for generalizing it across various unseen target domains. The key to solving the DG problem is disentangling domain-invariant features ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> semantic factors) from domain-specific features ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> variation factors) to facilitate generalizable representation learning. Previous studies either implicitly model the semantic and variation factors or ineffectively constrain the disentangling process, thus rendering the disentanglement incomplete and ineffective. In this study, we propose a novel approach, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DualVAE</i> , to explicitly model and disentangle both the semantic and variation factors. DualVAE is based on the variational autoencoder (VAE) architecture. However, it differs from the conventional VAE in that it consists of two paths, which explicitly model the semantic and variation factors. In addition to the reconstruction loss of VAE and the classification loss, three types of regularizations, namely statistical independence regularization, factorized prior regularization, and prediction consistency regularization, are proposed to further facilitate the disentanglement of factors. Experimental results on representative DG benchmarks show that our method performs favourably against previous state-of-the-art methods. Ablation and visualization results demonstrate that semantic and variation factors can be effectively disentangled.
The Environmental Impact of Leachate Processing Installations: Life Cycle Assessment (LCA) Study

This study aims to analyze waste management in the face of population growth and increasing human activity. As the population continues to grow and human activities intensify, the proper management of waste becomes crucial to safeguarding the environment. In this context, leachate treatment through the Leachate Treatment Plant (LTP) emerges as a key element in protecting the environment from the negative impacts of leachate. The Life Cycle Assessment (LCA) method serves as a valuable tool for analyzing the environmental implications of LTPs and aiding in sustainable decision-making. The research process commences with gathering essential data and constructing an inventory related to energy use, greenhouse gas emissions, water pollution loads, and the utilization of natural resources. Through the gate-to-gate approach using the SimaPro application with the CML-IA Baseline method, the environmental impacts are quantified. The study reveals the significant impacts of Global warming (3.03E-08 Pt), Photochemical Oxidation (3.70E-09 Pt), Human toxicity (2.09E-09 Pt), and Eutrophication (1.87E-06 Pt). Eutrophication stands out as the most prominent impact of wastewater treatment. The resultant eutrophication impact predominantly originates from the Biological Oxygen Demand (BOD) pollutant component, mainly generated from the equalization tub unit and constructed wetland. Furthermore, the control tank emerges as the primary source of the dominant pollutant.
The Application and Reflection of Informationization Teaching Methods in E-Commerce Majors in Vocational Schools

Informationization has had a significant impact on students' skill learning in the teaching process of secondary vocational education. The combination of new curriculum reform and internet teaching mode has enabled students to achieve personalized and autonomous learning, breaking free from the constraints of traditional teaching classrooms, stimulating students' subjective initiative, and enabling them to achieve comprehensive literacy improvement in a more diverse, relaxed, and free environment. This article analyzes the advantages of applying information technology in secondary vocational e-commerce teaching, the implementation of classroom teaching combining information technology with secondary vocational e-commerce teaching, and explores how to promote the application of information technology in secondary vocational e-commerce professional teaching.
Analyzing Audible Language Communication from the Perspective of Integrated Media

With the rapid development of the economy and society, we have entered the era of media convergence, in which the mode of transmission of spoken language has changed dramatically and faced many difficulties and challenges in terms of form and content. This paper firstly gives an overview of the spoken language, secondly analyzes the problems and reasons encountered in the process of dissemination, and finally discusses in depth the innovative strategies for the dissemination of the spoken language. It is hoped that it will provide opinions for the dissemination of spoken language.
Perbandingan Mutu Air di Bendungan yang Berada di Provinsi Jawa Timur

A dam is a body of water that is relied upon in raw water supply. Many dams have deteriorated due to human activities in their catchment areas. East Java Province Government is currently trying to improve the function of the dam as a water reserve in the dry season. This study aims to compare water quality in nine dams located in East Java Province. The Telaga Pasir and Gonggang Dams are in Magetan Regency. The Notopuro, Saradan, Kedung Brubus, and Dawuhan dams are in Madiun Regency. Kedung Bendo, Pondok, and Sangiran dams are in Ngawi Regency. The method for determining water quality is based on KepMenLH No 115/2003 concerning guidelines for determining water quality status. Water sampling was carried out on January 24 to February 7, 2022, representing the dry season by grab sampling. Water samples were taken from the part of the dam with a pool of water. The results showed that the water quality of all dams was categorized as good to slightly polluted, with index values ranging from 0.80 to 3.10. The cause of water quality being lightly polluted is the BOD, COD, Free Chlorine, Arsenic, Cadmium, Copper, Ammonia, and Nitrite, which do not meet quality standards. The presence of these parameters is predicted to originate from the activities of residents around the catchment area, such as settlements and agriculture.
Exploration of application strategies for 5G technology in the construction of smart libraries

As an important platform for the dissemination of human knowledge and ideas, library has always been the focus of national and social attention, and also an important part of the development of science and technology in China.5G technology has the advantages of low latency, high reliability and super high speed, which brings a new experience to the library information service. This paper starts with the construction principle of 5G technology application in smart library, discusses the main 5G technology applied in the construction of smart library, and puts forward the application strategy of 5G technology in the construction of smart library, in order to provide reference for the application of 5G technology in the construction of smart library.
Study on the Application of Digital Rural Tourism and Immersive Technology in Hebei Province

Hebei Province boasts abundant rural resources, yet traditional rural tourism methods no longer suffice to meet the diverse needs of modern tourists. Currently, it faces challenges related to diversification, insufficient experiences, and promotional difficulties. Hence, the integration of digital and immersive technologies into rural tourism in Hebei Province is crucial to enhance the tourist experience, promote rural revitalization, and preserve cultural heritage. This paper analyzes the current situation and issues of rural tourism, highlights the advantages and applications of immersive technology, proposes the advantages and implementation strategies of integrating digital and immersive technologies into rural tourism in Hebei Province, and outlines future trends in digital rural tourism and immersive technology.