Reviewerly/exHarmony huggingface dataset:
Using Bidirectional Federated Transfer Learning for Intrusion Detection in Heterogeneous Industrial Internet of Things

In the past few years, the development of Industry 4.0 has driven the demand for automation equipment in factories, thereby requiring better real-time monitoring tools to control the factory environment and equipment. Industrial Internet of Things (IIoT), as an important component of Industry 4.0, utilizes the capabilities of smart machines and real-time analytics to collect relevant data through various sensors, monitors, and other monitoring technologies deployed on devices, in order to improve operational efficiency. However, as the boundary between Information Technology (IT) and Operational Technology (OT) is rapidly disappearing, OT environments now require dedicated solutions for OT and IoT security. Due to the differences in packet features and labels collected in each domain, this study focuses on proposing a joint transfer learning approach combined with an intrusion detection system, and simulates datasets collected from different domains using three OT-related public datasets: Edge-IIoTset, WUSTL-IIOT-2021 Dataset, and Electra dataset (Modbus). It discusses the issues related to different attack types and dataset features. In addition, we compare the effects of different deep learning models on intrusion detection systems. The experimental results demonstrate that the proposed approach of bidirectional federated transfer learning enables cooperative learning of heterogeneous data.
Hamming Weight aware Fault Analysis for Lightweight Cryptography TWINE

Lightweight cryptography, which can be used in small embedded devices, is currently attracting attention. TWINE, which is the target of this study, is one of the most popular lightweight cryptography. The threat of fault analysis has also been reported as an analysis of cryptographic circuit. From the viewpoint of security evaluation, it is important to evaluate the resistance (tamper resistance) against various analysis methods. Therefore, in order to evaluate the tamper resistance of TWINE, this study proposes a new fault analysis method for TWINE that focuses on Hamming weights of intermediate value. Simulation experiments verified that the proposed method can be used to evaluate the security of TWINE for fault analysis. As a result, all round keys of TWINE were successfully estimated by using 30 faulty ciphertexts. Thus, the proposed method can evaluate the tamper resistance of TWINE against fault analysis.
CID-CJA: Co-Occurrence Information Discovery in Computer-Related Job Advertisements

The rapid advancements in technology have brought about significant disruptions in the job market for computer-related fields, necessitating a comprehensive understanding of emerging trends and evolving demands. This research focuses on the analysis of computer-related job advertisements to uncover co-occurrence information that employers seek within the context of disruptive technology and evolving business models. To address this need, we propose CID-CJA (Co-occurrence Information Discovery in Computer-related Job Advertisements), a novel method for extracting valuable insights from job advertisements. CID-CJA empowers job seekers to identify the knowledge and skills that are relevant in the current landscape, while also assisting universities in aligning their curricula with the dynamic needs of the industry. The flexibility of CID-CJA enables users to conduct detailed drill-down analyses on specific elements of information. The effectiveness of CID-CJA is validated through experimental studies conducted with real job advertisements, which demonstrate its ability to reveal valuable information that is beneficial for computer-related professionals.
Multi-secret sharing scheme based on XOR operations in combination with AES-GCM

Blakley and Shamir independently proposed the basic idea of (k, n)-threshold secret sharing schemes in 1979. To this day, most secret sharing schemes, represented by Shamir's scheme, often require arithmetic operations in the Galois field. Such schemes are difficult to meet the needs of low cost, low power consumption and high performance in practical applications. Therefore, we propose an efficient secret sharing scheme based on XOR operations and improve it according to the requirements of multiple secret sharing. Our scheme is similar to Kurihara's scheme, but our scheme is relatively more concise and easier to understand. In addition, our scheme also eliminates the security implications of the reduction of random vectors when multiple secrets are shared by incorporating AES-GCM. In combination with the AES-GCM, we can verify the integrity of the secret. From experimental simulations, our solution is feasible and efficient, with our simulated PC program taking only 50 seconds to distribute a 1G byte file.
Fast and Accurate Bolt Classification for a Retail Motorcycle Shop in Thailand

People have begun to improve their purchase of products and food via online applications because of the ongoing global pandemic. Especially, purchasing products through online applications is popular due to their convenience. Hence, numerous riders need motorcycles to work for delivery. As a result, critical spare parts such as durable and beautiful bolts are highly demanded for maintenance. We collect the best-selling bolts of 19 classes from a motorcycle shop to develop a bolt classification procedure and improve business opportunities. A combination of principal component analysis, median cut algorithm, and structural analysis, is used for extracting a set of features. Then, five classification models, which are multi-layer perceptron, random forest, decision tree, support vector machine, and logistic regression, are used to assign the most appropriate class for each bolt. The experiment results show that the multi-layer perceptron performs better than the other classification models.
Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems

Students “attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students” attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification.
Digital Forensics to Identify Damaged Part of Palm Leaf Manuscript

Ancient manuscripts have long been valued cultural and historical objects, offering a unique view into the past. However, many manuscripts have been damaged by natural decay, environmental conditions, and human intervention. Hence, the article aims to detect both undamaged and damaged ancient manuscripts. This study employed the palm leaf manuscript with the title L 536 Jatiswara by capturing the damaged and undamaged paper manuscript. The outcome demonstrates that the colors red and green might be used to distinguish between the probability and clear results for each portion of the damaged and undamaged paper. Furthermore, the training input of the Weka segmentation shows that the damaged part of the damaged paper manuscript is 256 while the undamaged paper shows 0 damage.
Human Action Recognition by Deep Learning Technique with Multiple-Searching Genetic Algorithm

The purpose of this paper was to review the hybrid method in the human action recognition approach utilizing deep learning and multiple-searching genetic algorithm. This study reported that the deep learning classifier stated the weights of the convolutional neural network in light of the arrangements created by reducing the classification error method from the multiple-searching genetic algorithm. Gradient descent was utilized to find a local minimum and train the convolutional neural network classifiers amid the fitness assessments of the chromosomes from the multiple-searching genetic algorithm. The local search capacities of the gradient descent and global search capacities of the multiple-searching genetic algorithm used could discover a solution that was nearer to the global optima. Furthermore, this paper demonstrated the confirmations of the classifiers produced by utilizing the multiple-searching genetic algorithm to enhance the execution. Moreover, the adequacy of the proposed arrangement framework of the human action recognition system was shown by using the action bank features from the UCF50 dataset.
Federated Learning for Network Traffic Classification: Impact of Non-IID Distribution on Model Performance

Network traffic classification is vital for network security. However, the non-IID nature of participant data poses challenges for federated learning in this context. This study examines the impact of non-IID on model performance in federated learning for network traffic classification. We analyze three non-IID data distribution scenarios: distribution-based label imbalance, quantity-based label imbalance, and quantity skew. We evaluate the effect of these distributions on federated learning performance using training models with varying degrees of non-IID data. Our experiments evaluate model accuracy and convergence efficiency. We also compare the influence of different non-IID levels on model performance. Results show that federated learning effectively handles non-IID challenges and performs well across all scenarios. However, increased data heterogeneity or label imbalance reduces model accuracy and convergence efficiency. Models perform relatively poorer with non-uniform label distributions. In conclusion, federated learning is a viable solution for non-IID in network traffic classification, emphasizing the need to consider the impact of data heterogeneity. These insights can guide the application of federated learning in network traffic classification and related fields.
Securing Patient Confidentiality in EHR Systems: Exploring Robust Privacy and Security Measures

Due to the digitization of healthcare, EHR systems are commonly used to maintain vast amounts of personally identifiable information and protected health information. There are, however, significant concerns regarding the security and privacy of EHR systems due to the dangers of unauthorized access, data intrusions, and identity theft. This study endeavors to identify methods and techniques that can be utilized to safeguard patient information contained in electronic health records. Examining the current security measures for EHR systems, highlighting the issues they cause, and proposing novel countermeasures against these attacks. Our research concentrates primarily on EHR system encryption techniques, authentication processes, and auditing procedures. In addition, the study also discusses the challenges associated with deployment and regulatory requirements, as well as the impending developments in EHR system security. By prioritizing the security and privacy of their electronic health record systems, healthcare providers can protect patient data, maintain regulatory compliance, and earn the trust of patients and other stakeholders.
Machine Learning Algorithm and Modeling in Solar Irradiance Forecasting

Forecasting solar irradiance is a very important requirement for determining the potential of the energy produced, especially to maintain the continuity of the availability of electrical energy. Creating a solar irradiance forecasting modeling algorithm is important to obtain the degree of closeness between the predicted and actual results. This study proposes a novel method to solve solar irradiance prediction by using a machine learning model based on the Bidirectional Long Short-Term Memory (BiLSTM) with one hidden layer and three input data variables and involves setting hyperparameters. The processed data is primary data obtained from the measurement results of three sensors over a certain period. The training technique and model validation use the backpropagation algorithm by adjusting the learning rate value. The proposed BiLSTM model has achieved convergence at epoch 183 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> and no overfitting occurs. Furthermore, the comparison between the BiLSTM model and the Baseline model shows that the BiLSTM model has better performance in predicting solar irradiance one minute ahead with mean absolute error (MAE) and root mean squared error (RMSE) values of 0.004326917696744204 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 0.00917678491314492 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> respectively. At the same time, the Baseline model shows MAE of 0.01438286076453574 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and RMSE of 0.033326815578768605 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Blockchain in Healthcare: A Case Study of Healthcare Insurance

Due to its distributed, decentralized database and capacity to offer immutability, tamper resistance, and transparency qualities, blockchain technology has attracted interest for use in the healthcare sector. Blockchain technology integration has the potential to enhance healthcare data management. By incorporating blockchain, healthcare networks can establish trust and ensure data security and privacy. However, existing studies mainly focus on the relationship between patients and hospitals, neglecting the incorporation of insurance aspects such as indemnity and insurance reimbursement. This paper proposed to address this issue by proposing a comprehensive blockchain-based healthcare system that encompasses the entire process, from patient registration to payment and insurance claiming procedures. The paper's focus on privacy rules, which enables the systematic management of hospital data in compliance with regulations including the Personal Data Protection Act (PDPA) and the Health Insurance Portability and Accountability Act (HIPAA), is a major strength. The legal framework has been carefully examined to facilitate the implementation of this project. The system implemented in this study utilizes Hyperledger Besu, a private blockchain platform. Given the sensitivity of healthcare data, a privacy group and permission settings are employed to prevent unauthorized access. The technical design of the healthcare network and an early-stage prototype are described in detail.
Comparison of Transformer Based and Traditional Models on Sentiment Analysis on Social Media Datasets

In the field of computers and natural language processing, there is an interesting sub-field, namely sentiment analysis. Currently, the use of social media Twitter to actively communicate between individuals contains various review data, opinion data, and emotional data from discussions of topics between users on social media. So that the data generated by Twitter social media can identify patterns in the data for the field of sentiment analysis. In the sentiment analysis sub-sector, there are approaches, namely transformer-based models and traditional models, the traditional model includes the naïve Bayes algorithm, support vector machine, and regression have weaknesses in overcoming data complexity in Indonesian language tweets on Twitter social media while the transformer model includes the BERT (Bidirectional Encoder Representations from Transformers) algorithm is often applied because it has advantages in overcoming the complexity of Indonesian sentences in tweets obtained from social media and in the process this model goes through a training stage with large amounts of data then is adjusted in the concept of sentiment analysis.
Optimizing Task Scheduling Algorithms for Efficient CPU Utilization in Enhancing Server Availability

As modern computing continues to develop, server technology must adapt to handle the increasing demands, particularly in the context of big data processing. This paper focuses on addressing the challenges in allocating and scheduling server CPU resources for efficient big data processing. In the event that the CPU has processed large volumes of big data until the CPU usage reaches 100%, if it is interrupted by another process, such as updating the operating system software, it will be affected, leading to a potential impact on server availability. The study proposes employing Task Scheduling Algorithms such as Earliest Due Date (EDD), Longest Processing Time (LTP), and Round-Robin (RR) to optimize CPU usage. Additionally, through the execution of operating system commands, CPU cores are efficiently allocated to tasks according to their priority and processing time. An experimental application of these techniques in processing 75 jobs showed successful report generation while maintaining CPU utilization below 100%. This paper concludes that capping CPU usage at approximately 80-90% allows for enhanced system stability and an improved ability to manage interrupts, thus optimizing server performance for big data processing.
Naruto Mobile: AI Sparring Partner Using Heterogeneous Deep Reinforcement Learning

Naruto Mobile is a popular mobile Fighting Game with over 100 million registered players. AI agents are deployed extensively to the game for a wide variety of applications such as level challenges and player training, which require them to fight like humans and imitate strong and weak players. Although deep reinforcement learning is an excellent approach to creating agents with diverse behaviors, it is difficult to apply to massive-scale games like Naruto Mobile which is built on a pool of more than 300 characters that have unique skills, speed, and attack range, as a traditional approach of self-play training at such scale may require a substantial computational cost and training time.In this paper, we present a new AI training approach called Heterogeneous Exploitation Self-Play (HESP) to improve AI agent generalization ability in Naruto Mobile and optimize its massive-scale self-play training so that the computational costs and train time are significantly reduced. The proposed algorithm has already been employed by the development team of Naruto Mobile to create AI agents, which, at the time of writing this paper, have been used in more than 300 million human-AI fighting matches. To the best of our knowledge, this is the first time that deep reinforcement learning has been employed by a commercial fighting game.
Bug-a-Tug: A Non-Interactive Game

Bug-a-Tug is a game without tangible gameplay – it omits any means of player input, and therefore, cannot give feedback. Bug-a-Tug accomplishes this by creating the illusion of player input, feedback, and impact on the game system. This unique design questions the importance of player perception and belief in constructing meaning and gameplay. It also questions whether gameplay and interactivity can be achieved even when a game subverts our traditional understandings of such terms.
Visualization-based Iterative Segmentation to Augment Video Game Analytics

Within the field of games, visualization of player log data is becoming an important method for its utility in providing an intuitive and informative way to understand players' experience, which is thus often used by game analytics personnel and game user researchers. Moreover, even players themselves show increased interest in using analytics to quantify and self-improve their performances. Among other types of visualizations, node-edge graphs have proven to be capable of revealing the process of individual and aggregated players, allowing analysts to discover play patterns that can inform game design. However, visualization of player traces often has several disadvantages. First, displaying players' process data does not trivially scale, as tendentially high variance often leads to complex and abstruse graphs. Second, when aggregating all players, individual variations are often overlooked. For example, data from minorities (e.g., casual players or players who played the game very differently than others) are often treated as outliers or noise. In this paper, we present an iterative segmentation approach that allows analysts to interact with the visualization and group players into different subcategories through meta-data or behavioral patterns. Using this approach, analysts can bypass complicated visualizations while protecting significant unique information.
Improvement of Soft-YOLOv5 Algorithm for Stray Cats and Dogs Detection

Real-time monitoring and instantaneous tracking of stray cats and dogs within a specific area using image recognition and object detection in computer vision technology is currently the predominant trend. Cat and dog recognition, as a classic object detection project, serves the purpose of identifying the quantity and categories of cats and dogs in a single image or video frame. This study builds upon the YOLOv5 framework and addresses the practical challenges of cat and dog detection, such as overlapping and occlusion. To enhance detection accuracy and stability, the Soft-NMS and Merge-NMS algorithms are contrasted with YOLOv5's NMS algorithm. The improved Soft-NMS and Merge-NMS algorithms are evaluated through performance testing and compared with the original NMS algorithm. As a result of these two algorithm enhancements for YOLOv5's NMS algorithm, the Soft-NMS algorithm demonstrates a 2.2% performance improvement over YOLOv5 using the NMS algorithm. Therefore, Soft-YOLOv5 is better suited for the task of detecting stray cats and dogs in urban communities and streets.
Forensics Method of Dialects Combine with ASR Post-Processing Correction

Electronic data forensics is an important part of public security work. Nowadays, the hardware and software technology of electronic devices is developing at a rapid pace, and how to use advanced data analysis techniques to analyse and forensically analyse the information in these devices is a pressing issue. Many electronic devices are now capable of instant messaging and can send both text and voice messages, which requires our staff to analyse these data messages simultaneously. One of the major problems we face with the analysis of voice information is the handling of dialects. Staff are not familiar with all the dialects and often do not understand them when they hear them. This makes forensic work difficult. In order to effectively solve the forensic problem when text data is mixed with dialectal speech data, we describe a model based on automatic speech recognition and text grammar correction, which is mainly divided into a speech recognition module and a text revision module.