Reviewerly/exHarmony huggingface dataset:
Blockchain Based Security Framework for Internet of Medical Things (IoMT) Applications

The growing demand for Internet of Medical Things (IoMT) sensors within the healthcare sector has emphasized the necessity for robust security measures. However, these devices, often constrained by limited resources, frequently require external assistance to guarantee the security of data. Blockchain technology emerges as a practical solution, offering a way to enhance system security and safeguard patients' sensitive health records against unauthorized access and data breaches. The fusion of IoMT and blockchain presents a range of benefits, such as cost reduction, improved speed, automation, and the guarantee of unmodifiable data. Nevertheless, security concerns persist, particularly in light of the ever-evolving encryption techniques. The effective implementation of IoMT hinges on the delivery of personalized, patient-centered care, which has the potential to enhance affordability, simplicity, and overall convenience, ultimately contributing to improved quality of life and extended lifespans. In this comprehensive exploration, we delve into the historical context, architecture, components, and challenges associated with IoMT. Additionally, we conduct an in-depth examination of blockchain technology, covering its essential elements and categories, as well as its seamless integration into IoMT, underscoring its critical role in reinforcing security measures within the healthcare domain. Furthermore, this paper outlines recent developments in blockchain technology within the IoMT framework, highlighting its ongoing evolution and sustained significance in the healthcare field.
Creating a Proctoring System Using Neural Network in the Educational Process

The professional activity of a person depends on his knowledge, skills and abilities, and secondly, on the knowledge he has acquired through the professional environment and information-educational resources. Therefore, the improvement of knowledge is connected with the use of information in an educational setting. The emergence of big data and high-performance devices, the direction of artificial intelligence, and the method of machine learning became the basis for creating a modern informational and didactic educational environment using neural networks. Therefore, in the context of the globalization of education, the issue of making and using an information portal is considered to improve the training of future computer science teachers based on machine learning and to increase the professional competence of computer science teachers currently working in the field of education. The article mentions the following parts of the use of machine learning algorithms in the creation and implementation of an information and education portal: organizing a server platform, using a second-level virtual hosting service, entering the system in real-time by creating a neural network through human face recognition, checking the performance of control work through voice recognition, passing the exam by proctoring the sections of the informational education portal, organizing a chatbot to establish feedback with users, and incorporating these topics into the general content of the informational education portal. Therefore, the article aims to develop an informative educational portal with the help of machine learning algorithms and master the methods of creating a system for students in the learning process. Theoretical and empirical research is being conducted to determine the prerequisites for training future computer science teachers based on machine learning methods. Research is being included in the educational process of students of the educational programs "6B01511-Informatics", "7M01511-Informatics", and "7M01525-STEM education" of the Eurasian National University named after L.N. Gumilyov. The result of creating a proctoring system based on neural networks is the basis for developing and implementing an informational educational portal. The development of the considered work is considered an information knowledge space that will be used in the training of future computer science teachers and in improving the professional competence of current computer science teachers.
Multi-Person Speech Curator for Minutes of Meetings along with Meeting Summarization and Language Translation

Multi-Person Speech Curator for Minutes of Meetings refers to a system where we can generate the summary of a meeting from the audio recording of the meeting using automatic speech recognition (ASR) and NLP algorithms. The system can identify and differentiate speakers and generate transcript with time stamp. ASR is used to transcribe the audio of the meeting and then NLP algorithm used to for speaker diarization and then extract the summary from the transcript. In this paper we use Whisper API from OpenAI for speech to text conversion. The model facebook/bart-large-cnn is used to generate the summary from the transcript. The model is fine-tuned with Samsum dataset that contains about 16k messenger-like conversations. mBART model is used for summary translation. The objective of the paper is to create minutes of meeting which has concise information about the complete meeting discussions, decisions, and actions. The system has the potential to significantly save the time and resources as it eliminates manual transcript and captures all important points of the meeting.
YOLOv5 ship: An improved ship target detection algorithm study

Aiming at the problem of difficult detection of maritime ship targets, which is prone to false detection, missed detection, and low recognition accuracy. In this paper, an improved ship target detection algorithm, YOLOv5, is proposed. firstly, the attention mechanism CBAM module is added to feature enhancement of image targets, and irrelevant interference information is suppressed based on retaining key features; secondly, the fusion structure of image feature extraction is improved, and the BiFPN structure is used instead of FPN+PAN structure, which realizes bi-directional multi-scale feature fusion and reduces target information loss and missed detection rate; finally, the loss function is optimized, and WIoU v3 with position and size focus factors is used to optimize the loss function, and the relative position and size of the prediction frame and the rear frame are learned more accurately, which effectively enhances the detection capability of small target ships. The experimental results show that the improved algorithm improves the target detection efficiency, the detection accuracy mAP50 value reaches 93.5%, and the FPS (Frames Per Second) goes to 51 frames per second, which can better meet the requirements of real-time detection and improve the target detection ability of ships in different environments with better robustness.
An Analysis of Large Language Models and LangChain in Mathematics Education

The development of large language models (LLMs) has led to the consideration of new approaches, particularly in education. Word problems, especially in subjects like mathematics, and the need to solve these problems by collectively addressing specific stages of reasoning, have raised the question of whether LLMs can be successful in this area as well. In our study, we conducted analyses by asking mathematics questions especially related to word problems using ChatGPT, which is based on the latest language models like Generative Pretrained Transformer (GPT). Additionally, we compared the correct and incorrect answers by posing the same questions to LLMMathChain, a mathematics-specific LLM based on the latest language models like LangChain. It was observed that the answers obtained were more successful with ChatGPT (GPT 3.5), particularly in the field of mathematics. However, both language models were found to be below expectations, particularly in word problems, and suggestions for improvement were provided.
A Trusted User Model for Collaborative Multi-Criteria Recommendation using User Review Elements

research-article Share on A Trusted User Model for Collaborative Multi-Criteria Recommendation using User Review Elements Authors: Sumaia Mohammed AL-Ghuribi Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Malaysia 0000-0001-9714-9677Search about this author , Shahrul Azman Mohd Noah Center for Artificial Intelligence , Universiti Kebangsaan Malaysia, Malaysia Center for Artificial Intelligence , Universiti Kebangsaan Malaysia, Malaysia 0000-0001-7683-4309Search about this author , Saeed Amer Al Ameri Center for Artificial Intelligence , Universiti Kebangsaan Malaysia, Malaysia Center for Artificial Intelligence , Universiti Kebangsaan Malaysia, Malaysia 0009-0002-8335-8480Search about this author , Masnizah Mohd Center for Cyber Security, Universiti Kebangsaan Malaysia, Malaysia Center for Cyber Security, Universiti Kebangsaan Malaysia, Malaysia 0000-0001-8908-8755Search about this author , Arifah Che Alhadi Department of Computer Science, Universiti Malaysia Terengganu, Malaysia Department of Computer Science, Universiti Malaysia Terengganu, Malaysia 0000-0002-5797-4784Search about this author Authors Info & Claims ICAAI '23: Proceedings of the 2023 7th International Conference on Advances in Artificial IntelligenceOctober 2023Pages 48–53https://doi.org/10.1145/3633598.3633621Published:22 January 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Development of Thai Real-time Captioning Service

Real-time captioning is a challenging task, as accurate text transcription must be produced within a few seconds after the word is uttered. We have explored several techniques that could be used to produce real-time captioning for Thai. An on-line real-time captioning platform which supports three different transcription techniques: simultaneous typing, re-voice, and Automatic Speech Recognition (ASR), has been developed. We evaluated the caption quality produced from the three techniques based on 2 aspects accuracy and delay. The evaluation results reveal that simultaneous typing is suitable for a less formal speaking style and for speech with a lot of code-switching between Thai and English. As the machine learning technology continues to improve the ASR technique is a promising choice as it achieved highest accuracy for more formal captioning tasks and used fewer number of human agents. Furthermore, the proposed 2-stages VAD (Voice Activity Detection) scheme can reduce the ASR transcription delay by 33% relatively.
EMS-BERT: A Pre-Trained Language Representation Model for the Emergency Medical Services (EMS) Domain

Emergency Medical Services (EMS) is an important domain of healthcare. First responders save millions of lives per year. Machine learning and sensing technologies are actively being developed to support first responders in their EMS activities. However, there are significant challenges to overcome in developing these new solutions. One of the main challenges is the limitations of existing methods for EMS text mining, and developing a highly accurate language model for the EMS domain. Several important Bidirectional Encoder Representations from Transformer (BERT) models for medical domains, i.e., BioBERT and ClinicalBERT, have significantly influenced biomedical text mining tasks. But extracting information from the EMS domain is a separate challenge due to the uniqueness of the EMS domain, and the significant scarcity of a high-quality EMS corpus. In this research, we propose EMS-BERT - a BERT model specifically developed for EMS text-mining tasks. For data augmentation on our small, classified EMS corpus which consists of nearly 2.4M words, we use a simultaneous pre-training method for transfer-learning relevant information from medical, bio-medical, and clinical domains; and train a high-performance BERT model. Our thorough evaluation shows at least 2% to as much as 11% improvement of F-1 scores for EMS-BERT on different classification tasks, i.e., entity recognition, relation extraction, and inferring missing information when compared both with existing state-of-the-art clinical entity recognition tools, and with various medical BERT models.
観測点から逆伝播する波面の重複点検出による簡易震源決定法

When a large earthquake occurs, understanding general earthquake activity immediately is important. However, arrival-time classifications, which involve grouping by earthquake and seismic phase discrimination are difficult to perform. Hence, we developed a new simple hypocenter determination method without these classifications. Only the arrival time was used. In our method, both the P and S phase wavefronts propagate reversely from the P and S arrival times from the seismic stations. The location where many wavefronts overlap is the seismic source, that is, a hypocenter. The wavefront is an isochronous surface of the travel time calculated using the ray tracing method. The target space-time volume was discretized, and travel times at all grid points in the volume from all seismic stations were calculated in advance. One point was added to the grid points at which each wavefront was present. The grid point of the local maximum score, where the wavefronts overlapped, was searched and extracted as a seismic source. Assigning points only to grid points on wavefronts is important to significantly reduce the computing time. We applied manually picked arrival time datasets of four typical seismic activities in Japan from Hi-net to our method, and successfully extracted sufficient source locations to understand the general distribution within an acceptably short computation time for each dataset.
Biological information monitoring

Biological information monitoring serves as a tool to detect anomalies related to individuals both temporally and spatially. In this paper, we discuss biological information monitoring, distinguishing between monitoring conducted in hospitals and that carried out at home. Furthermore, we argue the potential for extending the scope of monitoring not only to patients but also to medical professionals. Additionally, we explore the future possibilities of monitoring advancements.
Bitcoin Price Manipulation Regulation from a Game Perspective

Frequent price manipulation in the Bitcoin market will lead to market risk and seriously disrupt the financial order, but there is less research on its regulation. We address the Bitcoin price manipulation problem by building a regulatory game model. First, we study the price manipulation mechanism of the Bitcoin market based on behavioral finance and clarify the boundary conditions. Second, we introduce regulator constraints and establish a game model between the manipulator and the regulator. Further, through variable deconstruction, parameter verification, and simulation analysis, we explore how to achieve effective regulation of Bitcoin price manipulation. We find that the effective regulation of Bitcoin price manipulation can be achieved in three ways: (1) Adjust the penalty coefficient with a certain lower threshold so that the manipulator's expected return is negative; (2) Set the lowest possible price fluctuation standard while ensuring that it does not interfere with market-based transactions; (3) The simulation of price manipulation regulation is optimized and most efficiently controlled when the probability of investigation is dynamically adjusted by a concave function on the price fluctuation standard.
Improving Link Prediction Accuracy of Network Embedding Algorithms via Rich Node Attribute Information

Complex networks are widely used to represent an abundance of real-world relations ranging from social networks to brain networks. Inferring missing links or predicting future ones based on the currently observed network is known as the link prediction task. Recent network embedding based link prediction algorithms have demonstrated ground-breaking performance on link prediction accuracy. Those algorithms usually apply node attributes as the initial feature input to accelerate the convergence speed during the training process. However, they do not take full advantage of node feature information. In this paper, besides applying feature attributes as the initial input, we make better utilization of node attribute information by building attributable networks and plugging attributable networks into some typical link prediction algorithms and name this algorithm Attributive Graph Enhanced Embedding (AGEE). AGEE is able to automatically learn the weighting trades-off between the structure and the attributive networks. Numerical experiments show that AGEE can improve the link prediction accuracy by around 3% compared with SEAL, Variational Graph AutoEncoder (VGAE), and node2vec.
A lightweight deep-learning radar gesture recognition based on a structured pruning-NAS

This paper proposes a structured pruning-network architecture search (NAS) algorithm for a lightweight deep-learning radar foot gesture recognition in a conventional lightweight deep-learning models to quantitatively evaluate its performance. Our goal is to recognize foot gestures using a CW radar, generate their STFT unique signatures, and build a foot gesture recognition system that could be implemented on an edge device. The proposed scheme shows that model size and FLOPs were reduced, and a sub-optimal lightweight model for a foot gesture recognition device based on MobileNet was obtained with a slight decrease in accuracy.
Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums

The increasing sophistication of cyber threats necessitates proactive measures to identify vulnerabilities and potential exploits. Underground hacking forums serve as breeding grounds for the exchange of hacking techniques and discussions related to exploitation. In this research, we propose an innovative approach using topic modeling to analyze and uncover key themes in vulnerabilities discussed within these forums. The objective of our study is to develop a machine learning-based model that can automatically detect and classify vulnerability-related discussions in underground hacking forums. By monitoring and analyzing the content of these forums, we aim to identify emerging vulnerabilities, exploit techniques, and potential threat actors. To achieve this, we collect a large-scale dataset consisting of posts and threads from multiple underground forums. We preprocess and clean the data to ensure accuracy and reliability. Leveraging topic modeling techniques, specifically Latent Dirichlet Allocation (LDA), we uncover latent topics and their associated keywords within the dataset. This enables us to identify recurring themes and prevalent discussions related to vulnerabilities, exploits, and potential targets.
Vehicle Re-identification with Spatio-temporal Information

Vehicle re-identification (re-id) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based re-id models often fail to track vehicle across multiple cameras. To overcome the challenge, in this work, we utilize spatial and temporal information between cameras and individual vehicle. To this end, we build camera network topology that consists of transition time probabilities between camera pairs. Then, we merge the appearance similarity and spatio-temporal probability scores for the final reid similarity. Based on our methods, we improved vehicle reid performance on public dataset (VeRi776). The experiment results support that utilizing spatial and temporal information in vehicle re-id can leverage the baseline re-id performance and handle the vehicle appearance ambiguities.
LPI radar signal recognition with U<sup>2</sup>-Net-based denoising

Low Probability of Intercept (LPI) radar signals play a vital role in electronic warfare by maintaining informational superiority. Classifying these LPI radar waveforms is a key capability but remains a challenging task due to strong noise interference. Traditional signal processing techniques often show limitations in effectively removing complex noise signals. While deep learning-based modulation classification has exhibited superior performance, its effectiveness is compromised in the presence of significant noise. In this study, we propose a deep learning-based denoising method using the U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net for LPI radar signals, followed by modulation classification using a Convolutional Neural Network (CNN). We further compare the performance of U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net with other denoising models such as U-Net and denoising autoencoder. Experimental results demonstrate that the U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net outperforms other methods, achieving over 90% classification accuracy for signals with a signal-to-noise ratio above -14dB.
Latent-Variable Classifiers Based on Total Correlation for Dynamic Environments

This paper introduces a novel approach to latentvariable classifiers that employs the correlation explanation technique, an information-theoretic latent-variable clustering based on total correlation. The proposed classifier improves batch-mode classification accuracy in comparison to traditional training approaches. Additionally, it demonstrates the ability to incrementally learn from additional datasets involving new features, without accessing or reprocessing past data. These findings underscore the method's potential to serve as a robust machine learning model suitable for dynamic real-world environments.
Automation Testing with Appium Framework in IP Multimedia Subsystem

The IP Multimedia Subsystem (IMS) refers to the standard for a telecommunication system that controls multimedia services accessing different networks. Not only the IMS but other telecommunications networks are also required to operate continuously. In addition, any system error can lead to massive losses in revenue and productivity. Therefore, testing and quality control task has an important role in telecommunications networks, and it requires an efficient method to detect any inherent errors and failures. To facilitate the testing process, a solution using the test automation tool Appium is proposed. The Appium framework is mainly used to validate mobile browsers and mobile applications with UI. If the IMS can be implemented with the Appium framework, the burdensome testing tasks can be eased. Therefore, we developed an automated model in which the Appium framework can help in testing functions of the IMS. Our model contains some new features added to the basic function of the framework and these features are designed and developed specially for testing the IMS services. This proposed solution not only reduces the time and labor in testing but also can be applied to other systems that are related to mobile phone services.