Reviewerly/exHarmony huggingface dataset:
Blockchain-enabled Data Security Management for Optical Network Telemetry

We propose a blockchain-enabled security management scheme for telemetry data collection, storage, and access in optical networks. The designed smart contracts are deployed in the Ethereum blockchain, and its functions are tested and verified.
Exploring Blockchain for KYC: Deepening Client Understanding

Examining one’s client status in depth, also known by its acronym “Know Your Customer” (KYC), raises a significant problem that needs to be addressed thoroughly. As a result of this, the current paper examines the dynamics of client status and utilized “Blockchain Technology” to developed KYC. Since earlier research was lacking in its capacity to establish a KYC system, which is utilized in the financial sector, principally by banks, to collect information mandated by law regarding their customers in order to provide access to financial services, it is important to note this deficiency. The existing system makes use of databases that are hosted on servers operated by private corporations or by organizations that have their own infrastructures. Traditional KYC systems come with several potential drawbacks, including a single point of failure (SPOF), tedious and time-consuming processes for customers, and high costs to construct one’s own KYC identification and verification system. The aim of this project’s proof-of-concept is to mitigate the effects of the identified issues through the utilization of blockchain technology. The system’s ability to facilitate the data gathering and verification process was enhanced by the provision of previously registered KYC data by individual users through a single click, as well as the cross-checking of KYC data by bank users to verify their users’ information.
UAV Cooperative Path Planning Method Based on Deep Learning Technology

With the development of modern information technology, as one of the core technologies of mission planning, UAV track planning (TP) has become an essential technical means to realize precision guided weapons. How to quickly plan a better track is one of the key technologies to improve the effectiveness of UAV combat system. Based on deep learning technology, this paper studies the coordinated path planning method of UAV; This paper introduces the composition of path planning, analyzes the difficulties existing in path planning and tracking control methods, finally studies and designs the UAV path planning process, finally introduces the network neural algorithm of deep learning technology into UAV cooperative path planning, and analyzes the UAV path planning system and mission threat through simulation experiments. In the simulation process, 45 nodes are excited at t = 5.828. Finally, combined with the example and simulation results, the feasibility of the TP algorithm is proved. The algorithm can plan the better track more efficiently, and can still plan the reasonable track with the increase of obstacles in the combat environment.
Adaptive SLIC-Based Fuzzy Intensity Dissimilarity Thresholding for Color Image Segmentation

In order to make full use of the color information of the image and improve the accuracy of color image segmentation, this paper proposes an adaptive SLIC-based fuzzy intensity dissimilarity thresholding for color image segmentation, which does not need gray conversion. Firstly, the proposed algorithm adaptively selects the number of super-pixels through the sum of image information and image complexity, and uses SLIC technology to extract image super-pixels; Then, the median value of each channel pixel in each super-pixel block is used as the super-pixel value to calculate the super-pixel intensity information, and the super-pixel intensity histogram is counted; Finally, an intensity dissimilarity function based on IT2FS is constructed to search the optimal threshold. On Berkeley images and Weizmann images, the proposed algorithm is compared with the five related algorithms. The experiments show that the proposed algorithm has achieved good results in terms of visual effects and evaluation indicators, which proves the effectiveness of the algorithm.
Automating Intellectual Property License Agreements with DLT

Intellectual Property license agreements management is a business process that has still to be fully digitized and benefit from the derived advantages. To close this gap, in this paper we show how license agreements can be modernized and improved by Distributed Ledger Technology. We implement and refine the idea of representing a license agreement's logic with a smart contract. The obtained “Smart License” is executed by a DLT network following the contract specified logic, so without the possibility of being tampered with by its interested parties. This allows for licensing royalties to be computed in a transparent, trustworthy, and accurate way. The proposed approach further improves the IP licensing field by removing the need for audits, enabling novel business models too complex for traditional solutions, and reducing licensing costs, thus allowing for cheaper and wider participation to IP licensing markets. Beside studying the main concept of Smart License, we provide a possible architecture for its implementation and present an evaluation of a proof of concept implementation on Ethereum.
A study of Chinese Text Classification based on a new type of BERT pre-training

Chinese Text Classification (TC) is the process of mapping text to a pre-given topics category. In recent years, it has been found that TC is mainly based on RNN and BERT, so the development of different novel pre-training applied to Chinese TC is described as based on BERT pre-training. BERT combined with convolutional neural network is proposed to extend the BERT-CNN model for the problem of lack of semantic knowledge of BERT to derive a good classification effect. The second RoBERTa model performs feature extraction and fusion to obtain the feature word vector as the text output vector, which can solve the problem of insufficient BERT extracted features. The BERT-BiGRU model, on the other hand, mainly avoids the increase in the number of texts leading to long training time and overfitting, and uses a simpler GRU bi-word network as the main network to fully extract the contextual information of Chinese texts and finally complete the classification of Chinese texts; at the same time, it makes an outlook and conclusion on the new pre-training model for Chinese TC.
A Framework to Support the Delivery and Assessment of Practical-Based Concepts on Online Learning

In recent times, the shift towards online-based teaching and learning has become increasingly prevalent. This transition has been facilitated by the emergence of various platforms, such as E-learning, MS Teams, and Google Classroom. While these platforms are well-suited for delivering and assessing theory-based courses, they present challenges when it comes to practical-based courses. The delivery and assessment of practical skills and concepts require access to appropriate tools and supervision from educators during implementation. To evaluate learners’ practical knowledge, activity-based assessments can be employed. This assessment method involves observing and tracking learners’ progress, considering factors such as the time taken to complete an activity and the accuracy of their attempts. However, existing online platforms for teaching and learning lack the necessary features to effectively handle the delivery and assessment of practical skills. Therefore, there is a pressing need for a mechanism that can adapt these platforms to accommodate the requirements of practical-based concepts. This paper proposes a framework designed to integrate tools for online practices, thereby facilitating the delivery of practical-based concepts through online platforms. Additionally, the framework will leverage the data generated during the online learning process to gain insights into learners’ behavior and knowledge levels. By implementing this proposed framework, educators can bridge the gap between theory and practice in online learning environments. It enables the seamless delivery and assessment of practical skills, fostering a more comprehensive educational experience for learners.
Android Based Mobile Information and Management Application for University

Majority of us are using mobile phones to perform most of our daily work. However, many universities are still using websites to manage students’ course registration, timetables and fees payment which requires the students to use their laptop for better experience. Hence, we have proposed an Android based mobile information and management application for university. The proposed application provides numerous benefits for universities, staff, and students by creating a unified and user-friendly platform that streamlines various functions. For universities, the app reduces administrative workload and optimizes resource management, leading to more efficient operations. Staff members enjoy enhanced communication with students and colleagues, as well as easier access to essential information and tools for managing their courses. Students, on the other hand, experience greater convenience in accessing course materials, schedules, and other resources, resulting in improved engagement and satisfaction. Furthermore, the app’s QR code scanning capabilities facilitate seamless interactions with campus facilities, promoting a more connected and technologically advanced university environment.
Human Action Recognition (HAR) using Image Processing on Deep Learning

The advancement of artificial intelligence (AI) has bought many advances to human society as a whole. By using daily activities and integrating the technology from the fruits of AI, we can manage to gain further access to knowledge we can only begin to imagine. In identifying human action recognition (HAR); processing photos and videos to discern whether a human is present, then mapping the subject classified, which lastly determines the action being carried out is the objective. To achieve this, various steps are taken and careful approach is required, with the extensive amount of research, numerous troubleshooting and experimentation is required. The AI architecture has to learn from dataset collected for it to discern the identification of action properly. HAR is achieved by using Python code using real-time webcam feed. Human pose detection library known as MediaPipe Pose Detection detects human anatomy from input through joints key-points. MediaPipe algorithm that extract features in x-y-z axis with visibility (four variables) and the extracted data is trained using CNN-LSTM based on the trained and tested algorithm classifier model. The output obtained produced an RGB-skeleton and an action label on the detected subject as standing, waving, walking and sitting, has yielded good results.
Classification Performance of Directed Acyclic Graph Network on Potential Dyslexia Handwriting Images

The common learning disability known as dyslexia can have a negative impact on reading and writing abilities, so early diagnosis is essential for providing proper intervention and support. Furthermore, the traditional approach to dyslexia screening has addressed several drawbacks, including subjectivity, consumption of time, and a propensity for bias since it depends on the evaluator’s observation and rating. Technology has demonstrated that computer-aided methods for dyslexia screening are effective and reliable assessments. Directed Acyclic Graph (DAG) networks outperform sequential networks in Convolutional Neural Networks (CNNs) due to their capacity to capture both local and global features of the input data. This study investigates the classification performance of DAG Networks using dyslexic handwriting images. The study further explores the impact of skip connections in DAG networks on dyslexia screening by comparing four networks: DAG-I, DAG-2, DAG-3, and DAG4. The findings revealed a high accuracy of 99.01% in training and 89.68% in testing accuracy, indicating the expectations of DAG Networks as a dependable and helpful method for dyslexia screening. The results also demonstrated that skip connections could enhance the performance of DAG networks in identifying complex features in images of dyslexia handwriting. The proposed method can be incorporated into a computer-assisted dyslexia screening system to provide a rapid, accurate, and unbiased dyslexia assessment.
A Comparative Study on COCOMO II Model for Cost Estimation

Due to its capacity to increase capital accuracy, Constructive Cost Model II (COCOMO II) is frequently chosen for predicting the cost of software projects. The accuracy level is frequently impacted by the large error value difference between COCOMO II and the real project cost. This problem can be improved by various optimization methods, such as BCO, ANN, Fuzzy, ACO, Cuckoo, and Grey Wolf optimization (GWO). Therefore, this study aimed to comparatively analyze the COCOMO II model for cost estimation. In this case, the implemented datasets were Nasa 93 and Turkish. In comparison to other optimization techniques, the results showed that COCOMO II-GWO with Fuzzy Gaussian reduced the outputs of MMRE by more than 16%. This subsequently led to the improvement of project cost-estimate accuracy levels.
Learning Rate Optimization for Enhanced Hand Gesture Recognition using Google Teachable Machine

Developing efficient sign language recognition systems using wearable devices is a major challenge in Machine Learning. One obstacle is effectively translating gestures based on sensor data. Traditional methods involve complex programming using data fusion and mapping techniques. To address this, we need emerging technologies that simplify gesture data processing while maintaining accuracy. This study explores an artificial intelligence approach for detecting Bahasa Melayu using a ready-to-use machine learning framework-Google Teachable Machine. By experimenting with these tools, the research aims to improve the simplicity and accuracy of hand gesture detection. The study also investigates the impact of the learning rate, an important parameter in machine learning algorithms, on system performance, providing insights for optimizing gesture detection. The results of our study emphasize the significance of thoughtfully choosing the learning rate for successful model training. This underscores the importance of finding the optimal learning rate to ensure effective training, regardless of the specific machine learning framework employed.
User’s Acceptance and Intention of Blockchain Technology with UTAUT 3 Model: Beneficial or Detrimental in the Indonesian Banking Industry

The rapid development of technology has resulted in new technological innovations that continue to emerge and affect all aspects of life. Blockchain is a new technology that has attracted attention as it is considered an innovation that claims to be able to change the future of the banking side. Currently, banks have big data on the transactions that have taken place so far. Therefore, bankers also require technology to balance their job’s complexity. Here, blockchain technology is put forth as a way for bankers to stay current with changes during the industrial revolution, especially in the era of Banking 4.0. Therefore, in this study, we aim to predict the level of acceptance and intention of bankers in the Indonesian banking industry (Top ten banks in Indonesia based on Indonesia Stock Exchange (IDX) survey 2022) towards the use of blockchain technology using 8 factors in UTAUT 3 as our grand theory. This study employs quantitative methods by utilizing primary data by distributing questionnaires to accountants in the banking sector and testing hypotheses using SMARTPLS software. Based on the results of data processing, performance expectancy, social influence, habit, and personal innovativeness have a significant influence on behavioral intention. Meanwhile, effort expectancy, facilitating condition, hedonic motivation, and price value do not have a significant influence on behavioral intention. Bankers must be aware of the four variables that directly influence behavioral intention in using blockchain, including the need for increased knowledge and habits regarding the use of blockchain technology so that the adoption process will be better, in line with the benefits obtained from using blockchain technology.
Development of Sensory Unit Using Single IMU Sensor for Knee Joint Movement

People with paralysis due to neurological disorders such as stroke and SCI require scheduled rehabilitation exercise to regain muscle function and fitness. Most rehabilitation techniques involve some kind of devices or equipment that are dedicated to it. The sensory system is a key component in supporting the function of rehabilitation devices especially when it has elements of monitoring and control. IMU based systems that employ gyroscopes, accelerometer and magnetometer have a growing interest in measuring movement of body part during rehabilitation exercise. Thus, this study proposed the development of a wearable sensory unit using IMU to monitor the knee joint movement during sitting. The system used MPU9250 as a single sensing component to measure angular position and velocity of the knee. Based on the experimental work outcome, the system was proven to be efficient in performing the measurement of angular position and velocity and providing real time data logging for the purpose of data analysis and system control. Enhancement of this single used sensor to achieve the capabilities of two integrated sensors will be the aim for future work.
Evaluation of Cell Segmentation Using Pruning and Quantization

Cell segmentation is a challenging task due to the imaging modality employed, cell’s deformable construct and imaging settings. The use of Deep Neural Networks (DNN) has facilitated excellent results in segmentation when compared to traditional image segmentation techniques as it has shown significant improvement in several metrics including accuracy, efficiency, precision etc. From the end user perspective, a good trained model is one that has a high accuracy, is portable, and has a short execution time. The size of the trained model is essential in the age of tiny machine learning (TinyML) and several techniques are developed. Among the techniques that enable a good performance while operating with small size of the input data and small size of output data we can list data augmentation, pruning and quantization. Here we report results of cell segmentation while using pruning and segmentation.
Enhancing Recommender Systems with Semantic User Profiling through Frequent Subgraph Mining on Knowledge Graphs

Recommender systems play a crucial role in personalizing online user experiences by creating user profiles based on user–item interactions and preferences. Knowledge graphs (KGs) are intricate data structures that encapsulate semantic information, expressing users and items in a meaningful way. Although recent deep learning-based recommendation algorithms that embed KGs have demonstrated impressive performance, the richness of semantics and explainability embedded in the KGs are often lost due to the opaque nature of vector representations in deep neural networks. To address this issue, we propose a novel user profiling method for recommender systems that can encapsulate user preferences while preserving the original semantics of the KGs, using frequent subgraph mining. Our approach involves creating user profile vectors from a set of frequent subgraphs that contain information about user preferences and the strength of those preferences, measured by frequency. Subsequently, we trained a deep neural network model to learn the relationship between users and items, thereby facilitating effective recommendations using the neural network’s approximation ability. We evaluated our user profiling methodology on movie data and found that it demonstrated competitive performance, indicating that our approach can accurately represent user preferences while maintaining the semantics of the KGs. This work, therefore, presents a significant step towards creating more transparent and effective recommender systems that can be beneficial for a wide range of applications and readers interested in this field.
Privacy-friendly statistical counting for pedestrian dynamics

Relying on Wi-Fi signals broadcasted by smartphones became the de-facto standard in the domain of pedestrian crowd monitoring. This method got the edge over other traditional means owing to the fact that insights are built upon data which uniquely identifies individuals and, thus, allows highly accurate crowd profiling over time. On the other hand, handling such uniquely identifying data in such a way that it does not expose the sensed individuals to potential privacy infringements proves to be a difficult task. Although several protection techniques were proposed, they yield data which, combined with other external knowledge, can still be used for tracing back to specific individuals. To address this issue, we propose a construction which protects the short-term storage and processing of privacy-sensitive Wi-Fi detections under strong cryptographic guarantees and makes available in the clear, as end results, only statistical counts of crowds. To produce these statistical counts, we make use of homomorphically encrypted Bloom filters as facilitators for oblivious set membership testing under encryption. We implement the system and perform evaluation on both simulated data and a real-world crowd-monitoring dataset, demonstrating that it is feasible to achieve highly accurate statistical counts in a privacy-friendly way.
Virtual Dance Collaboratory: A Collaborative Model for Digital Media Education

This article outlines the authors’ collaborative approach to designing digital media curriculum rooted in distributed student leadership, health, and well-being. In collaboration with students, we launched the Virtual Dance Collaboratory (VDC), a fully remote, student-led artist collective created in response to the COVID-19 pandemic. Our pedagogical approach strikes a balance between teaching students new technology skills and cobuilding a student-led artist collective predicated on healthy leadership structures. In this article, we discuss how VDC addressed three COVID-related curricular objectives that subsequently evolved into new pedagogical philosophies for mentoring student-led dance company activity. Throughout the text, we provide concrete examples of the technology kits, organizational structures, and curricular approaches we developed.
Diagnostic Expert Advisor: A platform for developing machine learning models on medical time-series data

Setting up data structures, parallelizing code, and creating visualizations are tasks in almost any project aiming to develop healthcare AI solutions based on heterogeneous, high-dimensional data structures. While toolkits for individual parts of this workflow exist, a solution that provides integration of all steps is rarely found. We present the Diagnostic Expert Advisor, a platform for machine learning research on heterogeneous medical time-series data that aims to provide a robust environment for the rapid development of AI applications. It integrates a local web app through which whole patient cohorts, as well as the disease evolution of individual patients, can be analyzed with integrated tools for data handling, visualization, and parallelization. The platform provides sensible defaults while being flexible and extensible to fit various projects and working styles.