Reviewerly/exHarmony huggingface dataset:
BBB-Voting: Self-Tallying End-to-End Verifiable 1-out-of-k Blockchain-Based Boardroom Voting

Voting is a means to agree on a collective decision based on available choices (e.g., candidates), where participants agree to abide by their outcome. To improve some features of e-voting, decentralized blockchain-based solutions can be employed, where the blockchain represents a public bulletin board that in contrast to a centralized bulletin board provides extremely high availability, censorship resistance, and correct code execution. A blockchain ensures that all entities in the voting system have the same view of the actions made by others due to its immutability and append-only features. The existing remote blockchain-based boardroom voting solution called Open Vote Network (OVN) provides the privacy of votes, universal & End-to-End verifiability, and perfect ballot secrecy; however, it supports only 2 choices and lacks recovery from stalling participants. We present BBB-Voting, an equivalent blockchain-based approach for decentralized voting such as OVN, but in contrast to it, BBB-Voting supports 1-out-of-k choices and provides robustness that enables recovery from stalling participants. We make a cost-optimized implementation using an Ethereum-based environment respecting Ethereum Enterprise Alliance standards, which we compare with OVN and show that our work decreases the costs for voters by 13.5% in normalized gas consumption. Finally, we show how BBB-Voting can be extended to support the number of participants limited only by the expenses paid by the authority and the computing power to obtain the tally.
WatchLogger: Keyboard Typing Words Recognition Based on Smartwatch

Nowadays more and more people are wearing smart-watches in their daily lives. The various sensors embedded in smartwatches bring the ability to evaluate users' status as well as the risk of privacy issues. For example, if users are typing on key-boards while wearing smartwatches, the attacker could know the typing contents from the sensor data collected by the malicious applications that are installed on the targets' smartwatches. In this paper, we propose WatchLogger, the framework using audio and accelerometer signals to recognize the English words being typed, for demonstrating how to implement the smartwatch-based side-channel attack. Different from the previous studies that focused on the recognition of each key or pair of keys being pressed, WatchLogger aims to perform recognition on the scale of words. To achieve this goal, WatchLogger exploits the audio signals for segmentation and the accelerometer signals for classification. In addition, we propose an ensemble classification model to deal with the problem caused by too many words. At last, we build the dataset WTW -100 with 100 classes of words and 100 samples for each class, and we conduct experiments on the dataset. The experimental results show an accuracy of 98.5 % for keystroke recognition and 91.5 % for word classification, showing a considerable performance of WatchLogger.
Trusted Access Control Mechanism for Intelligent Manufacturing Based on Decentralized Identifier

Industry 4.0 intelligent manufacturing scenario has many types of enterprises, users, roles interconnected. A safe and reliable access control mechanism is an important technology to ensure efficient information sharing in all aspects of manufacturing. The traditional access control mechanism in engineering has problems such as management centralization and data leakage. Blockchain is a new application mode of computer technology, which could empower access control to achieve credible and secure sharing of data in all links. But most blockchain-based access control researches focus on the authorization part of access control, without considering authentication and auditing, which actually is a three-element separation problem. Meanwhile, the access control authority will be obtained by users. If the authority is stored on-chain, the privacy will be transparent; While if the authority is stored off-chain, it may cause the centralized management problems. This paper proposes a Trusted Access Control Mechanism for Intelligent Manufacturing Based on Decentralized Identifier(TACMIM_DID). It assigns hierarchical DID identifier to enterprises, users and resources involved in all aspects of intelligent manufacturing, in order to realize entity trusted identity management. Furthermore, we mark the access control authority with the role DID identifier, and use verifiable credential to automatically verify the access control authority. Finally, we design the user reputation model, where the average user unauthorized level and the number of user unauthorized access are used as indicators to evaluate user behavior. By auditing user behavior, a positive cycle is realized to improve user unauthorized access behavior. The experimental and analysis results show that TACMIM_DID can achieve better security, and realize customized reputation evaluation of user access behavior.
Towards Scalable Cross-Chain Messaging

Blockchains were originally designed as closed execution environments and lack the ability to communicate directly with external systems. To overcome this limitation, many blockchains employ relayers, external applications capable of transporting data between different blockchains. Typically, the process of relaying data is permissionless and multiple independent relayers work concurrently to transport the same information between two blockchains. While this model increases the reliability of data delivery by providing redundancy, it also introduces challenges that have not been previously discussed. In this work, we bridge this gap by discussing the shortcomings of permissionless cross-chain relaying systems and identifying three issues that adversely impact their performance, scalability and security. We take the first step towards addressing issues that hinder performance and scalability by proposing a novel protocol to enable coordination among independent relayers. Additionally, we provide an in-depth discussion about the tradeoffs associated with the design of relayer coordination protocols for permissionless settings. Through this work we provide a foundation for improving cross-chain relaying services.
Vehicle Collision Prediction from Visual Sizes of Objects via Car Dash Camera

The purpose of this research is to develop a method for vehicle collision prediction from visual sizes of objects. There are three main stages in the proposed method. The first stage is object detection and classification using You Only Look Once version 7 (YOLOv7) and object tracking using Simple Online and Realtime Tracking with a deep association metric (DeepSORT). Then, linear regression is applied with the data gathered from the first stage to predict the shape of the bounding box and the coordinates of the centroid of the bounding box of the object at various time intervals. The last stage is vehicle collision prediction along with the time interval in which the collision may occur. If the predicted bounding box of the object overlaps with the defined area in the video, the proposed method predicts that the collision will occur. From the experimental results, the proposed method can trigger collision warning and help reduce the risk of road accidents with car dash camera.
Blockchain International Certification Layer

Blockchain was developed as a decentralized public ledger that stores data among computer nodes. Online transactions and digital money systems were designed to ensure that people could interact freely and without bias. Due to ongoing fraud and the system’s shortcomings in protecting users from fraud and money laundering, this has recently been proven to be the contrary. This study examines the effects of the international certification layer (ICL) on the blockchain system. It also sheds light on the new ICL innovation, which adds a new layer to the blockchain system and its obligations toward users and the government. Additionally, this study will examine blockchain technology, including its functions and the benefits that the ICL offers to the overall system. All users’ safety is a major concern in this context, as is the user experience. the blockchain system also encourages government involvement and intervention in online transactions and access to consumer data. Insecurity and fraud are the key problems associated with digital money; this study aims to demonstrate the importance of the ICL in addressing these problems. It guarantees the functionality of the ICL and its roles through various technical tests and by adding additional layers to the blockchain system.
WiLearn: Design and Implementation of a Microlearning System that Utilizes a Captive Portal of Wi-Fi

Internet use is expanding along with the spread of smartphones and the promotion of digital use, but information security literacy is required to use the Internet safely. This paper addresses these challenges by focusing on the potential of microlearning and nudge theory to support continuous learning to solve these problems and presents a novel learning system that utilizes a Wi-Fi landing portal that appears as a dialog or a browser. To assess the effectiveness of the proposed system, we conducted a comparative analysis of information security test scores before and after the operation of the learning system and questionnaire-based evaluation. The results showed that the analysis of answer scores before and after the operation of the learning system was significant <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{p} &lt; 0.05)$</tex> , indicating an improvement in scores. It was also found that using interface information with nudges effectively motivated participants to engage in repeated learning behaviors. However, it should be noted that frequent interventions might reduce acceptability, and the psychological burden arising from task interruptions needs to be taken into account.
Navigating New Horizons in Electrical Engineering Education

This research explores the transformative landscape of electrical engineering education in the 21st century, focusing on interdisciplinary approaches, transformative technologies, critical thinking, inclusivity, and diversity. Emphasizing the importance of interdisciplinary pedagogy, we highlight its role in fostering well-rounded engineers who can address complex challenges by integrating knowledge from diverse disciplines. We delve into transformative technologies such as VR/AR, AI, and IoT, elucidating their potential to revolutionize the learning experience, offering interactive and personalized education to students. Cultivating critical thinking emerges as a foundational pillar, equipping students with analytical skills for innovative problem-solving and ethical considerations in engineering decisions. Recognizing inclusivity and diversity as cornerstones of progress, we advocate for creating inclusive environments that celebrate diverse perspectives. Addressing challenges and embracing future prospects, our research envisions a dynamic and empowered generation of electrical engineers navigating new horizons and contributing to a technologically advanced and inclusive world.
An Implementation of Intelligent Image Analysis Load Balancing for Virtualized Environment

In response to the widespread use of mobile devices to capture and upload numerous images for analysis in cloud services, we must manage the complexity of analysis requests and challenges of load balancing in a virtualized server environment (e.g., Docker environment). In this paper, we implement an intelligent load balancing approach based on deep reinforcement learning, allocating image analysis requests in a Docker container environment while evaluating the performance of each container. In the evaluation, our approach was able to find a reasonable allocation policy for minimizing the analysis time compared to other intelligent load balancers.
Design for Mobile Contexts: Evaluating Attitudes Towards Menu Systems in Responsive Websites

Designers face challenges when examining and implementing navigational patterns to accommodate smartphone users. While much research has focused on quantifying the effectiveness of menu systems, less attention has been paid to the role of context. This study examined how attitudes toward three menu systems vary across three contexts. The results showed an interaction effect between the menu systems and context. More specifically, the data revealed that all menu systems were rated lower in the brand page context than in the single-service and service directory contexts. In addition, the results showed that the transient menu system was rated lower than the top persistent and navigation hub menu systems were. The results can aid future designers in choosing a menu system for a specific context. They may also have implications for other digital artifacts that adopt menu systems in their graphical user interface.
An End-To-End Hybrid Overhead Facial Orientation Estimation System for Offline Group Discussions

Aiming to evaluate the concentration and engagement level in offline group discussion scenarios, we seek to capture the interaction information among meeting participants. In this paper, we focus on the facial orientation and present an end-to-end hybrid system capable of 2D overhead facial orientation estimation. Compared to common perspectives from which personal identity information is exposed, we explore approaches to perform overhead facial orientation estimation due to the privacy problem. The proposed system is a neural network-based architecture and composed of three modules. First we leverage and train from scratch the YOLOv8 model on the HollywoodHeads dataset, which allows full-perspective head detection in visual scenes. With the obtained head location, we then generate the corresponding head masks from the visual information within the bounding box. Finally, we fit them to ellipse so that 2D overhead facial orientation of all the meeting participants in the visual scenes can be estimated with the ellipse properties. Experiments demonstrate that our system achieves an MAE of 12.47 on the test dataset.
Wellbeing Insights in a Data-Driven Future

This article explores meaningful insights users might be able to obtain at the intersection of wearable technology and user-generated data. While wearables have become ubiquitous in monitoring health and well-being, the utility of the data they collect remains limited for end-users. Our TypeAware case study delves into users' challenges in interpreting and deriving actionable insights from their wearable data. The TypeAware application aims to enhance user understanding of digital well-being and sleep quality data. Our results indicate that, despite engagement, participants encountered difficulties generating actionable insights from their data. Leveraging the capabilities of large language models, our results demonstrate the potential for automating insight generation: transforming raw data into meaningful, user-friendly understandings. Ulti-mately, this work calls for a shift in wearable technology design, advocating for more user-centric approaches that empower individuals to unlock the full potential of their wearable data for improved well-being.
iTOChat: a Mobile Bot Recommending Engineering Departments and Open Campus Events

Chatbots are widely used in education, medicine, business, and other fields, among which natural language processing such as ChatGPT, especially chatbots based on large-scale language models (LLMs), are expected to flexibly answer users' complex questions. In this study, we present iTOChat, a mobile bot that uses ChatGPT to recommend departments and open campus events to students visiting Kyushu University, an open campus of the School of Engineering, by inputting students' personal information such as hobbies and future plans. Although iTOChat is expected to make accurate recommendations to students, this is not easy because ChatGPT does not contain detailed information about engineering departments and open campus events. Therefore, we developed a method to supplement the missing information in ChatGPT by embedding reasons and conditions for recommending departments in the ChatGPT prompts. This paper describes the features and objectives of the developed system and evaluates the satisfaction of students who used iTOChat at an open campus.
Optimal Energy Management System Using Probabilistic Day-ahead Forecasting

The power loss and economic revenue of a photovoltaic storage system (solar energy+storage system) can be optimized by integrating probabilistic photovoltaic (PV) forecasting into control strategies. An energy management system (EMS) aims to optimize energy utilization based on certain constraints (such as the grid code) and improve the reliability of energy injection into the electrical network. Solar energy production is dependent on factors such as weather conditions and time of day. Therefore, precise PV forecasting can aid an energy management system in predicting the solar energy quantity that will be available at any given time, allowing for optimal management of the storage system. In this article, we explore the performance of a PV storage system with integrated probabilistic forecasting under different scenarios. Our analysis focuses on evaluating the impact of the forecasting strategy on the energy balance and revenue maximization. We compare the results obtained using probabilistic, deterministic, and persistence-based forecasts over a one-year simulation period. Our findings demonstrate the effectiveness of the system in prioritizing electricity injection during peak hours, resulting in high electricity injection and low penalties. Overall, accurate PV forecasting plays a crucial role in optimizing the performance of a PV storage system and maximizing revenue.
Operation, optimization and taxation of large-scale combined solar and hydro pumped storage power plant

The planned and already observed increase in the use of photovoltaic power plants is partially constrained by the uncontrolled variability of energy generation and the need to allocate significant areas for the placement of photovoltaic panels. The weakening of the influence of these factors is possible through the construction of a floating photovoltaic plant on the reservoirs of a large-scale pumped storage hydropower plant. This paper is devoted to the operation of large-scale photovoltaic and pumped storage power plants under market conditions as well as the problem of taxation. We are determining an option that is acceptable to both the transmission network operator and the power plant.An optimization problem has been posed and solved, including the maximization of the profit of the plants and the transmission grid operator. The rationality of changing the taxes for transmission grid services is demonstrated. The methodology is based on forecasts of photovoltaic generation, changes in market prices and application of linear programming. The example of maximizing one week’s cash flow proves the importance and significance of the tax system as well as the need to change it.
JoTyMo: Joint Type and Movement Detection from RGB Images of Before and After Interacting with Articulated Objects

This paper presents a CNN-based network architecture aimed to classify and detect joint types within Articulated objects, specifically the Push-P joints, P-joints, R-joints and objects lacking joints. The movement modeling of the detected objects is explored by processing pre and post interaction RGB images within the SAPIEN PartNet-Mobility dataset. In order to achieve this, an architecture is proposed to leverage consecutive CNN encoders based on the VGG architecture, in order to classify joints based on pre and post-interaction images. Additionally, in order to detect the effect-point and movement vector, a convolutional encoder is applied for each joint type. Moreover, extensive evaluation is made to showcase the results, achieving 96% accuracy in joint classification and 94% accuracy in regression on the considered dataset. Elaborate details regarding network architectures, training procedures, and testing methodologies are available at source code repository.
A Design Of Blockchain-Based Happy Giving Application: Promoting Collaboration And Community Development

This paper proposed a design of the Happy Giving Application, which aims to support various social good activities, such as promoting a sharing economy, reducing waste, enabling transparent donations, and fostering gratitude and generosity. The article discusses the reasons why individuals may choose to give away their possessions instead of selling them, including altruism, decluttering, emotional attachment, practicality, environmentalism, and personal values. The Happy Giving Application is designed to maintain the cycle of giving and receiving by using Token of Heart (TOH) as a medium of exchange. The application utilizes blockchain technology to ensure transparency and global accessibility of TOH. Moreover, the application will also include a sponsorship system for those who wish to give their things but cannot afford packaging and transportation costs. Additionally, the application encourages giving by creating a Hall of Giver system, which recognizes and celebrates the generosity of givers. Ultimately, the Happy Giving Application aims to foster a culture of giving and sharing while reducing waste and promoting sustainability in society.
Explainable AI supported Evaluation and Comparison on Credit Card Fraud Detection Models

This research investigates credit card fraud detection through the lens of machine learning and explainable AI techniques. We employed four distinct models: K-Nearest Neighbors (KNN), Random Forests, Extreme Gradient Boosting (XGBoost), and Logistic Regression. The SHapley Additive exPlanations (SHAP) method was used to enhance the interpretability of our models. Model performance was assessed using key metrics such as accuracy, precision, recall, and F1 score before and after feature selection. Notably, despite a decrease in some performance metrics post feature selection, high precision scores were maintained, underscoring the robustness of our models. Our findings lay the groundwork for future research in this field, highlighting the potential of a broader range of models, advanced explainable AI techniques, and innovative feature selection methods in the ongoing pursuit of robust and interpretable fraud detection systems.
Text Compression Algorithms Employed Vowels and Article Pattern

This research article introduces the TCEVP and TCEVPaA data compression algorithms. They employ a novel dictionary design that utilizes patterns of word construction based on vowel and article formats to reference words during both compression and decompression. The compressed data is stored in a bit format before being written to the compressed file. In theoretical experimental results, when considering vowel patterns of 1-12 characters, the percentage of bit compression per word is as follows: when using ASCII encoding, compression ranges from 37.5% to 90.38%; Unicode achieves compression percentages ranging from 68.80% to 95.20%; and Long Unicode can compress data by 84.40% to 97.60%. When examining text ranging from 1 kiloword to 1 teraword with mixed formats, encoded using ASCII, Unicode, big endian Unicode, and Long Unicode encoding schemes, compression rates can reach from 82.42% to 95.61%, with compression ratios ranging from 5.69 to 22.80 times.