Reviewerly/exHarmony huggingface dataset:
Demo Paper: Anonymous Authentication on Trust in Blockchain-Based Mobile Crowdsourcing System

This paper presents a demonstration of anonymous authentication on trust within a blockchain-based Mobile Crowdsourcing System (MCS). MCS has gained substantial attention from both academic and industrial sectors as an effective method for data collection. Nevertheless, traditional MCS heavily relies on third parties to maintain system operations, which may lead to single-point failures and incur serious privacy concerns. The introduction of blockchain technology offers the potential to get rid of the third-party intermediaries. However, the inherent decentralized and transparent nature of blockchain also brings in new challenges related to privacy and trust. A critical challenge in the context of blockchain-based MCS is achieving anonymous authentication on trust. Despite the typical conflict between trust evaluation and anonymity, ensuring anonymity is a crucial privacy prerequisite for MCS, while trust plays a pivotal role in various MCS activities such as task selection and truth determination. In this paper, we present a demonstration of a novel blockchain-based MCS that provides a secure and efficient crowdsourcing platform for fair and effective allocation of tasks. Notably, we leverage Intel Software Guard Extensions (SGX) to achieve anonymous authentication within a decentralized environment. Through the implementation of a decentralized trust evaluation approach, our system enables accurate trust assessment.
Parallelization of DC Feeding Network Setting Time Reduction of Control Frequency

The authors have been conducting research and development on VG-Hub (Virtual Grid Hub) as a device to configure a distributed power source that utilizes renewable energy by integrating and cooperatively feeding power from small-scale storage batteries and power-generating devices such as solar panels. While the previous system allows for the configuration of power flow according to user-selected scenarios, the control to change the settings of each port of the VG-Hubs composing the network is a sequential process, and reducing the number of control cycles has been an issue. In this study, we address this issue as a constrained combinatorial optimization problem and report a method to reduce the number of control step cycles using Fixstars Amplify, a cloud platform for running quantum annealing machines, along with its fundamental evaluation.
A Benchmark for Different Implementations of Zero-Knowledge Proof Systems

Zero-Knowledge Proofs (ZKPs) ensure privacy in sensitive applications, including blockchains, anonymous verifiable voting, and secure auctions. In recent years, a number of ZKP implementations have been presented. The diversity of available solutions raises the need to compare the implementations to discover their strengths and weaknesses. Unfortunately, the collection of ZKP implementations is highly heterogeneous, aggravating a just comparison between different implementations. Hence, there is a strong need for a benchmarking solution to measure and collect metrics to compare different ZKP implementations. Therefore, we design and implement a benchmarking solution for ZKPs. The proposed solution allows users to gather performance metrics of different ZKP implementations and aids the decision-making process of selecting the right implementation for a specific use case. The system is highly configurable and allows the integration of novel ZKP systems. Further, we demonstrate the solution’ s applicability by benchmarking two popular ZKP implementations.
Mobile AR Interface for Instruction-Based Disaster Preparedness Guidelines

Disaster preparedness guidelines help citizens protect themselves against disasters. Regardless, the general public has been found not reading them. Augmented Reality (AR) interfaces are known to improve knowledge transfer in studies of education, industry, and elderly assistance. However, they achieve this by creating specific interfaces for their users, not the general public. To test the performance of these interfaces for general public guidance, we designed and implemented a novel AR-assisted disaster prevention guideline that leverages object detection models to identify targets of disaster preparedness advice. We compared our interface against a traditional paper-based preparedness guide by testing their usability, task load, and capacity to make users aware of their environmental hazards. We were able to observe the type of media influencing the performance of the subjects in the test according to their age group, suggesting that minors and seniors are performing worse than adults in guidelines intended for the general public. This study highlights the importance of guidance alternatives for the young and the less technology-aware population, contributing to the under-explored area of AR interfaces for the general public.
Privacy Filtering Using Word Embedding for 3D Point Cloud Based Spatial Sharing Systems

With the advancement of information technology and high-performance input/output interfaces, cyber-physical space sharing systems (hereafter CPSSS) have been proposed to enable highly immersive interactions by sharing spatial data in a virtual space. The main advantage of CPSSS is that users sharing a space can have highly immersive interactions by sharing three-dimensional images in multiple spaces. However, sharing all the information obtained from each space could violate the privacy of the participating users, thus necessitating the implementation of filtering, such as making certain aspects visible or invisible. On the other hand, making all images invisible compromises the sense of immersion; therefore, developing technologies for appropriate filtering according to various contexts is essential. This paper aims to develop privacy filtering technologies considering context and individual user characteristics by investigating privacy requirements for all possible objects to be shared in CPSSS. In preliminary research, we collected and analyzed data on different personality traits and privacy awareness for different objects through crowdsourcing with an online cooking class scenario. In this paper, we develop a method to infer privacy requirement levels using personality traits and object names as features. In the proposed method, first, we apply a text encoding using the distributed representation of object names to infer the privacy requirement level for unknown objects not included in the dataset. Second, to improve the inference accuracy of the privacy requirement level, we apply transfer learning to the requirement inference model for each user. As a result, we found that the method with word embedding improved the accuracy of recognizing the privacy requirement level by approximately 9 % compared to the baseline. In addition, implementing transfer learning improved accuracy by approximately 14%.
A Guitar Chord Recognition Method Based on Depth Images of Hand Shapes

In this paper, the authors propose a recognition method of guitar chords based on depth images of hand shapes. There are many constraints to practicing a guitar such as its portability and noise. On the other hand, it is possible to practice playing guitar anytime and anywhere if its played sound or chord can be recognized from hand shape in real-time without the actual guitar. The proposed method uses machine learning and depth estimation from RGB images of left hands. The experiment results showed that the proposed method achieves higher accuracy than that of RGB images only.
Research on the Development of Blockchain-based Distributed Intelligent Healthcare Industry: A Policy Analysis Perspective

As a pivotal innovation in digital infrastructure, blockchain ledger technology catalyzes the development of nascent business paradigms and applications globally. Utilizing Rothwell and Zegveld’s taxonomy of twelve innovation policy tools, this study offers a nuanced comparison of domestic blockchain policies, dissecting supply, environment, and demand-driven policy dimensions to distill prevailing strategic orientations towards blockchain healthcare adoption. The findings indicate that blockchain technology has seen rapid growth in the healthcare industry. However, a certain misalignment exists between the corporate and policy layers in terms of supply and demand. While companies focus more on technological applications, existing policies are geared towards regulations and governance. Government emphasis lies on legal supervision through environmental policies, aiming to guide the standardization and regulation of blockchain technology. This maintains a balance between encouraging innovation and market and legal regulatory order, thereby providing a reference for the development of the distributed intelligent healthcare industry in our country.
A Trusted Sharing Model for Risk Information of Food Full-process and All-information Based on Blockchain and Federated Learning

In view of the problems under the traditional sharing model of sensitive and easy to leak risk information of food full-process and all-information, the self-interest and complete autonomy of centralized servers and malicious nodes are prone to malicious behavior, the difficulty of cross-link interaction, and the low utilization of data caused by single purpose, a risk information of food full-process and all-information trusted sharing model based on blockchain and federated learning is proposed. Firstly, based on the analysis of the information sharing needs of food companies and regulatory agencies, federal learning is integrated into a hierarchical blockchain architecture, and combined with relay thinking, a risk information of food full-process and all-information trusted sharing model is constructed. Secondly, the model federated learning process is divided into horizontal and vertical federated learning processes based on the characteristics and categories of risk information, and two federated learning aggregation algorithms are used to aggregate data. Thirdly, in response to the different needs of sharing risk information data within and between enterprises and enterprise regulatory agencies, homomorphic encryption algorithms and zero-know ledge proof ideas are utilized to achieve hierarchical encryption and targeted trusted sharing of risk information with different sensitivities. Finally, taking food safety risk assessment as the simulation background, this model was validated through simulation based on the food risk information disclosure dataset and open source platform. The results showed that the trusted sharing model for risk information of food full-process and all-information based on blockchain and federated learning can meet the different sharing needs of enterprises and regulatory agencies for risk information data, and can achieve the full utilization, security, credibility, and efficient and accurate sharing of risk information data, thereby enhancing the determination of food enterprises and regulatory agencies to share data, promoting the development of data trustworthy sharing and food safety digitalization in the food industry.
The Design and Implement of Digital Literacy Tracking System for Undergraduate Students

In today’s modern world, digital literacy has become a crucial skill that empowers individuals to confidently navigate digital technologies, access information, and critically evaluate online content. Digital literacy plays a vital role in education, work, and daily life. Therefore, this study aims to design and implement a digital literacy tracking system for undergraduate students. The necessary digital literacy skills were explored to assess students’ digital competence. Drawing insights from previous research, this study condensed essential digital skills into a comprehensive framework. To validate the system’s efficacy, three experienced experts subjected it to rigorous testing and evaluation. Additionally, this research employed the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore students’ attitudes toward using the system. The results demonstrated that social influence strongly impacts students’ attitudes. The implications of this study extend to both students and educators as they prepare for the upcoming digital world.
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars. Our system employs a two-branch fusion method for vehicle image and tracking sensor data, leveraging the strengths of residual structures and identity mapping to enhance agent training. Through comprehensive comparisons, we demonstrate the efficacy of information fusion and establish the superiority of our selected algorithm over alternative approaches. Our work advances the field of autonomous driving and demonstrates the potential of reinforcement learning in enabling intelligent vehicle decision-making.
Eye-Tracking Based Control of a Robotic Arm and Wheelchair for People with Severe Speech and Motor Impairment (SSMI)

Eye-tracking technology has rapidly gained popularity as a revolutionary solution for individuals with severe physical disabilities, empowering them to engage in their daily activities with new found independence and efficiency. By utilizing advanced eye-tracking systems, individuals with limited mobility are able to control various devices and interfaces simply by moving their eyes. This paper utilizes deep learning techniques to create a low-cost real-time eye-tracking interface for controlling systems. A smart wheelchair and a robotic arm have been developed to design an eye-tracker, aiming to address the challenges faced by paralyzed people with severe physical limitations. The results demonstrate that eye-tracking is both fast and accurate, making it an effective tool for improving the interactions and accessibility for disabled individuals.
Design and Implementation of Nodding Recognition System Based on Chair Sway

In this paper, we propose a method to measure human head motion, especially nodding, without attaching any sensors to the person. Our proposed system focuses on the fact that the upper body moves along with nodding and that the body motion slightly shakes the chair. We challenge the problem of whether it is possible to recognize a nodding from the extremely slight sway of a chair. To reveal the optimal position of sensors, we collected data by attaching multiple accelerometers to various positions on a chair, including the backrest, the seat's underside, and the legs. Using a supervised learning approach, we determined the best positions and combinations of sensors for recognizing nodding more collectively. The Support Vector Machine (SVM) achieved a nodding recognition accuracy of 0.990. Further testing of the accuracy of nodding frequency measurements resulted in an accuracy of 0.947, suggesting that the best position for the accelerometer is the backrest. These results suggest that simply placing the accelerometer on the backrest can effectively quantify the nod frequency of seated participants.
Group-Oriented Multi-Signature supporting Monotonic Endorse Policies in Hyperledger Fabric

As multi-signature can produce one joint signature on the same message by an interactive protocol among a group of signers, it is widely used in all kinds of blockchain platforms (e.g., Bitcoin, Hypherledger Fabric, Algorand) to reduce the proportion of signatures in blocks, so that the platform scalability is improved. However, as existing multi-signature schemes mainly focus on “AND” relationship among multiple signers, they cannot adequately support the Fabric endorsement process that generally requires monotonic endorse policy with both “AND” and “OR” operations. Therefore, a Group-oriented Multi-Signature scheme (GMS) is proposed, which can aggregate the signatures of multiple signers from any group into one. Furthermore, GMS is used in the process of Fabric endorsement instead of ECDSA signature, so that the transaction protocol is improved. Analysis shows that, by aggregating multiple endorsement signatures according to the monotonic endorse policies, the proposed GMS has the security features of unforgeability, anonymity, revocability and traceability, and the improved transaction protocol has advantages in terms of smaller block size and faster verification time. In addition, it can protect the endorsement policies and the identities of the endorsement nodes anonymity.
Decision Models for Selecting Patterns in Governance-driven Blockchain Systems

Blockchain technology can construct a decentralised architecture design for diverse software applications. Nevertheless, the defects of on-chain algorithmic autonomy and subsequent tedious arguments for fixing the bugs raised concerns about whether blockchain can operate in a trustworthy way. Although multiple architectural patterns for blockchain governance have been summarised, concentrating on different governance dimensions (i.e., decision rights, incentives, and accountability), there is a need for guidance on the adoption of these patterns. In this paper, we present a set of decision models that can assist architects and developers in selecting appropriate patterns, to achieve governance in blockchain systems. This study provides a mapping between governance-related problems and each architectural pattern, and clarifies the trade-offs and conditions. The proposed decision models were evaluated based on expert opinions regarding the usability, correctness, and completeness collected via interviews.
User Estimation with Touch Panel Buttons Toward In-Home Activity Recognition

Smart homes that improve people's lives are at-tention. To realize a smart home, it is necessary to recognize user activity. Existing studies have collected user activity data by installing many sensors in the home, however, the collected activity data does not include user names, and it is not possible to recognize the activity of multiple users while distinguishing between them. In this study, we propose an in-home activity recognition method that identifies users by performing user estimation using data collected from home appliance operations. In this paper, we propose a user estimation method using button operations among various home appliance operations. We evaluated the user estimation performance of the proposed method using button operation data collected from 8 subjects and found that accuracy was 86.4% or higher when the amount of training data was 10 trials and only features obtained from the pressure were used. This result is comparable to existing studies that require multiple sensors, indicating the feasibility of user estimation with a single sensor.
Classification of Unsafe Children’s Toys Based on NLP-Generated Corpus

Playing with toys is essential for children as it is an integral part of the learning process and development. However, young children especially toddlers and babies are susceptible to dangers due to unsafe toys. This research aims to classify unsafe children’s toys based on a corpus derived by Natural Language Processing (NLP) techniques using different classification algorithms. The study collected 19087 unsafe and safe children’s toys data from the Europe Commission Rapid Alert System (Safety Gates) and Amazon online retail store. Three different scenarios in feature selection: ‘Brands’, ‘Unsafe Terms,’ and a combination of ‘Brands’ and ‘Unsafe Terms’ were used for classification. ‘Brands’ did not serve a vital role in the classification of safe and unsafe children’s toys compared to the classification with ‘Unsafe Terms’. Although combining ‘Brands’ and ‘Unsafe Terms’ or only ‘Unsafe Terms’ provided nearly similar accuracy, classification with only ‘Brands’ did not provide good accuracy. The classification accuracy of k-Nearest Neighbor is 78%, Logistic Regression is 83%, Support Vector Machine is 82%, Decision Tree is 82%, Naïve Bayes is 50%, and Random Forest is 81% respectively. The results showed that Logistic Regression revealed the best performance as it outperformed other algorithms.
The Ecological System of Digital Asset Markets: Based on the Perspective of Asset Trading and Valuation

With the application of big data, blockchain and other digital technologies, digital asset markets are rapidly expanding worldwide, digital products are becoming increasingly rich, and transaction subjects tend to be diversified. In the perspective of digital asset transaction and valuation, we firstly provide the classification of digital assets and the development of the digital asset markets. Then, we establish the ecosystem of the digital asset market, and summarize the specific transaction mechanism within such ecosystem. Finally, we analyze the price formation mechanism in centralized transaction scenario and distributed transaction scenario, respectively.
Multi-Blockchain Model for Data Sharing with Bell-LaPadula Access Control

In recent years, the technologies of blockchains, the Internet of Things (IoT), etc. technologies have been drawing attention across different industries. In our experience, during a pandemic, we look for innovations and personalization of secure data sharing on Electronic Health Records (EHRs) to provide better healthcare services. In the paper, we propose a decentralized record management system which is to share data over multiple independent blockchain entities. Each blockchain runs under the administration of an independent healthcare organization. In our design, data would be shared and integrated among multiple Hospital Information Systems (HIS) across different healthcare organizations. It gives doctors and healthcare personnel an easy and safe way to obtain the health records of patients across hospitals dynamically worldwide through negotiated Service-Layer Agreements (SLAs) to provide accurate medical services. Our system leverages blockchain properties to manage access authentication, retrieve medical data, and add new patient data entries. Without a centralized system and database, the design autonomously, dynamically, and securely governs the retrieval and storage of data traffic based on the SLAs. Further, we integrate the system design with the Bell-LaPadula access control model to maintain the confidentiality of security organized in a non-discretionary fashion for different layers of secrecy. Since an HIS runs within the security bounds of a healthcare authority, we can adopt the Proof-of-Authority (PoA) consensus algorithm, which is developed by the Go Ethereum (Geth) team. The PoA provides an efficient, low computational power consumption approach for securely validating the blockchains.