Reviewerly/exHarmony huggingface dataset:
The Economics of Consensus in Algorand

In the paper we investigate consensus formation, from an economic perspective, in a Proof-of-Stake (PoS) based platform inspired by the Algorand blockchain. In particular, we consider PoS in relation to governance, focusing on two main issues. First we discuss alternative sampling schemes, which can be adopted to select voting committees and to define the number of votes of committee members. The selection probability is proportional to one’s stake and increases with it. Participation in governance allows users to affect the platform’s decisions as well as to obtain a reward. Then, based on such preliminary analysis, we introduce a microeconomic model to investigate the optimal stake size for a generic user. In the model we conceptualize an optimal stake, for a user, as striking the balance between having Algos immediately available for transactions and setting aside currency units to increase the probability of becoming a committee member. Our main findings suggest that the optimal stake can be quite sensitive to the user’s preferences and to the rules for selecting committees. We believe the findings may support policy decisions in PoS based platforms.
RAFeL - Robust and Data-Aware Federated Learning-inspired Malware Detection in Internet-of-Things (IoT) Networks

Federated Learning (FL) is a decentralized machine learning in which the training data is distributed on the Internet-of-Things (IoT) devices and learns a shared global model by aggregating local updates. However, the training data can be poisoned and manipulated by malicious adversaries, contaminating locally computed updates. To prevent this, detecting malicious IoT devices is very important. Since the local updates are large because of the high volume of data, minimizing the communication overhead is also necessary. This paper proposes a "RAFeL" framework, comprising of two techniques to tackle the above issues, (1) a robust defense technique and (2) a "Performance-aware bit-wise encoding" technique. "Robust and Active Protection with Intelligent Defense (RAPID)" is a defense system that detects malicious IoT devices and restricts the participation of the contaminated local updates computed by these malicious devices. To minimize communication cost, "Performance-aware bit-wise encoding" selects the appropriate encoding scheme for individual split bits based on their significance and effect on FL performance. The results illustrate that the proposed framework shows a 1.2-1.8x higher compression rate than lossy and lossless encoding techniques and has an average accuracy drop of 3% to 10% even with a fraction of malicious devices.
A Key Escrow Free Anonymous Identity Based Encryption Scheme using Ring Signatures

Identity-based encryption (IBE) is an encryption scheme that is proposed to eliminate the need for certificates to certify the public keys of receivers in public-key cryptography (PKC). Nevertheless, the key escrow problem is a major problem to deploy identity-based encryption (IBE) in real-time. If the private key generator (PKG) is malicious, then he can decrypt all the messages. In this paper, we proposed an anonymous identity-based encryption scheme using ring signatures to solve this problem. In this process, the identity will get the corresponding private key from the key generation authority (KGA) by signing on masked identity using the ring signature generated by ring authority (RA). The KGA has no way of knowing who received which key. Hence, it solves the key escrow problem. While most existing techniques require certificates to ensure anonymity, which is also a motive for IBE, the proposed scheme will obtain the relevant key anonymously based on the ring signatures. In this paper, a proof of security of the proposed scheme is also presented along with the scheme.
Cybersickness Variability by Race: Findings From 6 Studies and a Mini Meta-analysis

With the influx of medical virtual reality (VR) technologies, cybersickness has transitioned from a nuisance experienced during leisure activities to a potential safety and efficacy concern for patients and clinicians. To improve health equity, it is important to understand any potential differences in cybersickness propensity among demographic groups, including racial groups.This study aims to explore whether cybersickness propensity differs across racial groups.We collected self-reported cybersickness ratings from 6 racially diverse independent samples within 1 laboratory group (N=931). In these studies, the participants were asked to perform tasks in VR such as traversing environments, pointing at and selecting objects, and interacting with virtual humans.Significant racial differences in cybersickness were found in 50% (3/6) of studies. A mini meta-analysis revealed that, on average, Black participants reported approximately one-third of SD less cybersickness than White participants (Cohen d=-0.31; P<.001), regardless of the nature of the VR experience. There was no overall difference in reported cybersickness between the Asian and White participants (Cohen d=-0.11; P=.51).Racial differences in cybersickness indicate that researchers, practitioners, and regulators should consider patient demographics when evaluating VR health intervention outcomes. These findings lay the groundwork for future studies that may explore racial differences in cybersickness directly.
TimeREISE: Time Series Randomized Evolving Input Sample Explanation

Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intelligence addresses this problem. However, most interpretability methods align to the imaging modality by design. The paper introduces TimeREISE, a model agnostic attribution method that shows success in the context of time series classification. The method applies perturbations to the input and considers different attribution map characteristics such as the granularity and density of an attribution map. The approach demonstrates superior performance compared to existing methods concerning different well-established measurements. TimeREISE shows impressive results in the deletion and insertion test, Infidelity, and Sensitivity. Concerning the continuity of an explanation, it showed superior performance while preserving the correctness of the attribution map. Additional sanity checks prove the correctness of the approach and its dependency on the model parameters. TimeREISE scales well with an increasing number of channels and timesteps. TimeREISE applies to any time series classification network and does not rely on prior data knowledge. TimeREISE is suited for any usecase independent of dataset characteristics such as sequence length, channel number, and number of classes.
Combining Virtual Reality and Machine Learning for Leadership Styles Recognition

The aim of this study was to evaluate the viability of a new selection procedure based on machine learning (ML) and virtual reality (VR). Specifically, decision-making behaviours and eye-gaze patterns were used to classify individuals based on their leadership styles while immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centred design approach. Interaction and gaze patterns were recorded in 83 subjects, who were classified as having either high or low leadership style, which was assessed using the Multifactor leadership questionnaire. A ML model that combined behaviour outputs and eye-gaze patterns was developed to predict subjects' leadership styles (high vs low). The results indicated that the different styles could be differentiated by eye-gaze patterns and behaviours carried out during immersive VR. Eye-tracking measures contributed more significantly to this differentiation than behavioural metrics. Although the results should be taken with caution as the small sample does not allow generalization of the data, this study illustrates the potential for a future research roadmap that combines VR, implicit measures, and ML for personnel selection.
Combining hand-crafted and deep-learning features for single sample face recognition

Single Sample Face Recognition (SSFR) is considered one of the most challenging issues in biometrics. This paper suggested a hybrid model to overcome the SSFR problem using two-dimensional face images. Two kinds of features were employed for recognition: the first type was extracted using the robust Multi-block Color Binarized Statistical Image Features (MB-C-BSIF) descriptor, also called hand-crafted features. The other was deep-learning features derived by employing a Convolutional Neural Networks (CNN) model on each face image. This is the first study that combines hand-crafted with deep-learning characteristics for the SSFR issue. We explored whether combining both features can improve recognition performance. Comparative experiments using the AR database indicate that performance improvements can be attained by combining both features, and the fusion of VGG-16-19 with MB-C-BSIF methods achieved the highest accuracy among all the combinations.
SODA-Stream: SDN Optimization for Enhancing QoE in DASH Streaming

Official statistics indicate that internet users all around the world watch more videos and play more games during the COVID-19 pandemic than at any time [18]. This unprecedented, challenging situation demands solutions to accommodate rapid growth while maintaining and/or enhancing the video quality. This paper proposes SODA-Stream, an SDN-based optimization framework for enhancing Quality-of-Experience (QoE) in DASH streaming. The optimization framework max-imizes the number of concurrent streaming sessions that can be accommodated in a network and maximize streaming quality. The practical implementation of the framework utilizes the dynamic routing and bandwidth allocation enabled by Software Defined Networking (SDN). The evaluation results show that SODA-Stream significantly outperforms the conventional network routing and resource allocation algorithms, accepting 52% more sessions, 45% improvement in bandwidth allocation, and 70% reduction in bandwidth wastage, smoother playback, and better viewing experience.
A Deep Learning Approach to Automatically Extract 3D Hand Measurements

Accurate hand measurement data is of crucial importance in medical science, fashion industry, and augmented/virtual reality applications. Conventional methods extract the hand measurements manually using a measuring tape, thereby being very time-consuming and yielding unreliable measurements. In this paper, we propose–to the best of our knowledge–the first deep-learning-based method to automatically measure the hand in a non-contact manner from a single 3D hand scan. The proposed method employs a 3D hand scan, extracts the features, reconstructs the hand by making use of a 3D hand template, transfers the measurements defined on the template and extracts them from the reconstructed hand. In order to train, validate, and test the method, a novel large-scale synthetic hand dataset is generated. The results on both the unseen synthetic data and the unseen real scans captured by the Occipital structure sensor Mark I demonstrate that the proposed method outperforms the state-of-the-art method in most hand measurement types.
Knowledge management in higher education: Theory and practice

Knowledge is now recognized as the driving force behind economic growth and productivity. The purpose of the article is study of the theory and practice of knowledge management as the basis for the competitive advantage of a modern organization on the example of a specific institution of higher education — Astrakhan State University. The methodological basis was a categorical apparatus of the knowledge management system, methods of description, analysis, synthesis, content analysis, as well as a systematic approach in relation to the description of the experience of a particular university. As part of the study, the systematization of theoretical views on the management of explicit and implicit knowledge was carried out, the features of this process in the higher education system were studied, and the barriers that prevent the free transfer of knowledge in organizations of this type were identified. The data considered in the article can be used in the institutions of higher education in Russia in the development of strategies in the relevant field of activity.
Structured Analysis on Movie Recommendation System using Machine Learning

Nowadays, the most common way to pass the time is to watch a movie. Using search engines, the user must wade through a sea of information to find the intended information. Helping consumers find content they like is where the Recommender System comes into play. Movies can be recommended in a variety of ways. Collaborative filtering is the best method. Recommendations based on previous viewing habits can help us find good movies. User interest models are used to anticipate the user's interests, and collaborative filtering uses these models to provide recommendations. Unknown information can be filtered out using machine learning methods. Combining content-based filtering and collaborative filtering is the goal of this essay, which uses the KNN algorithm to get the most relevant results. Because of this, the user would be able to view movies without having to spend additional time searching for the right one.
Dual‐blockchain‐based P2P energy trading system with an improved optimistic rollup mechanism

Blockchain technology has been recognised as a promising technology for supporting Peer-to-Peer (P2P) energy trading—an emerging paradigm in which energy trading is performed directly between end energy customers. The state-of-the-art blockchain designs for P2P energy trading can only support less compute-intensive energy trading tasks and/or rely on the set up of a long challenge time to ensure correctness of transactions, which restricts the systems' scalability and applicability in the diverse P2P energy trading applications. This paper proposes a new dual-blockchain system for P2P energy trading. The system consists of a primary blockchain and a secondary blockchain. The primary blockchain is mainly responsible for storing the data of P2P energy trading transactions while the compute-intensive tasks (e.g. pricing and transaction verification) are executed in the secondary blockchain. An Improved Optimistic Rollup (IOR) mechanism is proposed to facilitate the co-operation of the dual blockchains. Through a specific ‘executor-validator’ mechanism and a group of specific smart contracts with different functions, the IOR mechanism can effectively accommodate the dual blockchains to execute the P2P energy trading transactions in a computational efficient manner while ensuring the correctness and security of the transactions. A prototype of the proposed system is implemented on Fisco Bcos and Alibaba Cloud and comprehensive simulations are conducted to validate the proposed system.
CP-SGD: Distributed stochastic gradient descent with compression and periodic compensation

Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combined with a parallel communication mechanism method like pipeline, gradient compression technique can greatly alleviate the impact of communication overhead. However, there exist two problems of gradient compression technique to be solved. Firstly, gradient compression brings in extra computation cost, which will delay the next training iteration. Secondly, gradient compression usually leads to a decrease in convergence accuracy. In this paper, we combine parallel mechanism with gradient quantization and periodic full-gradient compensation, and propose a new distributed optimization method named CP-SGD, which can hide the overhead of gradient compression, overlap part of the communication and obtain high convergence accuracy. The local update operation in CP-SGD allows the next iteration to be launched quickly without waiting for the completion of gradient compression and the current communication process. Besides, the accuracy loss caused by gradient compression is solved by k-step correction method introduced in CP-SGD, which provides a gradient correction every k iterations. We prove that CP-SGD has a convergence guarantee and it achieves at least O(1K+1K) convergence rate, where K is the number of iterations. We conduct extensive experiments on MXNet to verify the convergence properties and scaling performance of CP-SGD. Experimental results on a 32-GPU cluster show that convergence accuracy of CP-SGD is close to or even slightly better than that of S-SGD, and its end-to-end time is 30% less than 2-bit gradient compression under a 56Gbps bandwidth environment. In addition, we analyze the performance of CP-SGD when training on 8, 16 and 32 GPUs. It is found that CP-SGD is suitable for most compression-supported update algorithms, and its scalability is approximately linear.
EB Algorithm for Effective Privacy and Security of Data Processing in MCC

Mobile Cloud computing (MCC) distributes amenities over the Internet, such as hardware, software, and bandwidth. Mobile gadgets smartphones are enabled for exploration. Customers and businesses are increasingly adopting mobile cloud computing technology. Network security, data access, authentication, web application security, authorization, data breach, and data confidentiality are the entire anxiety of MCC's protection. Mobile devices' data storage capacity is limited due to adequate data storage and processing. Security threats must be investigated and analyzed in order to build a secure MCC environment. The study's purpose is to identify a way out that can improve technological requirements in mobile cloud computing in terms of data privacy and security for clients. The enhanced blowfish (EB) approach is utilized to encrypt a person's data security, and the global private key is hashed using a basic hash function. The integrity and privacy of user data can be improved through hashing. The suggested technique is compared to a standard blowfish algorithm and DES with various parameters. The EB algorithm outperforms both the standard blowfish algorithm and the DES algorithm.
Product Ontology Construction for Crowdfunding Projects

Crowdfunding provides financial support to realize projects through the power of the public. Since crowdfunding is a voluntary activity, fraudulent crowdfunding projects cannot be regulated. During our research on how to detect fake crowdfunding projects, we found that the classification of product categories for crowdfunding project is crucial. Therefore, we want to implement the classification of product categories for crowdfunding projects on detecting fraudulent crowdfunding projects through the product ontology construction of crowdfunding projects.In this research, we proposed a novel method for product ontology construction based on the modified Nice Classification. To make the categories of products in the Nice Classification more closely match the crowdfunding projects, we have modified the Nice Classification according to the actual categories of products for crowdfunding projects. The method of product ontology construction based on the modified Nice Classification achieved an accuracy of 98% in classifying the categories of products for crowdfunding projects.
Technical Research on Moving Target Monitoring and Intelligent Tracking Algorithm Based on Machine Vision

Machine vision is an important branch of the rapid development of modern artificial intelligence, and it is a key technology to convert the image information of monitoring targets into digital signals. However, due to the wide range of machine vision applications, this research focuses on its application in video surveillance. In the era of artificial intelligence, the detection and tracking of moving objects have always been a key issue in video surveillance. The simulation of human vision is realized by combining the relevant functions of the computer and the image acquisition device, which enables the computer to have the ability to recognize the surrounding environment through images. The intelligent video analysis technology can automatically analyze and extract the key useful information from the video source with the powerful data processing ability of the computer, so as to realize the computer’s “understanding” of the video. It allows the computer to “understand” what is shown in the video or what kind of “event” happened and provides a new method and reliable basis for accident detection and accident analysis. Therefore, after a brief introduction to machine vision, moving target monitoring methods, and intelligent tracking algorithms, this paper will focus on moving target monitoring and intelligent tracking strategies for video surveillance. In addition, this paper will focus on introducing the principle of intelligent tracking algorithm through formulas and compare the accuracy and success rate of target monitoring and intelligent tracking between the machine vision-based algorithm and other algorithms during the experiment. Finally, experiments show that the monitoring and tracking effect of machine vision combined with “cloud” is the best, and the overall average can reach 85.7%. Based on this, this paper fully confirms the feasibility of the moving target monitoring and intelligent tracking algorithm based on machine vision.
REC: A Unity Tool to Replay, Export and Capture Tracked Movements for 3D and Virtual Reality Applications

We present REC12, a Unity open-source tool for developers and researchers to record, export and replay the movements of virtual reality users and virtual characters. Recording both real-time body tracking and animations, this tool makes it possible to export skeletal data in a comma-separated values (CSV) file allowing to process and analyze users' movements. We also provide a feature to reload and replay saved movements directly from the CSV file on virtual characters in a 3D environment.