Reviewerly/exHarmony huggingface dataset:
CLONE: Collaborative Learning on the Edges

The proliferation of edge computing technologies has boosted the development of new applications for a plethora of edge devices. However, many applications face privacy issues and bandwidth limitations. To solve these limitations, we propose a collaborative learning framework on the edges, named CLONE, which is steered by the real-world data sets collected from a large electric vehicle (EV) company and a grocery store of a shopping mall, respectively. We categorize two application scenarios for CLONE, i.e., CLONE in the training stage (CLONE_training) and CLONE in the inference stage (CLONE_inference). As to CLONE_training, we choose the failure prediction of EV battery and associated components as the first use case. While as for CLONE_inference, customer tracking in a grocery store is selected as another case study. In this work, the goal of the CLONE is to support real-time training and inference for connected vehicles and marketing intelligence services. Our experimental results on the EV data show that CLONE is able to reduce model training time without sacrificing algorithm performance. Furthermore, the experimental results on the video data from the grocery store reveal that CLONE is a useful approach to solve the multitarget multicamera tracking problem in a collaborative fashion.
Blockchain and Contact Tracing Applications for COVID-19: The Opportunity and The Challenges

Contact tracing mobile applications have been emerging as potentially automating surveillance technology to help stem the spread of the novel coronavirus (SARS-COV-2) by tracking individuals and those they come into exposure with.The avalanche of these apps left the software security researchers' with concerns about vulnerabilities in hastily written software.On the other hand, the COVID-19 pandemic has motivated the recent interest of leveraging blockchain for healthcare-related scenarios, including proposing and developing blockchain-based contact tracing apps.Utilizing the cryptographic concepts of blockchain to secure the collected data could help in winning the level of public engagement required to fight the spread of COVID-19.But will blockchain be a panacea to all the challenges accompanying these apps?Motivated by answering this question and following a twofold process, this paper: (i) explores the current landscape of contact tracing mobile apps, (ii) examines how blockchain technology can contribute positively to this landscape, and (iii) reports on the technical and social challenges that still accompany the deployment of blockchainbased contact tracing apps.
Risk Assessment of Pregnancy-induced Hypertension Using a Machine Learning Approach

This research aimed to develop a predictive model of the risk assessment of pregnancy-induced hypertension using a machine learning approach. Pregnancy-induced hypertension is a complication that has a serious impact on pregnant women and fetuses. It is the world's top three cause of death among pregnant women [1]. Nowadays, the exact cause of pregnancy-induced hypertension is unknown and therefore cannot be prevented. Early detection and received treatment can reduce the severity and danger. A public dataset of Logan (2020) was used in this research [2]. The dataset was collected from a casecontrol study on the determinants of 83 pre-eclampsia and five eclampsia cases among 352 pregnant women delivering in county hospitals in Nairobi, Kenya. According to the dataset, 75 percent of the pregnant women were healthy. Only 25 percent of the pregnant women were pre-eclampsia and eclampsia. Thus, this would result in a problem of an imbalanced classification when one of the two classes had more data than the other class. As such, this problem was resolved with the Synthetic Minority Over-sampling Technique (SMOTE). Risk assessment of pregnancy-induced hypertension was performed on seven machine learning algorithms, which were logistic regression (LR), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), multilayer perceptron neural network(MLP), support vector machines (SVM), and naive Bayes (NB). In the experimental results, RF had the highest accuracy at 89.62 percent compared to other machine learning algorithms.
Communication Strategy in the Design of a Unified Information and Analytical Space of a University

The article substantiates the need to use a communication strategy in the design of a unified information and analytical space of a university. An algorithm of actions for the communication strategy is described. Using such an algorithm, higher education organizations can quickly provide the necessary information, which can help to shape the opinion about the university and its activities in the minds of the target audience as an important part of the organization's image. The use of a communication strategy is also essential when forecasting specialties and areas of training. It will improve the interaction between the university and employers and will increase applicants' interest in the university. This study considers the development of the digital economy in the Russian Federation.The approbation of the main theoretical provisions of the proposed model and the practical implementation of the solution developed on its basis take place at the structural divisions of the Humanitarian and Pedagogical Academy of Sciences (branch) of V. I. Vernadsky Crimean Federal University in Yalta.
Dynamic Client Association for Energy-Aware Hierarchical Federated Learning

Federated learning (FL) has become a promising solution to train a shared model without exchanging local training samples. However, in the traditional cloud-based FL framework, clients suffer from limited energy budget and generate excessive communication overhead on the backbone network. These drawbacks motivate us to propose an energy-aware hierarchical federated learning framework in which the edge servers assist the cloud server to migrate the local models from the clients. Then a joint local computing power control and client association problem is formulated in order to minimize the training loss and the training latency simultaneously under the long-term energy constraints. To solve the problem, we recast it based on the general Lyapunov optimization framework with the instantaneous energy budget. We then propose a heuristic algorithm, which takes the importance of local updates into account, to achieve a suboptimal solution in polynomial time. Numerical results demonstrate that the proposed algorithm can reduce the training latency compared to the scheme with greedy client association and myopic energy control, and improve the learning performance compared to the scheme in which the associated clients transmit their local models with the maximal power.
Blockchain of Internet of Things-Based Earthquake Alarming System in Smart Cities

The worst natural catastrophes occurring in well-settled intelligent cities are earthquakes. A framework of earthquake warning minimizes destruction and protects countless lives. A system built on IoT to identify the earthquake in the S waves and then to warn people by showing them an alert and where the earthquake happened is proposed. An early warning system is generated by a seismic wave survey. The larger the earthquake, the heavier the tremor. The waves are also breaking down the driveway. So the earthquake in the S wave is safer to find. Therefore, determining the extent of the early warning system is essential for creating an earthquake. The chapter addresses the detection of the frequency of earthquakes by identifying the size of earthquakes. In this chapter, we will discuss the elevated processors and IoT (internet of things) that can efficiently deploy an early warning device that can capture and transmit data over networks without manual interference. The early earthquake warning system (EEW) can be used to support smart urban planning, making earthquake areas less sensitive to disasters.
StickyPie: A Gaze-Based, Scale-Invariant Marking Menu Optimized for AR/VR

This work explores the design of marking menus for gaze-based AR/VR menu selection by expert and novice users. It first identifies and explains the challenges inherent in ocular motor control and current eye tracking hardware, including overshooting, incorrect selections, and false activations. Through three empirical studies, we optimized and validated design parameters to mitigate these errors while reducing completion time, task load, and eye fatigue. Based on the findings from these studies, we derived a set of design guidelines to support gaze-based marking menus in AR/VR. To overcome the overshoot errors found with eye-based expert marking menu behaviour, we developed StickyPie, a marking menu technique that enables scale-independent marking input by estimating saccade landing positions. An evaluation of StickyPie revealed that StickyPie was easier to learn than the traditional technique (i.e., RegularPie) and was 10% more efficient after 3 sessions.
Non-Autoregressive Sequence-To-Sequence Voice Conversion

This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models. Inspired by the great success of NAR-S2S models such as FastSpeech in text-to-speech (TTS), we extend the FastSpeech2 model for the VC problem. We introduce the convolution-augmented Transformer (Conformer) instead of the Transformer, making it possible to capture both local and global context information from the input sequence. Furthermore, we extend variance predictors to variance converters to explicitly convert the source speaker’s prosody components such as pitch and energy into the target speaker. The experimental evaluation with the Japanese speaker dataset, which consists of male and female speakers of 1,000 utterances, demonstrates that the proposed model enables us to perform more stable, faster, and better conversion than autoregressive S2S (AR-S2S) models such as Tacotron2 and Transformer.
Development Path of Smart Agriculture Based on Blockchain

The agriculture of China is in a critical stage of transformation and development. The development of smart agriculture will help the country solve the problems in the existing agricultural production mode, which is lacking in efficiency and viability. Although the government proposes developing smart agriculture, the problems of China's agriculture have not been solved completely. Blockchain can greatly improve smart agriculture's efficiency and benefit through information encryption technology, software agent technology, distributed database technology. Studying the problems in the development process of smart agriculture, and the characteristics of blockchain technology decentralization, data transparency and non-tampering, and automatic enforcement of the smart contract, the author puts forward the design of progressive development path of smart agriculture based on blockchain-through the development path from regional blockchain farm to blockchain industry alliance to cross-industry cooperation of blockchain, to provide the reference for the development of smart agriculture and improve the efficiency of smart agriculture development step by step.
Fast and Secure Location-Based Services in Smart Cities on Outsourced Data

With the advancement of mobile Internet, cloud computing, and smart sensing devices, location-based services (LBSs) have become more and more indispensable in the Internet-of-Things (IoT)-based smart cities. Especially, spatial keyword queries have been widely deployed in real-life applications in recent years. Recently, several privacy-preserving spatial keyword queries schemes were proposed to guarantee data security and query privacy on outsourced data. However, these schemes support neither dynamic update nor diverse query types, which cannot meet the requirements in practical applications. This article proposes two secure dynamic spatial keyword queries (SDSKQs) constructions that support expressive query types and dynamic update. First, we present a basic SDSKQ construction based on hidden-vector encryption and order-revealing encryption. Specifically, we propose a secure hybrid index structure for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">spatio-textual</i> data, named encrypted textual signature quadtree (ETSQ-tree). Using ETSQ-tree, the server can prune the index tree according to search queries to reduce the search space. Besides, the ETSQ-tree can be updated dynamically. To resist the file-injection attack, which aims to infer query information according to newly inserted objects, we further improve the basic SDSKQ to achieve forward security. We implement our two constructions and evaluate them using real-world data sets. The experimental results show that they are efficient and feasible in practical applications, and the comparative evaluation confirms that the performance of our constructions outperforms that of the state-of-the-art schemes.
Evaluation of Body-centric Locomotion with Different Transfer Functions in Virtual Reality

Body-centric locomotion allows users to navigate virtual environments with body parts (e.g. head tilt, arm swing or torso lean). Transfer functions are an important determinant of the locus of such a locomotion method. However, there is little known about the effects of transfer functions on virtual locomotion with different body parts. In this work, we selected four typical transfer functions (linear function: L, power function: P, a piecewise function with constant and linear functions: CL, and a piecewise function with constant and power functions: CP) and four body parts (head, arm, torso, and knee) from existing works, and conducted an experiment to evaluate their effects on virtual locomotion under three distances (5, 10, and 15 m) in Virtual Reality (VR). Results show that (1) CP function generally led to the longest task time with a low rate of failed trials, while CL function had the shortest task time with a high rate of failed trials; (2) body parts significantly affected the rate of failed trials, but not task time and final position offset. Head and torso resulted in the lowest and highest rate of failed trials respectively; (3) body parts did not differ in User Experience Questionnaire-Short (UEQ-S), UEQ-S Pragmatic and UEQ-S Hedonic. L was rated as the highest score for UEQ-S, UEQ-S Pragmatic and UEQ-S Hedonic, but CP had the lowest score. According to the results, we provide implications of designing body-centric locomotion with different transfer functions in VR.
Fabricaide: Fabrication-Aware Design for 2D Cutting Machines

Designers of machine-cut objects must often consider whether and how their design can be fabricated with their available materials. In contrast to tools that support preparing finished designs for fabrication, we investigate shortening the feedback loop between design creation and fabrication preparation. To this end, we present Fabricaide, a fabrication-aware tool that interleaves the processes of creating and preparing designs for fabrication. By providing live feedback on how parts should be placed onto material sheets, analyzing how much material is consumed, and alerting users when designs are infeasible, Fabricaide enables users to proactively tailor their design to their available material. Fabricaide achieves this with a custom packing algorithm that arranges parts onto material sheets at interactive speeds. Our qualitative user study shows how Fabricaide can support different workflows, encourage material-conscious design practices, and provide insights on how to further improve similar interfaces in the future.
Learning-Empowered Privacy Preservation in Beyond 5G Edge Intelligence Networks

Edge intelligence is a key enabler for powerful applications through offering artificial-intelligence-empowered computation and caching service in proximity of end users. However, massive data interaction among edge nodes and distributed information storage in the service process make edge network vulnerable to privacy threats. Moreover, the upcoming beyond fifth generation (B5G) era brings new features for edge services in terms of system complexity and operation mode, which pose significant challenges to user privacy protection. To address the challenges, we first investigate privacy concerns in B5G edge intelligence networks and design privacy-driven application clarification strategies. In order to meet the different privacy requirements of various edge applications, we propose a learning-empowered privacy-preserving scheme, which adaptively applies data perturbation in a multi-mode differential privacy (DP) approach. Then we present a case study that implements our proposed schemes in edge caching service management. Numerical results demonstrate that the schemes efficiently improve edge service utility without loss of privacy.
Walking Outside the Box: Estimation of Detection Thresholds for Non-Forward Steps

Most virtual reality (VR) experiences are held in limited physical space; therefore, increasing the physical space's spatial efficiency is an essential task for the VR industry. Redirected walking maps a virtual path and a real path with unnoticeable distortion, enabling users to walk through a much bigger virtual space than physical space. To hide the distortion from the user, detection thresholds have been measured, entirely focusing on forward steps. However, it is not uncommon for the user to walk non-forward, that is, sideward and backward in VR. In addition to a forward step, adding options for a non-forward step can expand the VR locomotion in any direction. In this work, we measure the translation and curvature detection thresholds for non-forward steps. The results show similar translation detection thresholds with forward-step and wider detection thresholds for the curvature gain in both backward and sideward step experiments. Having sideward and backward steps in the redirected walking arsenal can add freedom to virtual world design and lead to efficient space usage.
Learning Connectivity with Graph Convolutional Networks

Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing convolutional operations to arbitrary non-regular domains. In particular, GCNs operating on spatial domains show superior performances compared to spectral ones, however their success is highly dependent on how the topology of input graphs is defined. In this paper, we introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function which learns not only the usual convolutional parameters in GCNs but also a transformation basis that conveys the most relevant topological relationships in these graphs. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method compared to handcrafted graph design as well as the related work.
Same File, Different Changes: The Potential of Meta-Maintenance on GitHub

Online collaboration platforms such as GitHub have provided software developers with the ability to easily reuse and share code between repositories. With clone-and-own and forking becoming prevalent, maintaining these shared files is important, especially for keeping the most up-to-date version of reused code. Different to related work, we propose the concept of meta-maintenance-i.e., tracking how the same files evolve in different repositories with the aim to provide useful maintenance opportunities to those files. We conduct an exploratory study by analyzing repositories from seven different programming languages to explore the potential of meta-maintenance. Our results indicate that a majority of active repositories on GitHub contains at least one file which is also present in another repository, and that a significant minority of these files are maintained differently in the different repositories which contain them. We manually analyzed a representative sample of shared files and their variants to understand which changes might be useful for meta-maintenance. Our findings support the potential of meta-maintenance and open up avenues for future work to capitalize on this potential.
Application of an Improved CHI Feature Selection Algorithm

Text classification is the critical content of machine learning, and it is widely applied in information filtering, sentimental analysis, and text review. It is very important to improve the accuracy of classification results, and this is also the main research purpose of researchers in this field in recent years. Feature selection plays an important role in text classification, which has the functions of eliminating irrelevant features, reducing dimensionality, and improving classification accuracy. So, this paper studies the CHI feature selection algorithm, and the main work and innovations are as follows: firstly, this paper analyzed the CHI algorithm’s flaws, determined that the introduction of new parameters will be the improvement direction of the CHI algorithm, and thus proposed a new algorithm based on variance and coefficient of variation. Secondly, experiment to verify the effectiveness of the new algorithm. In terms of language, the experiment in this paper includes two text classification systems, which were Chinese and English. In terms of classifiers, two classifier algorithms were used, which included the KNN classifier and the Naive Bayes classifier. In terms of data types, two distribution types of data were used: balanced datasets and unbalanced datasets. Finally, experiment and result analysis. This paper has conducted 3 comparative experiments and analyzed the results of each experiment. The experimental results obtained are all significantly improved compared to the results before the improvement.
Deep Hashing for Motion Capture Data Retrieval

In this work, we propose an efficient retrieval method for human motion capture (MoCap) data based on supervised deep hash code learning. Raw Mocap data is represented into three 2D images, which encode the trajectories, velocities and self-similarity of joints respectively. Such image-based representations are fed into a convolutional neural network (CNN) adapted from the pre-trained VGG16 network. Further, we add a hash layer to fine-tune the CNN and generate the hash codes. By minimizing the loss defined by classification error and constraints on hash codes, highly discriminative hash representations of the motion data can be generated. As experimentally demonstrated on the public HDM05 data set, our algorithm achieves high accuracy comparing with the state-of-the-art MoCap data retrieval algorithms. Besides, it achieves high efficiency due to the fast matching of hash codes.
End-To-End Multi-Accent Speech Recognition with Unsupervised Accent Modelling

End-to-end speech recognition has achieved good recognition performance on standard English pronunciation datasets. However, one prominent problem with end-to-end speech recognition systems is that non-native English speakers tend to have complex and varied accents, which reduces the accuracy of English speech recognition in different countries. In order to grapple with such an issue, we first investigate and improve the current mainstream end-to-end multi-accent speech recognition technologies. In addition, we propose two unsupervised accent modelling methods, which convert accent information into a global embedding, and use it to improve the performance of the end-to-end multi-accent speech recognition systems. Experimental results on accented English datasets of eight countries (AESRC2020) show that, compared with the Transformer baseline, our proposed methods achieve relative 14.8% and 15.4% average word error rate (WER) reduction in the development set and evaluation set, respectively.