Reviewerly/exHarmony huggingface dataset:
Measuring the Adoption of Biometric Verification Technique Implementations on Mobile Devices

Mobile device growth is rapid, and it has a significant impact on our private and professional lives. All mobile users want to be guaranteed that their data is safe, which is why biometrics exists for mobile devices. Countless surveys have been performed on the adoption of biometric verification techniques, however, only a limited number of these surveys have concentrated on mobile device-based biometrics, including this current research. This quantitative study’s overall model was put to test on 302 mobile users via survey questionnaire distribution and analyzed utilizing the Statistical Package for Social Science (SPSS). Validity and reliability tests were performed and the model proved to be fit. The findings showed that the SSN variable of the proposed model was not supported, indicating that more research is required. In addition, the research model’s functional elements have a greater impact on the participants’ desire to use the mobile biometric system than the social elements. The study adds to academic knowledge by proposing new constructs that combine MBTAM to assess the likelihood of mobile device users adopting biometric verification techniques.
Learning Based Efficient Federated Learning for Object Detection in MEC Against Jamming

Federated learning enables mobile edge computing (MEC) to train the object detection model with privacy protection and reduced communication overhead. However, the selection of the mobile devices and the training dataset that determines the energy consumption of the mobile devices and the detection accuracy and latency has to be optimized without relying on the known channel and jamming model against jamming attacks that aim to degrade the model training performance. In this paper, we propose a reinforcement learning (RL) based efficient federated learning training scheme against jamming. This scheme designs a fast RL algorithm with shared parameters to choose the training policy of the object detection model at the mobile devices based on the channel gain, the previous training, transmission and computation performance. The edge server uses a shared Q-table to determine the policy for each mobile device to accelerate the learning process. Simulation results show that this scheme can effectively improve the object detection accuracy, decrease the energy consumption and reduce the latency compared with the benchmark scheme.
TryItOut : Machine Learning Based Virtual Fashion Assistant

Image-based virtual try-on systems for fitting new in-shop clothes into a person image have attracted increasing research attention yet is still challenging. They are a future shopping method which can transform the way users shop. They not only change the target clothes into the most fitting shape seamlessly but also preserve the clothes identity such as texture, embroidery, prints etc. in the generated image. In this study, Generative adversarial networks (GAN) Model has been explored for generation of the clothing image and try-on image using CVPR Dataset. A novel approach to generate different poses using the state-of-the art Look into Person (LIP) Parser Model and superimposing the target cloth image. The segmentation of different clothing types was also done in order to identify the texture and clothing type of the person’s clothes and performs well for the images containing obstructions too. The proposed model overcome the limitations of low quality and clear background input images.
Autonomous Control of an Electric Vehicle by Computer Vision Applied to the Teaching–Learning Process

The electric vehicle is an attractive tool for the study of autonomous vehicles. In education, it can provide students with concepts on the hardware and software aspects of mobile robotics; therefore, this paper proposes the autonomous control of a low-cost car-like vehicle based on real-time image processing. The vehicle is developed to implement an autonomous control algorithm using image processing, detection of horizontal and vertical traffic signs on the road. In the design of the vehicle, the transmission and steering are modified to resemble the behaviour of an electric vehicle. The implementation of artificial vision uses low-cost elements and free software such as Raspberry Pi with its respective camera module and an Arduino. The Haar Cascade classifier is used to classify the vertical signage, which consists of fast object detection based on automatic learning.
Teacher–Researcher Co-designing of a Technology-Enhanced Classroom Inquiry Framework in Promoting Student-Generated Questioning: A Case Study

Questioning has been considered as central in helping students in fostering curiosity and capacity in learning and inquiry. For primary teachers, it remains a challenge to design a science curriculum to both embody classroom inquiry and encourage student questioning. This chapter aims to examine how a learning framework supporting student-generated questioning with technology has been developed through a teacher–researcher co-design process. As a whole, the use of technical platforms has assisted teachers to surface and address students’ doubts, which can help students, further their understanding by leading students in the construction of questions. The teacher–researcher co-design practices have also transited into teacher professional learning opportunities as they manage how to effectively access and use inquiry-based pedagogical understandings in their daily practice.
Secret Image-Sharing Scheme Based on Multinode Authentication in the Internet of Things

Internet of things (IoT) has been developed and applied rapidly because of its huge commercial value in recent years. However, security problem has become a key factor restricting the development of IoT. The nodes of IoT are easy to be impersonated or replaced when attacked, which leads to the mistake of the uploaded data, the abnormal use of the application, and so on. Identifying the authenticity of the data submitted by the nodes is the top priority. We propose a scheme to verify the authenticity of multinode data. In this scheme, the authenticity of node data is checked through visual secret recovery and XOR operation together. The least significant bit (lsb) operation converts data from nodes into a bit, which improves the efficiency of data verification and reduces the risk of data leakage. This scheme achieves the purpose of verifying the data provided by the node, which avoids malicious attacks from illegal nodes. By analyzing the experiment result and comparing with other works, our scheme has the advantages of high verification efficiency, lightweight storage of nodes, and security verification.
Two-Phase Real-Valued Multimodal Optimization with the Hill-Valley Evolutionary Algorithm

The aim of multimodal optimization (MMO) is to obtain all global optima of an optimization problem. In this chapter, we introduce a general framework for two-phase MMO evolutionary algorithms (EAs), in which different high-fitness regions (niches) are located in the first phase via clustering, and each of the located niches is separately optimized with a core search algorithm in the second phase. One such two-phase MMO EA is the Hill-Valley Evolutionary Algorithm (HillVall-EA). In HillVallEA, the remarkably simple hill-valley clustering method is used. The idea behind hill-valley clustering is that two solutions belong to the same niche (valley) when there is no hill in between them, which can be easily tested by performing additional function evaluations. We compare hill-valley clustering to two other recently introduced fitness-informed clustering methods: nearest-better clustering and hierarchical Gaussian mixture learning. We show how these clustering methods, as well as different core search algorithms, influence the resulting optimization performance of the two-phase MMO framework on the commonly used CEC 2013 niching benchmark suite. Our results show that HillVallEA, equipped with the core search algorithm Adapted Maximum-Likelihood Gaussian Model Univariate (AMu) as core search algorithm, outperforms all other MMO EAs, both within the limited benchmark budget, and in the long run. HillVallEA-AMu was the winner of the GECCO niching competition in 2018 and 2019, and is currently, to the best of our knowledge, the best performing algorithm on this benchmark suite.
First Steps on Gamification of Lung Fluid Cells Annotations in the Flower Domain

Annotating data, especially in the medical domain, requires expert knowledge and a lot of effort. This limits the amount and/or usefulness of available medical data sets for experimentation. Therefore, developing strategies to increase the number of annotations while lowering the needed domain knowledge is of interest. A possible strategy is the use of gamification, i.e. transforming the annotation task into a game. We propose an approach to gamify the task of annotating lung fluid cells from pathological whole slide images (WSIs). As the domain is unknown to non-expert annotators, we transform images of cells to the domain of flower images using a CycleGAN architecture. In this more assessable domain, non-expert annotators can be (t)asked to annotate different kinds of flowers in a playful setting. In order to provide a proof of concept, this work shows that the domain transfer is possible by evaluating an image classification network trained on real cell images and tested on the cell images generated by the CycleGAN network (reconstructed cell images) as well as real cell images. The classification network reaches an average accuracy of 94.73% on the original lung fluid cells and 95.25% on the transformed lung fluid cells, respectively. Our study lays the foundation for future research on gamification using CycleGANs.
Global News Sentiment Analysis

The Journalist’s Creed, a declaration of the principles, values, and standards of a journalist, states that a journalist should “believe that clear thinking and clear statement, accuracy and fairness are fundamental to good journalism”. However, in recent years there has been concern that personal, corporate, and government biases and opinions have had an effect on the fairness and integrity of modern journalism. Many mainstream media outlets are well known to have certain political inclinations and occurrences of “fake news” have become common. Studying mainstream international news sources is important for numerous reasons, such as national security, cultural stability, and the development of social/economic policy. Through the use of modern natural language processing techniques, we performed sentiment analysis on 10,809 articles published by ten globally distributed sources covering eleven globally impactful events, spanning a period of eleven years. We tested three open-source sentiment analysis tools and one commercial tool from Microsoft. We also tested three text processing approaches, i.e., sentence, paragraph, and article-based approaches. We observed that sentiment polarity was generally consistent across news agencies, however, the relative percent differences in sentiment magnitude varied by multiple standard deviations. Further, we found general agreement in sentiment polarity across the four analysis tools strengthening our finding regarding the news source content sentiment trends. The work provides both a reproducible computer-based metric for quantifying differences in global journalism and insights into the effective use of modern sentiment analysis tools.
Graph-AutoFS: Auto Feature Selection in Graph Neural

Graph embedding is an effective method to represent graph data in low-dimensional space for graph analysis. Based on the inspiration of graph convolutional network (GCN), researchers have made significant progress by learning the vector representation of graph topology and node features in this task. However, it is challenging to select the feature of nodes in the real-world data. Traditional feature selection method like one-hot encoding or description of the node brings large memory and computation cost. Even worse, useless features may introduce noise and complicate the training process. In this paper, we propose a two-stage algorithm for graph automatic feature selection (Graph-AutoFS) to improve existing models. Graph-AutoFS can automatically select important features as training inputs, and the computational cost is precisely equal to the convergence of the training target model. We did not introduce discrete candidate feature sets in the search stage but relax the choices to be continuous by introducing the architecture parameters. By implementing a regularized optimizer on the architecture parameters, the model can automatically identify and delete redundant features during the model's training process. In the re-train phase, we keep the architecture parameters serving as an attention unit to boost the performance. We use three public benchmark data sets and two popular graph embedding methods to conduct experiments to verify the performance of Graph-AutoFS in node clustering and link prediction tasks. Experimental results show that Graph-AutoFS consistently outperforms original graph embedding methods considerably on these tasks.
An Improved Random Walk Algorithm for Resource Scheduling in Cloud Datacenter

Resource scheduling plays a crucial role in improving resource utilization rate and user service quality of cloud datacenter. An efficient resource scheduling algorithm enables the datacenter to achieve load balancing, becoming the core of enterprise development. However, at present, the scheduling algorithm of cloud datacenter is usually lack of dynamics, and the calculation is relatively complex. When searching for the optimal scheme, it is easy to fall into the local optimal value, resulting in a large amount of calculation, high energy consumption, low QoS (Quality of Service) and low resource utilization. In this paper, we focus on the prevalent problems of lacking of dynamics, the high makespan and energy consumption in cloud datacenter and design a dynamic load balancing schedule framework. In this framework, we propose an improved random walk algorithm which searches the global optimal scheme with simpler computing. We compare our proposed improved random walk algorithm with Round Rabin algorithm and Particle Swarm Optimization (PSO) algorithm. The experimental results prove that our proposed algorithm improves the utilization rate of resources. Particularly, the makespan of our proposed random walk algorithm is 7% lower than PSO's and the overall energy consumption of ours algorithm is about 15% lower than PSO's.
Visual Tracking With Object Center Displacement and CenterNet

Modern artificial intelligence systems have revolutionized approaches to scientific and technological challenges in a variety of fields, thus remarkable improvements in the quality of state-of-the-art computer vision and other techniques are observed; object tracking in video frames is a vital field of research that provides information about objects and their trajectories. This paper presents an object tracking method basing on optical flow generated between frames and a ConvNet method. Initially, optical center displacement is employed to detect possible the bounding box center of the tracked object. Then, CenterNet is used for object position correction. Given the initial set of points (i.e., bounding box) in first frame, the tracker tries to follow the motion of center of these points by looking at its direction of change in calculated optical flow with next frame, a correction mechanism takes place and waits for motions that surpass a correction threshold to launch position corrections.
Evidence-based Fact-Checking of Health-related Claims

The task of verifying the truthfulness of claims in textual documents, or fact-checking, has received significant attention in recent years. Many existing evidence-based factchecking datasets contain synthetic claims and the models trained on these data might not be able to verify real-world claims. Particularly few studies addressed evidence-based fact-checking of health-related claims that require medical expertise or evidence from the scientific literature. In this paper, we introduce HEALTHVER, a new dataset for evidence-based fact-checking of health-related claims that allows to study the validity of real-world claims by evaluating their truthfulness against scientific articles. Using a three-step data creation method, we first retrieved real-world claims from snippets returned by a search engine for questions about COVID-19. Then we automatically retrieved and re-ranked relevant scientific papers using a T5 relevance-based model. Finally, the relations between each evidence statement and the associated claim were manually annotated as SUPPORT, REFUTE and NEUTRAL. To validate the created dataset of 14,330 evidence-claim pairs, we developed baseline models based on pretrained language models. Our experiments showed that training deep learning models on real-world medical claims greatly improves performance compared to models trained on synthetic and open-domain claims. Our results and manual analysis suggest that HEALTHVER provides a realistic and challenging dataset for future efforts on evidence-based fact-checking of health-related claims. The dataset, source code, and a leaderboard are available at https://github.com/sarrouti/healthver.
Management of Training Outcomes for Paud Teachers through Online Learning to Improve Skills Using Learning Media

This research is motivated by the problem of mastering the use of IT as a learning medium. Skill is a skill that is learned by training, in line with regulation Number 74 of 2008. Changing times, the development of science is coupled with the Covid-19 world outbreak, so that it requires teachers to be skilled at using IT media as an effective and efficient learning tool. This study aims to find out the description and description of planning, skills, implementation, barriers, evaluation, from the results of basic level training for PAUD teachers through online learning to improve skills in using learning media. The method used is descriptive method with a qualitative approach and data collection techniques through interviews, observations, and documentation. Grand theory of George Tery. The object of research in two schools, namely KB Rumah Bintang and Melati Kota Bandung. Based on the research findings, it was obtained that a) the planning carried out included online media learning training teachers because there were already many online learning media b) teacher skills continued to be improved to create interactive learning with online learning processes c) the implementation of training was very helpful according to relevant needs d) the obstacles in each school are different, the biggest factors are cost and opportunity e) evaluation of efforts to maintain competence and improve teacher performance f) the findings of the training have a very significant impact in terms of knowledge, skills and the application of the online learning process. The conclusion is that the management of PAUD teacher basic level training through online learning to improve the skills of using learning media has a positive and significant contribution.
Aim, Wait, Shoot: How the CacheSniper Technique Improves Unprivileged Cache Attacks

Microarchitectural side channel attacks have been very prominent in security research over the last few years. Caches proved to be an outstanding side channel, as they provide high resolution and generic cross-core leakage. All major cryptographic libraries provide countermeasures to hinder key extraction via cross-core cache attacks by now. In this paper, we analyze implementations protected by prefetch-based countermeasures aimed at preventing well-known cache attacks, and highlight the circumstances causing them to remain vulnerable. Further, we craft a novel attack technique that precisely synchronizes the attacking and the victim processes, enabling the attacking process to evict the target data from the cache at the desired instants. One key improvement of our approach is that it provides unprivileged attackers with a method to remove specific data from the cache with a single memory access and in absence of shared memory by leveraging the transient capabilities of TSX and relying on the L3 replacement policy. We show the feasibility of our approach by extracting an RSA key from the latest wolfSSL library and an AES key from the T-Table and S-Box implementations included in OpenSSL with CacheSniper. Both libraries implement prefetch-based methods as a protection against cache attacks.
State Estimation for Stochastic Non-linear Systems via Backstepping Design

This paper presents a stochastic differential equation based filter design algorithm for system state estimation. Motivated by backstepping procedure and the variance assignment, the non-linear system filtering has been achieved. In particular, the system dynamics have been described by stochastic differential equation which results in the structure of the filter. The estimation error has been stabilised by backstepping design. Then, the non-linear process of the estimation error is converted to Ornstein-Uhlenbeck process using the filter compensative signal. In the ideal case, the estimation error is a Brownian motion, which leads to the variance assignment of Ornstein-Uhlenbeck process, thus the parameter of the proposed filter can be further optimised. The numerical simulation has been given to show the effectiveness of the proposed design algorithm.
Model Construction and Empirical Research of Deep Interaction in Smart Learning Environment — Take Smart Classroom as an Example

Based on the fact that the smart learning environment mostly adopts the latest technology, many people purely pursue the advancement of technology, and then deviate from the original intention of the development of smart education, making education relatively lagging behind. In order to avoid the phenomenon of turning the cart before the horse in smart learning, the use of smart learning technology must remember the original intention, adapt to local conditions, and avoid mechanically copying. The article is based on the theory required for deep interaction in the smart learning environment, and constructs the deep interaction model according to the theoretical process, and finally conducts empirical research.
Pose Estimation for Mobile Robots with Monocular Vision

Pose estimation is crucial for visual servoing of mobile robots. This paper solves this problem by using only an off-board camera. First, a new process model customized for mobile robots is proposed. Then with the new process model and a generic camera model, an extended Kalman filter (EKF) pose estimation method appropriate for conventional and fish-eye cameras is designed. Theoretical analysis and simulation results demonstrate that our method is more robust than the traditional filtering method in the presence of occlusion.
A Control Method for Quadrotor Based on DDPG

This paper mainly discusses a method of bringing the copter to one target point rapidly and steadily at the same time. By building a physic-based offline simulation environment for a quadrotor, raw data from sensors are emulated. Meanwhile the states and motion locus can be estimated via a Kalman filter fusing the data from multi sensors. With the inputs of states and action of the copter, a control method based on Deep Deterministic Policy Gradient (DDPG) is introduced. In this paper, the feasibility of this approach is analyzed, and its availability is proved by the simulation experiment. The experimental results show this method enhances the robustness and rapidity in the control process, which will improve the performance of the whole copter system.