Reviewerly/exHarmony huggingface dataset:
A survey on security and privacy threats to federated learning

Federated learning (FL) has nourished a promising scheme to solve the data silo, which enables multiple clients to construct a joint model without centralizing data. The critical concerns for flourishing FL applications are that build a security and privacy-preserving learning environment. It is thus highly necessary to comprehensively identify and classify potential threats to utilize FL under security guarantees. This paper starts from the perspective of launched attacks with different computing participants to construct the unique threats classification, highlighting the significant attacks, e.g., poisoning attacks, inference attacks, and generative adversarial networks (GAN) attacks. Our study shows that existing FL protocols do not always provide sufficient security, containing various attacks from both clients and servers. GAN attacks lead to larger significant threats among the kinds of threats given the invisible of the attack process. Moreover, we summarize a detailed review of several defense mechanisms and approaches to resist privacy risks and security breaches. Then advantages and weaknesses are generalized, respectively. Finally, we conclude the paper to prospect the challenges and some potential research directions.
Internet of Things-Based Data Hiding Scheme for Wireless Communication

The substantial rise of information technology has facilitated the methods of access to digital information and internet of things (IOT). Digital image processing handles the digital material to store and distribute more effectively with decreased time and space complexity. However, these tactics undermine the privacy of digital materials. A recent study focuses on shielding digital materials from illicit use and distribution by making reversible data strategies to tackle the risk of privacy breaches for digital content. In this study, a composite reversible data hiding (CRDH) approach is suggested. CRDH employed the integer wavelet transform (HAAR transform) with the HH band’s eigenvalue decomposition. The suggested CRDH first performed the IWT transformation on the cover image (CI) and parsed it into four consecutive frequency subbands, namely, LL, HL, LH, and HH. Sensitive data of the proposed approach are incorporated by merging the HH band of the cover image’s individual values with the encrypted eigenvalues of the confidential data. The choosing of casing art is such a method that values are within a range. The confidential data picture and HH band’s frequency band are roughly the same; thus, modifying the individual values will not affect the quality of the confidential data image and the HH band’s content. The suggested strategy’s primary purpose is to design a data concealing technique that hinders the verification of digital information by maintaining a high rate of peak signal-to-noise ratio (PSNR). The PSNR of the existing technology is less than 50 per cent of the total accessible data set. The PSNR value shows the picture’s visual quality, where the PSNR increases the better image quality. Therefore, concealing data is essential for the technique that inhibits authentication and keeps a high rate of PSNR. The suggested approach fulfils this aim, gets a PSNR rate of above 50 per cent, and hits 59 per cent for line.
The Effect of Project-Based Learning through YouTube Presentations on English Learning Outcomes in Physics

This study aims to analyze the effect of project-based learning with YouTube presentations as a final project on the cognitive domain online learning outcomes in English for Physics subjects. This study used a quantitative approach with a quasi-experimental method. The design of the study was post-test with a non-equivalent design. The populations of this study were the fourth-semester students of Physics Tadris, which amounted to 32 people. The samples were taken by using the saturated sampling technique. The instrument used in this study was 20 multiple-choice questions. in analyzing the data, the researcher used the t-test and correlation test (phi (Φ) test) with a significance level of α = 0,05. The results of the t-test show that there is a difference in learning achievement where the experimental class is better than the control class. Then from the results of the correlation test (phi test (Φ)) shows that project-based learning with YouTube achievement as a final project has a significant effect on the cognitive domain.
VR Education Support System—A Case Study of Digital Circuits Design

Areas of experience allow for the acquisition and consolidation of both existing knowledge and skills. These are significant factors in the training of staff members for companies in the Industry 4.0 area. One of the currently available modern tools used in the teaching process is virtual reality (VR) technology. This technology, due to its high level of immersion and involvement of the different senses, and the need to focus on the performed activities, allows one to develop skills in solving various tasks and problems. The extended VR environment enables the creation of diverse teaching scenarios adapted to the needs of industry. This paper presents the possibility of building training scenarios in the field of digital techniques. The software solution, developed and presented by the authors, uses elements of computer game mechanics and is designed to familiarize students with the idea of digital circuits, their construction, logical implementation and application. This paper also presents a comparison of the features of different forms of education used in teaching digital techniques, as well as a comparison of these forms, from the point of view of the student and his/her perceptions.
Evaluation Algorithm of Ideological and Political Assistant Teaching Effect in Colleges and Universities under Network Information Dissemination

Ideological and political course is a key course to implement the fundamental task of building morality and cultivating people. Teaching evaluation is an important part of the construction of ideological and political courses. Constructing a perfect teaching evaluation index system is an urgent need to further deepen the teaching reform of ideological and political courses and improve the teaching quality of ideological and political courses. In order to improve the practical application effect of mixed teaching mode, an online and offline mixed teaching effect evaluation method based on big data analysis is proposed. Firstly, the big data in the process of mixed teaching are collected by using big data technology, and the evaluation index system is constructed from three dimensions. The required data are extracted according to the index, and then the association rules between the relevant data of the evaluation index are established, the phase space distribution of the data is obtained. Finally, the constraint parameter analysis method is used to fuse the control variables and explanatory variables of the index-related data to realize the online and offline mixed teaching effect evaluation. The application analysis results show that the method in this paper obtains ideal evaluation results of online and offline mixed teaching effects, which is conducive to improving teaching quality.
Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient (ρ) and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest ρ in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654,679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551,1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654,679) and V-RR-DSI(551,1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.
System for digital professional development of university teachers

The article highlights the issues of developing a system for digital competence of the teacher. The paper describes the research that resulted in the development and implementation of the system for digital professional development for university teachers at the Borys Grinchenko Kyiv University. The principle of system adaptation is realized in two directions: to time possibilities of testing and minicourse passing; to needs of teachers according to professional direction and disciplines taught. A model of organisation of this system based on self-assessment, self-education and micro-teaching principles has been developed. The main structural elements of the system are diagnostic test and sets of minicourses. The approaches to the formation of the diagnostic test, including ensuring its integration, variability and validity, as well as the principle of its use in order to establish the level of digital competence of teachers in accordance with the developed corporate standard of digital competence were applied in detail. Using the example of the compulsory level of digital competence Analyst-Researcher (A) the use of mini-course sets for Integrator (B1), Expert (B2), Leader (C1) and Innovator (C2) levels are described. The system allows teachers to build their own professional development trajectory as a digital footprint reflected in a personal study, and the use of embedded business intelligence tools provides a visualised holistic picture of digital professional performance. The place of the system and its further development for professional development in the digital twin of the educational institution is highlighted.
Examining the Uncharted Dark Web: Trust Signalling on Single Vendor Shops

Despite their growing popularity, cryptomarkets generate risks for participants. This has promoted the reemergence of a more personal transaction model on the dark web: single vendor shops. To date, little is known about how single vendors display trust to attract potential customers without relying on the structural trust provided by cryptomarkets’ review and escrow systems. A total of 108 single vendor shops were identified. A coding grid was used to determine whether vendors displayed any of the four categories of trust signals typically found on cryptomarkets (i.e., signals related to identity, marketing, security, and signals that directly express trust). While the majority of single vendor shops were involved in illicit drug dealing, other products such as electronics, weapons, and fake documents were also offered. On average, shops displayed few trust signals. However, variations between different kinds of vendors were found: while vendors involved in illicit drug dealing displayed more identity- and marketing-related trust signals, vendors involved in fraud displayed more security-related signals and signals that directly expressed trust. Differences between vendors might be due to the nature of the products they offer and to the level of competition in their respective markets.
“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

In this paper, we present the results of our long-term study on use of a voice assistant (Amazon Alexa via Amazon Echo Dot) in nine families with children and no previous experience with this technology. The study was conducted over the course of 5 weeks during which the families could interact with the device freely. Three house visits were made to collect empirical data from the adult participants in form of questionnaires. Additionally, conversational data from log files of the voice assistant were obtained. These data were annotated and analyzed with a focus on communication breakdowns during human-assistant interaction. We investigate user behavior for both adults and children in such situations, its reasons and consequences for user satisfaction. This article provides qualitative analysis of three particularly interesting breakdown cases, as well as statistical analysis along several hypotheses and research questions combining empirical and conversational data. Described cases of communication breakdown illustrate findings from existing literature on the topic. The statistical analysis paints a mixed picture, however, it helped us identify further avenues for research, some of which can be explored with our data set in the future. We found a significant negative effect of the number of abandoned failed requests on user satisfaction, contrary to the number of successfully repaired requests that had no influence on user satisfaction. We discovered that users are more inclined to use reformulation as repair strategy when they do not perceive the emergence of miscommunication as their fault. We could not identify a significant effect of internal reasons for the choice of other strategies, so we suggest that situational clues such as the immediate response of the voice assistant are more important for the choice of repair strategy. Our results also hint that users distinguish between repair strategies differently, as the self-perceived frequency of repetitions and abortions of requests were found to be positive predictors for the use of reformulation-based strategies. With regards to the long-term aspect of the study, use of repetition as a repair strategy by both children and adults significantly decreased with time, no other changes were found for other strategies. Additionally, no significant impact of age on the choice of repair strategy was found, as well as no interaction effect between age and time.
Rule mining over knowledge graphs via reinforcement learning

Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community. Many solutions have been proposed for the rule mining from large-scale KGs, which however are limited in the inefficiency of rule generation and ineffectiveness of rule evaluation. To solve these problems, in this paper we propose a generation-then-evaluation rule mining approach guided by reinforcement learning. Specifically, a two-phased framework is designed. The first phase aims to train a reinforcement learning agent for rule generation from KGs, and the second is to utilize the value function of the agent to guide the step-by-step rule generation. We conduct extensive experiments on several datasets and the results prove that our rule mining solution achieves state-of-the-art performance in terms of efficiency and effectiveness.
A hybrid-strategy-improved butterfly optimization algorithm applied to the node coverage problem of wireless sensor networks

To increase the node coverage of wireless sensor networks (WSN) more effectively, in this paper, we propose a hybrid-strategy-improved butterfly optimization algorithm (H-BOA). First, we introduce Kent chaotic map to initialize the population to ensure a more uniform search space. Second, a new inertial weight modified from the Sigmoid function is introduced to balance the global and local search capacities. Third, we comprehensively use elite-fusion and elite-oriented local mutation strategies to raise the population diversity. Then, we introduce a perturbation based on the standard normal distribution to reduce the possibility of the algorithm falling into premature. Finally, the simulated annealing process is introduced to evaluate the solution's quality and improve the algorithm's ability, which is helpful to jump out of the local optimal value. Through numerous experiments of the international benchmark functions, the results show the performance of H-BOA has been significantly raised. We apply it to the WSN nodes coverage problem. The results show that H-BOA improves the WSN maximum coverage and it is far more than other optimization algorithms.
Machine Learning in NextG Networks via Generative Adversarial Networks

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we investigate their use in next-generation (NextG) communications within the context of cognitive networks to address <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)</i> spectrum sharing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii)</i> detecting anomalies, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii)</i> mitigating security attacks. GANs have the following advantages. First, they can learn and synthesize field data, which can be costly, time consuming, and nonrepeatable. Second, they enable pre-training classifiers by using semi-supervised data. Third, they facilitate increased resolution. Fourth, they enable the recovery of corrupted bits in the spectrum. The paper provides the basics of GANs, a comparative discussion on different kinds of GANs, performance measures for GANs in computer vision and image processing as well as wireless applications, a number of datasets for wireless applications, performance measures for general classifiers, a survey of the literature on GANs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i)–iii)</i> above, and future research directions. As a use case of GAN for NextG communications, we show that a GAN can be effectively applied for anomaly detection in signal classification (e.g., user authentication) outperforming another state-of-the-art ML technique, an autoencoder.
VSAC: Efficient and Accurate Estimator for H and F

We present VSAC, a RANSAC-type robust estimator with a number of novelties. It benefits from the introduction of the concept of independent inliers that improves significantly the efficacy of the dominant plane handling and, also, allows near error-free rejection of incorrect models, without false positives. The local optimization process and its application is improved so that it is run on average only once. Further technical improvements include adaptive sequential hypothesis verification and efficient model estimation via Gaussian elimination. Experiments on four standard datasets show that VSAC is significantly faster than all its predecessors and runs on average in 1-2 ms, on a CPU. It is two orders of magnitude faster and yet as precise as MAGSAC++, the currently most accurate estimator of two-view geometry. In the repeated runs on EVD, HPatches, PhotoTourism, and Kusvod2 datasets, it never failed.
A Transfer Learning Framework for Power System Event Identification

The increasing uncertain components of power systems foster the wide applications of Machine Learning (ML) techniques. While traditional ML models demand a large set of data, data-scarce dilemmas exist for new meters, devices, and new grids. Further, for rich historical measurements, valuable data may still be limited, especially for targets like identifying system events that rarely occur in the power system. To enhance the event type differentiation and localization for a data-limited grid, we propose a Transfer Learning (TL) framework to transfer knowledge from a data-rich grid (source grid) to the target grid, using measurements from Phasor Measurement Units (PMUs). The transferring process is challenging because of (1) high-volume data with redundant information, (2) different measurement dimensionalities, (3) dissimilar data distributions, and (4) disjoint event-location-label spaces for two grids. To handle the challenges of (1) to (3), we propose a joint optimization to reduce dimensionality and maximize common knowledge in a shared low-dimensional feature space, where the commonality lies in the same dimensions and close data distributions. Such an optimization-based procedure is verified via rigid mathematical theorems given the same label space, i.e., event-type-label space. However, for event localization, challenge (4) obstructs the optimization. Therefore, we design a label space alignment method to relabel the event location by the event zone location and build an event zone estimation problem. Then, the framework is generalized to both tasks. Finally, comprehensive experiments demonstrate the advantages of the proposed methods over state-of-the-art transfer learning models.
Quantum walks of two correlated photons in a 2D synthetic lattice

Abstract Quantum walks represent paradigmatic quantum evolutions, enabling powerful applications in the context of topological physics and quantum computation. They have been implemented in diverse photonic architectures, but the realization of two-particle dynamics on a multidimensional lattice has hitherto been limited to continuous-time evolutions. To fully exploit the computational capabilities of quantum interference it is crucial to develop platforms handling multiple photons that propagate across multidimensional lattices. Here, we report a discrete-time quantum walk of two correlated photons in a two-dimensional lattice, synthetically engineered by manipulating a set of optical modes carrying quantized amounts of transverse momentum. Mode-couplings are introduced via the polarization-controlled diffractive action of thin geometric-phase optical elements. The entire platform is compact, efficient, scalable, and represents a versatile tool to simulate quantum evolutions on complex lattices. We expect that it will have a strong impact on diverse fields such as quantum state engineering, topological quantum photonics, and Boson Sampling.
Video Search with Context-Aware Ranker and Relevance Feedback

Interactive video search systems effectively combine text-image embedding approaches and smart user interfaces allowing various means of browsing in intermediate result sets. In this paper, we combine features from VIRET and SOMHunter systems into a novel approach for segment based interactive video retrieval. Based on our SOMHunter log analysis and VIRET tool performance in known-item search tasks, we focus on two specific features – a combination of context-aware ranking by text queries and Bayesian-like relevance feedback approach for refining scores using promising candidates.