Reviewerly/exHarmony huggingface dataset:
The state of ICT implementation in institutions of general secondary education: a case of Ukraine

The use of digital technology in various fields of education today is one of the most important trends in the educational process in the world. The article presents the results of the analysis of the current state of implementation of ICT in the educational process of institutions of general secondary education in Ukraine. For this purpose, a survey was conducted among students of the first year of the Zhytomyr Polytechnic State University, within which 17 questions were asked to students related to the use of information and communication technologies in the educational process. As a result of the research, the introduction of the discipline “Educational technologies and digital education” into the training of future information technology specialists was substantiated, as well as the certification educational program “Information systems and cloud technologies in the educational process”, designed for general education teachers, educators for higher education institutions, experts in the field of additional educational services, and other professionals.
Blockchain-empowered secure federated learning system: Architecture and applications

Federated learning (FL) is a promising paradigm to realize distributed machine learning on heterogeneous clients without exposing their private data. However, there is the risk of single point failure with FL because it relies on a central server to gather the model updates from clients, moreover, malicious behaviors of some clients may lead to low-quality or even poisoned global models. Blockchain as a revolutionary distributed ledger technology can alleviate the above problems to significantly enhance the security and scalability of FL systems. Therefore, this article presents a general framework of Blockchain-based Federated Learning (BFL) system with detailed description of its key technologies and operation steps. We then review and compare the most recent representative BFL applications. And we outlook some key challenges and opportunities of the future BFL system in terms of security, cost, and scalability. Finally, we propose PoS-BFL in IoT scenarios with malicious devices. The validator voting mechanism and role switching mechanism in PoS-BFL ensure the stakes of legitimate nodes, and effectively reduce the impact of malicious nodes on the accuracy of the system model. And the experiments are conducted to demonstrate that PoS-BFL can achieve 86% accuracy, which is much higher than vanilla FL and pFedMe, and PoS-BFL is robust to some extent by adjusting the ratio of workers, validators and miners.
An efficiency relation-specific graph transformation network for knowledge graph representation learning

Knowledge graph representation learning (KGRL) aims to infer the missing links between target entities based on existing triples. Graph neural networks (GNNs) have been introduced recently as one of the latest trendy architectures serves KGRL task using aggregations of neighborhood information. However, current GNN-based methods have fundamental limitations in both modelling the multi-hop distant neighbors and selecting relation-specific neighborhood information from vast neighbors. In this study, we propose a new relation-specific graph transformation network (RGTN) for the KGRL task. Specifically, the proposed RGTN is the first pioneer model that transforms a relation-based graph into a new path-based graph by generating useful paths that connect heterogeneous relations and multi-hop neighbors. Unlike the existing GNN-based methods, our approach is able to adaptively select the most useful paths for each specific relation and to effectively build path-based connections between unconnected distant entities. The transformed new graph structure opens a new way to model the arbitrary lengths of multi-hop neighbors which leads to more effective embedding learning. In order to verify the effectiveness of our proposed model, we conduct extensive experiments on three standard benchmark datasets, e.g., WN18RR, FB15k-237 and YAGO-10-DR. Experimental results show that the proposed RGTN achieves the promising results and even outperforms other state-of-the-art models on the KGRL task (e.g., compared to other state-of-the-art GNN-based methods, our model achieves 2.5% improvement using H@10 on WN18RR, 1.2% improvement using H@10 on FB15k-237 and 6% improvement using H@10 on YAGO3-10-DR). • We design a relation-specific graph transformation network (RGTN) for KGRL. • The RGTN is able to transform the relation-based graph to a new path-based graph. • The RGTN can aggregate multi-hop neighbors on the generated path-based graph. • Experimental results show that the proposed RGTN achieves promising results.
A graph based preordonnances theoretic supervised feature selection in high dimensional data

Generally, for high-dimensional datasets, only some features are relevant, while others are irrelevant or redundant. In the machine learning field, the use of a strategy for eliminating insignificant features from a dataset is very important for the classification task. Feature selection is the process of identifying the most informative features that help in predicting sample classes efficiently in order to achieve better classification performance. In this research paper, a new hybrid feature selection strategy for high-dimensional datasets is proposed to find the most discriminative subset of features for the dataset with the irrelevant and redundant features discarded. The proposed algorithm is called Maximal Clique based on the coefficients Ψ (MaCΨ algorithm). The MaCΨ method has the capability to handle categorical, numerical, and hybrid datasets. Furthermore, it can be applied either to binary or multi-class classification problems. The global structure of the MaCΨ algorithm can be described by three steps. In the first step, a weight is proposed to evaluate the importance of each feature in the dataset by balancing the trade-off between two novel measures of relevance and redundancy, and then the K most important features are selected to form the candidate subset, where K is taken as user input. In the second phase, a wrapper method based on graph theory is applied to the subset retained from the first step to extract the optimal subset of features. In the last stage, the final subset of features with the highest classification performance and the lowest number of features is obtained by applying the backward elimination algorithm to the optimal subset. The performance of the MaCΨ methodology is investigated on artificial as well as real-world datasets with different dimensionalities. The statistical analysis of the experimental results clearly indicates that the MaCΨ approach achieves competitive results in terms of the classification accuracy and the number of selected features compared with some state-of-the-art approaches.
Internet-of-Things-Based Suspicious Activity Recognition Using Multimodalities of Computer Vision for Smart City Security

Automatic human activity recognition is one of the milestones of smart city surveillance projects. Human activity detection and recognition aim to identify the activities based on the observations that are being performed by the subject. Hence, vision-based human activity recognition systems have a wide scope in video surveillance, health care systems, and human-computer interaction. Currently, the world is moving towards a smart and safe city concept. Automatic human activity recognition is the major challenge of smart city surveillance. The proposed research work employed fine-tuned YOLO-v4 for activity detection, whereas for classification purposes, 3D-CNN has been implemented. Besides the classification, the presented research model also leverages human-object interaction with the help of intersection over union (IOU). An Internet of Things (IoT) based architecture is implemented to take efficient and real-time decisions. The dataset of exploit classes has been taken from the UCF-Crime dataset for activity recognition. At the same time, the dataset extracted from MS-COCO for suspicious object detection is involved in human-object interaction. This research is also applied to human activity detection and recognition in the university premises for real-time suspicious activity detection and automatic alerts. The experiments have exhibited that the proposed multimodal approach achieves remarkable activity detection and recognition accuracy.
Global thermal spring distribution and relationship to endogenous and exogenous factors

Abstract Here we present digitization and analysis of the thermal springs of the world dataset compiled by Gerald Ashley Waring in 1965 into a collection of analog maps. We obtain the geographic coordinates of ~6,000 geothermal spring areas, including complementary data (e.g., temperature, total dissolved solids, flow rate), making them available in electronic format. Using temperature and flow rate, we derive the heat discharged from 1483 thermal spring areas (between ~10 −5 and ~10 3 MW, with a median value of ~0.5 MW and ~8300 MW in total). We integrate this data set with other global data sets to study the relationship between thermalism and endogenous and exogenous factors with a supervised machine learning algorithm. This analysis confirms a dominant role of the terrestrial heat flow, topography, volcanism and extensional tectonics. This data set offers new insights and will boost future studies in geothermal energy exploration.
Hermod

Serverless computing has seen rapid growth due to the ease-of-use and cost-efficiency it provides. However, function scheduling, a critical component of serverless systems, has been overlooked. In this paper, we take a fist-principles approach toward designing a scheduler that caters to the unique characteristics of serverless functions as seen in real-world deployments. We first create a taxonomy of scheduling policies along three dimensions. Next, we use simulation to explore the scheduling policy space and show that frequently used features such as late binding and random load balancing are sub-optimal for common execution time distributions and load ranges. We use these insights to design Hermod, a scheduler for serverless functions with two key characteristics. First, to avoid head-of-line blocking due to high function execution time variability, Hermod uses a combination of early binding and processor sharing for scheduling at individual worker machines. Second, Hermod is cost, load, and locality-aware. It improves consolidation at low load, it employs least-loaded balancing at high load to retain high performance, and it reduces the number of cold starts compared to pure load-based policies. We implement Hermod for Apache OpenWhisk and demonstrate that, for the case of the function patterns observed in real-world traces, it achieves up to 85% lower function slowdown and 60% higher throughput compared to existing production and state-of-the-art research schedulers.
CCAP: A Complete Cross-Domain Authentication Based on Blockchain for Internet of Things

The increasing diversity of Internet-of-Things (IoT) application scenarios and explosive growth of access devices have brought more frequent exchanges of resources between different administrative domains. Cross-domain authentication has become a key to safeguard communication and resource interaction among domains. Traditional centralized authentication schemes present heavy management overhead and trust challenges in cross-domain scenarios. Most of existing studies are incapable of establishing trust relationships between domains deployed with different authentication schemes, rendering such high-cost schemes difficult to be generalized. Further, the cross-domain scenario of IoT also raises additional requirements for device privacy and system overhead. In order to tackle these issues, this paper proposes a complete cross-domain authentication and privacy protection scheme, called CCAP, for the IoT based on consortium blockchain. CCAP achieves cross-domain authentication among the IoT domains which may have different configurations from each other. Further, CCAP can be cost-effectively deployed in resource-limited IoT domains and can offer privacy protection and efficient and secure communication for IoT devices. We demonstrate the effectiveness and efficiency of the scheme through experiments in virtual and physical experiment environments as well as comparing and analyzing CCAP with state-of-the-art work.
MLFW: A Database for Face Recognition on Masked Faces

As more and more people begin to wear masks due to current COVID-19 pandemic, existing face recognition systems may encounter severe performance degradation when recognizing masked faces. To figure out the impact of masks on face recognition model, we build a simple but effective tool to generate masked faces from unmasked faces automatically, and construct a new database called Masked LFW (MLFW) based on Cross-Age LFW (CALFW) database. The mask on the masked face generated by our method has good visual consistency with the original face. Moreover, we collect various mask templates, covering most of the common styles appeared in the daily life, to achieve diverse generation effects. Considering realistic scenarios, we design three kinds of combinations of face pairs. The recognition accuracy of SOTA models declines 5%–16% on MLFW database compared with the accuracy on the original images. MLFW database can be viewed and downloaded at http://whdeng.cn/mlfw .
How the emoji use in apology messages influences customers’ responses in online service recoveries: The moderating role of communication style

This research investigates the impact of using emojis (i.e., the pleading-face emoji) on customer forgiveness in relation to handling complaints on social media. Specifically, this research proposes that perceived firm sincerity and perceived firm empathy play mediating roles in the relationship between emoji use and customer forgiveness. In addition, the research identifies the moderating role of communication style in the proposed relationship. Results of three experimental studies indicated that the presence of the emoji in online complaint handling leads to an increase in perceived firm sincerity, which in turn increases perceived firm empathy and, subsequently, leads to customer forgiveness. The serial mediation effects (use of emojis → perceived firm sincerity → perceived firm empathy → customer forgiveness) were moderated by the service provider’s communication style. Specifically, the serial mediation effect occurs when an informal communication style, but not a formal communication style, is used. Furthermore, no difference exists in the research findings across different service types (hedonic vs. utilitarian).
Physical and digital worlds: implications and opportunities of the metaverse

The Metaverse is revolutionizing the world of the internet. It is the new “virtual” universe capable of going beyond the pure three-dimensional and immersive dimension combining the physical and digital worlds. The metaverse, which until recently was an abstract concept, is now assuming great importance and attracting the attention of consumers, investors, brands, and large global players. Certainly, the first sector to adapt to this new immersive reality is e-commerce. Obviously, e-commerce is not the only sector affected by this digital revolution. Interoperability and interconnection will revolutionize current business models. Although the business opportunities seem endless, the scenario is still not entirely clear. Thus, the aim of the present research is to provide an overview on the state of the art, technologies, applications, and challenges of metaverse. Ethical and social implications are also analized. The result is a first detailed scenario analysis on the Metaverse.
Modeling Based on the Analysis of Interval Data of Atmospheric Air Pollution Processes with Nitrogen Dioxide due to the Spread of Vehicle Exhaust Gases

The article deals with the issue of modeling taking into consideration nitrogen dioxide pollution of the atmospheric surface layer caused by vehicle exhaust gases. The interval data analysis methods were suggested. The method of identifying the mathematical model of the distribution of nitrogen dioxide as an atmospheric air pollutant based on the analysis of data with known measurement errors was proposed and grounded for the first time. The obtained mathematical model in the form of a difference equation is characterized by the guaranteed accuracy of forecasting nitrogen dioxide concentrations in a specified area of the city. It also adequately takes into account traffic changes which significantly reduces the costs of environmental control and monitoring. The proposed new model identification method is more effective in terms of computational time complexity compared to the known method and it is based on taking into account measurement errors which in the final case provides predictive properties of the model with guaranteed accuracy.
P-PBFT: An improved blockchain algorithm to support large-scale pharmaceutical traceability

To solve the problems of high latency, high system overhead, and small supported scale in the current application of pharmaceutical traceability combined with blockchain technology, an algorithm called Pharmaceutical-Practical Byzantine Fault Tolerance (P-PBFT) based on PBFT, grouping, and credit voting is proposed. The algorithm combines the characteristics of a pharmaceutical supply chain, optimizes the consistency protocol in the original algorithm, divides large-scale network nodes into different consensus sets by response speed, and performs grouping consensus. The algorithm's credit model and voting mechanism dynamically updates user status according to the behavior of nodes in consensus, evaluates the reliability of users, and also serves as a basis for electing management nodes. Experimental results show that the improved P-PBFT consensus algorithm provides smaller latency and higher throughput for pharmaceutical traceability systems, supports larger-scale traceability, effectively alleviates the dramatic increase in communication among network nodes, and reduces the influence of malicious nodes.
Exploring the global geography of cybercrime and its driving forces

Abstract Cybercrime is wreaking havoc on the global economy, national security, social stability, and individual interests. The current efforts to mitigate cybercrime threats are primarily focused on technical measures. This study considers cybercrime as a social phenomenon and constructs a theoretical framework that integrates the social, economic, political, technological, and cybersecurity factors that influence cybercrime. The FireHOL IP blocklist, a novel cybersecurity data set, is used to map worldwide subnational cybercrimes. Generalised linear models (GLMs) are used to identify the primary factors influencing cybercrime, whereas structural equation modelling (SEM) is used to estimate the direct and indirect effects of various factors on cybercrime. The GLM results suggest that the inclusion of a broad set of socioeconomic factors can significantly improve the model’s explanatory power, and cybercrime is closely associated with socioeconomic development, while their effects on cybercrime differ by income level. Additionally, results from SEM further reveals the causal relationships between cybercrime and numerous contextual factors, demonstrating that technological factors serve as a mediator between socioeconomic conditions and cybercrime.
Fields2Cover: An Open-Source Coverage Path Planning Library for Unmanned Agricultural Vehicles

This letter describes Fields2Cover, a novel open source library for coverage path planning (CPP) for agricultural vehicles. While there are several CPP solutions nowadays, there have been limited efforts to unify them into an open source library and provide benchmarking tools to compare their performance. Fields2Cover provides a framework for planning coverage paths, developing novel techniques, and benchmarking state-of-the-art algorithms. The library features a modular and extensible architecture that supports various vehicles and can be used for a variety of applications, including farms. Its core modules are: a headland generator, a swath generator, a route planner and a path planner. An interface to the Robot Operating System (ROS) is also supplied as an add-on. In this letter, the functionalities of the library for planning a coverage path in agriculture are demonstrated using 8 state-of-the-art methods and 7 objective functions in simulation and field experiments.
Quantum Simulation of Dissipative Collective Effects on Noisy Quantum Computers

Dissipative collective effects are ubiquitous in quantum physics, and their relevance ranges from the study of entanglement in biological systems to noise mitigation in quantum computers. Here, we put forward the first fully quantum simulation of dissipative collective phenomena on a real quantum computer, based on the recently introduced multipartite collision model. First, we theoretically study the accuracy of this algorithm on near-term quantum computers with noisy gates, and we derive some rigorous error bounds that depend on the timestep of the collision model and on the gate errors. These bounds can be employed to estimate the necessary resources for the efficient quantum simulation of the collective dynamics. Then, we implement the algorithm on some IBM quantum computers to simulate superradiance and subradiance between a pair of qubits. Our experimental results successfully display the emergence of collective effects in the quantum simulation. In addition, we analyze the noise properties of the gates that we employ in the algorithm by means of full process tomography, with the aim of improving our understanding of the errors in the near-term devices that are currently accessible to worldwide researchers. We obtain the values of the average gate fidelity, unitarity, incoherence and diamond error, and we establish a connection between them and the accuracy of the experimentally simulated state. Moreover, we build a noise model based on the results of the process tomography for two-qubit gates and show that its performance is comparable with the noise model provided by IBM. Finally, we observe that the scaling of the error as a function of the number of gates is favorable, but at the same time reaching the threshold of the diamond errors for quantum fault tolerant computation may still be orders of magnitude away in the devices that we employ.