Reviewerly/exHarmony huggingface dataset:
Intelligent Behavioural Analysis of Social Network Data for the Purposes of Accounting and Control

The growing socialization of digital communication has prompted the introduction of social networks into the economic activity of enterprises. Data derived from social networks is now recognized as a valuable information resource of intelligent behavioural analysis conducted for the purposes of accounting and control at the enterprise. The aim of this research is to provide an intelligent assessment of data from social networks with regards to the behavioural indicators of accounting users in order to improve the methodological and organizational regulations of accounting and control at an enterprise. The main directions of utilising social digital technologies for the purposes of setting up accounting and control at an enterprise are investigated. The paper suggests a new understanding of accounting in terms of non-linear processing of accounting data providing the enterprise, when the initiator of the start of the information process is a stakeholder, uses social networks. The methodology behind intelligent assessment of data from social digital communications is improved. In particular, the improvements are related to forecasting the possibility of interpersonal conflicts arising within the team; duplicated and competing messages, as well as official duties of specialists; perception of cyber risks and the possibility of actualized threats to cybersecurity; the need to update professional skills and knowledge of the staff; the necessity of introducing feedback in order to monitor the accuracy of perception of and reactions to the information. If the suggestions are implemented, it will help minimize the loss of financial resources and time spent on organizing accounting and control at the enterprise.
Why Has Digital Contact Tracing Worked Differently in Different Countries? Comment on Cao et al. The Impact of Digital Contact Tracing Apps Overuse on Prevention of COVID-19: A Normative Activation Model Perspective. Life 2022, 12, 1371

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Learning to Generalize in Heterogeneous Federated Networks

With the rapid development of the Internet of Things (IoT), the need to expand the amount of data through data-sharing to improve the model performance of edge devices has become increasingly compelling. To effectively protect data privacy while leveraging data across silos, federated learning has emerged. However, in the real world applications, federated learning inevitably faeces both data and model heterogeneity challenges. To address the heterogeneity issues in federated networks, in this work, we seek to jointly learn a global feature representation that is robust across clients and potentially also generalizable to new clients. More specifically, we propose a personalized <u>Fed</u>erated optimization framework with <u>M</u>eta <u>C</u>ritic (FedMC) that efficiently captures robust and generalizable domain-invariant knowledge across clients. Extensive experiments on four public datasets show that the proposed FedMC outperforms the competing state-of-the-art methods in heterogeneous federated learning settings. We have also performed detailed ablation analysis on the importance of different components of the proposed model.
An Abstract Summarization Method Combining Global Topics

Existing abstractive summarization methods only focus on the correlation between the original words and the summary words, ignoring the topics’ influence on the summaries. To this end, an abstract summarization method combining global topic information, ACGT, is proposed. A topic information extractor, based on Latent Dirichlet Allocation, is constructed to extract key topic information from the original text, and an attention module is built to fuse key topic information with the original text representation. The summary is then generated by combining a pointer generation network and coverage mechanism. With evaluation metrics of ROUGE-1, ROUGE-2, and ROUGE-L, the experimental results of ACGT in the English dataset CNN/Daily Mail are 0.96%, 2.44%, and 1.03% higher than the baseline model, respectively. In the Chinese dataset, LCSTS, ACGT shows a higher performance than the baseline method by 1.19%, 1.03%, and 0.85%, respectively. Our results demonstrate that the performance of summaries is significantly correlated with the number of topics that are introduced. Case studies show that the introduction of topic information can improve both the coverage of original text topics and the fluency of summaries.
The HEIC application framework for implementing XAI-based socio-technical systems

The development of data-driven Artificial Intelligence systems has seen successful application in diverse domains related to social platforms; however, many of these systems cannot explain the rationale behind their decisions. This is a major drawback, especially in critical domains such as those related to cybersecurity, of which malicious behavior on social platforms is a clear example. In light of this problem, in this paper we make several contributions: (i) a proposal of desiderata for the explanation of outputs generated by AI-based cybersecurity systems; (ii) a review of approaches in the literature on Explainable AI (XAI) under the lens of both our desiderata and further dimensions that are typically used for examining XAI approaches; (iii) the Hybrid Explainable and Interpretable Cybersecurity (HEIC) application framework that can serve as a roadmap for guiding R&D efforts towards XAI-based socio-technical systems; (iv) an example instantiation of the proposed framework in a news recommendation setting, where a portion of news articles are assumed to be fake news; and (v) exploration of various types of explanations that can help different kinds of users to identify real vs. fake news in social platform settings.
Stable factorization for phase factors of quantum signal processing

This paper proposes a new factorization algorithm for computing the phase factors of quantum signal processing. The proposed algorithm avoids root finding of high degree polynomials by using a key step of Prony&amp;apos;s method and is numerically stable in the double precision arithmetics. Experimental results are reported for Hamiltonian simulation, eigenstate filtering, matrix inversion, and Fermi-Dirac operator.
Prediction of the Share of Solar Power in China Based on FGM (1,1) Model

In recent years, fossil energy reserves have decreased year by year, and the development and use of renewable energy has attracted great attention of governments all over the world. China continues to promote the high-quality development of renewable energy such as solar power generation. Accurate prediction of the share of solar power in China is beneficial to implementing the goals of carbon peaking and carbon neutralization. According to the website of China’s National Bureau of statistics, the earliest annual data of China’s solar power generation is 2017, which leads to there being very few data on the share of China’s solar power generation. Therefore, the prediction accuracy of most prediction methods is low, and the advantages of the grey prediction model are shown. Based on the share of solar power in China from 2017 to 2020, this paper constructs an FGM (1,1) model, calculates r using the Particle Swarm Optimization (PSO) algorithm, and predicts the share of solar power in China in the next few years. r = 0.3858 and MAPE = 0.20% were obtained by calculation of the model. The prediction results show that the share of solar power generation in China will increase year by year, and it will reach about 4.2301% by 2030. In addition, it is found that the share of China’s solar power generation in 2021 is 2.1520%, and the predicted value is 2.1906%. It can be seen that the prediction error is small. Finally, the limitations and future research directions are elucidated. The prediction results presented in this paper will help to guide the development of solar power generation in China, and are of great significance in speeding up the pace of energy structural adjustment, accelerating the construction of a clean, low-carbon, safe and efficient energy system, and promoting sustainable development.
Quantum Communication with Polarization-Encoded Qubits under Majorization Monotone Dynamics

Quantum communication can be realized by transmitting photons that carry quantum information. Due to decoherence, the information encoded in the quantum state of a single photon can be distorted, which leads to communication errors. In particular, we consider the impact of majorization monotone dynamical maps on the efficiency of quantum communication. The mathematical formalism of majorization is revised with its implications for quantum systems. The discrimination probability for two arbitrary orthogonal states is used as a figure of merit to track the quality of quantum communication in the time domain.
Detection and Recognition of Spatial Non-Cooperative Objects Based on Improved YOLOX_L

In view of the intelligent requirements of spatial non-cooperative target detection and recognition tasks, this paper applies the deep learning method YOLOX_L to the task and draws on YOLOF (You Only Look One-Level Feature) and TOOD (Task-Aligned One-Stage Object Detection), which optimize and improve its detection accuracy to meet the needs of space Task Accuracy Requirements. We improve the FPN (Feature Pyramid Networks) structure and decoupled prediction network in YOLOX_L and perform a validation comparative analysis of the improved YOLOX_L on the VOC2007+2012 and spacecraft dataset. Our experiments conducted on the VOC2007+2012 benchmark show that the proposed method can help YOLOX_L achieve 88.86 mAP, which is higher than YOLOX_L, running at 50 FPS under the image size of 608 × 608. The spatial target detection method based on the improved YOLOX has a detection accuracy rate of 96.28% and a detection speed of 50 FPS on our spacecraft dataset, which prove that the method has certain practical significance and practical value.
Combination of texture feature extraction and forward selection for one-class support vector machine improvement in self-portrait classification

&lt;span lang="EN-US"&gt;This study aims to validate self-portraits using one-class support vector machine (OCSVM). To validate accurately, we build a model by combining texture feature extraction methods, Haralick and local binary pattern (LBP). We also reduce irrelevant features using forward selection (FS). OCSVM was selected because it can solve the problem caused by the inadequate variation of the negative class population. In OCSVM, we only need to feed the algorithm using the true class data, and the data with pattern that does not match will be classified as false. However, combining the two feature extractions produces many features, leading to the curse of dimensionality. The FS method is used to overcome this problem by selecting the best features. From the experiments carried out, the Haralick+LBP+FS+OCSVM model outperformed other models with an accuracy of 95.25% on validation data and 91.75% on test data.&lt;/span&gt;
Full-Stack Machine Learning Development Framework for Energy Industry Applications

Abstract Machine Learning (ML) has proved successful in various applications and delivered tremendous value across numerous domains. ML turns data into knowledge and intelligence, that can be used to make the right business decisions. The application of ML in the energy industry is increasing rapidly. This includes but is not limited to manufacturing, refining, energy distribution, and other related domains. Due to the unique and diverse domain requirements, various ML and AI solutions in the energy industry must be extensively customized. These specific requirements usually lead to different approaches to solutions and further introduce difficulties in developing, deploying, and scaling. These difficulties lead to higher costs and longer cycle time in productization. To reduce cost and save time, there is a growing business need to develop, deploy, and scale these energy industry ML applications in an agile way. To achieve this goal, we developed a full-stack ML development framework, which makes it easier to develop and deploy (energy industry) ML solutions with high efficiency, reproducibility, and scalability. It has been demonstrated that projects using this full-stack ML development framework successfully saved both turnaround time and costs.
Effective medical center finding during COVID-19 pandemic using a spatial DSS centered on ontology engineering

The global spread of the coronavirus has generated one of the most critical circumstances forcing healthcare systems to deal with it everywhere in the world. The complexity of crisis management, particularly in Iran, the unfamiliarity of the disease, and a lack of expertise, provided the foundation for researchers and implementers to propose innovative solutions. One of the most important obstacles in COVID-19 crisis management is the lack of information and the need for immediate and real-time data on the situation and appropriate solutions. Such complex problems fall into the category of semi-structured problems. In this respect, decision support systems use people's mental resources with computer capabilities to improve the quality of decisions. In synergetic situations, for instance, healthcare domains cooperating with spatial solutions, coming to a decision needs logical reasoning and high-level analysis. Therefore, it is necessary to add rich semantics to different classes of involved data, find their relationships, and conceptualize the knowledge domain. For the COVID-19 case in this study, ontologies allow for querying over such established relationships to find related medical solutions based on description logic. Bringing such capabilities to a spatial decision support system (SDSS) can help with better control of the COVID-19 pandemic. Ontology-based SDSS solution has been developed in this study due to the complexity of information related to coronavirus and its geospatial aspect in the city of Tehran. According to the results, ontology can rationalize different classes and properties about the user's clinical information, various medical centers, and users' priority. Then, based on the user's requests in a web-based SDSS, the system focuses on the inference made, advises the users on choosing the most related medical center, and navigates the user on a map. The ontology's capacity for reasoning, overcoming knowledge gaps, and combining geographic and descriptive criteria to choose a medical center all contributed to promising outcomes and the satisfaction of the sample community of evaluators.
Traffic Anomaly Detection in Intelligent Transport Applications with Time Series Data using Informer

Multivariate time series traffic dataset is usually large with multiple feature dimensions for long time duration under certain time intervals or sampling rates. In applications such as intelligent transportation systems, some machine learning methods being applied to traffic anomaly detections are computed under certain assumptions and require further improvements. Transport traffic time series data may also suffer from unbalanced number of training data where large amount of labelled training data available for a few popular classes, but with very small amount of labelled data for corner cases. In this paper, based on the recent long sequences prediction method Informer, an anomaly detection algorithm with an anomaly score generator is proposed that does not require any assumptions of data. The encoder-decoder architecture is adopted in the anomaly score generator. The encoder consists of three stacking ProbSparse self-attention mechanisms that significantly reduce computing complexity. The decoder incorporates two multi-head attention layers and a fully connected layer to obtain an output of anomaly scores. Then a One-Class Support Vector Machines (OCSVM) is applied to be the anomaly classifier. The proposed algorithm is capable of detecting anomalies for both vehicle traffic flows and pedestrian flows. It has been verified by applying to a real-world dataset consisting of traffic flows recorded in 2021, as well as to a public anomaly detection dataset.
Location Learning for AVs: LiDAR and Image Landmarks Fusion Localization with Graph Neural Networks

High-accuracy vehicular self-localization plays an important role in autonomous driving. In this paper, we investigate the problem of estimating an autonomous vehicle's location using computer vision and LiDAR information, with respect to (w.r.t.) a reference map composed of landmarks from the environment. The map is generated off-line using static roadside objects such as traffic signs, traffic lights and road-side poles, which are organized into a graph for calibration. We use deep learning techniques to perform automatic feature extraction from sensor measurements. Specifically, we use a Convolutional Neural Network (CNN) to extract features from RGB images captured by an on-vehicle camera and use a Graph Neural Network (GNN) to integrate measurements from LiDAR scans. The vehicle's location is estimated from a regression neural network by comparing the extracted features from the real-time measurements with the calibration landmark map. In our experiments, we perform evaluations using 2 datasets and demonstrate that our approach achieves the state-of-the-art localization accuracy.
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios

Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the devices. Such centralized FL techniques suffer from inherent problems such as failure of the central node and bottlenecks in channel bandwidth. When FL is used in conjunction with connected robots serving as devices, a failure of the central controlling entity can lead to a chaotic situation. This paper describes a mobile agent based paradigm to decentralize FL in multi-robot scenarios. Using Webots, a popular free open-source robot simulator, and Tartarus, a mobile agent platform, we present a methodology to decentralize federated learning in a set of connected robots. With Webots running on different connected computing systems, we show how mobile agents can perform the task of Decentralized Federated Reinforcement Learning (dFRL). Results obtained from experiments carried out using Q-learning and SARSA by aggregating their corresponding Q-tables, show the viability of using decentralized FL in the domain of robotics. Since the proposed work can be used in conjunction with other learning algorithms and also real robots, it can act as a vital tool for the study of decentralized FL using heterogeneous learning algorithms concurrently in multi-robot scenarios.
A Systematic Literature Review of Blockchain Technology for Internet of Drones Security

Internet of Drones (IoD) plays a crucial role in the future Internet of Things due to its important features such as low cost, high flexibility, and mobility. The number of IoD applications is drastically increasing from military to civilian fields. Nevertheless, drones are resource-constrained and highly vulnerable to several security threats and attacks. The use of blockchain technology for securing IoD networks has gained growing attention. To this end, this paper presents a systematic literature review to analyze the current research area regarding the security of IoD environments using the emerging blockchain technology. Forty relevant studies were selected from 129 published articles to answer the identified research questions. The selected studies were classified into three main classes based on blockchain type. Furthermore, a comparison of the reviewed articles in terms of different factors is provided. The research findings show that the blockchain can guarantee fundamental security requirements such as authentication, privacy-preserving, confidentiality, integrity, and access control. Finally, open issues and challenges related to the combination of blockchain and IoD technologies are discussed.
Cloud Servers: Resource Optimization Using Different Energy Saving Techniques

Currently, researchers are working to contribute to the emerging fields of cloud computing, edge computing, and distributed systems. The major area of interest is to examine and understand their performance. The major globally leading companies, such as Google, Amazon, ONLIVE, Giaki, and eBay, are truly concerned about the impact of energy consumption. These cloud computing companies use huge data centers, consisting of virtual computers that are positioned worldwide and necessitate exceptionally high-power costs to preserve. The increased requirement for energy consumption in IT firms has posed many challenges for cloud computing companies pertinent to power expenses. Energy utilization is reliant upon numerous aspects, for example, the service level agreement, techniques for choosing the virtual machine, the applied optimization strategies and policies, and kinds of workload. The present paper tries to provide an answer to challenges related to energy-saving through the assistance of both dynamic voltage and frequency scaling techniques for gaming data centers. Also, to evaluate both the dynamic voltage and frequency scaling techniques compared to non-power-aware and static threshold detection techniques. The findings will facilitate service suppliers in how to encounter the quality of service and experience limitations by fulfilling the service level agreements. For this purpose, the CloudSim platform is applied for the application of a situation in which game traces are employed as a workload for analyzing the procedure. The findings evidenced that an assortment of good quality techniques can benefit gaming servers to conserve energy expenditures and sustain the best quality of service for consumers located universally. The originality of this research presents a prospect to examine which procedure performs good (for example, dynamic, static, or non-power aware). The findings validate that less energy is utilized by applying a dynamic voltage and frequency method along with fewer service level agreement violations, and better quality of service and experience, in contrast with static threshold consolidation or non-power aware technique.