Reviewerly/exHarmony huggingface dataset:
Federated Learning via Unmanned Aerial Vehicle

Federated learning (FL) has emerged as a promising alternative to centralized machine learning for exploiting large amounts of data generated by networks while ensuring data privacy. Unlike previous FL works that rely on terrestrial base stations, this paper studies an unmanned aerial vehicle (UAV)-assisted FL system where a UAV collects local models from distributed ground devices. By leveraging the UAV’s high altitude and mobility, it can proactively establish short-distance line-of-sight links with devices to mitigate the communication straggler effect and improve communication efficiency in FL. Specifically, we present the convergence analysis of FL without convexity assumptions, demonstrating the effect of device scheduling on the global gradients. Based on the derived convergence bound, we aim to minimize the completion time of FL training by jointly optimizing device scheduling, UAV trajectory, and time allocation. This problem explicitly incorporates the devices’ energy budgets, dynamic channel conditions, and convergence accuracy of FL constraints. Despite the non-convexity of the formulated problem, we exploit its structure to decompose it into two sub-problems and further derive the closed-form solutions via the Lagrange dual ascent method. Simulation results show that the proposed design significantly improves the tradeoff between completion time and test accuracy compared to existing benchmarks.
Efficient Deep Learning Models for Predicting Super-Utilizers in Smart Hospitals

In healthcare, a huge amount is paid to meet the requirements of High-Need High-Cost (HNHC) patients, also known as super-utilizers.The major aim of the proposed study is to predict HNHC patients.This paper proposes hybrid Deep Learning (DL) models that identify HNHC patients, and help the management to manage their resources and budgets accordingly.We use the strategy of the Interquartile Range (IQR) that specifies the range of HNHC.The dataset used in this work has a lot of inconsistencies that are tackled by different techniques.Using three DL models, Gated Recurrent Unit (GRU), Fully Convolutional Network (FCN) and Vanilla Recurrent Neural Network (VRNN), we proposed 2 hybrid DL models, FCN-VRNN and GRU-FCN, for the prediction of HNHC patients.The performance of these models is evaluated based on different performance metrics, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), R-Squared (R 2 ) and execution time.The results depict that the proposed hybrid models show more competitive and outperforming results than the individual models.Proposed hybrid system model 1 FCN-VRNN achieves MSE of 0.4%, RMSE of 7%, MAPE of 29.8%, MAE of 3%, and R 2 of 99.4%.Proposed hybrid system model 2 GRU-FCN achieves MSE of 0.4%, RMSE of 6.5%, MAPE of 23.55%, MAE of 2.5%, and R 2 of 99.5%.
Prospects of blockchain governance: Understanding key public values, principles, challenges, and opportunities

Abstract The article elaborates on the potential of blockchain technology to transform governance in different sectors of economy. The research primarily relies on rich empirical data that have been collected from semistructured interviews and focus group studies with professional blockchain developers. In this regard, the article aims to answer the following questions: How could blockchain governance conceptually work? How could one illustrate schematically key principles of its implementation? What are the key features of blockchain governance? Why are they important? What benefits could the concept bring to different sectors of economy if its potential is realized? What are the typical barriers and limitations of blockchain governance? What could be recommended to overcome them?
Digital competence of K-12 pre-service and in-service teachers in China: a systematic literature review

Abstract This systematic review consisted of 50 theoretical and empirical articles taking China as a specific case study for identifying efforts and challenges to teachers' digital competence, establishing an understanding of their use of concepts, disciplines, regions, methods, and analysis. The review follows the methodology based on the PRISMA statement and PICO strategy using the 2010–2023 search period. This research has four primary findings: (1) “teachers' ICT competency” and “teachers’ information literacy” are the main terms used for describing teacher’s use of technology for teaching and learning; (2) the current research mainly focused on investigating the status of teachers' digital competence, the influencing factors, and teacher training; (3) non-experimental study for the population of in-service teachers is the leading research directions related to teachers’ digital competence in the context of China; (4) creating good ICT atmosphere by government and schools, conducting teacher training, and using good pedagogical strategies are three main proposals have been made to improve teachers' digital competence in China. The research findings provide the starting points for a subject of reflection and analysis of the Chinese teachers' digital competence status, and it can also support future empirical research.
Small object detection in UAV image based on improved YOLOv5

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The influence of artificial intelligence on the AISs efficiency: Moderating effect of the cyber security

The study designed to identify the influence of artificial intelligence on the efficiency of Accounting Information Systems (AIS) in the Jordanian industial companies, where the descriptive analytical approach was used. The study population included all Jordanian industrial companies listed in Amman Stock Exchange, includes (55) industrial companies. The respondents includes the managers, where (275) questionnaires were distributed to the study respondents electronically, with an average of (5) questionnaires for each company, and retrieved 142 valid questionnaires for statistical analysis. The results presented that there is a significant and positive effect of artificial intelligence's dimensions (i.e. expert systems, genetic algorithms, intelligent agents) on the efficiency of AIS in Jordanian industrial companies. But the resule revealed that neural network as an artificial intelligence's dimensions have non-significant effect on the efficiency of AISs. The result also found that the effective application of cyber security moderates the relationship between artificial intelligence and the the efficiency of AIS positively. Given the importance of the industrial companies' sector in the context of Jordan, the results are useful for these companies with regard to the issue of AIS efficiency and the role of artificial intelligence applications in this efficiency. The results also highlighted the importance of the effective application of cyber security in such a technological era. To the knowledge of the authors, this study is one of the first to address cybersecurity as a moderating variable on the relationship between artificial intelligence applications and AIS efficiency in industrial companies in Jordan, a developing country.
Computation and Communication Efficient Adaptive Federated Optimization of Federated Learning for Internet of Things

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster.
Feature selection: a perspective on inter-attribute cooperation

High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a learning algorithm. Over the decades, filter feature selection methods have evolved from simple univariate relevance ranking algorithms to more sophisticated relevance-redundancy trade-offs and to multivariate dependencies-based approaches in recent years. This tendency to capture multivariate dependence aims at obtaining unique information about the class from the intercooperation among features. This paper presents a comprehensive survey of the state-of-the-art work on filter feature selection methods assisted by feature intercooperation, and summarizes the contributions of different approaches found in the literature. Furthermore, current issues and challenges are introduced to identify promising future research and development.
Leveraging Self-Supervised Learning for Human Activity Recognition with Ambient Sensors

Human activity recognition (HAR) using ambient sensors has emerged as a promising approach to telemonitoring daily activities and enhancing the elderly quality of life. Deep learning models have demonstrated competitive performance in HAR on real-world datasets. However, acquiring large amounts of annotated sensor data for extracting robust features is costly and time-consuming. To overcome this limitation, we propose a novel model based on the self-supervised learning framework, SimCLR, for daily activity recognition using ambient sensor data. The core component of the model is the encoder module, which consists of two convolutional layers followed by a long short-term memory (LSTM) layer. This architecture allows the model to capture both spatial and temporal dependencies in the sensor data, enabling the extraction of informative features for downstream tasks. Through extensive experiments on three CASAS smart home datasets (Aruba-1, Aruba-2, and Milan), we showcase the superior performance of the model in semi-supervised learning and transfer learning scenarios, surpassing state-of-the-art approaches. The findings highlight the potential of self-supervised learning in extracting valuable information from unlabeled sensor data, reducing costly annotation efforts for real-world HAR applications.
The social impact of NFTs in the metaverse economy

The metaverse represents now one of the most promising innovations that lie under the umbrella of Web3-powered technologies. Its societal impact has important repercussions both on social good and on social computing. Among the technologies that are driving the metaverse, we find cryptocurrencies and Non-Fungible Tokens (NFTs) that are used to represent the property of goods and assets in the virtual worlds. In particular, NFTs are used to represent the parcels in which the metaverse is divided, with unknown consequences on the metaverse economy. In this paper, we explore the social impact of employing NFTs in the metaverse and propose an analysis of parcel sales taking Decentraland and The Sandbox as a case study. Our analysis uncovers that prices of the metaverse parcels are increasing over time and that acquiring parcels is becoming prohibitive. Additionally, we identify that the factors that drive the price of sales in the metaverse are the proximity to specific landmarks, like roads, squares, districts, or parcels owned by influencers or enterprises. We argue that this could be a factor in reducing the social good of the metaverse because it fuels a speculative market similar to the one of the physical estate.
Virtual Reality During Chemotherapy Infusion

Patients with cancer receiving infusional chemotherapy show negative symptoms such as worry about their survival, anxiety, anguish, depression, fear, magnified perception of the passage of time, and difficulty managing boredom. Patients also suffer various side effects produced by chemotherapy such as nausea, vomiting, pain, and fatigue, which, together with psychological distress, drastically reduce their quality of life and adherence to therapy with a corresponding reduction in the probability of the individual's survival. Virtual Reality is one of the most innovative and promising digital health interventions, capable of quickly and effectively producing a positive influence on the psychosomatic axis, improving patients' quality of life during chemotherapy. Virtual Reality, through its 3-dimensional multisensory technology, isolates sensory channels from the negative external environment and enables an experience of being physically and psychologically present within virtual scenarios, in which patients can perceive sensations, emotions, cognitions, and interactions as if they really were in different surroundings. This article systematically expounds the scientific conditions necessary for effective, appropriate, and safe implementation of Virtual Reality interventions in holistic nursing practice, describing the underpinning conceptual framework, the types, technological characteristics, methods of use, duration, type of virtual content, and implementation procedure of Virtual Reality.
Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation

Medical image segmentation is a vital task in medical imaging, aiming to extract meaningful and precise information from images. While traditional methods have been extensively used, they often suffer from drawbacks like poor accuracy and robustness. In contrast, deep learning methods, with their promising results in various applications, including medical image segmentation, are compared in this literature review. The review highlights the strengths and limitations of both approaches, providing insights into the current state-of-the- art techniques. Ultimately, it concludes that deep learning methods have demonstrated superior performance in medical image segmentation and are anticipated to make a significant impact in this field.
Speech-to-Text and Text-to-Speech Recognition Using Deep Learning

Speech-to-Text (STT) and Text-to-Speech (TTS) recognition technologies have witnessed significant advancements in recent years, transforming various industries and applications. STT allows for the conversion of spoken language into written text, while TTS enables the generation of natural-sounding speech from written text. In this research paper, we provide a comprehensive review of the latest advancements in STT and TTS recognition technologies, including their underlying methodologies, applications, challenges, and future directions. We begin by discussing the key components of STT and TTS systems, including Automatic Speech Recognition (ASR) and speech synthesis techniques. This research study highlights the evolution of these technologies, from traditional approaches to data-driven deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer based models. Further, this research study analyses various applications of STT and TTS recognition technologies in different domains, including healthcare, customer service, accessibility, and language translation and discusses about the benefits of STT and TTS in improving communication, accessibility, and user experience, and address the challenges and limitations of these technologies, such as accuracy in noisy environments, handling diverse accents and languages, context awareness, and ethical considerations. Moreover, this study highlights the ongoing research efforts to address these challenges and improve the performance and robustness of STT and TTS systems. Finally, we outline the future directions and potential research opportunities in STT and TTS, including advancements in deep learning techniques, multimodal integration, domain adaptation, and personalized speech synthesis and also emphasizes the importance of interdisciplinary research collaborations, data collection, and benchmarking efforts to further drive the development and deployment of STT and TTS recognition technologies in real-world applications.
A virtual try-on system based on deep learning

This study introduces a deep learning-driven virtual fitting system that allows users to virtually experiment with garments, resulting in visually appealing effects. Firstly, We utilize cutting-edge style transfer algorithms to apply the user's input image style onto the virtual scene. Then, we employ a generative adversarial network (GAN) on the modified image to generate content while maintaining image details. Specifically, The initial step in achieving a realistic try-on and detailed clothing representation involves predicting the semantic layout of the reference image to be altered after the try-on process, followed by determining its image contents based on the predicted semantic layout. The network model obtained by training on the clothing dataset can realize the virtual try-on system for people through the network. Finally, the user's try-on operation is completed in the virtual environment, and the final effect picture is generated. The implementation and construction of the overall system is based on Python Web. Experimental results show that the system achieves more accurate and satisfactory recommendations. In conclusion, this system can effectively achieve virtual try-on operations with high visual effects.
Masking and purifying inputs for blocking textual adversarial attacks

The vulnerability of deep neural networks (DNNs) to adversarial attacks has attracted attention in many fields, and researchers have sought methods to improve the robustness of DNNs. Most existing methods are empirical defenses that can only cope with known attack environments and specific attack conditions and are likely to be broken by more powerful attacks. In contrast, certified defense methods provide theoretical proof of robustness bounds and provide worst-case adversarial robustness that can guarantee reliable robustness for the target model. To ensure the security of DNNs against adversarial attacks, it is crucial to develop certified defense methods. However, the currently available study has paid little attention to certified defenses, and the currently certified defense commonly relies on unrealistic prior knowledge and assumptions, which is limited in practice. In this paper, we propose a model-agnostic and attack-agnostic certified defense method that implements denoising and refactoring of input samples through the Makser and the Purifier. The Makser is rule-based and the Purifier is trained by self-supervised fine-tuning on the improved BERT-MLM, and the whole implementation is independent of any target models or attack methods. We have theoretically demonstrated that the condition for method satisfying certified robustness and obtained its robustness boundary through simulation experiments. Meanwhile, experiments on three datasets demonstrate that our proposed method has superior performance in balancing the prediction accuracy on clean examples and the robustness against adversarial attacks compared with six state-of-the-art defense methods.
Deep learning-based scene understanding for autonomous robots: a survey

Autonomous robots are a hot research subject within the fields of science and technology, which has a big impact on social-economic development. The ability of the autonomous robot to perceive and understand its working environment is the basis for solving more complicated issues. In recent years, an increasing number of artificial intelligence-based methods have been proposed in the field of scene understanding for autonomous robots, and deep learning is one of the current key areas in this field. Outstanding gains have been attained in the field of scene understanding for autonomous robots based on deep learning. Thus, this paper presents a review of recent research on the deep learning-based scene understanding for autonomous robots. This survey provides a detailed overview of the evolution of robotic scene understanding and summarizes the applications of deep learning methods in scene understanding for autonomous robots. In addition, the key issues in autonomous robot scene understanding are analyzed, such as pose estimation, saliency prediction, semantic segmentation, and object detection. Then, some representative deep learning-based solutions for these issues are summarized. Finally, future challenges in the field of the scene understanding for autonomous robots are discussed.
Blockchain governance and governance via blockchain: decentralized utopia or centralized dystopia?

Is blockchain the technological blueprint of a utopian decentralized future or a dystopian centralized one? In search of an answer to this question, this article juxtaposes blockchain governance and the state’s utilization of blockchain to govern citizens. This juxtaposition reveals that whilst being a disruptive general-purpose technology emerging from mistrust of centralized institutions, blockchain could also be the vehicle delivering centralized state surveillance and behavioral control. This juxtaposition further reveals that in contrast to the expectations of blockchain enthusiasts, states’ approach to blockchain does not appear to be entirely antagonistic but it consists of a tripartite strategy of appropriation, regulation and rejection depending on which satisfies the state interests in a given context. As most constructs of governance via blockchain are still at an embryonic stage, it is difficult to reach definitive conclusions about what the future holds for blockchain’s impact on citizenship. Nevertheless, this article argues that it is necessary to follow blockchain-based governance critically to identify whether there will be a further divergence between the two worlds of blockchain.