Reviewerly/exHarmony huggingface dataset:
CCSFLF: Cloud‐edge‐terminal collaborative self‐adaptive federated learning framework

Authors:
raw_author_name: Tong Zhou
raw_affiliation_string: School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan China
raw_author_name: Haonan Yuan
raw_affiliation_string: School of Computer Science and Engineering, BDBC Beihang University Beijing China
raw_author_name: Bing Liu
raw_affiliation_string: State Grid Chongqing Electric Power Company Nanchuan Power Supply Branch Chongqing China
raw_author_name: Hongyang Zhao
raw_affiliation_string: State Grid Chongqing Electric Power Company Nanchuan Power Supply Branch Chongqing China

Abstract: Summary This article addresses the issue of ensuring model accuracy and training efficiency in a constrained federated learning environment. In an actual federated learning environment, each device's software, hardware, and network conditions are heterogeneous. Some terminal devices may not be able to undertake the work assigned by the server, resulting in poor model accuracy and slower convergence speed. However, existing research cannot ensure that each terminal device participating in training can handle the workload allocated by the system without collecting too much equipment information. This article proposes the cloud‐edge‐terminal collaborative self‐adaptive federated learning framework (CCSFLF) to solve this problem. This framework combines the advantages of federated learning and edge computing, reduces the probability that devices cannot handle the workload of system allocation, solves the system heterogeneity, and improves the efficiency of federated learning. CCSFLF can adaptively adjust the number of training tasks for terminal devices and select valuable training participants using a terminal device selection strategy. Multiple edge servers can simultaneously aggregate local models. Cloud servers are responsible for the aggregation and task distribution of global models. The above strategy enables the framework to have a faster convergence rate and higher model accuracy. The experimental results confirm that this framework can reduce the dropout rate of terminal devices by more than 5% in heterogeneous federated learning systems, improve the model accuracy by about 2%, and reduce the training time by 1/3 compared with similar methods, with better performance and applicability.
<scp>GMM‐LIME</scp> explainable machine learning model for interpreting sensor‐based human gait

Authors:
raw_author_name: Ronald Waweru Mwangi
raw_affiliation_string: School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya
raw_author_name: Agnes Mindila
raw_affiliation_string: School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya

Abstract: Abstract Machine learning (ML) has been used in human gait data for appropriate assistive device prediction. However, their uptake in the medical setup still remains low due to their black box nature which restricts clinicians from understanding how they operate. This has led to the exploration of explainable ML. Studies have recommended local interpretable model‐agnostic explanation (LIME) because it builds sparse linear models around an individual prediction in its local vicinity hence fast and also because it could be used on any ML model. LIME is however, is not always stable. The research aimed to enhance LIME to attain stability by avoid the sampling step through combining Gaussian mixture model (GMM) sampling. To test performance of the GMM‐LIME, supervised ML were recommended because study revealed that their accuracy was above 90% when used on human gait. Neural networks were adopted for GaitRec dataset and Random Forest (RF) was adopted and applied on HugaDB datasets. Maximum accuracies attained were multilayer perceptron (95%) and RF (99%). Graphical results on stability and Jaccard similarity scores were presented for both original LIME and GMM‐LIME. Unlike original LIME, GMM‐LIME produced not only more accurate and reliable but also consistently stable explanations.
Consumers’ Brand Experiences With Robotic Service Failures: Modeling Appraisal, Attribution, and Psychological Reactance

Authors:
raw_author_name: Hyejo Hailey Shin
raw_affiliation_string: School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
raw_author_name: Vincent Wing Sun Tung
raw_affiliation_string: School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
raw_author_name: Miyoung Jeong
raw_affiliation_string: School of Hospitality and Tourism Management, University of South Carolina, Columbia, SC, USA

Abstract: Due to the stretched capacities of human staff, consumers are increasingly placed in situations where they are “required” to use technology amidst their travel experiences, despite potential service failures in robotic technologies. Yet, research into how robotic service failures could potentially spill over to consumers’ brand experiences, robot experiences, and adoption intention remains unexplored. Drawing on appraisal, attribution, and psychological reactance theories, an interconnected research model of service failure, attribution, and (in)voluntary robot adoption was tested via two experiments. A mixed-design quasi-experiment (Study 1) found a significant interaction effect of service failure and attribution on brand experience in the hotel concierge context. Study 2 provided further evidence for the causal effects in Study 1 by employing a between-subject quasi-experiment in the hotel front desk context with additional measures. Collectively, this research contributes to the literature by highlighting how temporal, situational, and contextual factors in HRI may impact evaluations of brand and robot experiences.
Self-Adaptive Waste Management System: Utilizing Convolutional Neural Networks for Real-Time Classification

Authors:
raw_author_name: C. M. Vidhyapathi
raw_affiliation_string: School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
raw_author_name: S. Sundar
raw_affiliation_string: School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
raw_author_name: B. Karthikeyan
raw_affiliation_string: School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India

Abstract: This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training capabilities. The system utilizes sensors for object detection and a camera for image capture, enabling an accurate initial classification of waste objects into predefined categories such as food waste, metal, and plastic bottles. Notably, our proposed system sets itself apart by its unique ability to adapt and self-train based on classification errors, ensuring ongoing accuracy even in the face of changing waste compositions. Through dynamic adjustments of the conveyor belt’s destination, it efficiently directs waste objects to their appropriate bins for disposal or recycling. This research demonstrates the potential of SAWMS to revolutionize waste management practices, offering an agile and sustainable solution to the evolving challenges of waste sorting and disposal.
Techniques for Accelerometer Reading Processing on Railway Transport Using Wavelet Transform

Authors:
raw_author_name: A. M. Boronakhin
raw_affiliation_string: Saint Petersburg Electrotechnical University
raw_author_name: D.  M. Klionskiy
raw_affiliation_string: Saint Petersburg Electrotechnical University
raw_author_name: D. Yu. Larionov
raw_affiliation_string: Saint Petersburg Electrotechnical University
raw_author_name: R. V. Shalymov
raw_affiliation_string: Saint Petersburg Electrotechnical University

Abstract: Introduction. Safety issues of railway transport are inevitably connected with the condition of railway tracks and railway wheels. Various defects, such as irregularities of the railway track, may lead to emergencies and incidents. Therefore, it is important to measure and calculate short and impulse irregularities. A joint analysis of vibration acceleration signals is needed in order to study the types and size of railway track irregularities. Aim. Development of an algorithm for irregularity search based on accelerometer readings with the vertical measurement axis. Materials and methods. The research encompassed wavelet transformation and wavelet-based signal processing including discrete-time signal processing and continuous wavelet processing. In addition, time-frequency analysis based on Fourier transform and continuous wavelet scalogram was used. These methods provide for time-frequency localization for irregularity detection and measurement. Results. Algorithms for vibrational signal processing using the continuous and discrete-time wavelet transform are proposed. The results show that the discrete wavelet transform is effective for multiresolution and multiband analysis, and continuous wavelet transform and wavelet scalogram allows extraction of irregularities and determination of their parameters. The relative error for irregularity depth was improved by 18 %, and the absolute error for irregularity length determinations was reduced by 7 times. Conclusion. Application of the discrete Fourier transform and Fourier spectrogram provides for fine resolution in the frequency domain. However, separation of signal components in the time-frequency domain is impeded. The continuous-time wavelet transform ensures sufficient resolution in the low-frequency domain for component localization and visualization of irregularities.
Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism

Authors:
raw_author_name: Jiuyang Tang
raw_affiliation_string: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410072, China
raw_author_name: Hao Xu
raw_affiliation_string: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410072, China
raw_author_name: Changsen Liu
raw_affiliation_string: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410072, China
raw_author_name: Weixin Zeng
raw_affiliation_string: Laboratory for Big Data and Decision, National University of Defense Technology, Changsha 410072, China

Abstract: The inductive relation prediction of knowledge graphs, as an important research topic, aims at predicting the missing relation between unknown entities with many real-world applications. Existing approaches toward this problem mostly use enclosing subgraphs to extract the features of target nodes to make predictions; however, there is a tendency to ignore the neighboring relations outside the enclosing subgraph, thus leading to inaccurate predictions. In addition, they also neglect the rich commonsense information that can help filter out less convincing results. In order to address the above issues, this paper proposes a commonsense-guided inductive relation prediction method with a dual attention mechanism called CNIA. Specifically, in addition to the enclosing subgraph, we added the multi-hop neighboring relations of target nodes, thereby forming a neighbor-enriched subgraph where the initial embeddings are generated. Next, we obtained the subgraph representations with a dual attention (i.e., edge-aware and relation-aware) mechanism, as well as the neighboring relational path embeddings. Then, we concatenated the two embeddings before feeding them into the supervised learning model. A commonsense re-ranking mechanism was introduced to filter the results that conformed to commonsense. Extensive experiments on WN18RR, FB15k-237, and NELL995 showed that CNIA achieves better prediction results when compared to the state-of-the-art models. The results suggested that our proposed model can be considered as an effective and state-of-the-art solution for inductive relation prediction.
Special Issue on Information Security and Cryptography: The Role of Advanced Digital Technology

Authors:
raw_author_name: Abdullah Ayub Khan
raw_affiliation_string: Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi 75660, Pakistan; Department of Computer Science, Sindh Madressatul Islam University, Karachi 74000, Pakistan
raw_author_name: Lip Yee Por
raw_affiliation_string: Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia

Abstract: Information security has become a potential prospect that ensures information cannot be breached throughout the process of delivery while being exchanged over the Internet [...]
A Deep Learning-Based Preprocessing Method for Single Interferometric Fringe Patterns

Authors:
raw_author_name: Di Zhang
raw_affiliation_string: Shaanxi Province Key Laboratory of Thin Films Technology and Optical Test, Xi’an Technological University, Xi’an 710021, China
raw_author_name: Yilei Hao
raw_affiliation_string: Shaanxi Province Key Laboratory of Thin Films Technology and Optical Test, Xi’an Technological University, Xi’an 710021, China
raw_author_name: Bingcai Liu
raw_affiliation_string: Shaanxi Province Key Laboratory of Thin Films Technology and Optical Test, Xi’an Technological University, Xi’an 710021, China
raw_author_name: Hongjun Wang
raw_affiliation_string: Shaanxi Province Key Laboratory of Thin Films Technology and Optical Test, Xi’an Technological University, Xi’an 710021, China
raw_author_name: Ailing Tian
raw_affiliation_string: Shaanxi Province Key Laboratory of Thin Films Technology and Optical Test, Xi’an Technological University, Xi’an 710021, China

Abstract: A novel preprocessing method based on a modified U-NET is proposed for single interference fringes. The framework is constructed by introducing spatial attention and channel attention modules to optimize performance. In this process, interferometric fringe maps with an added background intensity, fringe amplitude, and ambient noise are used as the input to the network, which outputs fringe maps in an ideal state. Simulated and experimental results demonstrated that this technique can preprocess single interference fringes in ~1 microsecond. The quality of the results was further evaluated using the root mean square error, peak signal-to-noise ratio, structural similarity, and equivalent number of views. The proposed method outperformed U-NET, U-NET++, and other conventional algorithms as measured by each of these metrics. In addition, the model produced high-quality normalized fringes by combining objective data with visual effects, significantly improving the accuracy of the phase solutions for single interference fringes.
Comparative Analysis of Machine Learning Models for Prediction of Acute Liver Injury in Sepsis Patients

Authors:
raw_author_name: Xu Zeng
raw_affiliation_string: Department of Emergency Medicine, Lishui Municipal Central Hospital, Lishui, China

Abstract: Abstract Introduction: Acute liver injury (ALI) is a common complication of sepsis and is associated with adverse clinical outcomes. We aimed to develop a model to predict the risk of ALI in patients with sepsis after hospitalization. Methods: Medical records of 3196 septic patients treated at the Lishui Central Hospital in Zhejiang Province from January 2015 to May 2023 were selected. Cohort 1 was divided into ALI and non-ALI groups for model training and internal validation. The initial laboratory test results of the study subjects were used as features for machine learning (ML), and models built using nine different ML algorithms were compared to select the best algorithm and model. The predictive performance of model stacking methods was then explored. The best model was externally validated in Cohort 2. Results: In Cohort 1, LightGBM demonstrated good stability and predictive performance with an area under the curve (AUC) of 0.841. The top five most important variables in the model were diabetes, congestive heart failure, prothrombin time, heart rate, and platelet count. The LightGBM model showed stable and good ALI risk prediction ability in the external validation of Cohort 2 with an AUC of 0.815. Furthermore, an online prediction website was developed to assist healthcare professionals in applying this model more effectively. Conclusions: The Light GBM model can predict the risk of ALI in patients with sepsis after hospitalization.
Robust Chinese Short Text Entity Disambiguation Method Based on Feature Fusion and Contrastive Learning

Authors:
raw_author_name: Xuhui Li
raw_affiliation_string: Big Data Institute, Wuhan University, Wuhan 430072, China; School of Information Management, Wuhan University, Wuhan 430072, China

Abstract: To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which fuses LDA-based topic features and BERT-based semantic features, as well as using contrastive learning, to enhance the disambiguation process. Experiments on a publicly available Chinese short-text entity disambiguation dataset show that the proposed model achieves an F1-score of 84.0%, which outperforms the benchmark method by 0.6%. Moreover, our model achieves an F1-score of 74.5% with a limited number of training samples, which is 2.8% higher than the benchmark method. These results demonstrate that our model achieves better effectiveness and robustness and can reduce the burden of data annotation as well as training costs.
Translation Strategy Through Pragmatic Lens: Speech Act Analysis on The Campaign Media

Authors:
raw_author_name: Wida Mulyanti
raw_affiliation_string: Universitas Perjuangan Tasikmalaya
raw_author_name: Setyo Wati
raw_affiliation_string: Universitas Perjuangan Tasikmalaya

Abstract: Communication plays an essential role in transporting messages, feelings, ideas, or even an influence. They are transmitted both verbally and non-verbally. This study elaborates on the use of speech acts in campaign advertising, specifically on billboards and posters as a means of conveying a candidate’s message through written communication in non-English languages in Tasikmalaya City. The capacity to use language in an assortment of settings while reflecting the relationship between the speakers and social cultural context of the situation is known as pragmatic competence. This research is a descriptive qualitative study; data analysis was conducted in two steps. firstly, it was carried out through a translation process which was done by English Education Department students in varied study grades; furthermore, the translation result was analyzed by the researcher to seek Chesterman's translation strategy through the lens of Pragmatics. The writer applied Miles and Huberman's data reduction, data display, and conclusion drawing to analyse the data. The data reduction categorized the message into several categories of illocutionary acts using the translations that have been organized, then it was continued by classifying them according to Searle’s theory of speech act. This study shows that coherence change at 42.3%, trans editing at 23.1%, explicit change at 19.2%, cultural filtering at 11.5%, and illocutionary change at 3.8%; those reflect the translation strategy usage. Furthermore, the illocutionary act shows assertive 58.3%, directive 25%, and commissive 16.7%. Moreover, the pedagogical implications of the analysis are leading students to the awareness that translation can be done using various strategies, enhancing students’ understanding of translation strategies, and sharpening students’ critical thinking.
Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications

Authors:
raw_author_name: Ahmed M. Abdelmoniem
raw_affiliation_string: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK
raw_author_name: Mona Jaber
raw_affiliation_string: School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK

Abstract: The hype of the Internet of Things as an enabler for intelligent applications and related promise for ushering accessibility, efficiency, and quality of service is met with hindering security and data privacy concerns. It follows that such IoT systems, which are empowered by artificial intelligence, need to be investigated with cognisance of security threats and mitigation schemes that are tailored to their specific constraints and requirements. In this work, we present a comprehensive review of security threats in IoT and emerging countermeasures with a particular focus on malware and man-in-the-middle attacks. Next, we elaborate on two use cases: the Internet of Energy Things and the Internet of Medical Things. Innovative artificial intelligence methods for automating energy theft detection and stress levels are first detailed, followed by an examination of contextual security threats and privacy breach concerns. An artificial immune system is employed to mitigate the risk of malware attacks, differential privacy is proposed for data protection, and federated learning is harnessed to reduce data exposure.
Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

Authors:
raw_author_name: Gulshan Saleem
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
raw_author_name: Usama Ijaz Bajwa
raw_affiliation_string: Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
raw_author_name: Rana Hammad Raza
raw_affiliation_string: Electronics and Power Engineering Department, Pakistan Navy Engineering College (PNEC), National University of Sciences and Technology (NUST), Karachi 75350, Pakistan
raw_author_name: Fan Zhang
raw_affiliation_string: Ocean College, Zhejiang University, Hangzhou 316000, China

Abstract: Surveillance video analytics encounters unprecedented challenges in 5G and IoT environments, including complex intra-class variations, short-term and long-term temporal dynamics, and variable video quality. This study introduces Edge-Enhanced TempoFuseNet, a cutting-edge framework that strategically reduces spatial resolution to allow the processing of low-resolution images. A dual upscaling methodology based on bicubic interpolation and an encoder–bank–decoder configuration is used for anomaly classification. The two-stream architecture combines the power of a pre-trained Convolutional Neural Network (CNN) for spatial feature extraction from RGB imagery in the spatial stream, while the temporal stream focuses on learning short-term temporal characteristics, reducing the computational burden of optical flow. To analyze long-term temporal patterns, the extracted features from both streams are combined and routed through a Gated Recurrent Unit (GRU) layer. The proposed framework (TempoFuseNet) outperforms the encoder–bank–decoder model in terms of performance metrics, achieving a multiclass macro average accuracy of 92.28%, an F1-score of 69.29%, and a false positive rate of 4.41%. This study presents a significant advancement in the field of video anomaly recognition and provides a comprehensive solution to the complex challenges posed by real-world surveillance scenarios in the context of 5G and IoT.