Reviewerly/exHarmony huggingface dataset:
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition

Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive design of in-cabin cameras raises concerns about driver privacy. To address this issue, we propose a novel peer-to-peer (P2P) federated learning (FL) framework with continual learning, namely FedPC, which ensures privacy and enhances learning efficiency while reducing communication, computational, and storage overheads. Our framework focuses on addressing the clients’ objectives within a serverless FL framework, with the goal of delivering personalized and accurate NDAR models. We demonstrate and evaluate the performance of FedPC on two real-world NDAR datasets, including the State Farm Distracted Driver Detection and Track 3 NDAR dataset in the 2023 AICity Challenge. The results of our experiments highlight the strong competitiveness of FedPC compared to the conventional client-to-server (C2S) FLs in terms of performance, knowledge dissemination rate, and compatibility with new clients.
An Efficient Combination of Genetic Algorithm and Particle Swarm Optimization for Scheduling Data-Intensive Tasks in Heterogeneous Cloud Computing

Task scheduling is still an open issue for improving the performance of cloud services. Focusing on addressing the issue, we first formulate the task-scheduling problem of heterogeneous cloud computing into a binary non-linear programming. There are two optimization objectives including the number of accepted tasks and the overall resource utilizations. To solve the problem in polynomial time complexity, we provide a hybrid heuristic algorithm by combing both benefits of genetic algorithm (GA) and particle swarm optimization (PSO), named PGSAO. Specifically, PGSAO integrates the evolution strategy of GA into PSO to overcome the shortcoming of easily trapping into local optimization of PSO, and applies the self-cognition and social cognition of PSO to ensure the exploitation power. Extensive simulated experiments are conducted for evaluating the performance of PGSAO, and the results show that PGSAO has 23.0–33.2% more accepted tasks and 27.9–43.7% higher resource utilization than eight other meta-heuristic and hybrid heuristic algorithms, on average.
Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network

With the rapid development of information technology, the security of images has emerged as a significant area of research. This study presents an algorithm that integrates chaotic image encryption and a convolutional neural network (CNN) to enhance security and efficiency. The algorithm applies the properties of randomness and nonlinear mapping of chaotic sequences with the advanced feature extraction capabilities of a CNN model to achieve robust image encryption. First, we outline the fundamentals of chaotic image encryption and CNN. Chaotic image encryption employs chaotic sequence generation and nonlinear mapping to scramble pixel values for encryption purposes, while a CNN, as a deep-learning model with a local perceptual field and weight sharing, effectively extracts high-level image features. Subsequently, we provide a detailed description of the specific steps involved in combining chaotic image encryption and the CNN. These steps include chaotic sequence generation, pixel value mapping, feature extraction, and key management. The algorithm achieves high-strength encryption by performing dissimilarity operations between the chaotic sequence and image pixel values, as well as extracting high-level image features using the CNN. Finally, we conduct experimental evaluations of the algorithm and compare it with traditional chaotic image encryption methods. The experimental results demonstrate that the image encryption algorithm exhibits significant improvements in encryption quality and security while offering advantages in computational performance and encryption/decryption speed.
Ship detection in haze and low-light remote sensing images via colour balance and DCNN

Ship detection in marine remote sensing images (RSI) is an area of growing interest with a wide range of potential applications. However, current ship detection methods often suffer from low accuracy and poor robustness when dealing with RSI due to the influence of diffuse haze or low-light conditions. To overcome these issues, this study develops a hybrid deep learning framework for detecting ships in RSI, consisting of three main components: RSI dehaze, RSI enhancement, and an improved YOLO-v5s network. Firstly, the RSI dehaze uses colour space transformation to correct the colour cast of blurred RSI and performs dehaze using transmission mapping and haze-line clustering. Then the weakly illuminated RSI is enhanced by utilising illumination mapping between input and enhanced images to describe the correlation between images. Finally, the ship detection model is constructed by improving the YOLO-v5s model with channel split and shuffle, which effectively suppresses redundant features and accurately detects ships in RSI. The HRSC2016 and DIOR datasets are used for comprehensive performance assessment. The experimental results demonstrate that the proposed framework achieves superior ship detection precision, with mAP, Pre and Rec reaching 95.27%, 97.55% and 99.74%, respectively, on the HRSC2016 dataset.
Variational quantum and quantum-inspired clustering

Here we present a quantum algorithm for clustering data based on a variational quantum circuit. The algorithm allows to classify data into many clusters, and can easily be implemented in few-qubit Noisy Intermediate-Scale Quantum devices. The idea of the algorithm relies on reducing the clustering problem to an optimization, and then solving it via a Variational Quantum Eigensolver combined with non-orthogonal qubit states. In practice, the method uses maximally-orthogonal states of the target Hilbert space instead of the usual computational basis, allowing for a large number of clusters to be considered even with few qubits. We benchmark the algorithm with numerical simulations using real datasets, showing excellent performance even with one single qubit. Moreover, a tensor network simulation of the algorithm implements, by construction, a quantum-inspired clustering algorithm that can run on current classical hardware.
Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

In an actual industrial scenario, machines typically operate normally for the majority of the time, with malfunctions occurring only occasionally. As a result, there is very little recorded data on defects. Consequently, the fault diagnostic dataset becomes imbalanced, with a significantly lower number of fault samples compared to normal samples. Furthermore, with the rapid development of the manufacturing industry, the increasing complexity of machines and equipment leads to various challenges in collecting fault data, such as noise, within-class imbalance, multi-class imbalance, and time series imbalance. It is worth noting that this study is the first to comprehensively summarize these four specific challenges. Therefore, addressing these issues has become a critical research focus and a pain point in the field of fault diagnosis, and numerous solutions have emerged. This study provides a comprehensive overview of these solutions at three levels: data preprocessing, feature extraction, and classifier improvement. It also describes the applications of imbalanced data classification methods, including pure resampling techniques, as well as sampling techniques that combine resampling algorithms with feature extraction and classifier improvement in industrial scenarios. Finally, we summarize the challenges facing imbalanced data classification research and suggest potential directions for future studies.
CCDS-YOLO: Multi-Category Synthetic Aperture Radar Image Object Detection Model Based on YOLOv5s

Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management and wartime reconnaissance. However, complex scenes often influence SAR object detection, and the diversity of target scales also brings challenges to research. This paper proposes a multi-category SAR image object detection model, CCDS-YOLO, based on YOLOv5s, to address these issues. Embedding the Convolutional Block Attention Module (CBAM) in the feature extraction part of the backbone network enables the model’s ability to extract and fuse spatial information and channel information. The 1 × 1 convolution in the feature pyramid network and the first layer convolution of the detection head are replaced with the expanded convolution, Coordinate Conventional (CoordConv), forming a CRD-FPN module. This module more accurately perceives the spatial details of the feature map, enhancing the model’s ability to handle regression tasks compared to traditional convolution. In the detector segment, a decoupled head is utilized for feature extraction, offering optimal and effective feature information for the classification and regression branches separately. The traditional Non-Maximum Suppression (NMS) is substituted with the Soft Non-Maximum Suppression (Soft-NMS), successfully reducing the model’s duplicate detection rate for compact objects. Based on the experimental findings, the approach presented in this paper demonstrates excellent results in multi-category target recognition for SAR images. Empirical comparisons are conducted on the filtered MSAR dataset. Compared with YOLOv5s, the performance of CCDS-YOLO has been significantly improved. The mAP@0.5 value increases by 3.3% to 92.3%, the precision increases by 3.4%, and the mAP@0.5:0.95 increases by 6.7%. Furthermore, in comparison with other mainstream detection models, CCDS-YOLO stands out in overall performance and anti-interference ability.
Blockchain from the information systems perspective: Literature review, synthesis, and directions for future research

This paper profiled blockchain studies in information systems (IS) journals from 2016 to 2022. Drawing on the 443 selected articles from 77 IS journals, we proposed a classification scheme from the IS perspective. Current blockchain articles are highly skewed, focusing on research agendas and system design. We proposed a theoretical framework by summarizing the current status of the blockchain literature and highlighting 15 future research questions for the IS research community. We are optimistic that the proposed framework and future research questions can guide blockchain research and advance its scale and impact.
MedRSS: A blockchain-based scheme for secure storage and sharing of medical records

Due to the rapid advance of Healthcare Information Technology (HIT), the amount of medical data has increased dramatically and highly demands correct and secure storage and exchange. However, centralized medical record management systems suffer from privacy breaches, data manipulation, and insufficient shareability. In this article, we propose a blockchain-based solution for medical record data storage and sharing (MedRSS), where we apply Hyperledger fabric blockchain and encryption technology to create a secure environment for medical record data storage and sharing. Blockchain technology is used to ensure that medical records are proof of tampering, smart contracts are used to control access to data users, and cryptography is used to encrypt medical record data and digitalize sign messages. Experiments have shown that MedRSS ensures the accuracy, integrity, and security of medical records and high access throughput, outperforming existing approaches.
EventKGE: Event knowledge graph embedding with event causal transfer

Traditional knowledge graph embedding (KGE) aims to map entities and relations into continuous space vectors to provide high-quality data feature representation for downstream tasks. However, relations in most KGs often only reflect connections between static entities, but cannot represent dynamic activities and state changes of related entities, which makes the KGE models unable to effectively learn rich and comprehensive entity representation. In this paper, we verify the importance of embedding event knowledge in KG representation learning, and propose a novel event KGE model based on event causal transfer (EventKGE), which can effectively maintain the semantic information of events, entities, and relations in the event KG. First, we define a six tuple-based event representation model consisting of event trigger words, event arguments and event description text, which can effectively express events in a structured form. Second, for a given event KG, the event nodes and entity nodes in the KG are integrated through the constructed heterogeneous graph. Meanwhile, in the heterogeneous graph, the event nodes and entity nodes are connected through the event arguments type, and the event nodes are connected through the causal relationship. Finally, an information transfer method based on an attention network is designed, which is used for the relations between events, events and entities, and entities and entities, to integrate event information into KGE. Comprehensive experiments on the ACE2005 corpus and CEC2.0 dataset verify that the event KGE model we designed is efficient and stable in multiple downstream tasks.
An improved cascade RCNN detection method for key components and defects of transmission lines

Abstract Overhead transmission line detection based on deep learning of aerial images taken by UAVs has been widely investigated. Despite its success, it is limited by several factors, including inappropriate evaluation criteria and dramatic scaling of components in the images. To mitigate these issues, a relative mean Average Precision evaluation index is proposed to accurately measure the model's detection performance for smaller objects. A data enhancement strategy including multi‐scale transformation is adopted to alleviate the problem of drastic scaling. The existing Cascade RCNN target detection technology is enhanced by incorporating Swin‐v2 and a balanced feature pyramid to improve feature characterization capabilities, while side‐aware boundary localization is utilized to improve the positioning accuracy of the model. Experimental results demonstrate that the proposed method outperforms state‐of‐the‐art methods on CPLID and achieves 7.8%, 11.8%, and 5.5% higher detection accuracy than the baseline for mAP50, relative small and medium mAP, respectively. Additionally, the paper discusses the influence of adopted data enhancement on the robustness of the model.
Dysarthric Speech Transformer: A Sequence-to-Sequence Dysarthric Speech Recognition System

Automatic Speech Recognition (ASR) technologies can be life-changing for individuals who suffer from dysarthria, a speech impairment that affects articulatory muscles and results in incomprehensive speech. Nevertheless, the performance of the current dysarthric ASR systems is unsatisfactory, especially for speakers with severe dysarthria who most benefit from this technology. While transformer and neural attention-base sequences-to-sequence ASR systems achieved state-of-the-art results in converting healthy speech to text, their applications as a Dysarthric ASR remain unexplored due to the complexities of dysarthric speech and the lack of extensive training data. In this study, we addressed this gap and proposed our Dysarthric Speech Transformer that uses a customized deep transformer architecture. To deal with the data scarcity problem, we designed a two-phase transfer learning pipeline to leverage healthy speech, investigated neural freezing configurations, and utilized audio data augmentation. Overall, we trained 45 speaker-adaptive dysarthric ASR in our investigations. Results indicate the effectiveness of the transfer learning pipeline and data augmentation, and emphasize the significance of deeper transformer architectures. The proposed ASR outperformed the state-of-the-art and delivered better accuracies for 73% of the dysarthric subjects whose speech samples were employed in this study, in which up to 23% of improvements were achieved.
Institutional quality and economic growth in Sub-Saharan Africa: a panel data approach

Purpose The main purpose of this study is to examine the impact of different dimensions of institutional quality indices on the economic growth of Sub-Saharan African (SSA) countries. Design/methodology/approach The study uses a panel data set of 31 SSA countries from 1991 to 2015 and employs a two-step system-GMM (Generalized Method of Moments) estimation technique. Findings The study's empirical results indicate that investment-promoting and democratic and regulatory institutions have a significant positive effect on economic growth; however, once these institutions are taken into account, conflict-preventing institutions do not have a significant impact on growth. Practical implications The study's findings suggest that countries in the region should continue their institutional reforms to enhance the region's economic growth. Specifically, institutions promoting investment, democracy and regulatory quality are crucial. Originality/value Unlike previous studies that use either composite measures of institutions or a single intuitional indicator in isolation, the present study has employed principal component analysis (PCA) to extract fewer institutional indicators from multivariate institutional indices. Thus, this paper provides important insights into the distinct role of different clusters of institutions in economic growth.
Controllable AI - An Alternative to Trustworthiness in Complex AI Systems?

Abstract The release of ChatGPT to the general public has sparked discussions about the dangers of artificial intelligence (AI) among the public. The European Commission’s draft of the AI Act has further fueled these discussions, particularly in relation to the definition of AI and the assignment of risk levels to different technologies. Security concerns in AI systems arise from the need to protect against potential adversaries and to safeguard individuals from AI decisions that may harm their well-being. However, ensuring secure and trustworthy AI systems is challenging, especially with deep learning models that lack explainability. This paper proposes the concept of Controllable AI as an alternative to Trustworthy AI and explores the major differences between the two. The aim is to initiate discussions on securing complex AI systems without sacrificing practical capabilities or transparency. The paper provides an overview of techniques that can be employed to achieve Controllable AI. It discusses the background definitions of explainability, Trustworthy AI, and the AI Act. The principles and techniques of Controllable AI are detailed, including detecting and managing control loss, implementing transparent AI decisions, and addressing intentional bias or backdoors. The paper concludes by discussing the potential applications of Controllable AI and its implications for real-world scenarios.
An optimized backpropagation neural network models for the prediction of nanomaterials concentration for purification industrial wastewater

In this paper, an optimized backpropagation neural network (BPNN) prediction models have been proposed to estimate the concentration of titanium dioxide (TiO2) nanomaterials required for the removal of Acid Black 24 dye, a type of textile dye, from industrial wastewater. The research gap addressed in this study lies in the investigation of various optimization algorithms for BPNN models and their profound impact on the accuracy of nanomaterial concentration predictions, thus contributing to the advancement of nanotechnology-based wastewater treatment research and its practical applications in industrial settings. Although several studies have predicted the amount of nanomaterial needed to degrade the dyes in industrial wastewater using BPNN, few have investigated the effects of optimized BPNN and how greatly the amount of degradation can vary. Therefore, six different optimization algorithms, including genetic algorithms (GAs), ant colony optimization (ACO), particle swarm optimization (PSO), evaluation strategy (ES), biogeography-based optimization (BBO), and population-based incremental learning algorithm (PBIL), are applied to optimize and evaluate the BPNN models. Experimental data obtained from chemical laboratory experiments are used to train and test the models, with different experiments conducted for various factors like TiO2, H2SO4, NaIO4, K2S2O8, dye, and silver nitrate. The performance of the optimized BPNN models is assessed using metrics such as mean squared error (MSE), root means squared error (RMSE), and R-squared (R2). The results demonstrate the effectiveness of the proposed models, with ACO yielding the best outcomes across all models. The R2 values for the TiO2, H2SO4, NaIO4, and K2S2O8 models after optimization indicate perfect predictions, whereas the R2 values before optimization ranged from 0.888 to 0.984. PSO performs well for most models except for K2S2O8, GA improves most models except for the silver nitrate model, ES enhances only the Dye model, and PBIL does not affect the TiO2 model. Overall, the optimized BPNN models exhibit impressive accuracy in estimating the TiO2 concentration required for removing Acid Black 24 dye from industrial wastewater.
Efficient Multimodal Fusion via Interactive Prompting

Large-scale pre-training has brought unimodal fields such as computer vision and natural language processing to a new era. Following this trend, the size of multimodal learning models constantly increases, leading to an urgent need to reduce the massive computational cost of finetuning these models for downstream tasks. In this paper, we propose an efficient and flexible multimodal fusion method, namely PMF, tailored for fusing unimodally pretrained transformers. Specifically, we first present a modular multimodal fusion framework that exhibits high flexibility and facilitates mutual interactions among different modalities. In addition, we disentangle vanilla prompts into three types in order to learn different optimizing objectives for multimodal learning. It is also worth noting that we propose to add prompt vectors only on the deep layers of the unimodal transformers, thus significantly reducing the training memory usage. Experiment results show that our proposed method achieves comparable performance to several other multimodal finetuning methods with less than 3% trainable parameters and up to 66% saving of training memory usage.