Reviewerly/exHarmony huggingface dataset:
A YOLOv8-Based Approach for Real-Time Lithium-Ion Battery Electrode Defect Detection with High Accuracy

Targeting the issue that the traditional target detection method has a high missing rate of minor target defects in the lithium battery electrode defect detection, this paper proposes an improved and optimized battery electrode defect detection model based on YOLOv8. Firstly, the lightweight GhostCony is used to replace the standard convolution, and the GhostC2f module is designed to replace part of the C2f, which reduces model computation and improves feature expression performance. Then, the coordinate attention (CA) module is incorporated into the neck network, amplifying the feature extraction efficiency of the improved model. Finally, the EIoU loss function is employed to swap out the initial YOLOv8 loss function, which improves the regression performance of the network. The empirical findings demonstrate that the enhanced model exhibits increments in crucial performance metrics relative to the original model: the precision rate is elevated by 2.4%, the recall rate by 2.3%, and the mean average precision (mAP) by 1.4%. The enhanced model demonstrates a marked enhancement in the frames per second (FPS) detection rate, significantly outperforming other comparative models. This evidence indicates that the enhanced model aligns well with the requirements of industrial development, demonstrating substantial practical value in industrial applications.
Developing a Base Domain Ontology from Geoscience Report Collection to Aid in Information Retrieval towards Spatiotemporal and Topic Association

The efficient and precise retrieval of desired information from extensive geological databases is a prominent and pivotal focus within the realm of geological information services. Conventional information retrieval methods primarily rely on keyword matching approaches, which often overlook the contextual and semantic aspects of the keywords, consequently impeding the retrieval system’s ability to accurately comprehend user query requirements. To tackle this challenge, this study proposes an ontology-driven information-retrieval framework for geological data that integrates spatiotemporal and topic associations. The framework encompasses the development of a geological domain ontology, extraction of key information, establishment of a multi-feature association and retrieval framework, and validation through a comprehensive case study. By employing the proposed framework, users are empowered to actively and automatically retrieve pertinent information, simplifying the information access process, mitigating the burden of comprehending information organization and software application models, and ultimately enhancing retrieval efficiency.
“We Not Only Repair Our Devices, But Also Our Relationship With Them”: Repair-Led Designing at the Restart Parties in Barcelona

E-waste is the fastest-growing type of waste, leading to increases in waste management, valuation narratives and circular economy models. Despite the modest but fundamental role of repair as a sustaining and caring practice in everyday life—one that dates back generations as a practice—it remains quite invisible within the growing narratives of circular economies. The focus of this chapter is a case study about Restart Parties (RP), which are free public events where volunteers help participants to repair their own devices. The RP are evidence of repair community initiatives, also on the rise and aimed at tackling e-waste. To achieve large-scale validation, repair initiatives like the RP are required to demonstrate quantitative outcomes (for example, weight [kg] of avoided e-waste, and CO2 emissions). These metrics and reporting requirements often overlook broader socio-cultural values and achievements. Drawing from design ethnographic fieldwork in Barcelona, we analysed qualitative effects of the RP at ecological, economic, epistemic, social and material levels. In doing so, we have found examples of repair-led designing and design-led repairing to contribute towards a generative intersection between brokenness, repair and design.
The Potential Benefits and Challenges of a BRICS+ Agency for Cybersecurity Intelligence Exchange

The Brazil, Russia, India, China, South Africa (BRICS) nations lack a cohesive cybersecurity framework for intelligence exchange. The proposed expansion of the BRICS bloc calls for a BRICS+ agency dedicated to cybersecurity information sharing and analysis. Information Sharing and Analysis Centres (ISACs) are successful not-for-profit entities that centralise resources for gathering, analysing, and disseminating cybersecurity intelligence. However, founding a BRICS+ ISAC confronts challenges such as coordination complexity, financial constraints, trust deficits, linguistic diversity, and disparate legislative landscapes. This paper proposes a novel hybrid ISAC architectural model that amalgamates centralised and decentralised elements, presenting a tailored solution for the multifaceted needs of the expanding BRICS+ entity. The innovation of this model lies in its capacity to enhance cybersecurity resilience, promote efficient intelligence exchange, elevate the BRICS+ international standing, and solidify inter-nation collaboration, while being flexible enough to cater to the specific legal, cultural, and technological variances across member countries. The proposed model's uniqueness and adaptability position it as the premier choice for actualising the BRICS+ vision for a unified cyber front.
The Internet of Things (IoT) Forensic Investigation Process

The Internet of Things (IoT), a rapidly evolving network of connected devices, is expected to grow to an astounding 41.6 billion units by 2025. This exponential growth, while beneficial in terms of data collection and exchange, has also increased the vulnerability of these devices to sophisticated cyberattacks, notably the Mirai botnet malware. This paper centers on the distinctive challenges posed in the field of IoT forensics. These challenges are primarily due to the intricate and diverse nature of IoT devices and ecosystems, which complicate the application of standard forensic tools and methodologies. One of the most significant hurdles in IoT forensics is data acquisition, considering the vast diversity of devices and the lack of specialized forensic tools tailored to these unique environments. The paper conducts a thorough literature review to explore these challenges in depth, aiming to not only provide a comprehensive understanding of the current state of IoT forensics but also to identify potential avenues for future research and development. It also highlights key strategies and solutions to enhance the security of IoT devices and to support forensic investigators in navigating the complexities of IoT ecosystems. Through this exploration, the paper contributes valuable insights and guidelines, poised to shape the advancement of IoT device security and forensic investigation techniques.
Urbanity, Transgressive Digitality, and COVID-19: Hierarchical Cybercrime(s) and the Subaltern Nigerian Urban Youth in a Global Pandemic

The coronavirus disease (COVID-19) pandemic, among other things, has shown how the contemporary global community is inescapably wired to a virtual existentiality for continued human relations and correspondence. This human–digital relationship however spiked an unprecedented wave of global cybercrime during the pandemic’s 2020 upsurge. Reflecting on its (trans)national/situational peculiarities, I argue that Nigerian COVID-19 cybercrime uniquely revealed a heightened feature of deviant urban-youth subculture interrogating the dominant systemic neglect of the youth in Nigeria and its link to European colonialism/contemporary Western neoliberalism. The paper proposes that redressing Nigerian cybercrime would require (among other things) concerted global efforts to reintegrate the urban-youth.
Behavioural economics theories in information-seeking behaviour research: A systematic review

This study aims to systematically reveal the application of behavioural economics theories in information-seeking behaviour research to promote the theoretical development of information-seeking behaviour research. A systematic review method was utilised to collect 92 relevant articles published from 1989 to 2022. The temporal development of these articles, research themes, and the theoretical contributions of behavioural economics theories to information-seeking behaviour research were organised and analysed. The findings are as follows: First, behavioural economics theories have been applied in information-seeking behaviour research for more than30 years and have entered an accelerated development stage. Second, behavioural economics theories are widely adaptable to information-seeking behaviour research. Early behavioural economics theories are applicable to 10 research topics such as information-seeking stopping behaviour and information evaluation, while contemporary behavioural economics theories are applicable to 16 research topics: information-seeking strategies and health information-seeking. Third, behavioural economics theories have produced four contributions to information-seeking behaviour research by revealing the mechanisms underlying information-seeking behaviour, exploring behavioural and cognitive strategies in information-seeking behaviour, interpreting the biases underlying information-seeking behaviour, and constructing theoretical models for information-seeking behaviour.
Sigma-Delta and distributed noise-shaping quantization methods for random Fourier features

Abstract We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the random Fourier features (RFFs) associated with shift-invariant kernels. We prove that our quantized RFFs—even in the case of $1$-bit quantization—allow a high-accuracy approximation of the underlying kernels, and the approximation error decays at least polynomially fast as the dimension of the RFFs increases. We also show that the quantized RFFs can be further compressed, yielding an excellent trade-off between memory use and accuracy. Namely, the approximation error now decays exponentially as a function of the bits used. The quantization algorithms we propose are intended for digitizing RFFs without explicit knowledge of the application for which they will be used. Nevertheless, as we empirically show by testing the performance of our methods on several machine learning tasks, our method compares favourably with other state-of-the-art quantization methods.
Survey of Multimodal Medical Question Answering

Multimodal medical question answering (MMQA) is a vital area bridging healthcare and Artificial Intelligence (AI). This survey methodically examines the MMQA research published in recent years. We collect academic literature through Google Scholar, applying bibliometric analysis to the publications and datasets used in these studies. Our analysis uncovers the increasing interest in MMQA over time, with diverse domains such as natural language processing, computer vision, and large language models contributing to the research. The AI methods used in multimodal question answering in the medical domain are a prominent focus, accompanied by applicability of MMQA to the medical field. MMQA in the medical field has its unique challenges due to the sensitive nature of medicine as a science dealing with human health. The survey reveals MMQA research to be in an exploratory stage, discussing different methods, datasets, and potential business models. Future research is expected to focus on application development by big tech companies, such as MedPalm. The survey aims to provide insights into the current state of multimodal medical question answering, highlighting the growing interest from academia and industry. The identified research gaps and trends will guide future investigations and encourage collaborative efforts to advance this transformative field.
Automated Flight Technology for Integral Path Planning and Trajectory Tracking of the UAV

In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.
Evaluating Physical Stress across Task Difficulty Levels in Augmented Reality-Assisted Industrial Maintenance

Many studies have revealed the benefits of augmented reality (AR) to enhance productivity and fulfill a quality function in industry. Yet, the potential adverse effects of AR, such as physical stress, have not been extensively studied. This study investigates the effect of task difficulty (simple versus complex maintenance tasks) on the overall performance and physical stress of individuals while interacting with multimedia instructions (i.e., AR via HoloLens versus. paper-based guidance). Twenty-eight healthy male participants with a mean age of 32.12 years (standard deviation of 2.45 years) were randomly assigned to two groups: AR-based group and paper-based group. The metrics of total maintenance time, NASA-TLX scores, and heart rate variability (HRV) were used to evaluate overall performance, perceived workload, and physical stress, respectively. The findings suggest that AR-based instruction for a complex task significantly reduced both total maintenance time (by 14.94%) and physical stress in comparison with paper-based instruction. Paper-based instruction was found to destabilize heart rhythm, increase heart rate, and cause a shift in autonomic balance due to heightened sympathetic nerve activation. This study concludes that AR-based instruction is more efficient and less stressful than paper-based instruction, indicating that the AR system may offer independent benefits in reducing physical workloads.
Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques

Complex and high-computational-cost algorithms are usually the state-of-the-art solution for autonomous driving cases in which non-holonomic robots must be controlled in scenarios with spatial restrictions and interaction with dynamic obstacles while fulfilling at all times safety, comfort, and legal requirements. These highly complex software solutions must cover the high variability of use cases that might appear in traffic conditions, especially when involving scenarios with dynamic obstacles. Reinforcement learning algorithms are seen as a powerful tool in autonomous driving scenarios since the complexity of the algorithm is automatically learned by trial and error with the help of simple reward functions. This paper proposes a methodology to properly define simple reward functions and come up automatically with a complex and successful autonomous driving policy. The proposed methodology has no motion planning module so that the computational power can be limited like in the reactive robotic paradigm. Reactions are learned based on the maximization of the cumulative reward obtained during the learning process. Since the motion is based on the cumulative reward, the proposed algorithm is not bound to any embedded model of the robot and is not being affected by uncertainties of these models or estimators, making it possible to generate trajectories with the consideration of non-holonomic constrains. This paper explains the proposed methodology and discusses the setup of experiments and the results for the validation of the methodology in scenarios with dynamic obstacles. A comparison between the reinforcement learning algorithm and state-of-the-art approaches is also carried out to highlight how the methodology proposed outperforms state-of-the-art algorithms.
FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics

Federated learning is a distributed learning method that seeks to train a shared global model by aggregating contributions from multiple clients. This method ensures that each client’s local data are not shared with others. However, research has revealed that federated learning is vulnerable to poisoning attacks launched by compromised or malicious clients. Many defense mechanisms have been proposed to mitigate the impact of poisoning attacks, but there are still some limitations and challenges. The defense methods are either performing malicious model removal from the geometric perspective to measure the geometric direction of the model or adding an additional dataset to the server for verifying local models. The former is prone to failure when facing advanced poisoning attacks, while the latter goes against the original intention of federated learning as it requires an independent dataset; thus, both of these defense methods have some limitations. To solve the above problems, we propose a robust federated learning method based on geometric and qualitative metrics (FLGQM). Specifically, FLGQM aims to metricize local models in both geometric and qualitative aspects for comprehensive defense. Firstly, FLGQM evaluates all local models from both direction and size aspects based on similarity calculated by cosine and the Euclidean distance, which we refer to as geometric metrics. Next, we introduce a union client set to assess the quality of all local models by utilizing the union client’s local dataset, referred to as quality metrics. By combining the results of these two metrics, FLGQM is able to use information from multiple views for accurate poisoning attack identification. We conducted experimental evaluations of FLGQM using the MNIST and CIFAR-10 datasets. The experimental results demonstrate that, under different kinds of poisoning attacks, FLGQM can achieve similar performance to FedAvg in non-adversarial environments. Therefore, FLGQM has better robustness and poisoning attack defense performance.
Recommendation Algorithm Based on Heterogeneous Information Network and Attention Mechanism

Heterogeneous information networks (HINs) contain a rich network structure and semantic information, which makes them commonly used in recommendation systems. However, most of the existing HIN-based recommendation systems rely on meta-paths for information extraction, lack meta-path information supplements, and rarely learn complex structure information in heterogeneous graphs. To address these issues, we develop a novel recommendation algorithm that integrates the attention mechanism, meta-paths, and neighbor node information (AMNRec). In the heterogeneous information network, the missing information of the meta-path is supplemented by extracting the information of users and items’ neighbor nodes. The rich interactions between nodes are captured through convolution, and the embedded representation of nodes and meta-paths is obtained through the attention mechanism. TOP-N recommendation is completed by combining users, items, neighbor nodes, and meta-paths. Experiments on three public datasets show that AMNRec not only has the best recommendation performance but also has good interpretability of the recommendation results compared with the six recommendation benchmark algorithms.
Implementation of AI in program-based governance in Azerbaijan

In contemporary governance, program-based governance has gained widespread adoption across various sectors. However, the increasing demand for human labor in data management, transmission and analysis poses challenges to efficient administration. As societies grapple with increasingly complex challenges, the need for efficient and adaptive governance mechanisms becomes paramount. In response, the intersection of artificial intelligence (AI) technologies and program-based governance presents a way for enhancing overall managerial efficiency. This article provides a comprehensive overview of program-based governance within the framework of the monitoring and evaluation process using the insights of Azerbaijan. The exploration of case studies and practical applications - a personalized chatbot model created using advanced natural language processing such as GPT, the article identifies key functionalities where strategic AI integration can optimize and strengthen programmatic management. In the first implemented solution, users can swiftly obtain a summarized results on the progress reports, including details on goal achievement, challenges, and other pertinent information in Azerbaijani language, through the personalized bot on the portal.
Enhanced Chinese Domain Named Entity Recognition: An Approach with Lexicon Boundary and Frequency Weight Features

Named entity recognition (NER) plays a crucial role in information extraction but faces challenges in the Chinese context. Especially in Chinese paleontology popular science, NER encounters difficulties, such as low recognition performance for long and nested entities, as well as the complexity of handling mixed Chinese–English texts. This study aims to enhance the performance of NER in this domain. We propose an approach based on the multi-head self-attention mechanism for integrating Chinese lexicon-level features; by integrating Chinese lexicon boundary and domain term frequency weight features, this method enhances the model’s perception of entity boundaries, relative positions, and types. To address training prediction inconsistency, we introduce a novel data augmentation method, generating enhanced data based on the difference set between all and sample entity types. Experiments on four Chinese datasets, namely Resume, Youku, SubDuIE, and our PPOST, show that our approach outperforms baselines, achieving F1-score improvements of 0.03%, 0.16%, 1.27%, and 2.28%, respectively. This research confirms the effectiveness of integrating Chinese lexicon boundary and domain term frequency weight features in NER. Our work provides valuable insights for improving the applicability and performance of NER in other Chinese domain scenarios.
Neural Causal Information Extractor for Unobserved Causes

Causal inference aims to faithfully depict the causal relationships between given variables. However, in many practical systems, variables are often partially observed, and some unobserved variables could carry significant information and induce causal effects on a target. Identifying these unobserved causes remains a challenge, and existing works have not considered extracting the unobserved causes while retaining the causes that have already been observed and included. In this work, we aim to construct the implicit variables with a generator-discriminator framework named the Neural Causal Information Extractor (NCIE), which can complement the information of unobserved causes and thus provide a complete set of causes with both observed causes and the representations of unobserved causes. By maximizing the mutual information between the targets and the union of observed causes and implicit variables, the implicit variables we generate could complement the information that the unobserved causes should have provided. The synthetic experiments show that the implicit variables preserve the information and dynamics of the unobserved causes. In addition, extensive real-world time series prediction tasks show improved precision after introducing implicit variables, thus indicating their causality to the targets.