Reviewerly/exHarmony huggingface dataset:
Knowledge graph construction based on ship collision accident reports to improve maritime traffic safety

As an important data source, marine accident investigation reports are frequently used for accident analysis. However, it is hard to extract effective information since key knowledge is normally hidden in large blocks of text. In most cases, the collection of accident-related data is done manually. In this paper, a new knowledge graph construction approach to explore ship collision accidents is proposed, aiming to show the correlation among important factors of the accidents. In this research, 241 investigation reports on ship collision accidents from 2018 to 2021 published on the official website of the China Maritime Safety Administration (CMSA) were collected and analyzed. Then, the ship collision accident ontology module is constructed. According to the ontology information in the accident reports, entities were divided into context-based metadata and content-based metadata, which were used for describing different types of data. To extract the information for the accident report with semi-structured data, an information extraction module based on ontology was proposed. In this process, natural language processing (NLP) was used to obtain text information about the ontology. On this basis, the Ship Collision Accident Knowledge Graph (SCAKG) including 910 entity nodes and 1920 relation edges was constructed and stored in the graph database Neo4j. Finally, two case retrievals were conducted using the SCAKG to show the potential utilization of the method. The results show the effectiveness of the proposed approach in terms of discovering the internal relationship of the accident and could be used to expedite the judicial process, which simplifies the process of marine accident investigation.
ChatGPT and global public health: Applications, challenges, ethical considerations and mitigation strategies

The advancement of deep learning and artificial intelligence has resulted in the development of state-of-the-art language models, such as ChatGPT. This technology can analyze large amounts of data, identify patterns, and assist in the analysis and understanding of risk factors for diseases. Despite its potential, the applications, challenges, and ethical considerations have not been yet fully explored in global health research. This paper examines the applications of ChatGPT in global health research, assesses the challenges in its use, and proposes mitigation strategies. Additionally, it describes the ethical considerations around the use of ChatGPT in global health research and suggests potential avenues for addressing these issues. This paper summarizes that it is crucial to understand the capabilities and limitations of this technology in order to fully realize its potential and ensure its responsible integration into global health research.
A Systematic Literature Review of Information Security in Chatbots

Chatbots have become increasingly popular in recent years, but they also present security risks and vulnerabilities that need to be addressed. This systematic literature review examines the existing research relating to information security in chatbots, identifying the potential threats, proposed solutions, and future directions for research. The review finds that chatbots face various security threats, including malicious input, user profiling, contextual attacks, and data breaches, and that solutions such as blockchain technology, end-to-end encryption, and organizational controls can be used to mitigate these concerns. The review also highlights the importance of maintaining user trust and addressing privacy concerns for the successful adoption and continued use of chatbots. A taxonomy developed in this review provides a useful framework for categorizing the articles and their findings. The review concludes by identifying future research directions that include developing more sophisticated authentication and authorization mechanisms, exploring the use of privacy-enhancing technologies, and improving the detection and prevention of security threats, among others. This review contributes to the growing body of literature on information security in chatbots and can guide future research and practice in this field.
Holistic Evaluation of Language Models

Abstract Language models (LMs) like GPT‐3, PaLM, and ChatGPT are the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Holistic Evaluation of Language Models (HELM) to improve the transparency of LMs. LMs can serve many purposes and their behavior should satisfy many desiderata. To navigate the vast space of potential scenarios and metrics, we taxonomize the space and select representative subsets. We evaluate models on 16 core scenarios and 7 metrics, exposing important trade‐offs. We supplement our core evaluation with seven targeted evaluations to deeply analyze specific aspects (including world knowledge, reasoning, regurgitation of copyrighted content, and generation of disinformation). We benchmark 30 LMs, from OpenAI, Microsoft, Google, Meta, Cohere, AI21 Labs, and others. Prior to HELM, models were evaluated on just 17.9% of the core HELM scenarios, with some prominent models not sharing a single scenario in common. We improve this to 96.0%: all 30 models are now benchmarked under the same standardized conditions. Our evaluation surfaces 25 top‐level findings. For full transparency, we release all raw model prompts and completions publicly. HELM is a living benchmark for the community, continuously updated with new scenarios, metrics, and models https://crfm.stanford.edu/helm/latest/ .
Privacy-preserving malware detection in Android-based IoT devices through federated Markov chains

The continuous emergence of new and sophisticated malware specifically targeting Android-based Internet of Things devices is causing significant security hazards and is consequently fostering the need for effective detection models and strategies able to work with these hardware-constrained devices. In addition, since such models are often trained on confidential application data, many involved subjects are reluctant to share their data for this purpose. Accordingly, several Federated Learning-based solutions are emerging, which rely on the capabilities of Machine Learning models in malware detection/classification without sharing user data. However, Federated Learning methods are often adversely affected by non-independent and identically distributed data in terms of both the required training time and classification results. Therefore, a promising solution could be to overcome the Federated Learning-related issues by preserving the privacy of end-user data. In this direction, the capabilities of Markov chains and associative rules are extended within a federated environment to face malware classification tasks in the IoT scenario. The presented approach, evaluated on several malware families, has achieved an average accuracy of 99% in the presence of centralized and decentralized unbalanced training/testing data by overcoming the most common state-of-the-art approaches. Also, its runtime performance is comparable with centralized ones by considering several non-independent and identically distributed dataset partitions, splitting criteria, and clients, respectively.
Chaos-Based Image Encryption: Review, Application, and Challenges

Chaos has been one of the most effective cryptographic sources since it was first used in image-encryption algorithms. This paper closely examines the development process of chaos-based image-encryption algorithms from various angles, including symmetric and asymmetric algorithms, block ciphers and stream ciphers, and integration with other technologies. The unique attributes of chaos, such as sensitivity to initial conditions, topological transitivity, and pseudo-randomness, are conducive to cross-referencing with other disciplines and improving image-encryption methods. Additionally, this paper covers practical application scenarios and current challenges of chaotic image encryption, thereby encouraging researchers to continue developing and complementing existing situations, and may also serve as a basis of future development prospects for chaos-based image encryption.
Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering

Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propose a framework for cross-modal causal relational reasoning. In particular, a set of causal intervention operations is introduced to discover the underlying causal structures across visual and linguistic modalities. Our framework, named Cross-Modal Causal RelatIonal Reasoning (CMCIR), involves three modules: i) Causality-aware Visual-Linguistic Reasoning (CVLR) module for collaboratively disentangling the visual and linguistic spurious correlations via front-door and back-door causal interventions; ii) Spatial-Temporal Transformer (STT) module for capturing the fine-grained interactions between visual and linguistic semantics; iii) Visual-Linguistic Feature Fusion (VLFF) module for learning the global semantic-aware visual-linguistic representations adaptively. Extensive experiments on four event-level datasets demonstrate the superiority of our CMCIR in discovering visual-linguistic causal structures and achieving robust event-level visual question answering.
A new method for recommendation based on embedding spectral clustering in heterogeneous networks (RESCHet)

The advancement in internet technology has enabled the use of increasingly sophisticated data by recommendation systems to enhance their effectiveness. This data is comprised of Heterogeneous Information Networks (HINs) which are composed of multiple nodes and link types. A significant challenge is effectively extracting and incorporating valuable information from HINs. Clustering has been proposed as one of the main methods in recommender systems, but in Heterogeneous Information Networks for recommender systems has received less attention. In this paper, we intend to present a new method for Recommendation Based on Embedding Spectral Clustering in Heterogeneous Networks (RESCHet), which uses the embedding spectral clustering method, whose similarity matrix is generated by a heterogeneous embedding approach. Subsequently, we employed the concepts of submeta-paths and atomic meta-paths to uncover the relationships between users and items that are pertinent to each cluster. Finally, we generated recommendations for users by computing the Hadamard product between the relevant vectors. Experiments carried out on three open benchmark datasets have demonstrated that RESCHet outperforms current leading methods in a significant manner.
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures and benchmarks on how to evaluate these algorithms. This work proposes a standardized, exhaustive, and comprehensive experimental framework to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to a large-scale experimental study comparing state-of-the-art classifiers in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental framework is fully reproducible and easy to extend with new methods. This way, we propose a standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create complete, trustworthy, and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.
Agricultural UAV trajectory planning by incorporating multi-mechanism improved grey wolf optimization algorithm

Unmanned aerial vehicle (UAV) trajectory planning plays an essential role in agricultural production and biological control. To solve the agricultural UAV trajectory planning problem, a multi-mechanism collaborative improved grey wolf optimization algorithm (NAS-GWO) is proposed. In NAS-GWO, the evolutionary boundary constraint processing mechanism is introduced to update the position of the grey wolf individuals that cross the boundary in time to retain the position information of the optimal individuals to the largest degree to enhance the search accuracy of the algorithm. Then, the Gaussian mutation strategy and spiral function are used as perturbation mechanisms to help the algorithm jump out of the local optimum in time to strengthen the exploitation capability of NAS-GWO. Meanwhile, the improved Sigmoid function is used as a nonlinear convergence factor for balancing the exploitation and exploration of the NAS-GWO. By comparing NAS-GWO with ten advanced metaheuristic algorithms on 20 CEC2017 benchmark functions, the experimental results show that the NAS-GWO algorithm has superior merit seeking and robustness. Moreover, the agricultural UAV trajectory planning problem is solved using NAS-GWO. The experimental results show that the NAS-GWO algorithm plans a more viable and stable trajectory path in four different scale missions, while the most important is that it requires less cost. Among them, the algorithm reduces 27.93%, 38.15%, 32.32%, 34.11%, 10.63%, and 13.48% on average in cost function values compared to Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Aquila Optimizer (AO), Differential Evolution (DE), Dung Beetle Optimizer (DBO), and Grey Wolf Optimization algorithm (GWO), thus proving the effectiveness and significance of NAS-GWO in the agricultural UAV trajectory path planning problem.
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Machine-generated text is increasingly difficult to distinguish from text authored by humans. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine-generated text is a key countermeasure for reducing the abuse of NLG models, and presents significant technical challenges and numerous open problems. We provide a survey that includes 1) an extensive analysis of threat models posed by contemporary NLG systems and 2) the most complete review of machine-generated text detection methods to date. This survey places machine-generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models. While doing so, we highlight the importance that detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.
UGIF-Net: An Efficient Fully Guided Information Flow Network for Underwater Image Enhancement

Light traveling through water results in strong scattering across color channels, restricting visibility in underwater images. Many cutting-edge underwater image enhancement methods encounter limitations in color recovery accuracy and resilience against irrelevant feature interference. To tackle these degradation challenges, we propose an efficient and fully guided information flow network called UGIF-Net, for enhancing underwater images. Specifically, we propose a multi-color space-guided color estimation module that accurately approximates color information by incorporating features from two color spaces within a unified network. Subsequently, we employ a dense attention block to guide the network in thoroughly extracting color information from both color spaces while adaptively perceiving crucial color information. Moreover, we devise a color-guided map to steer the network’s focus toward color information and augment its response to color quality degradation. We incorporate the guided map into a guide color restoration module to achieve visually appealing enhancement results. Comprehensive experiments indicate that our approach surpasses state-of-the-art methods, showcasing favorable image restoration effects and their potential to aid other high-level vision tasks.
Levenberg-Marquardt backpropagation neural network procedures for the consumption of hard water-based kidney function

Abstract Water resources in Nusa Tenggara Timur have great concentrations based on magnesium and calcium ions thus being referred to as “hard water”. Prolonged hard water consumption has become a reason of kidney disfunction that can cause additional illnesses, like cerebrovascular pathologies and diabetes. Hence, it is crucial to comprehend how drinking hard water affects renal functions. The current study shows the kidney dysfunction model based on hard water consumption by applying the stochastic procedures of the Levenberg-Marquardt backpropagation neural networks (LMBNNs). The kidney dysfunction model of hard water consumption depends upon human components and water. Human dynamics is further divided into susceptible, infected and recovered, while water components are categorized into calcium and magnesium concentration. The log-sigmoid transfer function along with 20 hidden neurons is used to present the solutions of the model. Three cases of the model have been numerically stimulated and the correctness of the stochastic technique is perceived by using the comparison of proposed and reference Adam databased solutions along with the negligible absolute error. Training, validation and testing performances have been applied to reduce the values of the mean square error. Moreover, the statistical performances using the transition of state, error histograms and regression/correlation have been validated to authenticate the reliability of the scheme.
The impact of AI writing tools on the content and organization of students’ writing: EFL teachers’ perspective

The primary objective of this study was to examine the range of available Artificial Intelligence (AI) writing tools and assess their influence on student writing, particularly in terms of content and organization, as perceived by English as a Foreign Language (EFL) teachers. Utilizing a qualitative approach, the research was constructed within a case study design. The data was collected via semi-structured interviews, targeting information about the diversity of AI writing tools and their impact on students” writing quality. The study gathered data from four EFL teachers across three distinct universities in Indonesia, shedding light on the variety of AI writing tools used in their classrooms. These included applications like Quillbot, WordTune, Jenni, Chat-GPT, Paperpal, Copy.ai, and Essay Writer. Furthermore, these teachers unanimously agreed that the AI writing tools positively improved their students’ writing quality, particularly enhancing the quality of their content and organization. The findings of this study imply that integrating AI writing tools can prove beneficial in elevating the quality of EFL student writing. In response to this study’s limitations, recommendations for future research were also addressed.
The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction

It is essential to have accurate projections of the quantity of solar energy that will be generated in the future to improve the competitiveness of solar power plants in the energy market and reduce the dependence of both the economy and society on fossil fuels. This can be accomplished by having a better understanding of the amount of solar energy that will be generated in the future. We used databases containing information about California that span 2019 through 2021. These years encompass the state's forecast. These data were used in the analysis. The 10-fold cross-validation and Grid search has been used to enhance the performance of decision tree, light gradient boosting machine, and an extra tree in Solar Farm Power Generation Prediction.
Semantic segmentation using Vision Transformers: A survey

Semantic segmentation has a broad range of applications in a variety of domains including land coverage analysis, autonomous driving, and medical image analysis. Convolutional neural networks (CNN) and Vision Transformers (ViTs) provide the architecture models for semantic segmentation. Even though ViTs have proven success in image classification, they cannot be directly applied to dense prediction tasks such as image segmentation and object detection since ViT is not a general purpose backbone due to its patch partitioning scheme. In this survey, we discuss some of the different ViT architectures that can be used for semantic segmentation and how their evolution managed the above-stated challenge. The rise of ViT and its performance with a high success rate motivated the community to slowly replace the traditional convolutional neural networks in various computer vision tasks. This survey aims to review and compare the performances of ViT architectures designed for semantic segmentation using benchmarking datasets. This will be worthwhile for the community to yield knowledge regarding the implementations carried out in semantic segmentation and to discover more efficient methodologies using ViTs.
An Incentive Mechanism of Incorporating Supervision Game for Federated Learning in Autonomous Driving

Federated learning (FL), as a distributed machine learning technology, allows large-scale nodes to utilize local datasets for model training and sharing without revealing privacy, which has significant efficiency and advantages in artificial intelligence (AI)-based knowledge sharing of connected and autonomous vehicles (CAVs). However, for FL, there are challenges to ensure the security of knowledge, deal with the lazy behavior of participants, and enforce effective incentives. To bridge the gaps, in this paper, we first propose a hierarchical blockchain-supported FL architecture that utilizes the immutable and transparent properties of blockchain to enable secure storage and sharing of knowledge and transaction information with scalability. Then, considering the cost and laziness of the participants in the FL process, we propose an incentive mechanism combined with the supervision game to attract high-quality participants based on a comprehensive evaluation of model quality and participants’ reputation. Extensive simulation results validate that our proposal can improve learning accuracy and efficiency while ensuring security.