Reviewerly/exHarmony huggingface dataset:
A Mapping Review on Cyber-Physical Smart Contracts: Architectures, Platforms, and Challenges

Context: Smart contracts are software systems that monitor, automate, and control the execution of a process, react to violations, and enforce process terms and conditions. There is tremendous interest in developing smart contract applications in banking, finance, insurance, government, and supply chain markets. Many of these applications operate in a cyber-physical environment and adopt architectures based on Internet-of-Things (IoT) and blockchain technologies to support monitoring and ensure data integrity. Objective: To investigate how cyber-physical smart contracts are realized for compliance monitoring, together with associated challenges and research opportunities. Method: A mapping review of the literature that surveys underlying architectures and their evaluation. The publications considered came from four databases (Scopus, Web of Science, IEEE Xplore, and Google Scholar), supplemented by manual snowballing. All publications considered are peer-reviewed, written in English, and published in non-predatory venues. Results: A total of 368 publications were considered, with a final selection of 50 papers (all published between 2018 and 2023) that were analyzed along three dimensions: cyber-physical architectures, infrastructure failures, and technical challenges. Conclusions: Blockchain technologies are the most commonly used platform for smart contracts as they provide decentralized architectures deploying interesting communication patterns, as well as multiple technologies to simplify communication for producing and consuming events. Moreover, such architectures can lead to many types of infrastructure failures including sensor/actuator attacks, network outages, and hardware/software failures, resulting in five important technical challenge areas related to security, availability, robustness, privacy, and legal aspects. Key insights and directions for future research are also reported. This review will inform readers about how cyber-physical smart contracts are being built and deployed and the challenges that are faced by their builders and users.
BSTC: A Fake Review Detection Model Based on a Pre-Trained Language Model and Convolutional Neural Network

Detecting fake reviews can help customers make better purchasing decisions and maintain a positive online business environment. In recent years, pre-trained language models have significantly improved the performance of natural language processing tasks. These models are able to generate different representation vectors for each word in different contexts, thus solving the challenge of multiple meanings of a word, which traditional word vector methods such as Word2Vec cannot solve, and, therefore, better capturing the text’s contextual information. In addition, we consider that reviews generally contain rich opinion and sentiment expressions, while most pre-trained language models, including BERT, lack the consideration of sentiment knowledge in the pre-training stage. Based on the above considerations, we propose a new fake review detection model based on a pre-trained language model and convolutional neural network, which is called BSTC. BSTC considers BERT, SKEP, and TextCNN, where SKEP is a pre-trained language model based on sentiment knowledge enhancement. We conducted a series of experiments on three gold-standard datasets, and the findings illustrate that BSTC outperforms state-of-the-art methods in detecting fake reviews. It achieved the highest accuracy on all three gold-standard datasets—Hotel, Restaurant, and Doctor—with 93.44%, 91.25%, and 92.86%, respectively.
FLScalize: Federated Learning Lifecycle Management Platform

Federated learning (FL) that can train using machine learning methods without moving data have attracted interest owing to the focus on data privacy. Several FL platforms and frameworks are being developed with various open datasets. However, FL has not yet been fully utilized in real-world projects; instead, centralized ML models are still being used for AI. Since FL is composed of numerous clients and executed, it is necessary to manage the lifecycle such as model deployment and status management to multiple clients in order to operate FL. This study proposes FLScalize to enable AI researchers to apply their own custom data and models to FL environments and to deploy and manage the FL lifecycle. Researchers who develop these models should be able to easily and conveniently apply custom data and models developed in a centralized environment to FL environments, deploy and train multiple clients, and manage the lifecycle of the entire FL process. FLScalize can be used to simulate system heterogeneity and data heterogeneity, both of which are FL issues that occur in real FL environments. Furthermore, FLScalize provides a manager component that continuously manages the FL client and server required for real-world FL tasks and realizes an FL lifecycle management implementation that enables continuous integration, deployment, and training.
The Impact of Cyber Security on Business: How to Protect Your Business

This book presents knowledge and grasp of numerous sorts of computer attacks, as well as the reasons and targets of those attacks.The introduction of the article includes a brief dictionary of relevant terms that are laid out in a manner that is simple to comprehend.Following this, the paper analyzes "the patterns and severities of cyber attacks and their impact on routine computer-based operations, the furtherance of business, and electronic commerce, as well as on some Critical National Infrastructure (CNI), which supports such essential areas as power, transportation, communications, defense, and banking and finance".In the field of cyber security, which is now a highly popular topic of debate, the definition of cyberspace or cyber risk is currently a prominent issue of controversy.The major goal of this article is to educate the audience about the dilemma that is offered by cyber security and to make them aware of the possible attacks and cyber threats that now exist in the world of information technology or cyberspace.This piece will also make them aware of the attacks and cyber threats that now exist in the realm of information technology or cyberspace.
An Evolutionary Game Analysis of Stakeholders’ Decision-Making Behavior in Medical Data Sharing

In the era of big data, medical data sharing has become an inevitable requirement to improve the quality and efficiency of medical services. To advance the progress of medical data sharing and expedite the circulation of data value, it becomes crucial to examine the decision-making behavior of stakeholders involved in the medical-data-sharing process. To this end, we construct a three-way evolutionary game model applicable to the medical sharing scenario, analyzing the evolutionary trends in the selection strategies of data providers, the medical-data-sharing platform and data demanders. Furthermore, through theoretical analysis and simulation experiments, we explore the game equilibrium point of the system and analyze key factors that affect stakeholder strategy selection. The results of the experiment show that, in addition to data security, platforms and regulators should pay attention to the regulation and governance of the quality of data flows, which involves reasonable incentives–feedback–rewards and penalties. By strengthening the security technology and data governance system construction of sharing platforms, as well as promoting regulatory authorities to implement reward and punishment measures, etc., a stable state can be achieved in such systems. In addition, this article also proposes relevant management suggestions for medical data sharing in order to provide useful references for scientific decision making by stakeholders.
Into the unknown: active monitoring of neural networks (extended version)

Abstract Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. We consider the problem of monitoring the classification decisions of neural networks in the presence of novel classes. For this purpose, we generalize our recently proposed abstraction-based monitor from binary output to real-valued quantitative output. This quantitative output enables new applications, two of which we investigate in the paper. As our first application, we introduce an algorithmic framework for active monitoring of a neural network, which allows us to learn new classes dynamically and yet maintain high monitoring performance. As our second application, we present an offline procedure to retrain the neural network to improve the monitor’s detection performance without deteriorating the network’s classification accuracy. Our experimental evaluation demonstrates both the benefits of our active monitoring framework in dynamic scenarios and the effectiveness of the retraining procedure.
EPL-PRM: Equipotential line sampling strategy for probabilistic roadmap planners in narrow passages

Path planning is a crucial concern in the field of mobile robotics, particularly in complex scenarios featuring narrow passages. Sampling-based planners, such as the widely utilized probabilistic roadmap (PRM), have been extensively employed in various robot applications. However, PRM’s utilization of random node sampling often results in disconnected graphs, posing a significant challenge when dealing with narrow passages. In order to tackle this issue, we present equipotential line sampling strategy for probabilistic roadmap (EPL-PRM), a novel approach derived from PRM. This paper initially proposes a sampling potential field, followed by the construction of equipotential lines that are denser in the proximity of obstacles and narrow passages. Random sampling is subsequently conducted along these lines. Consequently, the sampling strategy enhances the likelihood of sampling nodes around obstacles and narrow passages, thereby addressing the issue of sparsity encountered in traditional sampling-based planners. Furthermore, we introduce a nodal optimization method based on an artificial repulsive field, which prompts sampled nodes to move in the direction of repulsion. As a result, nodes around obstacles are distributed more uniformly, while nodes within narrow passages gravitate toward the middle of the passages. Finally, extensive simulations are conducted to evaluate the proposed method. The results demonstrate that our approach achieves path planning with superior efficiency, lower cost, and higher reliability compared with traditional algorithms.
A Modified Differential Evolution Algorithm Based on Improving A New Mutation Strategy and Self-Adaptation Crossover

The differential evolution algorithm is one of the promising natural inspired population-based metaheuristic algorithms that attracted the attention of researchers in the recent years. This paper presents a new mutation strategy called DE/current-to-best/2 that presents a new mutated vector based on utilizing the distance between the best vector and the current vector along with another random vector. In addition, the crossover procedure is self-adapted to cover low locality and high locality based on the iteration number. To obtain the best results of the proposed modified differential evolution algorithm, design of experiments is done to optimize its parameters. The comparative results are done using 11 optimization problems to compare the classical version of differential evolution algorithm with the new modified version and the results show high efficiency of the proposed DE algorithm in terms of CPU time, evaluation, and accuracy The outline of the work done in this paper can be shown as follows:•The paper produces a new modification of one of the most promising metaheuristics algorithms, the differential evolution algorithm.•The mutation strategy of the algorithm is modified to work with the current solution, the global best solution, and a random solution. The resulted mutated vector from this procedure is used to produce a new modified crossover solution.•The crossover procedure is self-adapted to cover low locality and high locality based on the iteration number, where in case of the odd iterations, the high locality is applied to obtain more diversity, and in case of the even iterations the low locality is applied to obtain local neighbor solutions. The comparison is done with the classical version of the algorithm, and the results show efficiency in terms of CPU time, evaluation, and accuracy.
An intelligent detection method for plasmodium based on self-supervised learning and attention mechanism

Malaria remains a severe life-threatening disease caused by plasmodium parasites. Microscopy is widely used for malaria diagnosis. However, it relies heavily on the skills and experience of inspectors. Due to low-level medical services and the lack of skilled inspectors, misdiagnoses are frequently made in some areas.In recent years, many successful applications of CNN models have been reported. Unlike images in the ImageNet, the image of plasmodium only has a tiny defect area with a large amount of information. In addition, the dataset is extremely unbalanced: the number of positive samples is much less than that of negative samples. This paper proposes a classification network by combining attention mechanism and ResNeSt for plasmodium detection and using self-supervised learning to pre-train the network. First, the positive samples were adopted to pre-train the network. Then, attention modules were taken to highlight the feature area. To support current and future research, we also constructed a plasmodium dataset with Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, and Plasmodium malaria and non-Plasmodium. Through self-supervised learning, a large amount of unlabeled data is used to mine the representational features, thus improving the feature extraction capability of the model and achieving higher accuracy, while saving the physician's labeling time and improving the classification accuracy.The experiments show that our model exhibits an excellent performance and that the test accuracy, sensitivity, and specificity attain 97.8%, 96.5%, and 98.9%, respectively.The AI classification method proposed in this paper can effectively assist clinicians in the diagnosis and provide a basis for the automatic detection of malaria parasites in the future.
Federated Learning and Blockchain-Enabled Intelligent Manufacturing for Sustainable Energy Production in Industry 4.0

Intelligent manufacturing under Industry 4.0 assimilates sophisticated technologies and artificial intelligence for sustainable production and outcomes. Blockchain paradigms are coined with Industry 4.0 for concurrent and well-monitored flawless production. This article introduces Sustainable Production concerned with External Demands (SP-ED). This method is more specific about energy production and the distribution for flawless and outage-less supply. First, the energy demand is identified for internal and external users based on which sustainability is planned. Secondly, Ethereum blockchain monitoring for a similar production and demand satisfaction is coupled with the production system. From two perspectives, the monitoring and condition satisfaction processes are validated using federated learning (FL). The perspectives include demand distribution and production sustainability. In the demand distribution, the condition of meeting the actual requirement is validated. Contrarily, the flaws in internal and external supply due to production are identified in sustainability. The failing conditions in both perspectives are handled using blockchain records. The blockchain records reduce flaws in the new production by modifying the production plan according to the federated learning verifications. Therefore, the sustainability for internal and external demands is met through FL and blockchain integration.
HcLSH: A Novel Non-Linear Monotonic Activation Function for Deep Learning Methods

Activation functions are essential components in any neural network model; they play a crucial role in determining the network’s expressive power through their introduced non-linearity. Rectified Linear Unit (ReLU) has been the famous and default choice for most deep neural network models because of its simplicity and ability to tackle the vanishing gradient problem that faces backpropagation optimization. However, ReLU introduces other challenges that hinder its performance; bias shift and dying neurons in the negative region. To address these problems, this paper presents a novel composite monotonic, zero-centered, semi-saturated activation function called Hyperbolic cosine Linearized SquasHing function (HcLSH) with partial gradient-based sparsity HcLSH owns many desirable properties, such as considering the contribution of the negative values of neurons while having a smooth output landscape to enhance the gradient flow during training. Furthermore, the regularization effect resulting from the self-gating property of the positive region of HcLSH reduces the risk of model overfitting and ensures learning more robust expressive representations. An extensive set of experiments and comparisons is conducted that includes four popular image classification datasets, seven deep network architectures, and ten state-of-the-art activation functions. HcLSH exhibited the Top-1 and Top-3 testing accuracy results in 20 and 25 out of 28 conducted experiments, respectively, suppressing the widely used ReLU that achieved 2 and 5, and the reputable Mish that achieved 0 and 5 Top-1 and Top-3 testing accuracy results, respectively. HcLSH attained improvements over ReLU, ranging from 0.2% to 96.4% in different models and datasets. Statistical results demonstrate the significance of the enhanced performance achieved by our proposed HcLSH activation function compared to the competitive activation functions in various datasets and models regarding the testing loss Furthermore, the ablation study further verifies the proposed activation function’s robustness, stability, and adaptability for the different model parameter.
Improved Particle Swarm Path Planning Algorithm with Multi-Factor Coupling in Forest Fire Spread Scenarios

In this paper, a solution based on an improved particle swarm algorithm is proposed for the path planning problem without a road network in forest fire rescue scenarios. The algorithm adopts an adaptive inertia weight and a dynamically updated learning factor strategy to enhance the global and local search capabilities of the algorithm. In terms of cost function design, the article considers three factors: path length, terrain slope, and obstacle avoidance ability to ensure the safety and effectiveness of the path. The experimental results show that: (1) the path planning algorithm based on improved particle swarm optimization can effectively avoid spreading wildfire and reach the designated target point with a good “detour” effect; (2) the path planned by the improved PSO algorithm performs better than the original PSO algorithm in terms of fitness evaluation and average slope; and (3) changes in the particle population, dimensions, and learning factors in the particle swarm optimization algorithm can affect the convergence of the final path. Increasing the particle dimensions can bring more reasonable and specific paths; decreasing the learning factor increases the convergence iterations, but also obtains a better path planning solution and higher fitness.
CGRS: Collaborative Knowledge Propagation Graph Attention Network for Recipes Recommendation

In the age of big data, recipe recommendation is of great significance. It can recommend recipes in line with the user's eating habits in massive data. Compared with other recommendation tasks, recipe recommendation is influenced by multiple aspects and requires fine-grained learning to obtain entity representations. Therefore, the traditional recommendation method cannot meet people's requirements. In this paper, we propose the Collaborative Knowledge Propagation Graph Attention Network for Recipes Recommendation (CGRS). This method designs collaborative information propagation to make full use of user interaction and recipe attribute information to meet the needs of multiple influencing factors. Use the graph attention feature learning network to obtain the high-order feature information of the entity to meet the demand for fine-grained representation. Specifically, the method first obtains the multi-hop triplet sets of users and recipes through a collaborative message propagation strategy. Then utilises a graph attention feature learning layer to learn the topological proximity structure features of the triplet sets. Obtain high-level semantic information of entities by superimposing network layers. Design an attention aggregator at the prediction layer to refine the embedding representation of entities. Finally predict the user-recipe interaction probability. Experimental results prove the advancement and effectiveness of CGRS.
Perspectives on point cloud-based 3D scene modeling and XR presentation within the cloud-edge-client architecture

With the support of edge computing, the synergy and collaboration among central cloud, edge cloud, and terminal devices form an integrated computing ecosystem known as the Cloud-Edge-Client architecture. This integration unlocks the value of data and computational power, presenting significant opportunities for large-scale 3D scene modeling and XR presentation. In this paper, we explore the perspectives and highlight new challenges on 3D scene modeling and XR presentation based on point cloud within the cloud–edge-client integrated architecture. We also propose a novel cloud–edge-client integrated technology framework and a demonstration of municipal governance to address these challenges.
Rule-Based Expert Systems

A rule-based expert system is the most elementary form of AI, and it uses predetermined sets of steps to find an answer to a problem. The goal of an expert system is to encode the knowledge of a human expert as a set of rules that can be applied to data automatically.
Social accessibility in multiplayer games: Theory and praxis

In recent years, we have seen growing efforts towards facilitating the accessibility of games to people with disabilities. When it comes to multiplayer games, however, such efforts require not only facilitating the technical accessibility of the game (e.g., accessibility features) but also social inclusion in the game, what we refer to as social accessibility. Accessibility of multiplayer games is, hence, complex and multifaceted. While some advances have been made in terms of technical accessibility, much work is needed to facilitate social accessibility. This study had two goals 1) to propose a definition for social accessibility in multiplayer games, and 2) to present practical examples of how social accessibility can be facilitated in multiplayer games since these aspects were not showcased in the reviewed literature. The study suggests that social accessibility in multiplayer games is a combination of inclusive game design, in-game features, and social guidelines that aim to foster inclusive social structures, and multimodality in communications amongst players, while gaming or socially interacting with aspects of game culture. The study presents various in-game-features that could be used to facilitate social accessibility, such as pausing the game, co-piloting, and individualizable settings.