Reviewerly/exHarmony huggingface dataset:
Trajectory planning of autonomous mobile robots applying a particle swarm optimization algorithm with peaks of diversity

This paper presents a new quantum-behaved particle swarm optimization (QPSO) algorithm for the trajectory planning task of mobile robotic vehicles in static and dynamic environments—it is called enhanced diversity particle swarm optimization (EDPSO). The main characteristic of this algorithm is that it has peaks of diversity in its population, making it possible to escape from local minima effectively, avoiding stagnation. Through the proposed PSO, it is possible to obtain safe and efficient routes, avoiding energy waste and maintaining system integrity in several possible applications. The parameters of the proposed algorithm were tuned using the benchmarking functions. The same functions were used to compare the algorithm with those already established in the literature. Once the proposed algorithm showed promising results, it was simulated in four environments, each with different complexities, presenting dangerous regions and terrains unsuitable for robot navigation, and a large number of obstacles or even moving objects. Further, the algorithms used for comparison were also simulated and the EDPSO presented satisfactory results. Through simulations it was possible to notice that the proposed approach resulted in collision-free and planned routes, and the algorithm presented increased exploration features owing to the diversity peaks that occur during the optimization.
Empathy-based counterspeech can reduce racist hate speech in a social media field experiment

Despite heightened awareness of the detrimental impact of hate speech on social media platforms on affected communities and public discourse, there is little consensus on approaches to mitigate it. While content moderation-either by governments or social media companies-can curb online hostility, such policies may suppress valuable as well as illicit speech and might disperse rather than reduce hate speech. As an alternative strategy, an increasing number of international and nongovernmental organizations (I/NGOs) are employing counterspeech to confront and reduce online hate speech. Despite their growing popularity, there is scant experimental evidence on the effectiveness and design of counterspeech strategies (in the public domain). Modeling our interventions on current I/NGO practice, we randomly assign English-speaking Twitter users who have sent messages containing xenophobic (or racist) hate speech to one of three counterspeech strategies-empathy, warning of consequences, and humor-or a control group. Our intention-to-treat analysis of 1,350 Twitter users shows that empathy-based counterspeech messages can increase the retrospective deletion of xenophobic hate speech by 0.2 SD and reduce the prospective creation of xenophobic hate speech over a 4-wk follow-up period by 0.1 SD. We find, however, no consistent effects for strategies using humor or warning of consequences. Together, these results advance our understanding of the central role of empathy in reducing exclusionary behavior and inform the design of future counterspeech interventions.
A highly secure stream cipher based on analog-digital hybrid chaotic system

In recent decades, an avalanche of chaos-based cryptosystems have been proposed for information security. Most of these systems are not immune to the dynamical degradation of digital chaos and many have been shown to suffer from a lack of security. In this paper, a stream cipher system based on an analog–digital hybrid chaotic system is presented. The hybrid model can construct digital chaotic maps without degeneration and guarantee synchronization of analog chaotic systems for successful decryption. Moreover, focusing on the characteristics of low-dimensional chaotic maps, a modified three-dimensional Logistic map is proposed to improve the weaknesses of uneven distribution, low complexity and limited parameter space. Combining the three-dimensional Logistic map and the hybrid model, the proposed stream cipher has advantages of huge key space, virtually infinite cycle length and tight security. In particular, it is not affected by the dynamical degradation. Performance and security analyses indicate that the proposed stream cipher is highly resistant to various chaos-based attacks and cryptanalytic attacks.
Policy Search for Model Predictive Control With Application to Agile Drone Flight

Policy search and model predictive control (MPC) are two different paradigms for robot control: policy search has the strength of automatically learning complex policies using experienced data, and MPC can offer optimal control performance using models and trajectory optimization. An open research question is how to leverage and combine the advantages of both approaches. In this article, we provide an answer by using policy search for automatically choosing high-level decision variables for MPC, which leads to a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">policy-search-for-model-predictive-control framework</i> . Specifically, we formulate the MPC as a parameterized controller, where the hard-to-optimize decision variables are represented as high-level policies. Such a formulation allows optimizing policies in a self-supervised fashion. We validate this framework by focusing on a challenging problem in agile drone flight: flying a quadrotor through fast-moving gates. Experiments show that our controller achieves robust and real-time control performance in both simulation and the real world. The proposed framework offers a new perspective for merging learning and control.
CRN and 5G based IoT: Applications, Challenges and Opportunities

This article analyzes CRN and 5G-based IoT applications, the challenges and opportunities in their implementation. Initially, the relevance of the use of cognitive radio networks and 5G technologies in the implementation of IoT applications was revealed for today's science and technology. Then, the concepts of cognitive radio and cognitive IoT, their possibilities are explained. The IoT architecture based on CRN and 5G is presented and the principles of operation are explained. The application of CRN and 5G-based IoT in Smart Home, Smart City, Smart Grid, Smart Medicine, Smart Transportation, Environmental and other applications was analyzed. Also, the main problems in the implementation of CRN and 5G-based IoT: hardware design problems, rapid data analysis problems, spectrum resource management problems, Standardization link problems related to network construction and location, energy efficiency and security problems, as well as potential opportunities to solve them were analyzed.
Application of blockchain technology for agrifood supply chain management: a systematic literature review on benefits and challenges

Purpose This study aims to explore and analyze the application of blockchain technology (BKCT) in agrifood supply chain management (AFSM). Design/methodology/approach A systematic literature review (SLR) using Scopus, Emerald and Web of Science covering publications from 2016 to 2021 (till June). Out of 429 papers, 89 papers were shortlisted from journals based on citation analysis. The citation analysis was followed by a content analysis was for an in-depth analysis. Findings Regarding the application of BKCT in the agrifood supply chain, the study highlights the issue of food safety, traceability, transparency, eliminating intermediaries and integrating Internet of things (IoT) with BKCT as prominent applications in the agrifood sector. The study also uses the case of honey supply chain to use the explored applications in the agrifood sector and provides a traceability solution based on blockchain integrated with IoT. The challenges of BKCT as identified in the study are scalability, privacy, security, lack of regulations and lack of skills and training. Research limitations/implications The review considers only academic papers obtained from specific databases with their relevance based on number of citations. Originality/value The study contributes to filling the existing research gap through this SLR on the application of BKCT in AFSCM. The content analysis further assists in exploring the benefits and challenges of BKCT in the agrifood sector. Thus, the academic articles selected aid in this process.
Click-through rate prediction in online advertising: A literature review

Predicting the probability that a user will click on a specific advertisement has been a prevalent issue in online advertising, attracting much research attention in the past decades. As a hot research frontier driven by industrial needs, recent years have witnessed more and more novel learning models employed to improve advertising CTR prediction. Although extant research provides necessary details on algorithmic design for addressing a variety of specific problems in advertising CTR prediction, the methodological evolution and connections between modeling frameworks are precluded. However, to the best of our knowledge, there are few comprehensive surveys on this topic. We make a systematic literature review on state-of-the-art and latest CTR prediction research, with a special focus on modeling frameworks. Specifically, we give a classification of state-of-the-art CTR prediction models in the extant literature, within which basic modeling frameworks and their extensions, advantages and disadvantages, and performance assessment for CTR prediction are presented. Moreover, we summarize CTR prediction models with respect to the complexity and the order of feature interactions, and performance comparisons on various datasets. Furthermore, we identify current research trends, main challenges and potential future directions worthy of further explorations. This review is expected to provide fundamental knowledge and efficient entry points for IS and marketing scholars who want to engage in this area.
Skeleton Motion Recognition Based on Multi-Scale Deep Spatio-Temporal Features

In the task of human motion recognition, the overall action span is changeable, and there may be an inclusion relationship between action semantics. This paper proposes a novel multi-scale time sampling module and a deep spatiotemporal feature extraction module, which strengthens the receptive field of the feature map and strengthens the extraction of spatiotemporal-related feature information via the network. We study and compare the performance of three existing multi-channel fusion methods to improve the recognition accuracy of the network on the open skeleton recognition dataset. In this paper, several groups of comparative experiments are carried out on two public datasets. The experimental results show that compared with the classical 2s-AGCN algorithm, the accuracy of the algorithm proposed in this paper shows an improvement of 1% on the Kinetics dataset and 0.4% and 1% on the two evaluating indicators of the NTU-RGB+D dataset, respectively.
Curvature graph neural network

Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with adaptive locality ability, which enables the measurement of the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguishing the importance of nodes in the topology structure. We believe that the structural importance of neighboring nodes is closely related to their importance in aggregation. In this paper, we introduce discrete graph curvature (the Ricci curvature) to quantify the strength of the structural connection of pairwise nodes. We propose a curvature graph neural network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural properties of graph curvature. To improve the adaptability of curvature on various datasets, we explicitly transform curvature into the weights of neighboring nodes by the necessary negative curvature processing module and curvature normalization module. Then, we conduct numerous experiments on various synthetic and real-world datasets. The experimental results on synthetic datasets show that CGNN effectively exploits the topology structure information and that the performance is significantly improved. CGNN outperforms the baselines on 5 dense node classification benchmark datasets. This study provides a deepened understanding of how to utilize advanced topology information and assign the importance of neighboring nodes from the perspective of graph curvature and encourages bridging the gap between graph theory and neural networks. The source code is available at https://github.com/GeoX-Lab/CGNN.
Blockchain software patterns for the design of decentralized applications: A systematic literature review

A software pattern is a reusable solution to address a commonly occurring problem within a given context when designing software. Using patterns is a common practice for software architects to ensure software quality. Many pattern collections have been proposed for a large number of application domains. However, because of the technology's recentness, there are only a few available collections with a lack of extensive testing in industrial blockchain applications. It is also difficult for software architects to adequately apply blockchain patterns in their applications, as it requires deep knowledge of blockchain technology. Through a systematic literature review, this paper has identified 120 unique blockchain-related patterns and proposes a pattern taxonomy composed of multiple categories, built from the extracted pattern collection. The purpose of this collection is to map, classify, and describe all the available patterns across the literature to help readers make adequate decisions regarding blockchain pattern selection. This study also shows potential applications of those patterns and identifies the relationships between blockchain patterns and other non-blockchain software patterns.
Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design

The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.
Bift: A Blockchain-Based Federated Learning System for Connected and Autonomous Vehicles

Machine learning (ML) algorithms are essential components in autonomous driving. In most existing connected and autonomous vehicles (CAVs), a large amount of driving data collected from multiple vehicles are sent to a central server for unified training. However, data privacy and security have become crucial during the data-sharing process. Federated learning (FL) for data security has arisen nowadays, and it can improve the data privacy of distribute machine learning. However, the malicious attackers can still be able to attack the training process. Due to the complete reliance on the central server, FL is very fragile. To address the above problem, we propose Bift: 1) a fully decentralized ML system combined with FL and 2) blockchain to provide a privacy-preserving ML process for CAVs. Bift enables distributed CAVs to train ML models locally using their own driving data and then to upload the local models to get a better global model. More importantly, Bift provides a consensus algorithm named Proof of Federated Learning to resist possible adversaries. We evaluate the performance of Bift and demonstrate that Bift is scalable and robust, and can defend against malicious attacks.
Privacy-Preserving Bilateral Fine-Grained Access Control for Cloud-Enabled Industrial IoT Healthcare

The expeditious development in cloud-enabled industrial Internet of Things (IIoT) healthcare has significantly reduced the costs to monitor and protect people at home while notably improving the quality of human healthcare. Despite its considerable convenience and benefits, it confronts some security and privacy challenges in the aspects of bilateral fine-grained access control, the authenticity and tamper resistance of shared health data. To tackle these constraints, a secure privacy-preserving bilateral access control scheme with fine granularity (PBAC-FG) is proposed in this article. Our PBAC-FG exploits fine-grained access control and matchmaking encryption technologies to ensure both participants (e.g., patients and healthcare providers) can specify their respective fine-grained access control over the encrypted health data, such that only authorized counterparts can efficiently access the health data. Besides, the correct rigorous security proofs are indicated to verify that our PBAC-FG is indeed secure. We carry out comprehensive performance evaluations and comparisons to demonstrate the efficiency and practicality of the PBAC-FG for IIoT healthcare applications.
Mapping prospectivity for regolith-hosted REE deposits via convolutional neural network with generative adversarial network augmented data

The regolith-hosted rare earth elements (REE) deposits are the dominant source of the global heavy REE resources. This study proposed a convolutional neural network (CNN) architecture to integrate the multi-source data (e.g., geological, geochemical and geomorphological data) to map mineral prospectivity of regolith-hosted REE deposits. To solve the lack of labelled data for the training of the supervised CNN, a generative adversarial network (GAN) was applied for data augmentation. The proposed GAN is trained in an unsupervised way, which regards the random downscaled real samples as the inputs. The GAN-based augmented data is validated by comparison with real data and the peak signal-to-noise ratio value, respectively. A case study of mapping prospectivity for regolith-hosted rare earth elements deposits in Southern Jiangxi Province of China further illustrates and validates the procedure. The final mineral prospectivity map is obtained by the CNN with all geological, geochemical and geomorphological data augmented by GAN. The CNN reaches 99.7% training accuracy and 98.9% validation accuracy. All the known mineral deposits are located in the prospective areas delineated by the CNN, which only occupy 2.36% of the study area. The obtained results indicate that the proposed framework, integrating CNN and the GAN-based augmentation method is an effective way for the application of the supervised deep learning algorithms in mineral prospectivity mapping of the regolith-hosted REE deposits and the prospective areas could be used for guiding further exploration in the study area.
Liquid: Intelligent Resource Estimation and Network-Efficient Scheduling for Deep Learning Jobs on Distributed GPU Clusters

Deep learning (DL) is becoming increasingly popular in many domains, including computer vision, speech recognition, self-driving automobiles, etc. GPU can train DL models efficiently but is expensive, which motivates users to share GPU resource to reduce money costs in practice. To ensure efficient sharing among multiple users, it is necessary to develop efficient GPU resource management and scheduling solutions. However, existing ones have several shortcomings. First, they require the users to specify the job resource requirement which is usually quite inaccurate and leads to cluster resource underutilization. Second, when scheduling DL jobs, they rarely take the cluster network characteristics into consideration, resulting in low job execution performance. To overcome the above issues, we propose Liquid, an efficient GPU resource management platform for DL jobs with intelligent resource requirement estimation and scheduling. First, we propose a regression model based method for job resource requirement estimation to avoid users over-allocating computing resources. Second, we propose intelligent cluster network-efficient scheduling methods in both immediate and batch modes based on the above resource requirement estimation techniques. Third, we further propose three system-level optimizations, including pre-scheduling data transmission, fine-grained GPU sharing, and event-driven communication. Experimental results show that our Liquid can accelerate the job execution speed by 18% on average and shorten the average job completion time (JCT) by 21% compared with cutting-edge solutions. Moreover, the proposed optimization methods are effective in various scenarios.
Predicting the precise number of software defects: Are we there yet?

Defect Number Prediction (DNP) models can offer more benefits than classification-based defect prediction . Recently, many researchers proposed to employ regression algorithms for DNP, and found that the algorithms achieve low Average Absolute Error (AAE) and high Pred(0.3) values. However, since the defect datasets generally contain many non-defective modules, even if a DNP model predicts the number of defects in all modules as zero, the AAE value of the model will be low and Pred(0.3) value will be high. Therefore, the good performance of the regression algorithms in terms of AAE and Pred(0.3) may be questioned due to the imbalanced distribution of the number of defects. To revisit the impact of regression algorithms for predicting the precise number of defects. We examine the practical effects of 12 widely-used regression algorithms, two data resampling algorithm (SmoteR and ROS), and three ensemble learning algorithms (gradient boosting regression, AdaBoost.R2, and Bagging), one feature selection method (information gain) and one parameter optimization method (grid search) for predicting the precise number of defects on the 18 PROMISE datasets. We propose to evaluate the AAE and Pred(0.3) values for the modules with different numbers of defects separately. The AAE values for defective modules are very high and the Pred(0.3) values are very low, i.e., the regression algorithms are very inaccurate for predicting the precise number of defects in defective modules. The problem of predicting the precise number of defects via regression algorithms is far from being solved. We recommend that software testers use regression algorithms to rank modules for testing resource allocation, rather than predict the precise number of defects to evaluate the software reliability and maintenance effort. In addition, most existing DNP studies employing the whole AAE and Pred(0.3) values of all modules as the evaluation metrics for the proposed DNP algorithms should be revisited.