Reviewerly/exHarmony huggingface dataset:
A Lightweight Military Target Detection Algorithm Based on Improved YOLOv5

Military target detection technology is the basis and key for reconnaissance and command decision-making, as well as the premise of target tracking. Current military target detection algorithms involve many parameters and calculations, prohibiting deployment on the weapon equipment platform with limited hardware resources. Given the above problems, this paper proposes a lightweight military target detection method entitled SMCA-α-YOLOv5. Specifically, first, the Focus module is replaced with the Stem block to improve the feature expression ability of the shallow network. Next, we redesign the backbone network of YOLOv5 by embedding the coordinate attention module based on the MobileNetV3 block, reducing the network parameter cardinality and computations, thus improving the model’s average detection accuracy. Finally, we propose a power parameter loss that combines the optimizations of the EIOU loss and Focal loss, improving further the detection accuracy and convergence speed. According to the experimental findings, when applied to the self-created military target data set, the developed method achieves an average precision of 98.4% and a detection speed of 47.6 Frames Per Second (FPS). Compared with the SSD, Faster-RCNN, YOLOv3, YOLOv4, and YOLOv5 algorithms, the mAP values of the improved algorithm surpass the competitor methods by 8.3%, 9.9%, 2.1%, 1.6%, and 1.9%, respectively. Compared with the YOLOv5 algorithm, the parameter cardinality and computational burden are decreased by 85.7% and 95.6%, respectively, meeting mobile devices’ military target detection requirements.
Does investor sentiment predict bitcoin return and volatility? A quantile regression approach

The study investigates hypotheses relating to the effect of investor sentiment on predicting bitcoin returns and volatility. Using moments quantile regression, we present robust empirical evidence for the period 2017–2021. Our findings demonstrate that investor interest and emotions are significant predictors of bitcoin returns and volatility, while VIX and Bitcointalk.org forum are the most suitable predictors for representing investor emotions and interest, respectively. The findings also indicate a nonlinear relationship between investor sentiment and bitcoin returns and volatility, with predictable power changing based on the market conditions. Thus, the study enriches existing literature by providing empirical evidence to affirm the viability of behavioral finance theories in the bitcoin market and complements investors with more information to seek profits in different market conditions.
A hybrid approach using AHP–TOPSIS methods for ranking of soft computing techniques based on their attributes for prediction of solar radiation

Recent developments in solar equipment's have motivated researchers to formulate an accurate measurement system for solar radiation under varying environment circumstances that could prove to be economical and viable. To furnish precise estimators of solar radiation, multiple models of different background are employed which are capable of solving complex non-linear data collection and processing problems. These models are immensely proficient in predicting solar radiation by means of numerous algorithms for different functions. Current research compares the prediction capability of several prediction models on various criteria's such as accuracy, cost, time and skill requirement. The present study is accomplished in New Delhi, India using hybrid combination of two Multi Criteria Estimators methods. Weights were calculated by Analytical Hierarchical Process and assigned to each performance attribute. Accuracy is the most significant attribute as its percentage contribution is the maximum (48.28%) followed by skill requirement (31.38%), time (14.41%), and cost (5.9%). Furthermore, the research then ranks these models on the basis of their attributes with the aid of Technique for Order Preference by Similarity to Ideal Solution. Among all these models, Artificial Neural Network model was ranked as the best model for its application in the field of solar energy, followed very closely by Support Vector Machine model while Response Surface Methodology came out to be least favourite.
Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing

Whether AI explanations can help users achieve specific tasks efficiently (i.e., usable explanations) is significantly influenced by their visual presentation. While many techniques exist to generate explanations, it remains unclear how to select and visually present AI explanations based on the characteristics of domain users. This paper aims to understand this question through a multidisciplinary design study for a specific problem: explaining graph neural network (GNN) predictions to domain experts in drug repurposing, i.e., reuse of existing drugs for new diseases. Building on the nested design model of visualization, we incorporate XAI design considerations from a literature review and from our collaborators' feedback into the design process. Specifically, we discuss XAI-related design considerations for usable visual explanations at each design layer: target user, usage context, domain explanation, and XAI goal at the domain layer; format, granularity, and operation of explanations at the abstraction layer; encodings and interactions at the visualization layer; and XAI and rendering algorithm at the algorithm layer. We present how the extended nested model motivates and informs the design of DrugExplorer, an XAI tool for drug repurposing. Based on our domain characterization, DrugExplorer provides path-based explanations and presents them both as individual paths and meta-paths for two key XAI operations, why and what else. DrugExplorer offers a novel visualization design called MetaMatrix with a set of interactions to help domain users organize and compare explanation paths at different levels of granularity to generate domain-meaningful insights. We demonstrate the effectiveness of the selected visual presentation and DrugExplorer as a whole via a usage scenario, a user study, and expert interviews. From these evaluations, we derive insightful observations and reflections that can inform the design of XAI visualizations for other scientific applications.
Uncertainty-aware accurate insulator fault detection based on an improved YOLOX model

The surveillance and inspection of power line insulators, which act as essential components for the connection and insulation of power lines, play pivot roles in the daily maintenance of the power grid system since the failure of power line insulators could cause abrupt power cuts and accidents. Vision-assisted Unmanned Aerial Vehicle (UAV) technology has recently become an efficient and economical solution for automatic insulator fault inspection and demonstrated considerable accuracy with deep learning-based detection methods. However, current detection methods cannot predict and handle the uncertainty of insulator detection, and their capabilities are limited. This paper presents a novel deep learning-based methodology, namely YOLOD, to address the uncertainty issue by applying a Gaussian prior to the detection heads of YOLOX, the current state-of-the-art model of compact object detectors, for both bounding box regression and corresponding uncertainty estimation. Then, the estimated uncertainty scores are utilized to refine the bounding box prediction and further improve the robustness of the detection. Finally, in the comprehensive experiments, the proposed YOLOD model outperforms other benchmark models on a public insulator dataset and achieves the highest average precision (73.9%), which is 2.1% higher than that of YOLOX. Thus, the effectiveness and superiority of the proposed method for robust insulator defect inspection are validated.
A survey on Sign Language machine translation

Sign Languages (SLs) are employed by deaf and hard-of-hearing (DHH) people to communicate on a daily basis. However, the communication with hearing people still faces some barriers, mainly because of the scarce knowledge about SLs among hearing people. Hence, tools to allow the communication between users of either sign or spoken languages must be encouraged. A stepping stone in this direction is the research of the sign language translation (SLT) task, which aims to produce a spoken language translation of a sign language video or vice versa. By implementing these types of translators in portable devices, we will make considerable progress towards a barrier-free communication between DHH and hearing people. That is why, in this work, we focus on reviewing the literature on SLT and provide the necessary background about SLs. Besides, we summarise the available datasets and the results found in the literature for one of the most used datasets, the RWTH-PHOENIX-2014T. Moreover, the survey lists the challenges that need to be tackled within the SLT research and also for the adoption of SLT technologies, and proposes future research lines.
Vues

The emerging volumetric videos offer a fully immersive, six degrees of freedom (6DoF) viewing experience, at the cost of extremely high bandwidth demand. In this paper, we design, implement, and evaluate Vues, an edge-assisted transcoding system that delivers high-quality volumetric videos with low bandwidth requirement, low decoding overhead, and high quality of experience (QoE) on mobile devices. Through an IRB-approved user study, we build a first-of-its-kind QoE model to quantify the impact of various factors introduced by transcoding volumetric content into 2D videos. Motivated by the key observations from this user study, Vues employs a novel multiview approach with the overarching goal of boosting QoE. The Vues edge server adaptively transcodes a volumetric video frame into multiple 2D views with the help of a few lightweight machine learning models and strategically balances the extra bandwidth consumption of additional views and the improved QoE, indicated by our QoE model. The client selects the view that optimizes the QoE among the delivered candidates for display. Comprehensive evaluations using a prototype implementation indicate that Vues dramatically outperforms existing approaches. On average, it improves the QoE by 35% (up to 85%), compared to single-view transcoding schemes, and reduces the bandwidth consumption by 95%, compared to the state-of-the-art that directly streams volumetric videos.
Differential evolution with hybrid parameters and mutation strategies based on reinforcement learning

Differential evolution (DE) has recently attracted a lot of attention as a simple and powerful numerical optimization approach for solving various real-world applications. However, the performance of DE is significantly influenced by the configuration of control parameters and mutation strategy. To address this issue, we proposed reinforcement learning-based hybrid parameters and mutation strategies differential evolution (RL-HPSDE) in this paper. The RL-HPSDE is based on the Q-learning framework, the population individual is regarded as an agent. The optimization problem's dynamic fitness landscape analysis results are utilized to represent the environmental states. The ensemble of parameters and mutation strategy is employed as optional actions for the agent. Furthermore, a reward function is designed to guide the agent to perform the optimal action strategy. Based on its reinforcement learning experience stored by the corresponding Q table, the agent could adaptively select an optimal combination of mutation strategy and parameters to generate offspring individual during each generation. The proposed algorithm is evaluated using the CEC2017 single objective test function set. Several well-known DE variants are also compared with the proposed algorithm. Empirical studies suggest that the proposed RL-HPSDE algorithm is competitive with all other competitors.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation

Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation. It maximizes the agreements between paired sequence augmentations that share similar semantics. However, existing contrastive learning approaches in sequential recommendation mainly center upon left-to-right unidirectional Transformers as base encoders, which are suboptimal for sequential recommendation because user behaviors may not be a rigid left-to-right sequence. To tackle that, we propose a novel framework named Contrastive learning with Bidirectional Transformers for sequential recommendation (CBiT). Specifically, we first apply the slide window technique for long user sequences in bidirectional Transformers, which allows for a more fine-grained division of user sequences. Then we combine the cloze task mask and the dropout mask to generate high-quality positive samples and perform multi-pair contrastive learning, which demonstrates better performance and adaptability compared with the normal one-pair contrastive learning. Moreover, we introduce a novel dynamic loss reweighting strategy to balance between the cloze task loss and the contrastive loss. Experiment results on three public benchmark datasets show that our model outperforms state-of-the-art models for sequential recommendation. Our code is available at this link: https://github.com/hw-du/CBiT/tree/master.
A Scoping Review of Research on Online Hate and Sport

The rise of online hate speech in sports is a growing concern, with fans, players and officials subject to racist, sexist and homophobic abuse (in addition to many other prejudices) via social media platforms. While hate speech and discrimination have always been problems in sports, the growth of social media has seen them exacerbated exponentially. As a consequence, policy makers, sport governing bodies and grassroots anti-hate organisations are largely left playing catch-up with the rapidly shifting realm of online hate. Scholars have attempted to fill this vacuum with research into this topic, but such is the evolving nature of the issue that research has been diverse and fragmentary. We offer a scoping review into the scholarship of online hate in sport in order to encourage and facilitate further research into this urgent issue. Our review will achieve this through offering a comprehensive cataloguing of previously employed methodologies, case studies and conclusions. In doing so, it will not only equip future researchers with a concise overview of existing research in the field, but also illuminate areas and approaches in need of further examination.
RVC: A reputation and voting based blockchain consensus mechanism for edge computing-enabled IoT systems

By deploying edge servers around devices, edge computing brings computing resources far away from the cloud center close to the Internet of Things (IoT), which reduces latency and promotes the rapid development of IoT. Since edge servers and devices (hereafter referred to as NODEs) are highly scattered, blockchain is becoming one of the most promising solutions to enhance security issues for IoT. In the blockchain, a consensus mechanism determines how to achieve an agreement among nodes, hence it is an essential element for the operation and efficiency of the blockchain. However, due to the exponentially increasing of nodes in IoT, the consensus efficiency of the traditional consensus mechanism will be greatly reduced, and due to the lack of detection process for malicious nodes, its security will also reduce. To solve the above problems, in this paper, a Reputation and Voting based Consensus mechanism (RVC) is proposed. To reduce the time consumption of the consensus process, RVC adopts a reputation evaluation algorithm without complex hash calculations to select block proposers, which both consider the behaviors in edge computing and blockchain consensus. To prevent malicious nodes from participating in consensus, a filtering algorithm is designed for RVC, which can detect and filter nodes with malicious behaviors. Simulation results show that, RVC outperforms some traditional work. On time consumption, compared with AirBC and hybrid blockchain, RVC improved by 73.6% and 93.7% respectively. And on consensus security, RVC improved by 14% compared with LVBS. When the network scale and the proportion of malicious nodes change exponentially, RVC shows good scalability in terms of time consumption, successful consensus rate and transaction throughput.
A procedure for anomaly detection and analysis

Anomaly detection is often used to identify and remove outliers in datasets. However, detecting and analyzing the pattern of outliers can contribute to future business decisions or increase the accuracy of a learning algorithm. Selecting the applicable outlier detection method for a dataset requires human intervention and analysis due to the challenge of choosing an efficient technique suitable for all types of attributes. This work presents a procedure for anomaly detection and analysis. The procedure is feature-wise (i.e., processes each feature independently), uses T different anomaly detection techniques (for T>1), and estimates the best technique using predefined thresholds. It is a generic method that does not depend on the model type and can be applied to supervised and unsupervised learning. In addition, this method does not impute or remove the outliers, as they should be adapted according to the dataset or business requirements. The significant advantage of this method is the ability to use different techniques to detect anomalies since it is applied per feature and not per record, as in traditional anomaly detection methods. Furthermore, the method uses a new measure, Noise Ratio (NR), which describes the level of agreement between our method’s result and traditional anomaly detection techniques. The results showed that all the compared techniques identified non-anomalous features with consistent results between the various algorithms. In the proposed method, NR found up to 20% of the non-anomalous values marked as outliers and improved up to 10% in finding outliers in datasets compared to traditional anomaly detection algorithms.
An Energy-Efficient and Blockchain-Integrated Software Defined Network for the Industrial Internet of Things

The number of unsecured and portable Internet of Things (IoT) devices in the smart industry is growing exponentially. A diversity of centralized and distributed platforms have been implemented to defend against security attacks; however, these platforms are insecure because of their low storage capacities, high power utilization, single node failure, underutilized resources, and high end-to-end delay. Blockchain and Software-Defined Networking (SDN) are growing technologies to create a secure system and to ensure safe network connectivity. Blockchain technology offers a strong and trustworthy foundation to deal with threats and problems, including safety, privacy, adaptability, scalability, and security. However, the integration of blockchain with SDN is still in the implementation phase, which provides an efficient resource allocation and reduced latency that can overcome the issues of industrial IoT networks. We propose an energy-efficient blockchain-integrated software-defined networking architecture for Industrial IoT (IIoT) to overcome these challenges. We present a framework for implementing decentralized blockchain integrated with SDN for IIoT applications to achieve efficient energy utilization and cluster-head selection. Additionally, the blockchain-enabled distributed ledger ensures data consistency throughout the SDN controller network and keeps a record of the nodes enforced in the controller. The simulation result shows that the proposed model provides the best energy consumption, end-to-end latency, and overall throughput compared to the existing works.
DCMNet: Discriminant and cross-modality network for RGB-D salient object detection

It is well-acknowledged that depth maps contain affluent spatial information which is crucial to explicitly distinguish the foreground and background in Salient Object Detection (SOD). With the help of depth maps, the performance has been pushed to the peak in SOD. Nevertheless, some depth maps with low quality are not potent for capturing accurate spatial information. Hence, it is not desirable to utilize depth maps indiscriminately. To this end, we propose a Discriminant and Cross-Modality Network (DCMNet) for RGB-D salient object detection. In DCMNet, we integrate a module named Depth Decomposition and Recomposition Module (DDRM) to filter depth maps with low quality. Thereafter, we conduct a quality enhancement procedure towards these detrimental depth maps. Meanwhile, we propose a Multi-Cross Attention Module (MCAM), which combines spatial attention with channel attention in a multi-cross way for better exploiting rich details about the salient object from RGB-stream and depth-stream. In addition, we employ Res2Net model to efficiently excavate foreground information and it is named as Image Pretraining Model (IPM). By embedding DDRM, MCAM and IPM, the accuracy has increased by a large margin. Extensive experiments manifest our proposed approach (DCMNet) outperforms the other 14 state-of-the-art methods on five challenging public datasets.
Local Path Planning of Autonomous Vehicle Based on an Improved Heuristic Bi-RRT Algorithm in Dynamic Obstacle Avoidance Environment

The existing variants of the rapidly exploring random tree (RRT) cannot be effectively applied in local path planning of the autonomous vehicle and solve the coherence problem of paths between the front and back frames. Thus, an improved heuristic Bi-RRT algorithm is proposed, which is suitable for obstacle avoidance of the vehicle in an unknown dynamic environment. The vehicle constraint considering the driver's driving habit and the obstacle-free direct connection mode of two random trees are introduced. Multi-sampling biased towards the target state reduces invalid searches, and parent node selection with the comprehensive measurement index accelerates the algorithm's execution while making the initial path gentle. The adaptive greedy step size, introducing the target direction, expands the node more effectively. Moreover, path reorganization minimizes redundant path points and makes the path's curvature continuous, and path coherence makes paths between the frames connect smoothly. Simulation analysis clarifies the efficient performance of the proposed algorithm, which can generate the smoothest path within the shortest time compared with the other four algorithms. Furthermore, the experiments on dynamic environments further show that the proposed algorithm can generate a differentiable coherence path, ensuring the ride comfort and stability of the vehicle.