Reviewerly/exHarmony huggingface dataset:
Binary Restructuring Particle Swarm Optimization and Its Application

Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.
Improved Algorithm for Face Mask Detection Based on YOLO-v4

Abstract To reduce the chance of being infected by the COVID-19, wearing masks correctly when entering and leaving public places has become the most feasible and effective ways to prevent the spread of the virus. It is a concern to how to quickly and accurately detect whether a face is worn a mask correctly while reduce missed detection and false detection in practical applied scenarios. In this paper, an improved algorithm is proposed based on the YOLO-v4 algorithm. The attention mechanism module is added to the appropriate network level to enhance the key feature points of face wearing masks and suppress useless information. Apart from that, three attention mechanism modules are added to different layers of the YOLO-v4 network for ablation experiments, including CBAM (convolutional block attention module), SENet (squeeze-and-excitation networks) and CANet (coordinate attention networks). The path-aggregation network and feature pyramid are used to extract features from images. Two network models were compared and improved in the experiment, and it is found that adding the dual-channel attention mechanism CBAM before the three YOLO heads of YOLOv4 and in the neck network had better detection performance than the single channel attention mechanism SENet and the coordinated attention mechanism CANet. The experimental results show that when the attention module CBAM and the YOLO-v4 model are integrated, the accuracy of the selected MAFA + WIDER Face dataset reaches the highest value of 93.56%, which is 4.66% higher than that of the original YOLO-v4.
Capacity dependent analysis for functional online learning algorithms

This article provides convergence analysis of online stochastic gradient descent algorithms for functional linear models. Adopting the characterizations of the slope function regularity, the kernel space capacity, and the capacity of the sampling process covariance operator, significant improvement on the convergence rates is achieved. Both prediction problems and estimation problems are studied, where we show that capacity assumption can alleviate the saturation of the convergence rate as the regularity of the target function increases. We show that with properly selected kernel, capacity assumptions can fully compensate for the regularity assumptions for prediction problems (but not for estimation problems). This demonstrates the significant difference between the prediction problems and the estimation problems in functional data analysis.
Commonsense knowledge powered heterogeneous graph attention networks for semi-supervised short text classification

In real-world scenarios, considerable human power and expert knowledge are required to label data. Therefore, solving short text classification problems in a semi-supervised manner is a good method. Existing graph-based semi-supervised short text classification models can achieve good classification performance. However, these models do not introduce some prior knowledge related to short texts, such as commonsense knowledge and concepts, from the knowledge bases to alleviate the data sparsity of short texts, which limits the improvement of classification performance. To overcome the above limitations, this paper addresses the problem of semi-supervised short text classification by proposing a novel model called the Commonsense Knowledge-Powered Heterogeneous Graph Attention Network (CSK-HGAT). This model mines the short text-related commonsense knowledge from the definitions corresponding to extracted short text keywords and obtains the short text conceptual information from the existing knowledge bases by conceptualizing the identified short text entities. Next, the embeddings are generated separately for short texts, commonsense knowledge, and concepts. Then, a heterogeneous information network (HIN) containing diverse knowledge is constructed by using the semantic relations among the short texts, commonsense knowledge and the concepts. Finally, this HIN is embedded into the heterogeneous graph attention network model (containing node-level and type-level attentions) to fully utilize the mined prior knowledge (i.e., commonsense knowledge and concepts) to achieve effective semi-supervised short text classification. Experimental results on four benchmark datasets show that the proposed CSK-HGAT model outperforms state-of-the-art semi-supervised short text classification models in terms of classification accuracy.
Combining Reinforcement Learning and Spatial Proximity Exploration for New User and New POI Recommendations

Tourism Recommender Systems (TRSs) are unable to properly suggest new points of interest (POIs) to new users, i.e., to solve the combined new user and new item problem. To address this limitation we introduce a Reinforcement Learning TRS, which is called QEXP, and relies on a POI visits behaviour model mined from logs of POI visits data. This data is combined with general knowledge about the spatial range of tourists' movements in a destination to generate recommendations. QEXP can recommend new POIs possessing the features of POIs experienced by tourists in the past, while favouring the exploration of POIs in the proximity of the target tourist position. We compare QEXP with four state-of-the-art POI RSs and we show that it can successfully tame the new user and new item problems. QEXP can also mitigate the concentration and popularity biases of the compared RSs and can recommend diverse and geographically dispersed POIs.
Performance Evaluation of Machine Learning Techniques for Detecting Cross-Site Scripting Attacks

The entire world shares the digital form of information through the Internet by accessing multiple websites. The link to these websites can be shared by authenticated or unauthenticated sources. Cross-site scripting attacks are the category of cyberattack in which malicious code can be inserted into the webpage code. These attacks can be so severe that they can steal the entire data of the machine or server and can harm the network infrastructure. Ransomware attackers also utilize cross-scripting attacks as the medium for breaking the security policies of the victim’s network. Machine learning techniques are widely employed to detect different types of cyberattacks in the industry. In this research paper, machine learning techniques, Naïve Bayes, Logistic Regression, AdaBoost, XGBoost, and decision tree are used to detect the cross-scripting attacks from the information about scripts. The results prove that the AdaBoost technique gains 97.29% accuracy for detecting cross-scripting attacks and the Naïve Bayes methodology shows minimum accuracy among the employed machine learning methodology. These achieved results form the future directions for building smart extensions for Internet browsers to detect such attacks in advance.
Generating Image Description Using Machine Learning Algorithms

In the contemporary world, captions are one of the most essential tools. There are also integrated programmes that use deep neural network models to create and provide captions for particular photographs. This article compares deep learning models that create captions and explain images translation using machine mechanism, machine learning algorithms and computer vision. The projected work recognize and identify several things in an image and their relationships in order to produce captions. For the purpose of illustrating the suggested experiment, Flickr8k is the dataset, Python3 is the programming language, and ResNet, Xception, and Inception are utilised to create the model. This study also identifies the model that yields the best outcomes on the dataset being used. Natural language processing and machine vision both heavily rely on caption generation. To generate the caption of image Encode and decoder model of LSTM with CNN machine learning algorithm is used for correct caption of Images.
Transforming Payment Processes: A Discussion of AI-Enabled Routing Optimization

The utilization of Artificial Intelligence (AI)-enabled software and hardware is expected to enhance the effectiveness of monetary exchanges, which is the main objective of this research. Since transaction costs are based on the actual amount being transferred, customers feel more at ease making purchases using this approach. This work aims to investigate the role of financial systems in effective corporate administration, with a particular emphasis on gateways as key components of these systems. This study investigates the factors (price, procedures, risk assessments, etc.) that influence the connecting of merchant identities across several channels in a digital payment system. When multiple endpoints are connected to a central hub, they are referred to as a ‘‘terminal.’’ The study shows that utilizing AI-enabled software and hardware can reduce transaction costs and increase consumer confidence in purchasing decisions. This research can potentially improve financial systems for businesses and consumers by identifying the most effective applications of AI in this sector.
SEAL: A Strategy-Proof and Privacy-Preserving UAV Computation Offloading Framework

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads.
A state-of-the-art review of image motion deblurring techniques in precision agriculture

Image motion deblurring is a crucial technology in computer vision that has gained significant attention attracted by its outstanding ability for accurate acquisition of motion image information, processing and intelligent decision making, etc. Motion blur has recently been considered as one of the major challenges for applications of computer vision in precision agriculture. Motion blurred images seriously affect the accuracy of information acquisition in precision agriculture scene image such as testing, tracking, and behavior analysis of animals, recognition of plant phenotype, critical characteristics of pests and diseases, etc. On the other hand, the fast motion and irregular deformation of agriculture livings, and motion of image capture device all introduce great challenges for image motion deblurring. Hence, the demand of more efficient image motion deblurring method is rapidly increasing and developing in the applications with dynamic scene. Up till now, some studies have been carried out to address this challenge, e.g., spatial motion blur, multi-scale blur and other types of blur. This paper starts with categorization of causes of image blur in precision agriculture. Then, it gives detail introduction of general-purpose motion deblurring methods and their the strengthen and weakness. Furthermore, these methods are compared for the specific applications in precision agriculture e.g., detection and tracking of livestock animal, harvest sorting and grading, and plant disease detection and phenotyping identification etc. Finally, future research directions are discussed to push forward the research and application of advancing in precision agriculture image motion deblurring field.
CSIT: Channel Spatial Integrated Transformer for human pose estimation

Abstract Human keypoints detection is different from general detection tasks and requires networks that can learn visual information and anatomical constraints. Since CNN is excellent in extracting texture features of images and transformer can learn the correlation among keypoints well, many CTPNets (CNN+transformer type human pose estimation networks) have emerged. However, these networks are unconcerned with the processing of the features extracted from the CNN and naturally expand only from the channel dimension, ignoring the spatial features in the visual information that are essential for complex detection tasks like pose estimation. So the channel spatial integrated transformer for human pose estimation, termed CSIT, is proposed. The visual information are summarized as texture and spatial information, and a parallel network is used to expand the feature maps in the channel and spatial dimensions to learn texture features and spatial features respectively. In addition, anatomically constrained information is learned by keypoint embeddings. At the end of the network, the 1D vector representation method with more advanced performance and more compatible with transformer's characteristics is used to predict keypoints. Experiments show that CSIT outperforms the mainstream CTPNets on the COCO test‐dev dataset, and also show satisfactory results on the MPII dataset.
Paradigm gaps are associated with weird “distributional semantics” properties

Abstract This study investigates the phenomenon of defectiveness in Russian case and number noun paradigms from the perspective of distributional semantics. We made use of word embeddings, high-dimensional vectors trained from large text corpora, and compared the observed paradigms of nouns that are defective in the genitive plural, as suggested by Zaliznjak (1977) , with the observed paradigms for non-defective nouns. When the embeddings of about 20,000 inflected forms were projected onto a two-dimensional space, clusters of case and number within case were found, suggesting global semantic similarity for words with the same inflectional features. Moreover, defective lexemes were characterized by lower semantic transparency, in that inflected forms of the same lexeme are semantically less similar to each other, and their meanings are also more idiosyncratic. Furthermore, compared to non-defective lexemes, inflected forms from defective lexemes are further away from the idealized average case-number meanings, obtained by averaging over the vectors of all inflected forms of the same case-number combination. As a consequence, the semantics of defective forms are predicted less precisely by a simple model of conceptualization that assumes that the meaning of a given Russian inflected form is approximated well by the sum of pertinent embeddings of the lexeme, case, and number within case. We conclude that the relationship between defectiveness and semantics, at least the kind captured by word embeddings, is stronger than has been anticipated previously.
Healthcare data security and privacy in Data Warehouse architectures

Data Warehouse (DW) is a common term used in the Data Mining process for storing copious amounts of data ready for analysis. Organizations are starting to prioritize Data Warehouses, which are essential for mining their historical datasets. In the healthcare industry, the significance of data warehouses escalates due to the critical nature of managing and processing medical data, which plays a crucial role in facilitating optimal healthcare delivery and in the generation of valuable insights. While clinical data is complex, its timely integration and analysis are crucial to providing excellent patient care. The research looks into providing an efficient data warehousing solution with multiple privacy and security measures integrated by design. Securing the data at all stages: during data input, exploration, pre-processing, selection, analysis, and presentation, is very challenging. This paper presents a holistic perspective focusing on possible data warehouse architectures, data types, data schemes, storage, security and privacy in a healthcare environment. We discuss existing DW architectures dedicated to clinical data and suggest the best DW approaches for clinical data warehousing.
Multimedia Security Using Encryption: A Survey

Considering the current dependency on digital technology in modern society, the protection of multimedia is highly important. Encryption is vital in modern digital communication, ensuring data confidentiality, authentication, integrity, and non-repudiation. Multimedia encryption-based security techniques are becoming increasingly important as they allow for the secure sharing of multimedia content on digital platforms. This survey aims to review the state of secure and privacy-preserving encryption schemes applicable to digital multimedia, such as digital images, digital video, and digital audio. An extensive analysis of the existing cryptography schemes and multimedia encryption algorithms will be conducted to give an extensive overview of the current state of security encryption schemes specifically designed for digital multimedia technology. The survey results will be used to understand better the effectiveness and reliability of secure multimedia encryption schemes and contribute to developing efficient and secure encryption schemes in the future.
Breast cancer diagnosis using Orca predation optimization algorithm

Early detection and classification of breast cancer can be facilitated to initiate the most effective treatment. As the second leading cause of death among women, early breast cancer screening is essential for reducing mortality rates. In this context, Convolutional neural networks (CNNs) are the ideal candidate for increasing the rate of identification and classification of tumours with efficiency, particularly in medical imaging. This research proposes a hybridised CNN with the Orca Predation Optimization Algorithm (OPOA) as a novel classification model for the effective detection of abnormalities in breast cancer diagnosis. Specifically, the OPOA technique is used to determine the optimal hyperparameter values for the hybrid CNN architecture being deployed. As the pretrained CNN model, the suggested model utilizeds a ResNet50 residual network. It merged OPOA with the ResNet50 residual network to construct the OPOA-ResNet-50 Architecture. The experimental validation of the proposed OPOA-ResNet-50 model utilising the datasets of curated breast imaging subset of DDSM (CBIS-DDSM) shown improved classification accuracy of 99.04%, specificity of 98.56%, and sensitivity of 97.78% in comparison to the baseline techniques. The results also revealed that the proposed under mammographic image analysis society (MIAS) OPOA-ResNet-50 model demonstrated superior classification accuracy of 98.64%, specificity of 98.79%, and sensitivity of 98.82% compared to the benchmarked methods. The adopted OPOA algorithm is determined to achieve more optimal hyperparameter values for the ResNet50 architecture than the comparative algorithms Improved Marine Predator Optimization Algorithm (IMPOA), Whale Optimization Algorithm (WOA), Harris hawk’s optimization (HHO), and gravitational search algorithm (GSA).
Quantum dropout: On and over the hardness of quantum approximate optimization algorithm

A combinatorial optimization problem becomes very difficult in situations where the energy landscape is rugged, and the global minimum locates in a narrow region of the configuration space. When using the quantum approximate optimization algorithm (QAOA) to tackle these harder cases, we find that difficulty mainly originates from the QAOA quantum circuit instead of the cost function. To alleviate the issue, we selectively dropout the clauses defining the quantum circuit while keeping the cost function intact. Due to the combinatorial nature of the optimization problems, the dropout of clauses in the circuit does not affect the solution. Our numerical results confirm improvements in QAOA's performance with various types of quantum-dropout implementation.
An enhanced African Vulture Optimization Algorithm for solving the Unmanned Aerial Vehicles path planning problem

In recent years, research on Unmanned Aerial Vehicles has become one of the most interesting topics for industry and academics. UAVs path planning is one of the most important issues in terms of guaranteeing good performance in real-world applications. Its main objective is to determine and ensure an optimal and collision-free trajectory (path) between two positions from a starting point (source) to a destination point (target), while dealing with some requirements (e.g. safety, environment complexity, obstacle avoidance, energy consumption, etc.). In view of this topic's complexity, an efficient path planning algorithm is required. In this paper, we propose an improvement of the meta-heuristic African Vulture Optimization Algorithm (AVOA), named Chaotic Cauchy Opposition-based AVOA (CCO-AVOA), for solving the UAVs path planning problem in a 3D environment. The effectiveness of the proposed CCO-AVOA is validated in different environments with various numbers of waypoints and threats taking into account the fitness value, path cost, height cost, obstacles cost, UAV's angle cost, and execution time metrics. Compared to ten well-known meta-heuristics, simulation results demonstrate the efficiency of the proposed CCO-AVOA approach in most cases by obtaining a short, smooth, least costly, and collision-free path with better stability for UAVs in complex environments.