Reviewerly/exHarmony huggingface dataset:
Recommendation of Mix-and-Match Clothing by Modeling Indirect Personal Compatibility

Fashion recommendation considers both product similarity and compatibility, and has drawn increasing research interest. It is a challenging task because it often needs to use information from different sources, such as visual content or textual descriptions for the prediction of user preferences. In terms of complementary recommendation, existing approaches were dedicated to modeling either product compatibility or users’ personalization in a direct and decoupled manner, yet overlooked additional relations hidden within historical user-product interactions. In this paper, we propose a Normalized indirect Personal Compatibility modeling scheme based on Bayesian Personalized Ranking (NiPC-BPR) for mix-and-match clothing recommendations. We exploit direct and indirect personalization and compatibility relations from the user and product interactions, and effectively integrate various multi-modal data. Extensive experimental results on two benchmark datasets show that our method outperforms other methods by large margins.
Reducing Semantic Confusion: Scene-aware Aggregation Network for Remote Sensing Cross-modal Retrieval

Recently, remote sensing cross-modal retrieval has received incredible attention from researchers. However, the unique nature of remote-sensing images leads to many semantic confusion zones in the semantic space, which greatly affects retrieval performance. We propose a novel scene-aware aggregation network (SWAN) to reduce semantic confusion by improving scene perception capability. In visual representation, a visual multiscale fusion module (VMSF) is presented to fuse visual features with different scales as a visual representation backbone. Meanwhile, a scene fine-grained sensing module (SFGS) is proposed to establish the associations of salient features at different granularity. A scene-aware visual aggregation representation is formed by the visual information generated by these two modules. In textual representation, a textual coarse-grained enhancement module (TCGE) is designed to enhance the semantics of text and to align visual information. Furthermore, as the diversity and differentiation of remote sensing scenes weaken the understanding of scenes, a new metric, namely, scene recall is proposed to measure the perception of scenes by evaluating scene-level retrieval performance, which can also verify the effectiveness of our approach in reducing semantic confusion. By performance comparisons, ablation studies and visualization analysis, we validated the effectiveness and superiority of our approach on two datasets, RSICD and RSITMD. The source code is available at https://github.com/kinshingpoon/SWAN-pytorch.
Edge Enhanced Image Style Transfer via Transformers

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged, and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT (Style Transfer via Transformers) for image style transfer, and an edge loss function that can enhance the content details and avoid generating blurred results due to the excessive rendering of style features. Extensive qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer approaches while alleviating the content leak problem.
Graph Contrastive Learning on Complementary Embedding for Recommendation

Previous works build interest learning via mining deeply on interactions. However, the interactions come incomplete and insufficient to support interest modeling, even bringing severe bias into recommendations. To address the interaction sparsity and the consequent bias challenges, we propose a graph contrastive learning on complementary embedding (GCCE), which introduces negative interests to assist positive interests of interactions for interest modeling. To embed interest, we design a perturbed graph convolution by preventing embedding distribution from bias. Since negative samples are not available in the general scenario of implicit feedback, we elaborate a complementary embedding generation to depict users' negative interests. Finally, we develop a new contrastive task to contrastively learn from the positive and negative interests to promote recommendation. We validate the effectiveness of GCCE on two real datasets, where it outperforms the state-of-the-art models for recommendation.
Smart AI based Eye Gesture Control System

There are many people who are physically disabled or paralyzed due to some accident, because of which they are unable to do their everyday activities such as watching shows, TV, and internet browsing. Even after being paralyzed they can naturally move their eyes easily and make use of it. The main purpose of this research study is to solve the issue of any physically disabled people, who want to work independently on computer system, without relying on any other person for their day to day activities. It is a free and open source system that is available freely, here the webcam captures the face and then the pupil and according to the movement of the user it performs actions.
Decentralizing File Sharing: The Potential of Blockchain and IPFS

This research paper delves into the potential of using blockchain technology and the Interplanetary File System (IPFS) for file sharing. The current state of file sharing is analyzed, and it is found that centralized servers pose many challenges, such as security and privacy issues. The paper then investigates how blockchain and IPFS can provide a decentralized and secure solution for file sharing. The analysis of specific use cases and existing projects such as File Coin, which utilizes IPFS and a blockchain-based marketplace to allow users to rent out unused storage space, is also presented. The paper also addresses the limitations and challenges of using this technology, including scalability and regulatory issues. The research concludes that the combination of blockchain and IPFS has the potential to revolutionize the way digital content is shared and distributed, providing new opportunities for secure and efficient file sharing. The paper also explores other decentralized storage solutions, such as Sia, Storj, and MaidSafe, and how they compare to IPFS and blockchain-based file-sharing solutions. The research also evaluates the potential impact of this technology on various sectors, such as the media, entertainment, and healthcare industries. Overall, this research aims to provide a comprehensive understanding of the topic and its potential impact on the future of file sharing. The advancement of technology has impacted the way information is shared and distributed, with traditional file-sharing methods facing numerous security and privacy challenges. This research paper investigates the potential of using blockchain technology and the Interplanetary File System (IPFS) for file sharing as a means of addressing these challenges. This research paper argues that the combination of blockchain technology and IPFS holds great promise for the future of file sharing.
Smart Home IoT Forensics: Current Status, Challenges, and Future Directions

A ‘‘smart home’’ is a house that offers a number of automated services utilizing Internet of Things (IoT) gadgets that are outfitted with sensors, webcams, and lighting. Employing controls like smart speakers and smartphones these gadgets may be remotely controlled. IoT devices collect and analyze data on mobility, temperature, lighting control, and other elements in a smart home in addition to storing more intricate and varied user data. Because different smart home devices utilize different ways of storing data, it might be difficult to retrieve information that is valuable in forensic investigations. Data gathering from various smart home devices as well as the detection and evaluation of information that may be utilized in digital forensics are therefore essential. In this paper, the authors have focused on the advancements in the domain of IoT Forensics. Moreover, the open issues and challenges are also discussed in this paper.
Multi-Agent Personalized Recommendation System in E-Commerce based on User

As more sectors began to switch from conventional business models to e-commerce in response to the general trend toward mobile Internet use, the scale of e-commerce grew rapidly. There are three types of recommendation systems: hybrid, collaborative, content-based. Content based systems take into consideration the characteristics of the recommended objects. Then, titles in the database that have been classified as “romantic” are selected using a content-based recommendation method. Collaborative filtering systems utilize similarity measures to recommend items that are shared by individuals or objects with similar interests. Users are recommended items based on their preferences. In the recommendation system, collaborative filtering is the most popular and effective suggestion process. However, system performance impact as the amount of time required to locate the target user's closest neighbor across the entire user space increases with the number of users and products in the e-commerce system. The applied and designed Multi-Agent personalized recommendation system in E-commerce can be analyzed using user clustering in the Multi-Agent to E-commerce personalized recommendation system. An implementation strategy for recommendations based on user clustering is shown in this analysis. According to their scores for commodity categories, users are clustered, and only the nearest neighbours in their categories are searched, so that as many nearest neighbors as possible can be searched. The accuracy, recall, and specificity of this analysis are used to calculate its performance. In this analysis the presented method will give better results.
An Effective Partitional Crisp Clustering Method Using Gradient Descent Approach

Enhancing the effectiveness of clustering methods has always been of great interest. Therefore, inspired by the success story of the gradient descent approach in supervised learning in the current research, we proposed an effective clustering method using the gradient descent approach. As a supplementary device for further improvements, we implemented our proposed method using an automatic differentiation library to facilitate the users in applying any differentiable distance functions. We empirically validated and compared the performance of our proposed method with four popular and effective clustering methods from the literature on 11 real-world and 720 synthetic datasets. Our experiments proved that our proposed method is valid, and in the majority of the cases, it is more effective than the competitors.
An Enhanced PSO Algorithm for Scheduling Workflow Tasks in Cloud Computing

This paper proposes an enhanced Particle Swarm Optimization (PSO) algorithm in order to deal with the issue that the time and cost of the PSO algorithm is quite high when scheduling workflow tasks in a cloud computing environment. To reduce particle dimensions and ensure initial particle quality, intensive tasks are combined when scheduling workflow tasks. Next, the particle initialization is optimized to ensure better initial particle quality and reduced search space. Then, a suitable self-adaptive function is integrated to determine the best direction of the particles. The experiments show that the proposed enhanced PSO algorithm has better convergence speed and better performance in the execution of workflow tasks.
Complete coverage planning with clustering method for autonomous mobile robots

Summary Complete coverage planning (CCP) is a task to cover the entire area on the map, according to the job description of the autonomous mobile robot. The most widely used method for CCP in the literature is the grid‐based coverage method. In this method, the problem is processing the partially filled cell as completely filled, which reduces the coverage performance. The ability to use the clustering method, which will be created by considering the characteristics of the environment, was determined as a research question to solve this problem. In this direction, it is aimed to use K‐means++ algorithm, which is a widely used clustering algorithm and segmentation technique. In this context, an offline K‐means++ complete coverage planning (Km++CCP) method, in which the navigable area on the map of the indoor where a mobile robot will navigate is clustered using the K‐means++ algorithm and the centroids can be used as waypoints, is proposed. To test the proposed method, 2 simulations and 36 real‐world experiments were conducted. The indoor coverage ratio of Km++CCP was calculated higher than the grid‐based method in all experiments.
Security Issues in e-Banking

Banking system across the globe is facing challenge due to cyber security threats. This has led financial institutions to rethink and redesign their business models. To get rid of cyber-attacks and security breach, intervention of technology is imperative. Stakeholders in the banking industry are quite worried about upsurge in the rate of cyber-crimes. Generally, cyber-attacks are done through software system running on a computer system in a cyber space. To safeguard software system against cyber-attacks it is utmost to detect entities operating within the cyber space and dangers to application security separated after examining the vulnerabilities and creating defense mechanism to reduce risks of cyber-attacks on software systems. Hence it is pertinent to understand security issues being faced by e-banking so that suitable measures can be taken accordingly. This paper is an attempt to understand different theories related to cyber security and also discusses various security threats to which e-banking is exposed.
Jammer classification with Federated Learning

Jamming signals can jeopardize the operation of GNSS receivers until deying its operation. Given their ubiquity, jamming mitigation and localization techniques are of crucial importance, for which jammer classification is of help. Data-driven models have been proven useful in detecting these threats, while their training using crowdsourced data still poses challenges when it comes to private data sharing. This article investigates the use of federated learning to train jamming signal classifiers locally on each device, with model updates aggregated and averaged at the central server. This allows for privacy-preserving training procedures that do not require centralized data storage or access to client local data. The used framework FedAvg is assessed on a dataset consisting of spectrogram images of simulated interfered GNSS signal. Six different jammer types are effectively classified with comparable results to a fully centralized solution that requires vast amounts of data communication and involves privacy-preserving concerns.
Human Activity Recognition for People with Knee Abnormality Using Surface Electromyography and Knee Angle Sensors

With the onset of technological advancements, biosensors are being effectively used in a variety of contexts, including the diagnosis of diseases, the promotion of their prevention and rehabilitation, the monitoring of patient health, and human activity recognition (HAR). Recently, HAR research results have been obtained and applied in many commercial applications such as fall detection using smart watch sensors, activity classification using smart home sensors. However, previous HAR models utilizing wearable sensors primarily utilized data from healthy individuals, and such models are frequently inaccurate when applied to individuals with medical mobility impairments. In this work, surface electromyography and goniometer sensors were used to categorize various types of rehabilitation activities based on HAR models developed for individuals with knee abnormalities. To achieve the goal of our study, a deep residual network called ResNeXt was proposed to detect human activities with high performance. To investigate the classification performance of deep learning models, a 5-fold cross-validation technique was applied for both training and testing. Based on the results of our study, the combination of features extracted from the goniometer and electromyograph signals resulted in the highest F1-score (93.31%) and the best accuracy (93.52%).
A probabilistic perspective on predictability of solar irradiance using bootstrapped correlograms and ensemble predictability error growth

Recently in "Correlogram, predictability error growth, and bounds of mean square er-ror of solar irradiance forecasts" [Renew. Sustain. Energy Rev. 167 (2022) 112736], a new measure of predictability, applicable to solar irradiance in particular, was proposed. That predictability measure is expressed as one minus the ratio of the smallest attainable and highest tolerable root mean square errors (RMSEs) for a specific forecasting situation. Whereas the smallest attainable RMSE can be derived from the predictability error growth (PEG) of a numerical weather prediction model, the highest tolerable RMSE is computed from the forecasts of a naïve standard of reference. Despite that proposal has pioneered our understanding on the predictability of solar irradiance, answers to some technical challenges remain opaque. One of those technical challenges is the lack of uncertainty quantification of the predictability estimate, which is what this work seeks to resolve. Two conceptions, namely, ensemble PEG and bootstrapped correlograms, are herein introduced to facilitate the probabilistic representations of the smallest attainable and highest tolerable RMSEs, respectively. It is found that the uncertainty in predictability estimates is very small as compared to the range over which predictability varies across climatic conditions and forecast horizons.
Learning Trajectories from Spin-Wave Dynamics

The efficacy of a physical reservoir computer model based on traveling spin waves in a spin-wave delay-line active-ring resonator was demonstrated recently. In the present work, we investigate how this neuromorphic device can be adapted for sensing applications. In this ``reservoir computing for sensing'' framework, we exploit strong coupling of the physical reservoir to its environment to utilize the reservoir as a sensing element. The dynamics of traveling spin waves in delay-line active rings are strongly dependent on the magnetic field and carrier frequency of those spin waves. Treating the spin-wave frequency as an environmental variable, we excite the active ring into different dynamical states by modulating the carrier frequency of a drive signal of microwave pulses injected into the ring. Training a linear regression on the time-multiplexed output from the ring allows the periodic amplitude patterns of the spin waves to be mapped reproducibly onto two-dimensional trajectories, representing periodic ``behavioral'' targets. Our work demonstrates the versatility of a magnonic resonator as a multipurpose computing and sensing device.
Medical image mis-segmentation region refinement framework based on dynamic graph convolution

In medical image segmentation tasks, it is hard for traditional Convolutional Neural Network (CNN) to capture essential information such as spatial structure and global contextual semantic features since it suffers from a limited receptive field. The deficiency weakens the CNN segmentation performance in the lesion boundary regions. To handle the aforementioned problems, a medical image mis-segmentation region refinement framework based on dynamic graph convolution is proposed to refine the boundary and under-segmentation regions. The proposed framework first employs a lightweight dual-path network to detect the boundaries and nearby regions, which can further obtain potentially misclassified pixels from the coarse segmentation results of the CNN. Then, we construct the pixels into the appropriate graphs by CNN-extracted features. Finally, we design a dynamic residual graph convolutional network to reclassify the graph nodes and generate the final refinement results. We chose UNet and its eight representative improved networks as the basic networks and tested them on the COVID, DSB, and BUSI datasets. Experiments demonstrated that the average Dice of our framework is improved by 1.79%, 2.29%, and 2.24%, the average IoU is improved by 2.30%, 3.53%, and 2.39%, and the Se is improved by 5.08%, 4.78%, and 5.31% respectively. The experimental results prove that the proposed framework has the refinement capability to remarkably strengthen the segmentation result of the basic network. Furthermore, the framework has the advantage of high portability and usability, which can be inserted into the end of mainstream medical image segmentation networks as a plug-and-play enhancement block.
An efficient discrete rat swarm optimizer for global optimization and feature selection in chemoinformatics

Machine learning algorithms need feature selection (FS) as a significant step towards filtering unnecessary data. This paper proposes a wrapper FS approach that combines the rat swarm optimization (RSO) algorithm with genetic operators to avoid local optimal. In the proposed approach the transfer functions (TFs) are added to balance local and global search by converting a continuous search space into a discrete space. Eight variants of the bmRSO algorithm were applied for classification purposes using a support vector machine (SVM) to increase accuracy and decrease the number of features over several chemical datasets. The eight bmRSO proposed methods and the original RSO were evaluated using the CEC'20 test suite and twelve datasets (eight chemical and four toxicity effect datasets) to verify their performance in complex optimization problems and FS over real datasets, respectively. Moreover, the binary versions of other stable metaheuristic algorithms such as Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Farmland Fertility Algorithm (FFA), Artificial Gorilla Troops Optimizer (GTO), African Vultures Optimization Algorithm (AVOA), Runge Kutta Optimizer's (RUN), and Slime Mould Algorithm (SMA) were used to compare the results obtained by the best variant of the bmRSO. Eventually, the experimental results have revealed that in most of the tests, the proposed bmRSO1 has achieved efficient search results with higher convergence speeds without increasing additional computational efforts. From the twelve datasets, the MAO dataset reached the highest results compared with other datasets, so the proposed method, bmRSO1-SVM, achieved an accuracy of 98.201% and a 20.001 number of selected features.
Comparison Study between PoW and PoS Blockchains for Unmanned Aircraft System Traffic Management

This paper presents a novel consensus algorithm for Unmanned Aircraft System Traffic Management (UTM). Blockchain as a highly secured consensus protocol was used for airspace allocation. A smart contract in charge of airspace allocation was designed based on the Ethereum blockchain. Hence, the action of sharding could be done to reduce the computational complexity in the swarm flight zone. According to the sharded flight zone, a decentralized voting platform was developed using two main approaches, namely Proof of Work (PoW) and Proof of Stake (PoS). A swarm flight zone was arranged using a number of 1000 UAS-S4 at different locations and heading angles. The performance of the designed decentralized consensus system was evaluated in terms of error rate and validation time. Although the PoS approach was faster than the PoW, the comparison study showed that the PoW approach was better than the PoS in terms of error rate.