Reviewerly/exHarmony huggingface dataset:
A blockchain-based signature exchange protocol for metaverse

Electronic Commodity Trading has a pivotal role in our economic activities. In the literature, there is a classic problem called “Fair Exchange” which is fundamental to electronic commodity trading. It means the participants can receive the exchange item of the counterparty as they have agreed on before. Since the ownership of items online is often authorized by digital signatures, we focus on the problem of the fair exchange of digital signatures in this paper. As the concept of the Metaverse becomes popular recently, what we are concerned about is how the fair exchange will evolve in a metaverse. It is said that the metaverse is a vision of how the next generation of the internet will operate and the research for the fair exchange problem in it is essential for a wide range of applications like trading a (Non-fungible-token) NFT. Many cryptographic fair exchange protocols are proposed to deal with such a problem. However, most of them should rely on a Trusted Third Party (TTP) and the security is restricted by the TTP. The way the metaverse will work is still being defined. But it will probably provide users with a decentralized environment and a fair exchange protocol relying on a TTP seems unsuitable for it. Recently, researchers present some blockchain-based fair exchange protocols to get rid of or reduce the power of a TTP. But these blockchain-based protocols face new challenges. Firstly, cryptography pairing operations are often contained in such a TTP-free protocol and cost too much for the blockchain. Secondly, the player may hang for an unacceptably long time of the latency of the blockchain network. Thirdly, it is difficult to protect the privacy of the signatures while making the whole protocol verifiable at the same time. To bridge these research gaps, this paper presents a novel signature exchange protocol called DFSE which is decentralized, verifiable, efficient and autonomous. To give a real-world evaluation, we perform the experiment on the live test network of Ethereum and the results show that our protocol is feasible.
A Robust Medical Image Watermarking Scheme Based on Nature-Inspired Optimization for Telemedicine Applications

Medical images and patient information are routinely transmitted to a remote radiologist to assist in diagnosis. It is critical in e-healthcare systems to ensure that data are accurately transmitted. Medical images of a person’s body can be used against them in many ways, including by transmitting them. Copyright and intellectual property laws prohibit the unauthorized use of medical images. Digital watermarking is used to prove the authenticity of the medical images before diagnosis. In this paper, we proposed a hybrid watermarking scheme using the Slantlet transform, randomized-singular value decomposition, and optimization techniques inspired by nature (Firefly algorithm). The watermark image is encrypted using the XOR encryption technique. Extensive testing reveals that our innovative approach outperforms the existing methods based on the NC, SSIM, and PSNR. The SSIM and NC values of watermarked image and extracted watermark are close to or equal to 1 at a scaling factor of 0.06, and the PSNR of the proposed scheme lies between 58 dB and 59 dB, which shows the better performance of the scheme.
Hybrid recommendation by incorporating the sentiment of product reviews

Hybrid recommender systems utilize advanced algorithms capable of learning heterogeneous sources of data and generating personalized recommendations for users. The data can range from user preferences (e.g., ratings or reviews) to item content (e.g., description or category). Prior studies in the field of recommender systems have primarily relied on ”ratings” as the user feedback, when building user profiles or evaluating the quality of the recommendation. While ratings are informative, they may still fail to represent a comprehensive picture of actual user preferences. In contrast, there are other types of feedback data that differently or complementarily represent users and their preferences, including the reviews and the sentiments encapsulated within them. Such data can reveal important parts of a user’s profile that are not necessarily correlated with user ratings, and hence, they potentially reflect a different side of the user’s profile. In this paper, we propose a novel form of hybrid recommender system, capable of analyzing the reviews and extracting their sentiments that are incorporated into the recommendation process. We used advanced algorithms to generate recommendations for users capable of incorporating additional data, such as the review sentiment. We conducted analyses and showed that sentiments of user reviews are not always highly correlated with the ratings (e.g., in music domain). This might mean that sentiment can be indicative of a different aspect of user preferences and can be used as an alternative signal of user feedback. Hence, we have used both ratings and sentiments of reviews when evaluating our proposed hybrid recommender system. We selected two common datasets for the evaluation, Amazon Digital Music and Amazon Video Games, and showed the superior performance of the proposed hybrid recommender system compared to different baselines. The comparison were made in two evaluation scenarios, namely, when the ratings were considered the user feedback and when sentiments of the review were considered the user feedback.
Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.
Grey Wolf Optimizer for Reducing Communication Cost of Federated Learning

Federated Learning (FL) is a type of Machine Learning (ML) technique in which only learned models are stored on a server to sustain data security. The approach does not gather server-side data but rather directly shares only the models from scattered clients. Due to the fact that clients of FL frequently have restricted connection bandwidth, it is necessary to optimize the communication between servers and clients. FL clients frequently interact through Wi-Fi and must operate in uncertain network situations. Nevertheless, the enormous number of weights transmitted and received by existing FL aggregation techniques dramatically degrade the accuracy in unstable network situations. We propose a federated GWO (FedGWO) algorithm to reduce data communications. The proposed approach improves the performance under unstable network conditions by transferring score principles rather than all client models' weights. We achieve a 13.55% average improvement in the global model's accuracy while decreasing the data capacity required for network communication. Moreover, we show that FedGWO achieves a 5% reduction in accuracy loss compared to FedAvg and Federated Particle Swarm Optimization (FedPSO) methods when tested on unstable networks.
A Hybrid Approach With GAN and DP for Privacy Preservation of IIoT Data

There are emerging trends to use the Industrial Internet of Things (IIoT) in manufacturing and related industries. Machine Learning (ML) techniques are widely used to interpret the collected IoT data for improving the company’s operational excellence and predictive maintenance. In general, ML applications require high computational resource allocation and expertise. Manufacturing companies usually transfer their IIoT data to an ML-enabled third party or a cloud system. ML applications need decrypted data to perform ML tasks efficiently. Therefore, the third parties may have unacceptable access rights during the data processing to the content of IIoT data that contains a portrait of the production process. IIoT data may include hidden sensitive features, creating information leakage for the companies. All these concerns prevent companies from sharing their IIoT data with third parties. This paper proposes a novel method based on the hybrid usage of Generative Adversarial Networks (GAN) and Differential Privacy (DP) to preserve sensitive data in IIoT operations. We aim to sustain IIoT data privacy with minimal accuracy loss without adding high additional computational costs to the overall data processing scheme. We demonstrate the efficiency of our approach with publicly available data sets and a realistic IIoT data set collected from a confectionery production process. We employed well-known privacy six assessment metrics from the literature and measured the efficiency of the proposed technique. We showed, with the help of experiments, that the proposed method preserves the privacy of the data while keeping the Linear Regression (LR) algorithms stable in terms of the R-Squared accuracy metric. The model also ensures privacy protection for hidden sensitive data. In this way, the method prevents the production of hidden sensitive data from the sub-feature sets.
Achieving domain generalization for underwater object detection by domain mixup and contrastive learning

The performance of existing underwater object detection methods severely degrades when they face the domain shift caused by complicated underwater environments. Due to the limited domain diversity in collected data, deep detectors easily memorize a few seen domains, which leads to low generalization ability. There are two common ideas to improve the domain generalization performance. First, it can be inferred that the detector trained on as many domains as possible is domain-invariant. Second, their hidden features should be equivalent because the images with the same semantic content are in different domains. This paper further excavates these two ideas and proposes a domain generalization framework that learns how to generalize across domains from Domain Mixup and Contrastive Learning (DMCL). First, based on the formation of underwater images, an image in one kind of underwater environment is the linear transformation of another underwater environment. Therefore, a style transfer model, which outputs a linear transformation matrix instead of the whole image, is proposed to transform images from one source domain to another, enriching the domain diversity of the training data. Second, the Mixup operation interpolates different domains on the feature level, sampling new domains on the domain manifold. Third, a contrastive loss is selectively applied to features from different domains to force the model to learn domain-invariant features but retain the discriminative capacity. With our method, detectors will be robust to domain shift. Also, a domain generalization benchmark S-UODAC2020 for detection is set up to measure the performance of our method. Comprehensive experiments on S-UODAC2020 and two object recognition benchmarks (PACS and VLCS) demonstrate that the proposed method is able to learn domain-invariant representations and outperforms other domain generalization methods. The code is available inhttps://github.com/mousecpn/DMC-Domain-Generalization-for-Underwater-Object-Detection.git
A systematic literature review on phishing website detection techniques

Phishing is a fraud attempt in which an attacker acts as a trusted person or entity to obtain sensitive information from an internet user. In this Systematic Literature Survey (SLR), different phishing detection approaches, namely Lists Based, Visual Similarity, Heuristic, Machine Learning, and Deep Learning based techniques, are studied and compared. For this purpose, several algorithms, data sets, and techniques for phishing website detection are revealed with the proposed research questions. A systematic Literature survey was conducted on 80 scientific papers published in the last five years in research journals, conferences, leading workshops, the thesis of researchers, book chapters, and from high-rank websites. The work carried out in this study is an update in the previous systematic literature surveys with more focus on the latest trends in phishing detection techniques. This study enhances readers' understanding of different types of phishing website detection techniques, the data sets used, and the comparative performance of algorithms used. Machine Learning techniques have been applied the most, i.e., 57 as per studies, according to the SLR. In addition, the survey revealed that while gathering the data sets, researchers primarily accessed two sources: 53 studies accessed the PhishTank website (53 for the phishing data set) and 29 studies used Alexa's website for downloading legitimate data sets. Also, as per the literature survey, most studies used Machine Learning techniques; 31 used Random Forest Classifier. Finally, as per different studies, Convolution Neural Network (CNN) achieved the highest Accuracy, 99.98%, for detecting phishing websites.
Reducing bi-level feature redundancy for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) deals with the problem of transferring knowledge from a labeled source domain to an unlabeled target domain when the two domains have distinct data distributions. Therefore, the purpose of domain adaptation is to mitigate the distribution divergence between the two domains. Many existing UDA methods only use the traditional batch normalization layer, but this may lead to a large number of feature redundancy and lead performance degradation. In this paper, we introduce a novel deep learning paradigm called feature redundancy in UDA to enhance adaptation ability. Specifically, we first show that feature redundancy also exists on unsupervised domain adaptation (UDA), which has been ignored by most previous efforts. We utilize feature similarity as a metric to measure feature redundancy and then analyze the relationship between uniform feature spectrum and minimal feature similarity. Based on this relationship, we intend to reduce cross-domain feature redundancy for UDA by making the distribution of feature spectrum uniforms in a bi-level way. For the first level, we propose a cross-domain batch normalization with the whitening module (xBN) to ensure compact domain-specific features and learn domain-invariant features at the same time. With the domain-specific features from the first level that paves a way, on the second level, we suggest an alternative orthogonal regularizer (OR) that can make the distribution of the feature spectrum more uniform, thus domain-invariant feature redundancy is mitigated. Such a bi-level mechanism greatly reduces the feature redundancy for UDA. To evaluate the efficacy of the proposed bi-level mechanism, we plug those two novel modules (i.e., xBN and OR) into convolutional neural networks (CNNs) to form our UDA model and also conduct the corresponding empirical evaluations on five cross-domain object recognition benchmarks including both classical and large-scale image datasets. Experimental results show that the proposed UDA model could achieve state-of-the-art performance both in quantity and quality. Our source codes will be released after publication.
Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction

This work suggests an enhanced natural environment animal detection algorithm based on YOLOv5s to address the issues of low detection accuracy and sluggish detection speed when automatically detecting and classifying large animals in natural environments. To increase the detection speed of the model, the algorithm first enhances the SPP by switching the parallel connection of the original maximum pooling layer for a series connection. It then expands the model’s receptive field using the dataset from this paper to enhance the feature fusion network by stacking the feature pyramid network structure as a whole; secondly, it introduces the GSConv module, which combines standard convolution, depth-separable convolution, and hybrid channels to reduce network parameters and computation, making the model lightweight and easier to deploy to endpoints. At the same time, GS bottleneck is used to replace the Bottleneck module in C3, which divides the input feature map into two channels and assigns different weights to them. The two channels are combined and connected in accordance with the number of channels, which enhances the model’s ability to express non-linear functions and resolves the gradient disappearance issue. Wildlife images are obtained from the OpenImages public dataset and real-life shots. The experimental results show that the improved YOLOv5s algorithm proposed in this paper reduces the computational effort of the model compared to the original algorithm, while also providing an improvement in both detection accuracy and speed, and it can be well applied to the real-time detection of animals in natural environments.
An enhanced deep learning‐based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders

Abstract Phishing attacks have become one of the powerful sources for cyber criminals to impose various forms of security attacks in which fake website Uniform Resource Locators (URL) are circulated around the Internet community in the form of email, messages etc., in order to deceive users, resulting in the loss of their valuable assets. The phishing URLs are predicted using several blacklist‐based traditional phishing website detection techniques. However, numerous phishing websites are frequently constructed and launched on the Internet over time; these blacklist‐based traditional methods do not accurately predict most phishing websites. In order to effectively identify malicious URLs, an enhanced deep learning‐based phishing detection approach has been proposed by integrating the strength of Variational Autoencoders (VAE) and deep neural networks (DNN). In the proposed framework, the inherent features of a raw URL are automatically extracted by the VAE model by reconstructing the original input URL to enhance phishing URL detection. For experimentation, around 1 lakh URLs were crawled from two publicly available datasets, namely ISCX‐URL‐2016 dataset and Kaggle dataset. The experimental results suggested that the proposed model has reached a maximum accuracy of 97.45% and exhibits a quicker response time of 1.9 s, which is better when compared to all the other experimented models.
YOLO-Animal: An efficient wildlife detection network based on improved YOLOv5

With the continuous development of modern society, the rapid expansion of human civilization squeezes the living space of other organisms, and the extinction of more and more biological species has sounded the alarm for us. Therefore, in order to timely understand the changes of wild animals and other resources in a specific area, and facilitate relevant personnel to formulate effective restoration and protection measures, this paper proposes a wild Animal species recognition network based on deep learning and improved YOLOv5: YOLO-Animal. The application of artificial intelligence and computer vision has covered all aspects of human’s daily life and work. The benchmark YOLOv5 algorithm can quickly and accurately deal with problems associated with images. Through the fusion of weighted Bidirectional Feature Pyramid Network (BiFPN) and Effective Channel Attention (ECA) module, the original YOLOv5s network structure is enhanced, and the detection accuracy of small targets and occluded and fuzzy targets is effectively improved. The YOLO-Animal model outperforms the benchmark YOLOv5s model by 3.2% on mAP and achieves 95.5% accuracy on the test set.
An Empirical Study of Code Smells in Transformer-based Code Generation Techniques

Prior works have developed transformer-based language learning models to automatically generate source code for a task without compilation errors. The datasets used to train these techniques include samples from open source projects which may not be free of security flaws, code smells, and violations of standard coding practices. Therefore, we investigate to what extent code smells are present in the datasets of coding generation techniques and verify whether they leak into the output of these techniques. To conduct this study, we used Pylint and Bandit to detect code smells and security smells in three widely used training sets (CodeXGlue, APPS, and Code Clippy). We observed that Pylint caught 264 code smell types, whereas Bandit located 44 security smell types in these three datasets used for training code generation techniques. By analyzing the output from ten different configurations of the open-source fine-tuned transformer-based GPT-Neo 125M parameters model, we observed that this model leaked the smells and non-standard practices to the generated source code. When analyzing GitHub Copilot's suggestions, a closed source code generation tool, we observed that it contained 18 types of code smells, including substandard coding patterns and 2 security smell types.
An Evaluation of the Efficiency of the Floating Solar Panels in the Western Black Sea and the Razim-Sinoe Lagunar System

The development of novel solar power technologies is regarded as one of the essential solutions to meeting the world’s rising energy demand. Floating photovoltaic panels (FPV) have several advantages over land-based installations, including faster deployment, lower maintenance costs, and increased efficiency. Romania is considered a country with enormous solar energy potential, which is one of the most exploited sectors of the renewable energy sector. With this in mind, the purpose of this work is to assess the energetic potential provided by the sun, taking into account three lakes in Romania’s east and extending to the west of the Black Sea. In this context, we examine the hourly distribution of solar radiation for the year 2021. The solar radiation data were extracted using the ERA5 database, as well as data collected in situ near them. Following this research, we discovered that all of the chosen locations have a high energetic potential and could be used as locations for the exploitation of solar energy, thereby avoiding the use of land that could be used for agricultural purposes in these areas. We also noticed that there are minor differences between the solar radiation values obtained from the ERA5 database and the measured ones.