Reviewerly/exHarmony huggingface dataset:
DARI-Mark: Deep Learning and Attention Network for Robust Image Watermarking

At present, deep learning has achieved excellent achievements in image processing and computer vision and is widely used in the field of watermarking. Attention mechanism, as the research hot spot of deep learning, has not yet been applied in the field of watermarking. In this paper, we propose a deep learning and attention network for robust image watermarking (DARI-Mark). The framework includes four parts: an attention network, a watermark embedding network, a watermark extraction network, and an attack layer. The attention network used in this paper is the channel and spatial attention network, which calculates attention weights along two dimensions, channel and spatial, respectively, assigns different weights to pixels in different channels at different positions and is applied in the watermark embedding and watermark extraction stages. Through end-to-end training, the attention network can locate nonsignificant areas that are insensitive to the human eye and assign greater weights during watermark embedding, and the watermark embedding network selects this region to embed the watermark and improve the imperceptibility. In watermark extraction, by setting the loss function, larger weights can be assigned to watermark-containing features and small weights to noisy signals, so that the watermark extraction network focuses on features about the watermark and suppresses noisy signals in the attacked image to improve robustness. To avoid the phenomenon of gradient disappearance or explosion when the network is deep, both the embedding network and the extraction network have added residual modules. Experiments show that DARI-Mark can embed the watermark without affecting human subjective perception and that it has good robustness. Compared with other state-of-the-art watermarking methods, the proposed framework is more robust to JPEG compression, sharpening, cropping, and noise attacks.
HCBiLSTM: A hybrid model for predicting heart disease using CNN and BiLSTM algorithms

In human existence, healthcare is an inevitable obligation. Heart disease is a broad term that encompasses a variety of illnesses that affect the heart & veins. The early methods for assessing cardiovascular diseases aided in deciding on the progressions that should have occurred in high-risk individuals, therefore lowering their risks. The primary objective is to save human lives by detecting irregularities in heart conditions, which will be accomplished by the identification & processing of raw data derived from cardiac information. The purpose of this article is to construct a hybrid model based on deep learning methods for predicting whether or not a person has heart disease and providing awareness or diagnosis based on that prediction using CNN & Bi-LSTM. We solve the issue of missing data as well as the issue of imbalanced data in the publicly accessible Heart disease Cleveland UCI dataset using data processing methods. We have used an extra tree classifier for feature selection, and CNN-BiLSTM for classification. Experiments have been done on the Heart disease Cleveland UCI dataset which is collected from the Kaggle. The performance of the diagnosis model is obtained by using approaches like classification, accuracy, precision, recall & f1-score. Here a study is done based on the proposed technique and their performance analysis to propose an accurate model of predicting heart disease. Numerous existing methods were examined, with the hybrid model achieving an accuracy level of 96.66%.
Overcoming weaknesses of density peak clustering using a data-dependent similarity measure

Density Peak Clustering (DPC) is a popular state-of-the-art clustering algorithm, which requires pairwise (dis)similarity of data objects to detect arbitrary shaped clusters. While it is shown to perform well for many applications, DPC remains: (i) not robust for datasets with clusters having different densities, and (ii) sensitive to the change in the units/scales used to represent data. These drawbacks are mainly due to the use of the data-independent similarity measure based on the Euclidean distance. In this paper, we address these issues by proposing an effective data-dependent similarity measure based on Probability Mass, which we call MP-Similarity, and by incorporating it in DPC to create MP-DPC, a data-dependent variant of DPC. We evaluate and compare MP-DPC against diverse baselines using several clustering metrics and datasets. Our experiments demonstrate that: (a) MP-DPC produces better clustering results than DPC using the Euclidean distance and existing data-dependent similarity measures; (b) MP-Similarity coupled with Shared-Nearest-Neighbor-based density metric in DPC further enhances the quality of clustering results; and (c) unlike DPC with existing data-independent and data-dependent similarity measures, MP-DPC is robust to the change in the units/scales used to represent data. Our findings suggest that MP-Similarity provides a more viable solution for DPC in datasets with unknown distribution or units/scales of features, which is often the case in many real-world applications.
Low-cost multiclass-image encryption based on compressive sensing and chaotic system

In this paper, a compressible and low-cost cryptography system is investigated based a new memristor system for Internet of multimedia things to acquire and transmit multiclass-image signals, such as grayscale, color and 3D images (Stereolithography format). Compression sensing is first introduced to solve the limitation of computing and storage resources faced by detectors when sampling data from different scenarios. The batch data after sampling are fused into 3D cubes, and the confusion–diffusion framework is used to encrypt them in 3D space to ensure the privacy of information during transmission. For confusion procedure, a novel technique, like 3D jigsaw puzzles, is designed to improve the efficiency of scrambling. Furthermore, a new nonlinear primitive is proposed based on three-input majority gate , and the non-sequential diffusion path generated by depth-first search is used for diffusion operation. The experimental results and extensive security analysis show that the proposed encryption technique combined with the implementation of 3D space significantly improves the security and efficiency of batch image processing.
The Text Mining Analysis Approach for Electronic Information and Transaction (ITE) Implementation Based on Sentiment in the Social Media

Currently, the establishment of legal act in cyberspace is the inevitable consequence of globalization to ensure the legal certainty of the people who interacting within internet. With the number of legal infringement in cyberspace increasing each day, the needs of internet regulation have becoming a world concern. Indonesia is one of top ranking country with high number of internet users, which want to safeguard the public interest with a comprehensive law through "Undang-Undang Informasi dan Transaksi Electronik" (UU ITE). Therefore, the current use of this act is retrieving the diverse feedback and responses from citizens in Indonesia since it was signed or after the amendment for several aspects. The availability of social media networking enables them to react to the subject concerning legal issues within internet. Thus, this research work used text mining technique to find out the perceived issues, challenges, and benefits of ITE implementation from citizens' opinions, comments, and perceptions in social media of Twitter. The result of this study grouped into two main topics; legal consequences and precedence.
HateCircle and Unsupervised Hate Speech Detection Incorporating Emotion and Contextual Semantics

The explosive growth of social media has fueled an extensive increase in online freedom of speech. The worldwide platform of human voice creates possibilities to assail other users without facing any consequences, and flout social etiquettes, resulting in an inevitable increase of hate speech. Nowadays, English hate speech detection is a popular research area, but the prevalence of implicit hate content in regional languages desire effective language-independent models. The proposed research is the first unsupervised Hindi and Bengali hate content detection framework consisting of three significant concepts: HateCircle, hate tweet classification, and code-switch data preparation algorithms. The novel HateCircle method is proposed to detect hate orientation for each term by co-occurrence patterns of words, contextual semantics, and emotion analysis. The efficient multiclass hate tweet classification algorithm is proposed with parts of speech tagging, Euclidean distance, and the Geometric median methods. The detection of hate content is more efficient in the native script compared to the Roman script, so the transliteration algorithm is also proposed for code-switch data preparation. The experimentation evaluates the combination of various lexicons with our enriched hate lexicon that achieves a maximum of 0.74 F1-score for the Hindi and 0.88 F1-score for the Bengali datasets. The novel HateCircle and hate tweet detection framework evaluates with our proposed parts of speech tagging and Geometric median detection methods. Results reveal that HateCircle and hate tweet detection framework also achieves a maximum of 0.73 accuracy for the Hindi and 0.78 accuracy for the Bengali dataset. The experiment results signify that contextual semantic hate speech detection research with a language-independency feature offsets the growth of implicit abusive text in social media.
Software engineering research on the Robot Operating System: A systematic mapping study

The Robot Operating System (ROS) has become the de-facto standard framework for robotics software, and a great part of commercial robots is expected to have at least one ROS package on board in the coming years. For good quality, robotics software should rely on strong software engineering principles. In this paper, we perform a systematic mapping study on several works in software engineering on ROS, published at the top software engineering and robotics venues. Our goal is to analyze and evaluate such state-of-the-art regarding its relevance to the robotics software industry. The potentially-relevant studies are subject to a rigorously defined selection process. This results in a set of 63 primary studies on software engineering research on ROS. Those primary studies are then qualitatively analyzed according to a rigorously-defined classification framework. The results are of interest to both researchers and practitioners: (i) we provide an up-to-date overview of the state of the art on software engineering research on ROS and its potential for industrial adoption, (ii) a broad discussion of the research area as a whole, and (iii) point out routes of action for a better alignment between research and industry.
Image Desnowing via Deep Invertible Separation

Images taken on snowy days often suffer from severe negative visual effects caused by snowflakes. The task of removing snowflakes from a snowy image is known as image desnowing, which is challenging as image details are easily mistakenly treated and thus may be significantly lost during snowflake removal. Leveraging invertible neural networks (INNs), this paper presents a deep learning-based method for single image desnowing, which can remove snowflakes accurately while preserving image details well. Interpreting desnowing as an image decomposition problem, we propose an INN composed of two asymmetric interactive paths for predicting a latent image and a snowflake layer respectively. Such an INN is able to progressively refine the features of both latent images and snowflake layers for disentanglement, while retaining all information possibly relevant to latent image reconstruction. In addition, an attentive coupling layer supervised by snowflake masks is introduced to enhance feature dismantlement and a coupling-in-coupling structure is developed for further improvement. Extensive experiments show that, the proposed method outperforms existing ones on three benchmark datasets of synthetic and real-world images, and meanwhile it also shows advantages in terms of model size and computational efficiency.
Text Sentiment Analysis Based on a New Hybrid Network Model

The research of text sentiment analysis based on deep learning is increasingly rich, but the current models still have different degrees of deviation in understanding of semantic information. In order to reduce the loss of semantic information and improve the prediction accuracy as much as possible, the paper creatively combines the doc2vec model with the deep learning model and attention mechanism and proposes a new hybrid sentiment analysis model based on the doc2vec + CNN + BiLSTM + Attention. The new hybrid model effectively exploits the structural features of each part. In the model, the understanding of the overall semantic information of the sentence is enhanced through the paragraph vector pretrained by the doc2vec structure which can effectively reduce the loss of semantic information. The local features of the text are extracted through the CNN structure. The context information interaction is completed through the bidirectional cycle structure of the BiLSTM. The performance is improved by allocating weight and resources to the text information of different importance through the attention mechanism. The new model was built based on Keras framework, and performance comparison experiments and analysis were performed on the IMDB dataset and the DailyDialog dataset. The results have shown that the accuracy of the new model on the two datasets is 91.3% and 93.3%, respectively, and the loss rate is 22.1% and 19.9%, respectively. The accuracy on the IMDB datasets is 1.0% and 0.5% higher than that of the CNN-BiLSTM-Attention model and ATT-MCNN-BGRUM model in the references. Comprehensive comparison has shown the overall performance is improved, and the new model is effective.
Software Security Measurements: A Survey

Security metrics for software products give a quantifiable assessment of a software system’s trustworthiness. Metrics can also help detect vulnerabilities in systems, prioritize corrective actions, and raise the level of information security within the business. There is a lack of studies that identify measurements, metrics, and internal design properties used to assess software security. Therefore, this paper aims to survey security measurements used to assess and predict security vulnerabilities. We identified the internal design properties that were used to measure software security based on the internal structure of the software. We also identified the security metrics used in the studies we examined. We discussed how software refactoring had been used to improve software security. We observed that a software system with low coupling, low complexity, and high cohesion is more secure and vice versa. Current research directions have been identified and discussed.
BiRSwinT: Bilinear full-scale residual swin-transformer for fine-grained driver behavior recognition

Driver behavior recognition has attracted extensive attention recently. Numerous methods have been developed on the basis of various deep neural networks. However, the existing models still suffer from various challenges in solving the downstream fine-grained recognition task, because many driver behaviors share similar global shape / appearances, and can only be discriminated by subtle but meaningful actions of local body parts. To remedy this emerging issue of fine-grained driver behavior recognition, we present a bilinear full-scale residual Swin-Transformer network (BiRSwinT) to learn and fuse the global shape/appearance and the local discriminative cues of driver actions. Specifically, a transformer-based dual-stream structure is designed, which contains two parallel branches: a global representation branch and a local residual branch. To learn the multi-scale local cues implied in driver actions, a set of full-scale skip connections are introduced in the local residual branch. Then a bilinear fusion with multi-step training strategy is employed to leverage and combine the global representation and local detail features of driver actions. The proposed method has been validated on the AUC V1 and V2 datasets, and achieved the average Top-1 accuracy of 93.235% and 92.253%, respectively. Comparison experiments show that the proposed method has superior performance when compared to the state-of-the-art methods. The generalization experiments on the State-Farm dataset also demonstrate the superiority of the proposed framework and strategy. The source code is available at https://github.com/shadow2469/BiRSwinT.git.
Generating knowledge aware explanation for natural language inference

Natural language inference (NLI) is an increasingly important task of natural language processing, and the explainable NLI generates natural language explanations (NLEs) in addition to label prediction, to make NLI explainable and acceptable. However, NLEs generated by current models often present problems that disobey of commonsense or lack of informativeness. In this paper, we propose a knowledge enhanced explainable NLI framework (KxNLI) by leveraging Knowledge Graph (KG) to address these problems. The subgraphs from KG are constructed based on the concept set of the input sequence. Contextual embedding of input and the graph embedding of subgraphs, is used to guide the NLE generation by using a copy mechanism. Furthermore, the generated NLEs are used to augment the original data. Experimental results show that the performance of KxNLI can achieve state-of-the-art (SOTA) results on the SNLI dataset when the pretrained model is fine-tuned on the augmented data. Besides, the proposed mechanism of knowledge enhancement and rationales utilization can achieve ideal performance on vanilla seq2seq model, and obtain better transfer ability when transferred to the MultiNLI dataset. In order to comprehensively evaluate generated NLEs, we design two metrics from the perspectives of the accuracy and informativeness, to measure the quality of NLEs, respectively. The results show that KxNLI can provide high quality NLEs while making accurate prediction.
Hierarchical and Progressive Image Matting

Most matting research resorts to advanced semantics to achieve high-quality alpha mattes, and a direct low-level features combination is usually explored to complement alpha details. However, we argue that appearance-agnostic integration can only provide biased foreground (FG) details and that alpha mattes require different-level feature aggregation for better pixel-wise opacity perception. In this article, we propose an end-to-end hierarchical and progressive attention matting network (HAttMatting++), which can better predict the opacity of the FG from single RGB images without additional input. Specifically, we utilize channel-wise attention (CA) to distill pyramidal features and employ spatial attention (SA) at different levels to filter appearance cues. This progressive attention mechanism can estimate alpha mattes from adaptive semantics and semantics-indicated boundaries. We also introduce a hybrid loss function fusing structural similarity, mean square error, adversarial loss, and sentry supervision to guide the network to further improve the overall FG structure. In addition, we construct a large-scale and challenging image matting dataset comprised of 59,000 training images and 1,000 test images (a total of 646 distinct FG alpha mattes), which can further improve the robustness of our hierarchical and progressive aggregation model. Extensive experiments demonstrate that the proposed HAttMatting++ can capture sophisticated FG structures and achieve state-of-the-art performance with single RGB images as input.
AI-Based Techniques for Ad Click Fraud Detection and Prevention: Review and Research Directions

Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known as click fraud. Traffic statistics for online advertisements are artificially inflated in click fraud. Typical pay-per-click advertisements charge a fee for each click, assuming that a potential customer was drawn to the ad. Click fraud attackers create the illusion that a significant number of possible customers have clicked on an advertiser’s link by an automated script, a computer program, or a human. Nevertheless, advertisers are unlikely to profit from these clicks. Fraudulent clicks may be involved to boost the revenues of an ad hosting site or to spoil an advertiser’s budget. Several notable attempts to detect and prevent this form of fraud have been undertaken. This study examined all methods developed and published in the previous 10 years that primarily used artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the detection and prevention of click fraud. Features that served as input to train models for classifying ad clicks as benign or fraudulent, as well as those that were deemed obvious and with critical evidence of click fraud, were identified, and investigated. Corresponding insights and recommendations regarding click fraud detection using AI approaches were provided.
Adaptive Latent Graph Representation Learning for Image-Text Matching

Image-text matching is a challenging task due to the modality gap. Many recent methods focus on modeling entity relationships to learn a common embedding space of image and text. However, these methods suffer from distractions of entity relationships such as irrelevant visual regions in an image and noisy textual words in a text. In this paper, we propose an adaptive latent graph representation learning method to reduce the distractions of entity relationships for image-text matching. Specifically, we use an improved graph variational autoencoder to separate the distracting factors and latent factor of relationships and jointly learn latent textual graph representations, latent visual graph representations, and a visual-textual graph embedding space. We also introduce an adaptive cross-attention mechanism to perform feature attending on the latent graph representations across images and texts, thus further narrowing the modality gap to boost the matching performance. Extensive experiments on two public datasets, Flickr30K and COCO, show the effectiveness of our method.
Blockchain Changing the Outlook of the Sustainable Food Supply Chain to Achieve Net Zero?

The food supply chain (FSC), being a complex network, faces major issues such as traceability, food security, safety and sustainability. Blockchain technology (BLCT) is regarded as an innovative technology that can transform FSC by means of its traceable, irrevocable, tamperproof network. BLCT being a new technology, little work has been carried out on the FSC domain. The purpose of the study is to examine the most recent trends, benefits, challenges, and application of BLCT in the FSC and explore the comprehensive adoption and application of BLCT, stating how it helps to achieve a triple bottom line (TBL) and net zero in the supply chain. The methodology used in this article is a systematic literature review (SLR) comprising 55 papers spanning the years 2018 to 2022. The findings of the study state that BLCT helps to achieve food safety, security, and traceability and increases the performance of the FSC. It also contributes to achieving the TBL of sustainability which can further help to achieve net zero. Based on this work’s insight and observations, practitioners and academics can better understand how companies can implement BLCT and achieve TBL benefits in the FSC, which could eventually provide a path to achieving net zero.