Reviewerly/exHarmony huggingface dataset:
Active and Semi-Supervised Graph Neural Networks for Graph Classification

Graph classification aims to predict the class labels of graphs and has a wide range of applications in many real-world domains. However, most of existing graph neural networks for graph classification tasks use 90 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of labeled graphs for training and the remaining 10 <inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> for testing, which obviously struggle in solving the problem of the scarcity of labeled graphs in real-world graph classification scenarios. And it is arduous to label a large number of graph examples for training because of the difficulty and resource consumption in the tagging process. Motivated by this, we propose a novel active and semi-supervised graph neural network (ASGNN) framework, which endeavors to complete graph classification tasks with a small number of labeled graph examples and available unlabeled graph examples. In our framework, active learning selects high-uncertain and representative graph examples from the test set and add them to the training set after annotation. Semi-supervised learning is utilized to select the high-confidence unlabeled graph examples containing structural information from the test set, and add them to the training set after pseudo labeling. To improve the generalization performance of the graph classification model, multiple GNNs are trained collaboratively for promoting the expressiveness of each other and increasing the reliability of graph classification results. Overall, the ASGNN framework takes fully use of unlabeled graph examples to reinforce graph classification effectively, and can be applied to any existing supervised graph neural networks for graph classification. Experimental results on benchmark graph datasets demonstrate that the proposed framework yields competitive performance on graph classification tasks with only a small number of labeled graph examples.
3D Human Action Recognition: Through the eyes of researchers

Human Action Recognition (HAR) has remained one of the most challenging tasks in computer vision. With the surge in data-driven methodologies, the depth modality has been effectively leveraged to solve this problem by many researchers over the years, mitigating many of the challenges of the two dimensional modalities. In this work, we investigate the methodologies and applications of 3D HAR primarily using the depth modality through a study of the end-to-end process of the state-of-the-art techniques . Challenges, future directions, availability of the datasets, and critical analysis regarding existing methods constitute a part of this paper. This survey aims to provide the reader with an up-to-date analysis in 3D HAR for those interested in further research.
Industrial internet of things (IIoT) forensics: The forgotten concept in the race towards industry 4.0

Advances in Industrial Internet of Things (IIoT), inter-connectivity, continuous rise of smart ecosystems, and the quintessential need of process automation has not only opened formidable opportunities, but it has also extended the cyber-threat and attack landscape. Predominantly, this has been witnessed even as industries race towards aligning themselves with the on-demand industry 4.0 goals. Notably, it has become apparent that the convergence and amalgamation of Industrial Operational Technology (OT) with Information Technology (IT) brings about sophiscation, IIoT ecosystem complexities, paradigm shift and overall changes in security and digital forensic investigation architectures. Consequently, as the cybersecurity threat landscape also becomes more complicated in this context, with a widened attack surface, emergent and diverse system behaviors, the digital forensic perspective of IIoT is hardly integrated or addressed as many industries race to realize industry 4.0 objectives at the time of writing this paper. It is based on this premise, that this paper puts an argument that, at the time of writing this paper, there still lacks methodologies, standards, processes or maturity models that have a focus on IIoT forensics as the rat-race towards achieving Industry 4.0 persists. Based on these dimensions, the author explores the scope of IIoT forensics and posits the need of exploring and incorporating forensic standards and methods in IIoT. It is the authors’ opinion that the suppositions fronted herewith may provide fundamental building blocks for future IIoT-centered investigative frameworks.
Structure-Texture Aware Network for Low-Light Image Enhancement

Global structure and local detailed texture have different effects on image enhancement tasks. However, most existing works treated these two components in the same way, without fully considering the characteristics of the global structure and local detailed texture. In this work, we propose a structure-texture aware network (STANet) that successfully exploits structure and texture features of low-light images to improve perceptual quality. To construct STANet, a fine-scale contour map guided filter is introduced to decompose the image into a structure component and a texture component. Then, structure-attention and texture-attention subnetworks are designed to fully exploit the characteristics of these two components. Finally, a fusion subnetwork with attention mechanisms is utilized to explore the internal correlations among the global and local features. Furthermore, to optimize the proposed STANet model, we propose a hybrid loss function; specifically, a color loss function is introduced to alleviate color distortion in the enhanced image. Extensive experiments demonstrate that the proposed method improves the visual quality of images; moreover, STANet outperforms most other state-of-the-art approaches.
Learning a Contrast Enhancer for Intensity Correction of Remotely Sensed Images

Low-quality remotely sensed images (RSIs) are not beneficial for the analysis of many activities including agricultural growth, resident migration, forest fire, and etc. Many previous enhancement schemes improve their quality via changing their illumination. However, these approaches often fail in detail and brightness preservation as well as contrast improvement due to that the information from a single image is limited. To address this issue, an enhancement framework, named as global-local enhancement network (GLE-Net), is proposed to correct the intensity via learning extra information from collected training data, including the following three key steps: first, RSIs are decomposed by the discrete wavelet transformation (DWT) method into the low-frequency component and the detail components. Then, the low-frequency component is improved by the global enhancement network while the detail components are enhanced by the local enhancement network in parallel. Finally, the enhanced components are employed to produce high-quality images with the inverse DWT (IDWT) method. The quantitatively and qualitatively comparable experiments on both synthetic and real-world RSIs validate that the proposed GLE-Net method performs well on preserving brightness and fine details, and even outperforms the state-of-the-arts.
HumanGAN: A Generative Model of Human Images

Generative adversarial networks achieve great performance in photorealistic image synthesis in various domains, including human images. However, they usually employ latent vectors that encode the sampled outputs globally. This does not allow convenient control of semantically relevant individual parts of the image, and cannot draw samples that only differ in partial aspects, such as clothing style. We address these limitations and present a generative model for images of dressed humans offering control over pose, local body part appearance and garment style. This is the first method to solve various aspects of human image generation, such as global appearance sampling, pose transfer, parts and garment transfer, and part sampling jointly in a unified framework. As our model encodes part-based latent appearance vectors in a normalized pose-independent space and warps them to different poses, it preserves body and clothing appearance under varying posture. Experiments show that our flexible and general generative method outperforms task-specific baselines for pose-conditioned image generation, pose transfer and part sampling in terms of realism and output resolution.
A meta-analysis and systematic literature review of mixed reality rehabilitation programs: Investigating design characteristics of augmented reality and augmented virtuality

Researchers have shown great interest in the creation of rehabilitation programs using mixed reality (MR), which includes both augmented reality (inclusion of virtual elements in a real environment) and augmented virtuality (inclusion of real elements in a virtual environment). Due to its recent development and systematic limitations of associated studies (e.g., small sample sizes), many pivotal research questions for the study of MR rehabilitation (MRR) programs remain unanswered, and the current article seeks to answer these questions via a meta-analysis and systematic literature review. Our random-effects meta-analysis, including 29 studies, showed that MRR programs produce significant improvements in users’ outcomes, and they were significantly more effective than alternative rehabilitation programs when matched for duration. It also demonstrated that there was not a significant effect regarding all studied characteristics of MRR programs, including the incorporation of game elements, use of head-mounted displays, and task-technology fit. Our systematic literature review identified the most studied types of MRR programs, and it revealed that the most common research designs are the least methodologically robust (e.g., single group post-test only design). Our discussion suggests that MRR programs are still in the earliest stage of research, and the current results are encouraging for future development. Because none of our studied characteristics altered the effectiveness of MRR programs, we argue that characteristics that are generally consistent across all MRR programs are the drivers of their effectiveness, which can be categorized as fidelity and motivational characteristics.
Minimum logical representation of microcommands of cryptographic algorithms (AES)

The more optimally the algorithm is written into the device, the more energy, memory resources required by the device are saved and the speed increases. This article resolves the problem of optimal representation of modern Advanced Encryption Standard (AES) encryption algorithms on hard devices (microcontrollers). It is analyzed that the algorithms can be divided into microinstructions and used to determine their optimal (minimum) form by analyzing the type of graph-schemes, logical, algebraic and matrix representations.
Combining dynamic local context focus and dependency cluster attention for aspect-level sentiment classification

Aspect-level sentiment classification (ASC), as a subtask of Aspect-based sentiment analysis (ABSA), aims to analyze the sentiment polarity of different aspect terms in a sentence. Although the existing methods have been proven to be effective, they fail to effectively identify the range of local context and fully leverage the nonequivalent property of dependency relation. Hence, we propose a concept of dependency cluster and design two modules named Dynamic Local Context Focus (DLCF) and Dependency Cluster Attention (DCA) respectively. The DLCF can dynamically capture the range of local context based on the different max distance from the target aspect term to its context words and the DCA allows the model to pay more attention to the cluster which is more critical for sentiment classification. Together with the two modules, we then propose the DLCF-DCA model, in which the DLCF is equipped before DCA. Considering the DLCF has masked or weighted down the less-semantic-relative words, the semantic information can therefore be better extracted in DCA. Experiments conducted on six benchmark datasets demonstrate that DLCF-DCA achieves the state-of-the-art results. Moreover, the ablation experiment results also verify the effectiveness of each part in DLCF-DCA.
Development of An Algorithmic Model And Methods For Managing Production Systems Based On Algebra Over Functioning Tables

In this article, algorithmic methods and models of automated control of discrete production processes based on dynamic functioning tables are proposed. Control monitors are constructed, a method for describing and managing production systems based on algebra based on functioning tables is proposed. A complex of mathematical models and algorithms has been developed for solving problems of managing the organization of production and operational management in the tool shop of an industrial enterprise.
Hacker types, motivations and strategies: A comprehensive framework

Understanding and predicting cyber malfeasance is an emerging area of research with the increase in cybercrimes and heightened awareness about cybersecurity in recent years. The nature of cybercrimes is also becoming increasingly complex as hackers are more proficient and well-financed than earlier. Thus, our understanding of the various types of hackers and their motivations needs to be consistently updated. We identified and reviewed 11 classifications and typologies of hackers and their motivations published over three decades to consolidate our understanding in this area and summarize the state of the art. Following that, we present a unified framework of 13 hacker types and 7 unique motivations. In addition, we detail the strategies each hacker type typically employs. This framework, applicable to various domains, allows readers to map various motivations to each hacker type and understand that hackers often hold multiple motivations at once. It also allows for the identification of specific hacker types based on the strategies used during a cyberattack.
Comparison of the earthquake detection abilities of PhaseNet and EQTransformer with the Yangbi and Maduo earthquakes

PhaseNet and EQTransformer are two state-of-the-art earthquake detection methods that have been increasingly applied worldwide. To evaluate the generalization ability of the two models and provide insights for the development of new models, this study took the sequences of the Yunnan Yangbi M6.4 earthquake and Qinghai Maduo M7.4 earthquake as examples to compare the earthquake detection effects of the two abovementioned models as well as their abilities to process dense seismic sequences. It has been demonstrated from the corresponding research that due to the differences in seismic waveforms found in different geographical regions, the picking performance is reduced when the two models are applied directly to the detection of the Yangbi and Maduo earthquakes. PhaseNet has a higher recall than EQTransformer, but the recall of both models is reduced by 13%–56% when compared with the results reported in the original papers. The analysis results indicate that neural networks with deeper layers and complex structures may not necessarily enhance earthquake detection performance. In designing earthquake detection models, attention should be paid to not only the balance of depth, width, and architecture but also to the quality and quantity of the training datasets. In addition, noise datasets should be incorporated during training. According to the continuous waveforms detected 21 days before the Yangbi and Maduo earthquakes, the Yangbi earthquake exhibited foreshock, while the Maduo earthquake showed no foreshock activity, indicating that the two earthquakes’ nucleation processes were different.
Meta-Hashing for Remote Sensing Image Retrieval

With the explosive growth of the volume and resolution of high-resolution remote-sensing (HRRS) images, the management of them becomes a challenging task. The traditional content-based remote-sensing image retrieval (CBRSIR) technologies cannot meet what we expect due to the large volume of image archives and complex contents within HRRS images. As a successful approximate nearest neighborhood (ANN) search technique, Hash learning has received wide attention, especially when deep convolutional neural networks (DCNNs) appear. Due to DCNNs&#x2019; strong capacity of feature learning, many DCNN-based hashing methods have been proposed and achieved good performance for large-scale CBRSIR tasks. Nevertheless, their limitation is that a large of labeled training samples should be collected for training the deep models. To overcome this limitation, this article, therefore, develops a new supervised hash learning method for the large-scale HRRS CBRSIR task based on meta-learning, which could achieve well-retrieval performance with a few labeled training samples. First, taking the characteristics of HRRS into account, we develop a self-adaptive convolution (SAP-Conv) block and design a hashing net based on the block. SAP-Conv can learn robust features from HRRS images by exploring their multiscale information. Second, to enhance the generalization of the hashing net under a few labeled training samples, the hash learning is formulated in a meta-way, and we name it meta-hashing. Meta-hashing can effectively preserve the similarities between support and query set, and the similarities between samples within support set by the developed loss function. To further improve the performance of meta-hashing, we expand it to a dynamic version named dynamic-meta-hashing, in which the numbers of support and query are changeable in the training phase. Experimental results counted on the three widely used HRRS datasets demonstrate our dynamic-meta-hashing and meta-hashing can achieve promising performance in large-scale HRRS CBRSIR tasks based on a few training samples. Our source codes are available at <uri>https://github.com/TangXu-Group/Meta-hashing</uri>.
Unsupervised Eyeglasses Removal in the Wild

Eyeglasses removal is challenging in removing different kinds of eyeglasses, e.g., rimless glasses, full-rim glasses, and sunglasses, and recovering appropriate eyes. Due to the significant visual variants, the conventional methods lack scalability. Most existing works focus on the frontal face images in the controlled environment, such as the laboratory, and need to design specific systems for different eyeglass types. To address the limitation, we propose a unified eyeglass removal model called the eyeglasses removal generative adversarial network (ERGAN), which could handle different types of glasses in the wild. The proposed method does not depend on the dense annotation of eyeglasses location but benefits from the large-scale face images with weak annotations. Specifically, we study the two relevant tasks simultaneously, that is, removing eyeglasses and wearing eyeglasses. Given two face images with and without eyeglasses, the proposed model learns to swap the eye area in two faces. The generation mechanism focuses on the eye area and invades the difficulty of generating a new face. In the experiment, we show the proposed method achieves a competitive removal quality in terms of realism and diversity. Furthermore, we evaluate ERGAN on several subsequent tasks, such as face verification and facial expression recognition. The experiment shows that our method could serve as a preprocessing method for these tasks.
Multi-Label Emotion Classification on Code-Mixed Text: Data and Methods

The multi-label emotion classification task aims to identify all possible emotions in a written text that best represent the author's mental state.In recent years, multi-label emotion classification attracted the attention of researchers due to its potential applications in e-learning, health care, marketing, etc.There is a need for standard benchmark corpora to develop and evaluate multi-label emotion classification methods.The majority of benchmark corpora were developed for the English language (monolingual corpora) using tweets.However, the multi-label emotion classification problem is not explored for code-mixed text, for example, English and Roman Urdu, although the code-mixed text is widely used in Facebook posts/comments, tweets, SMS messages, particularly by the South Asian community.For filling this gap, this study presents a large benchmark corpus for the multi-label emotion classification task, which comprises 11,914 code-mixed (English and Roman Urdu) SMS messages.Each code-mixed (English and Roman Urdu) SMS message manually annotated using a set of 12 emotions, including anger, anticipation, disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust, and neutral (no emotion).As a secondary contribution, we applied and compared state-of-the-art classical machine learning (content-based methodsthree word n-gram features and eight character n-gram features), deep learning (CNN, RNN, Bi-RNN, GRU, Bi-GRU, LSTM, and Bi-LSTM), and transfer learning-based methods (BERT and XLNet) on our proposed corpus.After our extensive experimentation, the best results were obtained using state-of-the-art classical machine learning methods on word uni-gram (Micro Precision = 0.67, Micro Recall = 0.54, Micro F 1 = 0.67) with a combination of OVR multi-label and SVC single-label machine learning algorithms.Our proposed corpus is free and publicly available for research purposes to foster research in an under-resourced language (Roman Urdu).
A Privacy-Preserving Distributed Analytics Platform for Health Care Data

In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest.We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location.In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers.We show that our infrastructure enables the training of data models based on distributed data sources.Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.