Reviewerly/exHarmony huggingface dataset:
Multi-label feature selection based on stable label relevance and label-specific features

Multi-label feature selection can efficiently handle large amounts of multi-label data. However, two pressing issues remain in sparse learning for multi-label data. First, many methods explore label relevance through the original label matrix, which may introduce redundant and useless information, and they either consider local label relevance or global label relevance, which are complementary to obtaining potential label information. Second, most existing multi-label feature selection (MLFS) algorithms do not consider the characteristics of the labels themselves, i.e., label-specific features. To solve the above problems, an MLFS method based on stable label relevance and label-specific features are proposed in this study, which combines global and local label relevance to learn the label-specific features. A stable global label relevance strategy is designed for the self-representation model to avoid the effect of noise and outliers, and a manifold regularization term is adopted to explore the local label relevance. Moreover, ℓ1-norm is applied to the feature weight matrix and self-representation matrix to select the label-specific features. Then, a solution for optimizing the objective function is designed. Compared with eight related algorithms on sixteen public datasets. The results show that the proposed method can improve the classification performance of multi-label data.
Multi-similarity reconstructing and clustering-based contrastive hashing for cross-modal retrieval

In unsupervised cross-modal hashing, there are two notable issues that require attention. The inter- and intra-modal similarity matrices in the original and Hamming spaces lack sufficient neighborhood information and semantic consistency, while solely relying on the reconstruction of instance-level similarity matrices fails to effectively capture the global intrinsic correlation and manifold structure of the training samples. We propose a novel method that combines multi-similarity reconstructing with clustering-based contrastive hashing. Firstly, we construct image feature, text feature and joint-semantic feature multi-similarity matrices in their original space, along with their corresponding hashing code similarity matrices in the Hamming space, to enhance the semantic consistency of the inter-and intra-modal reconstructions. Secondly, the clustering-based contrastive hashing is proposed to capture the global intrinsic correlation and manifold structure of the image-text pairs. Extensive experiment results on Wiki, NUS-WIDE, MIRFlickr-25K and MS-COCO demonstrate the promising cross-modal retrieval performance of the proposed method.
SC<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si6.svg" display="inline" id="d1e2920"><mml:msup><mml:mrow /><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>-Net: Self-supervised learning for multi-view complementarity representation and consistency fusion network

Multi-view clustering (MVC) seeks to improve the original single-view clustering by exploring the complementarity and consistency contained in multi-view data. While most subspace-based multi-view clustering methods now focus on exploring one of the consistency or complementarity features associated with multi-view datasets, rather than balancing the exploration of them. Meanwhile, the favored approach of combining deep learning tends to design the network structure to be relatively complex and then superimpose the constraints of multiple loss functions. Additionally, training results with unlabeled datasets are often unsatisfactory. To solve the aforementioned concerns, we present an innovative deep convolutional clustering network (SC2-Net) backed by self-supervised learning. SC2-Net learns multi-view complementarity representation and consistency fusion between views which adheres to the two principles of MVC. The clustering labels will be obtained by cooperating with k-means, the overall structure is simple but efficient. In addition, we supervise the network training by using two self-supervised loss functions, making the training process free from using data with annotations. We test the proposed network under multiple sets of experimental parameter combinations and prove its effectiveness and robustness.
Multi-label feature selection based on correlation label enhancement

Feature selection is an effective data preprocessing technique that can effectively alleviate the curse of dimensionality in multi-label learning. The technique selects a subset of features with high discriminative power to maintain or improve the classifier's classification performance. In many practical applications, it is not possible to use multi-label data to express the relative importance of each label versus each sample. Therefore, all relevant or irrelevant labels are measured with the same importance, neglecting the potential relationship between labels and samples. To better account for the relationship between samples and labels, traditional logical labels are enhanced to label distributions. Aiming at the application of latent sample correlation in label enhancement, a new label enhancement algorithm is proposed. First, to extract potential correlation from the sample space, an objective function is proposed and solved. Second, in the process of label enhancement, the correlation is embedded into label distributions. Finally, label distributions are applied to the sparse linear regression model instead of logical labels. Experiments show that proposed method is superior to 5 advanced multi-label feature selection algorithms on 11 multi-label datasets.
MAML-SR: Self-adaptive super-resolution networks via multi-scale optimized attention-aware meta-learning

The deep-learning-based super-resolution (SR) methods require an avalanche of training images. However, they do not adapt model parameters in test time to cope with the novel blur kernel scenarios. Even though the recent meta-learning-based SR techniques adapt trained model parameters leveraging a test image’s internal patch recurrence, they need heavy pre-training on an external dataset to initialize. Also, the shallow internal information exploration and failure to amplify the salient edges impacts blurry SR image generation. Besides, model inferencing gets delayed due to a threshold-dependent adaptation phase. In this paper, we present Multi-scale Optimized Attention-aware Meta-Learning framework for SR (MAML-SR) to explore the multi-scale hierarchical self-similarity of recurring patches in a test image. Precisely, without any pre-training, we directly meta-train our model with a second-order optimization having the first-order adapted parameters from the intermediate scales, which are again directly supervised with the ground-truth HR images. At each scale, non-local self-similarity is maximized along with the amplification of salient edges using a novel cross-scale spectro-spatial attention learning unit. Also, we drastically reduce the inference delay by putting a metric-dependent constraint on the gradient updates for a test image. We demonstrate our method’s superior super-resolving capability over four benchmark SR datasets.
Adam-Dingo optimized deep maxout network-based video surveillance system for stealing crime detection

An enormous amount of video data is generated every day as a result of the widespread deployment of security cameras due to recent technological advancements. Analyzing such videos for identifying anomalies manually is an extremely challenging issue. Stealing is an issue occurring worldwide resulting in casualties and huge financial damages yearly. Efficient techniques to identify this anomaly haven’t been much researched, and hence here a novel technique is proposed. A hybrid optimized deep learning technique is proposed for detecting stealing crime from videos obtained by surveillance cameras. Firstly, the videos are summarized using Video summarization (VSUMM) method and then video is given input to the Deep Maxout Network. A deep maxout network whose weights are updated with the help of the introduced Adam-Dingo Optimizer for identifying the stealing crime event or normal event. The devised Adam-Dingo optimized Deep Maxout network is tested for its performance with metrics, such as True Positive Rate (TPR), True Negative Rate (TNR), and accuracy and is found to have attained values of 0.940, 0.936 and 0.945, respectively. Identifying stealing crime at different weather condition can be the future work. Identifying different crime events and reporting to control room can be the future work of proposed system.
Efficient asynchronous federated neuromorphic learning of spiking neural networks

Spiking Neural Networks (SNNs) can be trained on resource-constrained devices at low computational costs. There has been little attention to training them on a large-scale distributed system like federated learning. Federated Learning (FL) can be exploited to perform collaborative training for higher accuracy, involving multiple resource-constrained devices. In this paper, we introduce SNNs into asynchronous federated learning (AFL), which adapts to the statistical heterogeneity of users and complex communication environments. A novel fusion weight based on information age and average spike rate is designed, which aims to reduce the impact of model staleness. Numerical experiments validate SNNs on federated learning with MNIST, FashionMNIST, CIFAR10 and SVHN benchmarks, achieving better accuracy and desirable convergence under Non-IID settings.
GAA-PPO: A novel graph adversarial attack method by incorporating proximal policy optimization

The Graph Convolutional Network (GCN) has demonstrated impressive performance in processing graph structured data. However recent studies have revealed that GCN is vulnerable to adversarial attacks, where a small amount of data modification can significantly affect the performance of the GCN models. While most existing studies node injection attacks with graph reinforcement learning by considering gradient information, they still suffer from the problems that the step size of the policy gradient is difficult to determine, and the attack effect needs to be further improved. In light of the above issues, this paper proposes a Graph Adversarial Attack method by incorporating Proximal Policy Optimization named GAA-PPO, which fills subtasks of sequentially generating features and links for injected nodes without modifying existing nodes or edges. GAA-PPO comprises two main components: node injection attack network (actor network) and value prediction network (critic network). Specifically, the actor network leverages a node generator and an edge sampler to generate appropriate features and edges for the injected nodes. Notably, a novel edge sampler that incorporates Approximation Personalized Propagation of Neural Prediction (APPNP) is introduced to effectively propagate malicious features of the injected nodes. On the other hand, the critic network evaluates the performance of the perturbed graph at each stage. To enhance the stability of the algorithm, GAA-PPO employs the importance sampling technique of Proximal Policy Optimization (PPO) during the training process. Extensive experiments on three publicly benchmark datasets show that GAA-PPO yields significant performance advantages over the state-of-the-art method.
Exploration of transfer learning capability of multilingual models for text classification

The use of multilingual models for natural language processing is becoming increasingly popular in industrial and business applications, particularly in multilingual societies. In this study, we investigate the transfer learning capabilities of multilingual language models like mBERT and XLM-R across several Indian languages. We study the performance characteristics of a classifier model with mBERT/XLM-R as the front-end, which is trained only in one language for two tasks: text categorization of news articles and sentiment analysis of product reviews. News articles, on the same event but in different languages, are representative of what may be termed as ‘inherently parallel’ data; i.e. data that exhibits similar content across multiple languages, though not necessarily in parallel sentences. Other examples of such data would be customer inquiries/reviews about the same product, social media activity pertaining to the same topic, etcetera. After training in one language, we study the performance characteristics of this classifier model when applied to other languages. Our experiments reveal that by exploiting the inherently parallel nature of the data, XLM-R performs remarkably well when adapted for any Indian language dataset. Further, our study reveals the importance of simultaneously fine-tuning multilingual models with in-domain data from one language in order to express their cross-lingual and domain transfer learning abilities together.
CapsNet-based drift detection in data stream mining

For data streams, drift detection methods warn and detect the changes in patterns over time. For example, in smart manufacturing, many data streams are generated from sensors that monitor the real-time operation of manufacturing. Drift detection can be used to discover if and how the operation status changes. At present, there have been three main approaches in drift detection: error rate-based, distribution-based, and hypothesis-based. However, these approaches bear an impractical limitation: delays due to the demand for computational time. In a large-scale and high-speed data stream, a time-efficient detector is vital. To address this, this paper proposes a CapsNet-based drift detection algorithm (CapsNet-DDM). Our experimental results and comparative studies have found that CapsNet-DDM demonstrates a distinguishing advantage on computational time, with no compromise on accuracy, F1 score, and effective drift detection rates.
Analysis of Data Visualization Techniques Useful for Machine Learning and Visual Reality

Throughout the past couple of decades, machine learning (ML) has made its way into scientific research and engineering. Machine learning (ML) strategies are widely employed in processing information, data mining, especially scientific computation. Data visualization is essential. Despite the fact that numerous types of visualization tools are commonly used, the majority of them need sufficient coding knowledge, are developed for specific purposes, or are not free. Virtual reality (VR) provides intuitive interactivity and comprehensive visualization. Researchers use virtual reality to make it possible for any biomedical specialist to use a machine learning (DL) framework for picture analysis. Although ML models can be effective instruments for assessing information, they can additionally be difficult to comprehend and create. We have developed a ML development system based on virtual reality in order to render the technology more user-friendly and approachable. The intuitive interactivity and vivid visualisation are offered by virtual reality (VR). Any technical discipline can create a machine learning (ML) approach to recognising pictures using VR. This paper offers a thorough analysis of ML visualisation techniques, resources, and procedures. By looking at the visual analytical pipeline customers, and researchers place data visualisation into the visual analytics methodology. It present an analysis of the many chart types that are available for data visualisation and discuss guidelines for using each one while taking into account the unique circumstances of the given utilise case. There look more closely at a few of the latest and greatest exciting visualisation tools. We research visualisation challenges in each domain because each ML model is unique in terms to VR strategies. Finally, we present a summary of the main difficulties with ML visualisations.
Specific Emitter Identification Method based on Deep Ensemble Learning

In this paper, two deep ensemble algorithms, the hierarchical and multi-stage feature fusion(HMSFF) and Cooperative decision(Co_decision), are designed for specific emitter identification(SEI). Extracting the differential constellation trajectory amplitude, the energy spectrum as well as the spectrum symmetry as radio frequency fingerprint features, the proposed algorithms exhibit better recognition accuracy and robustness to time variation than existing researches. Furthermore, in the case of small number of training samples two data augmentation methods, noise augmentation and multipath channel augmentation, are used to further improve the recognition performance.
Robot service failure and recovery: Literature review and future directions

Robot service failure and subsequent user behavioral responses have emerged as a prominent scientific issue, warranting attention from multiple disciplines. A review of existing literature is crucial to synthesizing and comprehensively evaluating these studies. To this end, the present study undertook a structured systematic literature review to assess the relevant research on the concepts, dimensions, user response (including cognitional and behavioral), and recovery strategies related to service robot failure. Prior studies have largely followed interpersonal service interaction concepts and have identified several major consequences of service robot failure, including emotional and cognitive responses, negative attitudes, attributions of failure, and related behavioral and action-based responses. Notably, recovery strategies for robot service failure can be categorized into two main types: robot-initiated strategy and human intervention strategy. Further research on robot service failure is recommended in five key areas, including exploring the uniqueness of robot service failure, psychologically investigating user responses to robot failure, identifying novel remedy strategies for robot service failures, evolving the concepts of robot service failure and its remedies, and employing mixed-method and complementary research approaches.
The Architecture of Computing Power Network Towards Federated Learning: Paradigms and Perspectives

Computing Power Network (CPN) is a new network paradigm for next generation communication systems. Meanwhile, Federated Learning (FL) has attracted more and more attention nowadays. However, there are few researches on the resource scheduling problem of federated learning in computing power network. There are a large number of heterogeneous computing resources available in the computing power network, so efficient utilization of resources in CPN for federated learning is very important. Therefore, our research focuses on the resource scheduling problem of federated learning in computing power networks to make up for the shortcomings of current related research. In this paper, we propose a framework and functional architecture combining CPN and federated learning for the purpose of resource optimization in federated learning. Besides, we show that task offloading using split learning can significantly improve the computational performance of federated learning, especially on local computing.
Arabic Hate Speech Detection Using Different Machine Learning Approach

Hate speech is defined as an expression that targets an individual or community on the aspects like religion, sexual orientation, race, political opinion, and origin. Recently, hate speech on social media especially in the Arabic language has been exponentially increased and led to severe causes. Various studies had been conducted on social media platforms adopted by people to broadcast their opinions. This work aims to develop a model that is able to handle detection and classification of Arabic hate speech and offensive language. The experiments are carried out in using various machine learning (ML) and deep learning (DL) models. In this work, Arabic Hate Speech Detection (AHSD) model is proposed which composed of pre-processing, feature extraction, detection, and classification to identify hate speech on the Arabic benchmark dataset. The proposed model shows improved results. The transfer learning approach model exhibits superior performance compared to all other ML models in terms of accuracy, precision, recall, and F1 scores, achieving improvements of 84%, 79%, 80%, and 79%, respectively.
Financial Sentiment Analysis on Twitter During Covid-19 Pandemic in the UK

The surge in Covid-19 cases seen in 2020 has caused the UK government to enact regulations to stop the virus’s spread. Along with other aspects like altered customer confidence and activity, the financial effects of these actions must be taken into account. This later can be studied from the user generated content posted on social networks such as Twitter. In this paper, we provide a supervised technique to analyze tweets exhibiting bullish and bearish sentiments, by predicting a sentiment class positive, negative, or neutral. Both machine learning & deep learning techniques are implemented to predict our financial sentiment class. Our research highlights how word embeddings, most importantly word2vec may be effectively used to conduct sentiment analysis in the financial sector providing favorable solutions. In addition, comprehensive research has been elicited between our technique and a lexicon-based approach. The outcomes of the study indicate how well Word2Vec model with deep learning techniques outperforms the others with an accuracy of 87%.
Privacy Research on the Pulse of Time: COVID-19 Contact-Tracing Apps

Abstract In 2020, COVID-19 hit the World, and with it came the desire for a well-functioning and fast-working possibility to trace contacts of those people who tested positive for the virus, a method called contact tracing. Depending on how automated tracing is implemented, it is necessary to capture and store sensitive information about the user, such as where the user has been, who they were in contact with, and their health status. All of this entails the potential of mission creep and surveillance. In this chapter, we give a brief outline of the tracing technologies and their implications for the users’ data and, therefore, privacy. We look at scientific studies with end users and how their privacy concerns impacted their decision to install a contact tracing app. After reading this chapter, the reader will have an overview of the general privacy discussion and research on contact tracing apps in the context of COVID-19 and hints on where to find further information.
Semantic Integration of Data: From Theory to Social Research Practice

As mixed methodologies in social science research gain growing attention, the need to provide an integrated framework for combining qualitative, quantitative, and in general diverse types of data, is rapidly and analogously increasing. Semantic integration of data, a process of interrelating information from diverse sources and consolidating it into meaningful and valuable information using formal specifications of domain conceptualisations, i.e., taxonomies, can constitute a very valuable approach and an alternative view regarding mixed methods in social research. The present paper firstly outlines the basic theoretical definitions linked to semantic integration of data and then delves deeper into elements and assumptions that underlay the use of this specific methodology for organising and analysing social data from diverse sources made available through empirical social research. Finally, the study closes with the description of a recent, enlightening application in the field of ICT use and the digital transformations context.