Reviewerly/exHarmony huggingface dataset:
Detecting Dravidian Offensive Posts in MIoT: A Hybrid Deep Learning Framework

Hate speech and Offensive Posts (OP) detection on Smart Multimedia Internet of Things (MIoT) have been an active issue for researchers. MIoT media texts in non-native English-speaking countries are often code-mixed or script mixed/switched. This paper proposes an ensemble-based Deep Learning (DL) framework comprised of a Convolutional Neural Network (CNN) and a Dense Neural Network (DNN) for identifying hate and OP in Malayalam Code-Mixed (MCM), Tamil Code-Mixed (TCM), and Malayalam Script-Mixed (MSM) MIoT media postings. Word-level and character-level features are utilized in the convolutional neural network. In contrast, the dense neural network uses character-level Term Frequency-Inverse Document Frequency (TF-IDF) features. The inclusion of character-level features in the proposed ensemble framework resulted in state-of-the-art performance for TCM and MCM datasets, with weighted F 1 -score of 0.91 and 0.78, respectively, and comparable performance for MSM posts, with a weighted F 1 -score of 0.95.
Multi-Strategy Fusion of Sine Cosine and Arithmetic Hybrid Optimization Algorithm

The goal was to address the problems of slow convergence speed, low solution accuracy and insufficient performance in solving complex functions in the search process of an arithmetic optimization algorithm (AOA). A multi-strategy improved arithmetic optimization algorithm (SSCAAOA) is suggested in this study. By enhancing the population’s initial distribution, optimizing the control parameters, integrating the positive cosine algorithm with improved parameters, and adding inertia weight coefficients and a population history information sharing mechanism to the PSO algorithm, the optimization accuracy and convergence speed of the AOA algorithm are improved. This increases the algorithm’s ability to perform a global search and prevents it from hitting a local optimum. Simulations of SSCAAOA using other optimization algorithms are used to examine their efficacy on benchmark test functions and engineering challenges. The analysis of the experimental data reveals that, when compared to other comparative algorithms, the improved algorithm presented in this paper has a convergence speed and accuracy that are tens of orders of magnitude faster for the unimodal function and significantly better for the multimodal function. Practical engineering tests also demonstrate that the revised approach performs better.
Blockchain Technology for the Financial Market

Purpose: Blockchain technology has exploded in popularity in the social economy over the years. Because blockchain technology offers so many advantages, it was first employed for Bitcoin transactions. The interaction between blockchain technology and financial industries will grow more personal and interwoven with the introduction of Bitcoin. As the value of blockchain technology became evident, more financial companies began to test its use in monetary operations. Numerous financial companies have been experimenting with blockchain technology to reduce the transactional costs and to improve the efficiency of operations, specifically in the domain of financial notes, cross-border payments, and asset-backed securitisation. Blockchain will have a broad range of applications in the financial industry shortly.Methodology: Through financial tools and methods, trust may result in successful businesses. In the long term, blockchain technology is generally a significant source of the finance industry revolution. It allows you to create immutable transaction histories available to everyone on the network. A blockchain system consists of a sequence of blocks connected by a connection to the block before each block. Each block comprises more than one transaction representing changes in the ownership of assets on the ledger. A consensus procedure is employed to add new blocks to the existing chain, in which members of the public blockchain confirm that transactions are accurate.Findings: Blockchain technology is still in its infancy. The challenges of sustainability, privacy, secrecy, and latency, among others, have surfaced. Financial markets must get a deeper grasp of Blockchain to provide trustworthy alternatives.Significance: The book chapter highlights the architecture, protocols, and applications of Blockchain Technology. The chapter will also enlighten the readers about the challenges and solutions of Blockchain Technology in the Financial Market. Moreover, the chapter also discusses the future research scope of Blockchain Technology in the Financial Market.
The psychological premise of spatial presence in 360° virtual tours: the role of the spatial situation in first-time and repeat users

Purpose This study aims to construct a two-step model to examine how passive (mental imagery and immersion) and active (engagement) responses build a spatial presence in 360° virtual tours by constructing the spatial situation. This study further tests the difference between first-time and repeat users in building a spatial presence. Design/methodology/approach Systematic sampling was applied to collect data. Valid data from 355 participants were analysed using partial least squares structural equation modelling. Findings The results show that other than the direct effect of engagement on spatial presence, the spatial situation partially mediates the effects on the spatial presence of two passive responses. Through comparative analysis of the three models, this study confirms that the two-step model provides a better fit for the data. For repeat users, the spatial situation is the major source of spatial presence. Practical implications The results offer suggestions for destination marketers to develop effective virtual tours to induce the spatial presence of repeat users and first users. Originality/value This study contributes a two-step model for researchers to consider the mediating role of the spatial situation in further studies in the context of virtual reality (VR) tourism. This study also helps researchers design their future VR research in tourism by elucidating the roles of passive and active psychological responses in determining spatial presence.
Separable Convolution Network With Dual-Stream Pyramid Enhanced Strategy for Speech Steganalysis

Steganography based on fixed codebook has become one of the most important branches of speech steganography due to its high imperceptibility and having the largest available carrier space. As its countermeasure technique, this paper presents a novel steganalysis method based on separable convolution network (SepSteNet) with dual-stream pyramid enhanced strategy (DPES). Specifically, to better acquire discriminative representations, we design the pulse-aware separable block to capture the pulse correspondence along independent levels of pulse positions, where the pulse-aware excitation module is plugged to avoid noisy clue accumulation by adaptively emphasizing the salient part. Moreover, the global attending block is introduced to enhance correspondence features through calculating global responses at distinct subframes. In addition, to eliminate the negative impact of sample content, DPES is leveraged to incorporate cross-domain coherence features by the inverted connected dual-stream branches. With the original and calibration speech samples, two branches enable the correspondence of two detection feature domains to interact with each other to generate coherence features independent of sample content, thereby improving the detection performance. The performance of the presented method is comprehensively evaluated and compared with the state of the arts. The experimental results demonstrate that the presented method significantly outperforms the existing ones. Furthermore, DPES is shown to be a general enhancement strategy that can effectively improve the performance of the existing deep neural network for speech steganalysis. The source code for this work will be publicly available on GitHub.
Hierarchical Reinforcement Learning for Conversational Recommendation With Knowledge Graph Reasoning and Heterogeneous Questions

User interaction history with items is used to infer user preferences in conventional recommendation systems. Among these, conversational recommendation systems (CRSs), which provide effective recommendations based on a framework combining recommendations and conversations with users, have been proposed. However, existing CRS model still have some shortcomings, such as the lack of using dialogue records for recommendations. In this research, a method was proposed to solve multiround conversational recommendations with heterogeneous questions. The model included a hierarchical reinforcement learning framework and introduced methods of effectively incorporating user feedback for online recommender updates. Experiments were conducted on several datasets to verify model effectiveness; the model outperformed the current state-of-the-art method. Finally, the architecture was more realistic than other conversational recommendation scenarios and provided richer explanations.
Bangla E-Commerce Sentiment Analysis Using Machine Learning Approach

In this paper, a machine learning approach is used to predict user sentiments from Bangla texts about products available on e-commerce sites. In order to accomplish the task, we have constructed a Bengali corpus of the public views about products and services of multiple Bangladeshi E-commerce organizations. Besides, we have applied six different machine learning algorithms (Multinomial Naive Bayes (MNB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Stochastic Gradient Descent(SGD)) to predict and analyze the polarity of public sentiments. Term Frequency–Inverse Document Frequency (TF-IDF) technique has been applied by using Trigram features. Finally, after optimizing the hyperparameters using the Randomized-SearchCV algorithm, SVM classifier has been found to demonstrate the highest accuracy of 90.68% for predicting public sentiments.
A Corpus-Based Study on the Syllablic Form Selection of Ancient Chinese High-Frequency Nouns in the Middle Ages

Polysyllablization was one of the trends governing the development of Chinese lexis, and the Middle Ages, linking the preceding era and the next, was the key stage. Under the macro trend of lexical polysyllablization, different words could select different syllabic forms. Due to lack of basic data on syllablic form selection from Ancient Chinese to Middle Chinese, there remain considerable ambiguities and even blanks in research. Nouns are the bulk of the lexical system. Focusing on high-frequency nouns in Ancient Chinese, this paper constructs lexical data on syllablic form selection from Ancient Chinese to Middle Chinese based on the corpus linguistics method. After a quantitative survey and statistical analysis, it preliminarily reveals the rules of syllablic form selection in the evolution of Chinese lexis from various aspects, such as overlapping morphemes, nature of the source text, word formation, etc.
Creating a Metagraph Dataset Based on the ATOMIC Dataset

In this article we propose the creation of complex graph dataset based on the ATOMIC dataset. The example of the annotating metagraph model is given. The limitations of the RDF model are discussed; the possibility of overcoming the limitations using the metagraph model metavertex is proposed. The main ideas of ATOMIC dataset are discussed. The scheme demonstrates the principle of transforming ATOMIC data to RDF format is given. The first step of metagraph dataset creation implies using Markov clustering algorithm; algorithm describes the flow of Markov clustering for a flat graph. The second step of metagraph dataset creation implies using breadth clustering algorithm; this algorithm identifies objects, which could create miscellaneous context of a situation, described by common edge type. The experimental section discusses the metagraph creation using the five classes (xWant, oWant, oReact, oEffect, xEffect) of the inferential dimensions of the ATOMIC dataset.
Iterative Retinex-Based Decomposition Framework for Low Light Visibility Restoration

Images captured in low-light conditions often suffer from low brightness, low signal-to-noise ratio, low contrast, a narrow gray range, and color distortion, which can significantly impact human perception and limit the performance of various computer vision applications.Most existing low-light image restoration methods require assistance with a color cast, local over-exposure, glow, and artificial light sources.This paper proposes a new framework called RSD-Net, incorporating several innovative blocks, including a novel iterative Retinex network decomposition and enhancement algorithms, to improve the visibility and quality of images captured in low-light or nighttime conditions.We have extensively evaluated our proposed method on various benchmarking datasets and under different real-world scenarios, including challenging conditions such as glow, artificial light sources, low illumination, and noise.Moreover, we have evaluated our method on a face detection algorithm using extremely dark images and compared its performance with other state-of-the-art methods.The simulation results show that our proposed framework achieves a noticeable improvement compared to other low-quality image restoration techniques and enhances face detection accuracy in low-quality environments.The proposed framework has the potential to substantially impact human perception and enhance the performance of numerous computer vision applications.
Glomerular Lesion Recognition Based on Pathology Images With Annotation Noise via Noisy Label Learning

Background: Glomerular lesion recognition is one of the most crucial steps in the diagnosis of kidney disease. Deep learning, which relies on large numbers of pathology images, assists pathologists to access glomerular lesions more efficiently, objectively and accurately. However, due to different pathological development of glomeruli, complicated lesion patterns, and limited resolution of pathology images, there is annotation noise in datasets, making the deep learning model under- or over-fit. Methods: In this paper, we propose a novel noisy label learning model for lesion recognition in glomerular datasets with annotation noise. The model integrates uncertainty-based noisy label discriminator, contrastive learning, and consistency regularization to achieve high signal-to-noise supervision, pathology feature extraction, and utilization of pathology images. Results: We constructed large-scale glomerular datasets from 870 kidney disease cases using different stainings including Periodic acid–Schiff (PAS), Masson Trichrome (MT) and Periodic Schiff-Methenamine (PASM). Intensive experiments demonstrated the superiority of the proposed model for glomerular lesion recognition compared to other methods, as 25% of the lesions had f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - score above 85%, 43.75% had f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - score above 80%, and 75% had f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> - score at or above 70%. Additionally, further experiments demonstrate the effectiveness of each module. Conclusions: The noisy label learning model proposed is able to recognize the most glomerular lesions, with the annotation noise discrimination and large amounts of pathology images utilization, laying the foundation for the development of computer-aided evaluation system for renal pathology.
Spatiotemporal Learning Transformer for Video-Based Human Pose Estimation

Multi-frame human pose estimation has long been an appealing and fundamental issue in visual perception. Owing to the frequent rapid motion and pose occlusion in videos, this task is extremely challenging. Current state-of-the-art methods seek to model spatiotemporal features by equally fusing each frame in the local sequence, which weakens the target frame information. In addition, existing approaches usually emphasize more on deep features while ignoring the detailed information implied in the shallow feature maps, resulting in the dropping of crucial features. To address the above problems, we propose an effective framework, namely spatiotemporal learning transformer for video-based human pose estimation (SLT-Pose), which consists of a Personalized Feature Extraction Module (PFEM), Self-feature Refinement Module (SRM), Cross-frame Temporal Learning Module (CTLM) and Disentangled Keypoint Detector (DKD). To be specific, we propose PFEM which extracts and modulates the individual frame features to adapt to the varying human shape, and integrates single-frame features to obtain the spatiotemporal features. We further present SRM to establish global correlation spatial cues on the target frame to attain the refinement feature. Then, a CTLM is designed to search for the information most closely related to the target frame from the spatiotemporal features to intensify the interaction between the target frame and the local sequence, using both the shallow detailed and the deep semantic representations. Finally, we employ DKD to extract the disentangled characteristics of each joint and encode the articulated joint pairs in the human body, promoting the model to reasonably and accurately predict the keypoint heatmaps. Extensive experiments on three huamn motion benchmarks, including PoseTrack2017, PoseTrack2018, and Sub-JHMDB dataset, demonstrate that SLT-Pose plays favorably against state-of-the-art approaches in terms of both objective evaluation and subjective visual performance.
A Privacy-Preserving and Verifiable Statistical Analysis Scheme for an E-Commerce Platform

To know the most recent market conditions, an e-commerce platform needs to be aware of the sales situation of its sellers’ commodities. The most recent market conditions can help to forecast future market trends and develop policies to guide sellers in reasonably allocating their inventory proportion. Statistical analysis is a fundamental approach to studying the sales situation. However, the sales data of an e-commerce platform usually has a significant volume. Therefore, outsourcing statistical analysis to cloud servers is an effective method. Nevertheless, sellers do not want their sales data leaked to anyone or any other organization. Moreover, in many circumstances, we cannot fully trust cloud servers. Thus, we need to utilize cryptographic or non-cryptographic tools to realize the above outsourcing. Secret sharing is a lightweight and powerful non-cryptographic tool to realize privacy-preserving data analysis. However, it needs secure channels to distribute secret shares. On the other hand, homomorphic encryption is a powerful cryptographic tool for designing privacy-preserving data analysis schemes. Nevertheless, these schemes usually do not allow the entity that holds the decryption key to collude with other entities. We propose a privacy-preserving and verifiable statistical analysis scheme for an e-commerce platform that combines a threshold secret sharing scheme with a verifiable threshold homomorphic encryption scheme. Our solution’s demand for secure channels is reduced by 40% ~ 60% compared with a traditional threshold secret sharing scheme, thanking the designed novel distribution model for delivering secret shares. Furthermore, our solution has a stronger ability to resist collusive attacks, keep sales data private from any entity, and ensure that the platform can only obtain the analysis results with the help of some cloud servers, alleviating the single point of trust. And meanwhile, the novel distributed model makes our solution enjoy better robustness and fault tolerance. The proposed solution is validated through security analyses, performance evaluations, and comparison analyses.
You Are What You Use: Usage-based Profiling in IoT Environments

Habit extraction is essential to automate services and provide appliance usage insights in the smart home environment. However, habit extraction comes with plenty of challenges in viewing typical start and end times for particular activities. This paper introduces a novel way of identifying habits using an ensemble of unsupervised clustering techniques. We use different clustering algorithms to extract habits based on how static or dynamic they are. Silhouette coefficients and a novel noise metric are utilized to extract habits appropriately. Furthermore, we associate the extracted habits with time intervals and a confidence score to denote how confident we are that a habit is likely to occur at that time.
Trends in Cybercrime During the COVID-19 Pandemic

The COVID-19 pandemic accelerated many organisations' reliance on the Internet, with schools, companies, and government agencies, among other institutions, all shifting their operations online in a relatively short period of time. This sudden shift created opportunities for cybercriminals, as did the burden on the healthcare sector, the rush for unemployment benefits, and the desire for up-to-date information about outbreaks and health precautions. This chapter examines the trends in cybercrime during the COVID-19 pandemic that were spurred by these changes, as well as the mitigation measures implemented by government and private industry stakeholders to help protect critical infrastructure and online operations. It argues that the nature of cybercrime and the most common forms of criminal activity did not change substantially during the pandemic, but rather that criminals' focus on whom to target and how to exploit evolving current events enabled them to wield existing criminal tools to greater effect, and potentially also at greater scale, though reliable statistics on the frequency and size of cybercrimes are scarce. Finally, the chapter considers how these trends in cybercrime forced private organisations and governments to undertake new cybersecurity measures and programs and whether the long-term effects of the pandemic might actually include stronger cybersecurity if these efforts continue past the end of the public health crisis.
The Characteristics, Methods, Trends and Applications of Intelligent Systems

Interaction between intelligent systems and their human operators in dynamic, shifting, and unpredictable natural and social settings is novel area of study within the topic of intelligent computing systems. Robots of the past were not effective at making decisions on their own; instead, they routinely carried out the same set of actions in the same situations because they believed that the world was predictable. Nowadays, decisions may be made quickly and effectively by intelligent systems in practical settings. Modern intelligent systems include characteristics such as intelligent search and optimization, artificial evolution, and autonomous decision support that are unavailable in a traditional information system. An in-depth analysis of the methods used to create intelligent systems is presented in this paper. These techniques are often categorized as either artificial intelligence or soft computing. Some examples include the use of a neural network, fuzzy logic, a hybrid system, or a swarm of intelligent insects. In addition, this article gives an overview of two applications of intelligent systems and technologies such as Geothermal Heat Pumps (GHPs) and Heat exchangers (HEXs).