Reviewerly/exHarmony huggingface dataset:
An Approach of Hate Speech Identification on Twitter Corpus

In recent times, the Internet and social media are very well-known and popular among people. Usage of social media is increased exponentially during the last few years globally, and it allows people to engage with one another and share ideas, thoughts, opinions, etc. Every day, massive amounts of data are disseminated at breakneck speed via social media platforms, reaching a massive audience. Furthermore, the ability to write anonymous posts and comments makes expressing and spreading hate speech even easier. To improve the users’ experience, social media sites are attempting to remove hateful remarks. In this research work, the main focus is on developing automated hate speech and offensive language detection models. Started with traditional hate speech and offensive language identification approaches, than reaches to advanced hate speech recognition methods for social media. This brings a need for integrated datasets and the hate speech prediction method. This paper describes the study on hate speech and offensive content identification in English language by using the various approaches based on machine learning algorithms (Support vector machine, decision tree, and so on) and NLP, along with the features used for the classification problem. The models were tested on the HASOC (2021) datasets and concluded that the ensemble model perform better than other algorithms with the test dataset for different tasks. Results and analysis part of this paper offers researchers a comprehensive picture of approaches.
Blockchain-Enabled Data Sharing Framework for Intelligent Healthcare System

The rapid growth of health care 4.0 for sharing of health data between various stakeholders helps to save lives and improve treatment process. IoT-based intelligent healthcare system is a part of health care 4.0 which can be used in real-time health monitoring, sharing patient data for treatment, telecare medicine, etc. Normally this type of system suffers with different cyber-attacks like data interception, data stealing, unauthorized access, etc. To protect from such vulnerabilities, this paper proposes a blockchain-based framework for intelligent healthcare system where data sharing between multiple stakeholders is done in a secure manner. The proposed framework is implemented, and performance is evaluated using solidity.
Governing through big data: An ethnographic exploration of invisible lives in China's digital surveillance of the coronavirus disease 2019 pandemic

Since 2020, China has implemented unprecedented digital health surveillance over citizens and residents in response to the coronavirus disease 2019 pandemic. We explore the implementation of Health Code (jiankang ma), a contract-tracing and risk assessment app for coronavirus disease 2019, in China. By engaging with the concept of 'ocular ethics', we ask why and how some populations become invisible in China's Health Code surveillance system.This study used an ethnographic approach to critically examine the role of digital technology in the coronavirus disease 2019 pandemic governance. Three months of participant observation and 20 interviews were conducted to understand the design of Health Code and the situation of homeless population.We find that China's digital health surveillance during the coronavirus disease 2019 pandemic has failed to cover the homeless population, who either fail to access Health Code or find ways to avoid its mandatory health surveillance. We further summarize four problems resulting in their exclusion, including the loss of ID cards, access to smartphones and phone numbers, problematic design and elastic surveillance, and the neglect of homeless community's precarious living situation.Situating our work in the literature on theories of surveillance and anthropology of pandemics, we argue that without recognizing the structural problems embedded in homelessness, a large number of poor and homeless migrants are rendered invisible in this data-driven health surveillance, which further pushes them into social exclusion.
Software Defect Prediction Using ROS-KPCA Stacked Generalization Model

Software quality assurance is an area that deals with software defect prediction also. Identifying and eliminating defects is a crucial task that helps organizations deliver quality software products to customers. Machine learning approaches help in identifying software modules that are defective and which are not defective. The existing software defect prediction datasets contain data with features that could classify projects are defective or not. The machine learning model’s performance will be degraded with the existence of noisy attributes and class imbalance problems. In this work, we propose a ROS-KPCA-SG model (Random Over Sampling-Kernel Principal Component Analysis-Staked Generalization Model) model to solve the noisy dataset and class imbalance problems and to improve the software defect prediction accuracy. The performance of the ROS-KPCA-SG model is compared with individual models with different combinations of sampling techniques. The results show the proposed ROS-KPCA-SG model solves the problems and gives better performance than other models. The AUC-ROC score is between 0.9 and 1 for the ROS-KPCA-SG model on all the datasets, and the accuracy is near to 90% and above which is a higher value than other models. The proposed model gives accuracy on datasets CM1 is 98%; JM1 is 89%; PC1 is 98%; KC1 is 92% and with KC2 dataset is 89%.
A Novel COVID-19-Based Optimization Algorithm (C-19BOA) for Multimodal Optimization Processes

The advent of ongoing pandemic due to novel coronavirus disease-2019 (COVID-19) has rapidly unsettled the health sector with a considerable fatality rate. The main factors which help in minimizing the spread of this deadly virus are the proper use of masks, social distancing, and antibody growth rate in a person. Based on these factors, the authors propose a new nature-inspired meta-heuristic algorithm, named COVID-19-based optimization algorithm (C-19BOA). The proposed C-19BOA mimics the spread and control behavior of coronavirus disease centered on three containment factors: (1) social distancing, (2) use of masks, (3) antibody rate. Initially, the mathematical models of containment factors are presented, and further, the proposed C-19BOA is developed. To ascertain the effectiveness of developed C-19BOA, its performance is verified on standard mathematical benchmark functions. These performances are compared with established bio-inspired optimization algorithms available in literature. Performance analyzes reveal the developed C-19BOA which is at par with the established optimization algorithms in terms of minimization of benchmark functions and convergence to optimal values.
Universal Dependency Treebank for Ho Language

Language is the best way to share information. Each and every language has a grammatical structure to make complete sense. A sentence can be extracted with a dependency word sequence. The development of a large-scale dependency treebank for the Indian poor resource language is a great effort. The purpose of this paper is to build the first treebank of Ho language. The treebank contains 51 sentences of 315 tokens in the Ho language. The universal dependency guidelines have been followed for all the selected annotated sentences. The machine learning technique is used for morphological analysis of Ho treebank. The Ho language resources can be enhanced to create annotated treebanks. It will help in creation of language information systems for morphological analysis and cross-linguistic training. In the preliminary stages, we are using a machine learning approach to build Ho parsers. The linguistic study of the Ho universal dependency treebank is briefly mentioned in the conclusion section of this paper.
Sentimental Analysis of Twitter Users’ Text Using Machine Learning

Globally, most people are suffering from various mental health issues because of workload, relationship loss, death of close people, failure, etc. Social media has become a great place to share opinions related to particular issues. Among the different microblogging sites, Twitter is the best platform to analyze the mental status of its users as tweets support 280 characters. This paper aims to build a model which can predict Twitter users’ emotions based on their tweets. For preparing the dataset, Twitter scrapper has been used over the publicly available data with the help of Python Programming and to remove the noise from the dataset used nltk library and also used emotion-related hashtags and their relatable hashtags to scrap the tweets. Machine learning is used for text analysis. The results of different models were tested through measuring parameters like Precision, Recall, and F1 score, and the k-fold cross-validation has been done.
Head Pose Estimation and Validation of Medical Imaging Data for Image Guided Stereotactic Brain Surgeries

Computer assisted brain surgeries (also called stereotactic brain surgeries) are gaining prominence due to their effectiveness in handling the brain related ailments. As the surgeon cannot see the operative zone directly, due to the minimalistic invasive approach used in these surgeries, these procedures are heavily dependent on the medical imaging data obtained either through Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). In these surgeries, where the process of high accuracy localization of the tumors is crucial, the pose of the patient during the medical imaging procedure becomes utmost important. Though the head pose related information is embedded in the DICOM medical images as metadata, assertion of the correctness is highly desired owing to the clinical impact. As not much work was found in this direction, we propose a novel way to estimate head pose of the patient from medical imaging data. Our proposed method uses a modified deep residual network to estimate the head pose, i.e., yaw, pitch and roll angles of medical imaging data with 4° Mean Absolute Error (MAE), nearly 50% improvement over our experiments using a modified AlexNet architecture. In light of unavailability of a labeled dataset for medical imaging head pose, the work reports a data generation algorithm with a dataset of 150 k samples. Reported experiments show 22% and 50% increase in accuracy by using data-driven and combined data-model-driven strategy respectively. The estimated head pose can be used for validating MRI metadata, computing deviation and assistance in patient to MRI registration.
High-Utility Itemset Mining using Fuzzy Sets

High-utility itemset mining aims to mine itemsets with high utilities. Utility of an itemset is the profit value associated with it. However, high-utility itemsets are generated considering only the occurrence of the items in the database. These patterns do not contain any information about the utilities of the items in the transaction. This paper aims to generate high-utility itemsets containing information about the utilities of the items.
Technology-Assisted Mental Healthcare: A Novel Approach

Mental health issues are rising globally and the present healthcare system has to keep up with the dramatic fluctuations of mental health. Patients’ denial to express needs and assort to professional help are a risk factor for declining emotional balance in the society. Technology driven approach can play an integral role in identification, prevention, and treatment of this global health crisis. Access to personalized mental healthcare management is approaching a new frontier with emerging technological tools and applications. Moreover, the healthcare industry is a complex regulatory network and its inability to share and exchange information across patient and providers results in error and disputes. Newer technologies are opening avenues to replace the outdated healthcare payment process aiding in secure transfer of data and cutting down associated risk. This paper focuses on the existing state of the art applications of telemedicine, tricorder, blockchain technology, device tracking, and many more newer ways for management of mental health issues and strongly recommends their increased application to enhance patient care.
Information Resource to Support Voluntary Activities of Command-Professional Educational Institutions

The article examines and analyzes the existing information resources on the support of voluntary activity (VA). It is shown that VA participants often lack reliable information about volunteerism, which is scattered across various information resources. To meet their information needs, it is necessary to create a single information resource that will help not only motivate VA participants to implement socially significant programs and projects, but also attract new participants in VA. The purpose of the article is to propose the structure and main blocks of the information portal to support VA. As a result of the research, the following main blocks of the information resource are identified: the experience of VA, preparation of public speeches, creative thinking, project development and management. The data of these information blocks allows the creation of a single information resource to meet the information needs of volunteers.
Outsourced Privacy-Preserving SVM Classifier Model over Encrypted Data in IoT

The Internet of Things (IoT) enables the development of cloud computing by combining machine learning (ML) and big data technologies. Frameworks supporting ML typically process and classification manufacturing data services via cloud-based technologies. In order to achieve secure and efficient data collection and application, we design, implement and evaluate a new system employing a SVM classifier model over encrypted data (SVMCM-ED) based on multi-key Fully Homomorphic Encryption (multi-key FHE) in IoT. We first propose a new scheme that uses the cloud server and edge node to jointly implement SVM classification over the ciphertext to ensure the security of data and classification model. Our system significantly transfers cloud processing to the edge, and safely uses the model obtained from the data analysis to implement SVM classification among multiple users. We further design secure protocols based on multi-key FHE, which supports multiple users and satisfies the semi-honest security model where cloud computing is outsourced. Our protocol requires no interaction from the data analysis and users during online secure classification. Performance evaluation demonstrates that our new system can securely train a logistic regression model of multiple users. Performance evaluation demonstrates that our new system can securely implement the SVM classification model of multiple users in IoT, as well as share communication and computation overhead of the cloud.
A Lightweight and Precise Information Retrieval System for Organisational Wiki

Large organisations have vast amount of proprietary information in the form of internal wiki, which requires a fast and efficient information retrieval system to address employees’ informational needs. This paper lays down the design and development of an information retrieval system (IRS) for answering employees’ queries based on knowledge from the organisation’s internal wiki website. Organisational Wikis are not for public access and hence protected with stronger security mechanisms. An IRS that can understand the access level of the user and accordingly access the internal WiKi pages to fetch the precise needed information related to the query has been developed. This IRS is web based and specialist in nature as it serves the organisation’s employees with answers using the secure crawler developed here and the concise reference index built thereof. The query processing module of IRS uses natural language processing (NLP) tools. To enhance the speed of the process, a fast and secure database is maintained for indexing the most relevant information of the wiki by the crawler. The IRS serves updated answers from the wiki on the fly.
Disaster Remote Sensing Image Semantic Segmentation model with boundary constraints based on SegNeXt

Semantic segmentation has achieved significant breakthroughs in many fields. However, there are still many problems in disaster remote sensing semantic segmentation. For example, there are boundary anomalies in the prediction results of densely distributed objects, and there are inevitably unreasonable results in the segmentation prediction. This paper proposes a SegNeXt-based network architecture with boundary constraints which combines a boundary module with output branch different from main branch, and improves the sampling layer of SegNeXt. The improved SegNeXt was tested on the FloodNet dataset, and the final model result, mIou, reached 67.2%, with an accuracy of 90.1%. The mIou was 2.1% higher than the unmodified SegNeXt. In addition, the segmented image shows that the model has solved the boundary anomaly to some extent.
Optical Transmitter Fingerprint Authentication Based on Chaotic Complexity Features for Secure Optical Transmission

For secure optical communication, an optical transmitter fingerprint authentication scheme based on optical chaotic features is proposed and demonstrated. We propose to combine a chaotic system with a traditional optical transmitter and use the nonlinear complexity feature of chaotic time series as the fingerprint of optical transmitters, completing the optical transmitter authentication by identifying the fingerprint. In this article, four-wave mixing technology is used to ensure the security and reliability of the optical transmitter fingerprint. A chaotic complexity feature extraction algorithm based on empirical mode decomposition and fuzzy entropy is proposed. After time stability and distinguishability verification, the multi-dimensional features extracted by this algorithm can be used as the fingerprint of the optical transmitter for identity authentication, and then the fingerprint of the optical transmitter is classified by a support vector machine. The computer simulations and experiments have achieved feature extraction and authentication of chaotic systems, and the recognition accuracy is over 96%.
License Plate Recognition in Wild with Super-Resolution

License plate recognition is an important part of intelligent transport systems. Although there are various license plate recognition algorithms used in commercial activities, most of them have several limitations, requiring high image pixel and low image skew. In this paper, we propose a new license plate recognition system in unconstrained environments, which incorporates plate super-resolution. We use a generative adversarial network for super-resolution(SR) of license plate image to solve the problem of low recognition accuracy due to low-resolution images. Then, the SR image will be input an elaborate designed plate recognition network. We use Chinese City Parking Dataset(CCPD) as the evaluation dataset, on which the experiments show that the proposed method performs better than current license plate recognition methods, especially on low- resolution plate images.
Generating Real-Time Explanations for GNNs via Multiple Specialty Learners and Online Knowledge Distillation

Graph Neural Networks (GNNs) have become increasingly ubiquitous in numerous applications and systems, necessitating explanations of their predictions, especially when making critical decisions. However, explaining GNNs is challenging due to the complexity of graph data and model execution. Post-hoc explanation approaches have gained popularity due to their versatility, despite their additional computational costs. Although intrinsically interpretable models can provide instant explanations, they are usually model-specific and can only explain particular GNNs. To address these challenges, we propose a novel, general, and fast GNN explanation framework named SCALE. SCALE trains multiple specialty learners to explain GNNs, as creating a single powerful explainer for examining the attributions of interactions in input graphs is complicated. In training, a black-box GNN model guides learners based on an online knowledge distillation paradigm. During the explanation phase, explanations of predictions are generated by multiple explainers corresponding to trained learners. Edge masking and random walk with restart procedures are used to provide structural explanations for graph-level and node-level predictions, respectively. A feature attribution module provides overall summaries and instance-level feature contributions. We compare SCALE with state-of-the-art baselines through quantitative and qualitative experiments to demonstrate its explanation correctness and execution performance. Furthermore, we conduct a user study and a series of ablation studies to understand the strengths and weaknesses of the proposed framework.
CNNs Avoid the Curse of Dimensionality by Learning on Patches

Despite the success of convolutional neural networks (CNNs) in numerous computer vision tasks and their extraordinary generalization performances, several attempts to predict the generalization errors of CNNs have only been limited to a posteriori analyses thus far.A priori theories explaining the generalization performances of deep neural networks have mostly ignored the convolutionality aspect and do not specify why CNNs are able to seemingly overcome curse of dimensionality on computer vision tasks like image classification where the image dimensions are in thousands.Our work attempts to explain the generalization performance of CNNs on image classification under the hypothesis that CNNs operate on the domain of image patches.Ours is the first work we are aware of to derive an a priori error bound for the generalization error of CNNs and we present both quantitative and qualitative evidences in the support of our theory.Our patch-based theory also offers explanation for why data augmentation techniques like Cutout, CutMix and random cropping are effective in improving the generalization error of CNNs.
Frequency aware task scheduling using<scp>DVFS</scp>for energy efficiency in Cloud data centre

Abstract Reliable processing capacity and flexible storage space make Cloud computing the most recent favourable technology. Many organizations have converted their conventional processing data centre to a Cloud data centre. Cloud computing provides promising execution and storage, which leads to massive growth in processing demand by Cloud users. It makes the Cloud data centre increase the number of virtual machines (VM) to execute the users tasks. Hence, it causes high frequency disbursed and has increased energy consumption. Many techniques were proposed, which focuses on Cloud energy saving. However, there is still a lack of trade‐off between energy‐efficient task allocation and frequency scaling for a given workload. In this work, we propose a task scheduling algorithm that aims to minimize energy consumption through the frequency scaling technique while improving task execution time. Specifically, our scheduler comprises two modules, which are the scaling frequency module and frequency‐aware task scheduling module. In our first module, we utilize Dynamic Voltage and Frequency Scaling‐Optimal Frequency (DVFS) to determine the optimal frequency and selecting the best server for the incoming tasks. The number of VM is created upon the best server. As for the second module, the VM processing capacity is scaled to the required frequency of the task. We identify it as a required processing capacity for executing the tasks. The experiment result shows that our algorithm has outperformed and efficiently minimized the energy consumption in the Cloud data centre as compared with existing energy‐saving techniques. Meanwhile, the task allocation also has met the system's Quality of Service (QoS). Significantly, leveraging the resource processing frequency is able to gain better trade‐off between performance and energy consumption in the Cloud data centre.