Reviewerly/exHarmony huggingface dataset:
Use of Blockchain to Prevent Distributed Denial-of-Service (DDoS) Attack: A Systematic Literature Review

A Distributed Denial-of-Service (DDoS) assault overwhelms a server, network, or service with Internet traffic, disrupting regular traffic. As the Internet becomes more accessible, the new attack vector includes insecure, Internet-connected IoT devices, and Denial-of-Service (DoS) vulnerabilities in SDN architecture, such as southbound channel saturation. Blockchain technology holds immutable data in blocks without login or address. A coded username established through a unique mechanism makes hacking the blocks virtually impossible. This paper reports on a systematic literature review (SLR) aimed at identifying and structuring research on the use of blockchain to prevent DDoS attacks. We systematically selected and reviewed articles published in suitable venues in this review. The research outcome provides insight into what constitutes the main contributions of the field, identifies vacancies and opportunities, and purifies several predominant future research directions.
<i>VENUS</i>: A Geometrical Representation for Quantum State Visualization

Abstract Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely‐used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well‐designed case studies and an in‐depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits.
Belief Decay or Persistence? A Mixed‐method Study on Belief Movement Over Time

Abstract When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.
Perceptual video quality assessment: the journey continues!

Perceptual Video Quality Assessment (VQA) is one of the most fundamental and challenging problems in the field of Video Engineering. Along with video compression, it has become one of two dominant theoretical and algorithmic technologies in television streaming and social media. Over the last 2 decades, the volume of video traffic over the internet has grown exponentially, powered by rapid advancements in cloud services, faster video compression technologies, and increased access to high-speed, low-latency wireless internet connectivity. This has given rise to issues related to delivering extraordinary volumes of picture and video data to an increasingly sophisticated and demanding global audience. Consequently, developing algorithms to measure the quality of pictures and videos as perceived by humans has become increasingly critical since these algorithms can be used to perceptually optimize trade-offs between quality and bandwidth consumption. VQA models have evolved from algorithms developed for generic 2D videos to specialized algorithms explicitly designed for on-demand video streaming, user-generated content (UGC), virtual and augmented reality (VR and AR), cloud gaming, high dynamic range (HDR), and high frame rate (HFR) scenarios. Along the way, we also describe the advancement in algorithm design, beginning with traditional hand-crafted feature-based methods and finishing with current deep-learning models powering accurate VQA algorithms. We also discuss the evolution of Subjective Video Quality databases containing videos and human-annotated quality scores, which are the necessary tools to create, test, compare, and benchmark VQA algorithms. To finish, we discuss emerging trends in VQA algorithm design and general perspectives on the evolution of Video Quality Assessment in the foreseeable future.
A novel stacking framework with PSO optimized SVM for effective disease classification

Disease diagnosis is very important in the medical field. It is essential to diagnose chronic diseases such as diabetes, heart disease, cancer, and kidney diseases in the early stage. In recent times, ensembled-based approaches giving effective predictive performance than individual classifiers and gained attention in assisting doctors with early diagnosis. But one of the challenges in these approaches is dealing with class-imbalanced data and improper configuration of ensemble classifiers with optimized parameters. In this paper, a novel 3-level stacking approach with ADASYN oversampling technique with PSO Optimized SVM meta-model (Stacked-ADASYN-PSO) is proposed. Our proposed Stacked-ADASYN-PSO model uses base models such as Logistic regression(LR), K-Nearest neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP) in layer-0. In layer-1 three meta classifiers namely LR, KNN, and Bagging DT are used. In layer-2 PSO optimized SVM used as the final meta-model to combine the previous layer predictions. To evaluate the robustness of the proposed model It is tested on five benchmark disease datasets from the UCI machine learning repository. These results are compared with state-of-the-art ensemble models and non-ensemble models. Results demonstrated that the proposed model performance is superior in terms of AUC, accuracy, specificity, and precision. We have performed statistical analysis using paired T-tests with a 95% confidence level and our proposed stacking model is significantly differs when compared to base classifiers.
Security in Health Information Management Records through Blockchain Technology

Medical records play a crucial role in delivering high-quality healthcare by facilitating accurate diagnosis, treatment, and patient monitoring. However, manual, paper-based record-keeping systems present various challenges, including storage burden and retrieval difficulties. Although electronic medical records (EMRs) have gained popularity, they are not risk-free, such as data manipulation. Inadequate medical records administration contributes to inefficiencies in the healthcare system, increased costs, incorrect diagnoses, and compromised patient care. This literature review investigates the potential of blockchain technology in enhancing medical record management and improving the quality, efficiency, and limitations associated with traditional manual methods. Specifically, the study explores the benefits of utilizing blockchain in medical records management. The findings suggest that blockchain technology can enhance the security, interoperability, and accessibility of medical records thereby mitigating concerns related to data manipulation while providing a more cost-effective and efficient solution for medical records management.
Quantum adversarial metric learning model based on triplet loss function

Abstract Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher ϵ -robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning.
Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

We aim to preserve a large amount of data generated inside base station-less sensor networks (BSNs) while considering that sensor nodes are selfish. BSNs refer to emerging sensing applications deployed in challenging and inhospitable environments (e.g., underwater exploration); as such, there do not exist data-collecting base stations in the BSN to collect the data. Consequently, the generated data has to be stored inside the BSN before uploading opportunities become available. Our goal is to preserve the data inside the BSN with minimum energy cost by incentivizing the storage- and energy-constrained sensor nodes to participate in the data preservation process. We refer to the problem as DPP: d ata p reservation p roblem in the BSN. Previous research assumes that all the sensor nodes are cooperative and that sensors have infinite battery power and design a minimum-cost flow-based data preservation solution. However, in a distributed setting and under different control, the resource-constrained sensor nodes could behave selfishly only to conserve their resources and maximize their benefit. In this article, we first solve DPP by designing an integer linear programming (ILP)-based optimal solution without considering selfishness. We then establish a game-theoretical framework that achieves provably truthful and optimal data preservation in BSNs. For a special case of DPP wherein nodes are not energy-constrained, referred to as DPP-W, we design a data preservation game DPG-1 that integrates algorithmic mechanism design (AMD) and a more efficient minimum cost flow-based data preservation solution. We show that DPG-1 yields dominant strategies for sensor nodes and delivers truthful and optimal data preservation. For the general case of DPP (wherein nodes are energy-constrained), however, DPG-1 fails to achieve truthful and optimal data preservation. Utilizing packet-level flow observation of sensor node behaviors computed by minimum cost flow and ILP, we uncover the cause of the failure of the DPG-1. It is due to the packet dropping by the selfish nodes that manipulate the AMD technique. We then design a data preservation game DPG-2 for DPP that traces and punishes manipulative nodes in the BSN. We show that DPG-2 delivers dominant strategies for truth-telling nodes and achieves provably optimal data preservation with cheat-proof guarantees. Via extensive simulations under different network parameters and dynamics, we show that our games achieve system-wide data preservation solutions with optimal energy cost while enforcing truth-telling of sensor nodes about their private cost types. One salient feature of our work is its integrated game theory and network flows approach. With the observation of flow level sensor node behaviors provided by the network flows, our proposed games can synthesize “microscopic” (i.e., selfish and local) behaviors of sensor nodes and yield targeted “macroscopic” (i.e., optimal and global) network performance of data preservation in the BSN.
Recent Advances in Cyber Security Laws and Practices in India

The growth of the internet and proliferation of applications, products, and services has given rise to cyber threats which require far more stringent security measures than ever before. Some common types of cybercrimes are job fraud, phishing, baiting, vishing, smishing, credit and debit card fraud, child pornography, cyberbullying, etc. Cyber laws need constant upgrading and refinement to keep pace with the increasing technology. In India, various statutes and initiatives have been launched to ensure its cyber security such as Information Technology Act, 2000 (IT Act), Indian Penal Code, 1860 (IPC), National Cybersecurity Framework (NCFS), financial assistance, Cyber Crime Prevention against Women &amp; Children (CCPWC), Indian Cyber Crime Coordination Centre (I4C), National Cyber Crime Reporting Portal, Citizen Financial Cyber Fraud Reporting and Management System, Indian Computer Emergency Response Team (CERT-In), and Ministry of Electronics &amp; Information Technology (MeitY).
Combined regional homography-affine warp for image stitching

Image stitching is the process of combining multiple images with narrow fields of view into a panoramic image with high resolution. Conventional global warp-based image stitching algorithm has limited alignment accuracy and causes shape distortion while spatially-varying warp-based ones have high computational complexity. To address these problems, we proposed a novel regional warp which adopts various transformation models to handle different areas of the image. Images can be divided into overlapping and non-overlapping regions based on the distribution of matched features. For the overlapping area, two kinds of projective transformation are adopted in combination to warp each pixel in this region. For the non-overlapping area, it is further partitioned into two regions where a projective transformation and an affine transformation are utilized separately. Experimental results show that the proposed warp not only provides good alignment accuracy but also avoids severe shape distortion.
Political economy of financial development: empirical evidence from Ethiopia

Purpose The main purpose of this study is to examine the political economy of financial development in Ethiopia, specifically, to test the empirical relevance of the interest group theory of financial development in the context of Ethiopia. Design/methodology/approach The autoregressive distributive lag model to co-integration is applied to Ethiopia’s time series data from 1990 to 2020 to identify the long- and short-run effects of the political regime characteristics on financial development of the country. Findings The findings reveal that the degree of democracy in the political system (a proxy for narrow elites) was found to have a significant positive effect on financial development in the long run but has negatively affected financial development in the short run. Similarly, the political regime durability indicator shows a positive and statistically significant effect both in the long run and short run. The macroeconomic policy indicators which are used as control variables in this study reveal significant effects on the financial development of Ethiopia. Generally, the finding supports the interest group theory of financial development. Originality/value This paper is the original work on the effect of political regime characteristics on financial development in Ethiopia. Thus, it brings substantial value to studying determinants of financial development as it goes beyond the conventional determinants by considering the role of political power in the process of financial development.
Double groups of gates based Takagi-Sugeno-Kang (DG-TSK) fuzzy system for simultaneous feature selection and rule extraction

Embedded feature selection methods based on fuzzy model have been well studied, in which the gate functions are usually used to distinguish the essential features. Fuzzy rules play a critical role in constructing fuzzy systems. However, few works simultaneously evaluate the quality of fuzzy rules during the fuzzy system based feature selection approaches. This article proposes double groups of gates based Takagi-Sugeno-Kang (DG-TSK) fuzzy model to simultaneously conduct feature selection and rule extraction in one training phase. Firstly, a novel gate function is introduced, which is embedded into the system. We call this function as M-gate since it is shaped like an M. Secondly, a new type of fuzzy rule base (FRB) is designed by transforming each data point into one fuzzy rule, which is named point-based FRB (P-FRB). Based on the M-gate and P-FRB, DG-TSK model is developed, in which M-gates are embedded in the antecedents for feature selection and consequents for rule extraction. Note that these two procedures are performed at the same time. We test DG-TSK on 18 classification datasets and compare it with other similar methods. The experiment results demonstrate that DG-TSK is effective.
Blockchain Scalability: Solutions, Challenges and Future Possibilities

In recent years, blockchain has received a lot of interest and has also been widely adopted. Yet, blockchain scalability is proving to be a difficult problem. To create a new node in platforms like Bitcoin takes few days of time. This scalability problem has few proposed solutions. The present alternatives to blockchain scalability are divided into two groups in this paper: first layer and second layer techniques. Second layer solutions suggest procedures that are deployed outside of the blockchain, while first layer methods propose adjustments to the blockchain (i.e., altering the blockchain design, such as block size). We concentrate on sharding as a viable first-layer solution to the scalability problem. The thought behind sharding is to split the blockchain network into numerous groups, each processing a different set of transactions. Furthermore, we compare few of the already available sharding-based blockchain solutions and present a performance-based comparative analysis in form of the benefits and drawbacks of the existing solutions.
A secure physical health test data sharing scheme based on token distribution and programmable blockchains

With the rapid development of computers and networks, more and more convenient services are provided relying on the Internet. In this paper, we focus on the data sharing in students’ physical health tests. To reduce the burden of filling in information manually, physical health test service providers have designed a mode of obtaining students’ information, including personal information and test results, from a trusted third-party tester for gathering all physical health items and automatically filling. This mode is called one network unified management. However, if the trusted third-party organization sends too much unnecessary information to service requester, dishonest requester will maliciously use the students’ redundant information, which causes great security risk to students’ information. To solve the problem, this paper designs a token distribution scheme adapted to that mode based on programmable blockchains. Secure and fast data sharing is approached. Both traceability and evidence collection are available. For a network with 500 participants and size of maximum token set is 5×210, data sharing can be completed within 13.04 microseconds while traceability can be completed within 3.61 microseconds of extra time consumption. Ensuring safety and traceability, the time consumption of the scheme is proved to be linear with the increasing population of participants. It is also tested with real physical health test data, proving that the scheme is available in reality.
UNLOCKING CUSTOMER SENTIMENT INSIGHTS WITH AZURE SENTIMENT ANALYSIS: A COMPREHENSIVE REVIEW AND ANALYSIS

The paper analyzes the accuracy of the Azure Sentiment Analysis service for five languages, namely Romanian, French, Italian, Portuguese, and Spanish. The study generated 300 texts for each language type expressing positive and negative sentiments with varying lengths (less than 100 characters, between 100 and 250 characters, and more than 250 characters). The Azure Sentiment Analysis Review custom-made application was developed using C# language with .NET Framework and Entity Framework for the Microsoft SQL database, and it is used to make a request to the Azure Sentiment Service, and the response sets the label into the database. The expected and Azure labels for each type of analyzed text were described as well. The accuracy of sentiment recognition for different languages and text lengths is presented in the form of statistics, with the global accuracy of the service being 81.8%. The challenges of accurately classifying the sentiment of short and long texts were highlighted. The results suggest that texts of moderate length are easier to classify.
Mask Guidance Pyramid Network for Overlapping Cervical Cell Edge Detection

An important indicator of cervical cancer diagnosis is to calculate the proportion of diseased cells and cancer cells, so it is necessary to segment cells and judge the cell status. The existing methods are difficult to deal with the segmentation of overlapping cells. In order to solve this problem, we put forward such a hypothesis by reading a large number of literature, that is, image segmentation and edge measurement tasks have unity in high-level features. To prove this hypothesis, in this paper, we focus on the complementary between overlapping cervical cell edge information and cell object information to get higher accuracy cell edge detection results. Specifically, we present a joint multi-task learning framework for overlapping cell edge detection by the mask guidance pyramid network. The main component of the framework is the Mask Guidance Module (MGM), which integrates two tasks and stores the shared latent semantics to interact in the two tasks. For semantic edge detection, we propose the novel Refinement Aggregated Module (RAM) fusion to promote semantic edges. Finally, to improve the edge pixel accuracy, the edge consistency constraint loss function is introduced to our model training. Our extensive experiments have proved that our method outperforms other edge detection efforts.