Reviewerly/exHarmony huggingface dataset:
Blockchain-based verifiable and dynamic multi-keyword ranked searchable encryption scheme in cloud computing

Rational miners in the blockchain may admit the validity of search results directly to conserve calculation overhead without executing the validation. Such phenomenon is known as the Verifier's Dilemma that the search results of the blockchain-based searchable encryption scheme would be claimed to have been verified but devoid of valid verification. Besides, the top-k query technique utilized by most existing searchable encryption schemes fails to filter the search results from the user demands during retrieval, which results in massive ineffective access lists with high complexity. Aiming at the two problems, we present a blockchain-based multi-keyword ranked searchable encryption scheme and design a fair payment protocol for multi-user scenarios. Our scheme may achieve public verifiability and user fairness by leveraging the TrueBit network, while the data owner (DO) can withdraw search privileges from the authorized data user (DU). Also, we extend the searchable encryption scheme based on inverted index to multi-DO/multi-DU scenarios, and the HashMap index is used to diminish the response time and space complexity. Moreover, the proposed scheme supports the update of index keywords and files dynamically. To enhance retrieval accuracy, we modify the information divergence algorithm in machine learning and utilize the monotonically weighted aggregation function, which improves retrieval accuracy to around 97%. Finally, Security analysis illustrates correctness and resilience against typical attacks to verify the robustness and utility of the proposed scheme.
A Novel Method for Image to Text Extraction Using Tesseract-OCR

Text extraction process can play a vital role for detecting valuable information from a selected image. This text extraction process involves text detection, localization, marking, tracking, extraction, enhancement and finally recognition task. It is a difficult task to detect these text characters, because of their variation of size, style, font, orientation, alignment, contrast, color and textured background. There is a growing demand of information detection, indexing and retrieval from various multimedia documents nowadays. Several methods have been developed for extraction of text from an image. This article proposes a novel method for image to text extraction. In this paper, we are presenting a multiresolution morphology based text segmentation process suitable for various types of non-text elements like drawing, pictures, halftones or etc. For image processing, python library OpenCV is used and for text extraction Tessaract is used. Python Imaging Library (PIL) is capable to handle the opening and manipulation of images in many formats in Python. Also we are in testing of such an application that can give output in every language correctly.
Transformations of Stabilizer States in Quantum Networks

Stabilizer states and graph states find application in quantum error correction, measurement-based quantum computation and various other concepts in quantum information theory. In this work, we study party-local Clifford (PLC) transformations among stabilizer states. These transformations arise as a physically motivated extension of local operations in quantum networks with access to bipartite entanglement between some of the nodes of the network. First, we show that PLC transformations among graph states are equivalent to a generalization of the well-known local complementation, which describes local Clifford transformations among graph states. Then, we introduce a mathematical framework to study PLC equivalence of stabilizer states, relating it to the classification of tuples of bilinear forms. This framework allows us to study decompositions of stabilizer states into tensor products of indecomposable ones, that is, decompositions into states from the entanglement generating set (EGS). While the EGS is finite up to $3$ parties [Bravyi et al., J. Math. Phys. {\bf 47}, 062106~(2006)], we show that for $4$ and more parties it is an infinite set, even when considering party-local unitary transformations. Moreover, we explicitly compute the EGS for $4$ parties up to $10$ qubits. Finally, we generalize the framework to qudit stabilizer states in prime dimensions not equal to $2$, which allows us to show that the decomposition of qudit stabilizer states into states from the EGS is unique.
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation

Recently, privacy issues in web services that rely on users’ personal data have raised great attention. Despite that recent regulations force companies to offer choices for each user to opt-in or opt-out of data disclosure, real-world applications usually only provide an “all or nothing” binary option for users to either disclose all their data or preserve all data with the cost of no personalized service. In this article, we argue that such a binary mechanism is not optimal for both consumers and platforms. To study how different privacy mechanisms affect users’ decisions on information disclosure and how users’ decisions affect the platform’s revenue, we propose a privacy-aware recommendation framework that gives users fine control over their data. In this new framework, users can proactively control which data to disclose based on the tradeoff between anticipated privacy risks and potential utilities. Then we study the impact of different data disclosure mechanisms via simulation with reinforcement learning due to the high cost of real-world experiments. The results show that the platform mechanisms with finer split granularity and more unrestrained disclosure strategy can bring better results for both consumers and platforms than the “all or nothing” mechanism adopted by most real-world applications.
Hardware Optimizations of Fruit-80 Stream Cipher: Smaller than Grain

Fruit-80, which emerged as an ultra-lightweight stream cipher with 80-bit secret key, is oriented toward resource-constrained devices in the Internet of Things. In this article, we propose area and speed optimization architectures of Fruit-80 on FPGAs. Our implementations include both serial and parallel structure and optimize area, power, speed, and throughput, respectively. The area optimization architecture aims to achieve the most suitable ratio of look-up-tables and flip-flops to fully utilize the reconfigurable unit. It also reuses NFSR and LFSR feedback functions to save resources for high throughput. The speed optimization architecture adopts a hybrid approach for parallelization and reduces the latency of long data paths by pre-generating primary feedback and inserting flip-flops. Besides, we recommend using the round key function to optimize serial or parallel implementations for Fruit-80 and using indexing and shifting methods for different throughput. In conclusion, our results show that the area optimization architecture occupies up to 35 slices on Xilinx Spartan-3 FPGA and 18 slices on Xilinx 7 series FPGA, smaller than that of Grain and other common stream ciphers. The optimal throughput/area ratio of the speed optimization architecture is 7.74 Mbps/slice, better than that of Grain v1, which is 5.98 Mbps/slice. The serial implementation of Fruit-80 with round key function occupies only 75 slices on Spartan-3 FPGA. To the best of our knowledge, the result sets a new record of the minimum area in lightweight cipher implementation on FPGA.
Gaussian Mixture Model-Based Online Anomaly Detection for Vectored Area Navigation Arrivals

Identifying anomalous flight trajectories is crucial in airspace operations, as they can potentially lead to safety risks. One of the challenges in identifying abnormal aircraft trajectories in the vectored area navigation (RNAV) terminal airspace is distinguishing between anomalies and trajectories vectored from the structured procedures. Applying existing trajectory pattern identification algorithms to vectored trajectories could create patterns with wide variation within; hence, they cannot effectively discern anomalies from nominal vectored trajectories. In addition, most existing anomaly detection algorithms are developed to detect anomalies in historical air traffic surveillance data, and thus, they cannot be readily applicable for online implementation. To address these problems, an online anomaly detection algorithm for vectored flights in RNAV terminal airspace based on the Gaussian mixture model (GMM) is proposed, which can deal with highly complex airspace operations by incorporating the GMM with dynamic trajectory pattern classification and hybrid trajectory prediction. The proposed algorithm is demonstrated with real air traffic surveillance data at Incheon International Airport (ICN), South Korea.
Prediction of Venous Thrombosis Chinese Electronic Medical Records Based on Deep Learning and Rule Reasoning

Aiming at the problems of heavy workload of medical staff in the process of venous thrombosis prevention and treatment, error evaluation, missed evaluation, and inconsistent evaluation, we propose a joint extraction model of Chinese electronic medical records based on deep learning. The approach was to first construct the handshake annotation, then use bidirectional encoder representations from transformers (BERT) as the word vector embedding, then use the bidirectional long short-term memory network (BiLSTM) to extract the contextual features, and then integrate the contextual information into the process of normalizing the word vector. Experiments show that our proposed method achieves 93.3% and 94.3% of entity and relation F1 on the constructed electronic medical record dataset, which effectively improves the effect of medical information extraction. At the same time, the venous thromboembolism (VTE) risk factors extracted from the electronic medical record were used to judge the risk factors of venous thrombosis by means of rule reasoning. Compared with the assessment of clinicians on the Wells and Geneva scales, the accuracy rates of 84.7% and 86.1% were obtained.
Smart Scalable ML-Blockchain Framework for Large-Scale Clinical Information Sharing

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results.
The role of mobile health technologies in promoting COVID-19 prevention: A narrative review of intervention effectiveness and adoption

Researchers have found innovative ways of using mobile health (mHealth) technologies to prevent the spread of coronavirus disease 2019 (COVID-19). However, fewer studies have been done to determine their adoption and effectiveness.This review summarises the published evidence on the effect of mHealth technologies on the adoption of COVID-19 preventive measures, prevention knowledge acquisition and risk perception as well as technology adoption features for COVID-19 prevention.PubMed, IEEE and Google Scholar databases were searched for peer-reviewed literature from 1 January 2020 to 31 March 2022 for studies that evaluated the effect of mHealth technologies on COVID-19 preventive measures adoption, prevention knowledge acquisition and risk perception. Thirteen studies met the inclusion criteria and were included in this review. All the included studies were checked for quality using the mHealth evidence reporting and assessment (mERA) checklist.The review found out that the utilisation of mHealth interventions such as alert text messages, tracing apps and social media platforms was associated with adherence behaviour such as wearing masks, washing hands and using sanitisers, maintaining social distance and avoiding crowded places. The use of contact tracing was linked to low-risk perception as users considered themselves well informed about their status and less likely to pose transmission risks compared to non-users. Privacy and security issues, message personalisation and frequency, technical issues and trust concerns were identified as technology adoption features that influence the use of mHealth technologies for promoting COVID-19 prevention.Utilisation of mHealth may be a feasible and effective way to prevent the spread of COVID-19. However, the small study samples and short study periods prevent generalisation of the findings and calls for larger, longitudinal studies that encompass diverse study settings.
On the Explainability of Graph Convolutional Network With GCN Tangent Kernel

Graph convolutional network (GCN) is a powerful deep model in dealing with graph data. However, the explainability of GCN remains a difficult problem since the training behaviors for graph neural networks are hard to describe. In this work, we show that for GCN with wide hidden feature dimension, the output for semisupervised problem can be described by a simple differential equation. In addition, the dynamics of the behavior of output is decided by the graph convolutional neural tangent kernel (GCNTK), which is stable when the width of hidden feature tends to be infinite. And the solution of node classification can be explained directly by the differential equation for a semisupervised problem. The experiments on some toy models speak to the consistency of the GCNTK model and GCN.
Self-aware Optimization of Adaptation Planning Strategies

In today’s world, circumstances, processes, and requirements for software systems are becoming increasingly complex. To operate properly in such dynamic environments, software systems must adapt to these changes, which has led to the research area of Self-Adaptive Systems (SAS). Platooning is one example of adaptive systems in Intelligent Transportation Systems, which is the ability of vehicles to travel with close inter-vehicle distances. This technology leads to an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. However, the No-Free-Lunch theorem states that the performance of one adaptation planning strategy is not necessarily transferable to other problems. Moreover, especially in the field of SAS, the selection of the most appropriate strategy depends on the current situation of the system. In this article, we address the problem of self-aware optimization of adaptation planning strategies by designing a framework that includes situation detection, strategy selection, and parameter optimization of the selected strategies. We apply our approach on the case study platooning coordination and evaluate the performance of the proposed framework.
Awareness and Use of Remote Access to Electronic Resources by Postgraduate Students in a University

AbstractAbstractOff-campus access provides seamless and reliable access to licensed resources of libraries to multiple users, anywhere and anytime simultanously. Using a purposive sampling design, one hundred and forty-two postgraduate students were selected during information literacy training sessions to participate in this study. This study used a structured questionnaire as the data collection instrument. The study results revealed that the students are aware of the remote access facility. Many respondents were accessing the off-campus access facility from their homes and offices. Some recommendations were made to encourage the use of the remote access facility.Keywords: Off-campus accessremote accessremote authenticatione-resourceselectronic journalselectronic resources Additional informationFundingThe author(s) received no financial support for this article’s research, authorship, and/or publication.
Scalability and Performance of LiDAR Point Cloud Data Management Systems: A State-of-the-Art Review

Current state-of-the-art point cloud data management (PCDM) systems rely on a variety of parallel architectures and diverse data models. The main objective of these implementations is achieving higher scalability without compromising performance. This paper reviews the scalability and performance of state-of-the-art PCDM systems with respect to both parallel architectures and data models. More specifically, in terms of parallel architectures, shared-memory architecture, shared-disk architecture, and shared-nothing architecture are considered. In terms of data models, relational models, and novel data models (such as wide-column models) are considered. New structured query language (NewSQL) models are considered. The impacts of parallel architectures and data models are discussed with respect to theoretical perspectives and in the context of existing PCDM implementations. Based on the review, a methodical approach for the selection of parallel architectures and data models for highly scalable and performance-efficient PCDM system development is proposed. Finally, notable research gaps in the PCDM literature are presented as possible directions for future research.
A Comprehensive Survey on the Non-Invasive Passive Side-Channel Analysis

Side-channel analysis has become a widely recognized threat to the security of cryptographic implementations. Different side-channel attacks, as well as countermeasures, have been proposed in the literature. Such attacks pose a severe threat to both hardware and software cryptographic implementations, especially in the IoT environment where the attacker may easily gain physical access to a device, leaving it vulnerable to tampering. In this paper, we provide a comprehensive survey regarding the non-invasive passive side-channel analysis. We describe both non-profiled and profiled attacks, related security metrics, countermeasures against such attacks, and leakage-assessment methodologies, as available in the literature of more than twenty years of research.
Automatic Shuttlecock Fall Detection System in or out of a Court in Badminton Games—Challenges, Problems, and Solutions from a Practical Point of View

We built an Instant Review System (IRS) for badminton, also named a Challenge System. It allows players to verify linesmen in/out decisions and makes the game fairer. Elements such as lighting, the influence of air-conditioning on the flight trajectory, or the moving mats can significantly impact the final in/out decision. Due to the construction of the shuttlecock, it behaves differently during the flight than, for example, a tennis ball. This publication discusses the problems we encountered during our work with the proposed solution. We present the evolution of the system's architecture: the first version with the cameras mounted above the court and placed around the court close to the lines, tracking the shuttlecock in 3D; and the second, improved version with cameras placed only around the court, without 3D reconstruction. We used our system during the BWF World Senior Badminton Championships in Katowice. We present the system's results from this tournament and compare them with linesmen's decisions. We describe the system's verification process by the Badminton World Federation and Polish Badminton Federation and discuss evaluation methods for such systems. Our solution is comparable to the commercial product used in the biggest badminton tournaments in regard to processing time and accuracy. Still, our architecture and algorithms make installing it much easier and faster, making the system more adaptive, reliable, flexible, and universal in relation to the practical requirements of sports halls.
Snooker Statistics and Zipf’s Law

Zipf's law is well known in linguistics: the frequency of a word is inversely proportional to its rank. This is a special case of a more general power law, a common phenomenon in many kinds of real-world statistical data. Here, it is shown that snooker statistics also follow such a mathematical pattern, but with varying (estimated) parameter values. Two types of rankings (prize money earned and centuries scored), and three time-frames (all-time, decade, and year) are considered. The results indicate that the power law parameter values depend on the type of ranking used as well as the time-frame considered. Furthermore, in some cases the resulting parameter values vary significantly over time, for which a plausible explanation is provided.
Systemic Acquired Critique of Credit Card Deception Exposure through Machine Learning

A wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.