Reviewerly/exHarmony huggingface dataset:
Entanglement transfer via chiral and continuous-time quantum walks on a triangular chain

We investigate the chiral quantum walk (CQW) as a mechanism for an entanglement transfer on a triangular chain structure. We specifically consider two-site spatially entangled cases in short-time and long-time regimes. Using the concurrence as an entanglement measure; fidelity, and the Bures distance as the measure of the quality of the state transfer, we evaluate the success of the entanglement transfer. We compare the entangled state transfer time and quality in CQW against a continuous-time quantum random walk. We also observe the effect of mixed states on the entanglement transfer quality.
Ultrasonic logging image denoising algorithm based on variational Bayesian and sparse prior

An image denoising method is proposed for ultrasonic logging images with severe noise. The proposed method works on a variational Bayesian framework using block sparse prior. First, the sparse coefficients are simulated by a more appropriate distribution—Laplacian distribution. Then the variational Bayesian denoising model in which Laplacian distribution is used as a prior term of sparse coefficients is proposed. Finally, semiquadratic regularization is used to solve the model with a simplified process. Moreover, during the denoising process, a relaxation factor is introduced to improve the accuracy. In the vast majority of cases, the proposed method obtained better results in both the visual quality and the objective evaluation. It achieves better denoising performance than the existing denoising methods when the edge details of the images are contaminated by noise, especially severe noise. The experimental results show that the proposed method is practical in ultrasonic logging images.
Estimated Cases Averted by COVID-19 Digital Exposure Notification, Pennsylvania, USA, November 8, 2020–January 2, 2021

We combined field-based data with mathematical modeling to estimate the effectiveness of smartphone-enabled COVID-19 exposure notification in Pennsylvania, USA. We estimated that digital notifications potentially averted 7-69 cases/1,000 notifications during November 8, 2020-January 2, 2021. Greater use and increased compliance could increase the effectiveness of digital notifications.
A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities

There is an increasing demand for processing large volumes of unstructured data for a wide variety of applications. However, protection measures for these big data sets are still in their infancy, which could lead to significant security and privacy issues. Attribute-based access control (ABAC) provides a dynamic and flexible solution that is effective for mediating access. We analyzed and implemented a prototype application of ABAC to large dataset processing in Amazon Web Services, using open-source versions of Apache Hadoop, Ranger, and Atlas. The Hadoop ecosystem is one of the most popular frameworks for large dataset processing and storage and is adopted by major cloud service providers. We conducted a rigorous analysis of cybersecurity in implementing ABAC policies in Hadoop, including developing a synthetic dataset of information at multiple sensitivity levels that realistically represents healthcare and connected social media data. We then developed Apache Spark programs that extract, connect, and transform data in a manner representative of a realistic use case. Our result is a framework for securing big data. Applying this framework ensures that serious cybersecurity concerns are addressed. We provide details of our analysis and experimentation code in a GitHub repository for further research by the community.
Study and Design of Distributed Badminton Agility Training and Test System

In order to improve the agility of college students, this paper designs a distributed agility training system. The system includes an upper computer and nine lower computers, in which the lower computer realizes the functions of data acquisition and communication with the upper computer and calculates the reaction time. The Android-based system software was installed in the upper computer to complete the functions of network connection, setting training times and showing the exercise time. In order to test the effectiveness of the equipment, nine university students were invited to complete agility training over 8 weeks with the help of agility training equipment in preparatory, enhancement and special stages. A t-test (Student’s t test) was conducted on the test results at different positions on the front and middle and back areas of the court before and after the training. The results show that the agility of the experimental objects was significantly improved after training, from the midpoint to any point at the front, middle and back court (p &lt; 0.01). This shows that using equipment designed to develop agility for long-term training can promote the sensitive quality in badminton learners.
Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain

With the advancement of cyber threats, blockchain technology has evolved to have a significant role in providing secure and reliable decentralized applications. One of these applications is a remote voting system that allow voters to participate in elections remotely. This work proposes a privacy-preserving e-voting system supporting score voting using blockchain technology. The main challenge with score voting compared to the regular yes/no voting approach is that a voter is allowed to assign a score from a defined range for each candidate. To preserve privacy, votes shall be encrypted before submission to the Blockchain, however, a malicious voter can modify the score value before encrypting it to manipulate the elections result for the favor of a certain candidate. To address this challenge, the proposed scheme allows voters to first prove that the submitted score lies in the predefined range before the vote is added to the Blockchain to ensure fairness of the election. The performance of our scheme is evaluated against a set of comprehensive experiments designed to determine optimal bounds for workload and transaction send rates and measure the impact of exceeding these bounds on critical performance metrics. The results of these simulations and their implications therefore indicate that the proposed scheme is secure while being able to handle up to 10,000 transactions at a time.
Short-Term Photovoltaic Power Forecasting Based on a Novel Autoformer Model

Deep learning techniques excel at capturing and understanding the symmetry inherent in data patterns and non-linear properties of photovoltaic (PV) power, therefore they achieve excellent performance on short-term PV power forecasting. In order to produce more precise and detailed forecasting results, this research suggests a novel Autoformer model with De-Stationary Attention and Multi-Scale framework (ADAMS) for short-term PV power forecasting. In this approach, the multi-scale framework is applied to the Autoformer model to capture the inter-dependencies and specificities of each scale. Furthermore, the de-stationary attention is incorporated into an auto-correlation mechanism for more efficient non-stationary information extraction. Based on the operational data from a 1058.4 kW PV facility in Central Australia, the ADAMS model and the other six baseline models are compared with 5 min and 1 h temporal resolution PV power data predictions. The results show in terms of four performance measurements, the proposed method can handle the task of projecting short-term PV output more effectively than other methods. Taking the result of predicting the PV energy in the next 24 h based on the 1 h resolution data as an example, MSE is 0.280, MAE is 0.302, RMSE is 0.529, and adjusted R-squared is 0.824.
Comparative Case Study of Teamwork on Zoom and Gather.Town

Teamwork is a strategy for successful learning. With the Coronavirus outbreak, many universities began to rely on synchronous video conferencing and/or metaverse platforms. This study examines the difference between undergraduate students’ perceptions and experiences of teamwork on Zoom and Gather.Town. A mixed-method comparative case study was conducted in which a questionnaire survey was administered to 20 undergraduate students in Korea, followed by in-depth interviews and participant observation; reflective journal writing was also examined. The data were quantitatively and qualitatively analyzed. The results show that the students had a higher perception of teamwork on Gather.Town than on Zoom. Gather.Town was effective because of the sense of presence and mobility of space it afforded, the social presence it facilitated through avatars, empowerment, and openness of emotions, and the differences in the interface and social platform. The findings can aid in the selection of platforms to suit the needs of students and instructors and in the design and implementation of effective teamwork activities on the selected platform.
Extractive Arabic Text Summarization-Graph-Based Approach

With the noteworthy expansion of textual data sources in recent years, easy, quick, and precise text processing has become a challenge for key qualifiers. Automatic text summarization is the process of squeezing text documents into shorter summaries to facilitate verification of their basic contents, which must be completed without losing vital information and features. The most difficult information retrieval task is text summarization, particularly for Arabic. In this research, we offer an automatic, general, and extractive Arabic single document summarizing approach with the goal of delivering a sufficiently informative summary. The proposed model is based on a textual graph to generate a coherent summary. Firstly, the original text is converted to a textual graph using a novel formulation that takes into account sentence relevance, coverage, and diversity to evaluate each sentence using a mix of statistical and semantic criteria. Next, a sub-graph is built to reduce the size of the original text. Finally, unwanted and less weighted phrases are removed from the summarized sentences to generate a final summary. We used Recall-Oriented Research to Evaluate Main Idea (RED) as an evaluative metric to review our proposed technique and compare it with the most advanced methods. Finally, a trial on the Essex Arabic Summary Corpus (EASC) using the ROUGE index showed promising results compared with the currently available methods.
Descriptive Markers for the Cognitive Profiling of Desktop 3D Spaces

3D virtual reality spaces, whether running on desktop environments or on immersive displays, have been noted to support a radically new and highly stimulating way of working with digital content in a variety of application domains. At the same time, research in recent decades has produced a number of experimental results showing that the use of 3D, as opposed to 2D interfaces, can lead to performance improvements from a wide range of aspects, including the ability to comprehend and retain knowledge, ability to work collaboratively in more creative and effective ways, and ability to carry out workflows integrating numerous sources of information in less time. In this paper, we first review the relevant literature; then, we describe an exploratory study that we carried out with test subjects, both in a 3D desktop virtual environment and in a 2D web-based environment, while collecting eye tracking data. In the study, subjects were presented with a set of multimedia content on a range of topics within the field of astronomy, based on which they were subsequently asked to fill out a set of questionnaires. By comparing the 2D and 3D cases in terms of correctness of answers, time taken to perform the task, pupil dilation measurements, subjects’ self-reported difficulty assessments, as well as various kinds of high-level interaction patterns employed during the task (in 3D), we were able to identify a set of descriptive markers which may be relevant to the prediction of users’ effectiveness in virtual reality workspaces. In a weaker sense, the results also seem to support previous research works claiming improved effectiveness in 3D spaces compared to 2D web-based interfaces, although further work is needed to more clearly identify the constraints within which such benefits can be guaranteed.
Blockchain-Based Authentication Scheme for Collaborative Traffic Light Systems Using Fog Computing

In the era of the Fourth Industrial Revolution, cybercriminals are targeting critical infrastructures such as traffic light systems and smart grids. A major concern is the security of such systems, which can be broken down into a number of categories, such as the authentication of data collection devices, secure data transmission, and use of the data by authorized and authenticated parties. The majority of research studies in the literature have largely focused on data integrity and user authentication. So far, no published work has addressed the security of a traffic light system from data collection to data access. Furthermore, it is evident that the conventional cloud computing architecture is incapable of analyzing and managing the massive amount of generated data. As a result, the fog computing paradigm combined with blockchain technology may be the best way to ensure data privacy in a decentralized manner while reducing overheads, latency, and maintaining security. This paper presents a blockchain-based authentication scheme named VDAS using the fog computing paradigm. The formal and informal verifications of the proposed solution are presented. The evaluation of the proposed scheme VDAS showed that it has low communication and computation costs compared to existing lightweight authentication techniques.
Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection

Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects.
Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications

The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.
Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection

Anomalies are a set of samples that do not follow the normal behavior of the majority of data. In an industrial dataset, anomalies appear in a very small number of samples. Currently, deep learning-based models have achieved important advances in image anomaly detection. However, with general models, real-world application data consisting of non-ideal images, also known as poison images, become a challenge. When the work environment is not conducive to consistently acquiring a good or ideal sample, an additional adaptive learning model is needed. In this work, we design a potential methodology to tackle poison or non-ideal images that commonly appear in industrial production lines by enhancing the existing training data. We propose Hierarchical Image Transformation and Multi-level Features (HIT-MiLF) modules for an anomaly detection network to adapt to perturbances from novelties in testing images. This approach provides a hierarchical process for image transformation during pre-processing and explores the most efficient layer of extracted features from a CNN backbone. The model generates new transformations of training samples that simulate the non-ideal condition and learn the normality in high-dimensional features before applying a Gaussian mixture model to detect the anomalies from new data that it has never seen before. Our experimental results show that hierarchical transformation and multi-level feature exploration improve the baseline performance on industrial metal datasets.
Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts

Abstract Objectives To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts. Methods 430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset ( n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden’s index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity. Results The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden’s index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve. Conclusion Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.
Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

Abstract Metaheuristic optimization algorithms (MOAs) are computational randomized search processes which draw inspiration from physical and biological phenomena, with an application spectrum that extends to numerous fields, ranging from engineering design to economics. MOAs were originally developed for solving unconstrained NP-complete problems, and hence their application to constrained optimization problems (COPs) requires the implementation of specialized techniques that facilitate the treatment of performance and bound constraints. While considerable research efforts have been oriented towards the development and subsequent enhancement of novel constraint handling techniques (CHTs) for MOAs, a systematic review of such techniques has not been conducted hitherto. This work presents a state-of-the-art review on CHTs used with MOAs and proposes eight novel variants based on the feasibility rules and ε-constrained techniques. The distinctive feature of the new variants is that they consider the level and number of constraint violations, besides the objective function value, for selection of individuals within a population. The novel variant performance is evaluated and compared with that of four well-known CHTs from the literature using the metaheuristic pity beetle algorithm, based upon 20 single-objective benchmark COPs. The computational results highlight the accuracy, effectiveness, and versatility of the novel variants, as well as their performance superiority in comparison with existing techniques, stemming from their distinctive formulation. The complete code can be downloaded from GitHub ( https://github.com/nikoslagaros/MOAs-and-CHTs ).
A privacy-dependent condition-based privacy-preserving information sharing scheme in online social networks

Privacy leakage resulting from information sharing in online social networks (OSNs) is a serious concern for individuals. One of the culprits behind the problem is that existing privacy policies developed for OSNs are not fine-grained or flexible enough, resulting in privacy settings that could hardly meet the privacy requirements of individuals. Neither would such privacy settings allow individuals to control where the information could go. In addition, there are hardly any effective mechanisms for measuring potential threats to privacy during information propagation. To alleviate the situation, in this paper, we propose a novel privacy-preserving information sharing scheme for OSNs in which information flow can be controlled according to the privacy requirements of the information owner and the context of the information flow. Specifically, we first formally define the privacy-dependent condition (PDC) for information sharing in OSNs and then design a PDC-based privacy-preserving information sharing scheme (PDC-InfoSharing) to protect the privacy of individuals according to the heterogeneous privacy requirements of individuals as well as the potential threats that they may face. Furthermore, to balance information sharing and privacy protection, the techniques of reinforcement learning is utilized to help individuals reach a trade-off. PDC-InfoSharing would allow the privacy policies for specific information audience to be derived based on PDC to achieve dynamical control of the flow of information. Theoretical analysis proves that the proposed scheme can assist individuals in adopting fine-grained privacy policies and experiment shows that it can adapt to different situations to help individuals achieve the trade-off between information sharing and privacy protection.