Reviewerly/exHarmony huggingface dataset:
Phase‐coded radar waveform design with quantum annealing

Abstract The Integrated Side Lobe Ratio (ISLR) problem the authors consider here consists in finding optimal sequences of phase shifts in order to minimise the mean squared cross‐correlation side lobes of a transmitted radar signal and a mismatched replica. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP‐hard. In their work, the authors aim to take advantage of the scalability of quantum computing to find new optima, by solving the ISLR problem on a quantum annealer. This quantum device is designed to solve quadratic optimisation problems with binary variables (QUBO). After proposing suitable formulation for different instances of the ISLR, the authors discuss the performances and the scalability of their approach on the D‐Wave quantum computer. More broadly, their work enlightens the limits and potential of the adiabatic quantum computation for the solving of large instances of combinatorial optimisation problems.
Maintaining User Security in Consumer Electronics-Based Online Recommender Systems Using Federated Learning

The recommender system is an important component of applications that are used in the real world. In this era of information overload, it has evolved into a necessity and a lucrative market for many online businesses, like consumer electronics and others. The service cant make personalized recommendations without having access to private data like a users likes and dislikes, location, and purchasing history. Users are worry of giving out too much personal data, though, because it can be mistreated so readily. Thus, there are inescapable security issues that will be exposed through attempts at unauthorized entry while offering the recommendation solutions, especially in consumer electronics. While providing the recommendation, the important details must be hidden from the network to avoid privacy violations. This is accomplished by employing a federated learning method that is both successful and cost-effective. The users in the Federated Learning approach utilize their privately saved data and algorithms for prediction and computing model changes after receiving a primary machine learning model from the server. This work suggests a federated learning-based paradigm to perform matrix factorization through collaborative filtering. Our suggested method is easier to use and more reliable in practical settings when contrasted to a bench-mark federated recommender system. Our algorithm is tested on benchmark data, and its performance is measured in terms of the root-mean-squared error (RMSE) of user evaluation predictions. The suggested methodology outperforms other current solutions and displays considerably higher accuracy (96.32%) in terms of computational and communication overhead.
Empowering Privacy: Harnessing Hyperledger Fabric to Safeguard EHR Systems

The Blockchain boom began with the debut of Bitcoin. The application of blockchain technology is expanding rapidly. Various sectors such as supply chain, logistics, research, healthcare, government, banking, media, and entertainment have already embraced this ground-breaking, decentralized technology. The healthcare industry is at the top of the list with significant blockchain potential. This article discusses the permissioned blockchain powered by Hyperledger Fabric and its privacy-preserving features like identity mixer, multichannel, private data collections, and transient field. This study considers the EHR systems scenario and proves how these privacy protection techniques of Fabric could protect the privacy of healthcare organizations' sensitive data. We evaluate existing studies on the use of the Hyperledger Fabric framework for EHR systems. We discovered that their implementation has data privacy and user privacy concerns that can be addressed in our future studies.
A Decision Fusion Framework for ISAR Images Recognition Based On Scattering Center and Zernike Moment

Under the condition of large difference in imaging angle, Inverse Synthetic Aperture Radar (ISAR) images vary greatly and are difficult to identify. To solve this problem, a decision fusion framework based on scattering centers and Zernike Moment is proposed. In this framework, each feature corresponds to a classifier, then these classifiers are combined together by assigning different weights which reflect the ability of the classifiers. Experiments conducted on simulated ISAR images demonstrate that the proposed method can combine the outputs of the classifiers effectively and achieve better performance than single classifier.
VANET Network Traffic Anomaly Detection Using GRU-Based Deep Learning Model

The rise of Vehicular Ad-hoc Networks (VANETs) has led to the growing significance in intelligent transportation systems. This research suggests a deep learning model for anomaly detection based on GRU over VANET network traffic to address this challenge. Consumer electronics technologies can be successfully introduced to the market in one of two ways: either there is a clear benefit for the customer from using this technology, or it is required by a regulatory order that prevents the use of alternatives. It is possible to detect unknown assaults and DoS floods using traffic anomalies. Users can keep track of the security features of multimedia services by using Traffic Anomaly Detection, which provides an overview of traffic anomaly detection analysis. Anomaly detection methods fall into three categories: unsupervised, semi-supervised, and supervised. The right anomaly detection technique basically depends on the labels that are present in the dataset. To further improve the accuracy of proposed model, a new semi-supervised technique for detecting VANET network activity anomalies called SEMI-GRU has been proposed. The results demonstrate that proposed GRU-based deep learning model outperforms existing methods in detecting network anomalies with low false positive rates.
Design and implementation of scenic area information management system based on WebGIS

Location-based map services are developing rapidly, and detailed scenic area information has important reference value for tourists. However, it is difficult to obtain scenic area information and the information obtained is not detailed and accurate, and the overexploitation of scenic areas has led to the lack of attention to the precious natural resources of scenic areas. This system collects the basic information resources and basic geographic information resources of scenic areas, selects the development scheme of the system from the perspective of users, and realizes the specific module development of scenic area management system with WebGIS technology OpenLayers, PostgreSQL and Vue framework. Design the database through E-R diagram and database table, and build the overall system architecture and various functional module design to realize the integration, management, query and analysis of scenic area information. Then, taking the Baili Cuckoo Scenic Area as the test and research area data, the system module was tested and improved, and the results showed that the system basically met the expectations and operated stably.
Is it the new Google: Impact of ChatGPT on Students' Information Search Habits

The traditional subject of information search and retrieval (IR) paradigm shifted to an entirely new era since artificial intelligence (AI) techniques were introduced into the field. Browser-based IR solutions powered by AI for personalised recommendations-based information retrieval, such as the Google search engine, were one of the early examples. The IR field has advanced to its next level with the newest conversational applications based on large language model (LLM) techniques. It is becoming clear that Generative Pretrained Transformer (GPT) applications such as ChatGPT will significantly impact information retrieval behaviour in the education sector. Though this application has become widespread in acclaim, no previous study has shown its impact on information seeking and retrieval. However, based on the observation of the fast penetration of this technology and the growth of public interest, a pre-assumption was built that it is essential to investigate if students may also be showing a similar interest in this new tool. Hence, this study is set up to systematically and empirically explore how ChatGPT influences the IR behaviour of students in HEIs. A survey approach is utilised to collect the perceived IR behaviour through a questionnaire administered to 60 students in HEIs. The findings reveal that the tool is already widely known among HEI students. They also perceived the use of the tool in the context of information retrieval and proclaimed its usefulness, acknowledging its efficiency (reduced time) in finding information. Furthermore, the technology has considerably affected the typical use of other conventional information retrieval and search engine tools. On the contrary, 10% of the respondents are less likely to use ChatGPT during information seeking for various reasons, from credibility and relevance to technology infrastructure issues such as connectivity. Although a deeper analysis is required to establish a general conclusion on how and in which ways GPT-based models will override contemporary IR practices, the study outcome provides evidence for a possible behavioural change among HEI students in their IR habits in the future.
Simulation of hybrid mobile navigation information free switching in virtual reality

Aiming at the problems of poor real-time performance and low success rate of mobile navigation information free switching caused by improper use of mobile navigation information free switching parameters, a hybrid mobile navigation information free switching method is designed by introducing virtual reality technology. The accelerated robust feature algorithm is used to identify massive navigation scenes, track the feature points of navigation scenes, and obtain 2D navigation information. On this basis, X3D technology is used to create virtual reality scene and obtain 3D navigation information. According to the obtained two-dimensional and three-dimensional navigation information, the navigation information switching process is designed, the switching parameters are defined, and the navigation information switching execution rules are specified, so as to realize the free switching of hybrid mobile navigation information in virtual reality. The simulation results show that compared with the existing hybrid mobile navigation information free switching methods, the hybrid mobile navigation information free switching method proposed in this paper greatly improves the real-time performance and success rate of switching, which fully shows that the proposed hybrid mobile navigation information free switching method has better navigation information switching effect.
A Cybersecurity Transaction Energy System Using Multi-Tier Blockchain

Cybersecurity incidents are becoming more frequent due to the high degree of penetration that information and communication technologies have in our daily lives. One of the critical infrastructures that has benefited the most in recent years from the broad integration of technologies has been the smart grid. Smart metering systems allow, among other things, the monitoring of energy consumption and production readings that are translated into monetary transactions. The tampering and manipulation of the smart meter readings are reflected in economic losses for the utilities and loss of confidence in the end-users. This work presents a cybersecurity architecture based on a multitier blockchain capable of adapting to smart metering systems' architecture through an edge-fog-cloud distributed computing scheme. The proposed architecture is highly scalable to the various components of smart metering systems and improves the performance of blockchains in aspects such as storage and processing. This blockchain uses its own consensus algorithm proof-of-efficiency, which allows benefiting end-users through more efficient use of their energy consumption considering the power quality, the forecast of the demand, and the support for detecting theft and energy fraud. The consensus algorithm uses the same architecture proposed to determine users' rewards through data analytics and machine learning techniques. All of this lays the foundation for a more intelligent, more transactional, and cybersecure metering system. The architecture developed was tested to guarantee the cybersecurity of the transactions carried out in the smart metering systems. The results obtained suggest that using a blockchain architecture allows improving the cybersecurity of smart metering systems and giving end-users greater confidence in their energy transactions, being able to receive better economic incentives by making more efficient use of their energy consumption.
Hyperspectral Image Denoising with Discrete Cosine Transform and CNN Denoiser

Hyperspectral image (HSI) captures rich spectral information in more than hundreds of spectral bands, which allows far better discrimination between ground objects compared with the conventional optical images. Therefore, HSIs find a number of applications in Earth observation, precision agriculture and environmental monitoring. However, due to the effect of poor imaging condition, hardware limitation and sensor noise, the acquisition of HSIs is inevitably affected by noise, which hinders accurate interpretation of HSI in real applications. A number of denoising methods have been proposed for HSI, including BM4D [1] , LRMR [2] , LRTV [3] . Recent works such as HSID-CNN [4] , which adopt deep learning based technique, have yielded the advanced performance. However, most of them design neural network architecture to process noisy data in a spatial domain. It is known that HSI has significantly different properties in high-frequency and low-frequency domains. For instance, in low-frequency domain, smoothing regions of HSI are more relevant while the details of HSI and noise are often more salient in the high-frequency domain. Current methods restore clean HSIs in a spatial domain, and thus neglect the properties of HSI in different frequency domains, which lead to limited denoising performance.
ClinicalRisk: A New Therapy-related Clinical Trial Dataset for Predicting Trial Status and Failure Reasons

Clinical trials aim to study new tests and evaluate their effects on human health outcomes, which has a huge market size. However, carrying out clinical trials is expensive and time-consuming and often ends in no results. It will revolutionize clinical practice if we can develop an effective model to automatically estimate the status of a clinical trial and find out possible failure reasons. However, it is challenging to develop such a model because of the lack of a benchmark dataset. To address these challenges, in this paper, we first build a new dataset by extracting the publicly available clinical trial reports from ClinicalTrials.gov. The associated status of each report is treated as the status label. To analyze the failure reasons, domain experts help us manually annotate each failed report based on the description associated with it. More importantly, we examine several state-of-the-art text classification baselines on this task and find out that the unique format of the clinical trial protocols plays an essential role in affecting prediction accuracy, demonstrating the need for specially designed clinical trial classification models.
Homophily-enhanced Structure Learning for Graph Clustering

Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results. Despite the success of existing GNN-based graph clustering methods, they often overlook the quality of graph structure, which is inherent in real-world graphs due to their sparse and multifarious nature, leading to subpar performance. Graph structure learning allows refining the input graph by adding missing links and removing spurious connections. However, previous endeavors in graph structure learning have predominantly centered around supervised settings, and cannot be directly applied to our specific clustering tasks due to the absence of ground-truth labels. To bridge the gap, we propose a novel method called homophily-enhanced structure learning for graph clustering (HoLe). Our motivation stems from the observation that subtly enhancing the degree of homophily within the graph structure can significantly improve GNNs and clustering outcomes. To realize this objective, we develop two clustering-oriented structure learning modules, i.e., hierarchical correlation estimation and cluster-aware sparsification. The former module enables a more accurate estimation of pairwise node relationships by leveraging guidance from latent and clustering spaces, while the latter one generates a sparsified structure based on the similarity matrix and clustering assignments. Additionally, we devise a joint optimization approach alternating between training the homophily-enhanced structure learning and GNN-based clustering, thereby enforcing their reciprocal effects. Extensive experiments on seven benchmark datasets of various types and scales, across a range of clustering metrics, demonstrate the superiority of HoLe against state-of-the-art baselines.
Understanding public perceptions of virtual reality psychological therapy: development of the attitudes towards virtual reality therapy (AVRT) Scale (Preprint)

Background: Virtual reality (VR) psychological therapy has the potential to increase access to evidence-based mental health interventions by automating their delivery while maintaining outcomes.However, it is unclear whether these more automated therapies are acceptable to potential users of mental health services. Objective:The main aim of this study was to develop a new, validated questionnaire to measure public perceptions of VR therapy (VRT) guided by a virtual coach.We also aimed to explore these perceptions in depth and test how aspects such as familiarity with VR and mental health are associated with these perceptions, using both quantitative and qualitative approaches. Methods:We used a cross-sectional mixed methods design and conducted an exploratory factor analysis of a questionnaire that we developed, the Attitudes Towards Virtual Reality Therapy (AVRT) Scale, and a qualitative content analysis of the data collected through free-text responses during completion of the questionnaire. Results:We received 295 responses and identified 4 factors within the AVRT Scale, including attitudes toward VRT, expectation of presence, preference for VRT, and cost-effectiveness.We found that being more familiar with VR was correlated with more positive attitudes toward VRT (factor 1), a higher expectation of presence (factor 2), a preference for VRT over face-to-face therapy (factor 3), and a belief that VRT is cost-effective (factor 4).Qualitative data supported the factors we identified and indicated that VRT is acceptable when delivered at home and guided by a virtual coach. Conclusions:This study is the first to validate a scale to explore attitudes toward VRT guided by a virtual coach.Our findings indicate that people are willing to try VRT, particularly because it offers increased access and choice, and that as VR becomes ubiquitous, they will also have positive attitudes toward VRT.Future research should further validate the AVRT Scale.
Simplifying Specialized Texts with AI: A ChatGPT-Based Learning Scenario

This paper introduces a methodology for facilitating the acquisition of specialized language using artificial intelligence tools, specifically ChatGPT, to simplify complex texts. The methodology consists of five steps: summarizing, simplifying, extracting terminology, creating mind maps, and visualizing, by generating multimodal content such as short informative videos or infographics. All five steps were performed with the assistance of ChatGPT. The methodology was applied in a translation class in partnership with the Law School, where students worked on texts outside their area of expertise. This article offers a comprehensive explanation of each step and showcases the practical application of the output produced by the students. This methodology is flexible and can be tailored to discipline-specific tasks. Its objective is to help them easily understand specialized texts and at the same time to equip them with science communication skills. Creating multimodal content is an effective way to bridge the gap between scientific expertise and the general public, and it is a valuable skill for students to develop in order to become effective science communicators.
Pilot of Outpatient Preoperative Evaluation Through Teleassistance

The pilot project of pre-anesthetic evaluation through telemedicine at the Pedro Ernesto University Hospital (HUPE) of the State University of Rio de Janeiro (UERJ) is a commendable initiative that aims to address the challenges faced by patients in accessing preoperative care. The objective of this study was to reduce the waiting time between the surgical recommendation and its clinical clearance for the procedure. A service flow was established to enable patients to undergo a comprehensive evaluation, including examination and complementary tests, during a single visit with a general practitioner. Based on the type of surgery and the patient’s comorbidities, the Teleconsultants Center assessed the case and provided the necessary guidance. A total of 20 patients were attended to in face-to-face sessions during morning shifts, with the participation of Internal Medicine and Anesthesiology. Subsequently, these patients’ evaluations were scheduled for teleconsultation to assess their surgical risk. There has been a significant reduction in the time between the surgical recommendation and the clearance for the procedure with a notable improvement compared to the previous protocol. These initial outcomes demonstrate the project’s potential to enhance the efficiency and effectiveness of the preoperative evaluation process through teleassistance.
A Content Analysis of the Cultural Products in High-School English Language Learning Materials in Indonesia (Sebuah Analisis Konten terhadap Produk Budaya yang terdapat di dalam Materi Pembelajaran Bahasa Inggris SMA di Indonesia

This study aims to describe the representation of cultural products in English language learning materials as depicted in narrative texts for Indonesian high school students. To this end, the design of this study employed qualitative content analysis to know how cultural products are represented in narrative texts for Indonesian high school students. Initially, the narrative texts were collected from the English learning materials which were compiled and used in the teaching and learning process by the English teachers. The data of this study was gathered through document review and then coded within the analysis process. The findings of this study revealed that cultural products as depicted in those narrative texts represent the category of places , followed by artifacts and institutions . However, the category of art form has not been identified yet. These findings indicate that the cultural products have not been widely represented in the learning materials. In addition, the teacher-selected narrative texts still only showed Indonesian local cultures while their students needed to know other target cultures from around the world. This study suggests that teachers should include more cultural learning in their learning materials to build student’s knowledge and awareness of culture.