Reviewerly/exHarmony huggingface dataset:
Visually meaningful image encryption based on 2D compressive sensing and dynamic embedding

Conventional image encryption methods typically encrypt images into noise-like images, which can provide a degree of security for the image, but the non-visually meaningful appearance can easily trigger the suspicion of hackers. To address this issue, visually meaningful image encryption (VMIE) which can produce a visually meaningful cipher image is developed. Nevertheless, current VMIE algorithms still exhibit certain limitations in embedding capacity and the quality of the cipher images. To solve the above issues, a novel VMIE algorithm based on the improved sine-tangent (IST) map, two-dimensional compressive sensing (2DCS), and dynamic embedding is proposed in this paper. First, to enhance the embedding capacity of the proposed algorithm, 2DCS is applied to the plain image to obtain the compressed data, wherein the measurement matrix is generated by an IST map to reduce the use of storage space and optimized by the simulated annealing algorithm to reduce the distortion of the decrypted image. Then, scrambling and diffusion operations are applied to the compressed image to disguise its statistical properties. Finally, to adapt to plain images with different texture features and enhance the quality of VMIE, a dynamic embedding algorithm is devised to embed the encrypted image into the host image. Simulation results and robustness analysis confirm that our scheme is feasible. Additionally, the proposed algorithm shows the superior visual quality and reconstruction quality compared to existing related schemes.
Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.
Assessment of the high-resolution estimations of global and diffuse solar radiation using WRF-Solar

Compared with physical models, WRF-Solar, as an excellent numerical forecasting model, includes abundant novel cloud physical and dynamical processes, which enablesenable the high-frequency output of radiation components which are urgently needed by the solar energy industry. However, the popularisation of WRF-Solar in a wide range of applications, such as the estimation of diffuse radiation, suffers from unpredictable influences of cloud and aerosol optical property parameters. This study assessed the accuracy of the improved numerical weather prediction (WRF-Solar) model in simulating global and diffuse radiation. Aerosol optical properties at 550 nm, which were provided by a moderate resolution imaging spectroradiometer, were used as input to analyse the differences in accuracies obtained by the model with/without aerosol input. The sensitivity of WRF-Solar to aerosol and cloud optical properties and solar zenith angle (SZA) was analysed. The results show the superiority of WRF-Solar to WRF-Dudhia in terms of their root mean square error (RMSE) and mean absolute error (MAE). The coefficients of determination between WRF-Solar and WRF-Dudhia revealed no statistically significant difference, with values greater than 0.9 for the parent and nested domains. In addition, the relative RMSE (RRMSE%) reached 46.60%. The experiment on WRF-Solar and WRF-Dudhia revealed a negative bias for global radiation, but WRF-Solar attained a slightly lower RMSE and higher correlation coefficient than WRF-Dudhia. The WRF-Solar-simulated results on diffuse radiation under clear sky conditions were slightly poorer, with RMSE, RRMSE, mean percentage error and MAE of 181.93 W m−2, 170.52%, 93.04% and 138 W m−2, respectively. Based on Himawari-8 cloud data, statistical results on cloud optical thickness (COT) for cloudy days revealed that WRF-Solar overestimated diffuse radiation at COTs greater than 20. Moreover, when the aerosol optical depth was greater than or equal to 0.8, WRF-Solar also overestimated the diffuse radiation, with a mean difference of 58.57 W m−2. The errors of WRF-Solar simulations in global and diffuse radiation exhibited a significant dependence on the SZA. The dispersion degree of deviation increased gradually with the decrease in the SZA. Thus, WRF-Solar serves as an improved numerical tool that can provide high temporal and high-spatial-resolution solar radiation data for the prediction of photovoltaic power. Studies should explore the improvement of cumulus parameterisation schemes to enhance the accuracy of solar radiation component estimation and prediction under cloudy conditions.
Active Adjustment of the Subreflector Shape for the Large Dual-Reflector Antenna

A shape adjustment method for subreflectors based on minimizing the residual wavefront error of the large dual-reflector antenna is presented. This method is used to compensate for the antenna structural deformation caused by environment loading. The shape of the subreflector is adjusted using actuators fixed under the panels. The shape adjustment response function for the subreflector shape and the actuators' adjustment amount is established, which is based on the inverse distance weighting function, and then the control function of the subreflector shape is obtained. The actuators' adjustment amount can be calculated using the least squares matrix transformation with the minimum residual wavefront error. Analysis of the experiment's results shows the residual wavefront error and primary aberration are greatly reduced under different elevation angles, and the effectiveness of the proposed method is verified.
Blockchain Enabled Lateral Transshipment System for the Redistribution of Unsold Textile Products in a Circular Economy

With the rise of carbon footprint awareness and the circular economy, the need to improve supply chain traceability and visibility has increased. Traditionally, companies have used database-driven platforms to improve traceability, reuse, and reduce waste. In practice, lateral transshipment among stakeholders also aims to improve reuse and reduce waste by redistributing unsold products. Although the optimization issues are well-studied in this field, there are many challenges in sharing data about ingredients, processes, delivery stages, and the lifecycle of a product. Manufacturers, retailers, and consumers have only limited access to this information. Blockchain technology can help to manage the complexities of managing a circular supply chain, especially in the context of lateral transshipment. This study presents a framework for blockchain and smart contract-enabled lateral transshipment among fast fashion retailers. There are three main components of the framework: inventory management system by the organizer, introducing retailers’ excess inventory and orders into the inventory sharing pool, delivery with execution of smart contracts. The proposed framework not only contributes to the visibility and traceability of the system but also increases responsiveness, flexibility and efficiency.
Health chain-block chain based electronic healthcare record system with access and permission management

An electronic health record (EHR) is a technology that allows you to keep track of your health information. It keeps computerized records of several healthcare organizations. Records are exchanged via enterprise-wide data systems as well as other networking technologies and exchanges. Patients nowadays expect immediate access to their health information. However, the health sector comes with immediate access to data, and there are worries about the privacy and security of medical records of patients. As a result, a blockchain-based solution can assist in resolving this issue. The blockchain has the potential to beat the conventional centralized system, which suffers from a severe lack of accessibility. The current healthcare system is often seen as being both costly and convoluted. However, with improved health data management and insurance providers, this may be reduced via the use of Blockchain Technology (BT). It is the goal of the developers of BT to make it possible to keep track of financial transactions without having to rely on a central authority or a bank. Innovations in BT have allowed for better health records, insurance billing, and smart contract transactions, and it also enable a decentralized data structure of activities, as well as eternal availability and security over health information. Access to medical records, instrument tracking, prescription databases, hospital facilities, and property lifetime all inside a blockchain architecture is made possible by BT that also greatly improves the seamless integration of health information. The use of BT in the medical field has this advantage as one of its primary advantages. Access to a patient's medical history is crucial for prescribing the right treatment, and BT has the potential to greatly enhance the current state of public healthcare delivery. This paper offers a block chain-based scheme called "Health Chain" as a solution for maintaining medical records. Health Chain is made to be better than the existing systems since it gives patients, health professionals, and other external stakeholders safe, efficient, and ubiquitous medical information available while maintaining the patients' confidentiality. To regulate operations and restrict access to digital health records, Health Chain uses timed-based smart contracts. In addition, it uses cutting-edge encryption methods to boost security. This study suggests a new reward system that uses healthcare professionals to track patients' medical information and construct additional units. Numerous tests are run to assess the proposed Health Chain model's performance, and the findings demonstrate that the proposed suggestion effectively manages a huge dataset called (nCOV19) from the Covid-19 study with little delay. Maximum efficiency will be expected to be attained by 98%, transaction latency by 95%, overall system execution time by 96%, data security by 92%, and data scalability by 95%, according to the suggested research.
Implications of virtual reality (VR) for school teachers and instructional designers: An empirical investigation

The present study involved an experimental inquiry wherein the researcher examined the possible impact of “textual cues” and “summarising scaffolding” on cognitive load, mental model, and learning performance. The study was conducted on a sample of 122 primary school pupils from Hyderabad, India. It commenced on 20 January 2021 and lasted till 28 October 2021. Using a randomization process, the participants were allocated to one of four experimental groups (D1, D2, D3, and D4). The results of the ANOVA analysis indicate that the utilisation of textual cues had a noteworthy and favourable effect on both the learning performance and the mental model. The implementation of summarising scaffolding yielded a remarkable enhancement in the mental model. The absence of significant “interaction effects” suggests that the implementation of immersive virtual reality, accompanied by textual cues or summarising scaffolding, can be advantageous for young learners. These discoveries hold particular significance for stakeholders involved in the field of education, particularly teachers and students, and possess noteworthy ramifications for the development of efficacious immersive learning environments.
SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing

Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In this research, a Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm is incorporated with the Improved Particle Swarm Optimization (IPSO) algorithm for enhancing the task scheduling behavior by utilizing parameters like load balancing and resource allocation. The conventional EHO and PSO algorithms are improved utilizing a sine cosine-based clan-updating operator and human group optimizer that improve the algorithm’s exploration and exploitation abilities and avoid being trapped in the local optima problem. The efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. The numerical investigation indicates that the SCEHO-IPSO algorithm has a minimum memory storage of 309 kb, a latency of 1510 ms, and an execution time of 612 ms on the Kafka platform, and the obtained results reveal that the SCEHO-IPSO algorithm outperformed other conventional optimization algorithms. The SCEHO-IPSO algorithm converges faster than the other algorithms in the large search spaces, and it is appropriate for large scheduling issues.
Evaluating the viewer experience of interactive virtual reality movies

Abstract Significant advances in virtual reality (VR) technology have called into question the traditional methods of cinema storytelling and dissemination. New VR devices, such as the Meta (Oculus) Quest, have expanded the possibilities for viewing movies. The purpose of this study is to compare the emotional and cognitive impacts of VR and traditional 2D movies. In this study, sixty volunteers were divided into two groups and presented a movie (Gala) in 2D or VR format. We employed a multimodal method to assess the cognitive and emotional effects of the film both during and after watching. Our technique combined self-reports, interviews, questionnaires, and objective heart rate and EEG brain activity data. After quantitative and qualitative evaluation, it was discovered, that regardless of media, there was a substantial influence of the movie on the emotional state of the participant’s mood. Moreover, compared to the traditional 2D-movie, the VR movie led to more consistent and robust positive effect on all aspects of self-rated affect. The difference in self-reported mood was corroborated by reduced EEG amplitudes in the beta frequency band, indicating higher levels of positive affectivity, which was only observed for the VR movie. Lastly, the VR movie also leads to overall higher self-rated immersion and engagement than the 2D version. Our results highlight the potential of VR movies to engage and emotionally affect audiences beyond traditional cinema. Moreover, our study highlights the value of using a multidisciplinary method for analysing audience impacts.
The secret life of crime labs

Houston TX experienced a widely known failure of its police forensic laboratory. This gave rise to the Houston Forensic Science Center (HFSC) as a separate entity to provide forensic services to the City of Houston. HFSC is a very large forensic laboratory and has made significant progress at remediating the past failures and improving public trust in forensic testing. HFSC has a large and robust blind testing program, which has provided many insights into the challenges forensic laboratories face. HFSC's journey from a notoriously failed lab to a model also gives perspective to the resource challenges faced by all labs in the country. Challenges for labs include the pervasive reality of poor-quality evidence. Also that forensic laboratories are necessarily part of a much wider system of interdependent functions in criminal justice making blind testing something in which all parts have a role. This interconnectedness also highlights the need for an array of oversight and regulatory frameworks to function properly. The major essential databases in forensics need to be a part of blind testing programs and work is needed to ensure that the results from these databases are indeed producing correct results and those results are being correctly used. Last, laboratory reports of "inconclusive" results are a significant challenge for laboratories and the system to better understand when these results are appropriate, necessary and most importantly correctly used by the rest of the system.
MYCRUNCHGPT: A LLM ASSISTED FRAMEWORK FOR SCIENTIFIC MACHINE LEARNING

Scientific machine learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seamlessly without the need of employing elaborate and computationally taxing data assimilation schemes. However, preprocessing, problem formulation, code generation, postprocessing, and analysis are still time- consuming and may prevent SciML from wide applicability in industrial applications and in digital twin frameworks. Here, we integrate the various stages of SciML under the umbrella of ChatGPT, to formulate MyCrunchGPT, which plays the role of a conductor orchestrating the entire workflow of SciML based on simple prompts by the user. Specifically, we present two examples that demonstrate the potential use of MyCrunchGPT in optimizing airfoils in aerodynamics, and in obtaining flow fields in various geometries in interactive mode, with emphasis on the validation stage. To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a web app based guided user interface, that includes options for a comprehensive summary report. The overall objective is to extend MyCrunchGPT to handle diverse problems in computational mechanics, design, optimization and controls, and general scientific computing tasks involved in SciML, hence using it as a research assistant tool but also as an educational tool. While here the examples focus on fluid mechanics, future versions will target solid mechanics and materials science, geophysics, systems biology, and bioinformatics.
Random forest feature selection for partial label learning

Partial Label Learning (PLL) aims to induce a multi-class classifier to deal with the problem that each training instance is associated with a set of candidate labels, among which only one is valid but unknown. Feature selection, which choses some features related to the task (e.g., classification) and omits the unrelated ones, is challenging for PLL due to the ambiguous labeling information. In this paper, a random forest based method, namely, RFUTE, is developed to deal with the feature selection challenge in PLL. Due to the fact that the ground-truth labels are inaccessible in the training process, RFUTE first disambiguates the candidate labels, and then operates feature selection by calculating and sorting the total change of information entropy over all trees in the forest for all features. More importantly, a supplement approach is proposed after disambiguation to avoid the class imbalance problem. Comprehensive experiments over both synthetic and real-world partial label data sets empirically justify the effectiveness of RFUTE in improving the generalization performance of well-established PLL methods.
Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Deep Neural Networks (DNNs) have shown impressive performance in computer vision tasks; however, their vulnerability to adversarial attacks raises concerns regarding their security and reliability. Extensive research has shown that DNNs can be compromised by carefully crafted perturbations, leading to significant performance degradation in both digital and physical domains. Therefore, ensuring the security of DNN-based systems is crucial, particularly in safety-critical domains such as autonomous driving, robotics, smart homes/cities, smart industries, video surveillance, and healthcare. In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Furthermore, we explore the specific requirements and challenges associated with executing attacks in the physical world. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications, including classification, detection, face recognition, semantic segmentation and depth estimation. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness. We examine how each technique strives to ensure the successful manipulation of DNNs while mitigating the risk of detection and withstanding real-world distortions. Lastly, we discuss the current challenges and outline potential future research directions in the field of physical adversarial attacks. We highlight the need for enhanced defense mechanisms, the exploration of novel attack strategies, the evaluation of attacks in different application domains, and the establishment of standardized benchmarks and evaluation criteria for physical adversarial attacks. Through this comprehensive survey, we aim to provide a valuable resource for researchers, practitioners, and policymakers to gain a holistic understanding of physical adversarial attacks in computer vision and facilitate the development of robust and secure DNN-based systems.