Reviewerly/exHarmony huggingface dataset:
Designing the Museum of Instructional Design, a 3D Learning Environment: A Learning Experience Design Case

This paper describes the iterative design of a three dimensional collaborative virtual learning environment (3D CVLE) called the Museum of Instructional Design (MID) that was developed using learning experience design processes. A detailed articulation of our three-phased learner experience design process will be outlined. Findings will provide insight into how other instructional designers can use formative learner experience design processes to create highly usable and effective 3D learning environments.
The Impact of Experience, Overconfidence and Optimism on Future Cryptocurrency Ownership

Using recent survey data from Sweden, Denmark, and Finland, we analyze the effect of experience with cryptocurrencies, overconfidence and economic expectations on future cryptocurrency ownership. We document that negative experiences with cryptocurrencies and overconfidence positively affect the odds of having cryptocurrency in the future. The results show that owners of ESG products are the most optimistic, with the highest optimism score. This is followed closely by owners of Crypto assets / NFTs and Antiques / Art.
Enhancing Memory Utilization For On-Device Training of TinyML Models Utilizing Enhanced Grey Wolf Optimization Pushing State-of-the-Art Limits - TinyWolf

Training a deep learning model generally requires a huge amount of memory and processing power. Once trained, the learned model can make predictions very fast with very little resource consumption. The learned weights can be fitted into a microcontroller to build affordable embedded intelligence systems which is also known as TinyML. Although few attempts have been made, the limits of the state-of-the-art training of a deep learning model within a microcontroller can be pushed further. Generally deep learning models are trained with gradient optimizers which predict with high accuracy but require a very high amount of resources. On the other hand, nature-inspired meta-heuristic optimizers can be used to build a fast approximation of the model's optimal solution with low resources. After a rigorous test, we have found that Grey Wolf Optimizer can be modified for enhanced uses of main memory, paging and swap space among α, β, δ and ω wolves. This modification saved up to 71% memory requirements compared to gradient optimizers. We have used this modification to train the TinyML model within a microcontroller of 256KB RAM. The performances of the proposed framework have been meticulously benchmarked on 13 open-sourced datasets.
The WEIRD Governance of Fact-Checking and the Politics of Content Moderation

In this paper we chart the conflicting standards of fact-checking outside Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries that shifted their focus from holding politicians to account to acting as content moderators. We apply reflexive thematic analysis to a set of interviews with 37 fact-checking experts from 35 organizations in 27 countries to catalog the pressures they face and their struggle with tasks that are increasingly different from the journalistic values underpinning the practice. We find that fact-checkers have to balance the number of checks across each side of the partisan divide, an exercise in ‘bothsidesism’ to manage the expectations of partisan social media users; that they increasingly prioritize the checking of viral content; and that Meta’s third-party fact-checking program prevents them from holding local politicians to account. We conclude with a discussion of our findings and recommendations for content moderation outside WEIRD countries.
Certificateless Public Auditing for Cloud-Based Medical Data in Healthcare Industry 4.0

In the context of healthcare 4.0, cloud-based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper-proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy-preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state-of-the-art ones.
Performance Comparison of Machine Learning Models for Handwritten Devanagari Numerals Classification

This work focuses on comparing the suitability of different machine learning models for the classification of handwritten digits in the Devanagari script. The models that will be compared in this study are: K-Nearest Neighbours (K-NN), Support Vector Machine (SVM), Convolutional Neural Network (CNN), GoogLeNet (Inception v1), and ResNet-50. GoogLeNet and ResNet-50 are complex, deep neural networks. They possess a large number of hidden layers, and are generally used for more complex image classification tasks. The use of these models in this project is to gauge how well they perform on simpler image data. The foundation of this research is based on the ever increasing demand for accurate and efficient digit classification models in India, for purposes such as document scanning, ID card recognition, and the digitization of institutional records. The primary objective of this research project is to identify the most accurate and efficient digit classification model for numbers in the Devanagari script. Surprisingly, proposed simple CNN model outperforms the other complex GoogleNet and ResNet-50 models. Accuracy and Fl score of proposed CNN model is 99.522% and 0.9978 respectively. Also, the proposed CNN model used in this study outperforms other CNN model considered for Devanagari numerals classification.
Prediction of Hydrodynamic Loads on a Flexible Bio-Inspired Underwater Propulsor Using Physical Reservoir Computing

Abstract Bio-inspired autonomous underwater vehicles (BAUVs) have the potential for stealthy navigation and operation in sensitive underwater environments by mimicking the natural behaviors and compliance of marine animals. However, BAUVs still face challenges where maneuverability and control are difficult to achieve, especially in high-energy coastal zones. Maneuverability and control could be improved by implementing real-time sensing of the hydrodynamic forces acting on a BAUV’s compliant body. In this study, we predict the hydrodynamic forces generated by a soft-robotic propulsor using Physical Reservoir Computing (PRC), with the future aim of improving the propulsor’s maneuverability. Physical reservoir computing is a machine learning technique that utilizes the nonlinear dynamics of a physical system for information processing. Compared to software-based machine learning frameworks, PRC requires significantly lower computational costs. With PRC, a majority of the information is processed within the physical system itself, enabling real-time prediction of time-varying target signals. In this project, we use PRC to leverage the nonlinear fluid-structure dynamics of a soft-robotic propulsor to predict its time-varying thrust. Our soft-robotic propulsor is powered by an antagonistic pair of embedded hydraulically amplified self-healing electrostatic (HASEL) actuators. We conducted experiments to measure the thrust generated by the soft robot in quiescent water. Using the PRC framework, we predicted the thrust using a linear weighted sum of the time-varying kinematic strains measured at multiple locations on the propulsor’s compliant body. We trained the linear weights by minimizing the error between the PRC prediction and the experimentally measured thrust. We then analyzed various sensor placement configurations to achieve the best performance. By using the PRC framework, we were able to accurately predict the time-varying thrust. Using the sensor configuration that yielded the best performance, we achieved R-squared errors of 0.908 and 0.898 on the training set and test set, respectively. This PRC method holds significant potential for predicting time-varying hydrodynamic loads in real-time which can be used to improve control and maneuverability of BAUVs.
Load Balancing in Cloud Environment: A State-of-the-Art Review

IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms.
Designing Practical End-to-End System for Soft Biometric-Based Person Retrieval from Surveillance Videos

Video surveillance improves public safety by preventing and sensing criminal activity, enhancing quick counteractions, and presenting evidence to investigators. This is effectively performed by firing a natural language query containing soft biometrics to retrieve a person from a video. State-of-the-art (SOTA) approaches focus on improving retrieval results; thus, the building blocks of any person retrieval system are not accorded due attention, putting novice researchers at a disadvantage. This study aims to provide a design methodology by showcasing the block-by-block construction of a person retrieval system using video and natural language. For each subsystem - natural language processing, person detection, attribute recognition, and ranking- we discuss the available design selections, provide empirical evidence, and discuss bottlenecks and solutions. We thereafter select and integrate the best choices to create an end-to-end system. We highlight the integration challenges and demonstrate that the proposed method achieves an average intersection over union and the true positive rate of ≥ 60%. This is the first study to provide practical guidance to researchers for fast prototyping of person retrieval with subsystem-level understanding and achieve SOTA performance.
Improved Grey Wolf Optimization Algorithm Based on Hyperbolic Tangent Inertia Weight

The Grey Wolf Optimization Algorithm (GWO) is an algorithm that replicates the leadership and foraging mechanisms of the natural grey wolf, which excels at solving problems in a variety of domains. However, the algorithm tends to converge on local optimal and has a slow convergence rate. This paper proposes an enhanced Grey Wolf optimization algorithm (HTGWO) based on hyperbolic tangent inertia weights to solve this problem. HTGWO employs inertia weight based on hyperbolic tangent functions to balance GWO’s global and local search capabilities. HTGWO has a quicker convergence rate and more accurate solutions than GWO. Five classical test functions construct comparative experiments between HWGWO and five classical intelligent optimization algorithms. The comparison results indicate that HWGWO has superior convergence speed, solution precision, and stability compared to the other five classical intelligent optimization algorithms. Moreover, experimental evidence suggests that HWGWO is more effective at solving multi-modal than uni-modal functions.
Detecting Dark Patterns Using Generative AI: Some Preliminary Results

This article presents preliminary investigations of generative AI technologies as tools for detecting dark patterns. This work is motivated by growing regulatory interest in dark patterns and deceptive online choice environments, and the need to development meaningful behavioural auditing tools as a response to these practices. We propose and offer initial explorations into three approaches to using generative AI to simulate the behaviour of online users with differing levels of digital skill. Our initial testing shows the ‘AI Vision’ approach to hold promise at present; the ‘Choose your own adventure’ approach to hold potential; and the ‘Decision Network’ approach to raise interesting technical challenges.
Survival-Based Treatment Planning Using Stage-Specific Machine Learning Models

The significance of prognostic survivability in determining optimal treatment strategies for critical illnesses is widely acknowledged. However, there has been a lack of emphasis on the advancement of treatment planning models based on survival outcomes within clinical decision support systems. The research presented in this paper proposes an innovative framework for the planning of treatment strategies based on survival outcomes in the context of multi-stage diseases, with the aim of effectively tackling this issue. Our proposed system aims to predict a comprehensive list of treatment combinations for cancer patients, specifically focusing on their expected survival outcomes. The proposed solution aims to enhance the decision-making process of medical professionals by providing them with comprehensive and comprehensible treatment recommendations. To conduct survivability classification and regression analysis for patients with identical cancer stages, a two-step approach is employed. This involves the development of stage-specific Machine Learning models using breast cancer data that includes treatment information. Based on a real dataset on cancer patients, we aim to investigate the performance of the models under different balancing strategies. Our contribution in this work is the formulation of a treatment planning inference system, which focuses on prognostic considerations. This system utilizes patient data and estimates the survivability associated with each treatment plan in order to predict the recommended course of action. This facilitates the integration of the developed survival prediction models into the process of treatment planning. Ultimately, the system generates visual representations that illustrate the comparative significance of different features, as well as the decision-making process employed by the model in order to yield easily comprehensible outcomes for a specific patient. The study presents experimental findings that illustrate the efficacy of our proposed framework in the domains of treatment planning and survival estimation.
Hardware-Based Software Control Flow Integrity: Review on the State-of-the-Art Implementation Technology

Code reuse attacks (CRA) represent a type of control flow hijacking that attackers exploit to manipulate the standard program execution path, resulting in abnormal processor behaviors. In response to the security concern, proposals for Control Flow Integrity (CFI) verification have emerged. The CFI scheme diligently monitors program jumps during execution, effectively restraining abnormal program execution and robustly safeguarding against CRA. This paper provides a comprehensive analysis and synthesis of the current state of hardware-based CFI implementations. In this survey, we initially discuss common attack methods and variations of predominant CRA, elucidating the general procedural steps intrinsic to such attacks. We delve into the protective capacities inherent in contemporary hardware-based CFI implementations. By conducting a thorough examination and organization of diverse research endeavors on hardware-based CFI, we systematically classify CFI based on implementation methodologies, including label verification, instruction encryption, stack edge detection, instruction tracing, sensitive data isolation, and basic block validation. We provide comprehensive explanations and critical evaluations for each category followed by comparative analyses while offering personal insights on the evolution of hardware-based CFI.
Research on transmission line hardware identification based on improved YOLOv5 and DeblurGANv2

In order to accurately identify the high voltage transmission line fittings and guide the line patrol robot to achieve the corresponding obstacle-crossing action according to different fittings, a real-time detection network based on deep learning DeblurGANv2 and YOLOv5 target detection algorithm was proposed. In view of the problems of large network model, large amount of computation and low operation efficiency of YOLOv5 algorithm, the lightweight improvement of YOLOv5 algorithm was carried out to improve the detection speed of the algorithm. Shufflenetv2 was used to replace CSPDarknet-53 in the original network, and ECA module was introduced into Shufflenetv2.Enhance the model’s focus on valid features. At the same time, DeblurGANv2 super-resolution reconstruction algorithm is introduced to deblur the image of the fittings to generate high-quality images and improve the recognition accuracy. Experimental results based on homemade datasets show that,compared with the original YOLOv5 model, the parameter amount of the proposed method is reduced by 50.2%, the weight file size is only 5.3M, a reduction of 62.7%, and mAP@ (0.5) is increased by 1.3%. The proposed method can successfully detect the high voltage transmission line fittings and ensure the accuracy of fuzzy target detection.
Root Cause Analysis of Communication Network Based on Deep Fuzzy Neural Network

In the realm of communication networks, root cause analysis plays a vital role in maintaining efficient and reliable operation. However, existing root cause analysis methods face limitations and drawbacks, including their inability to handle complex data and disturbances, as well as inaccuracies in identifying root causes. To this end, we present the Deep Fuzzy Neural Network approach as an innovative solution. Integrating the strengths of deep learning and fuzzy logic inference, where the deep learning technique utilizes the parallel computing fusion of convolutional neural network and long short-term memory to extract the spatial-temporal features from sophisticated fault data of communication network. By leveraging this parallel computing fusion module, the proposed framework effectively addresses the flaws of traditional root cause analysis methods. Furthermore, the incorporation of fuzzy logic enables our model to manage disturbances such as uncertainty and noise inherent in the data, ensuring robust performance. Experimental results also demonstrate our proposed deep fuzzy neural network approach is an effective method for network root cause analysis in overcoming limitations inherent in existing methods and providing superior accuracy and resilience.
The Analysis of Nutrition Toxicology Detection Based on Big Data and Deep Learning

Public health and safety are increasingly concerned as public awareness of health-related issues grows. To find a rapid, convenient, and non-destructive testing method for detecting human nutritional toxicology detection, this study selects sildenafil, phenolphthalein, and metformin hydrochloride—commonly found additives in health products—as the focal point. The research endeavors to tackle the paramount issue of public health and safety. The study begins by elucidating the public health and safety concept and then outlines the computational process for determining the terahertz (THz) optical properties. Subsequently, it provides a brief overview of deep learning (DL) methods, including the Back Propagation Neural Network (BPNN), Convolutional Neural Network (CNN), Residual Network (ResNet), and MobileNet model. Finally, the study compares and tests the THz absorption spectrum data of 22 pure samples containing sildenafil, phenolphthalein, and metformin hydrochloride by DL technique to evaluate the model’s classification performance. The findings demonstrate that, with increased training iterations, the model’s accuracy consistently improves and stabilizes. For instance, after 12 training iterations, CNN’s accuracy under the verification set stabilizes, frequently reaching nearly 100%. After 83 iterations, the accuracy remains steady at 98.96%. Similarly, the MobileNet model reaches stability after 17 iterations, achieving 100% accuracy. The BPNN demonstrates the fastest prediction time among the four DL algorithm models, at 310-5 seconds. Meanwhile, the MobileNet model exhibits the highest accuracy and stability. This study using THz waves to identify contaminants in medical items can significantly enhance public health and safety.
Performance Comparison between OpenPose and TRT_Pose for Self-Practice Yoga on Embedded GPU Platform

In order to enhance the performance of human pose estimation applications on embedded systems, accurately identifying human keypoints and interpreting pose-related information are crucial. In this study, two different pose estimation methods, namely "Openpose" and "TRT_pose", are applied, and their performances are compared by realizing on an embedded platform. The results obtained from these two pose estimation methods are evaluated by analyzing the positional differences of human joint keypoints with Euclidean distance. The study aims to provide insights into improving human pose estimation for embedded systems and facilitating the correct interpretation of human body poses. Additionally, a Yoga self-training system is utilized as the target, and Yoga movements are used as the comparison data.
Enhancing tumor visualization through 3D Slicer-based segmentation and MATLAB-based image enhancement

Accurate visualization of any pathology plays a crucial role in medical diagnosis and treatment planning, especially when a tumor is involved. In this document, we present a comprehensive approach for enhancing tumor visualization by integrating the capabilities of 3D Slicer-based segmentation and MATLAB-based image enhancement techniques. The proposed methodology leverages the strengths of both software tools to achieve improved accuracy and quality in tumor segmentation and subsequent image enhancement. The slicer software is utilized for precise tumor segmentation, enabling the extraction of tumor regions from medical imaging data, in this case a tomography study. Subsequently, the programming environment algorithms are applied to enhance the tumor visualization by improving contrast, reducing noise, and refining details to generate a sharper image. The synergistic combination of this two software facilitates a more refined and accurate analysis of tumor regions, aiding in the understanding and interpretation of medical images, going from a 2D point of view to a 3D model allows a better surgical planning to approach the patient Experimental results demonstrate the efficacy of the proposed approach, showcasing enhanced tumor visualization with improved clarity and diagnostic value. The integration of slicer-based segmentation and MATLAB enhancement techniques holds great promise in advancing tumor analysis and assisting clinicians in making informed decisions for patient care.