Reviewerly/exHarmony huggingface dataset:
Secure Voting Website Using Ethereum and Smart Contracts

Voting is a democratic process that allows individuals to choose their leaders and voice their opinions. However, the current situation with physical voting involves long queues, paper-based ballots, and security challenges. Blockchain-based voting models have appeared as a method to address the limitations of traditional voting methods. As blockchain is distributed and decentralized, which uses hash functions for securing transactions, it dramatically improves the existing voting system. These digital platforms eliminate the need for physical presence, reduce paperwork, and ensure the integrity of votes through transparent and tamper-proof blockchain technology. This paper introduces a blockchain-based voting model to enhance accessibility, security, and efficiency in the voting process. The research focuses on developing a robust and user-friendly voting system by leveraging the advantages of decentralized technology. The proposed model employs Ethereum as the underlying blockchain platform through an innovative and iterative approach. The model uses Smart contracts to record and validate votes, while AI-based facial recognition technology is integrated to verify the identity of voters. Rigorous testing and analysis are conducted to validate the effectiveness and reliability of the proposed blockchain-based voting model. The system underwent extensive simulation scenarios and stress tests to evaluate its performance, security, and usability.
Multi-tasking Federated Learning meets Blockchain to Foster Trust and Security in the Metaverse

The Metaverse is a term that refers to a shared virtual space where users can interact with each other and with a virtual environment in real-time. The Metaverse has gained a lot of attention in recent years owing to the accelerated advancements in virtual and augmented realty technologies, which made it possible to create more immersive and highly interactive experiences for users. However, as users engage with the Metaverse, they generate a lot of data, which may include their location, activity, and interactions with other users. This data can be used to train multiple machine learning (ML) models to personalize content and ads, but it can also be exploited for more nefarious uses. The problem of user data collection and the sharing of sensitive and private data in the Metaverse is a challenge. Federated learning (FL) is a distributed machine learning (ML) mechanism that can avoid problems related to data sharing by having multiple devices work together to create a ML model while maintaining their local training data private. The utilization of FL in the Metaverse involves numerous contributors, thereby increasing the susceptibility to malicious activity, including attacks. To address this issue, a promising solution is the integration of blockchain technology, which can provide a trusted solution to the metaverse that allows for secure model learning. Due to frequent communication between FL and blockchain, FL needs to be adapted to the limited communication bandwidth and limited resources of the device. In this paper, we propose a blockchain-based FL framework to enhance security and trustworthiness in the Metaverse. The proposed solution utilizes a multi-task FL approach that leverages blockchain sharding to enhance the system’s throughput while simultaneously reducing resource requirements. By breaking the blockchain network into smaller shards, the processing power required for each shard is reduced, leading to an increase in overall throughput. The multi-task FL approach allows for the simultaneous training of multiple ML models, reducing the time and resources required to train each model individually. We formulate the model learning problem into a blockchain sharding problem and propose a bipartite matching solution for shard creation. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with a reliable behavior and informative dataset priority. Numerical results demonstrated that our framework has better performance than referenced solutions on the selected indicators.
An Enhanced Pseudorandom Number Generator Based Differential Lorenz system by using Runge-Kutta method

As networks become more interconnected, pseudo-random number generators (PRNGs) are essential for security. These generators produce random numbers at high speeds and with good unpredictability quality. This paper details our efforts to create a secure communication channel using the Lorenz system. With the aid of Runge Kutta’s 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> Order approach, the Lorenz system has been achieved. The suggested map performs better than other previously created improved chaotic maps because of its structure. On a MATLAB 2020a, the unpredictable system is implemented. The suggested chaotic system’s binary sequences are evaluated using the NIST (National Institute of Standards and Technology) test suit to determine their unpredictability. The outcomes show that the chaotic structure shows a variety of dynamical behaviors and is sensitive to the initial circumstances and exhibits good randomness. As a result, the stream cipher can employ this system. Our system’s shows an increase in keyspace and speed compared to previous systems, making it more appropriate for security applications, spread spectrum wireless communication, computer simulation.
An In-Depth Review of AI-Based Techniques for Early Diagnosis of Breast Cancer: Evaluation of CAD System Design and Classification Methodologies

One of the most prevalent forms of cancer among women worldwide is breast cancer, the leading cause of mortality. The vital procedure of early breast cancer detection can help with the right medical care, prevent the spread of cancer cells, as well as lower the risk of death and disability. The two most prevalent techniques for correctly detecting and classifying breast cancer through the use of Computer-Aided Diagnosis (CAD) technology are artificial intelligence (AI) and machine learning (ML). This work intends to highlight the significance of applying AI for breast cancer early detection to reduce its risks. The various techniques for recognizing breast cancer are also addressed. Also, the design process of the CAD system, including pre-processing stage, segmentation, extraction of features, and feature selection, is explained. The classification methodologies for detecting breast cancer will then be evaluated.
Tech-Care: A High-Tech Eye-Controlled Wheelchair for Paralyzed Patients

Quadriplegia is a devastating condition that can have a profound impact on an individual's quality of life. It is a result of spinal cord injury that affects all four limbs, severely limiting mobility and independence. However, with the rapid advancement in technology, there is hope for people with quadriplegia to regain some degree of independence.This paper proposes a revolutionary eye-controlled wheelchair that can provide independent mobility for people with disabilities. The system utilizes cutting-edge face landmark recognition technology to track eye movements and control the motorized movement of the wheelchair. The Python Dlib library was used to locate facial landmarks using a group of regression trees using Kazemi and Sullivan's face alignment in one millisecond. In this approach, face and eye recognition are accomplished using facial landmarks. It also features an ultrasonic sensor that can detect obstacles and prevent accidents. The whole system is controlled by Raspberry Pi, which is cost-effective and user-friendly.The proposed technology is a game-changer for people with disabilities. It eliminates the need for physical support and allows for greater independence and freedom of movement. With its advanced eye-tracking technology and reliable safety features, this eye-controlled wheelchair represents a significant breakthrough in the field of assistive technology. The proposed system is not just a technological innovation but a source of hope and empowerment for people with quadriplegia.
Recent Advances Towards Securing Smart Grids with Blockchain

The smart grid was introduced as an evolved version of the traditional power grid to enable two-way communication between consumers and electricity providers. It aims to distribute energy generation points that utilize renewable energy and enable energy trading among users. However, this grid has an increasing number of components, including consumers, prosumers, generation plants, and electric vehicles. Managing them using a centralized architecture approach introduces several challenges such as single points of failure and cyberattacks. To address this issue, blockchain technology was proposed as an infrastructure to secure the smart grid. The latter provides a distributed ledger that handles transactions in a safe environment. However, despite all its advantages, blockchain has several scalability and privacy protection issues. In this paper, we highlight the state-of-the-art of blockchain-based smart grids works. The article also provides a comprehensive survey on the challenges that face blockchain-based smart grids and points out timely future research topics.
Deep Action Recognition for Cognitive Assessment

Skeleton-based human action recognition has received increasing attention in recent years. It aims at extracting features on top of human skeletons and estimating human pose. However, existing methods capture only the action information, but in a real-world application such as cognitive assessment, we need to measure the executive functioning that helps psychiatrists to identify some mental disease such as Alzheimer, Schizophrenia and ADHD. In this paper, we propose a skeleton-based action recognition named Deep-Cogn for cognitive assessment. Deep-Cogn integrates a pose estimator to extract the human body joints and then automatically measures the executive functioning employing the distance and elbow angle calculation. Three score functions were designed to measure the executive functioning: the accuracy score, the rhythm score and the functioning score. We evaluate our model on two different datasets and show that our approach significantly outperforms the existing methods.
Explainable AI for Medical Image Processing: A Study on MRI in Alzheimer’s Disease

Alzheimer's disease is the most common type of dementia, characterised by memory-related brain changes that impair the patient's cognitive abilities. Early detection of this disease is critical from a clinical point of view to increase the chances of treating a patient at risk of further cognitive degeneration. The rate of development and expansion in the field of deep learning for medical analysis is rapidly increasing alongside the increased incidences of neurodegenerative diseases. Machine learning algorithms are often applied to automate tasks and alleviate issues, but modern methods such as neural networks often present as black boxes. In the field of medicine, it is crucial to understand why a machine learning algorithm has made a prediction. In this study, we initially utilise a CNN-based approach for computer vision in the detection of Alzheimer's disease from the ADNI MRI dataset. On unseen magnetic resonance imaging scans, our algorithm achieves a classification accuracy of 94.96%. We then implement the Local Interpretable Model Agnostic Explanations (LIME) algorithm to reveal visual evidence to support the predictions made by the model, and automatically visualise image segments contributing to predictions via Felzenszwalb's segmentation algorithm. The objectives of explainable AI in this field are to provide medical professionals with specific, easy-to-understand information to support efficient, consistent, and convenient diagnoses.
Human Experts’ Perceptions of Auto-Generated Summarization Quality

In this study we addressed automatic summarizations generated using modern artificial intelligence techniques. Several mathematical methods for evaluating the performance of automatic summarization exist. Such methods are commonly used as they allow many test cases to be assessed with little human effort as manual assessments are challenging and time consuming. One question is whether the output of such measures matches human perception of summarization quality. In this study we document a study involving the human evaluation of the automatic summarization of 22 academic texts. The unique aspect of this study is that our participants had strong familiarity with the texts as they had studied these texts in depth. The results are quite varied but do not give the impression of unanimous agreement that automatic summarizations are of high quality and are trusted.
Design, development, and usability evaluation of a dashboard for supporting formal caregivers in managing people with dementia.

Dementia is a condition that affects more and more elderly people around the world. The number of people affected by dementia is expected to increase in the future due to increasing longevity and the growing elderly population. According to the World Health Organization (WHO), the number of people with dementia worldwide is expected to double by 2030 and triple by 2050. This will pose a significant challenge to the health and social systems of many nations and will require increased attention and resources for the prevention, diagnosis and treatment of dementia. As the disease spreads, the workload of formal caregivers will inevitably increase. The HAAL project (HeAlthy Aging eco-system for peopLe with dementia) responds to this challenge by developing a dashboard that integrates all the relevant information coming from a set or a subset of nine already experimented devices. The dashboard is designed to show the most relevant information regarding the patient (fall events, sleep data, wellbeing data) to the formal caregiver. The purpose of this paper is therefore to present the experimental results obtained from the usability test performed on the HAAL dashboard by 26 formal caregivers in three pilot sites: Italy, Taiwan, and the Netherlands. Quantitative and qualitative results of this preliminary study confirm the suitability of the dashboard, highlighting inter-country differences.
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents

Reinforcement Learning (RL) has become an increasingly important research area as the success of machine learning algorithms and methods grows. To combat the safety concerns surrounding the freedom given to RL agents while training, there has been an increase in work concerning Safe Reinforcement Learning (SRL). However, these new and safe methods have been held to less scrutiny than their unsafe counterparts. For instance, comparisons among safe methods often lack fair evaluation across similar initial condition bounds and hyperparameter settings, use poor evaluation metrics, and cherry-pick the best training runs rather than averaging over multiple random seeds. In this work, we conduct an ablation study using evaluation best practices to investigate the impact of run time assurance (RTA), which monitors the system state and intervenes to assure safety, on effective learning. By studying multiple RTA approaches in both on-policy and off-policy RL algorithms, we seek to understand which RTA methods are most effective, whether the agents become dependent on the RTA, and the importance of reward shaping versus safe exploration in RL agent training. Our conclusions shed light on the most promising directions of SRL, and our evaluation methodology lays the groundwork for creating better comparisons in future SRL work.
Optimization of Datacenter Selection Policy in Cloud Computing using Differential Evolution Algorithm

Cloud computing has become one of the most influential inventions in modern-day computer science. It has become one of the integral parts of our day-to-day life. The success of our regular activities relies on the efficiency and effectiveness of the cloud services. The datacenter selection policy is one of the vital elements in cloud computing. The user requests are processed in the datacenter. Therefore, an optimal datacenter ensures user satisfaction with cloud services. In this research, we propose a differential evolution-based datacenter selection policy to choose the optimal datacenter for the userbase. Differential evolution mainly depends on the evolutionary process of mutation, which helps the algorithm explore the whole search space and find the optimal datacenter. We have used the Cloud Analyst simulator to experiment with the correctness and performance of our algorithm. The simulation results demonstrate that the proposed differential evolution algorithm performs better than the existing datacenter selection algorithm, such as the closest datacenter selection policy and optimized response time policy.
What Affects the Adoption of Metaverse in Education? A SEM-based Approach

Metaverse is an emerging 3D digital space that augments the real world and the virtual world. It has been envisioned to be the trendsetter for future education having tremendous potential. However, the success of any new technology depends on its mass adoption and widespread societal diffusion. Therefore, it becomes important to understand the factors that can lead to its success by ensuring widespread adoption. In this work, we investigate the different psychological gratifications of the educational usage of Metaverse under the lens of the Uses and Gratification (UGT) theory. Particularly, we propose and test a research model by using the Structural Equation Modelling (SEM) approach by considering four fundamental psychological gratifications of autonomy, achievement, affiliation, and dominance, together with two additional factors of hedonic motivation and student personality. Data is collected from two Asian universities, and results show the importance of autonomy, hedonic motivation, and personality in predicting Metaverse adoption.
Hybrid Quantum Encoding: Combining Amplitude and Basis Encoding for Enhanced Data Storage and Processing in Quantum Computing

This research applies Bit-Partition hybrid quantum encoding methods to store and process classical data in quantum systems efficiently. By combining amplitude encoding for representing the index and basis encoding for the data values, we introduce a novel technique that leverages the strengths of both encoding methods. We describe the process of encoding and decoding the hybrid states, highlighting the potential benefits of this approach in terms of data storage and computational efficiency. Furthermore, we explore the decoding process, addressing the inherent uncertainty associated with quantum measurements and discussing strategies to minimize such uncertainty. Our findings suggest that hybrid encoding can improve quantum information processing tasks, making it a promising technique for future quantum computing applications. Further research is needed to optimize the encoding and decoding processes and explore the full potential of this approach in various quantum algorithms.
Enhanced Machine Learning-Based Code Smell Detection Through Hyper-Parameter Optimization

To preserve software quality and maintainability, machine learning-based code smell detection has been proposed, and the results are promising. This research proposes an enhanced version of machine learning-based code smell detection. We improve the performance of machine learning-based code smell classifiers by applying hyper-parameter optimization techniques in Particle swarm optimization and Bayesian optimization to decision tree and random forest. The models were trained and evaluated on 74 open source projects to identify god class, data class, feature envy, and long method. The experimental results confirm that the optimized machine learning classifiers c an achieve up to 99.183% and 99.155% of accuracy for both class-level and function-level code smell classification, respectively. In term of recall, the enhanced machine learning-based code smell classifiers achieved 9 9.514% when identifying data class and 98.806% for long method. The comparison results also indicated that the enhanced machine learning classifiers outperform the original versions in the code smell detection context.
Examining the Link Between Corruption and Bank Credit

This research examines the influence of corruption on the expansion of bank credit in Sub-Saharan Africa from 2000 to 2017. By utilizing the generalized method of moments (GMM) technique, the investigation reveals a significant positive correlation between corruption and bank credit. Additionally, the findings indicate that while stability in the banking sector leads to a decrease in bank credit, economic growth and inflation contribute significantly to the growth of bank credit. Based on these results, the study offers essential recommendations to foster integrity and responsible lending practices in the banking sector. It proposes that banks establish robust whistleblowing procedures, encouraging employees to promptly report any instances of corrupt transactions associated with credit advancement. Through the cultivation of a transparent and accountable culture, banks can effectively address and mitigate the adverse impacts of corruption on credit growth. Furthermore, the research advocates for the enforcement of strict non-price constraints to regulate lending practices.
Midnight: An Efficient Event-driven EVM Transaction Security Monitoring Approach For Flash Loan Detection

Decentralized Finance (DeFi) has been on a roller coaster of swift changes for some years. In the DeFi world, the principal ingredients are smart contracts, which are often vulnerable to security issues. To work with these contracts, transaction submission is required. Arguably, advances in transaction analysis can benefit a whole Ethereum Virtual Machine (EVM) and non-EVM networks alike. Unfortunately, only a few open-source tools and commercial products exist that focus specifically on transactions. This research studies real-time event-driven EVM transaction monitoring and experiments with its effectiveness in performing the enormous task of transaction analysis and compares it with historical analysis approaches. In this study, EVM events were studied extensively, and Midnight, an open-source, full-stack proof of concept framework was used to experiment with the alternative approach. The research included a practical experiment on monitoring specific events called flash loan on the latest version of the popular DeFi protocol called AAVE.
A Comparison of Machine Learning and Neural Network Algorithms for An Automated Thai Essay Quality Checking

Checking the quality of essay writing in Thai language is still a complicated task because Thai language is very complex language in terms of punctuation, sentence structure, word repetition, spelling, commenting, and reasoning in content. Therefore, checking the quality of an essay and scoring require the reviewer's skills in reading and interpreting that make long time to review. In addition, if in reviewing process using more than one reviewer, it might affect different quality checking standards. We collected essay in Thai language which is written by student who registered paragraph writing course from The Sirindhorn Thai Language Institute of Chulalongkorn University. This work implemented LSTM model, CNN model, BERT model and WangchanBERTa model to compare the effectiveness of checking the quality of Thai essay writing. Our experimental result shows that classification analysis compiled with WangchanBERTa can achieve high accuracy up to 90%. However, CNN model compiled with classification analysis can achieve high accuracy up to 87% while compiled with regression analysis can achieve high accuracy in the range 90%. In conclusion, the system that we proposed can predict the quality of Thai essays with high accuracy. Therefore, we recommended Wangchanberta model for classification problem and CNN model for regression problem.