Reviewerly/exHarmony huggingface dataset:
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown Environments

Self-reinforcement algorithms, especially Q-learning and value iteration have been popular and effective for navigating mobile robots. To provide a deeper insight through their performance and usage, this paper investigates solving a simple Q-Learning problem for various working environments for mobile robots. No matter how quick or reliable they are, all temporal difference algorithms employ iterative methods to produce desired results. For this reason, in this paper, we looked at the relationship between episode steps, mesh sizes, and-most importantly-the impact of the environment on both convergence rate and solution reliability. As previously indicated, the majority of the conclusions of this study about the relationship between computation cost and environment and also dependability can be transferred to more sophisticated temporal difference based algorithms because all methods are iterative. As many robotic applications for reinforcement learning have two or three-dimensional state space for path planning, knowing the approximate convergence episode count would be a useful tool for researchers to tackle non-optimal episodes, especially on embedded systems with computational constraints. The content also discussed the relationship between three mesh sizes and tried to guide researchers to implement their reinforcement learning pipeline more effectively. As shown by this work, the performance and reliability of iterative reinforcement learning algorithms depend deeply on the environment. In certain cases, increasing the number of episodes would not alter the results entirely. Therefore, when solving these kinds of maps, researchers would refrain from using Q-learning techniques.
Utilizing Artificial Intelligence in Cryptocurrency Trading: a Literature Review

The rapid growth and unique advantages of cryptocurrencies have made them an attractive asset for investment portfolios. However, investing in cryptocurrencies comes with risks, especially market volatility. This paper explores the benefits of Bitcoin’s market dominance, including liquidity, stability, and practical utility. It also reviews multiple previous articles about using Artificial Intelligence (AI) for cryptocurrency trading to enhance the feasibility and profitability of cryptocurrency investments, focusing specifically on Bitcoin. It further discusses the potential advantages of using AI in developing predictive models for market trends and managing grid trading strategies to stabilize returns and lower risks. While research on the application of grid trading strategy with AI in the cryptocurrency market is limited, existing studies in other asset classes demonstrate the effectiveness of AI-based methods. Finally, this paper outlines novel potential future research that aims to integrate AI with the Grid Trading Strategy. This integration seeks to enhance the sustainability of cryptocurrency investments while simultaneously amplifying their profitability.
Analyzing the Federated Byzantine Agreement Blockchain Network for Liveness Using Multiple Online Analytical Processing

Blockchain technology is widely implemented in various fields, such as government, health, and agronomy. However, various security vulnerabilities and issues related to the validation speed hinder the adoption of blockchain technology. Blockchain consensus systems have been developed to address these issues, including the Proof of Stake and its variants, as well as the Practical Byzantine Fault Tolerance (PBFT) and its variants. PBFT excels in quickly resolving system validations but faces capacity limitations. To address the limitations of this consensus system, the Federated Byzantine Agreement (FBA) was developed. FBA relies significantly on network liveliness and security, necessitating an investigation of the underlying causes of network liveliness issues within the FBA framework. This study focuses on a real FBA network that operates on a stellar network. The analysis employed the Multiple Online Analytical Processing (MOLAP) method, revealing that MOLAP is a viable approach for examining networks affected by inactive nodes that pose a challenge in establishing a quorum, albeit with potential solutions through the use of thresholds. Nevertheless, a security loophole is identified due to the quorum being concentrated within a single organization, and further issues arise when nodes become overloaded, rendering them unverifiable.
Blockchain-based Trustless Fair Payment Protocol for Verifiable Confidential Outsourcing Computation

Outsourcing computation has emerged as a highly appealing service for companies seeking cost-effective alternative to perform on-premises expensive computational tasks. However, the conventional payment protocols employed by service providers and clients exhibit certain drawbacks, including inherent unfairness and reliance on a trusted third party. Furthermore, these protocols often assume the service providers are honest and will return valid results, which may not be a realistic assumption in practice. Additionally, conventional outsourcing computation fails to adequately address data confidentiality concerns, thereby giving rise to privacy issues. To address these challenges, this paper proposes a trustless fair payment protocol for verifiable confidential outsourced computation by leveraging blockchain technology and cryptography. It then provides an instantiation with efficiency optimizations. Lastly, it highlights limitations of the protocol and suggests potential directions for future work. The proposed protocol can remove the need of trusts between outsourcing computation service providers and their clients and achieve fairness. It provides assurance that results were computed correctly in a privacy-preserving manner, instilling confidence in clients.
Human Action Classification Based on Pose Estimation and Artificial Neural Network

Human computer interaction is an interesting topic in the current research work. Human action is useful for interaction with the computer. However, human action classification is a challenging topic. In this paper, we present the human pose classification using artificial neural network. There are ten classified actions including Both Hands Up, Right Hand Up, Left Hand Up, Both Hands Bye, Right Hand Bye, Left Hand Bye, Both Hands Fly, Right Hand Fly, Left Hand Fly, and Both Hands Down. We apply pose estimation to identify the body’s key points with results in 2-dimensional data as the basis of human skeleton. The action model is generated from the human skeleton by employing an Artificial Neural Network. The data has been processed by classification model with an accuracy of 95.7 percent, a precision of 96.2 percent, a recall of 95.8 percent and F1-score of 96.5 percent.
Face Recognition using Deep Learning

The primary objective of this research is to advance facial recognition by evaluating the performance of four distinct algorithms: Multi Task Cascaded Convolutional Networks (MTCNN), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and Histogram of Oriented Gradients (HOG). To accomplish this, a dataset comprising 200 images featuring 10 individuals was meticulously curated. This dataset encompasses various facial attributes, including front-facing, side-facing, and mask-wearing poses. Each algorithm’s effectiveness was assessed by measuring its accuracy in face recognition. Upon comparing the results, it was evident that MTCNN outperformed the other algorithms. For front-facing poses, MTCNN achieved an impressive average accuracy of 93.98%, while for side-facing poses, it maintained an average accuracy of 88.91%. Even for mask-wearing faces, MTCNN demonstrated commendable performance, with an average accuracy of 83.42%. Importantly, these results were obtained with a remarkable speed of face recognition every 0.5 seconds. In summary, MTCNN’s algorithm consistently exhibited superior average accuracy when compared to CNN, SVM, and HOG algorithms, respectively.
A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual’s behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained.
Knowledge Graph Based System for Community Enterprises Product Enhancement

Community enterprises aim to enhance products by standardizing goods and services through the incorporation of knowledge. However, the absence of a centralized knowledge map as a unified database for knowledge integration results in information fragmentation, leading to the challenges of intricate knowledge retrieval, data inaccuracies, resource inefficiencies, and insignificant data accessibility. To address these problems, researchers propose a knowledge graph-based system for community enterprises’ product enhancement using a knowledge-guided approach to enhance the efficiency of collecting and retrieving integrated knowledge. This involves developing a data structure based on the knowledge graph concept as well as improving the knowledge retrieval process fostering advanced knowledge incorporation for community enterprises’ product enhancement.
Image processing and fuzzy controller design for Robotic Walker

Due to the gradual increase in average age in some human societies, the need for tools to make life easier for the elderly is felt more. One of the types of equipment necessary for these people is a helping tool for their mobility. Until now, the designed tools have either been inactive and disturbed the user’s walking rhythm or were active but prevented movement, such as motorized wheelchairs. The aim of this research is to develop an active robotic walker in order to facilitate voluntary movement and return the person to a normal standing position in cases of loss of balance. The input of this system will be the image of the camera. After the image processing stage, the user’s position is determined and by using a fuzzy controller, the outputs of the problem, which are the forces of the robotic walker, are obtained to restore balance in forward and sideways directions. Inspired by the Prewitt filter, the developed filtering method is used to distinguish the user from the environment in the image processing stage. Then the fuzzy controller is designed to apply the forces in case of imbalance. The obtained results show the acceptable performance of the proposed image filter in most environmental conditions. Using the designed controller at varying heights and weights has rendered the desired outputs that are shown in 10-second time intervals.
SrNFT: Simple Restricted Non-Fungible Token Framework for Transactions With Risk Detection

Due to the rapid growth of Non-Fungible Tokens (NFTs), the current total market value of worldwide NFTs is at 1.05 billion dollars. The massive trading market increases the issue of blockchain supervision — transactions employing NFT to money laundering occur on a regular basis. However, most of the current domestic platforms do not support trading in the secondary market of digital collectibles, while there is a lack of foreign research on the regulation of NFT trading. The economic benefits of NFT have considerable potential to greatly enliven the market. The main purpose of this paper is to study the regulation of NFT ecology. Therefore, we provide a regulatory framework for NFT that realizes ex-ante regulation, and study two major modules of money laundering detection and regulatory control for NFT. In addition, to enhance compatibility, we also provide alternative ways to the proposed framework, aiming to provide new standards and guidance on the regulation of NFTs.
Learning 2D Mobile Casual Musical Pitch Training Game Application (Pitchero)

Musical skills help be an exceptional talent, emotional development, or intellectual development. Singing is an important musical skill. However, listening and singing correctly in the music’s key is something only some can do. Therefore, musicians create equipment for practicing vocal techniques, such as pitch matching which can help in improving singing and listening skills. Especially in young children who can easily build their skills; thus, the training design needs to be fun, easy to understand, accessible, and effective. We designed and created a game to enhance vocal techniques according to the high or low pitch of musical notes. That can indicate the pitch matching of the player. It shows visually on the screen whether the voice of the player is too high or too low to the note. Because visual information is perceived and analyzed more clearly than sound information. This makes it easier for players to control their voice, and practicing by playing games will allow players to improve singing skills and enjoy the game.
Secure Image Encryption Algorithm based on Two-Level Diffusion and Hybrid Chaotic Maps

Security is an essential aspect of a communications network. With the availability of the internet, a lot of private data is transferred daily, requiring robust protection of these data. In this paper, an image encryption algorithm is proposed based on two-level diffusion and two hybrid chaotic maps. The two-level diffusion consists of bit-level decomposition and shifting of the pixel. Firstly, the two hybrid chaotic maps, namely, the sine-cubic hybrid maps (SCHM) and the sine-logistic hybrid map (SLHM), are developed from the conventional 1D chaotic maps. Secondly, two keys are generated from the hybrid maps. The sine-cubic hybrid map generates the first key, and the sine-logistic hybrid map generates the second key. The first key is used for shifting the decomposed image. A simple logic is used to shift the decomposed image clockwise or anticlockwise. Followed by shifting the bitwise-XOR operation is done between the second key and the diffused image. Finally, different attack and security analyses are performed to measure the robustness of the encryption techniques, such as encryption correlation coefficient, histogram analysis, entropy, PSNR, MSE, differential, and noise attack analysis are performed. The results reveal that the proposed image encryption algorithm is able to resist several attacks.
Account Abstraction, Analysed

Ethereum recently unveiled its upcoming roadmap’s Splurge phase, highlighting the integration of EIP-$\underline{4337}$ as a foundational standard for account abstraction (AA). AA aims to enhance user accessibility and facilitate the expansion of functionalities. Anticipatedly, the deployment of AA is poised to attract a broad spectrum of new users and ignite further innovation in DApps. In this paper, we elucidate the underlying operating mechanisms of this new concept, as well as provide a review of concurrent advancements in accounts, wallets, and standards related to its development. We step further by conducting a preliminary security evaluation to qualitatively assess the extent of security enhancements achieved through AA updates.
Automating Academic Assessment: A Large Language Model Approach

In educational settings, providing timely and quality feedback can significantly enhance student engagement and learning outcomes. However, this task becomes increasingly challenging in larger classrooms. To address this issue, this study introduces a method that leverages Large Language Models (LLMs) — already proven useful in text generation and summarization — for automatically assessing students’ short-answer responses. Demonstrating effectiveness across a variety of use-cases such as answer matching, keyword extraction, and clustering, this approach achieves an impressive 99.03% accuracy rate. More than just an automated grading tool, the method also offers the capability to generate tailored, real-time feedback, thus enhancing the efficiency of teachers’ evaluation processes. The study further provides suggestions for effectively utilizing LLMs in student assessment tasks.
Beyond Words : Harnessing GPT-2 to Continue Stories with Imagination

Are you a writer or someone who just likes to write creatively? You know how challenging it may be at times to get your words out on paper if you've ever tried to sit down and write out a narrative [3]. Your imagination may not always come to you readily, and there may be occasions when you wish you had a random tale generator to provide direction and inspiration. Nowadays, with artificial intelligence becoming more and more popular, you may locate an AI story generator to assist you in coming up with an original and entertaining narrative or book.
The Validator’s Dilemma in PoW Blockchain: An Evolutionary Game Perspective

Mining pools serve as organized structures of miners and play a crucial role in maintaining the stable operation of the blockchain. They are responsible for validating transactions during mining and ensuring their legitimacy to generate new blocks and receive block rewards. Validating does not provide any additional economic benefits. Some mining pools may choose to skip validating the transactions to save time and earn rewards more quickly. This behavior poses a significant threat to the security of the blockchain. However, existing studies have not taken into account the validity of blocks, and it is important to note that skipping validation does not always result in errors. This study has developed a game model to depict the scenario and examined the influence of penalties on mining pool strategies. By employing evolutionary game theory, we investigated the evolution of mining pool strategies and identified the crucial factors that determine strategy choices. Our findings suggest that penalties alone are insufficient to change the equilibrium state of the system. Nevertheless, formulating policies that promote cooperation can effectively resolve the validator’s dilemma and enhance the stability of the blockchain.
Olive: An Instruction Following LLaMA Model For Odia Language

The AI community is experiencing a profound impact from Large Language Models (LLMs), and the introduction of ChatGPT and GPT-4 is prompting a reconsideration of the potential of artificial general intelligence(AGI). However, most of the LLMs are trained in English and other high-resource languages, resulting in the unavailability of LLM and its related technologies and services for many low-resource languages. In India, where only 10% of the population is proficient in English, the need for LLM models adapted to regional languages becomes crucial.In this paper, we emphasized the need for LLM for the low-resource Odia language by evaluating the available LLM-supporting Odia language. We describe the development process of the instruction-tuning LLM model for Odia. The developed instruction tuning Odia LLM is available freely for research and non-commercial purposes.
Deep Learning based Spoof Detection: An Experimental Study

This paper presents a novel approach to detect audio spoofing in the ASVspoof 2019 dataset. The proposed method utilizes Spectral Delta Coefficient (SDC) as a feature vector and employs Residual Neural Networks (ResNet) as a classifier. Audio spoofing poses a considerable challenge in forensic investigations and Automatic Speaker Verification (ASV) systems, with impersonation, voice conversion, speech synthesis, and replay attacks being common forms of spoofing techniques. Modern methods for generating spoofed speech using voice conversion and speech synthesis have reached a level of perceptual indistinguishability from genuine speech. Additionally, replay attacks allow perpetrators to deceive an ASV system by replaying a recorded voice of a legitimate human speaker. The paper evaluates and compares different feature vectors and classifier combinations. The feature vectors used in this study include Mel-frequency cepstral coefficients (MFCC), Constant Q Cepstral Coefficients (CQCC), Linear Frequency Cepstral Coefficients (LFCC), Spectrogram, and Spectral Delta Coefficients (SDC). Various classifiers, such as Neural Networks, Gaussian Mixture Models (GMM), Support Vector Machines (SVM), and Residual Networks (ResNet), are employed. Equal Error Rate(EER) is the evaluation metric used in the ASV spoof challenge. The results demonstrate that the SDC-ResNet combination outperforms other combinations, including LFCC-GMM, CQCC-GMM, MFCC-GMM, MFCC-SVM, SDC-GMM, SDC-SVM, and Spec-ResNet, in terms of EER.
Mitigating Undercutting Attacks: Fee-Redistribution Smart Contracts for Transaction-Fee-Based Regime of Blockchains with the Longest Chain Rule

In this paper, we review the undercutting attacks in the transaction-fee-based regime (i.e., without block rewards) of proof-of-work (PoW) blockchains with the longest chain fork-choice rule. Next, we focus on the two related problems: (1) fluctuations in mining revenue and (2) the mining gap – i.e., a situation, in which the immediate reward from transaction fees does not cover miners’ expenditures. To mitigate these issues, we propose a solution that splits transaction fees from a mined block into two parts-(1) an instant reward for the miner of a block and (2) a deposit sent to one or more fee-redistribution smart contracts $(\mathcal{F}\mathcal{RSC}\mathrm{s})$ that are part of the consensus protocol. At the same time, these $\mathcal{F}\mathcal{RSC}\mathrm{s}$ reward the miner of a block with a certain fraction of the accumulated funds over a predefined time. This setting enables us to achieve several interesting properties that improve the incentive stability and security of the protocol, which is beneficial for the honest miners. With our solution, the fraction of DEFAULT-COMPLIANT miners who strictly do not execute undercutting attacks is lowered from the state-of-the-art result of 66% to 30%.
A Survey on Extraction of Relations using Knowledge Graphs in Various Applications

Over the past ten years, knowledge graphs (KG) have rapidly become a significant field. A growing number of studies are focusing on knowledge graphs to structurally represent various relationships that exist between the entities. Knowledge graphs have quickly become a significant area in AI. A directed, labelled, multi-relational graph with some type of semantics is what is meant by the definition of a knowledge graph, which continues a longstanding tradition of graphs in the AI community. To provide such a synthesis, this paper presents a review of the KG research landscape first demonstrates what the main research areas are, and how they relate to various communities, such as in question-answering, error-recognition, and recommendation systems. Within these communities, the many but related focus of KG research are presented in a unified framework. Deep learning techniques are used by the majority of cutting-edge systems, primarily to target relationships between entities.