Reviewerly/exHarmony huggingface dataset:
Efficiency-enhanced Blockchain-based Client Selection in Heterogeneous Federated Learning

In Federated Learning (FL), blockchain has been extensively used to achieve distributed and tamper-resistant data processing. However, typical Blockchain-based Federated Learning (BFL) rarely considers clients’ resource and computing limits. High-capacity clients may be sacrificed when all clients train on the same neural network. This paper proposes a Blockchain-based Heterogeneous Federated Learning (BlocFL) model to address the challenges above. BlocFL replaces the central server with a consortium blockchain, and several neural networks are employed for local training. Considering the challenges in resource allocation in BFL, especially in heterogeneous networks, we propose a consortium blockchain-based heterogeneous federated learning client selection method. The proposed method optimizes the choice of client nodes under the limits of computational resources. Experiment results demonstrate that our method can allocate appropriate neural network models to each client and effectively improve the efficiency of local training in HFL. It also can achieve a comparable level of accuracy to the baseline approach with similar training parameters.
MACS: A Multi-Asset Coin Selection Algorithm for UTXO-based Blockchains

In this paper, we propose MACS - a new coin selection algorithm for multi-asset UTXO-based blockchains. MACS addresses the primary coin selection requirements regarding transaction fee, transaction size, UTXO pool size, transaction age, and privacy. We compare the performance of MACS with other coin selection algorithms. The performance evaluation shows that MACS achieves the mentioned requirements while outperforming other coin selection algorithms in terms of having a wider distribution of their number of UTXO inputs. Moreover, UTXOs will not remain in the UTXO pool forever when the MACS algorithm is used since MACS keeps the fraction of old UTXOs oscillating between $0 \%$ and $20 \%$.
Securing IoT Firmware Dispatch Systems with Blockchain

Internet-of-Things (IoT) devices have become a prevalent part of everyday life, making the need for IoT firmware security greater than ever. Firmware dispatch systems play a crucial role in maintaining the secure operations of IoT devices, where a compromise on the integrity of dispatches may simply ruin an IoT device. Hence it is essential to have robust solutions to safeguard such systems. With the advent of blockchain and Web3 technologies, there are new options for tackling security-related challenges. Blockchain provides a tamper-proof, secure, and fully distributed protocol, making it an excellent choice for a firmware dispatch system. In this work, we explore the existing blockchain-based solutions for distributing IoT firmware updates, and then propose a secure firmware dispatch system for IoT devices using Ethereum. Our system employs Web3.Storage, with a hosted implementation of the InterPlanetary File System (IPFS) and a peer-to-peer hypermedia protocol. In the evaluation, we demonstrate that our designed architecture is fast, reliable, and scalable by offering a firmware integrity validation. It can benefit a wide range of manufacturers who intend to increase the security of their IoT devices.
An Integration of a Neural-Network-Based Computer Vision Model and a 2-DOF Object Tracker Robot

This paper presents a system for object tracking that integrates a neural-network-based computer vision model and a 2-DOF robot. The system consists of two main parts: the first part detects objects in the environment and enables the user to select a target for tracking, while the second part establishes a control loop and tracks the selected target using a PID controller. The system uses Yolo neural network for object detection and StrongSort neural network for sorting and assigning IDs to objects. Two methods are proposed for distinguishing the target right before beginning the control loop phase: Landmark-Based Identification (LBI) and Feature-Based Identification. LBI detects a specific and unique sign in the environment to locate the target, while Feature-Based Identification utilizes the StrongSort neural network to extract features of detected objects and compares them to match the desired target. Finally, the tracking procedure begins with the help of StrongSort model and the system calculates the position of the target, generates a control signal to track it even if it’s moving. This system demonstrates the potential of integrating advanced technologies for object tracking applications.
Machine Learning-based Multiclass Classification Methods for Sentiment Analysis

Sentiment analysis, also known as opinion mining, is the process of identifying the sentiment or emotion conveyed in a textual review. This requires categorizing the expressed opinions into several sentiment classes, namely positive, negative, or neutral. Typically, machine learning algorithms are employed to construct a sentiment classifier, which is subsequently utilized to automatically assign appropriate sentiment to individual textual reviews. Numerous machine learning methods have been utilized for these purposes. Determining the most suitable algorithm for sentiment analysis is a challenge. One potential methodology is doing a comparative examination of the algorithm’s performance with the dataset under consideration, and then choose the most optimal sentiment classifier for adoption. In this study, we conducted a comparative analysis of many machine learning algorithms with lexicon-based approach, including multinomial naïve bayes, support vector machine, k-nearest neighbors, random forest and an ensemble approach combining these algorithms, with the purpose of developing a sentiment classifier model using the TripAdvisor dataset. The objective was to classify hotel customer reviews into three distinct categories: positive, neutral, and negative. After evaluation of recall, precision, F1, and accuracy metrics, it can be concluded that the ensemble approach yields superior outcomes compared to other approaches.
Optimization and Scalability of Blockchain Enabled Demand Response Smart Contracts using Sharding and Neural Networks

Utilities have a need for demand reduction in response to energy events that occur throughout the year such as extreme weather or other heavy energy usage periods. In addition, utilities must mitigate the increased variability of load flow on the grid due to increased renewable energy generation being added. Demand response (DR) is one solution utilities use to address these issues. However, DR is primarily for large energy consumers. As such, smart contracts on a permissioned blockchain are proposed in order to enable smaller residential energy customers to participate in demand response mitigation and peer-to-peer energy trading without the need for a trusted third party. Thus, increasing the available pool of demand reduction participants with minimal additional management. A demand response smart contract is created on Ethereum using the Remix – Ethereum IDE. LSTM (Long Short-Term Memory), a deep learning based Neural Network, is used to predict the future energy trading prices based on past power purchase agreement (PPA) prices, which is then used in our bidding model for P2P energy trading. This allows both prosumers and consumers to make informed decisions for the energy trading based on potential profit from revenue generated less transaction costs. The blockchain is sharded in order to improve scalability especially around throughput due to the large number of IoT devices and associated data that will be added to the blockchain.
The Blockchain Trilemma Described by a Formula

The blockchain trilemma was first introduced in a 2017 blog post written by Vitalik Buterin, one of the cofounders of Ethereum. This trilemma claims that it is impossible to simultaneously achieve the three properties of decentralization, scalability, and security in a blockchain. While extensive analysis of blockchain performance has been conducted, the claim has been empirically accepted as true. However, no one has formulated this trilemma mathematically. In this paper, we present a formula that explicitly expresses the trilemma for Proof of Work blockchains. Furthermore, based on our mathematical representation of the trilemma, we illustrate the two categories of approaches for improving blockchain performance under the constraints of the trilemma.
State Definition in Deep Reinforcement Learning: A Smoothness Index Approach

In the pursuit of developing autonomous agents capable of learning optimal behaviors, reinforcement learning has emerged as a foundational frame-work. This paper introduces a novel approach for state definition in deep reinforcement learning systems, with a focus on solving pixel-based control scenarios. The proposed frame-work harnesses the capabilities of Convolutional Variational Auto-Encoders to extract compact and meaningful latent representations from high-dimensional pixel observations. These representations serve as the foundation for constructing an efficient state space for reinforcement learning agents. To further enhance the state construction process, the concept of the Smoothness Index is introduced. Smoothness index acts as a criterion for feature selection within the latent space, facilitating the identification of the most relevant and informative states. Demonstrating the effectiveness of iterative smoothness index-based forward selection, this process notably compresses the observation space while retaining essential information. This compression enables the creation of more efficient reinforcement learning agents. The procedure involves collecting and preprocessing observation data given by the OpenAI Gym car racing environment, encoding observations using a convolutional variational auto-encoder, and iteratively selecting features based on smoothness index. Subsequently, the Proximal Policy Optimization algorithm is employed as a policy-based controller to evaluate the effectiveness of the proposed frame-work. Results indicate that this approach yields a remarkably compact state space that either maintains or enhances reinforcement learning agent performance. This innovative frame-work paves the way for the development of more efficient and adaptive reinforcement learning agents in real-world image-based applications.
Statically Checking Missing Input Validations in Solidity Smart Contracts - A Case Study

Smart contracts, running on blockchains, enable transparent interactions without intermediaries. However, program-related bugs and flaws in translating business logic into code can introduce vulnerabilities, making them attractive targets for malicious users. For instance, using input from untrusted users in critical operations without proper validation may lead to exploitable functionalities. We refer to this vulnerability as Missing Input Validation (MIV). This paper focuses on the issues caused by MIV (aka MIV-related issues) in Solidity smart contracts executing on the Ethereum blockchain. In particular, we conduct a case study emphasizing how MIV-related issues can lead to unexpected behavior in smart contracts, rendering them vulnerable to malicious manipulation. We also implement an information flow analysis-based analyzer, MIV-Checker, to statically detect instances of MIV in Solidity smart contracts. We evaluated MIV-Checker against a state-of-the-art smart contract analysis tool, Securify, using 3399 distinct contracts collected from the Ethereum blockchain. Securify identified only three contracts, whereas MIV-Checker detected 86 contracts as potentially susceptible to MIV. Our manual assessment confirms that MIV-Checker outperforms Securify by detecting more true MIV cases and reducing execution time. This case study helps determine that many recently used contracts on Ethereum may still be potentially vulnerable to MIV. Additionally, we position user-provided input as a non-deterministic factor in Ethereum contract execution and discuss future research avenues for mitigating issues arising from such factors.
Voxelmorph Learns to Pay Attention

This paper proposes a novel attention-based 3D image registration architecture to use in medical image registration. Previous registration methods have employed deep neural networks such as convolutional neural networks (CNNs) to extract the direct relationship between two pairs of image volumes. They do not pay extra attention to the particular regions of the images, as the medical image contexts might need. Our proposed architecture uses a Spatial Attention Module (SAM) and a deep VoxelMorph-based U-net convolutional neural network to guarantee the necessary attention to the particular regions of medical images in the registration process. The experimental results on a 4D cardiac Computed Tomography scans (Cardiac CT scans) dataset show that our registration method has outperformed state-of-the-art registration algorithms and reached better accuracy scores in both unsupervised and semi-supervised training approaches.
Encrypted Traffic Classification for Early-Stage Anomaly Detection in Power Grid Communication Network

Secure communication networks are crucial for the proper functioning of power grid infrastructure. Anomaly detection is essential for maintaining network security, but most existing methods rely on power system measurement anomalies that appear only after an attack has been effectively executed. Identifying attacks in their early stages, primarily through communication network-based anomalies, is therefore essential. However, the fast-paced proliferation of encrypted traffic conceals a significant amount of malicious activities. This paper proposes a novel framework for early-stage anomaly detection using deep-learning-based traffic classification. In our approach, wavelet transforms and byte-to-image transformation is used to extract features from encrypted traffic flow in the networks secured by the IPsec protocol suite. Experimental results show that our proposed method achieves 93% accuracy in detecting abnormal traffic. The framework presents a promising solution for early-stage anomaly detection in power grid communication networks and could serve as a valuable addition to critical infrastructure security measures.
Improving Quality of Experience in Cloud Gaming Using Speculative Execution

Cloud gaming has surged in popularity with the rise of mobile games. Cloud gaming systems allow players to enjoy the latest high-performance games from any location on their mobile devices. However, cloud gaming presents challenges, notably the increased latency due to network delays, creating an issue of end-to-end latency. Minimizing network delays like processing, transmission, and propagation is crucial to offer a user experience similar to playing a local game. Our research explores a speculative video transmission method on cloud gaming servers to overcome these limitations and reduce network latency. In our proposed method, the cloud game server analyzes input patterns from the user, then pre-encodes and sends the corresponding game frames. This process reduces the perceived network delay, making the game more responsive. This paper evaluates the estimated Quality of Experience (QoE) based on an existing QoE model.
Machine Learning-Based Fall Detection Algorithm Using Data Fusion Techniques

For} elderly people, accurate fall detection offers prompt medical assistance. This study describes a reliable fall detection system that combines multi-level data with machine learning. The SisFall dataset, which is open to the public, was chosen because of its benefits. Butterworth filtering and sliding window segmentation were used as preprocessing techniques. There were three degrees of data fusion used: data-level fusion (Kalman filtering) fused accelerometer and gyroscope signals; feature-level fusion (PCA) aggregated extracted features; and decision-level fusion (weighted majority vote bagging) combined classifier outputs. XGBoost, Naive Bayes, Random Forest, SVM, KNN, and logistic regression were among the machine learning models that were assessed. Accuracy and robustness were improved through the use of data fusion techniques and machine learning algorithms. The effectiveness of fusing complementary data at various levels was demonstrated by the final multi-level fusion technique, which had a fall detection accuracy of 99.0%.
Unveiling Vulnerabilities in DAO: A Comprehensive Security Analysis and Protective Framework

Decentralized Autonomous organizations (DAOs) have emerged as blockchain technology evolves beyond cryptocurrencies. Despite being the first project in this ecosystem, The DAO encountered a significant exploit due to inadequate implementation; nevertheless, it still paved the way for future projects. While decentralized autonomous organizations continue to thrive, there is a shortage of academic papers analyzing the associated risks. Therefore, this paper aims to comprehensively examine the current vulnerabilities in these organizations by systematically analyzing past attack incidents. 54 real-world events spanning from 2016 to July 2023 have been collected for identifying and summarizing major attack vectors. The results showcase that flash loan attacks, oracle manipulation, governance takeovers, and reentrancy issues are the critical vulnerabilities within this field. For further protection, this research also provides both general and specific countermeasures against each vulnerability, serving as an evaluation framework for both existing and future projects.
Sentiment Analysis Product using Text Mining with Word Relationship Represented by Graph

This paper presents a combination of text classifier and word centrality for analyzing product reviews. The product reviews collected from the Amazon website consist of 10,374 office product reviews. The classifier selected Random Forest algorithm and word centrality measures are Degree Centrality (DC), Centrality (BC), Closeness Centrality (CC), and Eigenvector Centrality (EC) and set threshold values ranking from 0.2-1.0. The result showed that Random Forest algorithm combining the world centrality was higher than the Random Forest alone with a threshold greater than 0.2. Classification accuracy is 85.50% to 87.50%.
Crisis of Trust: Analyzing the Verifier’s Dilemma in Ethereum’s Proof-of-Stake Blockchain

Blockchain technology has become deeply integrated into modern life, offering diverse applications beyond its origins in cryptocurrency. Ethereum’s transition to a Proof-of-Stake (PoS) consensus mechanism marks a significant milestone in blockchain development. However, this shift brings new challenges, including the Verifier’s Dilemma. This paper focuses on Ethereum’s Proof-of-Stake (PoS) implementation and delves into the Verifier’s Dilemma, a challenge initially prevalent in PoW protocols concerning the efficient confirmation of transaction validity without excessive computational costs. In the context of PoS, validators strive to optimize rewards through timely attestation, knowing that delayed or erroneous attestation can lead to penalties. To tackle this dilemma, we introduce a quantitative model based on the Performance Evaluation Process Algebra (PEPA). This research offers two significant contributions: (i) the identification of the Verifier’s Dilemma problem within Ethereum’s PoS protocol and (ii) the introduction of a base PEPA model that facilitates a comprehensive study and validation of potential solutions to this challenge. We believe that our work advances the understanding of the Verifier’s Dilemma in PoS blockchain networks, laying the groundwork for addressing this crucial issue.
Plexus Search – A Search Enumeration

Data Structures and Algorithms, in the field of computer studies, stand out as one of the most captivating and intellectually stimulating topics within this domain. What makes it even more intriguing is the intertwining of harmonics from the realm of mathematics, birthing something entirely novel and enthralling. These interactions sometimes lead to modifications of existing algorithms, while at other times, they give birth to entirely fresh ideas that push the boundaries of computational knowledge. Throughout our research, we have meticulously focused on presenting a groundbreaking algorithm designed to revolutionize the way data is sought and retrieved within data clusters, particularly in the first and second dimensions. This innovative algorithm, which we have proudly named Plexus Search, serves as a beacon of progress in tackling the complexities of intricate 2-D or 1-D data structures. Our motivation behind the title "Plexus Search" lies in the profound significance of searching clusters – those intricate configurations of data points that challenge conventional search methodologies. By harnessing the power of this algorithm, we aim to empower researchers, developers, and computer scientists to explore vast datasets with unprecedented efficiency and accuracy, unraveling new possibilities and discoveries in the ever-evolving world of computer studies.
Assessing Human Activity Recognition Performances of Different Machine Learning Algorithms Using Sensor Data

With how extensively various kinds of sensors have become a part of modern life, a vastness of application and research areas has sprung up in its wake. One such use case is the budding field of Human Activity Recognition (HAR) which powers medical care, security purposes, assistive technology in homes, navigation systems, etc. In this work, the focus is to assess the performance of different machine learning principles using sensor inputs to form an understanding of their suitability in the context of HAR. Random forest, SVM, neural network and kNN algorithms are chosen for training and testing experiments conducted on a benchmark dataset, the UCI-HAR. At the outset, base models are developed and their accomplishments are observed to highlight those performing encouragingly, after which randomized type of hyperparameter tuning is utilized to further upscale the selected classifiers’ effectiveness in activity detection. This tuning method is packed with some useful advantages like lessening the computational burden while maintaining the quality. The results obtained show that out of all the models presented, the best accuracy of 96.54% is achieved by the tuned SVM model on the aforementioned dataset. Confusion matrix analysis suggested that standing and sitting activities see confusion to some extent across most models. Moreover, tweaking with the hyperparameters proved most fruitful for the Neural Network model.
Towards Full-page Offline Bangla Handwritten Text Recognition using Image-to-Sequence Architecture

In this work, I have developed an Offline Handwritten Text Recognition (HTR) model architecture based on Neural Networks that can be taught to recognise whole pages of handwritten Bangla (Bengali) text without image segmentation. The model is developed in order to aim the extraction of text from an image and accurately sequence it without imposing any limits on the orientation, layout, and size of the text. Bengali being a resource-constrained Indic language, there is a lack of proper annotated dataset consisting scanned images of Bangla handwritten scripts. The publicly available datasets are either not annotated or the annotations are wrong in some cases. Therefore, in this work, I have introduced a new dataset, ‘Bongabdo’, which consists of full-page handwritten scripts collected from a wide variety of contributors of various age groups, occupation and gender. Significant time has been spent on preparing and modifying the datasets which are currently available publicly. Further, recently proposed State-of-the-art Image-to-Sequence architecture with different settings of hyperparameters have been applied on these images and they have been evaluated in terms of Character Error Rate (CER), Word Error Rate (WER) and Sequence Error Rate (SER) to finally come up with a comparative study.