Reviewerly/exHarmony huggingface dataset:
Efficient Quantization and Hardware Implementation of AlexNet on Resource-limited FPGAs

As Deep Neural Networks (DNNs) have been growing to more and more complex models, their computation and storage requirements have increased dramatically. Recent research works on quantized neural networks (QNNs) have shown their great advantages for real-time machine learning on resource-limited embedded devices. FPGAs have emerged as a popular and efficient hardware platform to accelerate QNNs and DNNs when considering both performance and energy efficiencies. On the other hand, with the increasing complexity of hardware design, high-level synthesis tools are becoming popular to facilitate fast implementation. In this paper, we present our quantization and hardware implementation of the AlexNet using the Xilinx FINN framework. Extensive experiments with QNNs of various bits on a low-cost and resource-limited FPGA board demonstrate their good performance with much smaller memory footprint and lower computation complexity.
Efficient Cloud Pipelines for Neural Radiance Fields

Since their introduction in 2020, Neural Radiance Fields (NeRFs) have taken the computer vision community by storm. They provide a multi-view representation of a scene or object that is ideal for eXtended Reality (XR) applications and for creative endeavors such as virtual production, as well as change detection operations in geospatial analytics. The computational cost of these generative AI models is quite high, however, and the construction of cloud pipelines to generate NeRFs is neccesary to realize their potential in client applications. In this paper, we present pipelines on a high performance academic computing cluster and compare it with a pipeline implemented on Microsoft Azure. Along the way, we describe some uses of NeRFs in enabling novel user interaction scenarios.
Assessing Blockchain Consensus in Robotics: A Visual Homing Approach

Blockchain technology has evolved drastically in recent years, with a wide range of applications in various industries, including robotics. This paper investigates how blockchain technology enhances visual homing in robotics, addressing challenges in navigation without relying on GPS or maps. By simplifying complexities and potential access issues, blockchain establishes an unchangeable data repository for critical information like landmarks and robot perspectives, enabling efficient data retrieval. The study explores blockchain integration into visual homing robotics, emphasizing two consensus algorithms- Proof of Work (PoW) and Proof of Stake (PoS). The evaluation covers suitability, throughput, and latency, unveiling distinct strengths and limitations. PoW ensures high security and decentralization but requires substantial resources and faces scalability hurdles, while PoS offers energy efficiency with decentralization compromises. We utilize Gazebo to simulate the environment and a visual homing based Wide Area Visual Navigation (WAVN) algorithm implemented in ROS, effectively managing the navigation of multiple robots.
Proven Transaction Flow Control for zk-Rollups

In blockchains, the number of transactions in each block is limited. In order to determine which transactions are included, a fee-based model is regularly used. At the same time, zk-rollups, which are often used as a layer 2 scalability solution for blockchains, are based on the same underlying fee model. In this paper we propose studying the inclusion of transaction flow control algorithms in zk-proofs to investigate alternative transaction fee models in zk-rollups or validiums by enabling proven flow control. We study the provability and the cost of control algorithms that manage the flow of transactions, preventing excessive volume and spamming. The evaluation of our proposition, based on our minimal zk-rollup implementation, demonstrates the effectiveness of these algorithms and their relative proof generation complexity. This can lead to new applications that need anti-spam protection or new fee models in a secure environment.
Prioritised Sharding: A Novel Approach to Enhance Blockchain Scalability

In Blockchain technology, distributed computing is based on trust assumption among independent nodes. Researchers are in never ending race to address the Blockchain scalability issues. In this research paper, Prioritised Sharding is introduced which is an innovative strategy based on prioritising nodes according to their performance using real-time analytics and advanced algorithms to enhance the scalability of Blockchain systems. Experimental results demonstrate notable improvements in query response times, data availability, and overall system throughput. Furthermore, our model reduces the potential bottlenecks typically associated with traditional sharding techniques. This work contributes significantly to the discourse on scalable data management strategies and offers promising applications for large-scale, data-intensive enterprises.
Blockchain-Based, Privacy-Preserving, First Price Sealed Bid Auction (FPSBA) Verifiable by Participants

The transparent, decentralized, and immutable properties of public blockchains make them appealing for applications that could benefit from these properties, including e-auctions. However, the usage of blockchains for such applications is hindered by concerns related to privacy and performance. We introduce a hybrid solution for e-auctions, leveraging the advantages of both centralized processing and the immutable registration offered by a public blockchain. In this approach, bids are processed by the auctioneer, while the masked bids are recorded on the blockchain. Consequently, the identity of the winner and their bid value is publicly accessible, while the remaining bid values remain confidential, known only to the bidder and the auctioneer and verifiable against the public results of the auction. These bids remain sealed unless the bidder chooses to disclose them during a dispute event. In this paper, we explain the proposed solution and present empirical analysis of its performance drawing on the outcomes of our preliminary experiments. We elaborate achieving promised privacy while showing 50% improvement over blockchain-based verifiable sealed-bid auction involving fewer than 15 bidders.
Improving Agile Teams Effectiveness Through the Metrics

This text explores the enhancement of Agile team effectiveness through the utilization of metrics, with a specific focus on the concept of “Flow.” In the context of knowledge work, particularly in Software Development, the ability to visualize and manage workflow is pivotal for achieving quicker and more consistent project delivery. Four core Kanban principles, namely visualizing work, limiting Work in Progress (WIP), concentrating on Flow, and continuous improvement, underpin this approach.
Exploring Locality in Ethereum Transactions

Early open blockchain designs face low throughput, high latency, and prohibitive costs for setting up a full node. New designs improve this with innovative mechanisms for handling transactions and the blockchain state, often assuming locality properties in the workload of transactions. Temporal locality allows efficient space management such as light nodes or snapshot-based bootstrap. Disjoint access parallelism, which depends on spatial locality, enables parallel processing of non-conflicting transactions. We analyze locality properties and their interplay in the largest transactional workload available to date, that of Ethereum. Our results show that, although transactions generally display good locality, a minority of accounts are responsible for caching- or parallelism-unfriendliness, calling for specific identification and handling in future blockchain designs.
Eiffel: Extending Formal Verification of Distributed Algorithms to Utility Analysis

The security of distributed systems, such as blockchains, relies heavily on the active participation of sufficient participants within the network. In order to incentivize such participation, different blockchains implement different rewarding mechanisms. However, for these mechanisms to be effective, it is essential that they are fair and accurately implemented, taking into account the rational participants. Consequently, utility analysis plays a vital role in assessing the efficacy of these mechanisms. Formal verification methods, such as TLA+, are commonly employed to ensure the correctness of distributed systems, including blockchains. However, the application of these methods for utility analysis remains relatively unexplored. In this paper, we propose Eiffel, a novel approach that extends the formal verification of distributed systems to encompass utility analysis. By leveraging TLA+, Eiffel enables the analysis of rewarding mechanisms and the detection of potential attacks. Eiffel focuses explicitly on the concept of Nash equilibrium, a fundamental notion in game theory that holds significant importance in utility analysis. Through the use of Eiffel, we demonstrate its versatility with different specifications, highlighting its applicability in committee-based blockchains and the Tendermint consensus algorithm. Our analyses show the effectiveness of Eiffel in evaluating rewarding mechanisms and identifying potential vulnerabilities.
Analysis of the Behavior of Company Employees as Users of Information Systems or Tools, Based on Employees Clustering with K-means Algorithm

While analyzing individual user's behavior in information systems or tools used for performing daily duties, identifying inefficient behavior scenarios can be challenging. To address these issues, it is necessary to automate the analysis and grouping of users behavior patterns using AI/ML and clustering mechanisms. With this approach, users can be classified and grouped based on their behavioral similarities. The K-means algorithm can be utilized as a clustering method to group employees with similar behavior and analyze their actions in comparison to the expected behavior of their respective groups. This allows for the identification of less efficient or ambiguous actions scenario and targeted training of according users. The proposed research aims to enhance the user's grouping, improve behavioral analysis and optimize efficiency of work in information systems by introducing a user behavior model-driven analysis approach.
We Are Launching our Own NFT! Characterizing Fashion NFT Transactions - Preliminary Results

Blockchain technology and Non Fungible Tokens (NFTs) have been a hot topic for several years now, as proven by the multitude of brands launching their own NFT projects. In this paper, we will consider some popular fashion NFT collections, namely: adidas Originals Into the Metaverse, AMBUSH OFFI-CIAL POW! REBOOT, Azuki x AMBUSH IKZ, CULT & RAIN - The Genesis Collection, Dolce& Gabbana: DGFamily, Dolce& Gabbana: DGFamily Glass Box, Chito x Givenchy NFT, MUGLER - We Are All Angel, RTFKT x Nike Dunk Genesis CRYPTOKICK, Prada Timecapsule. First, we will analyze and examine if we can find salient characteristics of transactions pertaining to these collections. Second, we will attempt to propose a first taxonomy of fashion NFT transactions. From the results, we can state that most transactions occur at the NFT launch and that they belong to the Memberships category. Secondly, the results show that we can propose a taxonomy of four transaction groups or clusters. The findings can have practical implications for both researchers and practitioners, indeed the results: (i) can be a stepping stone for future research on (fashion) NFTs, (ii) can help practitioners analyze transactions using our preliminary taxonomy.
Leveraging Machine Learning for Predictive Analysis in Incomplete Data Scenarios

The workplace conditions, such as optimal CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> level and humidity are essential factors to ensure the safety of employees. The work safety concerns established practices to protect employees' health and ensure wellbeing. Studies show that various human and environmental factors impact infections spreading across the office space. By monitoring these factors a less infectious workplace can be created. To reach this goal, an IoT enabled solution DROVIDS with continuous environment monitoring capabilities is proposed. The solution works with near real-time data and integrates predictive analysis, allowing to gain a comprehensive understanding of the environment and detect risks precisely. The solution enables near real-time response and provides a straightforward and immediate operational setup. The DROVIDS analytical service offers adjustability, it can be used without the missing data prediction component if there is no intention of calculating risk and receiving possible mitigations for future planned events. This paper presents DROVIDS architecture, design and operational principles.
Blockchain-Enabled Car Sharing Platform: Enhancing Reliability and Vehicle History Management

The rising expenses associated with car ownership have driven individuals to seek more affordable alternatives, such as car rentals. However, conventional car rental services often come with high costs due to leasing company overhead expenses. Consequently, car sharing has emerged as a popular and cost-effective solution that not only reduces expenses but also promotes eco-friendliness by reducing the overall number of vehicles on the roads. Nonetheless, centralization and reliability remain persistent challenges in car-sharing implementation. To address these issues, we propose a decentralized crowd car sharing and renting platform called Crowd-CarLink, leveraging blockchain technology's power. This innovative platform enables both individuals and leasing companies to rent out vehicles while securely recording the maintenance history of each vehicle on the blockchain. Within CrowdCarLink, garages are pivotal contributors, adding vehicle information in a reliable and immutable manner. By utilizing blockchain technology, our platform ensures transparency and fosters trust, effectively overcoming the limitations imposed by centralization. Our architectural design incorporates smart contracts which help streamline processes and facilitate seamless transactions within the platform. To demonstrate the feasibility of our approach, we have developed a prototype utilizing a private Ethereum blockchain with Proof of Authority (PoA) consensus. We believe that the architectural design and the practical solution presented here will play an integral role in shaping the future of smart transportation. By offering a cost-effective and efficient solution, our platform aims to benefit individuals and the environment alike, paving the way for a more sustainable and advanced transportation ecosystem.
Denoising Diffusion Algorithm for Single Image Inplaine Super-Resolution in CBCT Scans of the Mandible

Deep Learning models are currently the cornerstone of artificial intelligence in medical imaging. The performance of Deep Learning in medical imaging is significantly influenced by the amount and quality of training data. Diffusion models have recently attracted the attention of the computer vision community as they enable photorealistic synthetic image-to-image translation. Previous attempts to use diffusion models for super-resolution imaging have produced satisfactory high-resolution images from low-resolution inputs. However, the drawback is the slow speed of inference, which severely hinders practical applications in medicine. To speed up inference and further improve performance, we propose an accelerated algorithm based on denoising diffusion probability modelling approach for medical image super-resolution. Instead of sampling from pure Gaussian noise, the intermediate distributions of noisy low- and high-resolution images are compared and used to generate super-resolution images. Our proposed algorithm is used to convert low-resolution panoramic X-ray images from Cone-beam Computed Tomography scans of the mandible into high-resolution images for the identification of osteoporosis.
Who is Smarter? An Empirical Study of AI-Based Smart Contract Creation

The introduction of large language models (LLMs) like ChatGPT and Google Palm2 for smart contract generation seems to be the first well-established instance of an AI pair programmer. LLMs have access to a large number of open-source smart contracts, enabling them to utilize more extensive code in Solidity than other code generation tools. Although the initial and informal assessments of LLMs for smart contract generation are promising, a systematic evaluation is needed to explore the limits and benefits of these models. The main objective of this study is to assess the quality of generated code provided by LLMs for smart contracts. We also aim to evaluate the impact of the quality and variety of input parameters fed to LLMs. To achieve this aim, we created an experimental setup for evaluating the generated code in terms of validity, correctness, and efficiency. Our study finds crucial evidence of security bugs getting introduced in the generated smart contracts as well as the overall quality and correctness of the code getting impacted. However, we also identified the areas where it can be improved. The paper also proposes several potential research directions to improve the process, quality and safety of generated smart contract codes.
Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.
Effects of students’ learning environment and personality on online distance learning (ODL)

During the disruption of COVID-19, Malaysian Higher Education Institutions have been consistent in employing online distance learning (ODL).While the health and safety of the students and staff were prioritized, the quality of the education could not be put to a halt.For courses which could be taught online, accessibility of the students and the lecturers were critical to effective learning.Throughout the implementation of online learning in many countries, its prototypes have become the main interest of many.One of the many challenges of online learning was students' readiness to shift from face-to-face learning to non-face-to-face.This was related to students' personalities in coping with the new norm along with having a different environment, especially while being conscious of the COVID-19 pandemic.This research focused on theoretical, laboratory and project-based courses and was made to study the effect of students' learning environment and learning personality on theoretical, laboratory and project-based courses.The questionnaire was circulated to twenty-seven Information Systems Engineering (ISE) students of UiTM Malacca Campus in Jasin to analyze the issues of Online Distance Learning (ODL) during the three months of the academic session.The survey had two goals: (1) to investigate the effect of students learning environment based on three different modules of courses (theoretical, laboratory and project-based subject) and ( 2) to investigate the effect of students learning personality based on three different modules of courses (theoretical, laboratory and project-based subject).To achieve the first and the second goals, data were analyzed using a non-parametric statistical hypothesis testing, namely Wilcoxon Signed-Rank Test.Results of the study revealed a significant difference in the effect of ODL for the three courses on students learning personality.However, it was proven that colleagues and family were supposed to have a general perspective on online learning for the students' learning environment without considering different modules or courses taken separately.Therefore, the students' learning environment and learning personality in ODL were deemed essential aspects that successfully facilitated the teaching and learning process.
Enhancing Few-Shot Object Detection with Modified Faster R-CNN and Transfer Learning

Few-shot object detection, which aims to improve the generalization ability of the object detection model with limited data, has gained significant attention. Many existing methods use standard Faster R-CNN as the basic model and add modules to assist the detector to adapt to novel classes. However, Faster R-CNN as a method designed for the traditional object detection task, does not consider the few-shot condition and lacks relevant design for the few-shot detection. In this paper, we present an effective transfer learning-based approach for few-shot object detection. We propose modifications to the Faster R-CNN structure to address specific challenges in few-shot scenarios. Our method introduces a decoupled feature extraction network to obtain clearer and more generalized features, mitigating conflicts between classification and regression. Additionally, a feature complementing branch is introduced to obtain discriminative features for novel classes during fine-tuning. Furthermore, we design a margin cosine loss to enhance the classifier’s performance. Compared with baseline, our method achieves an average improvement of +7.0 nAP50 on PASCAL VOC and +3.0 nAP on COCO. Experimental results show that our method can outperform many existing algorithms with the state-of-the-art performance achieved.