Reviewerly/exHarmony huggingface dataset:
Multi-Object Pedestrian Tracking Using Improved YOLOv8 and OC-SORT

Multi-object pedestrian tracking plays a crucial role in autonomous driving systems, enabling accurate perception of the surrounding environment. In this paper, we propose a comprehensive approach for pedestrian tracking, combining the improved YOLOv8 object detection algorithm with the OC-SORT tracking algorithm. First, we train the improved YOLOv8 model on the Crowdhuman dataset for accurate pedestrian detection. The integration of advanced techniques such as softNMS, GhostConv, and C3Ghost Modules results in a remarkable precision increase of 3.38% and an mAP@0.5:0.95 increase of 3.07%. Furthermore, we achieve a significant reduction of 39.98% in parameters, leading to a 37.1% reduction in model size. These improvements contribute to more efficient and lightweight pedestrian detection. Next, we apply our enhanced YOLOv8 model for pedestrian tracking on the MOT17 and MOT20 datasets. On the MOT17 dataset, we achieve outstanding results with the highest HOTA score reaching 49.92% and the highest MOTA score reaching 56.55%. Similarly, on the MOT20 dataset, our approach demonstrates exceptional performance, achieving a peak HOTA score of 48.326% and a peak MOTA score of 61.077%. These results validate the effectiveness of our approach in challenging real-world tracking scenarios.
Echo state network implementation for chaotic time series prediction

The implementation of an Echo State Neural Network (ESNN) for chaotic time series prediction is introduced. First, the ESNN is simulated using floating-point arithmetic and afterwards fixed-point arithmetic. The synthesis of the ESNN is done in a field-programmable gate array (FPGA), in which the activation function of the neurons' outputs is a hyperbolic tangent one, and is approximated with a new design of quadratic order b-splines and four integer multipliers. The FPGA implementation of the ESNN is applied to predict four chaotic time series associated to the Lorenz, Chua, Lü, and Rossler chaotic oscillators. The experimental results show that with 50 hidden neurons, the fixed-point arithmetic is good enough when using 15 or 16 bits in the fractional part: using more bits does not reduce the mean-squared error prediction. The neurons are limited to four inputs in the hidden layer to achieve a more efficient hardware implementation, guaranteeing a prediction of more than 10 steps ahead.
Quantum trajectories of dissipative time crystals

The authors address the boundary time-crystal transition at the level of individual quantum trajectories that result from continuous monitoring. Signatures of critical behavior and of the time-crystal phase are identified for the photon count signal and homodyne current, while each monitoring scheme is shown to imprint its own characteristic features.
Challenges affecting bank consumers’ intention to adopt green banking technology in the UAE: a UTAUT-based mixed-methods approach

Purpose Sustainability has become a global need for survival in every field since the side effects of financial development have resulted in environmental devastation. Green banking (GB) has been proposed as a way to reduce the carbon footprint caused by banking operations by promoting paperless financial services through the intensive use of technology. However, the adoption level of GB technology remains unsatisfactory among customers in the United Arab Emirates (UAE). Therefore, using the unified theory of acceptance and use of technology (UTAUT), this study aims to comprehensively investigate the challenges affecting the intention of bank consumers in the UAE to adopt GB technology. Design/methodology/approach This study used exploratory sequential mixed-methods research. Preliminary semi-structured interviews were conducted with ten banking professionals using a purposive sampling technique to explore the challenges affecting consumers’ intention to adopt GB technology. Sequentially, the study tested various factors through a quantitative cross-sectional online survey of a sample of 332 bank customers and used the convenience sampling technique to obtain further empirical support for the research framework. Thematic content analysis using NVivo 11 was used for the qualitative data analysis. Meanwhile, partial least square structural equation modelling in Smart PLS 3.3 was used for the quantitative data analysis. Findings The qualitative analysis identified six new challenges affecting customers’ intention to adopt GB technology, including customer awareness, personal innovativeness, bank reputation, security and privacy, system quality and government support. The preliminary qualitative findings were confirmed mainly through quantitative data analysis, whereby customer awareness, personal innovativeness, system quality and bank reputation were found to significantly impact customers’ intention to adopt GB technology. However, the effects of security and privacy and government support were insignificant. Originality/value To the best of the authors’ knowledge, this study is the first to propose a comprehensive model that considers individual, technological, organisational and environmental factors to address the issue of customers’ low GB technology adoption rates in the UAE. Meanwhile, this study extends the UTAUT by integrating new factors. This paper is also among the first to investigate customers’ GB technology adoption intention using a mixed-methods approach, which combines the strengths of quantitative and qualitative methods within the same study to offer better insights than a single-method approach.
Blockchain for Intelligent Transportation Systems: Applications, Challenges, and Opportunities

Blockchain technology has the potential to revolutionize the way intelligent transportation systems (ITSs) operate in smart cities. By providing a secure and decentralized platform for data exchange and storage, blockchain can enhance the security, privacy, and interoperability of ITS systems. Blockchain technology can be used for various applications in ITS, including secure data exchange between vehicles, infrastructure, and service providers, smart contracts for autonomous vehicles, and decentralized marketplaces for transportation services. However, implementing blockchain in ITS comes with its own set of challenges, including scalability and high computational power requirements. Despite the challenges, blockchain technology offers significant opportunities for ITS in smart cities, enabling new business models and promoting innovation in transportation services. In this article, we study existing challenges, applications, and future requirements for ITS. We discuss the challenges of the ITS and their impact on smart cities. Blockchain-enabled applications are provided with performance analysis based on the critical parameters of ITS. We also derive the security requirements for future ITS. Finally, we provide some opportunities and possible research areas within the ITS to develop smart cities.
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis

The advancement of aspect-based sentiment analysis (ABSA) has highlighted the lack of a user-friendly framework that can significantly reduce the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet this demand, we present PyABSA, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to incorporate new models, datasets, and other related tasks. Additionally, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. The project is available at: https://github.com/yangheng95/PyABSA.
A Comparative Analysis of Multi-Label Deep Learning Classifiers for Real-Time Vehicle Detection to Support Intelligent Transportation Systems

An Intelligent Transportation System (ITS) is a vital component of smart cities due to the growing number of vehicles year after year. In the last decade, vehicle detection, as a primary component of ITS, has attracted scientific attention because by knowing vehicle information (i.e., type, size, numbers, location speed, etc.), the ITS parameters can be acquired. This has led to developing and deploying numerous deep learning algorithms for vehicle detection. Single Shot Detector (SSD), Region Convolutional Neural Network (RCNN), and You Only Look Once (YOLO) are three popular deep structures for object detection, including vehicles. This study evaluated these methodologies on nine fully challenging datasets to see their performance in diverse environments. Generally, YOLO versions had the best performance in detecting and localizing vehicles compared to SSD and RCNN. Between YOLO versions (YOLOv8, v7, v6, and v5), YOLOv7 has shown better detection and classification (car, truck, bus) procedures, while slower response in computation time. The YOLO versions have achieved more than 95% accuracy in detection and 90% in Overall Accuracy (OA) for the classification of vehicles, including cars, trucks and buses. The computation time on the CPU processor was between 150 milliseconds (YOLOv8, v6, and v5) and around 800 milliseconds (YOLOv7).
Augmented Reality and Virtual Reality in Cultural Heritage Tourism Enhancing Visitor Experiences

This book chapter explores the use of AR and VR in cultural heritage tourism, enhancing visitor experiences with historical locations and artefacts. AR and VR allow for immersive interactions, enabling visitors to comprehend and appreciate heritage places better. These technologies revive old structures and artefacts, offering personalized experiences and interactive storytelling. However, challenges like cost, hardware limitations, and maintaining authenticity need to be addressed. Future opportunities include wider accessibility, advancements in technology, and collaborations fostering innovation. AR and VR have the potential to revolutionize cultural heritage tourism by transforming visitor interactions and appreciation of historical sites.
Overview of the SISAP 2023 Indexing Challenge

This manuscript presents the premiere SISAP 2023 Indexing Challenge, which seeks replicable and competitive solutions in the realm of approximate similarity search algorithms. Our aim is recall, all while optimizing build time, search time, and memory consumption. Using a subset of the deep features of a neural network model provided by the LAION-5B dataset, the challenge posed three tasks, each with its unique focus: Notably, an innovative and competitive binary mapping method emerged from the challenge. It also spotlighted graph methods as the preferred indexing technique for binary and real-valued high-dimensional vectors. However, these methods have little room for improvement. Enhancing memory efficiency, refining navigational strategies, and tackling the secondary memory challenge are pivotal next steps.
Mastering 3D Modeling in Blender: From Novice to Pro

The open-source 3D modeling and animation software Blender has become a powerhouse for artists, designers, and hobbyists. Blender's capabilities and creative industry effects are summarized in this abstract. Due to its comprehensive features and unwavering community support, Blender has grown from a simple tool to an industry standard. This flexible instrument suits beginners and experts. Blender has an excellent user interface, several modeling and sculpting capabilities, a robust material and texture system, and powerful rendering engines like Cycles and Eevee. Open-source development keeps Blender available to anyone, making it a pioneer in democratizing 3D modeling and animation technologies. Blender is a creative movement that lets anyone with an idea realize their vision. Blender is our entry to 3D modeling and animation, novice or experienced. This study discusses the essential techniques of 3D modeling, shading, materials, rendering, textures in blender, rigging and animation, and physical simulation. The review presents a guide step-by-step, ensuring that develop a solid foundation in each aspect of Blender's powerful toolset.
Privacy and Security of Blockchain in Healthcare: Applications, Challenges, and Future Perspectives

Blockchain offers a cutting-edge solution for storing medical data, carrying out medical transactions, and establishing trust for medical data integration and exchange in a decentralized open healthcare network setting. While blockchain in healthcare has garnered considerable attention, privacy and security concerns remain at the center of the debate when adopting blockchain for information exchange in healthcare. This paper presents research on the subject of blockchain’s privacy and security in healthcare from 2017 to 2022. In light of the existing literature, this critical evaluation assesses the current state of affairs, with a particular emphasis on papers that deal with practical applications and difficulties. By providing a critical evaluation, this review provides insight into prospective future study directions and advances.
First-Shot Anomaly Sound Detection for Machine Condition Monitoring: A Domain Generalization Baseline

This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring. First-shot ASD does not allow systems to do machine-type dependent hyperparameter tuning or tool ensembling based on the performance metric calculated with the grand truth. To show benchmark performance for First-shot ASD, this paper proposes an anomaly sound detection system that works on the domain generalization task in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: “Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Technique” while complying with the First-shot requirements introduced in the DCASE 2023 Challenge Task 2 (DCASE2023T2). A simple autoencoder-based implementation combined with a selective Mahalanobis metric is implemented as a baseline system. The performance evaluation is conducted to set the target benchmark for the forthcoming DCASE2023T2. The source code of the baseline system has been made available on GitHub.
Parallel window decoding enables scalable fault tolerant quantum computation

Large-scale quantum computers have the potential to hold computational capabilities beyond conventional computers. However, the physical qubits are prone to noise which must be corrected in order to perform fault-tolerant quantum computations. Quantum Error Correction (QEC) provides the path for realizing such computations. QEC generates a continuous stream of data that decoders must process at the rate it is received, which can be as fast as 1 μs per QEC round in superconducting quantum computers. If the decoder infrastructure cannot keep up, a data backlog problem is encountered and the computation runs exponentially slower. Today's leading approaches to quantum error correction are not scalable as existing decoders typically run slower as the problem size is increased, inevitably hitting the backlog problem. Here, we show how to parallelize decoding to achieve almost arbitrary speed, removing this roadblock to scalability. Our parallelization requires some classical feed forward decisions to be delayed, slowing-down the logical clock speed. However, the slow-down is now only polynomial in the size of the QEC code, averting the exponential slowdown. We numerically demonstrate our parallel decoder for the surface code, showing no noticeable reduction in logical fidelity compared to previous decoders and demonstrating the predicted speedup.
Evolutionary design of explainable algorithms for biomedical image segmentation

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting "black box" models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
Effective and Efficient Line Segment Detection for Visual Measurement Guided by Level Lines

Line segment detection is the basis for various visual measurement tasks. Numerous methods have been proposed to detect line segments from images, and edge fitting-based ones have gained significant attention because of their remarkable detection efficiency. However, most edge fitting-based methods primarily rely on gradient magnitude for edge detection and edge coordinates for line segment fitting, neglecting the importance of considering gradient orientation, which may reduce their effectiveness. In addition, most of them require the least squares for line segment fitting, involving the computationally inefficient squaring operation. To solve the above issues, this study proposes an effective and efficient line segment detection (E2LSD) algorithm based on two new findings regarding the level line of edge points, i.e., the unit vector orthogonal to the corresponding gradient orientation. (1) Utilizing double consistent constraints of both coordinates and level lines of edge points to fit line segments results in a more effective line segment detection than those relying on a single consistent constraint of coordinates. (2) Decoupling line segment orientation and position, followed by fitting them separately using level lines and coordinates of edge points, results in a computationally efficient line segment detection approach. It is more computationally efficient than those directly fitting line segments in the least squares sense based on the coordinates of edge points. In the E2LSD algorithm, edges are drawn with the guideline of level lines to improve accuracy. Numerical experiments based on natural and synthetic datasets showed that the E2LSD algorithm outperforms existing state-of-the-art methods regarding both effectiveness and computational efficiency. The E2LSD algorithm has also successfully been employed in a visual measurement system regarding feature-based visual localization. The code of the E2LSD algorithm will be publicly available at https://github.com/roylin1229/E2LSD.
A molecular perspective on quantum information

Some of the fundamentals of quantum information science are described, including the concepts of quantum resources, quantum states and mixedness of states. The explanations are detailed and include a combination of basic facts with fully worked examples, and some more advanced topics. The principles of quantum information are illustrated with chemical examples drawn from singlet fission, photophysics of radicals, molecular excitons, electron transfer and so on. Suggestions for prospects and challenges for the field are discussed.
Business Renaissance: Opportunities and Challenges at the Dawn of the Quantum Computing Era

Quantum computing is emerging as a groundbreaking force, promising to redefine the boundaries of technology and business. This paper provides an in-depth examination of the quantum realm, beginning with its fundamental principles and extending to its implications for today’s industries. We discuss how quantum algorithms threaten existing cryptographic measures while also uncovering vast opportunities in sectors like finance, healthcare, and logistics. The narrative then shifts to the evolution of new business models, exemplified by Quantum-as-a-Service (QaaS) and enhanced AI capabilities. Alongside the myriad opportunities, we address the challenges and ethical concerns surrounding the swift rise of quantum technologies. By emphasizing the importance of collaborative efforts among businesses, policymakers, and technologists, the article advocates for a balanced and responsible approach to quantum adoption. Through this analytical lens, the article paints a comprehensive picture of the impending quantum era, presenting both its transformative potential and the complexities it brings to our interconnected world.
MULTI-CRITERIA SELECTION OF STANDARDS FOR SYSTEM ANALYST ACTIVITIES IN ORGANIZATIONS

The paper defines the methodology for the selection of standards and tools that system analysts use for the purposes of analyzing the computer infrastructure of organizations. The goal of system analyst analysis is primarily to increase: the speed of processing information, the efficiency of data processing and data exchange. A large number of standards and tools complicates an adequate choice, and on the other hand system analysts are not the only ones who influence the choice. In the analysis of the choice of standards and tools, in the second chapter an algorithm for determining the weight coefficients for the criteria selected by the company and the ranking of the standards for selection is presented through an example. In order to choose the best standard, the choice was made between four standards described in the third chapter. The chapter defines the criteria that cover IT activities for the selection of standards according to selected criteria and defined weight coefficients in the observed company. Finally, on the assumption that the company together with the analyst chose “k” criteria, where the methodology (LBWA) for the selection of weight coefficients for the criteria was proposed, a new model for calculating the ranking of standards was presented, i.e. the choice of the best. The conclusion of the paper is that the presented procedure for choosing standards is not complicated, that it is very successful, and that in this way the proposals were more easily accepted and implemented in the fastest way.