Reviewerly/exHarmony huggingface dataset:
Smart Traffic Monitoring Through Pyramid Pooling Vehicle Detection and Filter-Based Tracking on Aerial Images

Increased traffic density, combined with global population development, has resulted in increasingly congested roads, increased air pollution, and increased accidents. Globally, the overall number of automobiles has expanded dramatically during the last decade. Traffic monitoring in this environment is undoubtedly a significant difficulty in various developing countries. This work introduced a novel vehicle detection and classification system for smart traffic monitoring that uses a convolutional neural network (CNN) to segment aerial imagery. These segmented images are examined to further detect the vehicles by incorporating novel customized pyramid pooling. Then, these detected vehicles are classified into various subcategories. Finally, these vehicles are tracked via Kalman filter (KF) and kernelized filter-based techniques to cope with and manage massive traffic flows with minimal human intervention. During the experimental evaluation, our proposed system illustrated a remarkable vehicle detection rate of 95.78% over the Vehicle Aerial Imagery from a Drone (VAID), 95.18% over the Vehicle Detection in Aerial Imagery (VEDAI), and 93.13% over the German Aerospace Center (DLR) DLR3K datasets, respectively. The proposed system has a variety of applications, including identifying vehicles in traffic, sensing traffic congestion on a road, traffic density at intersections, detecting various types of vehicles, and providing a path for pedestrians.
Reentrancy Vulnerability Detection and Localization: A Deep Learning Based Two-phase Approach

Smart contracts have been widely and rapidly used to automate financial and business transactions together with blockchains, helping people make agreements while minimizing trusts. With millions of smart contracts deployed on blockchain, various bugs and vulnerabilities in smart contracts have emerged. Following the rapid development of deep learning, many recent studies have used deep learning for vulnerability detection to conduct security checks before deploying smart contracts. These approaches show effective results on detecting whether a smart contract is vulnerable or not whereas their results on locating suspicious statements responsible for the detected vulnerability are still unsatisfactory.
Quantum machine learning of large datasets using randomized measurements

Quantum computers promise to enhance machine learning for practical applications. Quantum machine learning for real-world data has to handle extensive amounts of high-dimensional data. However, conventional methods for measuring quantum kernels are impractical for large datasets as they scale with the square of the dataset size. Here, we measure quantum kernels using randomized measurements. The quantum computation time scales linearly with dataset size and quadratic for classical post-processing. While our method scales in general exponentially in qubit number, we gain a substantial speed-up when running on intermediate-sized quantum computers. Further, we efficiently encode high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth. The encoding is characterized by the quantum Fisher information metric and is related to the radial basis function kernel. Our approach is robust to noise via a cost-free error mitigation scheme. We demonstrate the advantages of our methods for noisy quantum computers by classifying images with the IBM quantum computer. To achieve further speedups we distribute the quantum computational tasks between different quantum computers. Our method enables benchmarking of quantum machine learning algorithms with large datasets on currently available quantum computers.
Efficient Anytime CLF Reactive Planning System for a Bipedal Robot on Undulating Terrain

We propose and experimentally demonstrate a reactive planning system for bipedal robots on unexplored, challenging terrain. The system includes: a multilayer local map for assessing traversability; an anytime omnidirectional control Lyapunov function for use with a rapidly exploring random tree star (RRT*) that generates a vector field for specifying motion between nodes; a subgoal finder when the final goal is outside of the current map; and a finite-state machine to handle high-level mission decisions. The system also includes a reactive thread that copes with robot deviations via a vector field, defined by a closed-loop feedback policy. The vector field provides real-time control commands to the robot's gait controller as a function of instantaneous robot pose. The system is evaluated on various challenging outdoor terrains and cluttered indoor scenes in both simulation and experiment on Cassie Blue, a bipedal robot with 20 degrees of freedom. All implementations are coded in C++ with the robot operating system and are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UMich-BipedLab/CLF_reactive_planning_system</uri> .
A novel image encryption scheme based on elliptic curves and coupled map lattices

The security of digital images has become the main concern due to the rapid rise in the sharing of data over the Internet and open networks. Recently many image cryptosystems are introduced to secretly share data but have some efficiency issues and drawbacks. Couple map lattices (CMLs) chaotic-systems and elliptic curves (ECs) are widely used in cryptography due to their long period and random behavior. A novel and secure image encryption algorithm based on ECs and CMLs is proposed for the real-time transmission of images with the aim to overcome the shortcomings of traditional encryption algorithms. The encryption procedure is mainly divided into two steps. In the first step, EC-based pseudo-random-numbers are used to create diffusion in the plain-image. In the second step, a substitution-box generator based on ECs and CMLs is designed to create confusion in the diffused image. The proposed encryption scheme has a large keyspace and is robust against the brute-force attacks. The use of a CMLs chaotic-system makes the encryption algorithm secure against the known-plaintext attack. The computation time of the newly constructed scheme is very low as it takes only 0.641 s to encrypt an image of size 256×256. The outcome of the experimental and statistical analysis shows that the proposed encryption algorithm is highly secure and can outperform some state-of-the-art schemes.
Non-functional requirements for machine learning: understanding current use and challenges among practitioners

Abstract Systems that rely on Machine Learning (ML systems) have differing demands on quality—known as non-functional requirements (NFRs)—from traditional systems. NFRs for ML systems may differ in their definition, measurement, scope, and comparative importance. Despite the importance of NFRs in ensuring the quality ML systems, our understanding of all of these aspects is lacking compared to our understanding of NFRs in traditional domains. We have conducted interviews and a survey to understand how NFRs for ML systems are perceived among practitioners from both industry and academia. We have identified the degree of importance that practitioners place on different NFRs, including cases where practitioners are in agreement or have differences of opinion. We explore how NFRs are defined and measured over different aspects of a ML system (i.e., model, data, or whole system). We also identify challenges associated with NFR definition and measurement. Finally, we explore differences in perspective between practitioners in industry, academia, or a blended context. This knowledge illustrates how NFRs for ML systems are treated in current practice, and helps to guide future RE for ML efforts.
Immersive virtual reality as a tool to improve bridge teaching communication

The objective of bridge teaching is to provide students with knowledge of bridge construction and lay a solid foundation for further in-depth study and work practice. However, the current bridge teaching has the following limitations: (1) the “spoon-feeding” teaching is dominated by teachers; (2) the teaching material lacks intuitiveness, practice, and interactivity. Immersive virtual reality (IVR) enables the possibility of representing abstract bridge concepts in a 3D manner while stimulating student engagement and motivation in the learning process. In this article, we attempt to use IVR as a tool to improve the student’s engagement and teaching communication. The automatic generation of bridge scenes in 3D, immersive interaction, and teaching communication evaluation are discussed in detail. Finally, a mega suspension bridge under construction is selected as a case for experiment analysis. The results of the experiment show that the combination of IVR and bridge teaching can effectively improve the students’ learning interests and teaching communication. The percentages of students who were very satisfied and satisfied with the immersive bridge teaching were 39.6% and 54.2% respectively, which proves that IVR has great potential and positive outcomes in education.
Research on risk assessment of blockchain-driven supply chain finance: A systematic review

Recently, blockchain-driven supply chain finance has been used in the scope of risk management to solve the financing problem of small and medium-sized enterprises. Among them, risk assessment is the core part of risk management. Therefore, this paper aims to review and analyze articles associated with risk assessment based on blockchain-driven supply chain finance mode. This research also aimed to provide the best practices and identify the academic challenges, motivations and recommendations related with the research. In addition, a methodological approach followed in previous research in this domain was discussed to give some insights for future comers with what to expect. Therefore, articles related to risk assessment based on blockchain-driven supply chain finance were searched systematically. The search was conducted on five major databases, namely, ScienceDirect, Scopus, Web of Science, Springer, Emerald from 2016 to 2022. These indices were considered sufficiently extensive and reliable to cover the scope of the literature. Articles were selected on the basis of the inclusion and exclusion criteria (n = 90). All these articles form a coherent taxonomy, describing the corresponding current views in the literature according to three main categories. The research is considered a major topic which warrants attention. This study emphasizes the current research positions and opportunities in this field and promotes the understanding of this research field.
BSKM-FC: Blockchain-based secured key management in a fog computing environment

With an increase in the number of devices in the edge layer connected to the fog server of the fog computing environment, it is found that vulnerable and unauthorized activities are also increasing relatively. Thus, there is a requirement for authorized access control in such an environment that is governed by secured and efficient key management between the communicating devices. Many researchers have presented conventional solutions for key management depending on a third party. The third party operated for a key generation or key distribution in a fog computing environment based on a centralized architecture which leads to the drawbacks like a single-point failure and inconsistency of key management. So, effective and secured key management between the edge devices which are willing to communicate is the major concern to be addressed in the current digital world to achieve access control. This paper proposes a BSKM-FC (Blockchain-based Secured Key Management in a Fog Computing Environment) which is a decentralized system in a fog computing environment without using a third party. The BSKM-FC makes use of a one-way hash chain for the generation of private and public key pairs and ECC (Elliptic Curve Cryptography) for secured sharing. Upon successful authentication, the session key generation at both edge devices is based on the key pair provided by the fog server and stored securely in the blockchain. The BSKM-FC system uses private blockchain technology in the fog layer to provide secured storage and management. The work is implemented in the Truffle Blockchain and found that BSKM-FC performs better in terms of overall block preparation time. The security analysis of the proposed scheme is carried out based on the ROR model and also verified using AVISPA for some known attacks. Informal security analysis of the proposed work is performed by considering some of the known attacks where we observe that the proposed scheme overcomes such attacks. Performance overhead analysis is demonstrated using MIRACL considering computation cost, communication cost, and storage cost. The results show that the proposed scheme meets security requirements and performs effectively. The computation overhead, communication overhead, storage overhead, and block preparation time of the proposed scheme were improved by 7% to 14%, 9% to 18%, 7% to 17%, and 14% to 28%, respectively, as compared to existing schemes.
Optimization of tips selection on the IOTA tangle for securing blockchain-based IoT transactions

Data integrity and security protection are needed in the Internet of Things. IOTA technology with a Directed Acyclic Graph (DAG) structure is a solution to realize secure and scalable data transactions. Recent research IOTA is still faced with the issue of being vulnerable to splitting attacks and left-behind transactions. The splitting attack causes the network to confirm conflict transactions. Then, left-behind transactions cause the network to generate transactions that will not be confirmed. The selection tip weighted random walk (WRW) algorithm uses the Markov Chain Monte Carlo (MCMC) to overcome these two issues by applying the appropriate bias parameter (α). However, when the α is too large, it will produce a lot of left-behind transactions. Determining the optimal value of α is still an important research topic today. An E-IOTA study that gives several α values statically with random selection but can still produce more left-behind transactions than pure WRW. This paper proposes an optimization of the tip selection algorithm (DA-IOTA) to determine the optimal alpha (α) using an approach to dynamically determine each WRW step. The experimental results show that DA-IOTA produces fewer left-behind transactions than MCMC (WRW) and E-IOTA which use α parameters statically.
Implementation of virtual reality technology for sports education and training: Systematic literature review

Virtual Reality (VR) is a computer-generated environment with scenes and objects that appear natural, immersing the user in their surroundings. Numerous Virtual Reality (VR) experiments have been applied in sports education and training. These can significantly contribute to and improve decision-making, such as prediction. This study aims to determine insights into the current state of virtual reality applications in sports. This exploration would lead to the most effective way to implement virtual reality technologies in sports education and training. A systematic search was performed in the most influential scientific electronic databases (e.g., Google Scholar) between 2010 to 2022. After reviewing past research on the issue, the researcher formulates research questions. We identified 30 related samples out of 995 searched articles that satisfied our inclusion criteria. There is a significant increasing pattern of VR application in sports education and training. In addition, the most popular method to implement VR in sports education and training is using a head-mounted display (HMD) combined with a motion capture system. This research aims to provide effectiveness, benefit, problems, and possible solution for VR implementation in sports education and training. Through this article, we hope future researchers can provide a new approach to VR technology in sports education and training.
Deep neural networks and weighted word embeddings for sentiment analysis of drug product reviews

One of the major tasks of natural language processing is sentiment analysis. The web is a source of unstructured and rich informa-tion with thousands of opinions and reviews. Individuals, businesses, and governments can all benefit from recognizing sentiment. As part of this study, we propose a deep learning-based approach for sentiment analysis on drug product review data obtained from the UCI machine learning repository. As an alternative to deep learning models, this architecture integrates glove word embedding with convolutional neural networks (CNN). Word2vec and GloVe word embedding schemes have been evaluated empirically for their predictive performance in CNN architectures. Based on a comparison of the deep learning architecture with RoBERTa, itcan be seen that BERT architecture outperforms both of them in training and validation. However, CNN models using Glove word embedding provided superior results in testing.
A High-Precision Vehicle Detection and Tracking Method Based on the Attention Mechanism

Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method.
MENet: Lightweight multimodality enhancement network for detecting salient objects in RGB-thermal images

Most red–green–blue and thermal (RGB-T) salient object detection methods require high memory consumption and incur large computational costs, which limit their applicability. To alleviate the computational resource requirements, we propose a lightweight multimodality enhancement network (MENet) for RGB-T salient object detection with relatively few parameters. As the RGB and thermal images are from different domains, the modality gap leads to unsatisfactory results if using simple feature concatenation. Instead, we introduce a multimodality complementary enhancement module with a nested residual structure to fuse RGB and thermal features. During decoding, we design a recursive sharpening module that is inspired by atrous spatial pyramid pooling and dense connections and has fewer parameters than similar methods. By reducing the number of parameters and computations of each component, MENet ends up having only 21.26 M parameters and running with 2.501G floating-point operations. Experiments were performed on three benchmark datasets. The results show that the proposed MENet can consistently outperform 16 state-of-the-art methods.
Continuous-variable quantum key distribution with on-chip light sources

Integrated quantum key distribution (QKD) systems based on photonic chips have high scalability and stability, and are promising for further construction of global quantum communications networks. On-chip quantum light sources are a critical component of a fully integrated QKD system; especially a continuous-variable QKD (CV-QKD) system based on coherent detection, which has extremely high requirements for the light sources. Here, for what we believe is the first time, we designed and fabricated two on-chip tunable lasers for CV-QKD, and demonstrated a high-performance system based on these sources. Because of the high output power, fine tunability, and narrow linewidth, the involved on-chip lasers guarantee the accurate shot-noise-limited detection of quantum signals, center wavelength alignment of nonhomologous lasers, and suppression of untrusted excess noise. The system’s secret key rate can reach 0.75 Mb/s at a 50 km fiber distance, and the secure transmission distance can exceed 100 km. Our results mark a breakthrough toward building a fully integrated CV-QKD, and pave the way for a reliable and efficient terrestrial quantum-secure metropolitan area network.
A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews

Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior training. This is possible by teaching the system how to codify a question and find its answer in the text. In many domains, especially health sciences, systematic reviews are evidence-based syntheses of information related to a specific topic. Producing them is demanding and time-consuming in terms of collecting, filtering, evaluating and synthesising large volumes of literature, which require significant effort performed by experts. One of its most demanding steps is abstract screening, which requires scientists to sift through various abstracts of relevant papers and include or exclude papers based on pre-established criteria. This process is time-consuming and subjective and requires a consensus between scientists, which may not always be possible. With the recent advances in machine learning and deep learning research, especially in natural language processing, it becomes possible to automate or semi-automate this task. This paper proposes a novel application of traditional machine learning and zero-shot classification methods for automated abstract screening for systematic reviews. Extensive experiments were carried out using seven public datasets. Competitive results were obtained in terms of accuracy, precision and recall across all datasets, which indicate that the burden and the human mistake in the abstract screening process might be reduced.
Explicit and size-adaptive PSO-based feature selection for classification

Feature selection (FS) aims to remove the irrelevant and redundant features to improve the classification accuracy of the algorithm, which is regarded as an NP-hard problem. Recently, particle swarm optimization (PSO) has shown promise in FS problems, but most previous PSO-based FS methods use implicit representation, whose particle size is equal to the number of original features. Such particle representation not only consumes a lot of memory and computational cost but also leads to a large search space when applied to high-dimensional data. In this paper, we propose a novel representation scheme called explicit representation (i.e. particles are directly represented by the corresponding selected feature subset) and redefine the particle update strategy for the new representation. Moreover, we adopt a feature grouping strategy based on feature importance and divide the original feature set into multiple groups. Finally, a size-adaptive expansion strategy is proposed, in which the swarm automatically determines the next feature group to increase the particle size. The proposed algorithm, called ESAPSO, is able to effectively reduce the particle size as well as the computational cost and the memory occupation. We validate the performance of the proposed ESAPSO with several state-of-the-art algorithms on ten benchmark datasets. Experimental results show that the proposed ESAPSO is usually achieved by better classification performance as well as feature subsets with similar or smaller sizes. This study provides valuable and novel insight into the particle representation of the PSO-based feature selection problem.