Reviewerly/exHarmony huggingface dataset:
Blockchain-based anonymous authentication for traffic reporting in VANETs

The application of vehicle ad-hoc networks (VANETs) has brought huge potential to the development of intelligent transportation systems. By traffic event reporting, traffic efficiency can be significantly improved. However, people usually lack the motivation for reporting traffic events or driving violations, because once the identity of the reporter is leaked, he or she may be tracked by malicious attackers or be retaliated by violators. However, if the reporter is anonymous, it is difficult to guarantee the accuracy and authenticity of the reports. In order to solve this contradiction, a mechanism that combines certificateless message authentication and blockchain incentives is proposed in this paper. A certificateless message signature algorithm is designed to provide the anonymity and non-repudiation for traffic-related message reporters. Simultaneously, an adaptive t-threshold multi-signature mechanism is introduced in our scheme. Similar to crowdsourcing, it requires multiple participants to witness the authenticity of the message together. Aggregation signature verification improves message authentication efficiency. A trust currency called TCoin is introduced to motivate reporters' enthusiasm for participation in intelligent transportation. The security analysis and simulation results show that our scheme is secure and effective in the intelligent transportation system.
Multi-Attention DenseNet: A Scattering Medium Imaging Optimization Framework for Visual Data Pre-Processing of Autonomous Driving Systems

The vision system is important for almost all kinds of autonomous driving systems. However, visual data interfered by scattering media, such as smoke, haze, water, and other non-uniform media will be degraded seriously, showing the characteristics of detail loss, poor contrast, low visibility, or color distortion. These characteristics can significantly interfere with the reliability of autonomous driving systems. In real environments the image degradation mechanism is complex, and the estimation of degradation parameters is difficult. This issue remains to be solved. In this study, we employed dense blocks as the framework and introduced the attention mechanism to our model from four dimensions: Multi-scale Attention, Channel Attention, Structure Attention, and ROI (region of interest) Attention. With the help of the training data provided by the weakly supervised model, the proposed method achieved excellent performance in the task of scattering medium imaging optimization in different scenes. Comparative experiments show that the proposed method is robust, and is superior to other state-of-the-art methods in image dehazing, and underwater image enhancement tasks. It is of great significance to improve the reliability of autonomous driving systems in underwater and severe weather environments.
A knowledge graph-based method for epidemic contact tracing in public transportation

Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
A Resource-Efficient CNN-Based Method for Moving Vehicle Detection

There has been significant interest in using Convolutional Neural Networks (CNN) based methods for Automated Vehicular Surveillance (AVS) systems. Although these methods provide high accuracy, they are computationally expensive. On the other hand, Background Subtraction (BS)-based approaches are lightweight but provide insufficient information for tasks such as monitoring driving behavior and detecting traffic rules violations. In this paper, we propose a framework to reduce the complexity of CNN-based AVS methods, where a BS-based module is introduced as a preprocessing step to optimize the number of convolution operations executed by the CNN module. The BS-based module generates image-candidates containing only moving objects. A CNN-based detector with the appropriate number of convolutions is then applied to each image-candidate to handle the overlapping problem and improve detection performance. Four state-of-the-art CNN-based detection architectures were benchmarked as base models of the detection cores to evaluate the proposed framework. The experiments were conducted using a large-scale dataset. The computational complexity reduction of the proposed framework increases with the complexity of the considered CNN model's architecture (e.g., 30.6% for YOLOv5s with 7.3M parameters; 52.2% for YOLOv5x with 87.7M parameters), without undermining accuracy.
Improving Parameter Optimization in Decoy-State Quantum Key Distribution

Quantum key distribution (QKD) enables two remote users to share a string of key bits with information-theoretical security. Parameter optimization is a crucial step in achieving optimal performance in practical QKD systems. In general, such optimization is implemented using a local search algorithm (LSA). However, LSAs inevitably fail to find out the optimal values when the searched key rate function is nonconvex or has a discontinuity of first-order derivatives and a narrow parameter search space. This paper proposes a genetic algorithm-based method to overcome the limitations of LSAs for QKD parameter optimization. We tested the proposed method with various types of common QKD protocols and found that it has very high parameter optimization performance for QKD with a time consumption comparable to that using a standard LSA. We expect our method to be a valuable optimization tool for quantum information processing tasks.
A hybrid genetic–firefly algorithm for engineering design problems

Abstract Firefly algorithm (FA) is a new random swarm search optimization algorithm that is modeled after movement and the mutual attraction of flashing fireflies. The number of fitness comparisons and attractions in the FA varies depending on the attraction model. A large number of attractions can induce search oscillations, while a small number of attractions can cause early convergence and a large number of fitness comparisons that can add to the computational time complexity. This study aims to offer H-GA–FA, a hybrid algorithm that combines two metaheuristic algorithms, the genetic algorithm (GA) and the FA, to overcome the flaws of the FA and combine the benefits of both algorithms to solve engineering design problems (EDPs). In this hybrid system, which blends the concepts of GA and FA, individuals are formed in the new generation not only by GA processes but also by FA mechanisms to prevent falling into local optima, introduce sufficient diversity of the solutions, and make equilibrium between exploration/exploitation trends. On the other hand, to deal with the violation of constraints, a chaotic process was utilized to keep the solutions feasible. The proposed hybrid algorithm H-GA–FA is tested by well-known test problems that contain a set of 17 unconstrained multimodal test functions and 7 constrained benchmark problems, where the results have confirmed the superiority of H-GA–FA overoptimization search methods. Finally, the performance of the H-GA–FA is also investigated on many EDPs. Computational results show that the H-GA–FA algorithm is competitive and better than other optimization algorithms that solve EDPs.
Experimental Analysis of Effect of Tuning Parameters on The Performance of Diversity-Driven Multi-Parent Evolutionary Algorithm

In order to find a global optimal solution to real-world optimization issues, an algorithm that efficiently explores the search space is required. The DDMPEA is a newly developed algorithm that uses selection, crossover, and mutation to perform search operations. The search performance of this algorithm is depending upon the tuning parameters involves in this technique. There are four parameters in this algorithm which are required to tune to obtain better solutions. In this work, sixteen cases are developed which are tested on eight classical benchmark functions. The performance of proposed algorithm is tested and performance is obtained in terms of average, median, standard deviation, best and worst values. The results suggest better values of tuning parameters on which this algorithm gives global optimum solutions.
A formant modification method for improved ASR of children’s speech

Differences in acoustic characteristics between children's and adults' speech degrade performance of automatic speech recognition systems when systems trained using adults' speech are used to recognize children's speech. This performance degradation is due to the acoustic mismatch between training and testing. One of the main sources of the acoustic mismatch is the difference in vocal tract resonances (formant frequencies) between adult and child speakers. The present study aims to reduce the mismatch in formant frequencies by modifying formants of children's speech to better correspond to formants of adults' speech. This is carried out by warping the linear prediction (LP) spectrum computed from children's speech. The warped LP spectra computed in a frame-based manner from children's speech are used with the corresponding LP residuals to synthesize speech whose formant structure is closer to that of adults' speech. When used in testing of an ASR system trained using adults' speech, the warping reduces the spectral mismatch in speech between training and testing and improves the system performance in recognition of children's speech. Experiments were conducted using narrowband (8 kHz) and wideband (16 kHz) speech of adult and child speakers from the WSJCAM0 and PF_STAR databases, respectively, and by recognizing children's speech using acoustic models trained with adults' speech. The proposed method gave relative improvements of 24% and 11% for the DNN and TDNN acoustic models, respectively, for narrowband speech. For wideband speech, the technique gave relative improvements of 27% and 13% for the DNN and TDNN acoustic models, respectively. The performance of the proposed method was also compared to two speaker adaptation methods: vocal tract length normalization (VTLN) and speaking rate adaptation (SRA). This comparison showed the best recognition performance for the proposed method. We also combined the proposed method with VTLN and SRA, and found that the combined method gave a further reduction in WER. Moreover, our experiments carried out for noisy speech using various types of additive noise and signal-to-noise ratios showed that the proposed method performs well also for degraded speech.
Applying the quantum approximate optimization algorithm to the minimum vertex cover problem

The minimum vertex cover problem belongs to a NP- complete problem, which is difficult to obtain the near-optimal solution in the polynomial time range using classical algorithms. In this paper, a quantum circuit solution scheme based on the quantum approximate optimization algorithm is presented for the minimum vertex cover problem. Firstly, the quantum Ising model and Hamiltonian of the problem are obtained based on the Ising model corresponding to the problem, which is quantized by the rotation operator and Pauli operator. Secondly, the parametric unitary transformation with the initial Hamiltonian and the problem Hamiltonian as the generator is obtained respectively. Through the alternating evolution of two parametric unitary transformations, the final quantum state and the problem Hamiltonian expectation are derived. In the process of evolution, the parameters in the parametric unitary transformations which are optimized by the classical processor can adjust the problem Hamiltonian expectation, so as to improve the probability of the problem solution. Then, the initial state of the algorithm and the quantum logic gate corresponding to the parametric unitary transformation are derived to generate the quantum circuit which can be implemented on the quantum computer. Simulation results show that the scheme can obtain the problem solution with high probability in polynomial time, realizes exponential acceleration, and has certain feasibility, effectiveness and innovation.
Graph Neural News Recommendation with User Existing and Potential Interest Modeling

Personalized news recommendations can alleviate the information overload problem. To enable personalized recommendation, one critical step is to learn a comprehensive user representation to model her/his interests. Many existing works learn user representations from the historical clicked news articles, which reflect their existing interests. However, these approaches ignore users’ potential interests and pay less attention to news that may interest the users in the future. To address this problem, we propose a novel G raph neural news R ecommendation model with user E xisting and P otential interest modeling, named GREP. Different from existing works, GREP introduces three modules to jointly model users’ existing and potential interests: (1) Existing Interest Encoding module mines user historical clicked news and applies the multi-head self-attention mechanism to capture the relatedness among the news; (2) Potential Interest Encoding module leverages the graph neural network to explore the user potential interests on the knowledge graph; and (3) Bi-directional Interaction module dynamically builds a news-entity bipartite graph to further enrich two interest representations. Finally, GREP combines the existing and potential interest representations to represent the user and leverages a prediction layer to estimate the clicking probability of the candidate news. Experiments on two real-world large-scale datasets demonstrate the state-of-the-art performance of GREP.
Recent metaheuristic algorithms with genetic operators for high-dimensional knapsack instances: A comparative study

As a new attempt to effectively tackle the high-dimensional 0–1 knapsack (01KP) instances with uncorrelated, weakly-correlated, and strongly-correlated characteristics, in this paper, five lately-proposed meta-heuristic algorithms: horse herd optimization algorithm (HOA), gradient-based optimizer (GBO), red fox search optimizer (RFSO), golden eagle optimizer (GEO), and Bonobo optimizer (BO) have been transformed into binary ones by investigating the various V-shaped and S-shaped transfer functions to be applied to those high-dimensional 01KP problems, which are discrete ones; these binary variants are named BHOA, BGEO, BBO, BRFSO, and BBO. Furthermore, some genetic operators such as the one-point crossover operator and mutation operators have been borrowed to discover more permutations as a trying to avoid stuck into local minima for reaching better outcomes. These two operators are effectively integrated with those binary variants to propose other ones with better performance for achieving further improvements for tackling the high-dimensional 01KP instances called BIHOA, BIGEO, BIGBO, BIBO, and BIRFSO. Those genetic operators and recently-developed meta-heuristic algorithms-based high dimensional binary techniques have been extensively validated using 21 uncorrelated, weakly-correlated, and strongly-correlated 01KP instances with high-dimensions ranging between 100 and 10000, and the obtained outcomes were compared even witnessing which algorithm is the best. The experimental findings show the superiority of BIRFSO for the instances with dimensions greater than 500, and its competitivity for the others.
Lightweight and computationally faster Hypermetropic Convolutional Neural Network for small size object detection

Object detection has been an active area of research over the past two decades. The complexity of detecting an object increases with the increase in object speed and decrease in object size. Similar scenarios are observed in sports video analysis, vision systems of robots, driverless cars and much more. This led to the need for an efficient neural network that can detect small size objects. Further, most of the real-time applications use single board computers such as Jetson Nano, TX2, Xavier, Raspberry Pi and the like. The state-of-the-art of Deep Learning models such as YOLOv4, v3, YOLOR, YOLOX and SSD show poor run-time performance on these devices. Their lighter versions YOLOv3-tiny, YOLOv4-tiny and YOLOX-nano run nearly at 24 frames per second (fps) on Jetson Nano; however, their detection accuracy on small-sized objects is unsatisfactory. This paper focuses on developing a computationally lighter Convolutional Neural network(CNN) to detect small-sized objects efficiently. A novel hypermetropic CNN was developed to meet the above requirements. The improvement in detection is made by extracting more features from the shallow layers and transferring low-level features to the deeper layers. The network is hypermetropic because it performs well on distant objects and lags on nearby objects. The proposed model's performance is compared with the state-of-the-art models on various public datasets such as the VEDAI dataset, Visdrone dataset, and a few classes from the MS COCO and OID dataset. The proposed model shows impressive improvements in detecting small-size objects, and a 32% increase in the fps is observed on Jetson Nano.
A decomposition-based constrained multi-objective evolutionary algorithm with a local infeasibility utilization mechanism for UAV path planning

Unmanned Aerial Vehicle (UAV) path planning problems can be treated as constrained multi-objective optimization problems, which often have complicated constraints in real-world scenarios. Algorithms for solving them require a powerful constraint-handling technique to utilize infeasible information. However, this has seldom been explored in this field. To remedy this issue, this paper proposes a decomposition-based constrained multi-objective evolutionary algorithm (M2M-DW) with a local infeasibility utilization mechanism for UAV path planning. Therein, M2M-DW is adopted as a solution optimizer since it can utilize infeasible individuals. However, this may result in poor performance due to the arbitrary use of infeasible individuals. To solve this issue, a local infeasibility utilization mechanism is proposed to effectively utilize the infeasible information. Besides, an improved mutation scheme is designed to further explore the promising regions. Experimental studies are conducted on three sets of UAV path planning problems with different difficulties, and the results highlight the effectiveness of the proposed algorithm in terms of reliability and stability in finding a set of feasible optimal solutions.
Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection

The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving away from black-box / opaque AI towards explainable AI (XAI). In this article, we propose the first interpretable autoencoder based on decision trees, which is designed to handle categorical data without the need to transform the data representation. Furthermore, our proposed interpretable autoencoder provides a natural explanation for experts in the application area. The experimental findings show that our proposed interpretable autoencoder is among the top-ranked anomaly detection algorithms, along with one-class Support Vector Machine (SVM) and Gaussian Mixture. More specifically, our proposal is on average 2% below the best Area Under the Curve (AUC) result and 3% over the other Average Precision scores, in comparison to One-class SVM, Isolation Forest, Local Outlier Factor, Elliptic Envelope, Gaussian Mixture Model, and eForest.
Robust multi-view non-negative matrix factorization for clustering

Non-negative matrix factorization (NMF) has attracted much attention for multi-view clustering due to its good theoretical and practical values. Although existing multi-view NMF methods have achieved satisfactory performance to some extent, there still exist the following problems: 1) most existing methods only consider the first-order relationship to construct the similarity matrix, which cannot fully explore the neighborhood information; 2) most existing methods only consider the common information shared by different views, and the complementary information contained in different views is often ignored. To tackle the above-mentioned shortcomings, we present a novel multi-view clustering approach, called robust multi-view NMF (RMNMF). Specifically, in RMNMF, we consider the high-order relationship among features, where the first-order and second-order relationship is utilized to obtain an optimal neighborhood structure. Also, we introduce the Hilbert-Schmidt independence criterion (HSIC) to fully explore the complementary information in different views. Extensive experimental results on several benchmark datasets prove the effectiveness of our proposed approach.
OWSP-Miner: Self-adaptive One-off Weak-gap Strong Pattern Mining

Gap constraint sequential pattern mining (SPM), as a kind of repetitive SPM, can avoid mining too many useless patterns. However, this method is difficult for users to set a suitable gap without prior knowledge and each character is considered to have the same effects. To tackle these issues, this article addresses a self-adaptive One-off Weak-gap Strong Pattern (OWSP) mining, which has three characteristics. First, it determines the gap constraint adaptively according to the sequence. Second, all characters are divided into two groups: strong and weak characters, and the pattern is composed of strong characters, while weak characters are allowed in the gaps. Third, each character can be used at most once in the process of support (the frequency of pattern) calculation. To handle this problem, this article presents OWSP-Miner, which equips with two key steps: support calculation and candidate pattern generation. A reverse-order filling strategy is employed to calculate the support of a candidate pattern, which reduces the time complexity. OWSP-Miner generates candidate patterns using pattern join strategy, which effectively reduces the candidate patterns. For clarification, time series is employed in the experiments and the results show that OWSP-Miner is not only more efficient but also is easier to mine valuable patterns. In the experiment of stock application, we also employ OWSP-Miner to mine OWSPs and the results show that OWSPs mining is more meaningful in real life. The algorithms and data can be downloaded at https://github.com/wuc567/Pattern-Mining/tree/master/OWSP-Miner.