Reviewerly/exHarmony huggingface dataset:
Global Wagon Web: Enabling a Semantic-Enhanced Framework for Vehicle Infrastructure Cooperative System

The core technology of Vehicle Infrastructure Cooperative System (VICS) has progressed to a “cooperative perception” phase, which necessitates an efficient, reliable and cost-effective information sharing mechanism. However, existing approaches are insufficient to meet the requirements due to a number of critical issues. Firstly, data structures specified according to applications are unable to accommodate system dynamics. Secondly, the lack of open and common protocols and interfaces across heterogenous and distributed systems may lead to severe problems related to data integrity and redundancy. Thirdly, centralized data aggregation not only confronts with limited storage and network congestion, but is also a single point of failure. To address these issues, we propose a novel semantically enhanced architectural layer, Global Wagon Web (GWW), which is a service-oriented and fully decentralized infrastructure to enable live data integration, service reuse and composition, and semantic interoperation within the context of VICS. To illustrate its natures, relevant concepts and forms of structural and behavioral algebraic descriptions are presented with the help of Petri net. Additionally, its key supporting technologies and potential applications are outlined.
Multi-UAV Trajectory Optimization for Fair Coverage Considering No-fly Zones

Optimizing trajectories of unmanned aerial vehicles base stations (UAV-BSs) is crucial in maximizing the usage of UAV. However, existing works often fail to consider the mobility of ground users. Therefore, we propose a multi-UAV trajectory optimization problem that maximizes fair coverage of dynamic users within no-fly zones. We present an efficient UAV trajectory optimization algorithm based on improved Soft Actor Critic (SAC) for solving complex constrained problems. Finally, simulation experiments demonstrate the feasibility of our proposed algorithm.
The Threat of Adversarial Attacks Against Machine Learning-based Anomaly Detection Approach in a Clean Water Treatment System

The protection of Critical National Infrastructure is extremely important due to nations being dependent on their operation and steadiness. Any disturbance to this infrastructure could have a devastating consequence on physical security, economic wellbeing, and public health and safety. To deal with the growing number of attacks, with differing degrees of impact against such systems, various machine learning-based Intrusion Detection Systems have been employed given their success in the automated detection of known and unknown cyberattacks with high degrees of accuracy. However, since machine learning models are susceptible to attacks, also known as Adversarial Machine Learning, employing such Intrusion Detection Systems has also created an additional attack vector which could potentially help hackers to evade detection. This paper explores the robustness of both traditional and non-traditional supervised machine learning algorithms by studying their classification behaviour under adversarial attacks. This includes machine learning algorithms such as Support Vector Machine, Logistic Regression, and Deep Learning models, such as Artificial Neural Network. Additionally, this contains adversarial machine learning attacks such as random & targeted label flipping, Fast Gradient Sign Method, and Jacobian Saliency Map Attack. A genuine dataset captured from a model of a clean water treatment system was used to support the experiments presented in this paper. Overall, the adversarial attacks were successful to decrease the classification performance of the machine learning algorithms but with varying degrees of influence. For example, the targeted label flipping has a stronger impact on the classification performance reduction compared with the random label flipping attacks. Additionally, Deep Learning model and Support Vector Machine both show longer fight against the adversarial attacks compared with Logistic Regression.
Cryptographic Validation of Lightweight Block ciphers and Hash Functions

This paper outlines the design and implementation of a test vector generation module for the lightweight block ciphers PRESENT, CLEFIA, and the SPONGENT hash function for use in cryptographic module validation. These algorithms are part of the ISO/IEC 29192-2: 2012 standard and ISO/IEC 29192-5: 2016, respectively. The test vector generation module generates test vectors for being fed to the IUT (Implementation Under Test) for validating the algorithms as per the validation methods described in the ISO/IEC 18367:2016(E) standard for block ciphers and hash functions that can operate in different modes of operation. The implementation of test vector generation modules for these algorithms is done in the C programming language, which is described in this paper. These generated vectors can be used as reference test vectors in CMVT (Crypto Module Validation Testing) labs for validating the cryptographic modules that implement these cryptographic algorithms.
Traffic Target Detection and Recognition System Based on Improved YOLOv5 and DeepSORT

Traffic target detection and recognition is an important aspect in the field of intelligent transportation, which directly affects road safety. To address the problem of diverse traffic target categories and the high accuracy and real-time requirements of target detection, this paper proposes an improved YOLOv5 algorithm, using MoblieNetV3 as a lightweight feature extraction network instead of the original CSPDarknet53, which includes the introduction of SE attention mechanism, replacing the activation function withh-swish, while using the BiFPN structure to replace the original FPN+PAN structure to enhance multi-scale feature fusion, thus realizing the optimization of the YOLOv5 algorithm in terms of accuracy, speed and lightweight. Experimental results show that the improved YOLOv5 algorithm effectively improves mAP by 6% in the recognition of the traffic sign dataset TT100K, with detection speed up to 60 f/s and model size of only 11.4 MB; the improved YOLOv5 algorithm combined with DeepSORT algorithm can complete the recognition and tracking of dynamic traffic targets. It has achieved a 30.4% MOTA in a self-made pedestrian and vehicle dataset, while the number of identity transformations is reduced. By integrating the two algorithms in a dual-threaded way, a smart traffic system can be ultimately built to achieve traffic target detection and recognition selectively based on actual scene requirements.
Synthetic Data-Based Detection of Zebras in Drone Imagery

Nowadays, there is a wide availability of datasets that enable the training of common object detectors or human detectors. These come in the form of labelled real-world images and require either a significant amount of human effort, with a high probability of errors such as missing labels, or very constrained scenarios, e.g. VICON systems. On the other hand, uncommon scenarios, like aerial views, animals, like wild zebras, or difficult-to-obtain information, such as human shapes, are hardly available. To overcome this, synthetic data generation with realistic rendering technologies has recently gained traction and advanced research areas such as target tracking and human pose estimation. However, subjects such as wild animals are still usually not well represented in such datasets. In this work, we first show that a pre-trained YOLO detector can not identify zebras in real images recorded from aerial viewpoints. To solve this, we present an approach for training an animal detector using only synthetic data. We start by generating a novel synthetic zebra dataset using GRADE, a state-of-the-art framework for data generation. The dataset includes RGB, depth, skeletal joint locations, pose, shape and instance segmentations for each subject. We use this to train a YOLO detector from scratch. Through extensive evaluations of our model with real-world data from i) limited datasets available on the internet and ii) a new one collected and manually labelled by us, we show that we can detect zebras by using only synthetic data during training. The code, results, trained models, and both the generated and training data are provided as open-source at https://eliabntt.github.io/grade-rr.
FPGA Implementation of the AES Algorithm with Lightweight LFSR-Based Approach and Optimized Key Expansion

Cryptography is essential for ensuring the confidentiality, integrity, authenticity, and non-repudiation of information in the digital age. It serves as a fundamental building block for secure systems, protecting sensitive data and enabling trusted communication and transactions. Hardware-based cryptography offers higher levels of security, performance, and compliance. The Advanced Encryption Standard cipher offers enhanced cryptographic security by effectively scrambling the plain text, providing a robust level of protection. This work introduces a novel method to achieve an efficient implementation of the Advanced Encryption Standard block cipher cryptographic algorithm on the Intel DE-10 Lite FPGA development board. The design incorporates architectural optimizations for the forward and inverse AES SubBytes transformation (S-box and inverse S-box) using Linear Feedback Shift Registers. Furthermore, algorithmic optimizations are incorporated to reduce memory storage requirements during round-wise key expansion. The proposed design handles AES encryption and decryption seamlessly, offering a complete solution for both processes. The hardware implementation of the proposed design achieved a remarkable reduction in device utilization for AES-128, decreasing from 91% to 29%.
Research on Crop Planting Problem Automatic Answering System Based on Knowledge Graph

This study aims to provide accurate and reliable diagnosis and treatment services for crop pests and diseases. A knowledge graph was constructed using a large amount of literature and expert knowledge related to crop pests and diseases. Based on this graph, an efficient and accurate automatic question-answering algorithm was developed, which was implemented in a user-friendly prototype system utilizing the Pyqt5 architecture. The system has good scalability and flexibility to meet the needs of different user groups. The results of this study provide important support for agricultural production and crop pest control by promoting timely diagnosis and treatment of crop pests and diseases, improving agricultural production efficiency and economic benefits. This study also provides useful inspiration for the application of knowledge graphs in other fields.
Development of a Simulation Environment for Robot Soccer Game with Deep Reinforcement Learning and Role Assignment

The robot soccer game has been recognized as an excellent scenario to test the gaming algorithm of multi-agent systems. This paper develops a new simulation platform for the robot soccer game, and it has the advantage of open architecture, such that the formation control scheme, the path planning strategy for multiple robots, and many other algorithms can be implemented and tested. Specifically, both a Deep Reinforcement Learning (DRL) scheme and a role-assignment-based method have been successfully realized in this platform to drive multiple robots to play the soccer game, including 2V2,3V3,4V4, and so on. It is believed that the developed simulation environment can be used for data collection and transfer learning (TL), hence bridging the gap of Sim2Real technique in actual implementations.
Fine-Grained Sentiment Analysis with a Fine-Tuned BERT and an Improved Pre-Training BERT

As a subtask of text classification, sentiment classification is a domain of study that advances rapidly with applications in almost any field in our daily life. Although many models have performed well on multiple binary sentiment classification datasets, there is still much space for improvement in the process of fine-grained sentiment analysis, which is essential for understanding people's perceptions of all kinds of topics, services, and products. In this paper, both the pre-training language model BERT whose initial parameters were fine-tuned for this task, and the RoBERTa model that improved the original way of pre-training were applied to the fine-grained sentiment analysis problem on the benchmark of the SST-5 dataset. Over the experiments, it is found that the RoBERTa model achieves higher accuracy compared with the fine-tuned BERT, which indicates that an improved way of pre-training can be more effective than just fine-tuning the initially learned parameters for the BERT model in this sentiment analysis task.
Motion Planning and Tracking Control Method Based on CoppeliaSim for UGV in Irregular Terrain

Path planning and tracking algorithms are one of the cores of autonomous navigation technology for unmanned ground vehicle (UGV). In this paper, a local obstacle avoidance method based on event-triggered control is proposed to ensure that the UGV avoids obstacles in the tracking process. First, the path planning and tracking control problem of the UGV is modeled and formulated. Second, the improvement and logical correlation of the relevant algorithms are described in detail. Finally, ajoint simulation of MATLAB and V-rep is performed. The simulation results show that event-triggered control can largely improve the reliability of the UGV path planning and tracking process, and verify the effectiveness of the algorithm.
Deep Reinforcement Learning Integrated RRT Algorithm for Path Planning

Rapidly-exploring random tree (RRT) algorithm, featured with strong exploration capability, is widely used in path planning tasks. However, it is difficult for RRT to find the optimal path due to its inherent characteristics of random sampling. In this paper, we propose an improved RRT algorithm integrated with deep reinforcement learning (IRRT-DRL), which can effectively search for feasible paths and exploit previous experience for optimization. It includes a unique reward function and a dynamic waypoint selection mechanism that automatically adjusts the interval between adjacent waypoints to help the agent bypass obstacles. Experimental results have verified the feasibility and superiority of the proposed approach.
Spectrum Recommendation in High Frequency Communications: A Knowledge Inference Method

As an intelligent approach for efficient spectrum utilization, spectrum recommendation is significant but largely unexplored, especially in the few-shot scenario of inference on unknown channel qualities in High-Frequency (HF) communication. For the incompetence of data-driven approaches, we consider playing a guiding role of domain knowledge of HF. Firstly, a Knowledge Graph (KG) for spectrum recommendation is developed to organize and represent the propagation knowledge of HF, and modeled as an undirected graph for the convenience of extracting and reasoning knowledge. Inference on channel qualities for recommendation is further converted to the node regression problem on the undirected graph. Then, inference schemes based on Graph Convolutional Network (GCN) are proposed to solve the node regression problem, where we transfer knowledge extracted from the complete data of the source domain to the few-shot inference scenario of the target domain. Simulation experiments demonstrate the effectiveness of the proposed GCN-based inference algorithm with knowledge transfer.
Semantic Interoperability Toolkit for Data Marketplaces

Due to the countless number of data sources, an integration issue emerges, as the same data often have several representations. Therefore, this paper suggests a Natural Language Processing (NLP) based approach for the so-called Semantic Interoperability Toolkit. It comprises two components - the Semantic Matchmaking solution and the Semantic Engine - allowing users to identify similarities between concepts and manage queries against them respectively, regardless of their original representations. The solution is at use on the AI4PublicPolicy's Data Marketplace for supporting the exploitation of a wide range of data sets.
FedPredict: Combining Global and Local Parameters in the Prediction Step of Federated Learning

In traditional Federated Learning (FL), such as FedAvg, the main objective is to compute a generalized model applied to all clients. This approach is not effective in the non-IID scenario, where each client has a specific data distribution. As an alternative, personalized FL has proven to be an important research direction for dealing with clients' particularities. However, part of these solutions must be reexamined when a new client (i.e., a few times trained or never trained) is added to the FL process. To address these problems, we propose FedPredict, a simple but effective federated learning approach that combines global and local (i.e., personalized) model parameters of neural networks, considering their evolution and update levels. This combination is essential because our method is a plugin that operates in the prediction/inference step on the FL client side, which means that there is no modification in the learning process, and it can be coupled with other techniques. Compared to state-of-the-art solutions, FedPredict converges faster while achieving greater accuracy in various scenarios, including when new clients are added.
Research on Multi-class Road Obstacle Recognition and Decision Based on YOLOP Combined YOLOV5 Algorithm

The paper proposes a visual real-time sensing system. It carries YOLOv5 and YOLOP models and can be carried on the vehicle embedded device. The system simultaneously performs vehicle, pedestrian, traffic light, traffic sign detection, driveable area segmentation and lane detection. The paper not only considers the high accuracy, but also considers the computational cost of running on the vehicle when selecting the model to get the sensing information. The paper chooses YOLOP, which enables FPS to reach 23 (after TRT acceleration) on embedded devices. YOLOv5 also has extremely fast execution speed and accuracy on embedded devices. The combination of the two well meets the speed and accuracy required by the automatic driving decision control. And upload all sensing information to the embedded device. At the same time, a priority decision system for automatic driving is also proposed: pedestrian and vehicle distance information (divided into high, medium and low risk) <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;$</tex> information of signal lights and traffic signs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;$</tex> distance information of lane line <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;$</tex> information of driving area. Finally, this paper combines perception information with decision control and carries out visual output. Through experiments, it can be seen that the decision has achieved good results and the automatic driving function can be realized in most Chinese real street scenes.
Detecting Writing Micro-Events using Motion Sensors in Smartwatches

Handwriting is integral to the education system, where students write notes, assignments, and exams using handwritten words. We explore the potential of motion sensors in a smartwatch to understand the writing micro-events — hand shift, line change, and actual writing. The ability to detect these writing micro-events opens up different possible studies, such as studying the students' cognitive load, comparing students of a different or similar caliber, their familiarity with given content, and cheating during examinations. Unlike existing work on handwriting using commercial smartwatches, which focuses on writing content or writer detection, we focus on detecting the writing micro-events. In our offline evaluation with data from ten participants, we observe F1 scores up to 0.84 for writing conditions of copying, writing from memory, and writing original text. A user-specified sample increases the best F1 score to 0.91.
Construction of Satellite Spectrum Knowledge Graph: Ontology Design and Knowledge Application

The explosive growth in the scale of satellite networks poses challenges in spectrum resource allocation, and forms an urgent demand for intelligent spectrum management methodology. However, the current spectrum management methodology is unable to effectively handle the enormous amount of heterogeneous multi-dimensional data. In order to overcome this drawbacks, the Satellite Spectrum Knowledge Graph is constructed in this paper. The ontology of satellite spectrum domain is designed by utilizing top-down method. Additionally, the raw data are processed by knowledge extraction, data mapping, knowledge fusion, and knowledge storage to complete the knowledge graph in graph database form. Moreover, the constructed knowledge graph is also applied in satellite information retrieval and frequency bands recommendation for satellite in this paper, which exhibits the potential in advanced satellite spectrum management framework.
Privacy-Preserving Machine Learning for Image Data: From Grayscale Single Feature to Color Multi-feature

The increasing demand for privacy protection in machine learning applications has led to the development of various privacy-preserving techniques. Our paper focuses on privacy-preserving machine-learning techniques applied to image classification tasks. Our approach addresses scenarios involving grayscale images with a single sensitive feature and color images with multiple sensitive and public features. We propose a novel approach combining adversarial training and modified loss functions to balance privacy and utility preservation. Specifically, we tackle the data representation problem as (x, s, u), where x represents the raw high-dimensional data, s denotes the sensitive features, and u corresponds to the public or utility features. Through extensive experiments conducted on diverse datasets, we demonstrate the effectiveness of our methods. Our approach safeguards sensitive information while achieving high classification accuracy for public features. The findings from our study provide valuable insights and practical guidance for developing privacy-preserving machine learning models in real-world applications involving image data.
Bayesian Optimization of Double-SAC Robotic Arm Path Planning Using Recent Prioritized Experience Replay

As for the path planning of robotic arms, Deep Reinforcement Learning (DRL) algorithms have recently been developed. However, the high-dimensional action space and state space will result in reward sparsity and low training efficiency of robotic arm path planning. We propose a Bayesian optimization of Double-SAC algorithm using recent priority experience replay to solve these issues. We first propose the sampling method of recent prioritized experience replay (RPER), which combines recent sampling and prioritized experience replay (PER). The interval of experience pool is divided according to the recent sampling method, and sampling is performed according to the sample priority of the interval. Then we adopt a Bayesian optimization method that reduces the variance of the random strategy and improve the quality of the sampled actions. The larger variance is more beneficial for exploring the environment. Smaller variance is advantageous for exploiting good experiences to obtain higher returns. Our approach finds a good balance between exploration and exploitation. We train two Soft Actor Critic (SAC) agents to obtain two independent probability distributions, and a Bayesian optimizer is adopted to combine the two distributions to design a hybrid strategy. The hybrid strategy can output better actions according to the uncertainty estimation of the distribution, and further enhance training efficiency and stability. Finally, simulation tests are performed on the ROS-Gazebo platform, and the simulation results illustrate that the algorithm proposed in this paper converges faster and has a shorter training time than the original algorithm in terms of path planning performance.