Reviewerly/exHarmony huggingface dataset:
Generating Heuristic Policies from Optimization in Large Scale Cloud Computing VM scheduling

The VM placement problem has emerged as a critical challenge in cloud resource scheduling. This type of problem, often formulated as a vector bin packing problem, is known to be NP-hard. For practical large-scale problems, the optimization-based algorithm fails to promptly accommodate on-demand user requests, while the heuristic algorithms face the scalability issues. To tackle the online VM placement problem, this paper shows a VM placement model considering NUMA architecture and presents an algorithm that converts optimal fine-grained solutions into coarse-grained placement policies so that the online implementation is simply the heuristic placement policies. The placement policies, which are generated from the offline optimal solutions of the past few time-steps, are refreshed (time- or event-triggered) every few steps. Our experiments demonstrate that the algorithm we proposed can balance the quality of the solution with execution time compared to BestFit and FirstFit in large scale cloud computing backgrounds.
Image Super-Resolution of Residual-Dense Connected Networks in Unmanned Deep-Sea Exploration Equipment

Deep-sea unmanned exploration equipment is an important tool for exploring and developing the resources in the ocean, and it can survey the deep-sea environment more visually with the help of visual images. However, the complex and variable environment and the low resolution of the underwater lens lead to the poor resolution of the images acquired by the equipment. In this paper, we propose a residual-dense connected method applied to unmanned deep-sea exploration equipment to improve it's image resolution. The method uses dense connections within the residual structure to improve the model detail information acquisition to ensure accuracy and model stability of model. Secondly, through the study of the model performance, a high precision residual-dense connected model with less computational effort is designed. Finally, the model is trained and tested using environmental images in deep-sea conditions, and it is demonstrated that the method can be applied to deep-sea unmanned exploration equipment for fast, accurate, and stable image super-resolution processing.
Semi-autonomous operation of a mobile concrete pump

The developments of mobile-concrete pumps steadily increase the complexity of their operation. At the same time, increasing computational power and sensor performance give rise to new smart assistance systems for the operator. This paper presents an optimization-based trajectory planning and motion control concept for a mobile concrete pump that can ensure secure support and obstacle avoidance during setup. The proposed concept allows to determine the truck's optimal support configuration and is able to plan the boom's unfolding and folding motion to desired target configurations. The effectiveness of the proposed concept is demonstrated by extensive simulation studies and experimental results on a real-world mobile concrete pump.
Multi-UAV Cooperative Path-Planning Under Complex Threat Environment

This paper proposes a cooperative path-planning method based on multi-Dubins path segments to meet the penetration requirements of UAVs in a complex threat environment. A pruning strategy is used in path-planning to sift path segments, and the diversity of threat avoidance paths is considered under a reasonable calculation amount. A threat intrusion strategy is designed for no safety path condition, which makes the cooperative path-planning method have excellent robustness. A cooperative path optimization method based on particle swarm optimization is adopted for the path length and collision avoidance requirements. The simulation results show that the UAV cooperative paths can avoid the threat area under any starting and ending points, and path lengths are close to the expected length.
EMHDRL: Adversarial Attacks on Graph Neural Networks via Hierarchical Reinforcement Learning

Graph neural networks (GNNs), with its powerful ability to explore the topological structure of graph data, have achieved great success in various downstream tasks. However, recent studies have shown that due to the lack of robustness, GNNs are easily fooled by perturbations. For node classification tasks, the current attack methods mainly focus on manipulating edges, modifying features and adding fake nodes. Although these attacks are effective, the changes to the original graph data are easy to be detected. Therefore, to misclassify the target nodes, we propose an Edge Manipulation method via Hierarchical Deep Reinforcement Learning (EMHDRL) by taking accounting to the imperceptibility of perturbation on graph data. Through modeling the topology perturbation as Finite Horizon Markov Decision Process, EMHDRL adopts a hierarchical Q-function network to manipulate edges of graph, while trying to ensure that the degree of the attacked node and the total number of edges of the whole graph remain unchanged. Our experimental results show that the proposed method can effectively reduce the performance of GNNs in classifying the target nodes and has strong transferability.
Decentralized Multi-Robot Navigation in Unknown Environments via Hierarchical Deep Reinforcement Learning

Multi-robot navigation in complex scenarios such as container terminalss is a challenging problem where each robot can only perceive a subset of the states and intentions of other robots. In this paper, we propose a multi-robot navigation framework based on option-based hierarchical deep reinforcement learning (DRL) for rapid and safe navigation. The framework comprises two control models: a low-level model that generates actions using sub-policies, and a high-level model that learns a stable and reliable behavior selection policy automatically. Additionally, we design a PID-based target drive controller and an emergency braking controller to enhance obstacle avoidance efficiency and generalization ability in hazardous scenarios. We evaluate the proposed method against existing DRL-based navigation methods in various simulated scenarios with thorough performance evaluations. Our results indicate that the proposed framework significantly improves multi-robot navigation performance in complex scenarios and exhibits excellent generalization ability to new scenarios.
End-to-End Aspect-Level Sentiment Analysis Based on Directed Syntactic Dependency Trees

As aspect-based sentiment analysis tasks (ABSA) continue to evolve, end-to-end ABSA tasks (E2E-ABSA) are emerging as a new research direction. Most models for this type of task obtain valid classification features from syntactic and semantic information of the text, but the commonly used syntactic dependency trees ignore the direction of word dependencies when generating syntactic information. To resolve this problem, a combined model of Bi-directional Graph Convolutional Network incorporating Attention Mechanisms (Bi-ATGCN) and Bi-directional Long Short-Term Memory Network (Bi-LSTM) is proposed. Firstly, Bert is used as a pre-training model to obtain dynamic word vectors containing contextual information. Secondly, a directed syntactic dependency tree is introduced, and the syntactic information of the text is captured using the Bi-directional Graph Convolutional Network (Bi-GCN), and the dependency relationship between words is optimized by fusing Attention Mechanism when generating the adjacency matrix of Bi-ATGCN. Finally, the contextual semantic information of the text is captured using Bi-LSTM and thus better perform the E2E-ABSA task. The classification performance of the combined model is improved when compared with related benchmark model experiments on LAPTOP and REST datasets.
Research on Image Denoising and Multi-Target Tracking Algorithm in Nuclear Radiation Environment

In this paper, an image denoising and multi-target tracking algorithm suitable for nuclear radiation scenes is designed, and the complexity of the filtering algorithm is reduced through an improved adaptive median filtering algorithm. Reduce the computational load of the network, and adapt to the nuclear radiation scene by introducing the acceleration component into the Kalman filter of the DeepSORT algorithm. Experiments show that the improved adaptive median filtering algorithm can reduce the computing time by about 53% without reducing the filtering effect; the improved YOLOv5s and DeepSORT multi-target tracking algorithms do not significantly sacrifice the tracking effect, and the FPS has been effectively improved. More meet the realtime requirements.
Spatially Dense Multi-Target Track Segment Association Algorithm

In the field of radar data processing, track interruption seriously affects target tracking, track fusion, and other tasks. The existing track segment association algorithms have low correlation accuracy in dense distributed or long-time interruption situations. To this purpose, a dense multi-target track segment association (DMTTSA) algorithm is proposed. Firstly, two identical networks based on the multi-head probability sparse (ProbSparse) self-attention are used to capture the long-term dependencies of the tracks. Then, the bidirectional quadruplet hard sample loss (BiQuaHard loss) is constructed to make the tracks belonging to the same targets closer and the tracks belonging to the different targets farther. Finally, DMTTSA takes the closest track pairs in the feature space as the associated tracks and divides the unassociated tracks into the birth and dead tracks in chronological order. Some comparative experiments are carried out to show the anti-noise performance of the DMTTSA, as well as the effectiveness of solving the problem of dense multi-target track interruption.
Multiple Pedestrian Tracking Based on Coordinate Attention and Camera Motion Compensation

Multiple pedestrian tracking (MPT) is essential for social robots to perform various tasks for the convenience of humans. In these crowd scenarios, occlusions are very common, especially from the perspective of mobile robot. However, most current approaches ignore the mobile platform, and result in poor performance of a high number of ID switches and missed detections when deployed on mobile robots. To address the problem, a novel MPT algorithm named C2-FairMOT is proposed in this paper, in which the coordinate attention mechanism and camera motion compensation are introduced to achieve more stable feature extraction and data association, respectively. Compared with the other three baseline algorithms, the proposed C2-FairMOT can reduce IDS and FN by at least 4.0% and 2.9%, respectively.
Motion Planning of an Automatic Blood Sampling Manipulator Using DDPG Algorithm

In this paper, an automatic blood sampling mechanism is proposed to reduce the working pressure of clinicians and the high risk of cross-infection caused by doctor-patient contact. The simulation environment for this mechanism is built via Gazebo in the Robot Operating System (ROS). Using Deep Deterministic Policy Gradient (DDPG) algorithm, the mechanism is trained to accomplish the blood sampling task. The reward function and the neural network is adjusted to optimize the training result. Simulations prove that the trajectory of automatic blood sampling mechanism using DDPG algorithm has excellent performance in rapidity and stability, thus the effectiveness and usefulness of the designed system.
Hierarchical Reinforcement Learning Based Multi-Agent Collaborative Control Approach

To address the issues of slow convergence and poor interpretability, this paper proposes a novel hierarchical reinforcement learning framework consisting of an upper-level macro-decision model and a lower-level micro-execution model. To enable agents to explore in an orderly manner, expert knowledge is incorporated into the framework to design explainable subtasks. Furthermore, a hierarchical multi-agent reinforcement learning algorithm with explainable subtasks is developed and evaluated in the SC2LE scenario. Experimental results show that the proposed algorithm outperforms the traditional MARL approach in complex scenarios involving heterogeneous agents' cooperation, effectively solves the multi-agent behavior interpretability challenge, and significantly improves the training convergence speed.
Frontier-Based Exploration Planning for Amphibious Robot with Gaussian Process Occupancy Map

The autonomous observation of amphibious environments necessitates the robot to possess the capability of simultaneous exploration and mapping. In this article, we present an efficient exploration planning framework for amphibious robot. This method utilizes the Gaussian Process Occupancy Map (GPOM) as a representation of the environment to improve map quality, and Rapidly-exploring Random Trees (RRT) as a global frontier detector for rapid detection of frontiers. Our proposed method has been evaluated through extensive simulations and real-world experiments utilizing a wheel-propeller integrated amphibious robot. The experimental results demonstrate that our approach outperforms other methods in terms of exploration efficiency.
Hybrid TISM and GRA Methods for Constructing Indicator Systems in Multi-Criteria Decision-Making Problems: The Case of Large Ships

Evaluating and selecting alternative solutions are multi-criteria decision-making (MCDM) problems. Indicator systems are critical for MCDM problems because they significantly impact the MCDM method selection and final decision-making results. However, the existing methods for constructing indicator systems need more rationality and better operability, especially in complicated MCDM problems with numerous indicators and complex structures. This paper presents a hybrid methodological approach for constructing indicator systems using Total Interpretive Structural Modeling (TISM) and Grey Relational Analysis (GRA) methods. The TISM method is used for the structural analysis of evaluation systems, and the GRA method is used for the quantitative analysis of group consensus. Furthermore, the corresponding iterative algorithm is developed to promote the rationality and operability of indicator system construction. Finally, a case study of constructing a shipequipment environmental suitability indicator system is examined to illustrate the feasibility and effectiveness. The results reveal that the proposed method improves the rationality and operability of indicator system construction and is suitable for complicated MCDM problems with numerous indicators and complex structures.
Abdominal MRI Edge Detection Algorithm Based on Flow-XDoG Operator and Multi-Strategy Fusion

As the basis of medical image processing, edge detection technology can extract many valuable diagnostic information contained in the image, which helps to improve the efficiency and accuracy of doctor's diagnosis of diseases. However, abdominal magnetic resonance images have the characteristics of noise interference, uneven gray distribution, complex background texture, etc., and the boundaries between organs and tissues are often blurred. Traditional edge detection algorithms will generate edges when applied to such images. In order to address issues related to positioning errors and edge fractures in abdominal magnetic resonance images, a multi-strategy fusion edge detection algorithm and a flow-based extended difference of Gaussian (Flow-XDoG) operator are proposed. First, the image is denoised using bilateral filtering and fractional differential filtering while retaining the edge information of the original image. This helps suppress the influence of noise on the image. Next, the improved difference of Gaussian operator and edge tangential flow are combined, and soft threshold extension is performed to obtain the image contour line. Afterwards, the two-parameter neighborhood growth strategy and the weighted full variational edge discrimination strategy are utilized to filter out error edges generated during the extraction process. Finally, fuzzy membership is used to connect broken edges and form a closed contour. The experimental results show that the algorithm has a good edge detection effect, overcomes the disadvantage that the traditional method cannot detect the edge in the area where the gray level changes gently, and effectively solves the problem of wrong edge and broken edge through multi-strategy optimization, and the extracted contour is more complete and clear.
Design and Implementation of Distributed Multi-Unmanned Vehicle Formation Control Based on Leader-Following Method

For the formation navigation, formation keeping and formation obstacle avoidance problems in the formation control of multiple unmanned vehicles (UGVs), the commonly used all-in-one algorithms have the disadvantage of high information interaction costs. In this paper, a distributed formation control method based on the leader-following method is constructed by distinguishing the leader vehicle and the follower vehicle, configuring a high intelligence trajectory tracking algorithm for the leader vehicle and a low intelligence formation control algorithm and obstacle avoidance algorithm for the follower vehicle. In this paper, the dynamic window approach, the error control-based leader-following method and the behavior-based method are used to achieve the work of trajectory tracking, formation control and formation avoidance respectively. With this distributed approach, the information interaction cost of the system is reduced and better navigation results are obtained for formation navigation compared to the old manual potential field method.
Incomplete Data Classification Based on the Tracking-Removed Autoencoder

The existence of missing values increases the difficulty of classification in the real world. In this paper, we propose a classification method based on the tracking-removed autoencoder (TRAE) for incomplete data. Specifically, we use the tracking-removed autoencoder as an imputation model to estimate missing values by constructing mutual fitting relationships between attributes on a network structure. On this basis, the multi-layer perceptron (MLP) is used as the classification model to classify the complete data after imputation. In the training phase, we designed a new training scheme for the imputation model, which treats missing values as variables and dynamically optimizes them together with network parameters, making it easier for missing values to match existing attribute values in incomplete datasets. Then, we use the complete training set after imputation to train the parameters in the MLP network model. In the testing phase, the network parameters of the imputation model are fixed and the missing values in the test set are considered as variables, which are optimally updated to obtain the complete test set. The classification model predicts its class label based on the optimized complete test set. The training scheme make the missing values in incomplete data approach the actual value with higher precision, and weaken the impact of missing data on classification. Experimental results on several incomplete datasets demonstrate that the proposed method can effectively improve the imputation performance, thereby improving the classification performance of incomplete data.
Privacy-Preserving Average Consensus via Homomorphic Encryption

With increased security awareness and increased attack incidents, it is becoming increasingly important to protect the privacy of data during storage and communication. As the basis of distributed systems, consensus is widely used in automatic control, signal processing and optimization. In this paper, a consensus protocol based on partially homomorphic encryption is applied for undirected networks, which can achieve privacy-preserving average consensus. In this method, each agent can participate in information interaction with its neighbors using a virtual initial state which is generated based on the Paillier cryptosystem, thus protects the privacy of the initial state value. Different from the original cryptography-based methods, our method is fully decentralized without the help of any aggregators or third parties. Numerical simulation shows the effectiveness of this method and is compared with other methods.
An Underwater Maneuvering Target Tracking Algorithm Based on UKF with Adaptive Sampling Range

Due to relatively high measurement errors and low data rate of the active sonar system, large tracking delay may occur during the target's maneuvering phase in the IMM algorithm. Instead of setting high-level process noise artificially to improve maneuver detection ability while sacrificing the tracking accuracy, an adaptive strategy based on range rate measurement is proposed to adjust the sampling range of the unscented transform (UT) online to cover maneuvers. In addition, a heading check mechanism is employed to alleviate the model misjudgment when the heading and the bearing of the target are nearly collinear. The proposed method does not rely on a priori knowledge about the maneuver level and achieves high tracking accuracy and quick maneuver response at the same time.
Full Attention Tracker: A Good Combination of Pixel-Level and Region-Level Cross-Correlation

The tracker based on Siamese neural network is currently a technical method with high accuracy in the tracking field. With the introduction of transformer in the visual tracking field, the attention mechanism has gradually emerged in tracking tasks. However, due to the characteristics of attention operation, Transformer usually has slow convergence speed, and its pixel-level correlation discrimination in tracking is more likely to lead to overfitting, which is not conducive to long-term tracking. A brand new framework FAT was designed, which is the improvement of MixFormer. The operation for simultaneous feature extraction and target information integration in MixFormer is retained, and the Mixing block is introduced to suppress the background as much as possible before the information interaction. In addition, a new operation is designed: the result of region-level cross-correlation is used as a guidance to help the learning of pixel-level cross-correlation in attention, thereby accelerating the model convergence speed and enhancing the model generalization. Finally, a joint loss function is designed to further improve the accuracy of the model. Experiments show that the presented tracker achieves excellent performance on five benchmark datasets.
A Rear and Side Object Detection System for Autonomous Driving Bus

To guarantee the safety of autonomous buses when changing lanes, this paper improves and implements a reliable and cost-effective object detection system that suits low-speed automatic driving buses. In this system, the ECX-1400-PEG embedded industrial control computer served as the primary computing unit. The detecting system and control unit communicates via the Redis database. Our System's detection part uses YOLOv5-Rep, an improved network whose kernel size is as large as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$31\times 31$</tex> . Experimental results show that compared with YOLOv5s, the number of YOLOv5-Rep's parameters was reduced to 6.67M, and the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{F}_{1}$</tex> score increased to 0.9236. The system can meet the detection requirements of the rears and sides of low-speed autonomous driving buses when changing lanes.