Reviewerly/exHarmony huggingface dataset:
Motion Planning and Control of Quadrotor Based on MPC in Frenet Frame

The autonomous flight of Unmanned Aerial Vehicles (UAVs) hinges on the interplay between trajectory planning and flight control. This study centers on a specific case: the autonomous flight of a quadcopter at low altitudes. To enhance this autonomous flight process, we address the estimation and prediction of trajectories for dynamic obstacles that may intersect the UAV's low-altitude flight path. This is achieved through the utilization of Extended Kalman Filtering (EKF) and Joint Probabilistic Data Association (JPDA) techniques within the Frenet coordinate system. Subsequently, leveraging both global planning and real-time environmental data, the local motion planner constructs an optimal trajectory for the UAV on a local scale. This ensures that the UAV navigates its immediate surroundings efficiently. To execute this trajectory, the quadrotor UAV undertakes low-altitude flight while adhering to the locally optimized path. This is accomplished through the application of Model Predictive Control (MPC), which enables the UAV to autonomously avoid collisions in real time. Finally, the validity and viability of the proposed approach are substantiated.
Optimal Trajectory Planning of UAVs in Transport Aircraft Delivery Mode

When the mission points are far away from the drone base, the UAVs may not be able to reach the mission points due to the limited flight distance of the UAVs. In this paper, a transport aircraft is used to convey the UAVs to the vicinity of the mission points, and then the UAVs are released to perform the tasks. Considering that the mission points need to be approached or reached at a specific angle, and to meet the maximum curvature constraint of the fixed-wing UAVs, the paths are generated using the Dubins curves. In this paper, a hierarchical solution architecture is suggested to solve the NP-hard problem of minimizing the total energy consumption of UAVs. Firstly, the target points are clustered. Then use the simulated annealing algorithm in each cluster to find the delivery position and angle of the transport aircraft that minimizes the total energy consumption of the UAVs. Finally, the genetic algorithm is used to solve the delivery sequence with the shortest flight path of the transport aircraft and generate the flight trajectories of the UAVs and the transport aircraft. Simulation results verify the effectiveness of the proposed planning method.
Transductive-Transfer Learning Based Deep Neural Networks for Day-Ahead PV Power Forecasting in Smart Grid Application: A Comparative Analysis

For effective grid management and the integration of renewable energy sources, accurate forecasting of photovoltaic (PV) power output is essential. This paper presents a novel approach to PV power forecasting using deep learning models with Transductive Transfer Learning (TTL) assistance. The proposed models, namely LSTM, Bi-LSTM, and Feed Forward Neural Networks, are trained and tested on a dynamic PV sites dataset that captures the diverse and dynamic nature of PV power generation. The efficiency of day-ahead power forecasting is evaluated on Winter and Summer days to account for seasonal variations. A detailed comparative analysis is conducted, comparing the performance of the proposed models with and without transfer learning assistance, as well as other conventional deep learning models. Additionally, comprehensive statistical analysis is performed to assess the sensitivity and reliability of the proposed models. The results demonstrate that TTL significantly improves the accuracy and reliability of the forecasting models, leading to more effective PV power management. The findings of this research contribute to the field of PV power forecasting and provide valuable insights for practical implementation in renewable energy systems.
A Two-Stage Pose Graph Optimization Method for Multi-Robot Distributed Cooperative SLAM

With the rapid development of robotics research, functional requirements of robots have become more and more complex, and it is difficult for single robot to satisfy the actual working requirements. However, multi-robot collaborative SLAM systems suffer from high computational resource requirements. This is due to the fact that the operation of a multi-robot collaborative SLAM system needs to process the pose information collected by its own sensors and the relative pose information of other robots obtained through inter-robot communication. In this context, the multi-robot distributed cooperative SLAM system is proposed. However, the distributed cooperative technology still faces the problems of difficult to guarantee the consistency of the poses in different coordinates and complicated pose optimization solution when the poses of different robots are inputted into a single robot system for processing. Therefore, how to efficiently complete the pose optimization of multi-robot cooperative SLAM has become a hot topic in recent years. In this paper, we propose a two-stage pose graph optimization method in a multi-robot distributed collaborative SLAM system to reduce the computational effort of the pose graph optimization process in multi-robot collaborative SLAM system. It is shown that our two-stage pose graph optimization method significantly improves the computational speed of pose graph optimization while maintaining similar accuracy as the existing common multi-robot collaborative SLAM pose graph optimization methods.
Adversarial Training Based on Dimension Extension of Label Coding

Facing the threat of adversarial samples, adversarial training comes into being. Since adversarial samples and clean samples obey different data distributions, it is difficult to weigh the clean classification accuracy and robustness of the adversarial training model. To solve this problem, we propose an adversarial training method based on dimension extension of label coding. Specifically, we first modify the target classifier model by replacing batch normalization with group normalization unrelated to batch data, so that the normalization operation of features is no longer related to the statistical characteristics of samples. Secondly, in view of the difference of samples under the same class, the label coding dimension extension is introduced to enlarge the distance between classes while reasonably considering the distance within the label class, so as to make the adversarial training objectives clearer. Finally, an auxiliary classification head in parallel with the original classification head of the target classifier is designed to provide feasibility for training the model with extended labels in the training stage. Experiments have been carried out to demonstrate the effectiveness of our proposed adversarial training method.
Fast and Robust Training and Deployment of Deep Reinforcement Learning Based Navigation Policy

The application of Deep Reinforcement Learning (DRL) as a comprehensive solution to automobile control and navigation has seen extensive exploration. Essentially, the agent refines the control policy, which maps the robot's actions from its inputs, by interacting with the environment. Despite this, the practical implementation of DRL faces significant challenges due to the prolonged training time required and the sim-to-real disparity. In this study, we trained a DRL-based motion planner under carefully designed simulation scenarios capable of emulating physical interaction with humans. To address the mentioned challenges, human experiences were incorporated into the experience buffer domain to expedite the convergence of training. It could also enable the agent to acquire effective strategies for interaction with dynamic objects. Furthermore, to enhance the robustness of the system, we introduced noise into both the input and target action networks. The efficacy and generalizability of our training framework were validated using an Ackermann mobile robot equipped solely with an economical 2D LiDAR and odometry. Both ROS Gazebo simulations and real-world experiments demonstrated the high performance and efficiency of the proposed approach, underscoring its potential utility in real-world navigation scenarios. The video link is: https://www.youtube.com/@TAOWeizhi/playlists.
Enhancing Human-AI Trust by Describing AI Decision-Making Behavior

Air combat game is a highly complex and dynamic decision-making problem that is crucial for ensuring national security and improving combat efficiency. In recent years, artificial intelligence (AI) technologies such as deep reinforcement learning have made significant progress in the air combat game field, surpassing human experts' capabilities. However, the decision-making process of AI algorithms often lacks transparency and interpretability, resulting in low trust in them, which limits their promotion and application in practical scenarios. To enhance human-AI trust, this paper proposes a decision explanation method based on natural language generation. As the most direct means of information transmission, natural language can help people quickly understand the behavior and intent of AI algorithms. Taking a one-on-one air combat game as an experimental scenario, this paper constructs a combat dataset mapping temporal states to behavioral explanations and designs an attention-based Encoder-decoder architecture (AED) capable of generating natural language descriptions of current AI decision-making behavior based on a period of combat data. Experimental results show that AED can accurately describe the decision-making behavior of AI algorithms and help improve the level of human-AI trust.
Adaptive Formation Tracking of Swarm Jumping Robots Using Multiagent Deep Reinforcement Learning

Swarm jumping robots have attracted increasing attention due to their multimodal motion performance, fault tolerance, and high efficiency. Formation maintenance is the core part of cooperative control of swarm robots, but the mainstream strategies still suffer from poor dynamic adaptability and difficulty in convergence. Here, we propose an adaptive formation tracking system for insect-inspired swarm jumping robots based on Multiagent Deep Reinforcement Learning with Artificial Potential Field (MADRL-APF). It incorporates a hierarchical control architecture for the jumping robots capable of multiple motion modes (jump, turn left, turn right) to autonomously track a moving target. We validated the feasibility of the proposed method in simulated environments with/without obstacles. Multiple jumping robots are able to approach, track and catch a moving target while avoiding obstacles and self-organizing formations during different tasks. Compared with the previous multiagent deep reinforcement learning algorithm, the results show that our method has better convergence and stability. In addition, the method exhibits good scalability and can be scaled to swarm robotic systems with more agents.
Path Smoothing via Policy Gradient Parameter Optimization with Utilization to Quadrotor Tracking

This paper proposes a novel global path smoothing approach based on policy gradient parameter optimization. Firstly, an algorithm is introduced to sample path points suitable for computer processing on grid maps, resulting in a discrete set of sampled points defining the path. Secondly, a path smoothing strategy is designed by resorting to differential iteration. The smoothing parameters are optimized and trained with a policy gradient which allows the coordinates of each path point to converge to a state where a smooth optimized trajectory can be generated. Finally, the simulation and experimental results indicate the smooth output of continuous achievable paths can be obtained on the basis of discrete paths of rasterized maps for the quadrotor UAV.
The Cooperation Experience of Peter the Great Saint Petersburg Polytechnic University and Samarkand State University Using Remote Labs

At the present time cooperation between universities plays an important role, allowing them to adopt best practices and improve the educational process. In the current year, cooperation has been established between Peter the Great St. Petersburg Polytechnic University, Russian Federation and Samarkand State University, Uzbekistan. As part of this cooperation, a set of remote laboratory works in electronics based on the NI ELVIS III platform and on the real laboratory equipment was tested at Samarkand State University. The paper describes the experience of using the NI ELVIS III platform, the oscilloscope Rigol DS1054z and the spectrum analyzer Rigol DSA1020 in organizing of remote laboratory works in electronics at Samarkand State University.
Quantum Computing for Cable-Routing Problem in Solar Power Plants

The solar power plant is a large-scale photovoltaic (PV) system aimed to generate solar power for the electricity grid. It includes PV arrays (PVAs), cables, and other electrical accessories. Moreover, the solar power plant builder must consider various parameters and design regulations. The cable routing problem (CRP) is critical in large-scale solar power plant design. The objective of our CRP is to minimize the installation cost of the cable by determining the partition of the photovoltaic array and the cable routing. In this study, we use the quantum computer to solve the CRP, an NP-hard integer linear programming (ILP) problem, and show its advantages over classic computers. We transfer the ILP CRP into the quadratic unconstrained binary optimization (QUBO) model and solve it by the advanced quantum annealer. Finally, we analyze the computational results and discuss the advantages of our approach to solving the CRP.
Power System Quasi-Steady State Estimation: An Echo State Network Approach

The operating point of a power system may change due to slow enough variations of the power injections. Rotating machines in the bulk system can absorb smooth changes in the dynamic states of the system. In this context, we present a novel reservoir computing (RC) method for estimating power system quasi-steady states. By exploiting the behavior of an RC-based recurrent neural network, the proposed method can capture the inherent nonlinearities in the power flow equations. Our approach is compared with traditional methods, including least squares, Kalman filtering, and particle filtering. We demonstrate the estimation performance for all the methods under normal operation and sudden load change. Extensive experiments tested on the standard IEEE 14-bus and 300-bus cases corroborate the merit of the proposed approach.
A Visualization Method of Knowledge Graphs for the Computation and Comprehension of Ultrasound Reports

Knowledge graph visualization in ultrasound reports is essential for enhancing medical decision making and the efficiency and accuracy of computer-aided analysis tools. This study aims to propose an intelligent method for analyzing ultrasound reports through knowledge graph visualization. Firstly, we provide a novel method for extracting key term networks from the narrative text in ultrasound reports with high accuracy, enabling the identification and annotation of clinical concepts within the report. Secondly, a knowledge representation framework based on ultrasound reports is proposed, which enables the structured and intuitive visualization of ultrasound report knowledge. Finally, we propose a knowledge graph completion model to address the lack of entities in physicians' writing habits and improve the accuracy of visualizing ultrasound knowledge. In comparison to traditional methods, our proposed approach outperforms the extraction of knowledge from complex ultrasound reports, achieving a significantly higher extraction index (η) of 2.69, surpassing the general pattern-matching method (2.12). In comparison to other state-of-the-art methods, our approach achieves the highest P (0.85), R (0.89), and F1 (0.87) across three testing datasets. The proposed method can effectively utilize the knowledge embedded in ultrasound reports to obtain relevant clinical information and improve the accuracy of using ultrasound knowledge.
Enhancing Blockchain Performance via On-chain and Off-chain Collaboration

Transactions concurrent execution is one of the most promising solutions to enhance throughput for blockchain systems. Traditional concurrent execution schemes include on-chain concurrency and off-chain concurrency. However, they either increase hardware requirements to nodes or bring extra overheads for transaction verification, compromising the decentralization and security properties of blockchains. In this paper, we propose a new concurrent execution scheme that integrates off-chain execution into the on-chain concurrent execution scheme, by which a blockchain system can enhance performance without compromising security and decentralization. To achieve this, we first propose a consistent information scheduling mechanism. This mechanism divides scheduling information of transactions based on the execution-related information, improving the efficiency of scheduling information transmission and execution between on-chain and off-chain nodes. Then, to achieve secure and efficient collaboration between on-chain and off-chain nodes, our scheme proposes a secure collaboration validation mechanism without additional security assumptions. Finally, we implement our prototype based on Tendermint and compare it with the serial execution scheme in the blockchain. The experimental results show that our scheme can achieve a maximum throughput improvement of 2.6 $$\times $$ , 11.2 $$\times $$ less execution time, and 2.1 $$\times $$ less verification time.
Experimenting with Zero-Knowledge Proofs of Training

How can a model owner prove they trained their model according to the correct specification? More importantly, how can they do so while preserving the privacy of the underlying dataset and the final model? We study this problem and formulate the notion of zero-knowledge proof of training (zkPoT), which formalizes rigorous security guarantees that should be achieved by a privacy-preserving proof of training. While it is theoretically possible to design zkPoT for any model using generic zero-knowledge proof systems, this approach results in extremely unpractical proof generation times. Towards designing a practical solution, we propose the idea of combining techniques from MPC-in-the-head and zkSNARKs literature to strike an appropriate trade-off between proof size and proof computation time. We instantiate this idea and propose a concretely efficient, novel zkPoT protocol for logistic regression.
Verifiable Learning for Robust Tree Ensembles

Verifying the robustness of machine learning models against evasion attacks at test time is an important research problem. Unfortunately, prior work established that this problem is NP-hard for decision tree ensembles, hence bound to be intractable for specific inputs. In this paper, we identify a restricted class of decision tree ensembles, called large-spread ensembles, which admit a security verification algorithm running in polynomial time. We then propose a new approach called verifiable learning, which advocates the training of such restricted model classes which are amenable for efficient verification. We show the benefits of this idea by designing a new training algorithm that automatically learns a large-spread decision tree ensemble from labelled data, thus enabling its security verification in polynomial time. Experimental results on public datasets confirm that large-spread ensembles trained using our algorithm can be verified in a matter of seconds, using standard commercial hardware. Moreover, large-spread ensembles are more robust than traditional ensembles against evasion attacks, at the cost of an acceptable loss of accuracy in the non-adversarial setting.
A Systematic Evaluation of Automated Tools for Side-Channel Vulnerabilities Detection in Cryptographic Libraries

To protect cryptographic implementations from side-channel vulnerabilities, developers must adopt constant-time programming practices. As these can be error-prone, many side-channel detection tools have been proposed. Despite this, such vulnerabilities are still manually found in cryptographic libraries. While a recent paper by Jancar et al. shows that developers rarely perform side-channel detection, it is unclear if existing detection tools could have found these vulnerabilities in the first place.
Efficient Set Membership Encryption and Applications

The emerging area of laconic cryptography [Cho et al., CRYPTO'17] involves the design of two-party protocols involving a sender and a receiver, where the receiver's input is large. The key efficiency requirement is that the protocol communication complexity must be independent of the receiver's input size. In recent years, many tasks have been studied under this umbrella, including laconic oblivious transfer (ℓOT).
Adaptively Secure (Aggregatable) PVSS and Application to Distributed Randomness Beacons

Publicly Verifiable Secret Sharing (PVSS) is a fundamental primitive that allows to share a secret S among n parties via a publicly verifiable transcript T. Existing (efficient) PVSS are only proven secure against static adversaries who must choose who to corrupt ahead of a protocol execution. As a result, any protocol (e.g., a distributed randomness beacon) that builds on top of such a PVSS scheme inherits this limitation. To overcome this barrier, we revisit the security of PVSS under adaptive corruptions and show that, surprisingly, many protocols from the literature already achieve it in a meaningful way:
On the Security of KZG Commitment for VSS

The constant-sized polynomial commitment scheme by Kate, Zaverucha, and Goldberg (Asiscrypt 2010), also known as the KZG commitment, is an essential component in designing bandwidth-efficient verifiable secret-sharing (VSS) protocols. We point out, however, that the KZG commitment is missing two important properties that are crucial for VSS protocols.
Finding All Cross-Site Needles in the DOM Stack: A Comprehensive Methodology for the Automatic XS-Leak Detection in Web Browsers

Cross-Site Leaks (XS-Leaks) are a class of vulnerabilities that allow a web attacker to infer user state from a target web application cross-origin. Fixing XS-Leaks is a cat-and-mouse game: once a published vulnerability is fixed, a variant is discovered. To end this game, we propose a methodology to find all leak techniques for a given state-dependent resource and a set of inclusion method. We translate a website's DOM at runtime into a directed graph. We execute this translation twice, once for each state. The outputs are two slightly different graphs. We then get the set of all leak techniques by computing these two graphs' differences. The remaining nodes and edges differ between the two states, and the corresponding DOM properties and objects can be observed cross-origin.