Reviewerly/exHarmony huggingface dataset:
Semantic Traffic Element Removal by Learning to Follow Linguistic Instructions

We propose a novel task of semantic object removal in traffic scenes through human-guided image editing. Unlike traditional methods, our model does not require image masks; instead, it effectively removes semantic elements from a traffic scene image based solely on an input image and linguistic instruction. To achieve this objective, we use large-scale pre-trained models to construct a traffic scene image dataset suitable for human-guided image editing. We train a text-guided image translation model on this dataset. During inference, our model quickly generalizes to photorealistic traffic scene images and user-written instructions, obviating the need for fine-tuning and enabling rapid removal of semantic object elements from images within seconds. Experimental results demonstrate that our model achieves satisfying removal performance in the traffic scene semantic object removal task, showing the potential advantages of text guidance in image processing tasks.
IAPF-RRT*: An Efficient Iterative Optimization Path Planning Algorithm for Autonomous Vehicles

Path planning module is an important part of autonomous vehicles, and its efficiency directly affects the steering safety. Sampling based algorithm has been widely used in autonomous vehicles, and the combination of RRT algorithm and artificial potential field method (APF -RRT*) has been proved to greatly improve the efficiency of planning. However, when driving in long distance and complex scenarios, there are still a lot of repetitive work during the cycles, so this paper proposes an iterative APF-RRT* (IAPF-RRT*) algorithm, which reconstructs the historical path after pruning as a prior search tree of the current frame and uses the nearest neighbor method to retrieve nodes. Considering that the results of the RRT* algorithm cannot be directly applied to autonomous vehicles, this paper takes the results of the IAPF-RRT* algorithm as the initial solution for quadratic programming. The experimental results show that the algorithm has superior real-time performance and the trajectory quality is sufficient to be directly applied to automatic driving.
Unmanned Aerial Vehicle Path Planning Based on DP-DDPG Algorithm

This paper proposes a DP-DDPG (Double-Prioritized Deep Deterministic Policy Gradient) path planning algorithm to address the issues of reward sparsity, convergence difficulty, and low success rate in solving complex environment-based UAV path planning tasks using the DDPG algorithm. Firstly, the state and action spaces of the deep reinforcement learning task are refined by constructing complex environmental models and UAV kinematic models. Secondly, a reward function is designed based on a combination of the artificial potential field method. Additionally, improvements are made to the structure of the experience replay pool and the quality of data exchange between the UAV and the environment. Finally, the DP-DDPG algorithm is applied to train the model. Experimental results show that, compared to the traditional DDPG algorithm, the DP-DDPG algorithm achieves faster convergence and improves the success rate of path planning by 30 %.
An Unstructured Terrain Traversability Mapping Method Fusing Semantic and Geometric Features

This paper presents a terrain traversability assessment method for autonomous navigation of mobile robots in unstructured outdoor environments. The method extracts terrain features from 3D point clouds and RGB images and merges them into a global terrain traversal cost map for safe trajectory planning. First, in view of the sparsity of raw point cloud data, Bayesian generalized kernel inference is applied to estimate the attributes of unknown grids, and Kalman filtering is used to fuse multi-frame information to generate dense local elevation maps in real time. Subsequently, the terrain semantic mapping takes into account the sensor noise and the semantic segmentation uncertainty and probabilistically updates the surface semantic information based on Bayesian filtering. Finally, geometric features are extracted from the elevation map and fused into the probabilistic semantic map to generate the terrain traversability map. The method is applied to a wheeled robot to perform traversability mapping tests in unstructured outdoor terrain. The experimental results show that the success rate of trajectory planning is improved by 7.7%-33.3% compared with other advanced terrain traversability analysis method.
Efficient Fine-Grained Pedestrian Detection on Edge Devices

Currently, the prevailing approach in pedestrian detection involves leveraging deep learning techniques. However, effectively detecting pedestrian objects at small scales continues to pose a significant challenge in this field of research. Moreover, many algorithms prioritize accuracy improvement, making it difficult to deploy them in real-world scenarios. This paper proposes an edge device-friendly object detection method. We introduce a target extraction module with a minimal number of non-linear units to preserve fine-grained information in the network. Additionally, we design a trapezoidal FPN structure based on the structural re-parameterization technique, and optimize it specifically for edge devices. Furthermore, we construct a hardware platform to validate the feasibility of our algorithm in real-world applications. In the experimental section, we demonstrate that our proposed method outperforms comparative algorithms on the Caltech pedestrian dataset, while maintaining high speed on edge devices.
Parameter-Based Optimization Approach for Chinese Named Entity Recognition in Navy Equipment Unmanaged System

With continuous updates and replacements of naval equipment in various countries around the world, a large amount of text data containing essential entity information has been accumulated, possessing significant research and application value. However, due to the high confidentiality of naval equipment information, publicly available data, and mature knowledge graph construction methods specific to this vertical domain are scarce. Furthermore, the parameter settings for Chinese Named Entity Recognition (NER) techniques mainly rely on experience and subjective methods currently. To solve these issues, this paper collects publicly available information to build a dataset for the naval equipment domain and proposes a Dynamic Particle Swarm Optimization algorithm (D-PSO) for optimizing parameters in the BERT-BiLSTM-CRF naval equipment NER method. The research results demonstrate that the dynamic learning factors and dynamic weighting strategies in the proposed D-PSO algorithm significantly enhance the training efficiency of the DPSO+BERT-BiLSTM-CRF model, resulting in precision, recall, and F1 score reaching 90.66%, 92.08%, and 91.36%, respectively. This fully validates the advantages of the proposed improved model in terms of accuracy, effectiveness, and feasibility.
A Novel Multiscale Fusion Algorithm for Underwater Image Dehazing and Edge Preserving

When we use visual devices for navigation and location in underwater environment, there is a great demand for image quality. However, underwater images have drawbacks such as severe color quality degradation, blurring, and low contrast, so it is necessary to preprocess the image to some extent. To address this problem, this paper proposes an underwater image dehazing algorithm based on a weight pyramid based multiscale image fusion framework. Specifically, for blue images, we use quadtree hierarchical search strategy and least squares filter to improve the acquisition process of background light in the dark channel prior method, making it more suitable for underwater images. For greenish images, we have improved saturation and value separately. Then the processed images are regarded as inputs to the multiscale pyramid to obtain the normalized weight mapping, making the image reconstructed. To verify the performance of proposed method, an experiment is conducted. As a result, the algorithm significantly corrects color degradation and the edges are also well preserved.
A Forward Propagation Motion Planning Algorithm Based on Generative Model

The random-forward-propagation-based approach can solve the kinodynamic motion planning(KMP) problem without requiring solving the boundary value problem (BVP). This technique ensures probability completeness and nearly asymptotic optimality by randomly propagating nodes and uti-lizing appropriate node selection methods. Random sampling control can help find a feasible solution, while it also generates numerous unnecessary extensions that reduce computational efficiency. In this paper, we propose a forward propagation motion planning method based on a generative model. The motion sequence is no longer entirely dependent on random generation but instead through the neural network heuristic, resulting in a faster solution of high quality. Specifically, a VAE-GAN is employed as the generative model in our approach. The shared generator in both VAE and GAN generates a set of control candidates simultaneously, from which the discriminator selects the optimal one for propagation. A large number of simulation experiments are conducted on different environments to verify the effectiveness of our algorithm.
Thickness Measurement of Brake Pads Based on Keypoint and Object Detection

The wear condition of the brake pad, as one of the core components of high-speed trains, needs to be detected and identified based on its thickness. However, existing measurement methods primarily rely on direct detection of the edges of brake pads, which is sensitive to lighting and contrast conditions. As a result, the accuracy of the algorithm for applications is limited. In this paper, we propose a brake pad thickness measurement method based on keypoint and object detection. We apply YOLOV5 for brake pad localization and adopt the HRNet network for keypoint detection in order to calculate thickness. The positions of keypoints are adjusted based on detected edge features. Precise distance is achieved by utilizing depth infor-mation and world coordinates of keypoints to obtain the actual thickness of the brake pad. Experimental results demonstrate that compared with the original method, our proposed algorithm achieves an average error rate of 0.53mm, which is accurate enough for real-world applications using our self-made dataset in realistic environments.
Fast and Efficient Multistage Pedestrian Detection Method Using Computer Vision with Applications to 5G/6G Communications

The paper presents a fast an efficient pedestrian detection method using computer vision. The method is based on a multistage approach combining motion or change detectors with heuristics and small size convolutional neural networks (CNN). The method is faster than direct application of larger CNNs but produces comparable results. Possible application of the method in 5G/6G millimeter wave communication systems is discussed.
Applying Quantum Computing to Simulate Power System Dynamics' Differential-Algebraic Equations

Power system dynamics are generally modeled by high dimensional nonlinear differential-algebraic equations due to a large number of generators, loads, and transmission lines.Thus, its computational complexity grows exponentially with the system size.In this paper, we aim to evaluate the alternative computing approach, particularly the use of quantum computing algorithms to solve the power system dynamics.Leveraging a symbolic programming framework, we convert the power system dynamics' DAEs into an equivalent set of ordinary differential equations (ODEs).Their data can be encoded into quantum computers via amplitude encoding.The system's nonlinearity is captured by Taylor polynomial expansion and the quantum state tensor whereas state variables can be updated by a quantum linear equation solver.Our results show that quantum computing can solve the dynamics of the power system with high accuracy whereas its complexity is polynomial in the logarithm of the system dimension.
Deep Learning-Based Sentiment and Stance Analysis of Tweets About Vaccination

Sentiment analysis and stance detection are interrelated problems of affective computing, and their outputs commonly complement each other. The focus of this article is to determine sentiments and stances of Twitter users about vaccination. A tweet dataset on COVID-19 vaccination is compiled and jointly annotated with sentiment and stance. This deep learning approach employs BERT, which is a model based on pre-trained transformers. The generative deep learning model, ChatGPT, is also used for stance and sentiment analysis on the dataset. ChatGPT achieves the best performance for stance detection, while BERT is the best performer for sentiment analysis. This study is the first one to observe stance and sentiment detection performance of ChatGPT on health-related tweets. This article also includes a full-fledged system proposal based on automatic sentiment and stance analysis. COVID-19 pandemic is an impactful global public health phenomenon, and hence, joint extraction of sentiments and stances from health-related tweets can profoundly contribute to health-related decision-making processes.
Statistical Image Watermark Algorithm for FAPHFMs Domain Based on BKF–Rayleigh Distribution

In the field of image watermarking, imperceptibility, robustness, and watermarking capacity are key indicators for evaluating the performance of watermarking techniques. However, these three factors are often mutually constrained, posing a challenge in achieving a balance among them. To address this issue, this paper presents a novel image watermark detection algorithm based on local fast and accurate polar harmonic Fourier moments (FAPHFMs) and the BKF–Rayleigh distribution model. Firstly, the original image is chunked without overlapping, the entropy value is calculated, the high-entropy chunks are selected in descending order, and the local FAPHFM magnitudes are calculated. Secondly, the watermarking signals are embedded into the robust local FAPHFM magnitudes by the multiplication function, and then MMLE based on the RSS method is utilized to estimate the statistical parameters of the BKF–Rayleigh distribution model. Finally, a blind image watermarking detector is designed using BKF–Rayleigh distribution and LO decision criteria. In addition, we derive the closed expression of the watermark detector using the BKF–Rayleigh model. The experiments proved that the algorithm in this paper outperforms the existing methods in terms of performance, maintains robustness well under a large watermarking capacity, and has excellent imperceptibility at the same time. The algorithm maintains a well-balanced relationship between robustness, imperceptibility, and watermarking capacity.
Speranza: Usable, Privacy-friendly Software Signing

Software repositories, used for wide-scale open software distribution, are a significant vector for security attacks. Software signing provides authenticity, mitigating many such attacks. Developer-managed signing keys pose usability challenges, but certificate-based systems introduce privacy problems. This work, Speranza, uses certificates to verify software authenticity but still provides anonymity to signers using zero-knowledge identity co-commitments.
Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning

Federated learning (FL) represents an innovative decentralized paradigm in the field of machine learning, which differs from traditional centralized approaches. It facilitates collaborative model training among multiple participants and transfers only model parameters without directly exchanging raw data to maintain confidentiality. Data valuation for each data provider becomes a critical issue to guarantee the fairness of federated learning by estimating the dataset quality of each data provider based on the contribution to the global model prediction performance. To value datasets in FL, the concept of Shapley value is introduced to estimate the contribution of each dataset to a trained global model by measuring the effects of including and excluding a local model parameter in various combinations of global model parameters. However, the contribution measurement to each dataset performed by an aggregator or certain central component as a verifier becomes irrational as the verifier is under the control of an organization. Thus, this work presents a contribution measurement framework or data valuation with strong fairness, where forged results from the contribution measurement procedure are impossible. The new framework allows every participant (data provider) to verify the results of contribution measurement.
FINNCH: Cooperative Pursuit Navigation for a Pursuer Team to Capture a Single Evader in Urban Environments

The development of a cooperative pursuit strategy for capturing escaping criminals or dangerous animals in urban public safety emergencies is becoming increasingly in demand. An ideal strategy should consider both the encirclement needed to prevent criminals from evading and the distance that pursuers need to move. This article proposes a fine-grained navigation network-based cooperative hunting (FINNCH) method. A fine-grained navigation network is created to provide detailed information about the traversability in urban areas. Three interaction rules inspired by biological behaviors in nature are introduced to achieve dynamic cooperation between pursuers. An heuristic search strategy is used to guide the pursuers toward potentially good directions, which consequently reduces the search effort. Two spatial constraints, namely, the direction centrality constraint (DCC) and encirclement distance constraint (EDC), are then constructed to evenly distribute the pursuers around the evader. Several experiments are conducted to evaluate the effectiveness and efficiency of the proposed method. The results show that FINNCH can provide navigation for multiple pursuers in complex urban environments comprised of roads, meadows, trees, water, and buildings. These findings point to the promising future of FINNCH for practical applications.
SysPart: Automated Temporal System Call Filtering for Binaries

Restricting the system calls available to applications reduces the attack surface of the kernel and limits the functionality available to compromised applications. Recent approaches automatically identify the system calls required by programs to block unneeded ones. For servers, they even consider different phases of execution to tighten restrictions after initialization completes. However, they require access to the source code for applications and libraries, depend on users identifying when the server transitions from initialization to serving clients, or do not account for dynamically-loaded libraries. This paper introduces SYSPART, an automatic system-call filtering system designed for binary-only server programs that addresses the above limitations. Using a novel algorithm that combines static and dynamic analysis, SYSPART identifies the serving phases of all working threads of a server. Static analysis is used to compute the system calls required during the various serving phases in a sound manner, and dynamic observations are only used to complement static resolution of dynamically-loaded libraries when necessary. We evaluated SYSPART using six popular servers on x86-64 Linux to demonstrate its effectiveness in automatically identifying serving phases, generating accurate system-call filters, and mitigating attacks. Our results show that SYSPART outperforms prior binary-only approaches and performs comparably to source-code approaches.
FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis

Deep learning classifiers achieve state-of-the-art performance in various risk detection applications. They explore rich semantic representations and are supposed to automatically discover risk behaviors. However, due to the lack of transparency, the behavioral semantics cannot be conveyed to downstream security experts to reduce their heavy workload in security analysis. Although feature attribution (FA) methods can be used to explain deep learning, the underlying classifier is still blind to what behavior is suspicious, and the generated explanation cannot adapt to downstream tasks, incurring poor explanation fidelity and intelligibility.
Tainted Secure Multi-Execution to Restrict Attacker Influence

Attackers can steal sensitive user information from web pages via third-party scripts. Prior work shows that secure multi-execution (SME) with declassification is useful for mitigating such attacks, but that attackers can leverage dynamic web features to declassify more than intended. The proposed solution of disallowing events from dynamic web elements to be declassified is too restrictive to be practical; websites that declassify events from dynamic elements cannot function correctly.
Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters

In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove that every valid privacy filter and odometer for noninteractive mechanisms extends to the concurrent composition of interactive mechanisms if privacy loss is measured using (ε, δ)-DP, ƒ-DP, or Rényi DP of fixed order. Our results offer strong theoretical foundations for enabling full adaptivity in composing differentially private interactive mechanisms, showing that concurrency does not affect the privacy guarantees. We also provide an implementation for users to deploy in practice.
Put Your Memory in Order: Efficient Domain-based Memory Isolation for WASM Applications

Memory corruption vulnerabilities can have more serious consequences in WebAssembly than in native applications. Therefore, we present \tool, the first WebAssembly runtime with memory isolation. Our insight is to use MPK hardware for efficient memory protection in WebAssembly. However, MPK and WebAssembly have different memory models: MPK protects virtual memory pages, while WebAssembly uses linear memory that has no pages. Mapping MPK APIs to WebAssembly causes memory bloating and low running efficiency. To solve this, we propose \acfdilm, which protects linear memory at function-level granularity. We implemented \acdilm into the official WebAssembly runtime to build \tool. Our evaluation shows that \tool can prevent memory corruption in real projects with a 1.77% average overhead and negligible memory cost.
RetSpill: Igniting User-Controlled Data to Burn Away Linux Kernel Protections

Leveraging a control flow hijacking primitive (CFHP) to gain root privileges is critical to attackers striving to exploit Linux kernel vulnerabilities. Such attack has become increasingly elusive as security researchers propose capable kernel security mitigations, leading to the development of complex (and, as a trade-off, brittle and unreliable) attack techniques to regain it. In this paper, we obviate the need for complexity by proposing RetSpill, a powerful yet elegant exploitation technique that employs user space data already present on the kernel stack for privilege escalation.