Robotic grasping is a fundamental skill for complex tasks and is essential to intelligent behavior. While most prior methods for general 6-DoF grasping focus on extracting scene-level semantic or geometric information, they often neglect adaptability to diverse downstream applications, such as target-oriented grasping. To dress this, we rethink 6-DoF grasp detection from a grasp-centric perspective and propose FlexLoG, a versatile framework designed to unify scene-level and target-oriented grasping within a single system. Our framework integrates two core components: the Flexible Guidance Module and the Local Grasp model. The Flexible Guidance Module accommodates both global guidance (e.g., grasp heatmaps) and local guidance (e.g., visual-language grounding), ensuring robust grasp generation across varied task requirements. The Local Grasp model focuses on object-agnostic regional points, enabling precise, localized grasp predictions. Experiments demonstrate FlexLoG surpasses existing methods, achieving a new state-of-the-art on the GraspNet-1Billion dataset. To validate the practical applicability, we reconstruct the dataset in a simulated digital twin environment, verifying consistency of performance between simulated and benchmark results. Real-world robotic experiments across diverse scenarios further confirm the framework's efficacy, achieving a success rate exceeding 90%.

Binary Software Composition Analysis (BSCA) is a technique for identifying the versions of third-party libraries (TPLs) used in compiled binaries, thereby tracing the dependencies and vulnerabilities of software components without access to their source code. However, existing BSCA techniques struggle with cross-language invoked C/C++ binaries in polyglot projects due to two key challenges: (1) interference from heterogeneous Foreign Function Interface (FFI) bindings that obscure distinctive TPL features and generate false positives during matching processes, and (2) the inherent complexity of composite binaries (fused binaries), particularly prevalent in polyglot development where multiple TPLs are frequently compiled into single executable units, resulting in blurred boundaries between libraries and substantially compromising version identification precision.We propose DeeperBin, a BSCA technique that addresses these challenges through a high-quality, large-scale feature database with four key advantages: (1) high scalability that is capable of analyzing 74,647 C/C++ TPL versions, (2) efficient noise filtering to remove FFI bindings and common functions, (3) automated extraction of version string regexes for 31,855 TPL versions, and (4) generation of distinctive version features using the Minimum Description Length (MDL) principle. Evaluated on 418 cross-language binaries, DeeperBin achieves 81.2% precision and 84.6% recall for TPL detection, outperforming state-of-the-art (SOTA) techniques by 14.1% and 23.2%, respectively. For version identification, it achieves 70.3% precision, a 12.6% improvement over state-of-the-art techniques. Ablation studies confirm the usefulness of FFI filtering and MDL-based features, boosting precision and recall by 17.1% and 18.8%. DeeperBin also maintains competitive efficiency, processing binaries in 364.3 seconds while supporting the largest feature database.

Dynamic grasping of moving objects in complex, continuous motion scenarios remains challenging. Reinforcement Learning (RL) has been applied in various robotic manipulation tasks, benefiting from its closed-loop property. However, existing RL-based methods do not fully explore the potential for enhancing visual representations. In this letter, we propose a novel framework called Grasps As Points for RL (GAP-RL) to effectively and reliably grasp moving objects. By implementing a fast region-based grasp detector, we build a Grasp Encoder by transforming 6D grasp poses into Gaussian points and extracting grasp features as a higher-level abstraction than the original object point features. Additionally, we develop a Graspable Region Explorer for real-world deployment, which searches for consistent graspable regions, enabling smoother grasp generation and stable policy execution. To assess the performance fairly, we construct a simulated dynamic grasping benchmark involving objects with various complex motions. Experiment results demonstrate that our method effectively generalizes to novel objects and unseen dynamic motions compared to other baselines. Real-world experiments further validate the framework's sim-to-real transferability.

Robotic grasping, serving as the cornerstone of robot manipulation, is fundamental for embodied intelligence. Manipulation in challenging scenarios demands grasp detection algorithms with higher efficiency and generalizability. However, for general 6-Dof grasp detection, most data-driven methods directly extract scene-level features to generate grasp prediction, relying on a relatively heavy scene-level feature encoder and a significant amount of data with dense grasp labels for model training. In this letter, we propose a novel data-efficient 6-Dof grasp detection framework in cluttered scenes, named Region-Centric Grasp Detection (RCGD), consisting of an Iterative Search Module (ISM) and a Region Grasp Model (RGM). Concretely, ISM aims to retrieve potential region centers and aggregate multiple regions in a coarse-to-fine way. Then, RGM extracts aligned grasp-related embeddings and predicts grasps within these local regions. Benefiting from the region-centric paradigm and the training-free location strategy, RCGD significantly outperforms previous methods and shows minimal performance loss with even a very small portion of training data or labels. Furthermore, real-world robotic experiments in two distinct settings highlight the effectiveness of our method with a 95% success rate.

Towards understanding the ecosystem gap of fixed-release Linux that is caused by the evolution of mirrors, we conducted a comprehensive study of the Debian ecosystem. This study involved the collection of Debian packages and the construction of the dependency graph of the Debian ecosystem. Utilizing historic snapshots of Debian mirrors, we were able to recover the evolution of the dependency graph for all Debian releases, including obsolete ones. Through the analysis of the dependency graph and its evolution, we investigated from two key aspects: (1) compatibility issues and (2) security threats in the Debian ecosystem. Our findings provide valuable insights into the use and design of Linux package managers. To address the challenges revealed in the empirical study and bridge the ecosystem gap between releases, we propose a novel package management approach allowing for separate dependency environments based on native Debian mirrors. We present a working prototype, named ccenv, which can effectively remedy the inadequacy of current tools.

Fast and robust object grasping in clutter is a crucial component of robotics. Most current works resort to the whole observed point cloud for 6-Dof grasp generation, ignoring the guidance information excavated from global semantics, thus limiting high-quality grasp generation and real-time performance. In this work, we show that the widely used heatmaps are underestimated in the efficiency of 6-Dof grasp generation. Therefore, we propose an effective local grasp generator combined with grasp heatmaps as guidance, which infers in a global-to-local semantic-to-point way. Specifically, Gaussian encoding and the grid-based strategy are applied to predict grasp heatmaps as guidance to aggregate local points into graspable regions and provide global semantic information. Further, a novel non-uniform anchor sampling mechanism is designed to improve grasp accuracy and diversity. Benefiting from the high-efficiency encoding in the image space and focusing on points in local graspable regions, our framework can perform high-quality grasp detection in real-time and achieve state-of-the-art results. In addition, real robot experiments demonstrate the effectiveness of our method with a success rate of 94% and a clutter completion rate of 100%.

A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent. In this paper, we propose Normalized Grasp Space (NGS) from a novel region-aware viewpoint, unifying the grasp representation within a normalized regional space and benefiting the generalizability of methods. Leveraging the NGS, we find that CNNs are underestimated for 3D feature extraction and 6-DoF grasp detection in clutter scenes and build a highly efficient Region-aware Normalized Grasp Network (RNGNet). Experiments on the public benchmark show that our method achieves significant > 20% performance gains while attaining a real-time inference speed of approximately 50 FPS. Real-world cluttered scene clearance experiments underscore the effectiveness of our method. Further, human-to-robot handover and dynamic object grasping experiments demonstrate the potential of our proposed method for closed-loop grasping in dynamic scenarios.

Vulnerability is a major threat to software security. It has been proven that binary code similarity detection approaches are efficient to search for recurring vulnerabilities introduced by code sharing in binary software. However, these approaches suffer from high false-positive rates (FPRs) since they usually take the patched functions as vulnerable, and they usually do not work well when binaries are compiled with different compilation settings. To this end, we propose an approach, named Robin , to confirm recurring vulnerabilities by filtering out patched functions. Robin is powered by a lightweight symbolic execution to solve the set of function inputs that can lead to the vulnerability-related code. It then executes the target functions with the same inputs to capture the vulnerable or patched behaviors for patched function filtration. Experimental results show that Robin achieves high accuracy for patch detection across different compilers and compiler optimization levels respectively on 287 real-world vulnerabilities of 10 different software. Based on accurate patch detection, Robin significantly reduces the false-positive rate of state-of-the-art vulnerability detection tools (by 94.3% on average), making them more practical. Robin additionally detects 12 new potentially vulnerable functions.

Third-party libraries (TPLs) are frequently used in software to boost efficiency by avoiding repeated developments. However, the massive using TPLs also brings security threats since TPLs may introduce bugs and vulnerabilities. Therefore, software composition analysis (SCA) tools have been proposed to detect and manage TPL usage. Unfortunately, due to the presence of common and trivial functions in the bloated feature dataset, existing tools fail to precisely and rapidly identify TPLs in C/C++ real-world projects. To this end, we propose OSSFP, a novel SCA framework for effective and efficient TPL detection in large-scale real-world projects via generating unique fingerprints for open source software. By removing common and trivial functions and keeping only the core functions to build the fingerprint index for each TPL project, OSSFP significantly reduces the database size and accelerates the detection process. It also improves TPL detection accuracy since noises are excluded from the fingerprints. We applied OSSFP on a large data set containing 23,427 C/C++ repositories, which included 585,683 versions and 90 billion lines of code. The result showed that it could achieve 90.84% of recall and 90.34% of precision, which outperformed the state-of-the-art tool by 35.31% and 3.71%, respectively. OSSFP took only 0.12 seconds on average to identify all TPLs per project, which was 22 times faster than the other tool. OSSFP has proven to be highly scalable on large-scale datasets.

Third-party libraries (TPLs) are frequently reused in software to reduce development cost and the time to market. However, external library dependencies may introduce vulnerabilities into host applications. The issue of library dependency has received considerable critical attention. Many package managers, such as Maven, Pip, and NPM, are proposed to manage TPLs. Moreover, a significant amount of effort has been put into studying dependencies in language ecosystems like Java, Python, and JavaScript except C/C++. Due to the lack of a unified package manager for C/C++, existing research has only few understanding of TPL dependencies in the C/C++ ecosystem, especially at large scale. Towards understanding TPL dependencies in the C/C++ ecosystem, we collect existing TPL databases, package management tools, and dependency detection tools, summarize the dependency patterns of C/C++ projects, and construct a comprehensive and precise C/C++ dependency detector. Using our detector, we extract dependencies from a large-scale database containing 24K C/C++ repositories from GitHub. Based on the extracted dependencies, we provide the results and findings of an empirical study, which aims at understanding the characteristics of the TPL dependencies. We further discuss the implications to manage dependency for C/C++ and the future research directions for software engineering researchers and developers in fields of library development, software composition analysis, and C/C++ package manager.

Third-party libraries (TPLs) are reused frequently in software applications for reducing development cost. However, they could introduce security risks as well. Many TPL detection methods have been proposed to detect TPL reuse in Android bytecode or in source code. This paper focuses on detecting TPL reuse in binary code, which is a more challenging task. For a detection target in binary form, libraries may be compiled and linked to separate dynamic-link files or built into a fused binary that contains multiple libraries and project-specific code. This could result in fewer available code features and lower the effectiveness of feature engineering. In this paper, we propose a binary TPL reuse detection framework, LibDB, which can effectively and efficiently detect imported TPLs even in stripped and fused binaries. In addition to the basic and coarse-grained features (string literals and exported function names), LibDB utilizes function contents as a new type of feature. It embeds all functions in a binary file to low-dimensional representations with a trained neural network. It further adopts a function call graph-based comparison method to improve the accuracy of the detection. LibDB is able to support version identification of TPLs contained in the detection target, which is not considered by existing detection methods. To evaluate the performance of LibDB, we construct three datasets for binary-based TPL reuse detection. Our experimental results show that LibDB is more accurate and efficient than state-of-the-art tools on the binary TPL detection task and the version identification task. Our datasets and source code used in this work are anonymously available at https://github.com/DeepSoftwareAnalytics/LibDB.

Grasping objects is one of the most important abilities in robot system. In this paper, we propose a novel grasp representation to match the multiple grasp rectangles with different orientations in the image and predict the pixel-wise quality map to locate the corresponding grasp centers. Based on this representation, we design a residual hourglass architecture to detect multiple robust grasp poses with the fusion of shallow and deep features. Our proposed network fully leverages the features and achieves state-of-the-art accuracy of 97.8% on Cornell dataset and 95.8% on Jacquard dataset with a real-time speed, which well demonstrates the effectiveness of our proposed method.

With the development of the open-source movement, third-party library reuse is commonly practiced in programming. Application developers can reuse the code to save time and development costs. However, there are some hidden risks in misusing third-party libraries such as license violation and security vulnerability. The identification of libraries written in C or C++ is impeded by compilation process which hides most features of code. The same open-source package can be compiled into different binary code by different compilation processes. Therefore, this paper proposes LibDX, a platform-independent and fully-automated system, to detect reused libraries in binary files. With a well-designed feature extractor, LibDX can overcome compilation diversity between binary files. LibDX novelly introduces the logic feature block concept which is applied to deal with the feature duplication challenge in a large-scale feature database. We built a large test data set covering multiple platforms and evaluated LibDX with 9.5K packages including 25.8K C/C++ binary files. Our results show that LibDX achieves a precision of 92% and a recall of 97%, and outperforms state-of-the-art tools. We have validated the performance of the system with closed source commercial applications and found some license violation cases.

Fast and efficient computational electromagnetic simulation is a long-standing challenge. In this article, we propose a data-driven model to solve Poisson's equation that leverages the learning capacity of deep learning techniques. A deep convolutional neural network (ConvNet) is trained to predict the electric potential with different excitations and permittivity distribution in 2-D and 3-D models. With a careful design of cost function and proper training data generated from finite-difference solvers, the proposed network enables a reliable simulation with significant speedup and fairly good accuracy. Numerical experiments show that the same ConvNet architecture is effective for both 2-D and 3-D models, and the average relative prediction error of the proposed ConvNet model is less than 3% in both 2-D and 3-D simulations with a significant reduction in computation time compared to the finite-difference solver. This article shows that deep neural networks have a good learning capacity for numerical simulations. This could help us to build some fast solvers for some computational electromagnetic problems.

Using open-source libraries can provide rich functions and reduce development cost. However, some critical issues have also been caused such as license conflicts and vulnerability risks. In this paper, we design and implement an open-source libraries detection tool OSLDetector which uses methods of matching features to detect third-party libraries for multi-platform software in binaries. We took a series of methods such as filtering features and novelty building an internal clone forest to cope with the challenge of feature duplication. The tool can also provide the conflict of licenses and identify possible corresponding vulnerabilities, so these potential risks can be resolved and avoided. To evaluate the efficiency of OSLDetector, we collect 5K libraries containing 9K versions and manage their respective license type and existing vulnerabilities. The experimental results with a precision of 96% and recall of 92.3% show that OSLDetector is effective and outperforms similar tools.

Open source movement boosts several open source communities and millions of open source repositories (repos) are available on these communities. Consequently, component-based development and code reuse greatly improve the efficiency of software development. However, they can also bring some problems, such as license violation and security weaknesses. While code reuse detection has been extensively studied in source form, third-party components detection for software in binary form especially based on large scale database like Github has been less researched. In this paper, we take a series of data cleaning processes to get filtered 22K C/C++ repos on Github. We extend the code reuse detection for binaries against such a large-scale data set and design a system called BCFinder as an assistant tool for binary analysis. BCFinder finds third-party components in binaries automatically by feature matching. We evaluate BCFinder with a number of real-word binary programs across platform and compiling configurations. Experiments show that BCFinder is an effective supplementary tool for binary analysis. BCFinder is, by far, the first lightweight, rapid and platform-independent tool to detect component reuse in binaries against a large-scale data base like Github.

In this work, we investigated the feasibility of applying deep learning techniques to solve 2D Poisson's equation. A deep convolutional neural network is set up to predict the distribution of electric potential in 2D. With training data generated from a finite difference solver, the strong approximation capability of the deep convolutional neural network allows it to make correct prediction given information of the source and distribution of permittivity. Numerical experiments show that the predication error can reach below one percent, with a significant reduction in CPU time compared with the traditional solver based on finite difference methods.

从1999年美国出现第一款商用计算机取证工具并随后引入中国、2013年《刑事诉讼法》首次确立“电子数据”作为证据的法律地位至今,电子数据取证技术和产品已经在司法机关得到广泛应用.然而,电子数据取证以技术为先的理念极大地限制了其自身发展的空间.电子数据侦查是跨越电子数据取证局限性的一种全新理念:在刑事案件侦查全过程中,以发现和收集案件相关证据、查明犯罪事实、确定和查获犯罪嫌疑人为目标,围绕以案件要素构成的研判模型,从海量电子数据中挖掘线索和证据,直至案件侦破终结的一系列案件研判活动.依此理念提出满足公安机关和检察机关侦查业务需求的解决方案,将为电子数据在刑事案件侦查领域的应用开辟更广阔的空间.