Benchmarking Software Vulnerability Detection Techniques: A Survey

Software vulnerabilities can have serious consequences, which is why many techniques have been proposed to defend against them. Among these, vulnerability detection techniques are a major area of focus. However, there is a lack of a comprehensive approach for benchmarking these proposed techniques. In this paper, we present the first survey that comprehensively investigates and summarizes the current state of software vulnerability detection benchmarking. We review the current literature on benchmarking vulnerability detection, including benchmarking approaches in technique-proposing papers and empirical studies. We also separately discuss the benchmarking approaches for traditional and deep learning-based vulnerability detection techniques. Our survey analyzes the challenges of benchmarking software vulnerability detection techniques and the difficulties involved. We summarize the challenges of benchmarking software vulnerability detection techniques and describe possible solutions for addressing these challenges

Matematičko modeliranje i neizrazito upravljanje mehanizmom za poravnavanje i podizanje

The moving process of a leveling and erecting mechanism is complicated, which involves six hydraulic cylinders. The research established mathematical model and optimized the moving process of the leveling and erecting mechanism. Kinematic analysis of the mechanism was accomplished. Mathematical model of the hydraulic system was established. Working scheme was designed consisting of workflow, trajectory planning, leveling strategy and control method. The mechanical, hydraulic and control models were respectively established in Pro/E, ADAMS, AMESim and Simulink software. Co-simulation was carried out to validate the designed scheme. Experiment was completed on a platform. The results of simulation and experiment indicate that the designed scheme is feasible. Fuzzy adaptive PID controller has an excellent effect in controlling the leveling and erecting mechanism.Gibanja mehanizma za poravnavanje i podizanje složeni je proces koji uključuje šest hidrauličkih cilindara. Istraživanje postavlja matematički model i optimizira proces gibanja mehanizma za poravnavanje i podizanje. Provedena je kinematička analiza mehanizma. Postavljen je matematički model hidrauličkog sustava. Radni program načinjen je uključujući tijek rada, planiranje trajektorije, strategiju poravnavanja i metodu upravljanja. Mehanički, hidraulički i upravljački modeli redom su izvedeni u Pro/E, ADAMS, AMESim i Simulink programskim paketima. Provedena je kosimulacija za validaciju načinjenog radnog programa. Eksperiment je proveden na stvarnoj platformi. Rezultati simulacije i eksperimenta ukazuju na izvedivost predloženog radnog programa. Neizraziti adaptivni PID regulator daje odličan efekt pri upravljanju mehanizma za poravnavanje i podizanje

Long and Diverse Text Generation with Planning-based Hierarchical Variational Model

Existing neural methods for data-to-text generation are still struggling to produce long and diverse texts: they are insufficient to model input data dynamically during generation, to capture inter-sentence coherence, or to generate diversified expressions. To address these issues, we propose a Planning-based Hierarchical Variational Model (PHVM). Our model first plans a sequence of groups (each group is a subset of input items to be covered by a sentence) and then realizes each sentence conditioned on the planning result and the previously generated context, thereby decomposing long text generation into dependent sentence generation sub-tasks. To capture expression diversity, we devise a hierarchical latent structure where a global planning latent variable models the diversity of reasonable planning and a sequence of local latent variables controls sentence realization. Experiments show that our model outperforms state-of-the-art baselines in long and diverse text generation.Comment: To appear in EMNLP 201

Defect Detection for Patterned Fabric Images Based on GHOG and Low-Rank Decomposition

In contrast to defect-free fabric images with macro-homogeneous textures and regular patterns, the fabric images with the defect are characterized by the defect regions that are salient and sparse among the redundant background. Therefore, as an effective tool for separating an image into a redundant part (the background) and sparse part (the defect), the low-rank decomposition model provides an ideal solution for patterned fabric defect detection. In this paper, a novel patterned method for fabric defect detection is proposed based on a novel texture descriptor and the low-rank decomposition model. First, an efficient second-order orientation-aware descriptor, denoted as GHOG, is designed by combining Gabor and histogram of oriented gradient (HOG). In addition, a spatial pooling strategy based on human vision mechanism is utilized to further improve the discrimination ability of the proposed descriptor. The proposed texture descriptor can make the defect-free image blocks lay in a low-rank subspace, while the defective image blocks have deviated from this subspace. Then, a constructed low-rank decomposition model divides the feature matrix generated from all the image blocks into a low-rank part, which represents the defect-free background, and a sparse part, which represents sparse defects. In addition, a non-convex log det as a smooth surrogate function is utilized to improve the efficiency of the constructed low-rank model. Finally, the defects are localized by segmenting the saliency map generated by the sparse matrix. The qualitative results and quantitative evaluation results demonstrate that the proposed method improves the detection accuracy and self-adaptivity comparing with the state-of-the-art methods