Excessive Data Exposure (EDE), where an API returns redundant data to the client beyond what is required for its functionality, has become a pervasive and severe security threat. However, automated detection techniques for such vulnerabilities remain underdeveloped, and existing methods, particularly black-box fuzzing, face significant bottlenecks in terms of accuracy and efficiency. To address these challenges, we propose Mockingbird, an automated detection tool based on a statically-assisted dynamic analysis approach. The tool leverages the JavaScript Proxy mechanism for efficient dynamic taint tracking to precisely identify the dangling data that is transmitted from an API response to the client but never consumed by any expected functionality, such as UI rendering or state management. Furthermore, to tackle the lack of a standardized benchmark in this domain, we have constructed and open-sourced EDEBench, the first persistent benchmark for EDE evaluation, comprising 8 popular open-source web projects built on diverse modern technology stacks. Experimental evaluation on EDEBench shows that, compared to the state-of-the-art, Mockingbird achieves an average F1-score improvement of 24.1% (Precision +15.8%, Recall +32.8%), enhances detection speed by nearly 20 times, and demonstrates broad applicability across all tested frameworks. These results provide a clear illustration of our tool’s accuracy, applicability, and efficiency. The source code is available at https://github.com/NeoSunJZ/Mockingbird-JS.

k-core is a subgraph where every node has at least k neighbors within the subgraph. The k-core subgraphs has been employed in large platforms like Network Repository to comprehend the underlying structures and dynamics of the network. Existing studies have primarily focused on finding k-core groups without considering their size, despite the relevance of solution sizes in many real-world scenarios. This paper addresses this gap by introducing the size-prescribed k-core search (SPCS) problem, where the goal is to find a subgraph of a specified size that has the highest possible core number. We propose two algorithms, namely the TSizeKcore-BU and the TSizeKcore-TD, to identify cohesive subgraphs that satisfy both the k-core requirement and the size constraint. Our experimental results demonstrate the superiority of our approach in terms of solution quality and efficiency. The TSizeKcore-BU algorithm proves to be highly efficient in finding size-prescribed k-core subgraphs on large datasets, making it a favorable choice for such scenarios. On the other hand, the TSizeKcore-TD algorithm is better suited for small datasets where running time is less critical.

Screws are important fasteners in industrial production and are also weak points in various mechanical devices. Therefore, screw tightening detection is of great significance. The traditional tightening quality detection method mainly collects the parameters of the tightening process and draws the rotation angle-torque curves, and draws conclusions through manual analysis, which is time-consuming and labor-intensive and low in efficiency. In order to solve this problem, this paper proposes a model based on LSTM that can automatically analyze the quality of the tightening curve, which improves the timeliness and accuracy of the test. The work of this paper is mainly divided into the following two phases. First, the original data is preprocessed using a feature extraction algorithm based on traditional sampling. In the second phase, we used a classification sample as training data to train a neural network based classifier. In the experiment, we compared the model with traditional machine learning methods, such as SVM, Random Forest. The result is better than traditional machine learning methods.

Automatic summarization technology is a method to obtain important information from documents, which can alleviate people's time and energy problems in the era of information explosion. This paper mainly studied the abstractive automatic summarization technology based on deep learning. Abstractive automatic summarization is consistent with the human habit of writing abstract, and has the characteristics of simplicity, flexibility and diversity. The experimental results based on English automatic summary data set (CNN/Daily Mail) and Chinese short text summary data set (LCSTS) showed that after the Attention Mechanism, Pointer Networks and Coverage Mechanism were added to the Seq2Seq model, the automatic summary Rouge evaluation index had an apparent improvement. In addition, comparative experiments were carried out from the neural network types (LSTM, GRU, SRU, etc.), the impact of Pointer Networks and Coverage Mechanism and the role of position features and Beam Search. After adding Batch Normalization and location features to the Point-Generator Network, there was a significant improvement in the Rouge1 and Rouge2 evaluation index.

Community detection amounts to one of the key methods in handling social networks with the aim of capturing global patterns of a network. This paper focuses on a situation where the network is unknown, which would render existing algorithms unusable. We propose exploratory community detection which aims to detect communities by utilizing samples taken from diffusion process over the network. For this problem, we propose a neural-based algorithm that develops a matrix representation of the network structure. This matrix is then the input of a spectral clustering algorithm to reveal communities in the network. We perform experiments on real-world and synthetic data sets with simulated diffusion samples.The results reveal that our algorithm has strong empirical performance.

Core-periphery structure refers to a prevalent property exhibited by many real-world complex networks. The formulation and identification of a network core-periphery structure have been a challenging problem. A classical framework (BE) proposed by Borgatti and Everett defines a core-periphery partition of the network by aligning its nodes with a block model and has been a standard method for this task. This method, however, suffers from high computational costs which make it inapplicable to large networks. Realizing this limitation, we proposed a new framework, which aims to efficiently evaluate core-ness of nodes. Our framework builds a model for core-periphery classification by integrating small samples. The experimental results of six real-world networks shows that our methods can efficiently and effectively identify network core, achieving a running time of less than three hours for a network with about 220,000 nodes.

Interpersonal ties are pivotal to individual efficacy, status and performance in an agent society.This paper explores three important and interrelated themes in social network theory: the center/periphery partition of the network; network dynamics; and social integration of newcomers. We tackle the question: How would a newcomer harness information brokerage to integrate into a dynamic network going from periphery to center? We model integration as the interplay between the newcomer and the dynamics network and capture information brokerage using a process of relationship building. We analyze theoretical guarantees for the newcomer to reach the center through tactics; proving that a winning tactic always exists for certain types of network dynamics. We then propose three tactics and show their superior performance over alternative methods on four real-world datasets and four network models. In general, our tactics place the newcomer to the center by adding very few new edges on dynamic networks with ~14000 nodes.

Influence maximization is a crucial problem in the analysis of large and complex networks. The problem asks for a set of nodes of a network from which message dissemination is maximized. This paper focuses on influence maximization from samples (IMFS) where the network is hidden. Instead, the input is a set of input-output samples of the influence function over a network. The recently proposed algorithm by Balkanski, Rubinstein, and Singer (BRS) for monotone submodular optimization achieves theoretically-tight optimization ratio. Empirically, however, no work is done to evaluate the actual performance of the BRS algorithm against parameters such as sample size, sample set size, and network topology. This paper provides an empirical analysis of algorithms for IMFS. In particular, we extends BRS by (1) factoring in effects caused by the complex network topology when approximating the marginal contribution of nodes and (2) removing constraints on the sampling distribution. We conduct experiments using two information diffusion models, and samples generated on both synthetic random networks and real-world networks. In general, our proposed algorithm significantly improves the output quality from the BRS algorithm.

With the grid applied in simulation widely, the traditional grid scheduling can not meet the requirements, which needs to server the appointed domain application not the simple computation jobs. The exiting grid scheduling mechanisms based on the physical computing nodes, which deployed with appointed environment, make the simulation resource usage less flexible and efficient due to the system structure limitation. According to the simulation application requirement, this paper integrated virtualization technology into the exited Simulation Grid to break the static dependence coupling and proposed a dynamic dependence injection scheduling mechanism. Furthermore, some solved key techniques are introduced, mainly including resource discovery and selection algorithm based the simulation resource virtualization encapsulation. This scheduling mechanism has a better efficiency in dynamic deployment, the real-time operation and the capabilities of current networked M&S platform on highly customizable resources sharing, collaboration and fault tolerant. Finally, the conclusion and some further works are given.

With the development of modeling and simulation (M&S) technology, more and more countries have taken the M&S as the main means of military training. Today's military oriented simulation exhibits a growing trend towards simulation processes that are more knowledge-intensive, distributed and collaborative. Although existing simulation grid frameworks has realized their effectiveness in dynamic discovery of model resource base on the existed computing grid technology, there is partially attributable to the lack of semantics-based discovery mechanism in the current simulation grid. Compared with existing methods, this paper extend the existed discovery mechanism of the simulation grid through embedding domain-specific ontology into model attributes descriptions and integrating a multistage matching mechanism into an improved classic matching algorithm (Paolucci et al. 's). It enables intelligent discovery mechanism to access and process the distributed, heterogeneous simulation model resources efficiently.

Process-based Web service composition is receiving significant momentum as an important strategy to allow enterprise collaboration. However the current Web service composition solutions are rather restricted and inflexible as they use a pre-defined model of the process environment. These solutions have assumed that the information in the models and consequently the compositions remain static and accurate throughout the service composition life cycle. The reflective framework presented here aims to improve the adaptability of BPEL based Web service composition. A meta-model was defined to represent the control and data flow of the composition model. To ensure the correctness of dynamic adaptation, a set of constrains and a verification algorithm were defined. A prototype adaptive service composition environment has been developed to implement our solution and demonstrate its effectiveness. In summary, it is stated that the reflective framework provides an considerable solution to the adaptive service composition without causing any control or dataflow errors.