Data Security Protection Mechanism of Video and Image Feature Modeling Based on Domestic Crypto Algorithm

Video and image monitoring is increasingly appearing in our home, travel and other aspects. Through video and image analysis and comparison of video and image monitoring data, it can provide strong analysis support capabilities for social security prevention and control, traffic command, safety production and so on. Video and image feature modeling is a necessary prerequisite for video and image analysis and comparison. Video and image feature modeling forms video and image feature data. Video and image feature data reflects the most essential information of various elements such as people, vehicles and objects. However, video and image data is easy to be damaged, changed and leaked in the process of collection, aggregation, analysis, modeling and storage, Facing data security risks. This paper proposes a set of video and image feature modeling data security protection mechanism based on domestic algorithm to realize the whole process encryption protection of data acquisition, transmission and storage.

A new framework based on features modeling and ensemble learning to predict query performance

A query optimizer attempts to predict a performance metric based on the amount of time elapsed. Theoretically, this would necessitate the creation of a significant overhead on the core engine to provide the necessary query optimizing statistics. Machine learning is increasingly being used to improve query performance by incorporating regression models. To predict the response time for a query, most query performance approaches rely on DBMS optimizing statistics and the cost estimation of each operator in the query execution plan, which also focuses on resource utilization (CPU, I/O). Modeling query features is thus a critical step in developing a robust query performance prediction model. In this paper, we propose a new framework based on query feature modeling and ensemble learning to predict query performance and use this framework as a query performance predictor simulator to optimize the query features that influence query performance. In query feature modeling, we propose five dimensions used to model query features. The query features dimensions are syntax, hardware, software, data architecture, and historical performance logs. These features will be based on developing training datasets for the performance prediction model that employs the ensemble learning model. As a result, ensemble learning leverages the query performance prediction problem to deal with missing values. Handling overfitting via regularization. The section on experimental work will go over how to use the proposed framework in experimental work. The training dataset in this paper is made up of performance data logs from various real-world environments. The outcomes were compared to show the difference between the actual and expected performance of the proposed prediction model. Empirical work shows the effectiveness of the proposed approach compared to related work.

MDTP

Traffic prediction has drawn increasing attention for its ubiquitous real-life applications in traffic management, urban computing, public safety, and so on. Recently, the availability of massive trajectory data and the success of deep learning motivate a plethora of deep traffic prediction studies. However, the existing neural-network-based approaches tend to ignore the correlations between multiple types of moving objects located in the same spatio-temporal traffic area, which is suboptimal for traffic prediction analytics. In this paper, we propose a multi-source deep traffic prediction framework over spatio-temporal trajectory data, termed as MDTP. The framework includes two phases: spatio-temporal feature modeling and multi-source bridging. We present an enhanced graph convolutional network (GCN) model combined with long short-term memory network (LSTM) to capture the spatial dependencies and temporal dynamics of traffic in the feature modeling phase. In the multi-source bridging phase, we propose two methods, Sum and Concat, to connect the learned features from different trajectory data sources. Extensive experiments on two real-life datasets show that MDTP i) has superior efficiency, compared with classical time-series methods, machine learning methods, and state-of-the-art neural-network-based approaches; ii) offers a significant performance improvement over the single-source traffic prediction approach; and iii) performs traffic predictions in seconds even on tens of millions of trajectory data. we develop MDTP + , a user-friendly interactive system to demonstrate traffic prediction analysis.

variED: an editor for collaborative, real-time feature modeling

Feature models are a helpful means to document, manage, maintain, and configure the variability of a software system, and thus are a core artifact in software product-line engineering. Due to the various purposes of feature models, they can be a cross-cutting concern in an organization, integrating technical and business aspects. For this reason, various stakeholders (e.g., developers and consultants) may get involved into modeling the features of a software product line. Currently, collaboration in such a scenario can only be done with face-to-face meetings or by combining single-user feature-model editors with additional communication and version-control systems. While face-to-face meetings are often costly and impractical, using version-control systems can cause merge conflicts and inconsistency within a model, due to the different intentions of the involved stakeholders. Advanced tools that solve these problems by enabling collaborative, real-time feature modeling, analogous to Google Docs or Overleaf for text editing, are missing. In this article, we build on a previous paper and describe (1) the extended formal foundations of collaborative, real-time feature modeling, (2) our conflict resolution algorithm in more detail, (3) proofs that our formalization converges and preserves causality as well as user intentions, (4) the implementation of our prototype, and (5) the results of an empirical evaluation to assess the prototype’s usability. Our contributions provide the basis for advancing existing feature-modeling tools and practices to support collaborative feature modeling. The results of our evaluation show that our prototype is considered helpful and valuable by 17 users, also indicating potential for extending our tool and opportunities for new research directions.

Cross-site Scripting Attack Detection Using Machine Learning with Hybrid Features

This study aims to measure the classification accuracy of XSS attacks by using a combination of two methods of determining feature characteristics, namely using linguistic computation and feature selection. XSS attacks have a certain pattern in their character arrangement, this can be studied by learners using n-gram modeling, but in certain cases XSS characteristics can contain a certain meta and synthetic this can be learned using feature selection modeling. From the results of this research, hybrid feature modeling gives good accuracy with an accuracy value of 99.87%, it is better than previous studies which the average is still below 99%, this study also tries to analyze the false positive rate considering that the false positive rate in attack detection is very influential for the convenience of the information security team, with the modeling proposed, the false positive rate is very small, namely 0.039%