scholarly journals Video transaction algorithm considering FISCO alliance chain and improved trusted computing

2021 ◽  
Vol 7 ◽  
pp. e594
Author(s):  
Yang Yang ◽  
Dingguo Yu ◽  
Chen Yang
Keyword(s):  
Information Extraction ◽  
Trusted Computing ◽  
Technical Support ◽  
Algorithm Complexity ◽  
Experimental Results ◽  
Computational Power ◽  
Media Centers ◽  
Computing Algorithm ◽  
Video Summary ◽  
Extraction Algorithm

With the advent of the era of self media, the demand for video trading is becoming more and more obvious. Alliance blockchain has the characteristics of traceable transaction records, tamper proof transaction records, decentralized transactions and faster transaction speed than public chains. These features make it a trading platform. Trusted computing can solve the problem of non Byzantine attack in the aspect of hardware. This paper proposes a video transaction algorithm considering FISCO alliance chain and improved trusted computing. First, an improved trusted computing algorithm is used to prepare a trusted transaction environment. Second, the video summary information extraction algorithm is used to extract the summary information that can uniquely identify the video. Finally, based on the video transactions algorithm of FISCO alliance chain, the video summary information is traded on the chain. Experimental results show that the proposed algorithm is efficient and robust for video transactions. At the same time, the algorithm has low computational power requirements and algorithm complexity, which can provide technical support for provincial and county financial media centers and relevant media departments.

Multi-Resolution-Based Contour Corner Extraction Algorithm for Computer Vision-Based Measurement

2018 ◽  
Author(s):  
Jianhua Li ◽  
Lin Liao
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Original Algorithm ◽  
Industry Standard ◽  
Extraction Algorithm ◽  
Corner Points ◽  
Multiple Resolutions ◽  
Key Steps

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

scholarly journals A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

2021 ◽  
Vol 11 (23) ◽  
pp. 11344
Author(s):  
Wei Ke ◽  
Ka-Hou Chan
Keyword(s):  
Probability Distribution ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Final Analysis ◽  
The State ◽  
Experimental Results ◽  
Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

scholarly journals Handwritten tifinagh character recognition using simple geometric shapes and graphs

2019 ◽  
Vol 13 (2) ◽  
pp. 598
Author(s):  
Youssef Ouadid ◽  
Abderrahmane Elbalaoui ◽  
Mehdi Boutaounte ◽  
Mohamed Fakir ◽  
Brahim Minaoui
Keyword(s):  
Character Recognition ◽  
Graph Matching ◽  
Recognition Rate ◽  
Recognition System ◽  
Features Extraction ◽  
Experimental Results ◽  
Feature Points ◽  
Matching Method ◽  
Extraction Algorithm ◽  
Novel Algorithm

<p>In this paper, a graph based handwritten Tifinagh character recognition system is presented. In preprocessing Zhang Suen algorithm is enhanced. In features extraction, a novel key point extraction algorithm is presented. Images are then represented by adjacency matrices defining graphs where nodes represent feature points extracted by a novel algorithm. These graphs are classified using a graph matching method. Experimental results are obtained using two databases to test the effectiveness. The system shows good results in terms of recognition rate.</p>

Automatic Partitioning of Large Scale Simulation in Grid Computing for Run Time Reduction

2012 ◽  
pp. 225-252
Author(s):  
Nurcin Celik ◽  
Esfandyar Mazhari ◽  
John Canby ◽  
Omid Kazemi ◽  
Parag Sarfare ◽  
...  
Keyword(s):  
Execution Time ◽  
Large Scale ◽  
Time Synchronization ◽  
Computational Grid ◽  
Experimental Results ◽  
Time Interval ◽  
Computational Power ◽  
Large Scale Systems ◽  
Large Scale Simulations ◽  
Reduce Execution Time

Simulating large-scale systems usually entails exhaustive computational powers and lengthy execution times. The goal of this research is to reduce execution time of large-scale simulations without sacrificing their accuracy by partitioning a monolithic model into multiple pieces automatically and executing them in a distributed computing environment. While this partitioning allows us to distribute required computational power to multiple computers, it creates a new challenge of synchronizing the partitioned models. In this article, a partitioning methodology based on a modified Prim’s algorithm is proposed to minimize the overall simulation execution time considering 1) internal computation in each of the partitioned models and 2) time synchronization between them. In addition, the authors seek to find the most advantageous number of partitioned models from the monolithic model by evaluating the tradeoff between reduced computations vs. increased time synchronization requirements. In this article, epoch- based synchronization is employed to synchronize logical times of the partitioned simulations, where an appropriate time interval is determined based on the off-line simulation analyses. A computational grid framework is employed for execution of the simulations partitioned by the proposed methodology. The experimental results reveal that the proposed approach reduces simulation execution time significantly while maintaining the accuracy as compared with the monolithic simulation execution approach.

scholarly journals A Privacy-Preserving Trajectory Publication Method Based on Secure Start-Points and End-Points

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yannian Zhao ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Zhaoyan Hu
Keyword(s):  
Theoretical Analysis ◽  
Privacy Preservation ◽  
Privacy Preserving ◽  
Third Parties ◽  
Algorithm Complexity ◽  
Experimental Results ◽  
End Points ◽  
End Point ◽  
Trajectory Similarity ◽  
Candidate Set

By judging whether the start-point and end-point of a trajectory conform to the user’s behavioral habits, an attacker who possesses background knowledge can breach the anonymous trajectory. Traditional trajectory privacy preservation schemes often generate an anonymous set of trajectories without considering the security of the trajectory start- and end-points. To address this problem, this paper proposes a privacy-preserving trajectory publication method based on generating secure start- and end-points. First, a candidate set containing a secure start-point and end-point is generated according to the user’s habits. Second, k−1 anonymous trajectories are generated bidirectionally according to that secure candidate set. Finally, accessibility corrections are made for each anonymous trajectory. This method integrates features such as local geographic reachability and trajectory similarity when generating an anonymized set of trajectories. This provides users with privacy preservation at the k-anonymity level, without relying on the trusted third parties and with low algorithm complexity. Compared with existing methods such as trajectory rotation and unidirectional generation, theoretical analysis and experimental results on the datasets of real trajectories show that the anonymous trajectories generated by the proposed method can ensure the security of trajectory privacy while maintaining a higher trajectory similarity.

GLOBAL RULE INDUCTION FOR INFORMATION EXTRACTION

2004 ◽  
Vol 13 (04) ◽  
pp. 813-828 ◽  
Author(s):  
JING XIAO ◽  
TAT-SENG CHUA ◽  
JIMIN LIU
Keyword(s):  
Natural Language ◽  
Information Extraction ◽  
Rule Induction ◽  
Important Task ◽  
Learning System ◽  
Experimental Results ◽  
Free Text ◽  
Global Feature ◽  
Test Set ◽  
Potential Applications

The ability to extract desired pieces of information from natural language texts is an important task with a growing number of potential applications. This paper presents a novel pattern rule induction learning system, GRID, which emphasizes the use of global feature distribution in all of the training instances in order to make better decision on rule induction. GRID uses chunks as contextual units instead of tokens, and incorporates features at lexical, syntactical and semantic levels simultaneously. The features chosen in GRID are general and they were applied successfully to both semi-structured text and free text. Our experimental results on some publicly available webpage corpora and MUC-4 test set indicate that our approach is effective.

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