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Journal ArticleDOI

TV program innovation and teaching under big data background in all media era

TL;DR: Wang et al. as mentioned in this paper adopted shot boundary detection technology to search the content of TV programs video, which mainly includes two aspects of decompressed domain and compressed domain, and adopted a new abrupt shot change detection algorithm for decompressed domains to analyze the whole search process of decompressive domain shot boundary.
Abstract: The purpose is to study how to innovate and teach TV programs in the background of big data. Shot boundary detection technology is adopted to search the content of TV programs video. The content retrieval of TV program is realized by shot boundary detection technology, which mainly includes two aspects of decompressed domain and compressed domain. Regarding the decompressed domain, a new abrupt shot change detection algorithm for decompressed domain is adopted to analyze of the whole search process of decompressed domain shot boundary. Regarding the compressed domain, the algorithm of video shot boundary detection on H.264/AVC code stream is used. Experimental results show that shot detection algorithm can detect not only abrupt shot change, but also gradual change. In the experiment, the comprehensive detection performance of various frequency sequences achieves 94% recall and 93.2% accuracy. The recall rate of abrupt shot change detection algorithm for experimental data is 94.5%, and the accuracy rate is 97.6%, which is superior to the detection performance of existing abrupt shot detection methods, and has a certain application value. Meanwhile, the similar video fast retrieval algorithm, the MinHash algorithm and LSH (Locality Sensitive Hashing) algorithm are compared. Similar video fast retrieval algorithm can achieve fast clustering of similar video faster, and can effectively retrieve similar video, so as to complete the fast retrieval of large-scale video data. The use of new abrupt shot change detection algorithm for decompressed domain and shot boundary detection algorithm in TV programs, to a large extent, optimizes the management of TV advertising and the manual broadcast of TV programs; moreover, it saves manpower and the broadcast cost of TV programs, which is a reform and innovation of traditional TV programs. In the future research, the boundary detection technology can be optimized to better play high-quality TV pictures.
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Journal ArticleDOI
TL;DR: This special issue aims at providing active researchers a platform to present recent advancements and address some of these challenges in the convergent research when big data meets knowledge graphs with ten original research papers out of sixteen.

2 citations

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Posted Content
TL;DR: In this article, optimal subsampling for quantile regression is investigated and algorithms based on the optimal sampling probabilities are proposed to obtain asymptotic distributions and optimality of the resulting estimators.
Abstract: We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator and the other minimizes that of the original parameter estimator. The former does not depend on the densities of the responses given covariates and is easy to implement. Algorithms based on optimal subsampling probabilities are proposed and asymptotic distributions and asymptotic optimality of the resulting estimators are established. Furthermore, we propose an iterative subsampling procedure based on the optimal subsampling probabilities in the linearly transformed parameter estimation which has great scalability to utilize available computational resources. In addition, this procedure yields standard errors for parameter estimators without estimating the densities of the responses given the covariates. We provide numerical examples based on both simulated and real data to illustrate the proposed method.

60 citations

Journal ArticleDOI
TL;DR: This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures, its advantages and disadvantages, and recommends potential future research paths.
Abstract: This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

57 citations

Journal ArticleDOI
TL;DR: In this article, optimal subsampling for quantile regression is investigated and algorithms based on the optimal sampling probabilities are proposed to obtain asymptotic distributions and optimality of the resulting estimators.
Abstract: We investigate optimal subsampling for quantile regression. We derive the asymptotic distribution of a general subsampling estimator and then derive two versions of optimal subsampling probabilities. One version minimizes the trace of the asymptotic variance-covariance matrix for a linearly transformed parameter estimator and the other minimizes that of the original parameter estimator. The former does not depend on the densities of the responses given covariates and is easy to implement. Algorithms based on optimal subsampling probabilities are proposed and asymptotic distributions and asymptotic optimality of the resulting estimators are established. Furthermore, we propose an iterative subsampling procedure based on the optimal subsampling probabilities in the linearly transformed parameter estimation which has great scalability to utilize available computational resources. In addition, this procedure yields standard errors for parameter estimators without estimating the densities of the responses given the covariates. We provide numerical examples based on both simulated and real data to illustrate the proposed method.

46 citations

Journal ArticleDOI
TL;DR: The trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence are identified and the smart grid's cybersecurity risk assessment methods for supervisory control and data acquisition are presented.
Abstract: Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The current security tools are almost perfect when it comes to identifying and preventing known attacks in the smart grid. Still, unfortunately, they do not quite meet the requirements of advanced cybersecurity. Adequate protection against cyber threats requires a whole set of processes and tools. Therefore, a more flexible mechanism is needed to examine data sets holistically and detect otherwise unknown threats. This is possible with big modern data analyses based on deep learning, machine learning, and artificial intelligence. Machine learning, which can rely on adaptive baseline behavior models, effectively detects new, unknown attacks. Combined known and unknown data sets based on predictive analytics and machine intelligence will decisively change the security landscape. This paper identifies the trends, problems, and challenges of cybersecurity in smart grid critical infrastructures in big data and artificial intelligence. We present an overview of the SG with its architectures and functionalities and confirm how technology has configured the modern electricity grid. A qualitative risk assessment method is presented. The most significant contributions to the reliability, safety, and efficiency of the electrical network are described. We expose levels while proposing suitable security countermeasures. Finally, the smart grid’s cybersecurity risk assessment methods for supervisory control and data acquisition are presented.

42 citations

Journal ArticleDOI
TL;DR: An improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed and can significantly improve latency reduction performance at the expense of small detection accuracy.
Abstract: The emergence of edge computing (EC) and intelligent vision-based driver assistance system is of great significance for the prospective development of Internet of Vehicle (IoV). The additional computation capability and extensive network coverage provides energy-limited smart devices with more opportunities to enable IoV system for time-sensitive applications. However, when implemented in a vision-based driver assistance system, the transmission of a large amount of redundant data not only causes delay but also severely compromises the accuracy of object detection. In this paper, an improved object detection algorithm based on video key-frame for latency reduction on edge IoV system is proposed. It can significantly improve latency reduction performance at the expense of small detection accuracy. In our proposal, we adopt an important coefficient and frame similarity comparison algorithm to filter redundant frames and achieve key frames for object detection. Then an improved Haar-like feature based classification algorithm is used for object detection under the edge computation model. Finally, a scalable cluster object detection system is implemented as a practical EC case to verify our proposal, and extensive simulations confirm the superiority of the proposed scheme over regular schemes. It can speed up about 84 times with 40% of the similar frames filtered in comparison.

34 citations