Zhongyang Wang, Hu Zhu, Feng Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 605-623, 2024, DOI:10.32604/cmc.2024.050959 - 18 July 2024

Abstract Visual object tracking plays a crucial role in computer vision. In recent years, researchers have proposed various methods to achieve high-performance object tracking. Among these, methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information. However, current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information. In this paper, we introduce self-calibration multi-head self-attention Transformer (SMSTracker) as a solution to these challenges. It employs a hybrid tensor decomposition self-organizing multi-head self-attention transformer mechanism, which not only… More >

Navdeep Bohra^1,2, Vishal Bhatnagar³, Amit Choudhary⁴, Savita Ahlawat², Dinesh Sheoran², Ashish Kumari^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 205-221, 2023, DOI:10.32604/iasc.2023.030708 - 29 September 2022

Abstract The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about… More >

Zhoubao Sun^1,*, Xiaodong Zhang¹, Haoyuan Li¹, Yan Xiao², Haifeng Guo³

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 621-630, 2021, DOI:10.32604/cmc.2020.012593 - 30 October 2020

Abstract Recommender system is an effective tool to solve the problems of information overload. The traditional recommender systems, especially the collaborative filtering ones, only consider the two factors of users and items. While social networks contain abundant social information, such as tags, places and times. Researches show that the social information has a great impact on recommendation results. Tags not only describe the characteristics of items, but also reflect the interests and characteristics of users. Since the traditional recommender systems cannot parse multi-dimensional information, in this paper, a tensor decomposition model based on tag regularization is More >

Xiaoding Guo¹, Hongli Zhang^{1, *}, Lin Ye¹, Shang Li¹

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 315-336, 2020, DOI:10.32604/cmc.2020.07506 - 30 March 2020

Abstract With the development of Internet technology and the enhancement of people’s concept of the rule of law, online legal consultation has become an important means for the general public to conduct legal consultation. However, different people have different language expressions and legal professional backgrounds. This phenomenon may lead to the phenomenon of different descriptions of the same legal consultation. How to accurately understand the true intentions behind different users’ legal consulting statements is an important issue that needs to be solved urgently in the field of legal consulting services. Traditional intent understanding algorithms rely heavily… More >

Shiming He¹, Zhuozhou Li¹, Yangning Tang¹, Zhuofan Liao¹, Feng Li^{1, *}, Se-Jung Lim²

CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.06130

Abstract With the popularity of deep learning tools in image decomposition and natural language processing, how to support and store a large number of parameters required by deep learning algorithms has become an urgent problem to be solved. These parameters are huge and can be as many as millions. At present, a feasible direction is to use the sparse representation technique to compress the parameter matrix to achieve the purpose of reducing parameters and reducing the storage pressure. These methods include matrix decomposition and tensor decomposition. To let vector take advance of the compressing performance of More >