Chuyuan Wei^*, Jinzhe Li, Zhiyuan Wang, Shanshan Wan, Maozu Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3299-3314, 2024, DOI:10.32604/cmc.2024.047811 - 15 May 2024

Abstract Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sentiment analysis and question-answering systems. However, previous studies ignored much unused structural information in sentences that could enhance the performance of the relation extraction task. Moreover, most existing dependency-based models utilize self-attention to distinguish the importance of context, which hardly deals with multiple-structure information. To efficiently leverage multiple structure information, this paper proposes a dynamic structure attention mechanism model based on textual structure information, which deeply… More >

Mengqi Zhang¹, Jiazhao Chai², Jianxiang Cao³, Jialing Ji³, Tong Yi^4,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3743-3762, 2024, DOI:10.32604/cmc.2024.048486 - 26 March 2024

Abstract In recent years, deep learning methods have developed rapidly and found application in many fields, including natural language processing. In the field of aspect-level sentiment analysis, deep learning methods can also greatly improve the performance of models. However, previous studies did not take into account the relationship between user feature extraction and contextual terms. To address this issue, we use data feature extraction and deep learning combined to develop an aspect-level sentiment analysis method. To be specific, we design user comment feature extraction (UCFE) to distill salient features from users’ historical comments and transform them More >

Yajing Ma^1,2,3, Gulila Altenbek^1,2,3,*, Yingxia Yu¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 695-712, 2024, DOI:10.32604/cmc.2023.045486 - 30 January 2024

Abstract Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events, we propose an Independent Recurrent Temporal Graph Convolution Networks (IndRT-GCNets) framework to efficiently and accurately capture event attribute information. The framework models the knowledge graph sequences to learn the evolutionary representations of entities and relations within each period. Firstly, by utilizing the temporal graph convolution module in the evolutionary representation unit, the framework captures the structural dependency relationships within the knowledge graph in each period. Meanwhile, to achieve better event… More >

YOHKO YAMAZAKI^1,*, MANABU KAWADA², ISAO MOMOSE¹

Oncology Research, Vol.31, No.6, pp. 845-853, 2023, DOI:10.32604/or.2023.030266 - 15 September 2023

Abstract The androgen receptor (AR) is a critical target in all the clinical stages of prostate cancer. To identify a new AR inhibitor, we constructed a new screening system using the androgen-dependent growth of prostate cancer cell lines as a screening indicator. We screened 50,000 culture broths of microorganisms using this screening system and found that the fermentation broth produced by a fungus inhibited androgen-dependent growth of human prostate cancer LNCaP cells without cytotoxicity. Purification of this culture medium was performed, and this resulted in deoxynortryptoquivaline (DNT) being identified as a novel inhibitor of AR function. More > Graphic Abstract

Stepan Konev¹, Victor A. Eremeyev^2,3, Hamid M. Sedighi^4,5,*, Leonid Igumnov², Anatoly Bragov², Aleksandr Konstantinov², Ayaulym Kuanyshova¹, Ivan Sergeichev¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2149-2161, 2023, DOI:10.32604/cmes.2023.028179 - 03 August 2023

Abstract This article presents the results of experimental studies concerning the dynamic deformation and failure of a unidirectional carbon fiber reinforced plastic (T700/LY113) under compression. The test samples were manufactured through the filament winding of flat plates. To establish the strain rate dependencies of the strength and elastic modulus of the material, dynamic tests were carried out using a drop tower, the Split Hopkinson Pressure Bar method, and standard static tests. The samples were loaded both along and perpendicular to the direction of the reinforcing fiber. The applicability of the obtained samples for static and dynamic… More >

Pearlyne Willie Wong, Huay Woon You^*

International Journal of Mental Health Promotion, Vol.25, No.8, pp. 891-901, 2023, DOI:10.32604/ijmhp.2023.028205 - 06 July 2023

Abstract This study aims to investigate the phenomenon of technological gadget usage among pre-university students, which include the time spent using them, as well as their purpose and influence. A descriptive research design was adopted in this study. 131 pre-university students were randomly selected to answer a structured questionnaire. They were informed two weeks earlier to keep track on their time spent on technological devices, before answering the questionnaire. Findings showed that 99.2% of the respondents owned at least two technological gadgets, and all respondents own a smartphone. The main two gadgets that respondents spend at… More > Graphic Abstract

Sheng Xiao^1,*, Yong Chen², Jing He³, Xi Yang⁴

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3527-3540, 2023, DOI:10.32604/csse.2023.027081 - 03 April 2023

Abstract Read-write dependency is an important factor restricting software efficiency. Timing Speculative (TS) is a processing architecture aiming to improve energy efficiency of microprocessors. Timing error rate, influenced by the read-write dependency, bottlenecks the voltage down-scaling and so the energy efficiency of TS processors. We proposed a method called Read-Write Dependency Aware Register Allocation. It is based on the Read-Write Dependency aware Interference Graph (RWDIG) conception. Registers are reallocated to loosen the read-write dependencies, so resulting in a reduction of timing errors. The traditional no operation (Nop) padding method is also redesigned to increase the distance More >

Bin He, Hao Meng, Zhejin Zhang, Rui Liu, Ting Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 403-419, 2023, DOI:10.32604/cmes.2023.023242 - 05 January 2023

Abstract A qualia role-based entity-dependency graph (EDG) is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese. Traditional neural solvers use end-to-end models to translate problem texts into math expressions, which lack quantity relation acquisition in sophisticated scenarios. To address the problem, the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects. Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems. Experimental result shows that the proposed method achieved an average accuracy of 82.2% on quantity relation extraction More >

L. Maria Michael Visuwasam^1,*, D. Paul Raj²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2435-2446, 2023, DOI:10.32604/csse.2023.024742 - 21 December 2022

Abstract Technological developments create a lot of impacts in the tourism industry. Emerging big data technologies and programs generate opportunities to enhance the strategy and results for transport security. However, there is a difference between technological advances and their integration into the methods of tourism study. The rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental issues. This study addresses the gap by using a synthesized application (app) for demographic surveys and Global Navigation Satellite System (GNSS) technology to implement research processes. This article focuses on managing More >

Qiuyun Lyu¹, Chenhao Ma¹, Yanzhao Shen², Shaopeng Jiao³, Yipeng Sun¹, Liqin Hu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1625-1647, 2023, DOI:10.32604/cmes.2022.021562 - 27 October 2022

Abstract Smart contracts running on public blockchains are permissionless and decentralized, attracting both developers and malicious participants. Ethereum, the world’s largest decentralized application platform on which more than 40 million smart contracts are running, is frequently challenged by smart contract vulnerabilities. What’s worse, since the homogeneity of a wide range of smart contracts and the increase in inter-contract dependencies, a vulnerability in a certain smart contract could affect a large number of other contracts in Ethereum. However, little is known about how vulnerable contracts affect other on-chain contracts and which contracts can be affected. Thus, we… More >