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SSRN 2024年

作者： Zheng, Cenyang Gong, Xun Fan, Lin Li, Jiao School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China Department of Gastroenterology The Third People’s Hospital of Chendu Affiliated Hospital of Southwest Jiaotong University Chengdu610031 China

Endoscopic ultrasound (EUS) is the primary diagnostic imaging technique for gastrointestinal stromal tumors. However, accurately identifying these tumors through EUS imaging alone remains challenging, even for experienced clinicians. They often rely on additional data, such as white light endoscopy (WLE) and EUS text reports, to aid in the diagnosis. Unfortunately, in practice, these supplementary modalities are sometimes overlooked during patient evaluation. To address these issues, we propose the Cross-modal Feature Reconstruction Network (CFRN), which integrates multimodal data modeling for medical diagnostic processes and performs specific learning for different modalities with missing data. The network fuses the multi-modal features based on the clinical medical process, realizes the feature-level missing modality reconstruction, and learns the refined features of different modal true cases by decoding the mode-specific missing. We trained and tested the model on two publicly available datasets, Audiovision-MNIST, MM-IMDb, and data sets collected from real clinical scenarios at three hospitals. Our results show that CFRN outperforms state-of-the-art methods, achieving higher prediction accuracy than using EUS alone. By simultaneously incorporating multimodal or reconstructed modal data for disease diagnosis, our single-modal EUS model proves to be more suitable for clinical scenarios. © 2024, The Authors. All rights reserved.

关键词： Endoscopy

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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On Star Coloring of Mycielski's Graphs for Special Graphs

On Star Coloring of Mycielski's Graphs for Special Graphs

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Cai Jin Shuangliang Tian Zhuomo An School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781728186696

Let G be a simple graph with vertex set V (G) and edge set E (G). A vertex coloring of G is called a star coloring of G if any path of 4 order is bicolored. The minimum number of colors required for a star coloring of G is denoted by χ s (G). Let G is a simple graph with vertex set V (G) = V and edge set E (G) = E, if the vertex set is V ∪ V' ∪ {w} where V' = {X' : x ∈ V} and the edge set is EU{xy' : xy∈V} U {y'w : y'∈V'}, then G is a Mycielski's graph. This paper mainly studies star coloring of Mycielski's graph for some special graphs, the specific results are as follows: if n = 2,3 , then χ s (M (P n )) = 4, otherwise, χ s (M (P n )) = 6 ; if n = 5, then χ s (M(C n )) = 8, otherwise, χ s (M(C n )) = 6 ; χ s (M(S n )) = 4 ; χ s (M (K n )) = n + 2.

关键词： Computer science Color

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The acyclic coloring of the Corona product and Edge corona product of special graphs

The acyclic coloring of the Corona product and Edge corona p...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Qiqi Lu Shuangliang Tian Lizhen Peng School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

An acyclic coloring of a graph is a proper vertex coloring such that there are no bichromatic cycles. The acyclic chromatic number of G, denoted a(G), is the minimum number of colors required for acyclic coloring of a graph G. We study the acyclic coloring of the corona product, edge corona product of paths or circles and any simple graph, and the corresponding acyclic chromatic number are determined.

关键词： Computer science Color Corona

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How to respond to emerging threats of cyberspace security via social engineering? A novel paradigm to solve cyberspace security issues

TechRxiv

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TechRxiv 2022年

作者： Lin, Hao Wang, Chun-Dong Gao, Hao-Yu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Ministry of Education Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China College of Data Science and Application Inner Mongolia University of Technology Hohhot China

Cybersecurity researchers always ignore the "people" behind the issues when dealing with cyberspace security issues. The human and technological aspects of cyberspace security must be simultaneously addressed to enable a secure cyber environment. Social engineering is a discipline whose research object is people, and the combination of social engineering and cyberspace security can provide new solution ideas for lots of cyberspace security issues. This paper attempts to answer the question —— "How to solve cyberspace security issues via social engineering?" We briefly review existing frameworks for social engineering attacks and propose a novel, recursive, and iterative framework for social engineering attacks in cyberspace. The existing research achievements of combination of social engineering and cyberspace security were reviewed in the order of cyber personality, hacker psychology, human flesh search, and social engineering defense. To answer this question, we summarized four "guidelines" for using social engineering. Finally, we look ahead, give our thoughts, and discuss answers of the question and future research. It is aimed at getting more researchers of cyberspace security and social science to pay attention to this field. © 2022, CC BY.

关键词： Cybersecurity

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Adjacent vertex distinguishing incidence coloring of semi-strong product of paths

Adjacent vertex distinguishing incidence coloring of semi-st...

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Computer Science and Management Technology (ICCSMT), International Conference on

作者： Lizhen Peng Shuangliang Tian Qiqi Lu School of Mathematics and Computer Science Northwest for Minzu University Key Laboratory of Streaming Data Computing Technologies and Applications Northwest Minzu University Lanzhou China

ISBN: (纸本)9781728186696

Adjacent vertex distinguishing incidence coloring of graph G is an incidence coloring that satisfies that adjacent vertices have different color sets, and the minimum color number is called the adjacent vertex distinguishing incidence coloring number of graph G, which is denoted as χ ai (G). In this paper, we study the adjacent vertex distinguishing incidence coloring of semi-strong products and strong products of paths of different orders, and obtain the exact values of the corresponding coloring numbers.

关键词： Computer science Color

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G3SR: Global Graph Guided Session-based Recommendation

arXiv

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arXiv 2022年

作者： Deng, Zhi-Hong Wang, Chang-Dong Huang, Ling Lai, Jian-Huang Yu, Philip S. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China College of Mathematics and Informatics South China Agricultural University Guangzhou China Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application China University of Illinois at Chicago ChicagoIL United States

Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendation under the condition that user-profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named G3SR (Global Graph Guided Session-based Recommendation). G3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G3SR method over the state-of-the-art methods, especially for cold items. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial applications

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： Big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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Retraction Note: Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 255-255页

作者： Lin, Qiang Li, Tongtong Shakeel, P. Mohamed Samuel, R. Dinesh Jackson School of Mathematics and Computer Science Northwest Minzu University LanzhouGansu China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University LanzhouGansu China Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University LanzhouGansu China Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Malaysia Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK

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Deep Learning Based Search and Recommendation of Literature in Wanfang Knowledge Base

Deep Learning Based Search and Recommendation of Literature ...

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6th International Conference on Intelligent computing and Signal Processing (ICSP)

作者： Tongtong Li Qiang Lin Xiaoyan Chen Yanru Guo Shaofang Zhao School of Mathematics and Computer Science Northwest Minzu University Lanzhou Gansu China Dynamic Flow Data Computing and Application Laboratory Northwest Minzu University Lanzhou Gansu China Key Laboratory of Chinese National Language and Information Technology Ministry of Education Northwest Minzu University Lanzhou Gansu China

Classifying Chinese text is a hot but difficult topic in natural language processing field. In this work, we aim to quickly recommend the required documents for users via searching keywords in a massive literature database. We use "Wanfang data Knowledge Service Platform Journal Document User Behavior Log data" as the object to research and build a text classifier based on deep learning techniques. It is proposed to perform data cleaning and data extraction for user browsing behavior logs and user download behavior logs. Firstly, Chinese word segmentation and keyword extraction are carried out on the document title information, and the keyword data set and document keyword data set based on the document name are constructed. Finally, using the traditional deep learning model and convolutional neural network model to build a text classifier for training and classification, as a model for users to search for keywords recommended for literature. Experimental results show that the constructed model is able to effectively classify and recommend documents with the users' search keywords, and extract keywords from document names in the construction model. The proposed model TEXTCNN V2 obtains a value for accuracy, precision and F-1 score is 0.9132, 0.9220 and 0.9154, respectively.

关键词： Deep learning Training Computational modeling Text categorization Search problems data models Convolutional neural networks

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