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On the peculiarities of the exchange of data between applications in high-level languages and matlab functions 1

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On the peculiarities of the exchange of data between applica...

1st Workshop computer Science and Engineering in the framework of the 5th International Scientific-Methodical Conference "Problems of Mathematical and Natural-Scientific Training in Engineering Education, CSITinMNSTinEE 2018

作者： Krasnovidov, Alexander V. Khomonenko, Anatoly D. Zabrodin, Andrew V. Smirnov, Alexander V. Department of Information and Computer Systems Emperor Alexander I St. Petersburg State Transport University St. Petersburg Russia Laboratory of Computer Aided Integrated Systems St.Petersburg Institute for Informatics and Automation of the RAS Saint Petersburg Russia

The possibilities of data exchange between functions in the language of the Matlab system with applications in high-level languages are considered. Programs in high-level languages appropriately to use in conjunction with mathematical packages to collect data, control various technological processes, and perform other similar actions. The instrumental means of organizing such an exchange are characterized. It had been shown the feasibility of integrating applications in high-level languages and the M-function of the Matlab system, if necessary, to organize input / output of large amounts of data. Copyright © by the papers’ authors.

关键词： Electronic data interchange

Convolutional ordinal regression forest for image ordinal estimation

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arXiv 2020年

作者： Zhu, Haiping Shan, Hongming Zhang, Yuheng Che, Lingfu Xu, Xiaoyang Zhang, Junping Shi, Jianbo Wang, Fei-Yue The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-inspired Technology Shanghai201210 China The GRASP Laboratory University of Pennsylvania PhiladelphiaPA United States The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology 999078 China The University of Chinese Academy of Sciences Beijing100049 China

Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers independently, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers simultaneously. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Decision trees

A hybrid deep-learning approach for complex biochemical named entity recognition

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arXiv 2020年

作者： Liu, Jian Gao, Lei Guo, Sujie Ding, Rui Huang, Xin Ye, Long Meng, Qinghua Nazari, Asef Thiruvady, Dhananjay HeFei University of Technology Hefei230009 China CSIRO Waite Campus UrrbraeSA5064 Australia Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Shanghai Engineering Center for Microsatellites Shanghai201203 China Xi’an Jiaotong University Xi’an710049 China College of Computer and Information Engineering Tianjin Normal University Tianjin300387 China School of Mechatronic Engineering and Automation Shanghai University Shanghai200072 China Faculty of Business Information Shanghai Business School Shanghai201400 China School of Information Technology Deakin University Geelong Australia Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei Anhui230601 China

Named entity recognition (NER) of chemicals and drugs is a critical domain of information extraction in biochemical research. NER provides support for text mining in biochemical reactions, including entity relation extraction, attribute extraction, and metabolic response relationship extraction. However, the existence of complex naming characteristics in the biomedical field, such as polysemy and special characters, make the NER task very challenging. Here, we propose a hybrid deep learning approach to improve the recognition accuracy of NER. Specifically, our approach applies the Bidirectional Encoder Representations from Transformers (BERT) model to extract the underlying features of the text, learns a representation of the context of the text through Bi-directional Long Short-Term Memory (BILSTM), and incorporates the multi-head attention (MHATT) mechanism to extract chapter-level features. In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels. Moreover, conditional random field (CRF) is used to label sequence tags because this probabilistic method does not need strict independence assumptions and can accommodate arbitrary context information. The experimental evaluation on a publicly-available dataset shows that the proposed hybrid approach achieves the best recognition performance;in particular, it substantially improves performance in recognizing abbreviations, polysemes, and low-frequency entities, compared with the state-of-the-art approaches. For instance, compared with the recognition accuracies for low-frequency entities produced by the BILSTM-CRF algorithm, those produced by the hybrid approach on two entity datasets (MULTIPLE and IDENTIFIER) have been increased by 80% and 21.69%, respectively. Copyright © 2020, The Authors. All rights reserved.

关键词： Random processes

The technique of structuring social network graphs for visual analysis of user groups to counter inappropriate, dubious and harmful information 2

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The technique of structuring social network graphs for visua...

2nd International Scientific and Practical Conference "Fuzzy Technologies in the Industry - FTI", FTI 2018

作者： Kalameyets, M. Chechulin, A. Kotenko, I. Laboratory of Computer Security Problems St. Petersburg Institute for Informatics and Automation Russian Academy of Sciences Saint-Petersburg194100 Russia International Laboratory of Information Security of Cyber-physical Systems ITMO University Saint-Petersburg197101 Russia

The paper proposes a data visualizing technique for analyzing social networks in order to identify and counteract inappropriate, dubious and harmful information. The proposed technique is based on the force-layout technique of drawing graphs in which the parameters of vertices and edges are calculated depending on the number of links. The paper provides an example of using the proposed data visualization technique for social networks analysis on the basis of visual analytics of several groups in the "VKontakte" social network in four display modes: without using the technique, in 2D, in 3D and in augmented reality. The example also contains a description of various drawing techniques and estimates them. © 2018 CEUR-WS. All rights reserved.

关键词： Data visualization

Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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The Innovation 2025年第6期6卷

作者： Jincai Huang Yongjun Xu Qi Wang Qi (Cheems) Wang Xingxing Liang Fei Wang Zhao Zhang Wei Wei Boxuan Zhang Libo Huang Jingru Chang Liantao Ma Ting Ma Yuxuan Liang Jie Zhang Jian Guo Xuhui Jiang Xinxin Fan Zhulin An Tingting Li Aiguo Fei Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Software Engineering Peking University Beijing 100871 China College of Systems Engineering National University of Defense Technology Changsha 410073 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing 100081 China Laboratory for Big Data and Decision National University of Defense Technology Changsha 410073 China School of Automation Beijing Institute of Technology Beijing 100081 China Huazhong University of Science and Technology Wuhan 430074 China University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of AI Safety Beijing 100190 China Department of Automation Tsinghua University Beijing 100084 China School of Information Science and Engineering Dalian Polytechnic University Dalian 116034 China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou 511453 China College of Information and Electrical Engineering China Agricultural University Beijing 100083 China State Key Laboratory of Efficient Utilization of Agricultural Water Resources Beijing 100083 China IDEA Research International Digital Economy Academy Shenzhen 518057 China School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing 100876 China

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM’s evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems.

关键词： artificial intelligence intelligent decision-making foundation models agent large language model

Improving pedestrian attribute recognition with weakly-supervised multi-scale attribute-specific localization

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arXiv 2019年

作者： Tang, Chufeng Sheng, Lu Zhang, Zhaoxiang Hu, Xiaolin State Key Laboratory of Intelligent Technology and Systems Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University College of Software Beihang University Institute of Automation Chinese Academy of Sciences

Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to localize the regions related to the attribute. However, in this task, the region annotations are not available. How to carve out these attribute-related regions remains challenging. Existing methods applied attribute-agnostic visual attention or heuristic body-part localization mechanisms to enhance the local feature representations, while neglecting to employ attributes to define local feature areas. We propose a flexible Attribute Localization Module (ALM) to adaptively discover the most discriminative regions and learns the regional features for each attribute at multiple levels. Moreover, a feature pyramid architecture is also introduced to enhance the attribute-specific localization at low-levels with high-level semantic guidance. The proposed framework does not require additional region annotations and can be trained end-to-end with multi-level deep supervision. Extensive experiments show that the proposed method achieves state-of-the-art results on three pedestrian attribute datasets, including PETA, RAP, and PA-100K. Copyright © 2019, The Authors. All rights reserved.

关键词： Heuristic methods

Poster: Tancreator: A tangible tool for children to create augmented reality games

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Poster: Tancreator: A tangible tool for children to create a...

2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable computers, ISWC 2018

作者： Jin, Qiao Wang, Danli Sun, Fang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Liaoning Normal University Dalian China

ISBN: (纸本)9781450359665

In this paper, we present TanCreator, a tangible authoring tool which facilitates children to create games based on Augmented Reality (AR) and sensor technologies. Combining AR elements and sensors in games bridges virtual world with realistic surroundings closely together, providing more joyful and intuitive creating experiment for children. Children could boost their creativity via creating their own maze games in daily life with paper tokens and sensors. It is also great training for children's motor skills such as hand-eye coordination. © 2018 Copyright is held by the owner/author(s).

关键词： Augmented reality

Blockchain-Based Incentive Announcement System for Internet of Vehicles

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Blockchain-Based Incentive Announcement System for Internet ...

IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yang Yang Jialiang Chen Xianghan Zheng Ximeng Liu Wenzhong Guo Hairong Lv College of Mathematics and Computer Science Fuzhou University Fuzhou China Guangxi Key Laboratory of Cryptography and Information Security Guangnxi China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology Beijing National Research Center for Information Science and TechnologyDepartment of Automation Tsinghua University Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the rapid development of computation and communication technologies, the traditional vehicle ad hoc networks (VANETs) are changing to Internet of vehicle (IoV). Vehicular announcement networks in IoV have been widely used in the communication of vehicles. Generally, we need to solve two problems while establishing a vehicular announcement system. First, we need to protect user's privacy when broadcasting the message. Second, participants usually lack the enthusiasm to reply to the announcement. To solve these two problems, we propose a novel blockchain-based incentive announcement system that not only allows participants to anonymously announce their message on the blockchain in a non-trusted environment, but also motivates witnesses to respond to the request of the traffic information with incentive mechanism. Meanwhile, traffic messages and signatures in our system are tamper-resistant, which are recorded on the blockchain. According to the security and performance analysis, it shows that our system is privacy-preserving and efficient in computation cost.

关键词： Blockchain Privacy Smart contracts Authentication Internet Remuneration Hash functions

Hierarchic neighbors embedding

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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology