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arXiv 2023年

作者： Wu, Yanmin Zhang, Yunzhou Zhu, Delong Deng, Zhiqiang Sun, Wenkai Chen, Xin Zhang, Jian Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China The Department of Electronic Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Augmented reality

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Fast Personalized Human Activity Recognition on Heuristic Parameter Estimation

Fast Personalized Human Activity Recognition on Heuristic Pa...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Shizhuo Deng Chuangui Yang Zhubao Guo Boqian Lin Dongyue Chen Tong Jia Botao Wang College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China Baidu Inc Beijing China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Personalized human activity recognition (HAR) on wearable sensor data is crucial for healthcare, industry and sport. Personalized HAR has two challenges: very few high-quality labeled personalized data and limited terminal computing resource. Most previous domain adaptation models ignore the transfer efficiency on the terminal, which is also one criterion of Artificial Intelligence of Things. Therefore, we propose a fast transfer HAR, FastTrans, to improve the efficiency and get a trade-off with recognition effectiveness. A fusion feature extraction module is designed to learn multi-scale features on the improved hybrid loss. The proposed heuristic parameter estimation method learns the approximate solutions of the classification weights in FastTrans by scanning the adaptation data only once. Besides, an efficient time series data augmentation is proposed as a plugin for dataset variety. The results on benchmark datasets show the dramatically competitive performance of FastTrans on transferring efficiency with close accuracy to other models.

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Predictive Modeling of Chronic Kidney Disease Progression Using Longitudinal Clinical Data and Deep Learning Techniques

Predictive Modeling of Chronic Kidney Disease Progression Us...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Farzaneh Rastegari Mahmoud Odeh Reyhaneh Mehdizadeh Baroughi Arad Bustan Computer Science and Engineering Department University of Connecticut Storrs United States Cyber security Department School of information technology Zarqa University Zarqa Jordan College of Business Administration University of Business and Technolog Jeddah Saudi Arabia Electrical and Computer Engineering Polytechnic University of Turin Turin Italy Graduate School of Biomedical Science and Engineering(GSBSE) University of Maine Orono United States

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Chronic Kidney Disease (CKD) is a prevalent condition with significant morbidity and mortality. Early and accurate detection is critical for effective treatment and management. This paper presents a comparative analysis of deep learning techniques, specifically Multi-layer Perceptron (MLP) models, for predicting CKD. We preprocessed a dataset containing clinical and demographic features, trained MLP models of varying complexities, and evaluated their performances. The results showed that simpler models achieved high accuracy, precision, recall, and F1 scores, comparable to more complex models. These findings suggest that deep learning can be a valuable tool in the early detection of CKD, with the potential to support clinical decision-making.

关键词： Deep learning Analytical models Accuracy Decision making Medical services Predictive models Chronic kidney disease

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Micro-Expression Recognition by Combining Progressive-Learning Intensity Magnification with Self-Attention-Convolution Classification

Micro-Expression Recognition by Combining Progressive-Learni...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Wei Zhao Ling Lei Tong Chen Shigang Li Jianfeng Li College of Electronic and Information Engineering Southwest University China College of Computer science Sichuan University China Graduate School of Information sciences Hiroshima City University Hiroshima Japan

ISBN: (纸本)9781665463959

How to detect the subtle intensity change and find the inherent relation of feature maps in a face image is the two key issues for micro-expression recognition. In this study, in order to cope with the subtle intensity change, we design an innovative Cascade Micro-Expression Magnification Network (Cascade-MEMN) to magnify the low amplitude of the intensity change for learning the intensity progressively, which helps suppress overlapping artifacts and produce more realistic magnification. In order to find the inherent relation of facial feature maps, we design a Self-attention-based Convolution Layer (SCL), which introduces self-attention to every pixel when calculating the weights in the sliding window, considering that the variation of micro-expressions is very subtle and local on face. Concretely, the SCLs are used to replace 3 Resblocks from the last stage of ResNet50 with 3 SCL blocks. Finally, a new micro-expression recognition system is realized by combining the progressive-learning-based intensity magnification network with the modified self-attention-convolution classification network. Consequently, the proposed method achieves competitive results for the composite database evaluation (CDE) protocol from MEGC 2019, as shown in the experimental result.

关键词： Protocols Image recognition Convolution Databases Face recognition Feature extraction Facial features

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Hybrid descriptor definition for content based image classification using fusion of handcrafted features to convolutional neural network features

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International Journal of information Technology (Singapore) 2021年第4期13卷 1365-1374页

作者： Das, Rik Kumari, Khusbu De, Sourav Manjhi, P.K. Thepade, Sudeep Department of Post Graduate Program in Information Technology Xavier Institute of Social Service Ranchi Jharkhand India Department of Computer Applications Yogoda Satsanga Mahavidyalaya Ranchi Jharkhand India Department of Computer Science & Engineering Cooch Behar Government Engineering College Cooch Behar India Department of Mathematics Vinoba Bhave University Hazaribag Jharkhand India Department of Computer Engineering Pimpri Chinchwad College of Engineering Pune India

The efficacy of content-based image classification is dependent on the richness of the feature vectors extracted from the image data. Traditional feature extraction techniques highlight single low level image characteristics like shape, size, texture, color etc. for feature generation. The process often fails to extract meaningful descriptors since considering a single image characteristic will ignore other rich properties of image contents. Mass adoption of Convolutional Neural Network (CNN) has significantly improved classification performances for content-based image data. Recent literature has documented high level of precision for image classification by carrying out transfer learning with open source pre trained Convolutional Neural Network (CNN). However, the concept of transfer learning experiences major setback in case of limited training data due to overfitting of training instances during finetuning. This work has identified this challenge and has attempted to capture probability distribution of input images to a pre trained Convolution Neural Network by utilizing it as a fixed weight feature extractor. The proposed approach has addressed the overfitting problem by removing the finetuning step involved in transfer learning. Further investigation on robust descriptor definition is carried out by concatenating the pre-trained CNN features to handcrafted features. The hybrid architecture has encouraging outcomes that have outclassed the classification accuracies of state-of-the-art handcrafted techniques. © 2021, Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Augmented feature learning Feature fusion Handcrafted features Image classification Pre trained Convolutional Neural Network (CNN) Representation learning

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Exponentially Enhanced Scheme for the Heralded Qudit GHZ State in Linear Optics

arXiv

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arXiv 2024年

作者： Chin, Seungbeom Ryu, Junghee Kim, Yong-Su Okinawa Institute of Science and Technology Graduate University Okinawa904-0495 Japan Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Center for Quantum Information R&D Korea Institute of Science and Technology Information Daejeon34141 Korea Republic of Division of Quantum Information KISTI School Korea University of Science and Technology Daejeon34141 Korea Republic of Seoul02792 Korea Republic of Division of Quantum Information Technology KIST School Korea University of Science and Technology Seoul02792 Korea Republic of

High-dimensional multipartite entanglement plays a crucial role in quantum information science. However, existing schemes for generating such entanglement become complex and costly as the dimension of quantum units increases. In this work, we overcome the limitation by proposing a significantly enhanced linear optical heralded scheme that generates the d-level N-partite GHZ state with single-photon sources and linear operations. Our scheme requires dN photons, which is the minimal required photon number, with substantially improved success probability from previous schemes. It employs linear optical logic gates compatible with any qudit encoding system and can generate generalized GHZ states with installments of beamsplitters. With efficient generations of high-dimensional resource states, our work opens avenues for further exploration in high-dimensional quantum information processing. © 2024, CC BY-NC-ND.

关键词： Photons

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In the Service of Online Order: Tackling Cyber-Bullying with Machine Learning and Affect Analysis

arXiv

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arXiv 2022年

作者： Ptaszynski, Michal Dybala, Pawel Matsuba, Tatsuaki Masui, Fumito Rzepka, Rafal Araki, Kenji Momouchi, Yoshio High-Tech Research Center Hokkai-Gakuen University Japan Graduate School of Information Science and Technology Hokkaido University Japan Graduate School of Engineering Mie University Japan Department of Computer Science Kitami Institute of Technology Japan Department of Electronics and Information Engineering Faculty of Engineering Hokkai-Gakuen University Japan

One of the burning problems lately in Japan has been cyber-bullying, or slandering and bullying people online. The problem has been especially noticed on unofficial Web sites of Japanese schools. Volunteers consisting of school personnel and PTA (Parent-Teacher Association) members have started Online Patrol to spot malicious contents within Web forums and blogs. In practise, Online Patrol assumes reading through the whole Web contents, which is a task difficult to perform manually. With this paper we introduce a research intended to help PTA members perform Online Patrol more efficiently. We aim to develop a set of tools that can automatically detect malicious entries and report them to PTA members. First, we collected cyber-bullying data from unofficial school Web sites. Then we performed analysis of this data in two ways. Firstly, we anal-ysed the entries with a multifaceted affect analysis system in order to find distinctive features for cyber-bullying and apply them to a machine learning classifier. Secondly, we applied a SVM based machine learning method to train a classifier for detection of cyber-bullying. The system was able to classify cyber-bullying entries with 88.2% of balanced F-score. © 2022, CC BY-NC-ND.

关键词： Websites

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Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations

arXiv

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arXiv 2022年

作者： Edemacu, Kennedy Jang, Beakcheol Kim, Jong Wook Department of Computer Science and Electrical Engineering Muni University Arua Uganda Graduate School of Information Yonsei University Seoul Korea Republic of Department of Computer Science Sangmyung University Seoul Korea Republic of

Due to privacy concerns, multi-party gradient tree boosting algorithms have become widely popular amongst machine learning researchers and practitioners. However, limited existing works have focused on vertically partitioned datasets, and the few existing works are either not scalable or tend to leak information. Thus, in this work, we propose SSXGB which is a scalable and secure multi-party gradient tree boosting framework for vertically partitioned datasets with partially outsourced computations. Specifically, we employ an additive homomorphic encryption (HE) scheme for security. We design two sub-protocols based on the HE scheme to perform non-linear operations associated with gradient tree boosting algorithms. Next, we propose a secure training and a secure prediction algorithms under the SSXGB framework. Then we provide theoretical security and communication analysis for the proposed framework. Finally, we evaluate the performance of the framework with experiments using two real-world datasets. © 2022, CC BY.

关键词： Machine learning

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Intelligent evaluation system for new energy vehicles based on sentiment analysis: An MG-PL-3WD method

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engineering Applications of Artificial Intelligence 2024年第PartE期133卷

作者： Zhang, Chao Wen, Qifei Li, Deyu Sangaiah, Arun Kumar Lin, Mingwei School of Computer and Information Technology Shanxi University Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Yunlin64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos1102-2801 Lebanon College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China

As a critical strategic tool within the framework of the digital economy (DE), artificial intelligence (AI) plays a crucial role in realizing an AI-assisted economy. This novel paradigm has gained widespread acceptance from governments globally, positioning itself as a cornerstone in the practical implementation of the DE. The increasing reliance on digital means for economic transformation, the treatment of data as a critical resource, and the integration of information technologies highlight the synergy between AI and the DE. Against this backdrop, online reviews and SA play a pivotal role, offering rich data streams continually emerging on various platforms. Therefore, this article utilizes a probabilistic linguistic term set (PLTS) to establish a multi-granularity probabilistic linguistic (MG-PL) information system. This approach, through sentiment analysis (SA), enables quantitative analysis of user emotional expressions. The integration of these technological aspects holds the promise of enhancing decision-making processes, facilitating the intellectualization of economic activities, and addressing existing challenges in the application of AI within the DE domain. Initially, SA is conducted on text-based online comment data, forming a probabilistic linguistic incomplete information system. The maximum similarity method is employed to establish a probabilistic linguistic complete information system. Subsequently, an adjustable MG-PL decision-theoretic rough set (DTRS) is developed, followed by the establishment of a three-way group decision model using three-way decisions (3WD) and a directed graph. The model's applicability, stability, and feasibility are demonstrated via a case study centered on the acquisition of new energy vehicles (NEVs). © 2024 Elsevier Ltd

关键词： Sentiment analysis

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Design of Coffee Yield Estimation System Using computer Vision by Crowd Counting Model

Design of Coffee Yield Estimation System Using Computer Visi...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Kuei-Chung Chang Yu-Lung Lin International Graduate School of Artificial Intelligence. National Yunlin University of Science and Technology Taiwan R.O.C Dept. of Information Engineering and Computer Science Feng Chia University Taiwan R.O.C

For coffee farmers, the yield of coffee is an issue that will affect their income. At present, the method used to estimate the coffee production is usually to estimate the production of the entire coffee garden by weighing the coffee till the harvest; however, this method does not have a way to understand the reasons of affecting coffee production in each period of the planting process. In this paper, we propose an intelligent system using a crowd counting model to estimate the yield of coffee, so that farmers can observe changes in the amount of coffee during the planting process from CCTVs remotely. Experimental results show that the proposed method can obtain estimated results with mean absolute error about 6.91.

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