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Attacking the Dialogue System at Smart Home 16th

Attacking the Dialogue System at Smart Home

16th EAI International Conference on Collaborative computing: networking, Applications, and Worksharing, CollaborateCom 2020

作者： Deng, Erqiang Qin, Zhen Li, Meng Ding, Yi Qin, Zhiguang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China Institute of Electronic and Information Engineering of UESTC Chengdu China Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong523808 China

ISBN: (纸本)9783030675363

Intelligent dialogue systems are widely applied in smart home systems, and the security of such systems deserves concern [1, 2]. In this paper, we design a threatening scenario of dialogue systems at a smart home. A trojan robot is disguised as one part of the whole system but generates dialogue adversarial examples to attack the normal robots according to the information of users. To achieve the goal in such a scenario, the responding speed, the correctness of the grammar, and the consistency of semantic is necessary. Based on these requirements, we propose a novel method named Attention weight Probability Estimation Attack (APE) to allocate the keys words in dialogue and substitute these words with synonyms in real-time. We perform our experiments on popular classification datasets in the DNN model, and the result shows that APE effectively attacks the system with low responding time and a high success rate. © 2021, ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Semantics

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Research on unified recognition model and algorithm for multi-modal gestures

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The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 30-42页

作者： Guo Xiaopei Feng Zhiquan Sun Kaiyun Liu Hong Xie Wei Bi Jianping School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan 250022China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Information and Electrical Engineering Harbin Institute of Technology at WeihaiWeihai 264209China Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250022China

In gesture recognition,static gestures,dynamic gestures and trajectory gestures are collectively known as multi-modal *** solve the existing problem in different recognition methods for different modal gestures,a unified recognition algorithm is *** angle change data of the finger joints and the movement of the centroid of the hand were acquired respectively by data glove and *** the preprocessing of the multi-source heterogeneous data,all hand gestures were considered as curves while solving hand shaking,and a uniform hand gesture recognition algorithm was established to calculate the Pearson correlation coefficient between hand gestures for gesture *** this way,complex gesture recognition was transformed into the problem of a simple comparison of curves *** main innovations:1) Aiming at solving the problem of multi-modal gesture recognition,an unified recognition model and a new algorithm is proposed;2) The Pearson correlation coefficient for the first time to construct the gesture similarity operator is *** testing 50 kinds of gestures,the experimental results showed that the method presented could cope with intricate gesture interaction with the 97.7% recognition rate.

关键词： Kinect data glove multi-modal gesture gesture interaction

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Personalized Federated Learning with Collaborative Aggregation networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control Engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

arXiv

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arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Xu, Daozhu Bai, Long Jin, Cheng Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Research Institute of Surveying and Mapping Xi’an710054 China

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Embedding 3D models in offline physical environments

Embedding 3D models in offline physical environments

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作者： Ertugrul, Egemen Zhang, Han Zhu, Fang Lu, Ping Li, Ping Sheng, Bin Wu, Enhua Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

This article introduces a novel approach for embedding 3D models in offline physical environments using quick response (QR) codes. Unlike conventional methods, we consider settings where 3D models cannot be retrieved from a remote server. Our method involves generating octree models from voxelized 3D models and storing them in QR codes using a space-efficient data structure. This allows storing 3D models that are both intelligible and purposeful on standard QR codes while addressing the major storage constraint that is present in offline situations. Furthermore, we explore 3D convolutional neural networks (CNN) and autoencoders (AE) to compress 3D models with high resolutions where using octrees alone does not suffice. To the best of our knowledge, our AE network is the first to employ octrees to further compress its encoded data. Through user-friendly desktop and mobile applications, we allow users to encode, decode and visualize 3D models in augmented reality (AR) using QR codes, thus experiment with our methods. The proposed approach enables unique applications and future research in ubiquitous computing, 3D data compression and transmission, 3D AEs, AR and Virtual Reality, low-cost autonomous robots, and 3D printing. © 2020 John Wiley & Sons, Ltd.

关键词： Digital storage

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A Fusion Neural network Incorporating Attention for Sensor-Based Human Activity Recognition 3

A Fusion Neural Network Incorporating Attention for Sensor-B...

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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Lu, Limeng Zhang, Chuanlin Cao, Kai Deng, Dao Institute of Chinese Ethnic Information Technology Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Lanzhou China Northwest Minzu University School of Mathematics and Computer Science Lanzhou China Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Lanzhou China

ISBN: (纸本)9781665459112

Even though the RNN, LSTM, and other networks are used to extract dependencies in time series, sensor-based human behavior recognition (HAR) still faces some difficulties, and the ability of deep learning (DL) networks to extract features still needs to be improved. We propose a fusion neural network in which an optimized small optimized Convolutional Block Attention Module (MP-CBAM) is suitable for HAR tasks to extract samples. The MP-CBAM is added to two branches Convolutional Neural network (CNN) with different convolution kernel sizes, and the fused features are labeled with GRU for temporal dependence. Then the softmax function is used for classification. We validate on benchmark datasets UCI-HAR and WISDM and accuracies of 97.15% and 9S.9S% are obtained, respectively, demonstrating the adaptation of our framework to both single-sensor and multi-sensor devices. © 2022 IEEE.

关键词： Convolution

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Generalized association and deep understanding method for big video data processing

Generalized association and deep understanding method for bi...

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2019 IEEE International Conferences on Ubiquitous computing and Communications and data science and Computational Intelligence and Smart computing, networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Xu, Lin Wu, Wei Lv, Wanghan Wang, Dong Science and Technology in Information System Engineering Lab Nanjing Research Institute of Electronic Engineering Nanjing China School of Information Science and Engineering University of Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781728152097

In Big data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory and uses Long-term and short-term memory networks (LSTM) algorithm to generate a summary of the content of video behavior track. It has already to apply the proposed method in target recognition and tracking, and the recognition rate achieves more than 86%. © 2019 IEEE.

关键词： Big data

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Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention network

arXiv

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arXiv 2021年

作者： Fang, Yuchen Qin, Yanjun Luo, Haiyong Zhao, Fang Xu, Bingbing Wang, Chenxing Zeng, Liang School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas;ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network;iii) the quadratic complexity of the full graph attention introduces heavy computational needs. To achieve the effective traffic flow forecasting, we propose an efficient spectral graph attention network with disentangled traffic sequences. Specifically, the discrete wavelet transform is leveraged to obtain the low- and high-frequency components of traffic sequences, and a dual-channel encoder is elaborately designed to accurately capture the spatio-temporal dependencies under long- and short-term schemas of the low- and high-frequency components. Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the attention. Extensive experiments on four real-world datasets show the superiority of our model, i.e., the higher traffic forecasting precision with lower computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

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A Load Balancing Strategy Of "Container Virtual Machine" Cloud Microservice Based On Deadline Limit 14

A Load Balancing Strategy Of "Container Virtual Machine" Clo...

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14th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2022

作者： Xia, Hong Liu, MengDi Chen, YanPing Jin, XiaoMin Wang, ZhongMin Wang, FengWei School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China ZTE Corporation No.55 Hi-tech Road South Shenzhen518057 China

ISBN: (纸本)9781665499781

Aiming at the problem of workflow scheduling under the "container virtual machine" two-tier structure in cloud microservice workflow application, a "container virtual machine" load balancing strategy based on deadline limit (container virtual machine load) is proposed. Balance strategy), CV LBMs strategy first obtains the optimal economic configuration of the corresponding instance of the micro service according to historical data, and calculated the list of remaining resources in the leased virtual machine;Determined whether the virtual machine is on the virtual machine according to the container image list in the virtual machine. Whether a corresponding container image exists, and create a new container on the virtual machine that already has a container image to reduce the time to extract the container image. The workflow simulation results showed that compared with the existing algorithms, the C-V LBMs strategy had the ability to meet the deadline success rate and reduced the cost. © 2022 IEEE

关键词： Containers

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Ranking enhanced dialogue generation

arXiv

引用

arXiv 2020年

作者： Hao, Changying Pang, Liang Lan, Yanyan Sun, Fei Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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