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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Balancing Activity recognition and Privacy Preservation with a Multi-objective Evolutionary Algorithm 7th

Balancing Activity Recognition and Privacy Preservation with...

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7th EAI international conference on Smart Objects and Technologies for social Good, GOODTECHS 2021

作者： Poli, Angelica Muñoz-Antón, Angela M. Spinsante, Susanna Florez-Revuelta, Francisco Dipartimento di Ingegneria dell’Informazione Università Politecnica delle Marche Ancona60131 Italy University of Alicante Alicante03690 Spain Department of Computing Technology University of Alicante Alicante03690 Spain

ISBN: (纸本)9783030914202

With the widespread of miniaturized inertial sensors embedded in wearable devices, an increasing number of individuals monitor their daily life activities through consumer electronic products. However, long-lasting data collection (e.g., from accelerometer) may expose the users to privacy violations, such as the leakage of personal details. To help mitigate these aspects, we propose an approach to conceal subject’s personal attributes (i.e., gender) while maximizing the accuracy on both the monitoring and recognition of human activity. In particular, a Multi-Objective Evolutionary Algorithm (MOEA), namely the Non-dominated Sorting Genetic Algorithm II (NSGA-II), is applied to properly weight input features extracted from the raw accelerometer data acquired with a wrist-worn device (Empatica E4). Experiments were conducted on a large-scale and real life dataset, and validated by adopting the Random Forest algorithm with 10-fold cross validation. Findings demonstrate that the proposed method can highly limit gender recognition (from 89.37% using all the features to 64.38% after applying the MOEA algorithm) while only reducing the accuracy of activity recognition by 5.45% points (from 89.59% to 84.14%). © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Wearable sensors

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UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images

UHRSNet: A Semantic Segmentation Network Specifically for Ul...

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international conference on pattern recognition

作者： Lianlei Shan Minglong Li Xiaobin Li Yang Bai Ke Lv Bin Luo Si-Bao Chen Weiqiang Wang University of Chinese Academy of Sciences Beijing China MOE Key Lab of Signal Processing and Intelligent Computing School of Computer Science and Technology Anhui University Hefei China

Semantic segmentation is a basic task in computer vision, but only limited attention has been devoted to the ultra-high-resolution (UHR) image segmentation. Since UHR images occupy too much memory, they cannot be directly put into GPU for training. Previous methods are cropping images to small patches or downsampling the whole images. Cropping and downsampling cause the loss of contexts and details, which is essential for segmentation accuracy. To solve this problem, we improve and simplify the local and global feature fusion method in previous works. Local features are extracted from patches and global features are from downsampled images. Meanwhile, we propose one new fusion called local feature fusion for the first time, which can make patches get information from surrounding patches. We call the network with these two fusions ultra-high-resolution segmentation network (UHRSNet). these two fusions can effectively and efficiently solve the problem caused by cropping and downsampling. Experiments show a remarkable improvement on Deepglobe dataset [1].

关键词： Training Image segmentation Computer vision Semantics Graphics processing units Feature extraction pattern recognition

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Network Traffic Anomaly Detection Based on Optimized Transfer Learning 10th

Network Traffic Anomaly Detection Based on Optimized Transfe...

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10th international conference on Computer Engineering and Networks, CENet 2020

作者： Huo, Yonghua Jiao, Libin Xie, Ping Fu, Zhiming Tao, Zhuo Yang, Yang The 54th Research Institute of CETC Shijiazhuang050000 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts Beijing China

ISBN: (纸本)9789811584619

With wide use of internet, the problem of anomaly detection in network security has become an import issue of concern. In order to solve the problem, this paper proposes a new method FEN-TLSAE which uses transfer learning to detect network traffic when plenty of labeled data are unavailable. First, we use feature extraction to get two feature subsets and use two different networks to train them. then we add BN and dropout to the improved autoencoder for regularization. Finally, we use joint inter-class and inter-domain distributional adaptation (JCDA) in the process of transfer learning. Minimizing the marginal and conditional distribution distance between the source and target domains while maximizing the distribution distance of samples between different classes in the source domain. the experiments on the NSL-KDD data set indicate that the proposed method is more efficient than that based on TLSAE. Also, the method has higher detection accuracy, recall and precision. © 2021, the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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A Blockchain and FOT (Fog of things) based Framework and Technique for Anticipating an Infectious Illness Sent by a Harmful Respiratory Infection

A Blockchain and FOT (Fog of Things) based Framework and Tec...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Naveen Tewari Rajeev Kumar Mukesh Joshi Sandeep Budhani School of Computing Graphic Era Hill University Bhimtal College of Computing Sciences and Information Technology Teerthankar Mahaveer University Moradabad

ISBN: (纸本)9781665439718

the current exploration is by and large identified with a framework and technique for anticipating an irresistible sickness, for example, COVID-19 communicated by a harmful respiratory infection. Revealed are a framework and technique for anticipating an irresistible infection communicated by a harmful respiratory infectionthe system joins a larger part of Internet of things (IoT) sensors, a larger part of fog center devices, and a greater part of handling contraptions in cloud server ranches. the IoT sensors are organized to be joined to a larger part of individuals to deliver a prosperity dataset. the Fog registering devices are connected with a haze lay-er to get the prosperity dataset from the IoT sensors to quantify and store the prosperity dataset over a square chain organization. the contraptions measure the prosperity da-taset at the mist layer by playing out haze handling. the figuring contraptions and cloud server ranches get the taken care of prosperity dataset from the haze center point devices over the blockchain network. this assessment is in like manner requested of for the patent in Indian Patent Office with application number – 202011021969.

关键词： COVID-19 Patents Cloud computing Organizations Market research Sensor systems Sensors

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Determination of pattern recognition Problems based on a Pythagorean Fuzzy Correlation Measure from Statistical Viewpoint

Determination of Pattern Recognition Problems based on a Pyt...

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international Workshop on Advanced Computational Intelligence (IWACI)

作者： Paul Augustine Ejegwa Yuming Feng Shiping Wen Wei Zhang Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Australian AI Institute University of Technology Sydney Ultimo Australia School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China

By considering all the three parameters describing Pythagorean fuzzy set, we introduce a new technique of computing correlation coefficient between Pythagorean fuzzy sets from statistical perspective. the correlation coefficient value obtain via this technique shows strength of correlation between the Pythagorean fuzzy sets and indicates whether the Pythagorean fuzzy sets under consideration are related negatively or positively in contrast to other existing correlation coefficient approaches in Pythagorean fuzzy context, which only assess the strength of relationship. Certain numerical examples are considered to ascertain the authenticity of this method over similar techniques studied in intuitionistic/Pythagorean fuzzy contexts. Some pattern recognition problems are resolved with the aid of the new technique. For higher productivity sake, this technique could be approached from an object-oriented perspective.

关键词： Correlation coefficient Productivity Fuzzy sets Correlation Uncertainty pattern recognition Performance analysis

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EEG-Based Cognitive State Assessment Using Deep Ensemble Model and Filter Bank Common Spatial pattern

EEG-Based Cognitive State Assessment Using Deep Ensemble Mod...

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international conference on pattern recognition

作者： Debashis Das Chakladar Shubhashis Dey Partha Pratim Roy Masakazu Iwamura Indian Institute of Technology Roorkee India Institute of Engineering and Management Kolkata India Osaka Prefecture University Japan

ISBN: (纸本)9781728188089;9781728188096

Electroencephalography (EEG) is the most used physiological measure to evaluate the cognitive state of a user efficiently. As EEG inherently suffers from a poor spatial resolution, features extracted from each EEG channel may not be efficiently used for the cognitive state assessment. In this paper, the EEG-based cognitive state assessment has been performed during the mental arithmetic experiment, which includes two cognitive states (task and rest) of a user. To obtain the temporal as well as the spatial resolution of the EEG signal, we combined the Filter Bank Common Spatial pattern (FBCSP) method and Long Short-Term Memory (LSTM)-based deep ensemble model for classifying the cognitive state of a user. Subject-wise data distribution has been performed due to the execution of a large volume of data in a low computing environment. In the FBCSP method, the input EEG is decomposed into multiple equal-sized frequency bands, and spatial features of each frequency bands are extracted using the Common Spatial pattern (CSP) algorithm. Next, a feature selection algorithm has been applied to identify the most informative features for classification. the proposed deep ensemble model consists of multiple similar structured LSTM networks that work in parallel. the output of the ensemble model (i.e., the cognitive state of a user) is computed using the average weighted combination of the individual model prediction. this proposed model achieves 87% classification accuracy, and it can also effectively estimate the cognitive state of a user in a low computing environment.

关键词： Solid modeling Computational modeling Filter banks Predictive models Brain modeling Feature extraction Electroencephalography

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DEEP LEARNING FOR pattern recognition OF INTERHARMONICS IN TIME-SERIES AND SPECTROGRAMS

DEEP LEARNING FOR PATTERN RECOGNITION OF INTERHARMONICS IN T...

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CIRED 2021 - the 26th international conference and Exhibition on Electricity Distribution

作者： R. A. Oliveira V. Ravindran S. Rönnberg M. H. J. Bollen Electric Power Engineering Luleå Technical University Skellefteå Sweden

this work applies an unsupervised deep feature learning to finding patterns of interharmonics. the main objectives of this work are to provide an additional graphical tool to handle two distinct data inputs: (a) individual interharmonics components in time-series; (b) broadband spectrum by employing spectrograms. Both data inputs are analysed employing an autoencoder based on convolutional neural networks followed by clustering. the application of the method results in the most common patterns in time-series or spectrograms. Two study cases are presented by applying the method to measurements from solar installations in Finland and Sweden. the results show the usefulness of the method to recognize interharmonics in a single frequency and broadband spectrum.

关键词： unsupervised learning feature extraction deep learning (artificial intelligence) time series convolutional neural nets pattern clustering power engineering computing

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Citational practices: Interrogating hegemonic knowledge structures in computing research in Latin America 2021

Citational practices: Interrogating hegemonic knowledge stru...

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10th Latin American conference on Human Computer Interaction, CLIHC 2021

作者： Collective, Citational Justice De Castro Leal, Debora Molina Leon, Gabriela Maestre, Juan F. Williams, Kristin Wong-Villacres, Marisol Reynolds-Cuéllar, Pedro Oswal, Sushil K. Cerratto Pargman, Teresa Sharma, Vishal India Siegen University Siegen Germany University of Bremen Bremen Germany Indiana University Indiana United States Carnegie Mellon University Pittsburgh United States Escuela Superior Politecnica Del Litoral Guayaquil Ecuador Massachusetts Institute of Technology CambridgeMA United States University of Washington Tacoma/SeattleWA United States Stockholm University Stockholm Sweden Georgia Institute of Technology AtlantaGeorgia United States

ISBN: (纸本)9781450384896

Citations are nodes in the networks of knowledge we create. Portals to conversations with the past and bonding material with the scholarship of the present. Choosing who we cite is a practice signaling who we recognize and respect as a knowledge source. therefore, we recognize citations as a relational practice. As this relational characteristic of citing is mediated by wealth we distribute across those who we cite, it is imperative to interrogate how just these practices are. thus, we ought to engage with Citational Justice. Building on recent work discussing citational practices within HCI, we use the opportunity of this workshop to expand this conversation into deeper reflection on how we cite and the practices and infrastructures surrounding citations. Our goal with this workshop is two-fold. First, to create a common language to collectively reflect, interrogate our own citational practices and reverberations, while fleshing out concrete steps to make these practices just in our work and communities we are part of. Second, to invite participants to re-imagine citational practices and the systems and infrastructures necessary to make such practices feasible. We invite a diverse group of participants from the CLIHC community interested in examining their citational practices and the systems surrounding them. © 2021 Owner/Author.

关键词： Human computer interaction

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Talking Face Generation via Learning Semantic and Temporal Synchronous Landmarks

Talking Face Generation via Learning Semantic and Temporal S...

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international conference on pattern recognition

作者： Aihua Zheng Feixia Zhu Hao Zhu Mandi Luo Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Heifei China Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China

ISBN: (纸本)9781728188089;9781728188096

Given a speech clip and facial image, the goal of talking face generation is to synthesize a talking face video with accurate mouth synchronization and natural face motion. Recent progress has proven the effectiveness of the landmarks as the intermediate information during talking face generation. However, the large gap between audio and visual modalities makes the prediction of landmarks challenging and limits generation ability. this paper proposes a semantic and temporal synchronous landmark learning method for talking face generation. First, we propose to introduce a word detector to enforce richer semantic information. then, we propose to preserve the temporal synchronization and consistency between landmarks and audio via the proposed temporal residual loss. Lastly, we employ a U-Net generation network with adaptive reconstruction loss to generate facial images for the predicted landmarks. Experimental results on two benchmark datasets LRW and GRID demonstrate the effectiveness of our model compared to the state-of-the-art methods of talking face generation.

关键词： Learning systems Visualization Face recognition Semantics Mouth Detectors Benchmark testing

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Simulation Based Protocols Comparison for Vehicular Ad-hoc Network Routing

Simulation Based Protocols Comparison for Vehicular Ad-hoc N...

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international conference on System Modeling & Advancement in Research Trends (SMART)

作者： Ravi Shankar Shukla Danish Ather College of Computing and Informatics Saudi Electronic University Tabuk Kingdom of Saudi Arabia Sharda University Greater Noida India

ISBN: (纸本)9781665439718

VANETs (vehicular ad hoc networks) are a rapidly developing technology that allows communication between moving cars and roadside devices without the need for infrastructure. Although MANET has a subtype called VANET, it is distinguished by the inclusion of automobiles as nodes. ITS makes extensive use of self-organizing networks. Because of the VANET’s extremely dynamic topology and frequent disconnection, developing an efficient approach is difficult. Since no one routing protocol is suitable for all VANET applications, it is essential to evaluate the benefits and drawbacks of each. this research project is focused on the VANET and its protocols for routing. this paper discusses the benefits and drawbacks of the DYMO, DSR, AODV and VANET routing protocols.

关键词： Wireless communication Vehicular ad hoc networks Jitter Self-organizing networks Routing Routing protocols Topology

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