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Age-Aware Query Evaluation for Big data Analytics in Mobile Edge Clouds 22

Age-Aware Query Evaluation for Big Data Analytics in Mobile ...

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22nd IEEE international conference on High Performance Computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Xia, Qiufen Ren, Wenhao Li, Mingchu Ren, Jiankang Dalian University of Technology International School of Information Science and Engineering Dalian116621 China Dalian University of Technology School of Software Dalian116621 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

ISBN: (纸本)9781728176499

The advancement of 5G technology and extensive usage of smart devices have led to a deluge of data. It is invaluable to analyze such big data by issuing queries to derive real-Time business insights for better, smarter and fact-based decisions. Timely big data analytics are crucial in many service domains, thus the age of data (AoD) required by queries is emerging as a novel metric that measures the freshness of data and evaluates the quality of data analytics. Traditional big data analytics that are performed in remote clouds cannot satisfy the AoD requirements of queries, due to the congested core networks and long transmission latency between users and clouds. The technique of mobile edge computing (MEC) is expected to reduce the age and guarantee the timeliness of queries for big data analytics, by processing data at edge cloudlets close to users. In this paper, we investigate a problem of age-Aware query evaluation for big data analytics in a mobile edge cloud network. We first formulate the problem with certain data processing latency as an Integer Linear Program (ILP). We then develop an online learning algorithm for the problem with uncertain data processing latency. We finally evaluate the performance of proposed algorithms against existing studies by simulations and testbed implementations. Evaluation results show that the proposed algorithms outperform existing works by achieving 20% lower AoD. © 2020 IEEE.

关键词： data Analytics

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Utilization of Neurophysiological data to Classify Player Immersion to Distract from Pain 22nd

Utilization of Neurophysiological Data to Classify Player Im...

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22nd international conference on Human-Computer Interaction,HCII 2020

作者： Stamp, Kellyann Dobbins, Chelsea Fairclough, Stephen Department of Computer Science Liverpool John Moores University LiverpoolL3 3AF United Kingdom School of Information Technology and Electrical Engineering The University of Queensland BrisbaneQLD4072 Australia School of Natural Sciences and Psychology Liverpool John Moores University LiverpoolL3 3AF United Kingdom

ISBN: (纸本)9783030601270

Painful experiences during clinical procedures can have detrimental effects on the physical and mental health of a patient. Current pain reduction methods can be effective in reducing pain, however these methods are not without fault. Active distraction via computer games have been proven to effectively reduce the experience of pain. However, the potential of this distraction to effectively alleviate pain is dependent on players’ engagement with the game, which is determined by the difficulty of the game and the skill of the player. This paper aims to model and classify immersion through increasingly difficult levels of game play, in the presence of pain, using functional Near Infrared Spectroscopy (fNIRS) and heart rate data. Twenty people participated in a study wherein fNIRS data (4 channels located at the prefrontal cortex, four channels located at the somatosensory cortex) and heart rate data were collected whilst participants were subjected to experimental pain, via the Cold Pressor Test (CPT). Participants played a computer game at varying difficultly levels as a distraction. data were then pre-processed using an Acceleration Based Movement Artefact Reduction Algorithm (AMARA) and Correlation Based Signal Improvement (CBSI). Classification was subsequently undertaken using Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Recursive Partitioning (rPart). The results demonstrate a maximum accuracy of 99.2% for the binary detection of immersion in the presence of pain. © 2020, Springer Nature Switzerland AG.

关键词： Infrared devices

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CCIED: Cache-Aided Collaborative Intelligence between Edge Devices 22

CCIED: Cache-Aided Collaborative Intelligence between Edge D...

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作者： Hu, Chuanwen Bai, Yuebin Wang, Rui Liu, Chang Wang, Xiaolin Beihang University School of Computer Science and Engineering Beijing China Sino-French Engineer School Beihang University Beijing China

ISBN: (纸本)9781728176499

Recently, deep learning technology has shined in the fields of computer vision, natural language processing and speech recognition, and related products have sprung up like mushrooms. Due to the storage and calculation of deep neural network (DNN) models are relatively large and mobile edge devices are often resource-constrained, how to efficiently deploy DNN models on resource-constrained edge devices has attracted great attention from academia and industry. There's strength in numbers, so we propose CCIED, a framework which lets edge devices cooperate with each other to complete DNN inference tasks. Due to task inputs in the mobile edge computing scenarios usually have great similarities, the outputs of the middle layer of the neural network and the corresponding labels are cached. When a similar input already exists in the cache, the device does not need to perform the remaining calculations, but directly returns the cached results. One of the challenges of collaborative inference is that the communication overhead associated with transferring intermediate data can be significant. We therefore perform weight pruning only on the layer that obtains the intermediate results, which can greatly reduce the redundant parameters of the intermediate results, thereby reducing the time for transferring data between devices, and basically does not reduce the complexity of the model. Experimental results show that CCIED can efficiently deploy the DNN model on edge devices with almost no loss of precision, and can significantly reduce the total latency during cache hits. © 2020 IEEE.

关键词： Deep neural networks

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Improving the Detection of User Uncertainty in automated Overtaking Maneuvers by Combining Contextual, Physiological and Individualized User data 22nd

Improving the Detection of User Uncertainty in Automated Ove...

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22nd international conference on Human-Computer Interaction, HCII 2020

作者： Trende, Alexander Hartwich, Franziska Schmidt, Cornelia Fränzle, Martin OFFIS e.V Escherweg 2 Oldenburg26121 Germany Cognitive and Engineering Psychology Chemnitz University of Technology Wilhelm-Raabe-Straße 43 Chemnitz09120 Germany University of Oldenburg Ammerländer Heerstraße 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783030507312

Highly automated driving will be a novel experience for many users and may cause uncertainty and discomfort for them [1]. An efficient real-time detection of user uncertainty during automated driving may trigger adaptation strategies, which could enhance the driving experience and subsequently the acceptance of highly automated driving [2]. In this study, we compared three different models to classify a user’s uncertainty regarding an automated vehicle’s capabilities and traffic safety during overtaking maneuvers based on experimental data from a driving-simulator study. By combining physiological, contextual and user-specific data, we trained three different deep neural networks to classify user uncertainty during overtaking maneuvers on different sets of input features. We evaluated the models based on metrics like the classification accuracy and F1 Scores. For a purely context-based model, we used features such as the Time-Headway and Time-To-Collision of cars on the opposing lane. We demonstrate how the addition of user heart rate and related physiological features can improve the classification accuracy compared to a purely context-based uncertainty model. The third model included user-specific features to account for inter-user differences regarding uncertainty in highly automated vehicles. We argue that a combination of physiological, contextual and user-specific information is important for an effectual uncertainty detection that accounts for inter-user differences. © 2020, Springer Nature Switzerland AG.

关键词： Deep neural networks

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EdgeLD: Locally Distributed Deep learning Inference on Edge Device Clusters 22

EdgeLD: Locally Distributed Deep Learning Inference on Edge ...

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作者： Xue, Feng Fang, Weiwei Xu, Wenyuan Wang, Qi Ma, Xiaodong Ding, Yi Beijing Jiaotong University School of Computer and Information Technology Beijing China Key Laboratory of Industrial Internet of Things Networked Control Ministry of Education Chongqing China Beijing Wuzi University School of Information Beijing China

ISBN: (纸本)9781728176499

Deep Neural Networks (DNN) have been widely used in a large number of application scenarios. However, DNN models are generally both computation-intensive and memory-intensive, thus difficult to be deployed on resource-constrained edge devices. Most previous studies focus on local model compression or remote cloud offloading, but overlook the potential benefits brought by distributed DNN execution on multiple edge devices. In this paper, we propose EdgeLD, a new framework for locally distributed execution of DNN-based inference tasks on a cluster of edge devices. In EdgeLD, DNN models' time cost will be firstly profiled in terms of computing capability and network bandwidth. Guided by profiling, an efficient model partition scheme is designed in EdgeLD to balance the assigned workload and the inference runtime among different edge devices. We also propose to employ layer fusion to reduce communication overheads on exchanging intermediate data among devices. Experiment results show that our proposed partition scheme saves up to 15.82% of inference time with regard to the conventional solution. Besides, applying layer fusion can speedup the DNN inference by 1.11-1.13X. When combined, EdgeLD can accelerate the original inference time by 1.77-3.57X on a cluster of 2-4 edge devices. © 2020 IEEE.

关键词： Deep neural networks

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Profit-driven Computation Offloading for Mobile Edge Computing in Wireless Metropolitan Area Networks 22

Profit-driven Computation Offloading for Mobile Edge Computi...

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作者： Huang, Hualong Peng, Kai Xu, Xiaolong Huaqiao University School of Engineering Quanzhou China Nanjing University of Science and Technology Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing China

ISBN: (纸本)9781728176499

The applications and services are growing computation-intensive vary with the advent of wireless communication technology, whereas the computing resources and capabilities of mobile devices (MDs) are finite. Mobile edge computing (MEC) is a compelling way to offload applications from MDs to the cloudlets for performing, by means of deploying cloudlets nearby the MDs, which is capable of alleviating the computing burden of MDs and reducing the completion time of applications. However, MDs are irregularly dispersed in the Wireless Metropolitan Area Networks (WMAN), and the service provider needs to recover the cost of the deployment and maintenance of cloudlets and achieve profitability. As a consequence, it poses a significant challenge to reasonably offload applications from MD to the proper cloudlet for MEC in the WMAN. In this paper, we study the user satisfaction of MDs, the profit of service provider and the resource utilization of cloudlets as a multi-objective optimization problem. In the sequel, we devise a profit-driven computation offloading method, under the collaboration of MD-cloudlet-cloud, to obtain the optimal offloading strategy such that maximizes the user satisfaction of MDs, the profit of service provider and the resource utilization of cloudlets. To this end, we conduct comprehensive experiments to validate the efficiency of the proposed method. The numerical results demonstrate the performance of the proposed algorithm is prominent. © 2020 IEEE.

关键词： computation offloading

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ICMI 2020 - Proceedings of the 2020 international conference on Multimodal Interaction

ICMI 2020 - Proceedings of the 2020 International Conference...

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22nd ACM international conference on Multimodal Interaction, ICMI 2020

ISBN: (纸本)9781450375818

The proceedings contain 125 papers. The topics discussed include: advanced multi-instance learning method with multi-features engineering and conservative optimization for engagement intensity prediction;a multi-modal system to assess cognition in children from their physical movements;a neural architecture for detecting user confusion in eye-tracking data;analysis of face-touching behavior in large scale social interaction dataset;attention sensing through multimodal user modeling in an augmented reality guessing game;bring the environment to life: a sonification module for people with visual impairments to improve situation awareness;combining auditory and mid-air haptic feedback for a light switch button;and depression severity assessment for adolescents at high risk of mental disorders.

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DatEthics: Ethical data-Centric Design of intelligent Behaviour 20

DatEthics: Ethical Data-Centric Design of Intelligent Behavi...

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22nd international conference on Human-Computer Interaction with Mobile Devices and Services

作者： Jacky Bourgeois Jered Vroon Aaron Yi Ding Ella Peltonen TU Delft Netherlands Industrial Design Engineering / Design Engineering / IoT Delft University of Technology Netherlands Center for Ubiquitous Computing University of Oulu Finland

ISBN: (纸本)9781450380522

The Internet of Things makes human activity data – what people do, how they move, how they socialise – an abundant resource. However, this rich and intimate perspective on people, which uniquely shape and characterise their behaviours, can have tremendous ethical implication if data is handled irresponsibly. Being personal, contextual and accessible, mobile devices are key facilitators of (ir)responsible collection and use of data. In this workshop, we will use the Future Workshop approach to develop a research agenda towards ethical data-centric design of intelligent behaviours. As part of this approach, we will (1) criticise the current mechanisms and infrastructure to frame ethical challenges, (2) fantasise on futures which support user and designer values, and (3) implement a research agenda for the MobileHCI community to emphasise the barriers to tackle. The outcomes of this workshop will foster ethical research and inspire the MobileHCI community.

关键词： data-Centric Design Responsibility Ethics Internet of Things

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Adversarial Training for Underwater Target Recognition in Complex Marine Conditions 22

Adversarial Training for Underwater Target Recognition in Co...

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作者： Zeng, Xin Liu, Xingang Song, Gaoyu Wang, Dayu Luo, Hengguang Zhang, Boxuan University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China 54th Research Institute of Cetc Escc Shijiazhuang China

ISBN: (纸本)9781728176499

Studies on underwater target recognition have been making impressive progress recently due to the application of many deep learning methods. However, when the test set and training set contain different marine background noises, the performance of the trained model deteriorates. According to our observations, such problem cannot be solved by traditional noise reduction techniques and alleviating overfitting. Considering the complexity of marine background noise and the dissimilarity in transmission channels under different marine conditions, we first compare various marine conditions to different generalized channels so that this problem can be compared to channel mismatch in the field of speaker recognition. Inspired by domain adaptation and channel compensation, we propose a Generalized Channel-Invariant Network (GCIN), which uses a gradient reversal layer (GRL) and an adversarial training framework to eliminate the dissimilarity in the received data caused by various marine conditions and then a more robust model is obtained. Experiments on two multi-Target and multi-marine-condition datasets confirmed that compared with other CNN-based methods that do not use GRL, GCIN can not only effectively alleviate the mismatch problem under marine conditions within given range, increase by 15% when the training set and test set are mismatched, but also improve the accuracy by at least 2.2% when two sets are matched. © 2020 IEEE.

关键词： Deep neural networks

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A novel soft exosuit based on biomechanical analysis for assisting lower extremity 22

A novel soft exosuit based on biomechanical analysis for ass...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Liu, Youfu Chen, Chunjie Lu, Jun Liu, Yida Zhang, Yu Wang, Zhuo College of Mechanical and Electrical Engineering Shaanxi University of Science and Technology Shenzhen China ShenZhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (纸本)9781728162676

In the past two decades, with growing focus of lower limb exoskeleton, large variety of rigid exoskeletons were designed for medical rehabilitation and the other purposes. Compared with the rigid exoskeletons, which added extra inertial to low limb and restricted wearers' movement, the soft wearable lower extremity exoskeleton minimized the impact of these factors on the human body locomotion. In this paper, a novel design of exosuit that provided assistive force for both hip extension and flexion through the variation in hip joint dynamics during strides was proposed. Based on the change of hip moment, an assistance strategy and assistance force curve were came up with. PD type iterative learning control (ILC) method was introduced to reduce the error caused by wearing position and biological characteristics to improve assistance performance. To evaluate assistance performance, the metabolic cost of four subjects wearing exosuit and walking on treadmill at 5km/h in the situations of that without assistance, assisting both hip extension and hip flexion, and assisting hip extension respectively was measured. Compared with assisting hip extension only and wearing exosuit with no assistance, results indicated that the decrease in average net metabolic cost of assisting both hip extension and flexion was 0.445W/kg and 1.027W/kg respectively, corresponding to the average net metabolic cost rate decrease was 7.45% and 15.67%. © 2021 IEEE.

关键词： Exoskeleton (Robotics)

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