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2nd International conference on computing and Applied Informatics

作者： Riwinoto Mahfud, N. Lumombo, L. Batam State Polytech Informat Dept Multimedia & Networking Engn Study Program Batam Ctr Jalan Ahmad Yani Batam 29461 Indonesia

The development of the android mobile operating system and the presence of Play Store services poses challenges for developers to produce exciting mobile games. Although publishing games in Play Store is not difficult, in fact developers have to face tough competition to make homemade games can become popular. This study analyzes the gameplay of three popular paid-free games in Play Store that can survive for a period of one year from the top 10 positions in October 2015- November 2016. Analysis performed on 8 elements of the game based on the definition of Fullerton and emotional expression analysis of respondents who appeared while playing the three choosen games. The analysis shows that scarce resources are the main attraction of all three games because they create conflicts, giving constraints and challenges to players. The multiplayer game has a pattern of results and the result makes the opponent become more negative than the player to win. While the single player game is analyzed has a pattern to make the player get positive to win. There are 3 basic emotions that most often appear that is joy, disgust and surprise. Multiplayer games tend to emphasize the emotions of joy players, while single player games tend to bring disgust emotions.

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Resource-Aware Cache Management for In-Memory Data Analytics Frameworks

Resource-Aware Cache Management for In-Memory Data Analytics...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Zhengyang Zhao Haitao Zhang Xin Geng Huadong Ma Beijing Key Lab of Intelligent Telecomm. Software and Multimedia Beijing University of Posts and Telecomm. Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Continuously increasing amounts of data have led to the emergence of distributed in-memory computing systems for higher data processing speeds. Finding an efficient memory management method has become key to improving the performance of these systems because of the confined memory resource. Recent research in dependency-aware caching, e.g., Least Reference Count (LRC) and Least Composition Reference Count (LCRC), has enabled significant improvements in efficiency by profiling the application's Directed Acyclic Graph (DAG). However, these methods do not consider the dynamic occupancy mechanism of the Unified Memory Manager (UMM) in Spark, which could cause heavily referenced and costly data blocks to be evicted and produce high recomputing overhead. To alleviate this defect, we propose a resource-aware cache management approach that uses both runtime resource metrics and dependency information. By applying an adaptive approach, we can retain the data blocks that have greater contributions to obtain the final results. We demonstrate the effectiveness of our cache management approach through a series of widely used benchmarks. The experimental results show that compared with current DAG-aware implementations, our approach improves performance by an average of 15% and up to 24%. Compared with the default Least Recently Used (LRU) strategy in Spark, our implementation reduces job runtime by 28% on average and by up to 61%.

关键词： Sparks Memory management Runtime Prefetching Random access memory Task analysis Data analysis

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SaFePlay: A Portable Biomechanics Measurement and Analysis System of Lower Limbs

SaFePlay: A Portable Biomechanics Measurement and Analysis S...

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CHI conference on Human Factors in computing Systems (CHI)

作者： Wei, Shi-Yao Chou, Yin-Yu Wang, Hsing-Man Lin, Tse-Yu Lin, Shih-Jie Chen, Min-Shin Yang, Zhi-Wei Huang, Jung-Tang Hung, Yi-Ping Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Inst Informat Ind Taipei Taiwan Natl Taipei Univ Technol Grad Inst Mechatron Engn Taipei Taiwan

ISBN: (纸本)9781450356213

Gait analysis of walking, running and exercising helps greatly to prevent injury and improves performance in sports activity. However, current mainstream gait detection and locomotion measurement techniques relying on motion capture need bulky devices such as pressure plates and motion capture cameras. They are installed in a laboratory environment and must be operated by professional technicians. In other words, the traditional system in the lab cannot measure outdoor activities. SaFePlay, Smart Footwear Platform, is a portable lower limb motion monitoring, biomechanics measurement, and analysis system. This system consists of smart insoles, smart knee braces and an analysis engine installed on a mobile device. Users are able to review data over a specific period of time to have a general understanding of his or her gait-related health conditions. The historical data can also be provided to professional coaches or physical therapists to help them do the further action to the users.

关键词： Mobile and ubiquitous application action recognition activity identification portable lower limb motion monitoring

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Cardea: Context-Aware Visual Privacy Protection for Photo Taking and Sharing 18

Cardea: Context-Aware Visual Privacy Protection for Photo Ta...

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9th ACM multimedia Systems conference (MMSys)

作者： Shu, Jiayu Zheng, Rui Hui, Pan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Helsinki Helsinki Finland

ISBN: (纸本)9781450351928

The growing popularity of mobile and wearable devices with built-in cameras and social media sites are now threatening people's visual privacy. Motivated by recent user studies that people's visual privacy concerns are closely related to context, we propose Cardea, a context-aware visual privacy protection mechanism that protects people's visual privacy in photos according to their privacy preferences. We define four context elements in a photo, including location, scene, others' presences, and hand gestures. Users can specify their context-dependent privacy preferences based on the above four elements. Cardea will offer fine-grained visual privacy protection service to those who request protection using their identifiable information. We present how Cardea can be integrated into: a) privacy-protecting camera apps, where captured photos will be processed before being saved locally;and b) online social media and networking sites, where uploaded photos will first be examined to protect individuals' visual privacy, before they become visible to others. Our evaluation results on an implemented prototype demonstrate that Cardea is effective with 86% overall accuracy and is welcomed by users, showing promising future of context-aware visual privacy protection for photo taking and sharing.

关键词： Visual privacy protection context-aware computing photo capturing and sharing

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multimedia at CHI: Telepresence at Work for Remote conference Participation

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IEEE multimedia 2017年第3期24卷 5-9页

作者： Boll, Susanne Carl von Ossietzky Univ Oldenburg Media Informat & Multimedia Syst Oldenburg Germany

Susanne Boll recounts her experience as a telepresence robot attending this year’s ACM conference on Human Factors in computing Systems (CHI), illustrating the fusion of the state of the art in robotics, long-distance interaction, and shared remote-audio-visual experiences. As a researcher in human computer interaction, yet with an international team facing restricted travel owing to the newly imposed US travel ban, she opted for remote participation at CHI 2017. Furthermore, as a member of the multimedia community, she wanted to explore first-hand how multimedia is at work in the field of telepresence.

关键词： Cameras Robot Vision Systems Telerobotics multimedia Communication Robots Pervasive computing Distributed Management Virtualization Human Computer Interaction Telepresence Robotics Human Computer Interaction multimedia networking Distributed Systems Intelligent Systems Internet Web Technologies Pervasive computing Virtualization

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ACM International conference Proceeding Series

ACM International Conference Proceeding Series

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16th International conference on Advances in Mobile computing and multimedia, MoMM 2018

ISBN: (纸本)9781450364522

The proceedings contain 20 papers. The topics discussed include: betweenness centrality based k-anonymity for privacy preserving in social networks;towards opinions analysis method from social media for multidimensional analysis;user time spent between persuasiveness and usability of social networking mobile applications: a case study of facebook and YouTube;a framework for the composition and formal verification of adaptable semantic web services;parameter recovery using radon transform;management of mobile objects location for video content filtering;a method to reduce processing time by parallelizing generation of Voronoi diagrams;and visual attention toward socially rich context information for autism spectrum disorder (ASD) and normal developing children: an eye tracking study.

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Automatically Migrating Sequential Applications to Heterogeneous System Architecture 16

Automatically Migrating Sequential Applications to Heterogen...

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International conference on High Performance computing & Simulation (HPCS)

作者： Liang, Chih-Yung Fu, Sheng-Yu Liu, Yu-Ping Hsu, Wei-Chung Natl Taiwan Univ Grad Inst Networking & Multimedia Taipei Taiwan Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781538678794

Heterogeneous System Architecture (HSA) is a hardware architecture for heterogeneous computing proposed by the HSA Foundation. Its Unified Memory Architecture (hUMA) enables data sharing between heterogeneous devices and its user-level Queuing Model (hQ) enables low overhead kernel launching. With such features, applications could enjoy more efficient and effective heterogeneous computing. However, most of today's heterogeneous-computing applications have not leveraged the hUMA and hQ features. Moreover, the majority of applications on the market are implemented in traditional sequential models. This paper looks at building a fully automatic framework to migrate sequential applications to HSA. The framework includes polyhedral-guided memory aliasing analysis, a staged dispatching predictor, and memory coalescing optimization. It also takes advantages of hUMA and hQ to achieve low overhead job dispatching on HSA-compliant systems. On an AMD Carrizo machine (HSA-compliant), a sequential application runs through our framework could be 8.66x faster on Carrizo than before. In several cases where workloads are considered insufficient to benefit from conventional or non-HSA heterogeneous computing, our framework could still deliver significant speedups. In addition, the performance obtained through our framework can sometimes exceed the performance gain from manual tuning for both HSA and non-HSA platforms, running on the same Carrizo machine. With this framework, many existing applications coded in traditional sequential models could get performance boost from HSA-based heterogeneous computing.

关键词： Automatic migration Heterogeneous System Architecture shared virtual memory fine-grained system SVM

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Advances in Network-Based Information Systems: The 21st International conference on Network-Based Information Systems (NBiS-2018)

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2018年

作者： Leonard Barolli Natalia Kryvinska Tomoya Enokido Makoto Takizawa

ISBN: (纸本)9783319985299

This book presents the latest research findings and innovative theoretical and practical research methods and development techniques related to the emerging areas of information networking and their applications. Todays networks and information systems are evolving rapidly, and there are several new trends and applications, such as wireless sensor networks, ad hoc networks, peer-to-peer systems, vehicular networks, opportunistic networks, grid and cloud computing, pervasive and ubiquitous computing, multimedia systems, security, multi-agent systems, high-speed networks, and web-based systems. These networks have to deal with the increasing number of users, provide support for different services, guarantee the QoS, and optimize the network resources, and as such there are numerous research issues and challenges that need to be considered and addressed.

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Parameterized Data Reduction Framework of Thermal Sensing for Gait Velocity Measurements 24

Parameterized Data Reduction Framework of Thermal Sensing fo...

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24th IEEE International conference on Embedded and Real-Time computing Systems and Applications (RTCSA)

作者： Chou, Jyun-Jhe Shih, Chi-Sheng Natl Taiwan Univ NTU IoX Res Ctr Dept Comp Sci & Informat Engn Grad Inst Networking & Multimedia Taipei Taiwan

ISBN: (纸本)9781538677599

In IoT systems, many sensing devices will periodically transmit data. However, most of the sensing data are either similar or redundant and the sensing device itself may not have effective rules standard to decide to send or not. Static rules maybe effective on specific scenario, and become ineffective when the use scenario of the sensor changes. Hence, we design an algorithm to solve the problem of data redundant for IoT devices. The algorithm iteratively separates a data region into smaller regions. In each round, it chooses a region with highest variability, and separates it into smaller regions. Finally, each region has different sizes and uses its average value to represent itself. If an area has more dynamical, diverse data, more data points will be used to present the data. In this paper, we present a method to reduce the file size of thermal sensor which can sense the temperature of a surface and outputs a two dimension gray scale image. In our evaluation result, we can reduce the file size to be 50% of JPEG format when 0.5% of distortion is allowed, and up to 93% less when 2% of distortion is allowed.

关键词： Temperature sensors Heating systems Legged locomotion Temperature measurement Velocity measurement

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Multi-Resource Fair Allocation for Composited Services in Edge Micro-Clouds

Multi-Resource Fair Allocation for Composited Services in Ed...

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IEEE International conference on Big Data and Cloud computing (BdCloud)

作者： Tongyu Guo Haitao Zhang Han Huang Jianli Guo Chenze He Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Science and Technology on Communication Networks Laboratory Shijiazhuang China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Edge micro-clouds can collaboratively provide computing ability to users nearby in the dynamic environments, such as disaster relief and battlefield. Generally, the applications deployed on edge micro-clouds platform may consist of a set of composited services, and different services require different amounts of multiple dimensional resources. So massive service deployment can lead to unbalanced resource utilization on the micro-clouds. On the other hand, the composited services can be placed in different nodes, and even different edge micro-clouds, and the Quality of Service (QoS) of applications cannot be guaranteed. In this paper, we first define two models of edge micro-clouds and composited services to measure the resource utilization balance degree and QoS constrains respectively. Based on the two models, we formulate the problem of maximizing the edge micro-clouds platform resource utilization balance degree while satisfying the application delay constraints. Subsequently, we propose a Deep Reinforcement Learning (DRL) based multi-resource fair allocation algorithm to obtain the resource allocation strategy by making online decision. In our algorithm, we design a shaped reward function to jointly consider the factors of resource utilization balance degree and application delay constrains, and an improved DRL model is used to improve the convergence performance and the accuracy of the obtained strategy. The experimental evaluation demonstrates the proposed algorithm can guarantee the QoS of applications while achieving a better resource utilization balance degree compared with the other representative algorithms.

关键词： Resource management Delays Quality of service Reinforcement learning Cloud computing Heuristic algorithms Servers

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