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Mining Human Mobility from Social Media to support Urban computing Applications 15

Mining Human Mobility from Social Media to support Urban Com...

引用

15th Annual international conference on distributed computing in Sensor Systems (DCOSS)

作者： Comito, Carmela Natl Res Council Italy CNR Inst High Performance Comp & Networking ICAR Via Pietro Bucci 8-9C I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781728105703

Analysis of people trajectories is key for implementing effective urban computing applications. Nowadays, social media represent one of the main sources of information concerning human dynamics within urban context, allowing to enhance the comprehension of people behaviour, including human mobility regularities. The paper presents an approach to predict human mobility by exploiting Twitter data. The prediction method is based on a hybrid approach combining frequent pattern mining, trajectory similarity and supervised classification. The trajectory pattern similarity allows to identify the more suitable historic patterns to exploit for the prediction of the user next location. If none of the patterns satisfies the similarity threshold, a set of spatio-temporal features characterizing locations and movements among them are combined into a supervised learning approach based on M5 model trees. The experimental results obtained by using a real-world dataset show that the proposed method is effective in predicting the user's next places achieving a remarkable accuracy and prediction rate.

关键词： Urban computing Trajectory Patterns Human Mobility Next-place Prediction

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FedIM: An Anti-attack Federated Learning Based on Agent Importance Aggregation

FedIM: An Anti-attack Federated Learning Based on Agent Impo...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Zhipeng Gao Chenhao Qiu Chen Zhao Yang Yang Zijia Mo Yijing Lin State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781665416597

Federated learning (FL) is a distributed framework for machine learning (ML) model training. Training agents upload local model parameters rather than original training data, and the central server performs parameter aggregation. FL can protect user data privacy and break the information island when training the ML model. Federated Average (FedAvg) is an aggregation method commonly used in the FL training task. The central server calculates the mean value of the local model parameters to obtain the new global parameters. FedAvg assumes that all the training agents are honest, which means the central server lacks terminal agents' knowability. When there are attackers in the training agents, the global model's performance may be deeply affected, and the training task cannot be completed normally. To solve this problem, we propose a Federated Learning method with aggregation based on the Importance of training agent (FedIM), in which the central server performs pre-evaluation on the agent parameters before aggregation, calculates the weights of parameters according to the historical behavior records of training terminals and performs federated aggregation to improve the anti-poisoning ability of learning task. Experiments show that our method can effectively improve the global model's anti-poisoning ability and accelerate the training speed compared with the FedAvg method when malicious agents are involved.

关键词： Training Privacy Data privacy Training data Machine learning Collaborative work Data models

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An adaptive intelligent collaborative optimization method based on inconsistent information

An adaptive intelligent collaborative optimization method ba...

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2020 international Seminar on Artificial Intelligence, networking and Information Technology, AINIT 2020

作者： Shu, Jun Ming Fu, Chao Liu, Jihong Xu, Wen Ting Yu, Hong Yan School of Mechanical Engineering and Automation Beihang University Beijing100191 China Beijing Institute of Mechanical and Electrical Engineering Beijing100191 China

Multidisciplinary Design Optimization (MDO) is a design optimization method for dealing with large-scale and multi coupling complex engineering systems. The collaborative optimization (CO) has the characteristics of high degree of discipline autonomy, multi-level optimization and distributed computing. It can effectively solve the design optimization problems of large-scale complex engineering systems, and has been widely used in aerospace, ship, automobile, machinery and other fields. Because of its own optimization model and principle, the CO method has the defects of low computational efficiency and difficult convergence. In this paper, in order to overcome the convergence difficulty caused by the internal definition defects of the CO method, combined with the adaptive mechanism, the position relationship between the system level optimization points and the constraint conditions is analyzed, and the adaptive penalty function is constructed based on the inconsistent information of the system. The system level optimization model of the CO method is reconstructed by transforming the system level constraints. Finally, an example is given to demonstrate the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： distributed computer systems

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From Facility to Application Sensor Data: Modular, Continuous and Holistic Monitoring with DCDB 19

From Facility to Application Sensor Data: Modular, Continuou...

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international conference for High Performance computing, networking, Storage and Analysis (SC)

作者： Netti, Alessio Mueller, Micha Auweter, Axel Guillen, Carla Ott, Michael Tafani, Daniele Schulz, Martin Leibniz Supercomp Ctr Garching Germany Tech Univ Munich Munich Germany Megware GmbH Chemnitz Germany

ISBN: (数字)9781450362290

ISBN: (纸本)9781450362290

Today's HPC installations are highly-complex systems, and their complexity will only increase as we move to exascale and beyond. At each layer, from facilities to systems, from runtimes to applications, a wide range of tuning decisions must be made in order to achieve efficient operation. This, however, requires systematic and continuous monitoring of system and user data. While many insular solutions exist, a system for holistic and facility-wide monitoring is still lacking in the current HPC ecosystem. In this paper we introduce DCDB, a comprehensive monitoring system capable of integrating data from all system levels. It is designed as a modular and highly-scalable framework based on a plugin infrastructure. All monitored data is aggregated at a distributed noSQL data store for analysis and cross-system correlation. We demonstrate the performance and scalability of DCDB, and describe two use cases in the area of energy management and characterization.

关键词： High-Performance computing Monitoring distributed Data Store Infrastructure Management Application Analysis

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A CPN-based information propagation model in Online Social Networks

A CPN-based information propagation model in Online Social N...

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international conference on Ubiquitous computing and Communications and international Symposium on Cyberspace and Security (IUCC-CSS)

作者： Jinming Kong Wangyang Yu Fei Hao Key Laboratory for Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China

ISBN: (纸本)9781665466684

In Online Social Networks (OSNs), information is propagated by interaction among users. A user interacts with others through a variety of behaviors, which can promote the flow of information resource. The previous researches on information propagation lack the analysis of user behavior characteristics and the intuition of the dynamics of information propagation. A OSN system is a distributed concurrent system which is suitable to be modeled graphically with Petri net theory. Taking the Weibo platform as an example, this paper models and simulates its information propagation process using Colored Petri net (CPN). We adopt a modular modeling method. First, based on seven typical user behaviors, namely browsing (no action), posting, reposting, liking, commenting, replying and deleting, we model the information propagation process, which describes the spread of a microblog posted by the initiator of a topic. Then we count the number of actions of each type generated by users during the propagation process, which can evaluate the users' participation for the topic, that is, the popularity of the topic. Finally, we combine the two modules to obtain a complete information propagation model using sharing composition. In addition, we run the model in CPN Tools to simulate the spread of a microblog posted by the initiator of a topic. Moreover, we analyze and verify the state changes and user behavior sequences of the information propagation system using State Space Tools. Our work reveals that it is feasible and advantageous to apply Petri Net theory to social network analysis, so as to discover more information propagation rules in the future.

关键词： Information resources Social networking (online) Computational modeling Petri nets Blogs Ubiquitous computing

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iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction 19

iFDK: A Scalable Framework for Instant High-resolution Image...

引用

international conference for High Performance computing, networking, Storage and Analysis (SC)

作者： Chen, Peng Wahib, Mohamed Takizawa, Shinichiro Takano, Ryousei Matsuoka, Satoshi Tokyo Inst Technol AIST Tokyo Tech Real World Big Data Computat Open Innovat Lab Natl Inst Adv Ind Sci & Technol Tokyo Japan Natl Inst Adv Ind Sci & Technol AIST Tokyo Tech Real World Big Data Computat Open Innovat Lab Tsukuba Ibaraki Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Tokyo Inst Technol Ctr Computat Sci RIKEN Kobe Hyogo Japan

ISBN: (数字)9781450362290

ISBN: (纸本)9781450362290

Computed Tomography (CT) is a widely used technology that requires compute-intense algorithms for image reconstruction. We propose a novel back-projection algorithm that reduces the projection computation cost to 1/6 of the standard algorithm. We also propose an efficient implementation that takes advantage of the heterogeneity of GPU-accelerated systems by overlapping the filtering and back-projection stages on CPUs and GPUs, respectively. Finally, we propose a distributed framework for high-resolution image reconstruction on state-of-the-art GPU-accelerated supercomputers. The framework relies on an elaborate interleave of MPI collective communication steps to achieve scalable communication. Evaluation on a single Tesla V100 GPU demonstrates that our backprojection kernel performs up to 1.6x faster than the standard FDK implementation. We also demonstrate the scalability and instantaneous CT capability of the distributed framework by using up to 2,048 V100 GPUs to solve 4K and 8K problems within 30 seconds and 2 minutes, respectively (including I/O).

关键词： Computed Tomography GPU distributed Heterogeneous computing

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A Load Balancing Method for distributed Key-Value Store Based on Order Preserving Linear Hashing and Skip Graph 20

A Load Balancing Method for Distributed Key-Value Store Base...

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20th IEEE/ACIS international conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed computing (SNPD 2019)

作者： Higuchi, Ken Murayama, Jin Tsuji, Tatsuo Univ Fukui Grad Sch Engn Fukui Japan Univ Fukui Fukui Japan

ISBN: (纸本)9781728116518

In this paper, a load balancing method for the distributed key-value store based on order preserving linear hashing and Skip Graph is proposed. In this system, data are divided by order preserving linear hashing and Skip Graph is used for overlay network. The routing table of this system is very uniform. Then, short detours can exist in the route of forwarding. By using these detours, the number of hops for the query forwarding is reduced. But since data is partitioned by linear hash, load balancing among nodes is difficult. In the proposed method, by dividing a physical node and a Skip Graph node, load balancing can be done. Furthermore, the maximum number of hops for message forwarding become smaller by this load balancing method.

关键词： distributed key-value store Skip Graph linear hashing load balancing

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Energy Efficient Workload Processing Technology for distributed computing Environment

Energy Efficient Workload Processing Technology for Distribu...

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international Black Sea conference on Communications and networking (BlackSeaCom)

作者： Larysa Globa Nataliia Gvozdetska National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Institute of Telecommunication Systems Kyiv Ukraine

The following topics are dealt with: cellular radio; telecommunication traffic; quality of service; resource allocation; learning (artificial intelligence); probability; 5G mobile communication; multi-access systems; ... 详细信息

ISBN: (数字)9781728171272

ISBN: (纸本)9781728171289

关键词： Power demand Energy efficiency Data centers distributed computing Monitoring User interfaces Scheduling algorithms

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Quantization improvements for LU Decomposition to Solve Linear Equations *

Quantization improvements for LU Decomposition to Solve Line...

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

作者： Lin Chen Kang He Hao Jiang Roberto Barrio Jie Liu Tiejun Li Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China University of Zaragoza Zaragoza Spain

Quantization is an effective method for compressing Deep Neural Networks. Now, it is considered to accelerate the traditional HPC applications. In this article, we present a quantization method (q8gemm) using integer 8-bits(INT8) for matrix multiplication, which is implemented by block techniques to overcome the overflow problem. We choose different scales for one matrix’s each row and those for the other matrix’s each column to keep the numbers in range. With the intel advanced matrix extension cblas_gemm_s8u8s32, our q8gemm can accelerate panel-matrix multiplication in LU factorization. Furthermore, we propose a new mixed-precision solver (q8gesv 1 ) for the linear equations. Using q8gemm in INT8, q8gesv modifies LU factorization in FP32 and then utilizes GMRES iterative refinement in FP64 to bring the solution back to 64 bits accuracy. Numerical tests show that on two Intel CPU platforms with avx2 and avx512, q8gemm achieves a speedup of 1.313-1.621 over cblas_sgemm of MKL. The execution time of cblas_gemm_s8u8s32 makes up 86.78%-90.85% of the q8gemm. By means of q8gemm, our q8gesv can achieve a speedup of 1.176-1.493 over the optimized Higham’s mixed-precision algorithm utilized in the HPL-AI benchmark and a speedup of 1.1-1.459 over the LAPACKE_dsgesv function from MKL LAPACK. Meanwhile, q8gesv runs up to 2.859X faster on Intel(R) Core(TM) i5-10600KF and to 2.224X faster on Intel(R) Xeon(R) Gold 6130 than the LAPACKE_dgesv function from MKL LAPACK.

关键词： Deep learning Gold Quantization (signal) Benchmark testing

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distributed Sensor Data Collection Using Mobile Clouds for Public Transportation

Distributed Sensor Data Collection Using Mobile Clouds for P...

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IEEE international conference on Intelligent Computer Communication and Processing (ICCP)

作者： Anders Skretting Tor-Morten Grønli Mobile Technology Lab Kristiania University College Oslo Norway Oslo Metropolitan University Oslo Norway

ISBN: (纸本)9781665409773

Today mobile solutions are expected to streamline all kinds of daily tasks from entertainment and communication to more specific tasks such as ordering food or buying a ticket for public transport. Social networking solutions might entail frequent image processing while mobility solutions might activate sensors in order to provide public transport operators with insights into travel patterns. All of these tasks lead to an elevation in resource expenditure which becomes a problem for everyday mobile users, especially in terms of battery life. This work proposes the usage of mobile clouds in mobility related solutions in order to offload tasks and share context specific data with nearby devices. The proposed solution is a mobile application capable of ranking nearby nodes based on their performance characteristics, and then request these nodes to participate in a shared sensor data collection according to computing principles of mobile clouds. The proposed solution can be used to both reduce energy consumption of devices and increase the amount of sensor data collected. We evaluate the solution based on both energy consumption and data loss. We measure and compare both battery drainage and data loss where the latter is evaluated by measuring sensor events sent versus received.

关键词： Cloud computing Energy consumption Sensor phenomena and characterization Data collection Loss measurement Battery charge measurement Mobile applications

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