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computing Places and Human Activity in Data-absent Informal Urban Settlements

Computing Places and Human Activity in Data-absent Informal ...

引用

20th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Lai, Yuan Liu, Yifeng Tsinghua Univ Sch Architecture Dept Urban Planning Beijing Peoples R China NYU Marron Inst Urban Management New York NY 10012 USA Tsinghua Univ Sch Architecture Dept Architecture Beijing Peoples R China

ISBN: (纸本)9781665416474

Informal urban settlements are critical for sustainable development but challenging for computational analytics due to a lack of data, raising issues on data representativeness and the digital divide. This study explores a hybrid approach integrating conventional urban design theory and digitizing methods to compute places and associated human activities in a data-absent environment. Using an informal settlement area in Beijing as a testing site, this study demonstrates a digitizing pipeline that could be applied to other data-poor sites. Research findings reveal spatial characteristics of observed human activity and associated factors relevant to the informal use of space. Such investigation in computing informal places and human activity brings vital value to address the current digital divide and spatial justice in urban development.

关键词： urban informatics urban design digital divide

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Q-EEGNet: an Energy-Efficient 8-bit Quantized parallel EEGNet Implementation for Edge Motor-Imagery Brain-Machine Interfaces 6

Q-EEGNet: an Energy-Efficient 8-bit Quantized Parallel EEGNe...

引用

6th IEEE international conference on Smart computing (SMARTCOMP)

作者： Schneider, Tibor Wang, Xiaying Hersche, Michael Cavigelli, Lukas Benini, Luca Swiss Fed Inst Technol Dept EE & IT Switzerland Zurich Switzerland Univ Bologna DEI Bologna Italy Huawei Technol Zurich Res Ctr Zurich Switzerland

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169972

Motor-Imagery Brain-Machine Interfaces (MI-BMIs) promise direct and accessible communication between human brains and machines by analyzing brain activities recorded with Electroencephalography (EEG). Latency, reliability, and privacy constraints make it unsuitable to offload the computation to the cloud. Practical use cases demand a wearable, battery-operated device with low average power consumption for long-term use. Recently, sophisticated algorithms, in particular deep learning models, have emerged for classifying EEG signals. While reaching outstanding accuracy, these models often exceed the limitations of edge devices due to their memory and computational requirements. In this paper, we demonstrate algorithmic and implementation optimizations for EEGNET, a compact Convolutional Neural network (CNN) suitable for many BMI paradigms. We quantize weights and activations to 8-bit fixed-point with a negligible accuracy loss of 0.4% on 4-class MI, and present an energy-efficient hardware-aware implementation on the Mr. Wolf parallel ultra-low power (PULP) System-on-Chip (SoC) by utilizing its custom RISC-V ISA extensions and 8-core compute cluster. With our proposed optimization steps, we can obtain an overall speedup of 64 x and a reduction of up to 85% in memory footprint with respect to a single-core layer-wise baseline implementation. Our implementation takes only 5.82 ms and consumes 0.627 mJ per inference. With 21.0 GMAC/s/W, it is 256x more energy-efficient than an EEGNET implementation on an ARM Cortex-M7 (0.082 GMAC/s/W).

关键词： brain-machine interface edge computing parallel computing machine learning deep learning motor imagery

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QCCF-tree: A New Efficient IoT Big Data Indexing Method at the Fog-Cloud computing Level

QCCF-tree: A New Efficient IoT Big Data Indexing Method at t...

引用

IEEE international Smart Cities conference (ISC2)

作者： Khettabi, Karima Kouahla, Zineddine Farou, Brahim Seridi, Hamid Univ 8 Mai 1945 Dept LabSTIC LabSTIC Guelma Algeria

ISBN: (纸本)9781665449199

Over the past decades, the volume of Internet of Things (IoT) data has exploded which raise the problem of indexing, storing and retrieving efficiently. Most studies are based on dividing the target dataset into subsets using balls with one or two pivots. However, in the era of big data, where efficient indexing is important, the subspace volumes grow exponentially, which could degenerate the index. This problem is due to the inherent inadequacy of space partitioning. The topology must avoid biased allocation of objects for separable sets and must not influence the index structure. To meet this criteria, in this paper a new indexing structure called QCCF-tree (Quad tree based on Containers at the Cloud- Fog computing level), is proposed, based on dividing the space into four balk with four pivots. For the enhancement of the retrieving time in this new structure, the query is searched in parallel at each node of the QCCF-tree. The experimental evaluation of the proposed system, compared with several indexing systems, show that QCCF-tree outperforms most indexing systems either in the construction or the similarity query search.

关键词： IoT data Indexing Metric space QCCF-tree parallel similarity query search

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Dynamic algorithm selection in parallel GAMESS calculations

Dynamic algorithm selection in parallel GAMESS calculations

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35th international conference on parallel Processing

作者： Ustemirov, Nurzhan Sosonkina, Masha Gordon, Mark S. Schmidt, Michael W. Iowa State Univ Ames Lab Dept Comp Sci Ames IA 50011 USA Iowa State Univ Ames Lab Ames IA 50011 USA Iowa State Univ Dept Chem Ames IA 50011 USA

ISBN: (纸本)0769526373

Applications augmented with adaptive capabilities are becoming common in parallel computing environments which share resources such as main memory, network, or disk I/O. For large-scale scientific applications, dynamic adjustments to a computationally-intensive part may lead to a large pay-off in facilitating efficient execution of the entire application while aiming at avoiding resource contention. Application-specific knowledge, often best revealed during the run-time, is required to initiate and time these adjustments. In particular General Atomic and Molecular Electronic Structure System (GAMESS) used for ab initio molecular quantum chemistry calculations has two different implementations of Self-Consistency Field (SCF) methods, each of which targets either disk I/O or memory. This paper describes a mechanism enabling switching of algorithms during GAMESS run-time and shows the effect of the adaptations on the performance of GAMESS calculations as well as on a parallel GAMESS execution for different resource availability. The test results indicate that, in the presence of I/O resource contention, parallel GAMESS enhanced with adaptive mechanism may sustain the performance similar to that of full resource availability.

关键词： Algorithms

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Simulation of Large Scale Computational Ecosystems with Alchemist: A Tutorial 21st

Simulation of Large Scale Computational Ecosystems with Alch...

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21st ifip WG 6.1 international conference on Distributed Applications and Interoperable Systems (DAIS) Held as Part of the 16th international Federated conference on Distributed computing Techniques (DisCoTec)

作者： Pianini, Danilo Univ Bologna Alma Mater Studiorum Dipartimento Informat Sci & Ingn I-47522 Cesena FC Italy

ISBN: (纸本)9783030781972;9783030781989

Many interesting systems in several disciplines can be modeled as networks of nodes that can store and exchange data: pervasive systems, edge computing scenarios, and even biological and bio-inspired systems. These systems feature inherent complexity, and often simulation is the preferred (and sometimes the only) way of investigating their behavior;this is true both in the design phase and in the verification and testing phase. In this tutorial paper, we provide a guide to the simulation of such systems by leveraging Alchemist, an existing research tool used in several works in the literature. We introduce its meta-model and its extensible architecture;we discuss reference examples of increasing complexity;and we finally show how to configure the tool to automatically execute multiple repetitions of simulations with different controlled variables, achieving reliable and reproducible results.

关键词： Simulation Pervasive computing Self-organization

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A Framework for Routing and Resource Allocation in network Virtualization

A Framework for Routing and Resource Allocation in Network V...

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16th international conference on High Performance computing (HiPC 2009)

作者： Solheim, Ashild Gronstad Lysne, Olav Skeie, Tor Sodring, Thomas Reinemo, Sven-Arne Simula Res Lab Networks & Distributed Syst Grp Lysaker Norway

ISBN: (纸本)9781424449217

Computer architectures for high performance computing have traditionally been based on an assumption of one parallel application running alone on one machine. The current trend is, however, that huge computer installations offer compute power to a set of users or customers, each demanding only a subset of the available compute resources. This places new requirements on the architecture, in that it must support dynamic partitioning of the resources into several virtual servers as demand changes. We introduce a novel framework which supports flexible formation of such virtual servers while preventing interference between the communication of different virtual servers. This paper investigates the impacts of a shared interconnection network on applications running on virtual compute servers. We show that the interconnect performance supplied to each job is highly unpredictable, and that a job can experience a performance degradation of 97% when its traffic interferes with the traffic of concurrent jobs. With a minor reduction in the utilization of each processing node, this can be considerably improved through a combination of routing-containment in the interconnection network and a carefully designed resource allocation strategy.

关键词： Computer architecture

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Swarm: Playground for Large-scale Decentralized Learning Simulations

Swarm: Playground for Large-scale Decentralized Learning Sim...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Lee, Sangsu Yu, Haoxiang Zheng, Xi Julien, Christine Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA Macquarie Univ Dept Comp N Ryde NSW Australia

ISBN: (纸本)9781665416474

Decentralized learning is an emerging field of research that opens doors for many novel pervasive computing applications. In decentralized learning, model training is offloaded to devices in the edge, and in some approaches, functions entirely without a central controller. SWARM is a tool for fast and large-scale simulations to test the performance of the practical implementations of decentralized learning algorithms that underlie many pervasive computing applications. In SWARM, developers can easily launch simulations for their algorithms by simply writing code that defines the behavior of a device when collaborating with others. The developer delegates to SWARM the emulation of the devices' encounters, given a pervasive computing scenario. By decoupling the encounter emulation and the learning algorithm execution, SWARM makes the configuration of diverse application scenarios easy and their simulations repeatable. Moreover, developers can evaluate the scalability of an algorithm in diverse and large-scale application contexts as SWARM can automatically deploy and manage multiple worker nodes. Finally, the SWARM Dashboard provides a visualization of the simulation progress and the algorithm performance.

关键词： mobile computing distributed machine learning computer simulation

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When CPN Meets AI: Resource Provisioning for Inference Query upon computing Power network 29

When CPN Meets AI: Resource Provisioning for Inference Query...

引用

29th IEEE international conference on parallel and Distributed Systems, ICPADS 2023

作者： Ji, Mingtao Qian, Zhuzhong Ye, Baoliu Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350330717

Performing machine learning inference at the network edge, named Edge Inference, showing benefits like low latency, reduced data traffic, and improved user privacy, has attracted massive attention. computing Power network (CPN) creates opportunities for edge inference, but also poses multiple challenges for service providers, including computing power selections from different enterprises, the time-coupled decision for resource provisioning and the unpredictable CPN network status. To overcome these challenges, this study formulates a time-varying integer program problem whose goal is to minimize long-term costs, involving switching costs, operational costs, communication costs, and queuing costs. Then we design a group of polynomial-time online algorithms to make online decisions, by taking into account stochastic inputs. Our algorithms adaptively make control decisions by solving delicately constructed subproblems based on the inputs predicted via online learning. Specifically, we first obtain fractional solutions which are transformed into integers for deployment with expectations preserving. Furthermore, we conduct a rigorous proof and establish the competitive ratio which highlights the difference between the performance of our proposed algorithms and the offline optimum. Our comprehensive evaluations, using datasets from real systems, demonstrate that our algorithms outperform multiple alternatives, up to an average of 35% cost reduction, confirming the effectiveness of our algorithms. © 2023 IEEE.

关键词： computing Power network Resource Provisioning

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Peer-to-peer lessons for cluster file systems

Peer-to-peer lessons for cluster file systems

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international conference on parallel and Distributed Processing Techniques and Applications

作者： Mache, J Garg, S Lewis and Clark College Portland OR 97219 United States Intel Corp. Beaverton OR 97006 United States

ISBN: (纸本)1892512459

parallel file systems provide high-performance disk access by transparently striping data across multiple disks and I/O nodes. Similar to peer-to-peer systems (e.g. Freenet, Oceanstore, Chord/ CFS, Past), parallel file systems for clusters are employed on networked computers whose nodes are not guaranteed to be always available, due to node failures or network failure. However, different from peer-to-peer systems, cluster file systems like PVFS do not handle these failures very well. In this work, we explore how cluster file system can utilize certain peer-to-peer techniques that can handle failing nodes and thus allow for high data availability.

关键词： Availability Cluster computing parallel file system Peer-to-peer Performance

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A Trajectory Prediction Method with Sparsity Data 15

A Trajectory Prediction Method with Sparsity Data

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15th IEEE international Symposium on parallel and Distributed Processing with Applications (ISPA) / 16th IEEE international conference on Ubiquitous computing and Communications (IUCC)

作者： Fan, Hengfei Yao, Wenbin Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Natl Engn Lab Mobile Network Secur Beijing Peoples R China

ISBN: (纸本)9781538637906

A timely and accurate prediction of the vehicle's trajectory can render a good service, help predict traffic flow, and in the more interesting case, detect potentially dangerous situations as early as possible. It is widespread to use historical data to predict trajectories. However, due to the sparsity of trajectory, sometimes it is difficult to predicate trajectory. A novel approach to trajectory prediction is proposed which has the capability to predict the trajectory with sparse data. To achieve this, we denote city route and trajectory as a series of fixed points. Under normal conditions, only the most similarly trajectory is used to predict trajectories, which may mislead the result. To remedy this problem, we use several most similarity trajectories to predict the next point, and regard the most points as our result. The experimental results show that the approach improves the prediction accuracy 8%, coverage 2%, and greatly outperform the baseline algorithm (30 ms) compared with Spatial Iterative Grid Partition (SIGP).

关键词： vehicle sparsity data trajectory prediction

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