Journal: ACM International Conference Proceeding Series

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Association for Computing Machinery

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Publications 1 - 10 of 32
  • Robotic Maintenance of Road Infrastructures: The HERON Project
    Item type: Conference Paper
    Katsamenis, Iason; Bimpas, Matthaios; Protopapadakis, Eftychios; et al. (2022)
    ACM International Conference Proceeding Series ~ PETRA '22: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments
    Of all public assets, road infrastructure tops the list. Roads are crucial for economic development and growth, providing access to education, health, and employment. The maintenance, repair, and upgrade of roads are therefore vital to road users' health and safety as well as to a well-functioning and prosperous modern economy. The EU-funded HERON project will develop an integrated automated system to adequately maintain road infrastructure. In turn, this will reduce accidents, lower maintenance costs, and increase road network capacity and efficiency. To coordinate maintenance works, the project will design an autonomous ground robotic vehicle that will be supported by autonomous drones. Sensors and scanners for 3D mapping will be used in addition to artificial intelligence toolkits to help coordinate road maintenance and upgrade workflows.
  • Communication and Timing Issues with MPI Virtualization
    Item type: Conference Paper
    Nigay, Alexandr; Mosimann, Lukas; Schneider, Timo; et al. (2020)
    ACM International Conference Proceeding Series ~ EuroMPI/USA '20: 27th European MPI Users' Group Meeting
    Computation–communication overlap and good load balance are features central to high performance of parallel programs. Unfortunately, achieving them with MPI requires considerably increasing the complexity of user code. Our work contributes to the alternative solution to this problem: using a virtualized MPI implementation. Virtualized MPI implementations diverge from traditional MPI implementations in that they map MPI processes to user-level threads instead of operating-system processes and launch more of them than there are CPU cores in the system. They are capable of providing automatic computation–communication overlap and load balance with little to no changes to pre-existing MPI user code. Our work has uncovered new insights into MPI virtualization: Two new kinds of timers are needed: an MPI-process timer and a CPU-core timer, the same discussion also applies to performance counters and the MPI profiling interface. We also observe an interplay between the degree of CPU oversubscription and the rendezvous communication protocol: we find that the intuitive expectation of only two MPI processes per CPU core being enough to achieve full computation–communication overlap is wrong for the rendezvous protocol—instead, three MPI processes per CPU core are required in that case. Our findings are expected to be applicable to all virtualized MPI implementations as well as to general tasking runtime systems.
  • Can matrix-layout-independent numerical solvers be efficient?
    Item type: Conference Paper
    Lampka, Kai; Harwarth, Stefan; Siegle, Markus (2007)
    ACM International Conference Proceeding Series ~ Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
  • Evaluation of NTP/PTP fine-grain synchronization performance in HPC clusters
    Item type: Conference Paper
    Libri, Antonio; Bartolini, Andrea; Cesarini, Daniele; et al. (2018)
    ACM International Conference Proceeding Series ~ ANDARE '18 Proceedings of the 2nd Workshop on AutotuniNg and aDaptivity AppRoaches for Energy efficient HPC Systems
    Fine-grain time synchronization is important to address several challenges in today and future High Performance Computing (HPC) centers. Among the many, (i) co-scheduling techniques in parallel applications with sensitive bulk synchronous workloads, (ii) performance analysis tools and (iii) autotuning strategies that want to exploit State-of-the-Art (SoA) high resolution monitoring systems, are three examples where synchronization of few microseconds is required. Previous works report custom solutions to reach this performance without incurring in extra cost of dedicated hardware. On the other hand, the benefits to use robust standards which are widely supported by the community, such as Network Time Protocol (NTP) and Precision Time Protocol (PTP), are evident. With today's software and hardware improvements of these two protocols and off-the-shelf integration in SoA HPC servers no expensive extra hardware is required anymore, but an evaluation of their performance in supercomputing clusters is needed. Our results show NTP can reach on computing nodes an accuracy of 2.6us and a precision below 2.7us, with negligible overhead. These values can be bounded below microseconds, with PTP and low-cost switches (no needs of GPS antenna). Both protocols are also suitable for data time-stamping in SoA HPC monitoring infrastructures. We validate their performance with two real use-cases, and quantify scalability and CPU overhead. Finally, we report software settings and low-cost network configuration to reach these high precision synchronization results.
  • From PIace2Vec to Multi-Scale Built-Environment Representation: A General-Purpose Distributional Embedding for Urban Data Analysis
    Item type: Conference Paper
    Wang, Zhangyu; Moosavi, Vahid (2020)
    ACM International Conference Proceeding Series ~ LocalRec'20: Proceedings of the 4th ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks, and Geoadvertising
    Built environments like cities, roads, communities are rich sources of urban data. Many downstream applications require comprehensive analysis like geographic information retrieval, recommender systems, geographic knowledge graphs, and in general, understanding urban spaces [28]. Points of Interests (POI), as one of the most researched aspects of urban data, has been successfully modeled using concepts borrowed from Machine Learning (ML) and Natural Language Processing (NLP). In the work of Place2Vec [28], a Word2Vec-like statistical model is proposed to represent spatial adjacency with a continuous embedding space. This method successfully models the functional semantics of POIs with regard to several human-assessment based evaluations. However, though the Place2Vec model addresses the distributional heterogeneity within a given spatial context with ITDL augmentation, it does not address the spatial heterogeneity among different regions. To solve this problem, we propose to introduce a hierarchical, density-based, self-adjusting clustering mechanism. The boundary of relatedness and unrelatedness is learned from the given context, where denser areas have tighter bounds while sparser areas have looser ones. We train our model on both the baseline Yelp hierarchical dataset [28] and our OpenStreetMap dataset. We demonstrate that 1) our model significantly improves the performance on 2 of the 3 baseline tasks and the stability of training, and 2) our model generalizes excellently across 112 cities of radically different scales (minimum 1725 POIs, maximum 2694070 POIs), regions (North America, Europe, Asia, Africa) and types (commercial, touristy, industrial, etc.) without the need of adjusting or tuning any hyperparameters. We also demonstrate that our model can be used to discover interesting facts about cities like inter-city semantic analogy and intra-city connectivity, which can be very useful in urban planning, social computing and public policy making. © 2020 Association for Computing Machinery.
  • Adaptive random sensor selection for field reconstruction in wireless sensor networks
    Item type: Conference Paper
    Santini, Silvia; Colesanti, Ugo (2009)
    ACM International Conference Proceeding Series ~ Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
  • Enabling agile rapid product development in K12 classrooms by enhancing an educational exoskeleton
    Item type: Conference Paper
    Schifferle, Tobias; Kollegger, Nina (2021)
    ACM International Conference Proceeding Series ~ Proceedings of 5th FabLearn Europe / MakeEd Conference 2021
    The Product Development Module of the CYBATHLON @school series brings rapid prototyping, hardware and software development to regular school lessons or project weeks, combines it with entrepreneurship and fosters inclusion of people with disabilities. Engineering students and persons with disabilities conduct the units. Pupils aged 12 to 18 team up in their virtual startup and identify day-to-day issues of a person with a paralyzed arm. They develop solutions to mitigate those challenges with the help an assistive technology, the educational exoskeleton Flexo. They use modern rapid prototyping technologies to iteratively develop a gripper for the Flexo, using special software-assisted laser cutting and 3D printing. A knowledge integrated software interface helps them to program it using Python. The teams present their grippers in a final competition. Preliminary feedback suggests that the module is well received.
  • Gesture spotting using wrist worn microphone and 3-axis accelerometer
    Item type: Conference Paper
    Ward, Jamie A.; Lukowicz, Paul; Tröster, Gerhard (2005)
    ACM International Conference Proceeding Series ~ Smart objects and ambient intelligence
  • Cluster analysis of heterogeneous rank data
    Item type: Conference Paper
    Busse, Ludwig M.; Orbanz, Peter; Buhmann, Joachim M. (2007)
    ACM International Conference Proceeding Series ~ Proceedings of the 24th International Conference on Machine Learning, ICML '07
  • NFTeller: Dual-centric Visual Analytics for Assessing Market Performance of NFT Collectibles
    Item type: Conference Paper
    Cao, Yifan; Xia, Meng; Shigyo, Kento; et al. (2023)
    ACM International Conference Proceeding Series ~ VINCI '23: Proceedings of the 16th International Symposium on Visual Information Communication and Interaction
    Non-fungible tokens (NFTs) have recently gained widespread popularity as an alternative investment. However, the lack of assessment criteria has caused intense volatility in NFT marketplaces. Identifying attributes impacting the market performance of NFT collectibles is crucial but challenging due to the massive amount of heterogeneous and multi-modal data in NFT transactions, e.g., social media texts, numerical trading data, and images. To address this challenge, we introduce an interactive dual-centric visual analytics system, NFTeller, to facilitate users' analysis. First, we collaborate with five domain experts to distill static and dynamic impact attributes and collect relevant data. Next, we derive six analysis tasks and develop NFTeller to present the evolution of NFT transactions and correlate NFTs' market performance with impact attributes. Notably, we create an augmented chord diagram with a radial stacked bar chart to explore intersections between NFT collection projects and whale accounts. Finally, we conduct three case studies and interview domain experts to evaluate the effectiveness and usability of this system. As such, we gain in-depth insights into assessing NFT collectibles and detecting opportune moments for investment.
Publications 1 - 10 of 32