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2021 International Conference on Bioinformatics and Intelligent computing, BIC 2021

作者： Su, Yuqi School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Guangdong China

ISBN: (纸本)9781450390002

A Brain-Computer Interface (BCI) aims at providing a way for controlling external devices through the utilization of brain signals. One of the challenges in electroencephalography (EEG)-based BCI is to adjust the brain signal decoder to detect a user's intention as accurately and efficiently as possible, as EEG signals are non-stationary. Therefore, adaptive classification, an approach to adapt to the changes of the EEG signals, would be effective in overcoming this problem. This paper provides a review of the representative adaptive classifiers used in BCI, and it can be divided into four categories: adaptive linear discriminant analysis, adaptive support vector machine, adaptive Bayesian classifiers and adaptive Riemannian Geometry-based classifiers. Besides, the pros and cons of these adaptive classification algorithms are further described. © 2021 acm.

关键词： Electroencephalography

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Snowflakes at the Edge: A Study of Variability among NVIDIA Jetson AGX Xavier Boards 4

Snowflakes at the Edge: A Study of Variability among NVIDIA ...

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4th International Workshop on Edge systems, Analytics and Networking (EdgeSys)

作者： Abdelhafez, Hazem A. Halawa, Hassan Pattabiraman, Karthik Ripeanu, Matei Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781450382915

While applications deployed at the edge often rely on performance stability (or, at a minimum, on a predictable level of performance), variability at the edge remains a real problem [4]. This study uncovers a surprising source of variability: intrinsic variability (in performance and power consumption) among edge platforms that are nominally identical. We focus on a popular platform designed for edge applications, the NVIDIA Jetson AGX, and aim to answer the following high-level questions through rigorous statistical analysis: (i) are the edge devices in our study statistically different from each other in terms of applications' runtime performance and power draw (although they are sold under the same product model and family)?, (ii) if the differences between these edge devices are statistically significant, what is the magnitude of these differences?, and (iii) do these differences matter from the application's perspective?

关键词： Performance and power variation Edge computing Jetson AGX

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"Taking care of myself as long as I can": How People with Dementia Configure Self-Management systems 21

"Taking care of myself as long as I can": How People with De...

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CHI Conference on Human Factors in computing systems

作者： Dixon, Emma Piper, Anne Marie Lazar, Amanda Univ Maryland College Pk MD 20742 USA Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9781450380966

Self-management research in HCI has addressed a variety of conditions. Yet, this literature has largely focused on neurotypical populations and chronic conditions that can be managed, leaving open questions of what self-management might look like for populations with progressive cognitive impairment. Grounded in interviews with seventeen technology savvy people with mild to moderate dementia, our analysis reveals their use of technological and social resources as part of the work of self-management. We detail how participants design self-management systems to enable desired futures, function well in their social world, and maintain control. Our discussion broadens the notion of self-management to include future-oriented, sociotechnical, self-determinate design. We advocate for expanding the way technologists, designers, and HCI scholars view people with mild to moderate dementia to recognize them as inventive creators and capable actors in self-management.

关键词： Self-management Dementia Self-management Technology Chronic Care Sociotechnical systems Self-determination

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MOST: Model-Based Compression with Outlier Storage for Time Series Data

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proceedings of the acm on Management of Data 2023年第4期1卷 1-29页

作者： Zehai Yang Shimin Chen Institute of Computing Technology CAS & University of Chinese Academy of Sciences Beijing China

Time series data are used in a wide variety of applications. The explosive growth of the amount of time series data poses a significant challenge in efficient data storage and query processing. Unfortunately, existing compression techniques either show only low to medium compression ratio on time series data, or incur significant decompression overhead during query *** propose a novel compression technique, MOST (Model-based compression with Outlier STorage) for time series data. As measurement values often change smoothly in a period of time, we divide a time series into segments of smooth changes, then compute a linear model for each segment. Since tiny errors are often acceptable in analysis tasks, we omit data points whose computed values are within a pre-specified error threshold from the actual values, thereby effectively reducing the data size. Outliers are rare but important for many applications, and therefore we store outliers explicitly. Moreover, for processing MOST compressed data, we propose a segment-outlier dual-mode query engine that computes segments as a whole as much as possible, and build a prototype MostDB. Experimental results on real-world data sets show that MOST achieves 9.45-15.04x compression ratios. Compared to existing time series databases, MostDB achieves up to 11.68x speedups for common queries from the IoTDB Benchmark.

关键词： dual-mode query engine model-based compression outlier

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DSM: A Case for Hardware-Assisted Merging of DRAM Rows with Same Content

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proceedings of the acm on measurement and analysis of computing systems 2020年第2期4卷 1–26页

作者： Ghahani, Seyed Armin Vakil Kandemir, Mahmut Taylan Kotra, Jagadish B. Penn State Univ State Coll PA 16802 USA AMD Res Austin TX 78735 USA

The number of cores and the capacities of main memory in modern systems have been growing significantly. Specifically, memory scaling, although at a slower pace than computation scaling, provided opportunities for very large DRAMs with Terabytes (TBs) capacity. Consequently, addressing the performance and energy consumption bottlenecks of DRAMs is more important than ever. DRAM memory refresh operation is one of the main contributing factors to the memory overheads, especially for large capacity DRAMs used in modern servers and emerging large-scale data centers. This paper addresses the memory refresh problem by leveraging the fact that most cloud servers host virtualized systems that use similar kernels, libraries, etc. We propose and experimentally evaluate a novel approach that exploits this observation to address the DRAM refresh overhead in such systems. More specifically, in this work, we present DSM, a light-weight hardware extension in memory controller to detect the pages with same content in memory and refresh only one of them and redirect the requests to the others to this page. Our detailed experimental analysis shows that the proposed DSM design can reduce 99th percentile memory access latency by up to 2.01x, and it also reduces the overall memory energy consumption by up to 8.5%.

关键词： DRAM Memory Refresh Virtualized systems KSM

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Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online 21

Viral Visualizations: How Coronavirus Skeptics Use Orthodox ...

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CHI Conference on Human Factors in computing systems

作者： Lee, Crystal Yang, Tanya Inchoco, Gabrielle Jones, Graham M. Satyanarayan, Arvind MIT 77 Massachusetts Ave Cambridge MA 02139 USA Wellesley Coll Wellesley MA 02181 USA

ISBN: (纸本)9781450380966

Controversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government's pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.

关键词： digital ethnography network analysis Twitter Facebook data literacy data visualization

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The Impact of a Live Refactoring Environment on Software Development

The Impact of a Live Refactoring Environment on Software Dev...

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International Conference on Software Engineering - Companion, ICSE, Companion

作者： Sara Fernandes Ademar Aguiar André Restivo Faculty of Engineering University of Porto INESC TEC Porto Porto Portugal

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

Reading, adapting, and maintaining complex software can be a daunting task. We might need to refactor it to streamline the process and make the code cleaner and self-explanatory. Traditional refactoring tools guide developers to achieve better-quality code. However, the feedback and assistance they provide can take considerable time. To tackle this issue, we explored the concept of Live Refactoring. This approach focuses on delivering real-time, visually-driven refactoring suggestions. That way, we prototyped a Live Refactoring Environment that visually identifies, recommends, and applies several refactorings in real-time. To validate its effectiveness, we conducted a set of experiments. Those showed that our approach significantly improved various code quality metrics and outperformed the results obtained from manually refactoring code.

关键词： measurement Visualization Codes Programming Real-time systems Software Task analysis

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Where did my 256 GB go? A measurement analysis of Storage Consumption on Smart Mobile Devices 21

Where did my 256 GB go? A Measurement Analysis of Storage Co...

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2021 acm SIGMETRICS / International Conference on measurement and Modeling of Computer systems, SIGMETRICS 2021

作者： Bijlani, Ashish Ramachandran, Umakishore Campbell, Roy Georgia Institute of Technology Atlanta United States University of Illinois at Urbana-Champaign Urbana United States

ISBN: (纸本)9781450380720

This work presents the first-ever detailed and large-scale measurement analysis of storage consumption behavior of applications (apps) on smart mobile devices. We start by carrying out a five-year longitudinal static analysis of millions of Android apps to study the increase in their sizes over time and identify various sources of app storage consumption. Our study reveals that mobile apps have evolved as large monolithic packages that are packed with features to monetize/engage users and optimized for performance at the cost of redundant storage consumption. We also carry out a mobile storage usage study with 140 Android participants. We built and deployed a lightweight context-aware storage tracing tool, called cosmos, on each participant's device. Leveraging the traces from our user study, we show that only a small fraction of apps/features are actively used and usage is correlated to user context. Our findings suggest a high degree of app feature bloat and unused functionality, which leads to inefficient use of storage. Furthermore, we found that apps are not constrained by storage quota limits, and developers freely abuse persistent storage by frequently caching data, creating debug logs, user analytics, and downloading advertisements as needed. Finally, drawing upon our findings, we discuss the need for efficient mobile storage management, and propose an elastic storage design to reclaim storage space when unused. We further identify research challenges and quantify expected storage savings from such a design. We believe our findings will be valuable to the storage research community as well as mobile app developers. © 2021 Owner/Author.

关键词： Android (operating system)

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Plant Health Evaluation Based on Bioimpedance Phase measurement 6

Plant Health Evaluation Based on Bioimpedance Phase Measurem...

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6th International Symposium on Instrumentation systems, Circuits and Transducers, INSCIT 2022

作者： Carvalho, Stephane Souza, Matheus Brusamarello, Valner Dos Santos, Tawan Chrysther Carvalho, Elyson Carvalho, Jose Freire, Raimundo C. S. Federal University of Sergipe Electrical Engineering Department São Cristóvão Brazil Federal University of Rio Grande Do Sul PPGEE Porto Alegre Brazil Federal University of Campina Grande Paraíba Brazil

ISBN: (数字)9781665471688

ISBN: (纸本)9781665471688

Crop diseases and pest infestation are one of the main causes of economic losses in the agriculture business and reduction in food production. In this context, the application of techniques for early diagnosis of crop health has been widely reported in recent literature and Electrical Impedance Spectroscopy (EIS) has been shown as a powerful tool. However, despite the EIS is the technique with the greatest potential to detect stresses in the initial stage, there are two main limitations that hinder the application of this technique: The sensitivity to environmental factors and the difficulty in classifying different types of stress. So, in this paper we propose a simple way of detecting plant stress through EIS from the analysis of the electrical model of plant tissue, measuring only one parameter (phase) of the bioimpedance. The results show that is possible to use single information, the phase change, and a particular range or even a fixed frequency, instead of dealing with a wide frequency range and several types of measurements simultaneously. © 2022 IEEE.

关键词： Losses

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Memory Space Recycling

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proceedings of the acm on measurement and analysis of computing systems 2022年第1期6卷 1–24页

作者： Ryoo, Jihyun Kandemir, Mahmut Taylan Karakoy, Mustafa Penn State Univ University Pk PA 16802 USA TUBITAK BILGEM Kocaeli Turkey

Many program codes from different application domains process very large amounts of data, making their cache memory behavior critical for high performance. Most of the existing work targeting cache memory hierarchies focus on improving data access patterns, e.g., maximizing sequential accesses to program data structures via code and/or data layout restructuring strategies. Prior work has addressed this data locality optimization problem in the context of both single-core and multicore systems. Another dimension of optimization, which can be as equally important/beneficial as improving data access pattern is to reduce the data volume (total number of addresses) accessed by the program code. Compared to data access pattern restructuring, this volume minimization problem has relatively taken much less attention. In this work, we focus on this volume minimization problem and address it in both single-core and multi-core execution scenarios. Specifically, we explore the idea of rewriting an application program code to reduce its "memory space footprint". The main idea behind this approach is to reuse/recycle, for a given data element, a memory location that has originally been assigned to another data element, provided that the lifetimes of these two data elements do not overlap with each other. A unique aspect is that it is "distance aware", i.e., in identifying the memory/cache locations to recycle it takes into account the physical distance between the location of the core and the memory/cache location to be recycled. We present a detailed experimental evaluation of our proposed memory space recycling strategy, using five different metrics: memory space consumption, network footprint, data access distance, cache miss rate, and execution time. The experimental results show that our proposed approach brings, respectively, 33.2%, 48.6%, 46.5%, 31.8%, and 27.9% average improvements in these metrics, in the case of single-threaded applications. With the multi-threaded vers

关键词： multi-core processors memory systems high performance computing compilers workload characterization

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