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IEEE International Conference on High Performance computing and Communications

作者： Mostafa Mehrabi Xing Fan Nasser Giacaman Oliver Sinnen Department of Electrical and Computer Engineering The University of Auckland Parallel and Reconfigurable Computing Lab

ISBN: (纸本)9781509042982

A reduction is a parallel programming mechanism for combining two or more elements into one. Many parallel programming languages, tools and frameworks (e.g., OpenMP, MPI, etc.) directly support simple forms of reductions (e.g., building a total sum out of partial sums). Some of those tools and frameworks allow more complex reductions to be implemented as custom reductions. However, the success of network-based application frameworks like Hadoop have shown that there is a strong need for reductions of aggregate data structures, such as the union of sets or maps. Usually parallel programming frameworks on shared-memory systems do not support these types of complex reductions directly, and a user needs to implement them manually. To address the gap thereof, this paper proposes an object-oriented reduction framework that supports reductions of aggregate types, and proposes the nesting of reduction objects for flexible extensions of reduction operations. Based on the proposed framework, a reduction library (RedLib) has been developed for Java with direct support for many common reduction operations on collections and maps. Furthermore, the paper studies the usage of the framework for common and complex cases and evaluates its performance, based on operations found in standard benchmarks.

关键词： object oriented reductions shared memory reductions nestable reductions aggregate reductions collections maps :

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Graph based Tensor Recovery for Accurate Internet Anomaly Detection

Graph based Tensor Recovery for Accurate Internet Anomaly De...

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2018 IEEE Conference on computer Communications, INFOCOM 2018

作者： Xie, Kun Li, Xiaocan Wang, Xin Xie, Gaogang Wen, Jigang Zhang, Dafang College of Computer Science and Electronics Engineering Hunan University China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Department of Electrical and Computer Engineering State University of New York at Stony Brook United States Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9781538641286

Detecting anomalous traffic is a crucial task of managing networks. Many anomaly detection algorithms have been proposed recently. However, constrained by their matrix-based traffic data model, existing algorithms often suffer from low detection accuracy. To fully utilize the multi-dimensional information hidden in the traffic data, this paper takes an initiative to investigate the potential and methodologies of performing tensor factorization for more accurate Internet anomaly detection. Only considering the low-rank linearity features hidden in the data, current tensor factorization techniques would result in low anomaly detection accuracy. We propose a novel Graph-based Tensor Recovery model (Graph-TR) to well explore both low rank linearity features as well as the non-linear proximity information hidden in the traffic data for better anomaly detection. We encode the non-linear proximity information of the traffic data by constructing nearest neighbor graphs and incorporate this information into the tensor factorization using the graph Laplacian. Moreover, to facilitate the quick building of neighbor graph, we propose a nearest neighbor searching algorithm with the simple locality-sensitive hashing (LSH). We have conducted extensive experiments using Internet traffic trace data Abilene and GEANT. Compared with the state of art algorithms on matrix-based anomaly detection and tensor recovery approach, our Graph-Trcan achieve significantly lower False Positive Rate and higher True Positive Rate. © 2018 IEEE.

关键词： Anomaly detection

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Alzheimer’s Disease and Frontotemporal Dementia: Differential Diagnosis Using Electroencephalogram Signal 1

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13th National Technical Symposium on Unmanned System Technology, NUSYS 2023

作者： Rostamikia, Mehran Sarbaz, Yashar Farzamnia, Ali Biomedical System Modeling Lab Biomedical Engineering Department Electrical and Computer Engineering Faculty University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Dementia is a neurological disorder that affects a person’s cognitive and social skills, leading to a decline in their overall mental functioning. Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) are two common types of dementia. Frontotemporal dementia mostly affects the frontal and temporal lobes of the brain. These areas are responsible for executive functions, decision-making, language, behavior regulation, and personality traits. Alzheimer’s disease primarily damages the cerebral cortex, which is responsible for higher cognitive functions such as memory, language, and perception. The electroencephalogram (EEG) signal has many advantages in diagnosing these disorders, including low cost and high temporal resolution. This study compares AD and FTD patients with healthy subjects by extracting specific features from EEG signals. Two machine learning algorithms were used for the separation, Support Vector Machines (SVMs) and k-Nearest Neighbors (KNNs), and 10-Fold Cross-Validation was applied to validate the performance of this method and an accuracy of 91.2% (sd = 7.8) was achieved using the SVM classifier for diagnosing the disease and 71.1% (sd = 8.6) for classifying AD and FTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition 60

Self-supervised Semantic-driven Phoneme Discovery for Zero-r...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Wang, Liming Feng, Siyuan Hasegawa-Johnson, Mark Yoo, Chang D. Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Multimedia Computing Group Delft University of Technology Netherlands Artificial Intelligence and Machine Learning Lab KAIST Korea Republic of

ISBN: (纸本)9781955917216

Phonemes are defined by their relationship to words: changing a phoneme changes the word. Learning a phoneme inventory with little supervision has been a longstanding challenge with important applications to under-resourced speech technology. In this paper, we bridge the gap between the linguistic and statistical definition of phonemes and propose a novel neural discrete representation learning model for self-supervised learning of phoneme inventory with raw speech and word labels. Given the availability of phoneme segmentation and some mild conditions, we prove that the phoneme inventory learned by our approach converges to the true one with an exponentially low error rate. Moreover, in experiments on TIMIT and Mboshi benchmarks, our approach consistently learns a better phoneme-level representation and achieves a lower error rate in a zero-resource phoneme recognition task than previous state-of-the-art self-supervised representation learning algorithms. © 2022 Association for Computational Linguistics.

关键词： Speech recognition

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Microwave spectral analysis using optical spectral holeburning

Microwave spectral analysis using optical spectral holeburni...

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2004 IEEE International Topical Meeting on Microwave Photonics - Technical Digest, MWP'04

作者： Mohan, R. Krishna Cole, Zachary Reibel, Randy R. Chang, Tiejun Merkel, Kristian D. Babbitt, Wm. Randall Colice, Max Schlottau, Friso Wagner, Kelvin H. Spectrum Lab. Montana State University Bozeman MT 59717-3510 Optoelectronic Computing Systems Center Department of Electrical and Computer Engineering University of Colorado at Boulder Boulder CO 80309-0425

A microwave spectrum analyzer capable of capturing multi-GHz spectra with sub-MHz resolution and unity probability of intercept based on optical spectral holebuming materials is proposed and initial demonstrations pre... 详细信息

关键词： Spectrum analysis

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Segmentation as postprocessing for hyperspectral image classification

Segmentation as postprocessing for hyperspectral image class...

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IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015

作者： Jimenez, L.I. Ayma, V.A. Achanccaray, P. Costa, G.A.O.P. Feitosa, R.Q. Plaza, A. Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Caceres Spain Computer Vision Lab Department of Electrical Engineering Catholic University of Rio de Janeiro Rio de Janeiro Brazil Department of Computer Engineering Rio de Janeiro State University Rio de Janeiro Brazil

ISBN: (纸本)9781479979295

Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images at differents wavelength values for the same area of the Earth. For instance the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor of NASA capable to obtain 224 spectral channels in a wavelength range between 40 and 250 nanometers. As a result each pixel of the image can be represented as a spectral signature. Image segmentation is the process of dividing a digital image into groups of pixels or objects. Hyperspectral image classification is an important and active area dedicated to identifying each pixel in the image with an exclusive material/object class. Several efforts had been done in this field using spectral and spatial information separately or simultaneously in order to improve the performance of the classification techniques. In this work we have developed a new technique that uses a segmentation algorithm to post-process the classification results obtained using a widely used classifier such as the support vector machine (SVM). Experimental results with a real hyperspectral data set collected over the city of Pavia, Italy, are provided. © 2015 IEEE.

关键词： classification hyperspectral imaging Segmentation

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Facilitating temporal synchronous target selection through user behavior modeling

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019年第4期3卷 1-24页

作者： Zhang, Tengxiang Yi, Xin Wang, Ruolin Gao, Jiayuan Wang, Yuntao Yu, Chun Li, Simin Shi, Yuanchun Tsinghua University Institute of Computing Technology Chinese Academy of Sciences Kexueyuan South Road Beijing100084 China Key Laboratory of Pervasive Computing Ministry of Education Beijing Key Lab of Networked Multimedia Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University China Tsinghua University China Global Innovation eXchange Institute Key Laboratory of Pervasive Computing Ministry of Education Beijing Key Lab of Networked Multimedia Department of Computer Science and Technology Tsinghua University Shuangqing Road Beijing100081 China Department of Electrical and Computing Engineering Beihang University Beijing100081 China Tsinghua University Shuangqing Road Beijing100081 China UCLA United States

Temporal synchronous target selection is an association-free selection technique: users select a target by generating signals (e.g., finger taps and hand claps) in sync with its unique temporal pattern. However, classical pattern set design and input recognition algorithm of such techniques did not leverage users' behavioral information, which limits their robustness to imprecise inputs. In this paper, we improve these two key components by modeling users' interaction behavior. In the first user study, we asked users to tap a finger in sync with blinking patterns with various period and delay, and modeled their finger tapping ability using Gaussian distribution. Based on the results, we generated pattern sets for up to 22 targets that minimized the possibility of confusion due to imprecise inputs. In the second user study, we validated that the optimized pattern sets could reduce error rate from 23% to 7% for the classical Correlation recognizer. We also tested a novel Bayesian, which achieved higher selection accuracy than the Correlation recognizer when the input sequence is short. The informal evaluation results show that the selection technique can be effectively scaled to different modalities and sensing techniques. Copyright © 2019 held by the owner/author(s).

关键词： Taps

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Principal Component Pursuit with reduced linear measurements

Principal Component Pursuit with reduced linear measurements

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IEEE International Symposium on Information Theory

作者： Arvind Ganesh Kerui Min John Wright Yi Ma Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Department of Electrical Engineering Columbia University USA Visual Computing Group Microsoft Research Asia China

ISBN: (纸本)9781467325790

In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data processing tasks such as aligning multiple images or rectifying regular texture, where the goal is to recover a low-rank matrix with a large fraction of corrupted entries in the presence of nonlinear domain transformation. We consider a natural convex heuristic to this problem which is a variant to the recently proposed Principal Component Pursuit. We prove that under suitable conditions, this convex program guarantees to recover the correct low-rank and sparse components despite reduced measurements. Our analysis covers both random and deterministic measurement models.

关键词： Sparse matrices Manganese Matrix decomposition Information theory Linear matrix inequalities Numerical models Robustness

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Quality of Service (QoS)-Driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions 1

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NIELIT's International Conference on Communication, Electronics and Digital Technology, NICE-DT 2023

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer and Information Sciences The University of Hyderabad Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

ISBN: (数字)9789819916993

ISBN: (纸本)9789819916986

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in health care and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses Edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage, and monitor the hospital systems in real time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Compressive principal component pursuit

Compressive principal component pursuit

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IEEE International Symposium on Information Theory

作者： John Wright Arvind Ganesh Kerui Min Yi Ma Department of Electrical Engineering Columbia University USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign USA Visual Computing Group Microsoft Research Asia China

ISBN: (纸本)9781467325790

We consider the problem of recovering a target matrix that is a superposition of low-rank and sparse components, from a small set of linear measurements. This problem arises in compressed sensing of structured high-dimensional signals such as videos and hyperspectral images, as well as in the analysis of transformation invariant low-rank recovery. We analyze the performance of the natural convex heuristic for solving this problem, under the assumption that measurements are chosen uniformly at random. We prove that this heuristic exactly recovers low-rank and sparse terms, provided the number of observations exceeds the number of intrinsic degrees of freedom of the component signals by a polylogarithmic factor. Our analysis introduces several ideas that may be of independent interest for the more general problem of compressive sensing of superpositions of structured signals.

关键词： Sparse matrices Matrix decomposition Manganese Robustness Q measurement Compressed sensing Image coding

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