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DEBS 2022 - Proceedings of the 16th ACM international conference on distributed and Event-Based Systems

DEBS 2022 - Proceedings of the 16th ACM International Confer...

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16th ACM international conference on distributed and Event-Based Systems, DEBS 2022

ISBN: (纸本)9781450393089

The proceedings contain 27 papers. The topics discussed include: CougaR: fast and eclipse-resilient dissemination for blockchain networks;optimizing complex event forecasting;predicate-based push-pull communication for distributed CEP;toward reducing cross-shard transaction overhead in sharded blockchains;substream management in distributed streaming dataflows;a multi-level caching architecture for stateful stream computation;travel light - state shedding for efficient operator migration;zero-shot cost models for distributed stream processing;window-based parallel operator execution with in-network computing;event-based data-centric semantics for consistent data management in microservices;knowledge graph stream processing at the edge;and deriving a realistic workload model to simulate high-volume financial data feeds for performance benchmarking.

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Studies of Micro Nets for Edge Computed Nonintrusive Load Monitoring 4

Studies of Micro Nets for Edge Computed Nonintrusive Load Mo...

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4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Sun, Mingyang Zheng, Zhe Liu, Rui Cui, Wenpeng Research Center Beijing Smart-Chip Microelectronics Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350337181

Deep learning approach has been widely used to achieve appliances recognition or energy disaggregation for nonintrusive load monitoring (NILM) problem. In real scenarios, NILM models should be deployed on MCUs in edge computing devices, where there are limited hardware resources with even less than one MB of SRAM and on-chip eFlash. Although there are several researches having explored to deploy NILM models on mobile devices, a mobile SoC usually has several GB DRAM and tens of GB Flash which are much more abundant than those on MCUs. To design a neural network that could meet the hardware constraints on MCUs while retaining a good enough recognition accuracy for NILM problem is a tough task. In this paper, we carry out a comparative study between several well-known lightweight CNN models. The units making up winning models are used as references to design a new base unit. Then, this new base unit is optimized to cut down the requirements on SRAM and eFlash as well as to improve the recognition performance. A SE block is added to this new unit. The impact of reduction ratio of SE block for NILM problem is studied in detail. The results demonstrate that a smaller value of reduction ratio should be adopted for micro nets deployed on MCUs. When reduction ratio is 2, the best performance of 96.75% in F1-score is achieved. A micro neural network consisting of the optimized base unit is put forward, achieving an improvement of about 1.7% in F1-score than others. © 2023 IEEE.

关键词： Neural networks

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Integration of Transfer Learning and Self-Attention for Spontaneous Micro-Expression Recognition 7

Integration of Transfer Learning and Self-Attention for Spon...

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7th international conference on parallel, distributed and Grid computing, PDGC 2022

作者： Indolia, Sakshi Nigam, Swati Singh, Rajiv Banasthali Vidyapith Department of Computer Science Rajasthan India

ISBN: (纸本)9781665454018

Micro-expressions are involuntary facial expressions which cannot hide true mental state of a person. These expressions appear for a very short duration of time and thus are very difficult to spot and recognize. Performance of deep learning models becomes stagnant due to overfitting;transfer learning helps to address this issue. Although, spotting and classification of micro-expression is a challenging task, but self-attention mechanism can easily detect relevant facial region required for micro-expression recognition. Considering these advantages of transfer learning and self-attention mechanism, in this work, two deep CNN models - Dual Stream ResNet18 (DSResNet) and Dual Stream ResNet18 Attention (DSResNetAtt) are proposed which exploits transfer learning through ResNet18 and self-attention for spontaneous Micro-expression recognition (MER). To demonstrate the effect of self-attention, the model is evaluated in terms of classification accuracy on three benchmark datasets CASME-I, CASME-II, and SAMM. Performance of the model has also been compared with state-of-the-art existing models and comparative analysis shows its efficiency in terms of classification accuracy. © 2022 IEEE.

关键词： Convolutional neural networks

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Enabling Massive Scalability in Low-Power Wide-Area networks 24

Enabling Massive Scalability in Low-Power Wide-Area Networks

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Rahman, Mahbubur Goala, Pushpen Bikash Graduate Center and Queens College City University of New York Computer Science United States Graduate Center City University of New York Computer Science United States

ISBN: (纸本)9798350319934

Low-power wide-area networks (LPWANs) have become ubiquitous in the Internet of Things (IoT) applications due to their ability to connect sensors across large geographic areas over a single hop. It is, however, very challenging to achieve massive scalability in LPWANs where numerous sensors can transmit data energy efficiently and with low latency, which may be required by the emerging IoT applications. In this paper, we address the above challenges by significantly advancing an LPWAN technology called SNOW (sensor network over white spaces). SNOW exploits D-OFDM (distributed orthogonal frequency division multiplexing) to enable parallel reception of data to a base station (BS) from multiple asynchronous sensors, each sensor using a different D-OFDM subcarrier. In this paper, we achieve massive scalability in SNOW by enabling the BS to decode parallel data from multiple asynchronous sensors on the same subcarrier while concurrently decoding from other subcarriers as well. To do this, we develop a set of pseudorandom noise (PN) sequences that are mutually non-interfering within and across the DOFDM subcarriers. Each sensor uses its own PN sequence (from the set) for modulating data on its subcarriers, enabling massive concurrency in SNOW. Our simulation results show that we can achieve approximately 9x more scalability in SNOW while being both energy efficient at the sensors and timely in data collection at the BS, thereby enabling emerging IoT applications that require longer sensor battery life as well as make time-critical, data-driven decisions. © 2022 IEEE.

关键词： Wide area networks

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Emotionally Aware Edtech: A Deep Learning Perspective

Emotionally Aware Edtech: A Deep Learning Perspective

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international conference on parallel distributed and Grid computing (PDGC)

作者： Mehak Goel Akshat Mittal Mitu Sehgal Sorabh Gupta Rashmi Makkar Department of Computer Science & Engineering (AI & DS) Panipat Institute of Engineering and Technology Panipat India Department of Information Technology Panipat Institute of Engineering and Technology Panipat India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Speech emotion recognition in affective computing is an area that is growing quickly. It has implications for how people and computers interact, how mental health is diagnosed, and how customer service is handled. To correctly pick up on emotions in real time, deep learning is used in this study. Our model looks at vocal pitch, tone, and cadence along with complicated neural network designs to accurately classify emotions. The method was taught with a variety of emotional expressions. It's strong and can work with a wide range of speakers and settings. Many tests show that the model is more accurate and can process emotions faster than most other ways. Deep learning can make automatic systems smarter about emotions, which can make them more caring and flexible, as this study shows

关键词： Deep learning Emotion recognition Affective computing Computational modeling Customer services Neural networks Speech recognition Mental health Grid computing Real-time systems

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CustTest: An Effective Testbed for Testing Customized Routing in Integrated Heterogeneous networks 29

CustTest: An Effective Testbed for Testing Customized Routin...

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

作者： Zhang, Jialei Xie, Xiaoxuan Yan, Zheng Wang, Haiguang Li, Tieyan Xidian University School of Cyber Engineering Xi'an China Singapore Research Center Huawei Technologies Shield Lab. Singapore

ISBN: (纸本)9798350330717

With the rapid development of the internet and the increasing demand for communication, customized routing has become one of the hot research topics in future network. In order to better meet various communication requirements, it is necessary to test and evaluate existing customized routing algorithms to optimize and explore better customized algorithms. However, existing research on customized routing simulation testing cannot provide a customized routing testbed that supports universality, stability, efficiency, and reliability to test and evaluate different customized routing algorithms. To address this issue, we proposes a testbed called CustTest. CustTest is designed based on Software Defined Network (SDN) and Network Simulator 3 (NS3), and consists of two sub platforms: a customized routing calculation platform and a customized routing simulation platform. The customized routing calculation platform is used to calculate customized routes and distribute routing tables. This platform can not only flexibly deploy different customized routing algorithms, but also calculate customized routes based on user needs and convert these routes into customized routing tables. The customized routing simulation platform can effectively evaluate the performance of different customized routing algorithms using statistical analysis tools in NS3. We ensure the stability, efficiency, and reliability of CustTest by adding a pool structure and daemons. In addition, we conduct functional and performance tests on CustTest to demonstrate its ability to effectively execute customized routing calculations and its excellent performance in high concurrency environments, as well as the effectiveness of testing the performance of different customized routing algorithms. © 2023 IEEE.

关键词： Customized Routing computing Platform Customized Routing Simulation Platform Customized Routing Testbed Integrated Heterogeneous Network

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Towards Ambiently Powered Inference on Wireless Sensor networks: Asynchrony is the Key! 17

Towards Ambiently Powered Inference on Wireless Sensor Netwo...

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17th Annual IEEE international conference on distributed computing in Sensor Systems (IEEE DCOSS)

作者： Papageorgiou, Vasileios Nichoritis, Athanasios Vasilakopoulos, Panagiotis Vougioukas, Georgios Bletsas, Aggelos Tech Univ Crete Sch Elect & Comp Engn Khania 73100 Greece

ISBN: (纸本)9781665439299

Is it possible to build ultra-low power wireless sensor networks (WSN) that exploit the inherent parallel and distributed nature of powerful message passing/inference algorithms, embrace ultra-low power communication principles and make autonomous, in-network decisions, solely powered by the environment? While edge and cloud computing emerge, this work points towards the opposite direction, inspired by the fact that ambient energy, either from radio frequency (RF), sun, motion, temperature or even living organisms, has fixed (on average) density per surface (or volume). It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, message passing problem. Examples from Gaussian Belief Propagation and Average Consensus are offered;ambient energy harvesting and availability are quantified, controling the probability of successful (or not) message passing. Such interrupted communication requires distributed algorithms robust to asynchrony, at the expense of increased overall delay. Simulation and experimental validation are offered in a WSN testbed with solar energy harvesting. Future work will focus on overall delay minimization.

关键词： Wireless communication Wireless sensor networks Message passing Solar energy Sensor systems Delays Low-power electronics

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Joint Routing and Packet Scheduling For URLLC and eMBB traffic in 5G O-RAN

Joint Routing and Packet Scheduling For URLLC and eMBB traff...

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IEEE international conference on Communications (ICC)

作者： Zhang, Cheng Cheng Nguyen, Kim Khoa Cheriet, Mohamed Univ Quebec Ecole Technol Super Montreal PQ Canada

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Open Radio Access Network (O-RAN) is an innovative RAN architecture designed to revolutionize 5G-and-beyond mobile networks. O-RAN virtualizes the fronthaul network functions into Open Centralized Unit (O-CU), Open distributed Unit (O-DU) and Open Radio Unit (O-RU). Unfortunately, there is no standard data communication mechanism to disaggregate Quality of Service (QoS) flow traffic into multiple routes to access O-DUs to leverage the distributed computing capability. Furthermore, there is no centralized scheduler to coordinate processors that are processing O-DU functions efficiently to meet fifth generation (5G) QoS services. Therefore, O-RAN performance is still questionable. This paper investigates an optimized solution for joint Routing and Packet Scheduling (RPS) which is implemented in the O-DU pool to replace individual O-DUs. We formulate two joint RPS problems to coordinate multiple routes and multiple parallel processors in the centralized O-DU pool to accommodate the Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services. We propose a greedy algorithm and a Min-Max algorithm to approximate the optimal result. Numerical results show that our proposed solution improves significantly system processing delay compared with a scheme of individual O-DUs which are selfishly maximized.

关键词： O-RAN 5G Joint Routing and packet Scheduling Centralized O-DU Pool Multiple Routes and parallel Processors

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Attention Mechanism, Linked networks, and Pyramid Pooling Enabled 3D Biomedical Image Segmentation 24

Attention Mechanism, Linked Networks, and Pyramid Pooling En...

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24th IEEE/ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing, SNPD 2022

作者： Ravi, Pooja Roy, Srijarko Dutta, Indira Kottursamy, Kottilingam Srm Ist Cse Srm Ist Computational Intelligence

ISBN: (纸本)9798350310412

We present an approach to detect and segment tumorous regions of the brain by establishing three varied segmentation architectures for multiclass semantic segmentation along with data-specific customizations like residual blocks, soft attention mechanism, pyramid pooling, linked architecture, and 3D compatibility to work with 3D brain MRI images. The proposed segmentation architectures namely, Attention Residual U-Net 3D (ARU-Net 3D), LinkNet 3D and PSPNet 3D, segment the MRI images and successfully isolate three classes of tumors. By assigning pixel probabilities, each of these models differentiates between pixels belonging to tumorous and non-tumorous regions of the brain. By experimenting and observing the performance of each of the three architectures using metrics like Dice Loss and Dice Score, on the BraTS2020 dataset, we successfully establish the following validation scores: 0.6488, 0.6485, and 0.6501 for the ARU-Net, LinkNet, and PSPNet 3D architectures respectively. Code has been made available at: https://***/indiradutta/BrainTumorSeg-III-D. © 2022 IEEE.

关键词： Medical imaging

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Multiclass Classification of Alzheimer’s Disease using Ensembling of Deep Learning Models

Multiclass Classification of Alzheimer’s Disease using Ense...

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international conference on parallel distributed and Grid computing (PDGC)

作者： Anushka Aggarwal Palak Goyal Rinkle Rani Bikram Pal Kaur Turka Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Department of Computer Science The College of New Jersey New Jersey USA

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Alzheimer's Disease (AD) is a cognitive illness that poses significant challenges in its early detection and classification. AD diagnosis and classification represent crucial challenges in neurodegenerative research. In this work, we present a unique method for multiclass AD classification that combines deep learning models and ensemble approaches. Our methodology integrates convolutional neural networks (CNNs) and various pre-trained models to extract features from MRI scans. Further, through ensembling strategy, we combine the predictions of multiple deep learning models to enhance classification accuracy. Our experiments on a comprehensive dataset comprising AD patients at different stages shows how well the suggested method works for correctly classifying AD patients. In addition, the results underscore the potential of ensembling deep learning models for improving AD classification accuracy, which could facilitate early diagnosis and intervention strategies in clinical settings.

关键词： Deep learning Accuracy Magnetic resonance imaging Pressing Predictive models Feature extraction Grid computing Genetics Convolutional neural networks Alzheimer's disease

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