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Multimodal Deep Neural Networks for Robust Sign Language Translation in Real-World Environments

Multimodal Deep Neural Networks for Robust Sign Language Tra...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： V. Ellappan R. Sathiskumar P. Saikrishna C. Sivakumar R. Selvam Department of ECE Mahendra Institute of Technology Namakkal Tamil Nadu

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Background: Sign language recognition aims to convert hand gestures used in sign language into spoken or written words, facilitating communication between hearing and deaf individuals. This technology has great potential to enhance accessibility and foster inclusivity for individuals who are deaf or hard-of-hearing. Various techniques can be employed for sign language recognition, including Computer vision algorithms for analyzing visual data captured by cameras, sensor-based approaches that utilize sensors attached to the hands or fingers to record motion data, and hybrid approaches that combine both methods Our research results imply that a hybrid approach combining computer vision and sensor-based techniques yields the most accurate and robust sign language recognition system. By leveraging the strengths of both methods, the hybrid approach can effectively capture and interpret intricate hand and finger movements, resulting in a higher recognition accuracy rate compared to using either technique independently. Developing precise and reliable sign language recognition systems. has the potential to revolutionize communication and accessibility for individuals with hearing loss. Our proposed hybrid approach offers a promising solution for overcoming the challenges associated with sign language recognition, paving the way for bridging the communication gap and promoting inclusivity for the deaf and hard-of-hearing community.

关键词： Sign language Computer vision Vocabulary Visualization Computer architecture Auditory system sensor systems

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Efficient Large Models Fine-tuning on Commodity Servers via Memory-balanced Pipeline Parallelism

Efficient Large Models Fine-tuning on Commodity Servers via ...

引用

ieee international conference on High Performance computing and Communications (HPCC)

作者： Yujie Liu Zhiquan Lai Weijie Liu Wei Wang Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

Large models have achieved impressive performance in many downstream tasks. Using pipeline parallelism to fine-tune large models on commodity GPU servers is an important way to make the excellent performance of large models available to the general public. Previous solutions fail to achieve an efficient memory-balanced pipeline parallelism. In this poster, we introduce a memory load-balanced pipeline parallel solution. This solution balances memory consumption across stages on commodity GPU servers via NVLink bridges. It establishes a new pathway to offload data from GPU to CPU by using the PCIe link of adjacent GPUs connected by the NVLink bridge. Furthermore, our method orchestrates offload operations to minimize the offload latency during large model fine-tuning. Experiments demonstrate that our solution can balance the memory footprint among pipeline stages without sacrificing training performance.

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Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition

Semi-supervised Multi-source Domain Adaptation in Wearable A...

引用

international conference on distributed computing in sensor systems (dcoss)

作者： Avijoy Chakma Abu Zaher Md Faridee Raghuveer Rao Nirmalya Roy Information Systems University of Maryland Baltimore County Army Research Lab United States

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.

关键词： Deep learning Adaptation models Buildings distributed databases Performance gain Data models Labeling

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Solar Irradiance Forecasting Utilizing distributed and Federated Learning

Solar Irradiance Forecasting Utilizing Distributed and Feder...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Hao Zhang Ke Yan College of Information Engineering China Jiliang University Hangzhou China China Jiliang University and National University of Singapore

Photovoltaic energy generation and prediction are crucial for the integration between solar plants and smart grid. Accurate solar radiation prediction is one of the most important research topics for solar energy generation. Existing deep learning methods require sufficient data for training for achieving the desired results. In traditional deep learning modeling, the data sets required for model training usually come from various data in different regions and organizations, and it is difficult to ensure data security and privacy protection. Federated learning can effectively utilize the data optimization model while ensuring data security and privacy, but there is still the risk of privacy leakage by analyzing the parameters uploaded by the client. To effectively utilize data optimization models while ensuring data security and privacy, we propose a method based on a federated learning framework and N-BEATS to achieve interpretable time series prediction of solar irradiance. The model builds the N-BEATS model on the solar irradiance datasets of multiple institutions in a distributed method, does not share local data to protect institutional data privacy, the concept of differential privacy (DP) is introduced, Gaussian noise is added to the parameters of the client before the server-side aggregation processing, the model parameters of each distributed node are collected through TCP communication for federated average, and the parameters are returned to each distributed node. The experimental results show that compared with other deep learning models, the model has better prediction accuracy under the premise of ensuring data security and privacy.

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Work in Progress paper: Experiment Planning for Heterogeneous Programmable Networks

Work in Progress paper: Experiment Planning for Heterogeneou...

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international conference on distributed computing in sensor systems (dcoss)

作者： Nik Sultana Dept. of Computer Science Illinois Tech Chicago Illinois

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Private and publicly-funded cloud infrastructure and testbeds increasingly feature programmable network hardware. Programmable network cards and switches support the execution of increasingly-complex in-network programs that can operate independently of end-hosts to improve the network’s performance, resilience and utilisation. Reasoning about in-network programs, their placement, and workloads is needed to plan jobs on programmable networks. On programmable testbed networks, this reasoning feeds into resource allocation, fairness and reproducible research. But this reasoning is made challenging by the performance and resource diversity of hardware and by the failure modes that can arise in a distributed *** is currently the most comprehensive reasoning system for distributed and heterogeneous in-network programs but it uses a custom formalism and tool implementation, making it difficult to understand, extend, and *** paper describes Lightplanner, a generalisation of Flight-planner’s reasoning system that has been implemented on Prolog. It provides an executable formalisation in a well-understood logic. By relying on Prolog’s proof search, Lightplanner is 10 smaller than Flightplanner’s implementation in C++, making×it better suited for others to understand, extend, and scale. A benchmark of publicly-available in-network programs is used to evaluate Lightplanner against Flightplanner. Though the time overhead is slightly larger, Lightplanner can find better allocations than the original, more complex C++ *** is being incubated to plan experiments in a local programmable network testbed at Illinois Tech, and as a future step it will be extended to work across federated networks such as FABRIC.

关键词： C++ languages Cognition Hardware sensor systems Fabrics Planning Resource management

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DENNI: distributed Neural Network Inference on Severely Resource Constrained Edge Devices

DENNI: Distributed Neural Network Inference on Severely Reso...

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40th ieee international Performance, computing, and Communications conference (IPCCC)

作者： Sahu, Rohit Toepfer, Ryan Sinclair, Mathew D. Duwe, Henry Iowa State Univ Ames IA 50011 USA Univ Wisconsin Madison WI USA AMD Res Madison WI USA

ISBN: (纸本)9781665443319

Pervasive intelligence promises to revolutionize society from Industrial Internet of Things (IIoT), to smart infrastructure and homes, to personal health monitoring. Unfortunately, many edge devices that are pervasively embedded into infrastructure or implanted into humans are severely resource-constrained. As performing computations at the edge becomes increasingly important to meet latency deadlines and retain sensitive data locally, severe resource constraints present a challenge because many algorithms are too large to fit on a single edge device. In this paper, we focus on distributing inference for neural networks (NNs) with convolution and fully connected layers across multiple edge nodes. In order to improve efficiency on severely resource-constrained edge nodes for diverse NN architectures we present an end-to-end, automated approach, DENNI, that optimizes NN distribution with minimal nodes while meeting memory constraints. When targeting a network of edge nodes with 256KB of nonvolatile memory connected with Bluetooth Low Energy, DENNI successfully distributes NN inference for a variety of machine learning algorithms across multiple edge nodes where other, static approaches cannot.

关键词： distributed computing neural networks wireless sensor nodes

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Federated Deep Reinforcement Learning Approach for Real-Time Target Coverage in Wireless sensor Networks

Federated Deep Reinforcement Learning Approach for Real-Time...

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ieee Internatonal conference on Mobile Adhoc and sensor systems (MASS)

作者： Corey Thomas Glodek Nazli Siasi College of Computing and Digital Media DePaul University Chicago IL USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Maximizing target coverage is crucial in wireless sensor networks (WSN), where sensor nodes collect data to monitor events or targets in specific areas. Efficient coverage optimization involves deploying sensors to cover the entire monitored region, addressing the NP-hard nature of the problem. Various algorithms, including learning-based automata, genetic algorithms, and probabilistic approaches, have been employed. Specifically, deep reinforcement learning (DRL), using deep neural network (DNN) as a predictor, has shown promise. However, the DRL models require a large amount of data for training, which may limit their adaptability to the ever-evolving statistics of the underlying environment. Moreover, wireless networks are inherently distributed systems, where centralized DRL approaches would require excessive data exchange, while fully distributed approaches may result in slower convergence rates and performance degradation. To overcome these challenges, we propose a novel federated learning (FL) approach to Deep Q-Network (DQN), called Federated Reinforcement Deep Q-Network (FEDQN), which allows sensors to collaboratively train the DQN by sharing only model weights, not training data. This paper demonstrates how FEDQN maximizes target coverage in a continuous, partially observable visual sensor network environment, simulating traffic flow with mobile targets.

关键词： Wireless sensor networks Computational modeling Wireless networks Artificial neural networks Deep reinforcement learning Data models Ad hoc networks Monitoring Load modeling Convergence

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The Smart Optimization of Security systems in Wireless Personal sensor Networks

The Smart Optimization of Security Systems in Wireless Perso...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Sunanda Das Sindhu Madhuri G Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore India

Wireless personal sensor networks (WPSNs) are an emerging technology that provides a platform for monitoring and collecting data from a variety of sources. These networks are used to monitor environmental, health, and even lifestyle factors. As the use of WPSNs continues to grow, it is important to consider the security implications of using these networks. WPSNs rely on a variety of technologies to ensure secure communication between devices. These include encryption, authentication, secure protocols, and secure key management. Encryption is used to protect data from being intercepted and decoded by an unauthorized user. Authentication is used to verify the personality of the user and the device attempting to access the WPSN. Secure protocols are used to ensure secure communication between the devices and the server. Finally, secure key management is used to manage and store the encryption keys used to protect the data. In addition to encryption, authentication, and secure protocols, WPSNs must also be designed with a robust access control system. Access control systems are used to limit access to the WPSN based on user credentials and other factors. This assist ensure that authorized users alone can access the data stored on the WPSN.

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Smart-Hop: Low-Latency Multi-hop Networking for LoRa

Smart-Hop: Low-Latency Multi-hop Networking for LoRa

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international conference on distributed computing in sensor systems (dcoss)

作者： Absar-Ul-Haque Ahmar Wouter Joosen Danny Hughes imec-DistriNet Computer Science KU Leuven Belgium

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Low Power Wide Area Networks (LPWANs) provide the Internet of Things (IoT) with energy-efficient, low cost, and long-range networking. LoRaWAN is one of the most widely deployed and studied LPWAN technologies due to its license-free operation and open standard, which has enabled 3rd parties to deploy millions of networks worldwide. LoRa allows transmission range and speed to be traded off by configuring the Spreading Factor (SF) setting. By tuning this parameter, the performance envelope of the network can be configured to favor a higher speed or longer range. While the SF of LoRA provides a simple and elegant means to tailor performance, at the highest spreading factor, maximum throughput drops to a few bits per second, dramatically increasing data extraction times. In this paper, we address this problem by proposing Smart-Hop, a novel multi-hop MAC layer protocol for LoRa which implements low-overhead routing. In contrast to prior work, Smart-Hop exploits the variable range afforded by different SFs to eliminate much of the complexity and overhead of traditional multi-hop mesh networking. Smart-Hop reduces the number of end-devices operating at high SFs while implementing low overhead routing in order to reduce data extraction times. These improvements are achieved while preserving the maximum range offered by LoRa in its highest SF configuration.

关键词： Wireless sensor networks Spread spectrum communication Routing Throughput sensor systems Internet of Things Data mining

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Data-driven soft sensing towards quality monitoring of industrial pasteurization processes

Data-driven soft sensing towards quality monitoring of indus...

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international conference on distributed computing in sensor systems (dcoss)

作者： Gabriel Filios Andreas Kyriakopoulos Stavros Livanios Fotis Manolopoulos Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Heineken Patras Plant Greece Department of Computer Science University of Liverpool UK

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

In the food and beverage industry many foods, beers and soft drinks usually need to get pasteurized, a process that holds a significant role in the quality and taste of the final product but is difficult to monitor due to the process nature. Soft sensing techniques, also called virtual sensing or surrogate sensing, can be leveraged to monitor the product quality, by using information available from other measurements and process parameters to calculate an estimation of the quantity of interest. In this paper, we develop a soft sensing methodology that is based on machine learning algorithms for continuous, end-to-end estimation of the temperature of products during the pasteurization process, with the vision to serve as an intermediate step towards monitoring live the final quality of the pasteurized products. This work studies a real beer pasteurization process in collaboration with Heineken’s plant in Patras, Greece and the results demonstrate notable performance in temperature prediction accuracy, with average root mean square error (RMSE) of 1.85°C in the test sets. Thus, we claim that it is possible to obtain measurements quite similar to the ones by the respective physical sensors with sufficient accuracy, and our methodology can be considered as a virtual low-cost solution for monitoring product quality in legacy pasteurizer operation.

关键词： Temperature measurement Temperature sensors Machine learning algorithms Soft sensors Estimation Product design sensor systems

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