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IMPROVING ULTRASOUND TONGUE CONTOUR EXTRACTION USING U-NET AND SHAPE CONSISTENCY-based REGULARIZER

IMPROVING ULTRASOUND TONGUE CONTOUR EXTRACTION USING U-NET A...

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Feng, Ming Wang, Yin Xu, Kele Wang, Huaimin Ding, Bo Tongji Univ Shanghai Peoples R China Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (纸本)9781728176055

B-mode ultrasound tongue imaging is widely used to visualize the tongue motion, due to its appearing properties. Extracting the tongue surface contour in the B-mode ultrasound image is still a challenge, while it is a prerequisite for further quantitative analysis. Recently, deep learning-based approach has been adopted in this task. However, the standard deep models fail to address faint contour when the ultrasound wave goes parallel to the tongue surface. To address the faint or missing contours in the sequence, we explore the shape consistency-based regularizer, which can take sequential information into account. By incorporating the regularizer, the deep model not only can extract frame-specific contours, but also can enforce the similarity between the contours extracted from adjacent frames. Extensive experiments are conducted both on the synthetic and real ultrasound tongue imaging dataset and the results demonstrate the effectiveness of proposed method. To better promote the research in this field, we have released our code at(1).

关键词： ultrasound tongue imaging speech production contour extraction shape consistency loss

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Energy-efficient Service Deployment based on Multi-Dimensional Features in Mobile Edge Computing: A Learning-based Approach

Energy-efficient Service Deployment Based on Multi-Dimension...

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IEEE conference on Global Communications (GLOBECOM)

作者： Xiangyi Chen Yang Li Huanlai Xing Danyang Zheng Min Li Lexi Xu Hai Zhao School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China China United Network Communications Beijing China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Mobile edge computing (MEC) decentralizes the computational and storage capabilities of the network to edge nodes, providing support for the dynamic deployment and rapid response of mobile services. However, the large-scale distributed deployment of edge nodes, their widespread geographic distribution, multi-dimensional and complexly dependent service characteristics, and the dynamically changing network environment pose challenges to energy-efficient service deployment. In this paper, we consider multi-dimensional features for service deployment, including dynamic traffic demand, geography information, service semantics, and service popularity, with the aim of improving the availability of edge services and reducing network energy consumption. Firstly, to handle the large volume of edge service data, we design a multi-dimensional feature extraction approach based on the Transformer model, which does not rely on the sequential order of data and can enhance computational efficiency of edge models through parallel processing. Then, to adapt to the dynamically changing edge network environment, we propose an Energy-Efficient Service Deployment algorithm (EESD) based on the improved Dueling Deep Q-network, which makes service deployment and base station switching decisions in a learning-based manner. Finally, simulation results demonstrate that EESD outperforms comparison algorithms in terms of model convergence, system total cost, and energy consumption.

关键词： Energy consumption Adaptation models Solid modeling Multi-access edge computing Computational modeling Simulation Switches Feature extraction Transformers Energy efficiency

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K-means ++ and Reinforcement Learning-based Clustering Method to Achieve Energy Efficiency in Underwater Acoustic Sensor networks

K-means ++ and Reinforcement Learning-Based Clustering Metho...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Tao Xiong Rui Hou Lingyun Zhou Guanglin Xing Deze Zeng College of Computer Science South-Central Minzu University Wuhan China Computer School China University of Geosciences Wuhan China

In recent years, underwater acoustic sensor networks (UASNs) have had a wide range of applications in the fields of underwater environment sensing, marine resource exploitation and water quality analysis. Sensor nodes operate in an underwater environment and are powered by embedded batteries, making it very difficult to replenish power. It is thus important to efficiently improve sensor energy efficiency in UASNs and to extend the network lifetime. Clustering can effectively reduce UASN energy consumption by dividing sensor nodes into clusters; however, energy efficiency is always affected by the imbalance in energy consumption due to the unreasonable selection of cluster heads (CHs) and routes. In this paper, a K-means ++ and reinforcement learning-based clustering (KRC) method is proposed to improve energy efficiency. First, the UASN clusters are uniformly distributed using the K-means ++ algorithm. Then, a combination of clustering and reinforcement learning is applied to CH selection, which calculates the energy of intercluster data transmission when each node in the cluster is a CH based on the node’s residual energy and location and selects the appropriate CHs to equalize the energy consumption of each network node. The optimal transmission route constructed by reinforcement learning is used to select suitable nodes for relaying data for sensor nodes. The simulation results show that the KRC method effectively balances UASN node energy consumption and improves energy efficiency, thereby extending network survival.

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DSUTO: Differential Rate SAC-based UAV-Assisted Task Offloading Algorithm in Collaborative Edge Computing

DSUTO: Differential Rate SAC-Based UAV-Assisted Task Offload...

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International conference on parallel and distributed Systems (ICPADS)

作者： Longxin Zhang Runti Tan Minghui Ai Huazheng Xiang Cheng Peng Zhihao Zeng College of Computer Science Hunan University of Technology Zhuzhou China

Mobile edge computing effectively enhances service quality and decreases system cost by processing resource-intensive tasks at the network edge. Today, unmanned aerial vehicles (UAVs) are increasingly being utilized for task offloading services in remote areas due to their convenient deployment and flexible mobility. However, the complex task environment when using UAVs brings great challenges to the optimization strategy’s capacity to solve and converge in a stable manner. To solve this issue, a differential rate rule (DRR) is proposed in this work with the goal of improving the update stability of the agent in the actor–critic reinforcement learning (RL). Second, a UAV-assisted task offloading algorithm called DSUTO is designed based on DRR and maximum entropy RL. Finally, a UAV-assisted mobile device-edge-cloud collaborative computing model is constructed with time-varying channel obstacles and user movement, thus solving a multi-objective joint optimization problem on the task completion cost (including delay and energy consumption) and UAV endurance under resource constraints. The experiment results demonstrate that DSUTO not only has excellent performance in terms of convergence and stability, but also significantly reduces the total system cost by 21.38% compared with the latest benchmark algorithms under complex environment conditions.

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VSNT: Adaptive network Traffic Forecasting via VMD and Deep Learning with SCI-Block and Attention Mechanism

VSNT: Adaptive Network Traffic Forecasting via VMD and Deep ...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Pingping Dong Yuning Zuo Liying Chen Shangyu Li Lianming Zhang College of Information Science and Engineering Hunan Normal University Changsha China

As the Internet evolves towards a dense network paradigm, advancements in mobile services have resulted in a consistent have consistently amplified the volume of mobile data. Consequently, precise network traffic forecasting has become critical for effective network planning and resource allocation. The Informer model, based on the Transformer architecture, has been optimized for time-series forecasting recently. However, the Informer tends to accentuate noise within individual time series, which leads to overfitting or decreased robustness, ultimately affecting forecasting performance. To address these challenges, an adaptive network traffic forecasting model named VSNT is introduced. VSNT employs Variational Mode Decomposition (VMD) to adaptively decompose original network traffic into Intrinsic Mode Function (IMF) components. For the preservation of the interrelation among IMF components, comprehensive representation learning is undertaken prior to their input into the neural network. This network utilizes an encoder-decoder structure and integrates tools such as the SCI-Block and attention mechanisms. The SCI-Block is instrumental in extracting multiscale network attributes, while the attention mechanism emphasizes pronounced features. Compared to the intricate Informer model, VSNT reduces the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE) by approximately 51% and 35% respectively. This positions VSNT as a robust solution for accurate network traffic forecasting, providing a fruitful path for research in network traffic forecasting and time-series analysis.

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RoBGP: A Nested Biomedical Named Entity Recognition Method based on RoBERTa and Global Pointer

RoBGP: A Nested Biomedical Named Entity Recognition Method B...

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Software Engineering, Artificial Intelligence, networking and parallel/distributed Computing (SNPD-Winter), ACIS International Winter conference on

作者： Chao Song Jiao Long Dongmei Li Xiaolong Qu Feng Lin Xiaoping Zhang School of Information Science and Technology Beijing Forestry University National Forestry and Grassland Administration Engineering Research Center for Forestry-oriented Intelligent Information Processing Beijing China National Data Center of Traditional Chinese Medicine China Academy of Chinese Medical Sciences Beijing China

Named entity recognition (NER) is one of the fundamental tasks in biomedical text mining, aiming to extract specific types of biomedical entities from texts. Compared to general-domain texts, biomedical texts often contain nested entities and local dependencies among entities, which pose challenges to existing NER models. To address these issues, we propose a nested biomedical NER method based on RoBERTa and Global Pointer (RoBGP). First, we dynamically obtain word-level semantic representations using the RoBERTa-wwm-ext-large model. Second, A BiLSTM model is used to capture long-distance semantic information in a bidirectional manner. Finally, we utilize a global pointer network to recognize all nested entities from innermost to outermost. Experimental results on the Chinese medical dataset CMeEE show that our method achieves the F1 score of 67.69%, outperforming other advanced models by 0.32% to 5.11%, validating the effectiveness of our approach in biomedical NER.

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Multi-Agent Reinforcement Learning based distributed Renewable Energy Matching for Datacenters 21

Multi-Agent Reinforcement Learning based Distributed Renewab...

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50th International conference on parallel processing (ICPP)

作者： Wang, Haoyu Shen, Haiying Gao, Jiechao Zheng, Kevin Li, Xiaoying Univ Virginia Charlottesville VA 22901 USA

ISBN: (纸本)9781450390682

The rapid growth of cloud computing in cloud datacenters in recent decades greatly increases the brown energy consumption in datacenters, and hence significant increase of carbon emission that negatively impacts on the environment as well as the monetary cost. More and more cloud service providers are adopting renewable energy as the energy supply to offset the consumption of brown energy. Meanwhile, an increasing number of renewable energy generators have been built to meet the needs. However, the instability of the renewable energy cannot guarantee the support to the datacenter and the energy competition of different datacenters may lead to datacenter energy outage. In this paper, we focus on the problem of how to match different renewable energy generators to the datacenters from different cloud providers to minimize the carbon emission, monetary cost, and service level objective (SLO) violation due to renewable energy shortage. The challenges here are that the datacenters may compete in energy requesting, the renewable energy generation is not stable and the decision should be made quickly. There have been no previous efforts devoting to this problem. To solve the problem, we first test several machine learning techniques on long-term prediction accuracy on renewable energy generation and energy demand using real traces and identify SARIMA for the prediction. We then propose a multi-agent reinforcement learning based method (MARL) for each datacenter to determine how much renewable energy to request from each generator based on the predicted results. We also propose a deadline guaranteed job postponement method (DGJP) to postpone executing unurgent jobs upon insufficient renewable energy supply. The trace-driven experiments show that MARL outperforms other methods by increasing up to 35% SLO satisfaction ratio, and reducing up to 19% (0.33 billion dollars in 90 days) total monetary cost and 33% total carbon emission, and DGJP further improves the performance.

关键词： Renewable energy resources

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A hardware acceleration Framework of Reconfigurable Edge Modules for Convolutional Neural networks

A hardware acceleration Framework of Reconfigurable Edge Mod...

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Annual conference of Industrial Electronics Society

作者： Yiming Qiao Zidi Jia Shixiang Li Lei Ren Beihang University Beijing China Zhongguancun Laboratory State Key Laboratory of Intelligent Manufacturing System Technology Beihang University Beijing China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

Edge computing exploits node devices situated in close proximity to terminals to deliver distributed computing services directly to users, with FPGAs serving as the predominant platform. With the amplification in volume and intricacy of deep learning models, effectively deploying these models on FPGA devices introduces substantial challenges. To counter this predicament, this paper introduces an FPGA-based hardware acceleration framework for reconfigurable edge devices tailored for Convolutional Neural networks (CNNs). Initially, a method for dynamically quantizing network models is devised to markedly curtail model memory consumption. Subsequently, a meticulously engineered hardware acceleration unit is formulated to attain augmented computing parallelism via meticulous temporal redesign. Finally, model deployment on FPGA devices for inference verification is showcased utilizing fully connected and convolutional neural networks as exemplars. On the MNIST dataset, the FPGA inference unit attains remarkable accuracy and computational efficiency in comparison to CPUs and GPUs

关键词： Industrial electronics Solid modeling Computational modeling Memory management parallel processing Convolutional neural networks distributed computing Field programmable gate arrays Hardware acceleration Edge computing

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Translation of Array-based Graph Programs to Spark SQL on Block Arrays

Translation of Array-Based Graph Programs to Spark SQL on Bl...

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9th IEEE International conference on Big Data (IEEE BigData)

作者： Noor, Md Hasanuzzaman Fegaras, Leonidas Univ Texas Arlington Arlington TX 76019 USA

ISBN: (纸本)9781665439022

Many graph algorithms can be expressed as repetitive computations that resemble matrix multiplication in which the addition and multiplication operations have been replaced with generalized operations that form an algebraic structure known as a semiring. Similar to matrix multiplication, these graph algorithms can be implemented in a distributed system using block arrays, which are distributed collections of non-overlapping dense arrays. In a distributed system, computations on block matrices are significantly faster than the same computations in the coordinate format in terms of computation and communication cost. Given the rise of Big Data and data-driven decision-making, our goal is to rewrite sequential programs on large graphs that are initially written to run on a single computer using loops and standard array operations to data-parallel programs on array blocks that can run on a distributed system. Our framework, called OSQLgen, automatically parallelizes graph programs with loops and arrays to distributed data-parallel programs. OSQLgen recognizes programs that are equivalent to a semiring structure and translates them to fast Spark SQL programs on block arrays that can run on a computer cluster in a distributed mode. We compare the performance of OSQLgen with GraphX, GraphFrames, and hand-written Spark SQL programs on coordinate and block arrays on various graph problems. On certain graph problems, OSQLgen is up to 36x faster than GraphX, 25x faster than GraphFrames, and up to 99x faster than hand-written Spark SQL programs on coordinate arrays, giving performance close to that of hand-written Spark SQL programs on block arrays.

关键词： Big Data arrays SQL Spark Graph processing

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MAS-DDPG: Multi-Sensor Self-Adaptive Scheduling for UAV Path Planning in network-Unavailable Environments

MAS-DDPG: Multi-Sensor Self-Adaptive Scheduling for UAV Path...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Na Lin Zhijiang Wang Liang Zhao Shenyang Aerospace University Shenyang China

In network-unavailable environments, the path planning of unmanned aerial vehicles (UAVs) becomes constrained by limitations in their energy resources and computing capabilities within a predefined mission timeframe. Additional low-power flight control must be considered to effectively complete a flight mission. To support UAV path planning in network-unavailable environments, this paper proposes a multi-sensor self-adaptive scheduling and optimized depth deterministic strategy gradient (MAS-DDPG) algorithm. Specifically, unlike considering activating all sensors in varying scenarios, a multi-sensor self-adaptive scheduling (MAS) strategy based on Multi-nominal Logit (MNL) is proposed. This approach allows the UAV to choose an appropriate scheduling strategy according to different scenarios. Furthermore, instead of relying on randomly chosen experiences from a pool, a mechanism based on screening experience is proposed. This mechanism takes into account successful experiences along with their contextual counterparts to enhance the learning process. Our goal is to maximize the average flight speed of the UAV and minimize the number of flight decisions and flight energy consumption. Simulation results show that the proposed approach can achieve significantly hinter speed and lower energy consumption than the benchmark and other typical algorithms.

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