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24th International conference on Parallel and distributed Computing, Applications and Technologies, PDCAT 2023

24th International Conference on Parallel and Distributed Co...

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24th International conference on Parallel and distributed Computing, Applications and Technologies, PDCAT 2023

ISBN: (纸本)9789819982103

The proceedings contain 28 papers. The special focus in this conference is on Parallel and distributed Computing, Applications and Technologies. The topics include: Insider Trading Detection Algorithm in Industrial Chain Based on Logistics Time Interval Characteristics;link Attributes Based Multi-service Routing for Software-Defined Satellite networks;A Fuzzy Logical RAT Selection Scheme in SDN-Enabled 5G HetNets;SSR-MGTI: Self-attention Sequential Recommendation Algorithm Based on Movie Genre Time Interval;fine Time Granularity Allocation Optimization of Multiple networks Industrial Chains in Task processing Systems;Ε-Maximum Critic Deep Deterministic Policy Gradient for Multi-agent Reinforcement Learning;effective Density-Based Concept Drift Detection for Evolving Data Streams;an End-to-End Multiple Hyper-parameters Prediction Method for distributed Constraint Optimization Problem;Formalization and Verification of the Zab Protocol Using CSP;dynamic Priority Coflow Scheduling in Optical Circuit Switched networks;Deep Reinforcement Learning Based Multi-WiFi Offloading of UAV Traffic;Triple-Path RNN network: A Time-and-Frequency Joint Domain Speech Separation Model;design of Query Based Gallery Selector and Mask-Aware Loss for Person Search;a Privacy-Preserving Blockchain Scheme for the Reliable Exchange of IoT Data;R-RPT-A Reliable Routing Protocol for Industrial Wireless Sensor networks;action Segmentation Based on Encoder-Decoder and Global Timing Information;Security Challenges and Lightweight Cryptography in IoT: Comparative Study and Testing Method for PRESENT-32bit Cipher;The Prediction Model of Water Level in Front of the Check Gate of the LSTM neural network Based on AIW-CLPSO;Using MPIs Non-Blocking Allreduce for Health Checks in Dynamic Simulations;parallelizable Loop Detection using Pre-trained Transformer Models for Code Understanding;list-Based Workflow Scheduling Utilizing Deep Reinforcement Learning.

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How Powerful are Shallow neural networks with Bandlimited Random Weights? 40

How Powerful are Shallow Neural Networks with Bandlimited Ra...

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40th International conference on Machine Learning

作者： Li, Ming Sonoda, Sho Cao, Feilong Wang, Yu Guang Liang, Jiye Zhejiang Normal Univ Key Lab Intelligent Educ Technol & Applicat Zheji Jinhua Zhejiang Peoples R China RIKEN AIP Deep Learning Theory Team Tokyo Japan China Jiliang Univ Coll Sci Hangzhou Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai Peoples R China Shanxi Univ Sch Comp & Informat Technol Key Lab Computat Intelligence & Chinese Informat Taiyuan Peoples R China

We investigate the expressive power of depth-2 bandlimited random neural networks. A random net is a neural network where the hidden layer parameters are frozen with random assignment, and only the output layer parameters are trained by loss minimization. Using random weights for a hidden layer is an effective method to avoid non-convex optimization in standard gradient descent learning. It has also been adopted in recent deep learning theories. Despite the well-known fact that a neural network is a universal approximator, in this study, we mathematically show that when hidden parameters are distributed in a bounded domain, the network may not achieve zero approximation error. In particular, we derive a new nontrivial approximation error lower bound. The proof utilizes the technique of ridgelet analysis, a harmonic analysis method designed for neural networks. This method is inspired by fundamental principles in classical signal processing, specifically the idea that signals with limited bandwidth may not always be able to perfectly reconstruct the original signal. We corroborate our theoretical results with various simulation studies, and generally, two main take-home messages are offered: (i) Not any distribution for selecting random weights is feasible to build a universal approximator;(ii) A suitable assignment of random weights exists but to some degree is associated with the complexity of the target function.

关键词： Convex optimization

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Split Computing based Quantized PoseNet Model for distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International conference on Communication Systems and networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep neural network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling Image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Selection of Deep neural network Models for IoT Anomaly Detection Experiments 29

Selection of Deep Neural Network Models for IoT Anomaly Dete...

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29th Euromicro International conference on Parallel, distributed and network-Based processing (PDP)

作者： Gaifulina, Diana Kotenko, Igor Russian Acad Sci Russian Acad Sci SPC RAS St Petersburg Inst Informat & Automat St Petersburg Fed Res Ctr 14th Liniya St Petersburg Russia

ISBN: (纸本)9781665414555

This research is about selection of deep neural network models for anomaly detection in Internet of Things network traffic. We are experimentally evaluating deep neural network models using the same software, hardware and the same subsets of the UNSW-NB 15 dataset for training and testing. The assessment results are quality metrics of anomaly detection and the time spent on training models.

关键词： deepp learning deep neural networks anomaly detection Internet of Things information security

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Optimal Gradient Sliding and its Application to distributed Optimization Under Similarity 36

Optimal Gradient Sliding and its Application to Distributed ...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Kovalev, Dmitry Beznosikov, Aleksandr Borodich, Ekaterina Gasnikov, Alexander Scutari, Gesualdo King Abdullah Univ Sci & Technol Thuwal Saudi Arabia MIPT Moscow Russia HSE Univ Yandex Russia Inst Informat Transmiss Problems RAS Moscow Russia Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781713871088

We study structured convex optimization problems, with additive objective r := p + q, where r is (mu-strongly) convex, q is L-q-smooth and convex, and p is L-p-smooth, possibly nonconvex. For such a class of problems, we proposed an inexact accelerated gradient sliding method that can skip the gradient computation for one of these components while still achieving optimal complexity of gradient calls of p and q, that is, O(root L-p/mu) and O(root L-q/mu), respectively. This result is much sharper than the classic black-box complexityO(root(L-p + L-q)/mu), especially when the difference between L-p and L-q is large. We then apply the proposed method to solve distributed optimization problems over master-worker architectures, under agents' function similarity, due to statistical data similarity or otherwise. The distributed algorithm achieves for the first time lower complexity bounds on both communication and local gradient calls, with the former having being a longstanding open problem. Finally the method is extended to distributed saddleproblems (under function similarity) by means of solving a class of variational inequalities, achieving lower communication and computation complexity bounds.

关键词： Convex optimization

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MoMo Strategy: Learn More from More Mistakes

MoMo Strategy: Learn More from More Mistakes

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Asia-Pacific-Signal-and-Information-processing-Association Annual Summit and conference (APSIPA ASC)

作者： Chulif, Sophia Lee, Sue Han Chang, Yang Loong Tsun, Mark Tee Kit Chai, Kok Chin Then, Yi Lung Swinburne Univ Technol Sarawak Campus Kuching Malaysia NEUON AI SDN BHD Kota Samarahan Sarawak Malaysia Univ Malaysia Sarawak Kota Samarahan Malaysia

ISBN: (纸本)9798350300673

Training accurate convolutional neural networks (CNNs) is essential for achieving high-performance machine learning models. However, limited training data pose a challenge, reducing model accuracy. This research investigates the selection and utilization of misclassified training samples to enhance the accuracy of CNNs where the dataset is long-tail distributed. Unlike classical resampling methods involving oversampling of tail classes and undersampling of head classes, we propose an approach that allocates more misclassified training samples into the training process to learn more (namely, MoMo strategy), with ratios of 50:50 and 70:30 for the wrongly predicted and correctly predicted samples, respectively. Additionally, we propose incorporating a balanced sample selection method, whereby the maximum training sample per class in an epoch is assigned to address the long-tail dataset problem. Our experimental results on a subset of the current largest plant dataset, PlantCLEF 2023, demonstrate an increase of 1%-2% in overall validation accuracy and a 2%-5% increase in tail class identification. By selectively focusing on more misclassified samples in training, at the same time, integrating a balanced sample selection achieves a significant boost in accuracy compared to traditional training methods. These findings emphasize the significance of adding more misclassified samples into training, encouraging researchers to rethink the sampling strategies before implementing more complex and robust network architectures and modules.

关键词： network architecture

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User Mobility Prediction in 5G networks Using Recurrent neural networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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2024 IEEE International conference on Communication, Computing and Signal processing, IICCCS 2024

作者： Shrestha, Anubhav Sharma, Vikalp Hussein, Layth Aishwarya, M. Satyanarayana, A.N. Saimanohar, T. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Saveetha Institute of Medical and Technical Sciences Department CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent neural networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets. © 2024 IEEE.

关键词： 5G mobile communication systems

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Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention 36

Dance of SNN and ANN: Solving binding problem by combining s...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Zheng, Hao Lin, Hui Zhao, Rong Shi, Luping Tsinghua Univ Dept Precis Instrument Ctr Brain Inspired Comp Res Beijing 100084 Peoples R China

ISBN: (纸本)9781713871088

The binding problem is one of the fundamental challenges that prevent the artificial neural network (ANNs) from a compositional understanding of the world like human perception, because disentangled and distributed representations of generative factors can interfere and lead to ambiguity when complex data with multiple objects are presented. In this paper, we propose a brain-inspired hybrid neural network (HNN) that introduces temporal binding theory originated from neuroscience into ANNs by integrating spike timing dynamics (via spiking neural networks, SNNs) with reconstructive attention (by ANNs). Spike timing provides an additional dimension for grouping, while reconstructive feedback coordinates the spikes into temporal coherent states. Through iterative interaction of ANN and SNN, the model continuously binds multiple objects at alternative synchronous firing times in the SNN coding space. The effectiveness of the model is evaluated on synthetic datasets of binary images. By visualization and analysis, we demonstrate that the binding is explainable, soft, flexible, and hierarchical. Notably, the model is trained on single object datasets without explicit supervision on grouping, but successfully binds multiple objects on test datasets, showing its compositional generalization capability. Further results show its binding ability in dynamic situations.

关键词： neural networks

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Hardware-aware Partitioning of Convolutional neural network Inference for Embedded AI Applications 18

Hardware-aware Partitioning of Convolutional Neural Network ...

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18th Annual International conference on distributed Computing in Sensor Systems (DCOSS)

作者： Kress, Fabian Hoefer, Julian Hotfilter, Tim Walter, Iris Sidorenko, Vladimir Harbaum, Tanja Becker, Juergen Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9781665495127

Embedded image processing applications like multicamera-based object detection or semantic segmentation are often based on Convolutional neural networks (CNNs) to provide precise and reliable results. The deployment of CNNs in embedded systems, however, imposes additional constraints such as latency restrictions and limited energy consumption in the sensor platform. These requirements have to be considered during hardware/software co-design of embedded Artifical Intelligence (AI) applications. In addition, the transmission of uncompressed image data from the sensor to a central edge node requires large bandwidth on the link, which must also be taken into account during the design phase. Therefore, we present a simulation toolchain for fast evaluation of hardware-aware CNN partitioning for embedded AI applications. This approach explores an efficient workload distribution between sensor nodes and a central edge node. Neither processing all layers close to the sensor nor transmitting all uncompressed raw data to the edge node is an optimal solution for each use case. Hence, our proposed simulation toolchain evaluates power and performance metrics for each reasonable partitioning point in a CNN. In contrast to the state of the art, our approach does not only consider the neural network architecture. In the evaluation, our simulation toolchain additionally takes into account hardware components such as special accelerators and memories that are implemented in the sensor node. Exemplary, we show the simulation results for three commonly used CNNs in embedded systems. Thereby, we identify advantageous partitioning points regarding inference latency and energy consumption. With the support of the toolchain, we are able to identify three beneficial partitioning points for FCN ResNet-50 and two for GoogLeNet as well as for SqueezeNet V1.1.

关键词： convolutional neural networks embedded systems hardware accelerator simulation toolchain hardware/software co-design

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distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees 36

Distributed Methods with Compressed Communication for Solvin...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Beznosikov, Aleksandr Richtarik, Peter Diskin, Michael Ryabinin, Max Gasnikov, Alexander Innopolis Univ Innopolis Russia MIPT Dolgoprudnyi Russia HSE Univ Moscow Russia Yandex Moscow Russia KAUSTI Riyadh Saudi Arabia RAS IITP Moscow Russia Innopolis Univ Res Ctr Artificial Intelligence Innopolis Russia

ISBN: (纸本)9781713871088

Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increasing data and problem sizes necessary to train high performing models across various applications, we need to rely on parallel and distributed computing. However, in distributed training, communication among the compute nodes is a key bottleneck during training, and this problem is exacerbated for high dimensional and over-parameterized models. Due to these considerations, it is important to equip existing methods with strategies that would allow to reduce the volume of transmitted information during training while obtaining a model of comparable quality. In this paper, we present the first theoretically grounded distributed methods for solving variational inequalities and saddle point problems using compressed communication: MASHA1 and MASHA2. Our theory and methods allow for the use of both unbiased (such as Randk;MASHA1) and contractive (such as Topk;MASHA2) compressors. New algorithms support bidirectional compressions, and also can be modified for stochastic setting with batches and for federated learning with partial participation of clients. We empirically validated our conclusions using two experimental setups: a standard bilinear min-max problem, and large-scale distributed adversarial training of transformers.

关键词： Optimization

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