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Examining Transfer Learning with neural network and Bidirectional neural network on Thermal Imaging for Deception Recognition 28th

Examining Transfer Learning with Neural Network and Bidirect...

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28th International conference on neural Information processing

作者： Qin, Zishan Zhu, Xuanying Gedeon, Tom Australian Natl Univ Canberra ACT Australia Curtin Univ Perth WA Australia

ISBN: (纸本)9783030923099;9783030923105

Deception is a common feature in our daily life, which can be recognised by thermal imaging. Previous research has attempted to identify deception with causality features extracted from thermal images using the extended Granger causality (eGC) method. As the eGC transformation is complicated, in this paper we explore whether a transfer learning model trained on the eGC-transformed thermal deception dataset can be applied to the original thermal data to recognise deception. We explore two feature selection methods, namely linear discriminant analysis (LDA) and t-distributed random neighborhood embedding (t-SNE), and three classifiers, including a support vector machine (SVM), a feed forward neural network (NN) and a bidirectional neural network (BDNN). We find that using features selected by LDA, a transfer learning NN is able to recognise deception with an accuracy of 91.7% and an F1 score of 0.92. We believe this study helps foster a deeper understanding of eGC and provides a foundation for building transfer learning models for deception recognition.

关键词： Bidirectional neural network Extended granger causality Transfer learning Feature extraction Linear discriminant analysis

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Evaluating a Deep Learning Model for Cyberattack Detection Based on network Traffic 5th

Evaluating a Deep Learning Model for Cyberattack Detection B...

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5th International conference on Cognitive Computing and Information processing, CCIP 2023

作者： Ha, Muon Tran, Duc–Manh Telecommunications University Nha Trang Viet Nam St. Petersburg State Electrotechnical University St. Petersburg Russia

ISBN: (纸本)9783031607240

In the current digital era, distributed Denial of Service (DDoS) attacks can be recognized as a prevalent and perilous network threat, posing substantial risks to both organizations and individuals. Consequently, the timely identification and notification of DDoS attacks play a vital role in their mitigation and reduction of ensuing harm. Long Short-Term Memory (LSTM) networks, a prevalent form of deep neural network, find extensive application in tasks like natural language processing and forecasting time series data. This study suggests the use of LSTM networks to train and categorize network data features, determining their association with cyberattacks. The effectiveness of our approach in detecting and signaling cyberattacks, especially DDoS attacks, is confirmed through experiments on real-world datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Denial-of-service attack

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DMAP: a distributed Morphological Attention Policy for Learning to Locomote with a Changing Body 36

DMAP: a Distributed Morphological Attention Policy for Learn...

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36th conference on neural Information processing Systems (NeurIPS)

作者： Chiappa, Alberto Silvio Vargas, Alessandro Marin Mathis, Alexander Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

ISBN: (纸本)9781713871088

Biological and artificial agents need to deal with constant changes in the real world. We study this problem in four classical continuous control environments, augmented with morphological perturbations. Learning to locomote when the length and the thickness of different body parts vary is challenging, as the control policy is required to adapt to the morphology to successfully balance and advance the agent. We show that a control policy based on the proprioceptive state performs poorly with highly variable body configurations, while an (oracle) agent with access to a learned encoding of the perturbation performs significantly better. We introduce DMAP, a biologically-inspired, attention-based policy network architecture. DMAP combines independent proprioceptive processing, a distributed policy with individual controllers for each joint, and an attention mechanism, to dynamically gate sensory information from different body parts to different controllers. Despite not having access to the (hidden) morphology information, DMAP can be trained end-to-end in all the considered environments, overall matching or surpassing the performance of an oracle agent. Thus DMAP, implementing principles from biological motor control, provides a strong inductive bias for learning challenging sensorimotor tasks. Overall, our work corroborates the power of these principles in challenging locomotion tasks. The code is available at the following link: DMAP

关键词： Controllers

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CDCP: A Framework for Accelerating Dynamic Channel Pruning Methods 29

CDCP: A Framework for Accelerating Dynamic Channel Pruning M...

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29th IEEE International conference on Parallel and distributed Systems, ICPADS 2023

作者： Xiang, Zhaohong Luo, Yigui Xie, Yin She, Haihong Liang, Weiming Zhao, Laigang Tongji University School of Software Engineering Shanghai China Company Limited China

ISBN: (纸本)9798350330717

Dynamic channel pruning is a technique aimed at reducing the theoretical computational complexity and inference latency of convolutional neural networks. Dynamic channel pruning methods introduce complex additional modules for dynamically selecting channels for images. Due to the additional modules, dynamic channel pruning methods never achieve optimal acceleration effect in real world. To address this problem, we propose Consecutive Dynamic Channel Pruning (CDCP), a novel dynamic channel pruning framework unified for almost all dynamic pruning methods designed for continuous image processing. The core idea of CDCP stems from our observation that adjusting the network for all frames in semantically continuous scenes is unnecessary since adjacent frames often share similar network structures in dynamic channel pruning. CDCP introduces a simple binary classifier to determine whether the network structure needs to be adjusted for a new frame. Our method can also be used for semantically non-continuous image processing tasks with a slightly lower probability of model reuse. We validate the effectiveness of CDCP on three dynamic channel pruning methods and better acceleration effects are achieved when applied them with CDCP to the semantically continuous Waymo dataset, the nuScenes dataset, and the semantically discontinuous COCO dataset. © 2023 IEEE.

关键词： dynamic channel pruning model acceleration network structure

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HKTGNN: Hierarchical Knowledge Transferable Graph neural network-based Supply Chain Risk Assessment 21

HKTGNN: Hierarchical Knowledge Transferable Graph Neural Net...

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21st IEEE International Symposium on Parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhou, Zhanting Bi, Kejun Zhong, Yuyanzhen Tang, Chao Li, Dongfen Ying, Shi Wang, Ruijin University of Electronic Science and Technology of China School of Information Science and Engineering Chengdu China Sichuan University School of Computer Science Chengdu China Chongqing Ceprei Industrial Technology Research Institute Co. Ltd. Chongqing China Sichuan Changhong Electronic Holding Group Co. Ltd. Chengdu China Chengdu University of Technology College of Computer Science and Cyber Security Chengdu China Wuhan University School of Computer Science Wuhan China

ISBN: (纸本)9798350329223

The strength of a supply chain is an important measure of a country's or region's technical advancement and overall competitiveness. Establishing supply chain risk assessment models for effective management and mitigation of potential risks has become increasingly crucial. As the number of businesses grows, the important relationships become more complicated and difficult to measure. This emphasizes the need of extracting relevant information from graph data. Previously, academics mostly employed knowledge inference to increase the visibility of links between nodes in the supply chain. However, they have not solved the data hunger problem of single node feature characteristics. We propose a hierarchical knowledge transferable graph neural network-based (HKTGNN1) supply chain risk assessment model to address these issues. Our approach is based on current graph embedding methods for assessing corporate investment risk assessment. We embed the supply chain network corresponding to individual goods in the supply chain using the graph embedding module, resulting in a directed homogeneous graph with just product nodes. This reduces the complicated supply chain network into a basic product network. It addresses difficulties using the domain difference knowledge transferable module based on centrality, which is presented by the premise that supply chain feature characteristics may be biased in the actual world. Meanwhile, the feature complement and message passing will alleviate the data hunger problem, which is driven by domain differences. Our model outperforms in experiments on a real-world supply chain dataset. We will give an equation to prove that our comparative experiment is both effective and fair.1Accepted by The 21st IEEE International Symposium on Parallel and distributed processing with Applications (IEEE ISPA 2023). © 2023 IEEE.

关键词： Supply chains

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52nd International conference on Parallel processing, ICPP 2023 - Main conference Proceedings

52nd International Conference on Parallel Processing, ICPP 2...

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52nd International conference on Parallel processing, ICPP 2023

ISBN: (纸本)9798400708435

The proceedings contain 82 papers. The topics discussed include: EC-SpMM: efficient compilation of SpMM kernel on GPUs;minimizing network and storage costs for consensus with flexible erasure coding;SNICIT: accelerating sparse neural network inference via compression at inference time on GPU;BIRP: batch-aware inference workload redistribution and parallel scheme for edge collaboration;CoTrain: efficient scheduling for large-model training upon GPU and CPU in parallel;OSP: boosting distributed model training with 2-stage synchronization;ITIF: integrated transformers inference framework for multiple tenants on GPU;parallel order-based core maintenance in dynamic graphs;fast parallel index construction for efficient K-Truss-based local community detection in large graphs;and improving the scaling of an asynchronous many-task runtime with a lightweight communication engine.

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Language Models as Zero-shot Lossless Gradient Compressors: Towards General neural Parameter Prior Models 38

Language Models as Zero-shot Lossless Gradient Compressors: ...

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38th conference on neural Information processing Systems, NeurIPS 2024

作者： Wang, Hui-Po Fritz, Mario CISPA Helmholtz Center for Information Security Germany

Despite the widespread use of statistical prior models in various fields, such models for neural network gradients have long been overlooked. The inherent challenge stems from their high-dimensional structures and complex interdependencies, which complicate effective modeling. In this work, we demonstrate the potential of large language models (LLMs) to act as gradient priors in a zero-shot setting. We examine the property by considering lossless gradient compression - a critical application in distributed learning - that depends heavily on precise probability modeling. To achieve this, we introduce LM-GC, a novel method that integrates LLMs with arithmetic coding. Our technique converts plain gradients into text-like formats, enhancing token efficiency by up to 38 times compared to their plain representations. We ensure that this data conversion maintains a close alignment with the structure of plain gradients and the symbols commonly recognized by LLMs. Our experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods, improving compression rates by 10% up to 17.2% across various datasets and architectures. Additionally, our approach shows promising compatibility with lossy compression techniques such as quantization and sparsification. These findings highlight the significant potential of LLMs as a model for effectively handling gradients. Code is available at https://***/hui-po-wang/LM-GC. © 2024 neural information processing systems foundation. All rights reserved.

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Joint Partitioning and Sampling Algorithm for Scaling Graph neural network 29

Joint Partitioning and Sampling Algorithm for Scaling Graph ...

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29th Annual IEEE International conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Das, Manohar Lal Jatala, Vishwesh Gupta, Gagan Raj Indian Inst Technol Bhilai Dept EECS Raipur Madhya Pradesh India

ISBN: (纸本)9781665494236

Graph neural network (GNN) has emerged as a popular toolbox for solving complex problems on graph data structures. Graph neural networks use machine learning techniques to learn the vector representations of nodes and/or edges. Learning these representations demands a huge amount of memory and computing power. The traditional shared-memory multiprocessors are insufficient to meet real-world data's computing requirements;hence, research has gained momentum toward distributed GNN. Scaling the distributed GNN has the following challenges: (1) the input graph needs to be efficiently partitioned, (2) the cost of communication between compute nodes should be reduced, and (3) the sampling strategy should be efficiently chosen to minimize the loss in accuracy. To address these challenges, we propose a joint partitioning and sampling algorithm, which partitions the input graph with weighted METIS and uses a bias sampling strategy to minimize total communication costs. We implemented our approach using the DistDGL framework and evaluated it using several real-world datasets. We observe that our approach (1) shows an average reduction in communication overhead by 53%, (2) requires less partitioning time to partition a graph, (3) shows improved accuracy, (4) shows a speed up of 1.5x on OGB-Arxiv dataset, when compared to the state-of-the-art DistDGL implementation.

关键词： Graph neural network Graph Partitioning distributed processing Deep Learning

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

23rd International Conference on Algorithms and Architecture...

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23rd International conference on Algorithms and Architectures for Parallel processing, ICA3PP 2023

ISBN: (纸本)9789819708000

The proceedings contain 165 papers. The special focus in this conference is on Algorithms and Architectures for Parallel processing. The topics include: MDCF: Multiple Dynamic Cuckoo Filters for LSM-Tree;a Game Theory Based Task Offloading Scheme for Maximizing Social Welfare in Edge Computing;research on Dos Attack Simulation and Detection in Low-Orbit Satellite network;malware Detection Method Based on Visualization;TOC: Joint Task Offloading and Computation Reuse in Vehicular Edge Computing;distributed Task Offloading for IoAV Using DDP-DQN;a Task Offloading and Resource Allocation Optimization Method in End-Edge-Cloud Orchestrated Computing;multi-agent Cooperative Intrusion Detection Based on Generative Data Augmentation;an Uncertainty-Aware Auction Mechanism for Federated Learning;Long Short-Term Deterministic Policy Gradient for Joint Optimization of Computational Offloading and Resource Allocation in MEC;query Optimization Mechanism for Blockchain-Based Efficient Data Traceability;research on the Evolution Path of network Hotspot Events Based on the Event Evolutionary Graph;Task Offloading in UAV-Assisted Vehicular Edge Computing networks;path Planning of Coastal Ships Based on Improved Hybrid A-Star;data Augmentation Method Based on Partial Noise Diffusion Strategy for One-Class Defect Detection Task;K Asynchronous Federated Learning with Cosine Similarity Based Aggregation on Non-IID Data;MPQUIC Transmission Control Strategy for SDN-Based Satellite network;an Energy Prediction Method for Energy Harvesting Wireless Sensor with Dynamically Adjusting Weight Factor;Bayesian Optimization for Auto-tuning Convolution neural network on GPU;persistent Sketch: A Memory-Efficient and Robust Algorithm for Finding Top-k Persistent Flows;faCa: Fast Aware and Competition-Avoided Balancing for Data Center network;Optimizing GNN Inference processing on Very Long Vector Processor;GDTM: Gaussian Differential Trust Mechanism for Optimal Recommender System;privacy-Enhanced Dyna

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Removal of Gaussian distributed Noise in Images with Deep neural network Models 30

Removal of Gaussian Distributed Noise in Images with Deep Ne...

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30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Kus, Zeki Aydin, Musa Fatih Sultan Mehmet Vaktif Univ Bilgisayar Muhendisligi Istanbul Turkey

ISBN: (纸本)9781665450928

The removal of noise caused by environmental factors in microscopic imaging studies has become an important research topic in the field of medical imaging. In the medical imaging stage made with any digital microscopy method (Confocal, Fluorescence, etc.), undesirable noises are added to the image obtained due to factors stemming from excessive or low illumination, high or low temperature, or electronic circuit equipment. The most basic noise model formed due to these environmental factors mentioned is the Gaussian normal distribution or a characteristic function close to this distribution. It is widely known that spatial filters (mean, median, Gaussian smoothing) are applied to eliminate Gaussian noise in digital image processing. However, undesirable results may occur in the images obtained when spatial filters are used to remove the noise in the images. In particular, because high frequencies are suppressed in images where spatial filters are applied, details are lost in the final image, and a blurred image is obtained. For this reason, four different convolutional neural network-based models are used for noise removal and to improve the PSNR values in this study. As a result, the modified U-Net improved the PSNR values for different noise levels as follows: +6.23, +7.88 and +10.52 dB

关键词： Deep neural networks Convolutional neural networks Image Denoising

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