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Asymmetric certified robustness via feature-convex neural networks 23

Asymmetric certified robustness via feature-convex neural ne...

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Proceedings of the 37th International conference on neural Information processing Systems

作者： Samuel Pfrommer Brendon G. Anderson Julien Piet Somayeh Sojoudi University of California Berkeley

Real-world adversarial attacks on machine learning models often feature an asymmetric structure wherein adversaries only attempt to induce false negatives (e.g., classify a spam email as not spam). We formalize the asymmetric robustness certification problem and correspondingly present the feature-convex neural network architecture, which composes an input-convex neural network (ICNN) with a Lipschitz continuous feature map in order to achieve asymmetric adversarial robustness. We consider the aforementioned binary setting with one "sensitive" class, and for this class we prove deterministic, closed-form, and easily-computable certified robust radii for arbitrary ℓp-norms. We theoretically justify the use of these models by extending the universal approximation theorem for ICNN regression to the classification setting, and proving a lower bound on the probability that such models perfectly fit even unstructured uniformly distributed data in sufficiently high dimensions. Experiments on Malimg malware classification and subsets of the MNIST, Fashion-MNIST, and CIFAR-10 datasets show that feature-convex classifiers attain substantial certified ℓ1, ℓ2, and ℓ∞-radii while being far more computationally efficient than competitive baselines. Code for reproducing our results is available on https://***/spfrommer/asymmetric_convex_certification.

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Artificial neural network for processing Fingerprint Image Noise

Artificial Neural Network for Processing Fingerprint Image N...

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Summer Virtual conference on Software Engineering, Artificial Intelligence, networking and Parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Ke Han Department of Fingerprint Technology Institute of Forensic Science Ministry of Public Security China Beijing China

ISBN: (纸本)9798350396386

Fingerprint images from crime scenes can be used to find and identify suspects in the field of forensic science. Fingerprint images are usually polluted by noise, which affects the visual effect of fingerprint images. It is important to process the noise of fingerprint images. A supervised learning 9-layer artificial neural network besides the input layer and the output layer is designed to remove the noise of fingerprint images in this paper. The first three layers of the neural network are convolutional layers. The purpose of designing convolutional layers is to extract the feature information of the images layer by layer and gradually generate the feature maps. The fourth layer to the sixth layer are fully connected layers. The number of neurons in the fourth layer is the same as that of the sixth layer. The number of neurons in the fourth layer is the number of elements in the vector flattened by the feature maps outputted from the last convolutional layer. The seventh layer to the ninth layer in the neural network are deconvolutional layers. These deconvolutional layers gradually restore the feature maps to the fingerprint image. Finally, the enhanced fingerprint image is outputted from the output layer. As the training samples of fingerprint images is inadequate, this paper provides a way to make training samples. The clear fingerprint image is blurred as the corresponding blurred fingerprint image, which solves the problem of lack of the training samples. The blurred fingerprint image and the corresponding clear fingerprint image are used to establish the training sample set and train the artificial neural network. The mean square error function is used as the loss function to adjust and optimize the parameters of the neural network. The neural network in this paper is evaluated on test fingerprint images. The neural network method is compared with the Gaussian low-pass filtering method and the Wiener filtering method. Experimental results show that the neura

关键词： Training Wiener filters Filtering Image matching Neurons Artificial neural networks Fingerprint recognition

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Prnet: A Progressive Regression network for No-Reference User-Generated-Content (UGC) Video Quality Assessment

Prnet: A Progressive Regression Network for No-Reference Use...

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IEEE International conference on Image processing

作者： Yang Yang Bo Jiang Kailin Wu NetEase Zhejiang Lab ByteDance

Objectively perceptual quality assessment of UGC videos is a challenge due to many reasons. First, the sources of UGC-videos are not available, which makes the appropriate technique is the no-reference video quality assessment (NR-VQA). Second, subjective mean option scores (MOS) of all the UGC-datasets are not uniformly distributed. Third, authentic degradations occurred in the videos are not unique, therefore, not predicable. For example, an over- or underexposure video, brightness and contrast static information is critical for evaluation. Only employing verified priori statistic knowledge or generalized learning knowledge may not cover all possible distortions. To solve these problems, we introduce a framework—Progressive Regression network (PRNet) in this paper. For the MOS problem, a PR module is proposed, which utilizes the coarse-to-fine strategy during the training process. It can turn sparse subjective human rating scores into integers with denser samples to address the problem. For the unpredictable distortions, a wide and deep feature extraction module is developed, which employs both low-level features generated from natural scene statistics (NSS) and high-level semantic features extracted by deep neural networks, to fuse memorizing priori knowledge and generalizing learning features. Experiments demonstrate that our PRNet achieves top performance in three main datasets.

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Pavlovian Conditioning Modeling Using Wireless Spiking neural network

Pavlovian Conditioning Modeling Using Wireless Spiking Neura...

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International SoC Design conference, ISOCC

作者： Jiaying Lin Ryuji Nagazawa Kien Nguyen Hiroo Sekiya Hiroyuki Torikai Mikio Hasegawa Won-Joo Hwang Graduate School of Science and Engineering Chiba University Chiba Chiba Japan Graduate School of Science and Engineering Hosei University Koganei Tokyo Japan Graduate School of Faculty of Engineering Tokyo University of Science Sinjuku Tokyo Japan Department of Information Convergence Engineering (Artificial Intelligence) Pusan National University Busan South Korea

To address the communication congestion and high energy consumption issues in IoT networks, prior research has proposed a distributed information processing system called WiBIC. As an exploratory step towards implementing the basic functionalities of the WiBIC system, this study focuses on the wireless integration of SNNs. Considering the data transmission and learning characteristics of SNNs, we propose using the APCMA protocol to effectively stabilize the operation of the wireless SNN. By modeling Pavlovian conditioning on this network, the learning capabilities of the implemented system can be determined.

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1st International conference on Applied Intelligence and Informatics, AII 2021

1st International Conference on Applied Intelligence and Inf...

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1st International conference on Applied Intelligence and Informatics, AII 2021

ISBN: (纸本)9783030822682

The proceedings contain 30 papers. The special focus in this conference is on Applied Intelligence and Informatics. The topics include: Key Techniques and Challenges for processing of Heart Sound Signals;enhanced Signal processing Using Modified Cyclic Shift Tree Denoising;a Machine Learning Driven Android Based Mobile Application for Flower Identification;a Generative Text Summarization Model Based on Document Structure neural network;human Gender Detection from Facial Images Using Convolution neural network;few-Shot Learning for Tamil Handwritten Character Recognition Using Deep Siamese Convolutional neural network;A CNN Based Model for Venomous and Non-venomous Snake Classification;recognition of Dysfluency in Speech: A Bidirectional Long-Short Term Memory Based Approach;distributed Denial of Service Attack Detection Using Machine Learning and Class Oversampling;glaucoma Detection Using Inception Convolutional neural network V3;scientific Metrological Analysis of Government Services Based on Big Data Analysis and Visualization Software Driven by Information Technology;violent Video Event Detection: A Local Optimal Oriented Pattern Based Approach;human Age Estimation Using Deep Learning from Gait Data;an Error Resilient Video Transmission in Ad Hoc network Using Error Diffusion Block Truncation Coding;ALO: AI for Least Observed People;COVID-Hero: Machine Learning Based COVID-19 Awareness Enhancement Mobile Game for Children;Literature Classification Model of Deep Learning Based on BERT-BiLSTM——Taking COVID-19 as an Example;identifying Relevant Stakeholders in Digital Healthcare;COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model;mixed Bangla-English Spoken Digit Classification Using Convolutional neural network;iConDet: An Intelligent Portable Healthcare App for the Detection of Conjunctivitis.

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Recognition of Moving Vehicle Number Plates using Convolutional neural network and Support Vector Machine Techniques

Recognition of Moving Vehicle Number Plates using Convolutio...

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2021 IEEE International conference on distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2021

作者： Fernandes, Roshan Madhu Rai, K. Rodrigues, Anisha P Mohan, B.A. Sreenivasa, N. Megha, N. NMAM Institute of Technilogy Nitte Department of CSE Udupi India Nitte Meenakshi Institute of Technology Department of CSE Bengaluru India Sahyadri College of Engg. Mgmt. Department of ECE Mangaluru India

ISBN: (纸本)9781665412445

Nowadays video cameras have become gradually deployed, hence the hassle of video enhancement has also been increased. Video enhancement is a process of illuminating the occurrence using gentle techniques to maintain the integrity of pixel quality. The standard of the original video recording gives the success for the enhancement. The purpose of video enhancement is to refine the visual look of the video or to give an extra changed illustration for future video processing which consists of analysis, detection, segmentation, recognition, and used for surveillance and the criminal justice system. In the proposed work vehicle number plate is enhanced and recognition of a number plate is performed using Convolutional neural network and Support Vector Machine. There are a lot of challenges in recognizing the number plate due to the presence of blur, low-intensity, snow, rain, hit and run cases. In such a case, recognizing the vehicle number plate is challenging. So to overcome all these problems video enhancement has to be performed. The proposed work involves converting the video into image frames, pre-processing the frames and then performing enhancement, and finally recognizing the vehicle number plate using CNN and Support Vector Machine. The result analysis proves that CNN gives better classification accuracy over the Support Vector Machine model. © 2021 IEEE.

关键词： Image enhancement

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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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Accelerated training via incrementally growing neural networks using variance transfer and learning rate adaptation 23

Accelerated training via incrementally growing neural networ...

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Proceedings of the 37th International conference on neural Information processing Systems

作者： Xin Yuan Pedro Savarese Michael Maire University of Chicago TTI-Chicago

We develop an approach to efficiently grow neural networks, within which parameterization and optimization strategies are designed by considering their effects on the training dynamics. Unlike existing growing methods, which follow simple replication heuristics or utilize auxiliary gradient-based local optimization, we craft a parameterization scheme which dynamically stabilizes weight, activation, and gradient scaling as the architecture evolves, and maintains the inference functionality of the network. To address the optimization difficulty resulting from imbalanced training effort distributed to subnetworks fading in at different growth phases, we propose a learning rate adaption mechanism that rebalances the gradient contribution of these separate subcomponents. Experiments show that our method achieves comparable or better accuracy than training large fixed-size models, while saving a substantial portion of the original training computation budget. We demonstrate that these gains translate into real wall-clock training speedups.

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

HKTGNN: Hierarchical Knowledge Transferable Graph Neural Net...

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

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

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 (HKTGNN 1 ) 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. 1 Accepted by The 21st IEEE International Symposium on Parallel and distributed processing with Applications (IEEE ISPA 2023).

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2021 International conference on Field-Programmable Technology, ICFPT 2021

2021 International Conference on Field-Programmable Technolo...

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20th International conference on Field-Programmable Technology, ICFPT 2021

ISBN: (纸本)9781665420105

The proceedings contain 51 papers. The topics discussed include: a high-performance and flexible FPGA inference accelerator for decision forests based on prior feature space partitioning;scalable and flexible high-performance in-network processing of hash joins in distributed databases;a hexagon-based honeycomb routing architecture for FPGA;an area-efficient multiply-accumulation architecture and implementations for time-domain neural processing;development of autonomous driving system based on image recognition using programmable SoCs;and StreamSVD: low-rank approximation and streaming accelerator co-design.

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