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

作者： Federico Gerardi Giacomo Iadarola Fabio Martinelli Antonella Santone Francesco Mercaldo Geckosoft Pisa Italy Institute of Informatics and Telematics National Research Council of Italy (CNR) Pisa Italy University of Molise Campobasso Italy

Considering the weaknesses of signature-based approaches adopted by current antimalware, from both academic and industrial side there is a boost in the development of techniques exploiting artificial intelligence, where one of the most promising are based on the representation of application under analysis as image. In order to understand whether these approaches can be effectively adopted in the real-world, starting from a detector based on deep learning, in this paper we evaluate the resilience of these approaches when morphed samples are considered. We present DexWave, a tool aimed to automatically inject perturbations techniques targeting the smali code representation of Android applications. The experimental analysis demonstrate that image-based malware classifier are vulnerable to simple perturbations attack.

关键词： Deep learning Codes Perturbation methods Detectors Malware Artificial intelligence Resilience

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A Dual-stage Attention Based SDN Traffic Prediction Method

A Dual-stage Attention Based SDN Traffic Prediction Method

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

作者： Chenjing Gao Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stage attention based Traffic Prediction method for the SDN traffic matrix prediction task. First, our method uses temporal pattern attention as the inter-flow attention mechanism before the encoding stage to realize adaptive feature extraction. Then, temporal attention is introduced as the intra-flow attention mechanism before the decoding stage to capture long-term temporal dependencies. Finally, we use the autoregressive module to handle the highly dynamic SDN traffic volume. Experimental results show that our proposed SDN traffic prediction method can capture more traffic features and improve the SDN traffic matrix prediction performance to some extent.

关键词： Network topology Time series analysis Prediction methods Machine learning Feature extraction Graph neural networks Encoding

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Research on Integrated Detection of SQL Injection Behavior Based on Text Features and Traffic Features 10th

Research on Integrated Detection of SQL Injection Behavior B...

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10th International conference on Computer Engineering and Networks, CENet 2020

作者： Li, Ming Liu, Bo Xing, Guangsheng Wang, Xiaodong Wang, Zhihui College of Intelligence Science and Technology National University of Defence Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9789811584619

With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens various Web application services and users data security. There are both traditional detection methods and emerging methods based on deep learning technology with higher detection accuracy for the detection of SQL injection. However, they are all for detecting a single statement and cannot determine the stage of the attack. To further improve the effect of SQL injection detection, this paper proposes an integrated detection framework for SQL injection behavior based on both text features and traffic features. We propose a SQL-LSTM model based on deep learning technology as the detection model at the text features level. Meanwhile, the features of the data traffic are merged. By this integrated method, the detection effect of SQL injection is further improved. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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A Power Sharing Method for Microgrid Consisting of HAPF and VSC

A Power Sharing Method for Microgrid Consisting of HAPF and ...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Mingming Zhang Ping Qin Yongjin Chen Hengjie Jia Wenyang Deng Shaoguan Power Supply Bureau of Guangdong Power Grid Co. Ltd Shaoguan Guangdong Guangzhou Power Electrical Technology Co. Ltd Guangzhou Guangdong

Converters play an imperative part in joining conveyed generators and directing control stream of microgrid and active distributed network (ADN). The hybrid active power filter is proposed to improve responsive control with a moo amplitude of operation's voltage. Its control extends and characteristics are distinctive based on a routine voltage source converter (VSC). Regulation of parallel-connected HAPF and VSC in active distributed networks is examined in this work. To completely utilize the control efficiency of every sort of converter, the power output allocation calculation among these two devices is considered for reduce add up to converter rating. A control reference assurance strategy is hence proposed and inserted in various leveled control system of the active distributed network. Comes about demonstrate that converter capacity is decreased by combining HAPF and VSC. Decoupling regulation method is utilized as the essential regulation strategy to every converter. Reenactment comes about are given to appear the legitimacy of the proposed strategy.

关键词： Simulation image processing Power control Microgrids Regulation Hybrid power systems Generators

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Removal of Gaussian distributed Noise in images with Deep Neural Network Models

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

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IEEE Signal processing and Communications Applications (SIU)

作者： Zeki Kuş Musa Aydin Bilgisayar M&#x00FC hendisli&#x011F i Fatih Sultan Mehmet Vak&#x0131 f &#x00DC niversitesi &#x0130 stanbul T&#x00FC rkiye

ISBN: (数字)9781665450928

ISBN: (纸本)9781665450935

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： Temperature Smoothing methods Noise reduction Neural networks Lighting Signal processing Spatial filters

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distributed methods with compressed communication for solving variational inequalities, with theoretical guarantees 22

Distributed methods with compressed communication for solvin...

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Proceedings of the 36th International conference on Neural Information processing Systems

作者： Aleksandr Beznosikov Peter Richtárik Michael Diskin Max Ryabinin Alexander Gasnikov Research Center for Artificial Intelligence Innopolis University Moscow Institute of Physics and Technology HSE University and Yandex Russia King Abdullah University of Science and Technology Saudi Arabia HSE University and Yandex Russia Yandex and HSE University Russia MIPT HSE University and Institute for Information Transmission Problems RAS 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.

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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Li Mocheng Liu Yang Ou Yang Chen Zhiguang Xiao Nong Chen Tao Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences(Beijing) Beijing Institute of Lifeomics Beijing China

The proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. The parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. The heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. This paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. This work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that.

关键词： Deep learning Scalability Neural networks Data preprocessing distributed databases Graphics processing units Clustering algorithms

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Design of a real-time face detection architecture for heterogeneous systems-on-chips

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INTEGRATION-THE VLSI JOURNAL 2020年 74卷 1-10页

作者： Spagnolo, Fanny Perri, Stefania Corsonello, Pasquale Univ Calabria Dept Informat Modeling Elect & Syst Engn I-87036 Arcavacata Di Rende Italy Univ Calabria Dept Mech Energy & Management Engn I-87036 Arcavacata Di Rende Italy

Object detection represents one of the most important and challenging task in computer vision applications. Boosting-based approaches deal with computational intensive operations and they involve several sequential tasks that make very difficult developing hardware implementations with high parallelism level. This work presents a new hardware architecture able to perform object detection based on a cascade classifier in real-time and resource-constrained systems. As case study, the proposed architecture has been tailored to accomplish the face detection task and integrated within a complete heterogeneous embedded system based on a Xilinx Zynq-7000 FPGA-based System-on-Chip. Experimental results show that, thanks to the proposed parallel processing scheme and the runtime adaptable strategy to slide sub-windows across the input image, the novel design achieves a frame rate up to 125fps for the QVGA resolution, thus significantly outperforming previous works. Such a performance is obtained by using less than 10% of on-chip available logic resources with a power consumption of 377 mW at the 100 MHz clock frequency.

关键词： Integral images Face detection image processing FPGA

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High-Speed and Low-Latency 3D Sensing with a parallel-Bus Pattern

High-Speed and Low-Latency 3D Sensing with a Parallel-Bus Pa...

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International conference on 3D Imaging, Modeling, processing, Visualization and Transmission (3DIMPVT)

作者： Leo Miyashita Satoshi Tabata Masatoshi Ishikawa The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo Japan The University of Tokyo Tokyo University of Science 7-3-1 Hongo Shjinjuku-ku Tokyo Japan

ISBN: (纸本)9781665456715

High-speed 3D shape sensing is an essential technology for three-dimensional recognition and manipulation in dynamic scenes. However, conventional high-speed sensing methods mainly focus on the image capturing speed and the actual processing time to obtain a point cloud is not optimized in the measurement scheme and sensing pattern configuration. On the other hand, measurement latency is critical to respond in real-time and physically handle the dynamic scenes. This paper introduces a physically stereo-rectified projector-camera system for high-speed and low-latency 3D sensing with fast sequential memory access in the decoding process. Moreover, we configure a structured light pattern named “parallel-bus pattern” with a De Bruijn torus and clock lines to maximize information density and robustly decode the pattern as data transfer in a parallel bus interface. We measured dynamically moving and deforming objects with the proposed system and evaluated the measurement performance. As a result, the developed 3D sensing system with the parallel-bus pattern achieved 27K-points measurement at higher than 1000 fps with 0.336 ms latency and 0.838 mm accuracy on average.

关键词： Three-dimensional displays Shape Shape measurement Robot sensing systems Real-time systems Sensors Decoding

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Automated image and Video Quality Assessment for Computational Video Editing 9th

Automated Image and Video Quality Assessment for Computation...

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9th International conference on Analysis of images, Social Networks and Texts, AIST 2020

作者： Lomotin, Konstantin Makarov, Ilya HSE University Moscow Russia Samsung-PDMI Joint AI Center St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia

ISBN: (纸本)9783030726096

We study non-reference image and video quality assessment methods, which are of great importance for computational video editing. The object of our work is image quality assessment (IQA) applicable for fast and robust frame-by-frame multipurpose video quality assessment (VQA) for short videos. We present a complex framework for assessing the quality of images and videos. The scoring process consists of several parallel steps of metric collection with final score aggregation step. Most of the individual scoring models are based on deep convolutional neural networks (CNN). The framework can be flexibly extended or reduced by adding or removing these steps. Using Deep CNN-Based Blind image Quality Predictor (DIQA) as a baseline for IQA, we proposed improvements based on two patching strategies, such as uniform patching and object-based patching, and add intelligent pre-training step with distortion classification. We evaluated our model on three IQA benchmark image datasets (LIVE, TID2008, and TID2013) and manually collected short YouTube videos. We also consider interesting for automated video editing metrics used for video scoring based on the scale of a scene, face presence in frame and compliance of the shot transitions with the shooting rules. The results of this work are applicable to the development of intelligent video and image processing systems. © 2021, Springer Nature Switzerland AG.

关键词： Deep neural networks

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