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Space Filling Curve Mapping for Malware Detection and Classification 20

Space Filling Curve Mapping for Malware Detection and Classi...

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3rd international conference on Computer Science and Software Engineering, CSSE 2020

作者： Ren, Zhuojun Chen, Guang Lu, Wenke College of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9781450375528

Shannon entropy can reflect whether malware is encrypted or compressed, so it is important that security analysts can locate obfuscated regions by higher-entropy values. Moreover, malware files belonging to the same families share similar modules that can be shown in the form of entropy. So we present a new method that uses space filling curve mapping (SFCM) to visualize malware, extracts image features by using the deep convolution neural networks and classifies the generated images by SVM (support vector machine) classifier. We verified the proposed method with 7162 samples of 24 Kaspersky malware families, and obtained 99.44% detection accuracy and 98.56% classification accuracy. © 2020 ACM.

关键词： pattern recognition

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Action recognition Using 3D CNN and LSTM for Video Analytics 3rd

Action Recognition Using 3D CNN and LSTM for Video Analytics

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3rd international conference on Intelligent Computing and Communication (ICICC)

作者： Umamakeswari, A. Angelus, Jonah Kannan, Monicaa Rashikha Bragadeesh, S. A. SASTRA Univ Thanjavur India

ISBN: (纸本)9789811510847;9789811510830

With the advent of growing digital technology, large amount of video data is being generated, making video analytics a promising technology. Human activity recognition in videos is currently receiving increased attention and activity recognition systems are a large field of research and development with a focus on advanced machine learning algorithms, innovations in the field of hardware architecture, and on decreasing the costs of monitoring while increasing safety (Guo and Lai in pattern Recognit 47:3343-3361, 2014, [1]). The existing system for action recognition involves using Convolutional Neural Networks (CNN). Videos are taken as a sequence of frames and frame-level CNN sequence features generated are fed to Long Short-Term Memory (LSTM) model for video recognition. However, the abovementioned methodology takes frame-level CNN sequence features as input for LSTM, which may fail to capture the rich motion information from adjacent frames or multiple clips. It is important to consider adjacent frames that allow for salient features, instead of mapping an entire frame into a static representation. Thereby, to mitigate this drawback, a new methodology is proposed wherein initially, saliency-aware methods are applied to generate saliency-aware videos. Then, an end-to-end pipeline is designed by integrating 3D CNN with LSTM, followed by a time series pooling layer and a softmax layer to predict the activities in video.

关键词： Saliency mask Three-dimensional convolutional neural network Action recognition Long Short-Term Memory Time series pooling Softmax function

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Smart Cyber-Physical System for pattern recognition of Illegal 3D Designs in 3D Printing 3rd

Smart Cyber-Physical System for Pattern Recognition of Illeg...

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3rd international conference on Smart Applications and Data Analysis for Smart Cyber Physical Systems

作者： Vedeshin, Anton Dogru, John Mehmet Ulgar Liiv, Innar Ben Yahia, Sadok Draheim, Dirk 3D Control Syst Inc 3DPrinterOS San Francisco CA 94103 USA Tallinn Univ Technol Dept Software Sci Tallinn Estonia Tallinn Univ Technol Informat Syst Grp Tallinn Estonia

ISBN: (纸本)9783030451820;9783030451837

The method to protect intellectual property (IP) in automated manufacturing (AM) and 3D printing industry particularly, presented in this paper, is based on a smart cyber-physical system and the radical improvement of preventive and detective controls to find potential cases of automated manufacturing copyrights infringement. The focus of this paper is not the ecosystem of managing a large network of physical 3D printers, but a smart application and data analysis of data flow within the ecosystem to solve a problem of IP protection and illegal physical objects manufacturing. In this paper, we focus on the first step in this direction - pattern recognition of illegal physical designs in 3D printing, and detection of firearms parts particularly. The proposed method relies on several important steps: normalization of 3D designs, metadata calculation, defining typical illegal designs, pattern matrix creation, new 3D designs challenging, and pattern matrix update. We classify 3D designs into loose groups without strict differentiation, forming a pattern matrix. We use conformity and seriation to calculate the pattern matrix. Then, we perform the analysis of the matrix to find illegal 3D designs. Our method ensures simultaneous pattern discovery at several information levels - from local patterns to global. We performed experiments with 5831 3D designs, extracting 3728 features. It took 12 min to perform pattern matrix calculation based on the test data. Each new 3D design file pattern recognition took 0.32 s on four core, 8GB ram, 32GB SSD Azure VM instance.

关键词： pattern recognition Intelligent manufacturing systems Technology social factors Distributed computing

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3rd international conference on Computer Vision and Image Processing, CVIP 2018

3rd International Conference on Computer Vision and Image Pr...

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3rd international conference on Computer Vision and Image Processing, CVIP 2018

ISBN: (纸本)9789813290877

The proceedings contain 38 papers. The special focus in this conference is on Computer Vision and Image Processing. The topics include: Mushroom Classification Using Feature-Based Machine Learning Approach;deep Learning Framework for Detection of an Illicit Drug Abuser Using Facial Image;diagnosis of Prostate Cancer with Support Vector Machine Using Multiwavelength Photoacoustic Images;a Deep Learning Framework Approach for Urban Area Classification Using Remote Sensing Data;things at Your Desk: A Portable Object Dataset;storm Tracking Using Geostationary Lightning Observation Videos;pneumonia Detection on Chest X-Ray Using Machine Learning Paradigm;eigenvector Orientation Corrected LeNet for Digit recognition;retinal-Layer Segmentation Using Dilated Convolutions;vehicle Speed Determination and License Plate Localization from Monocular Video Streams;detecting Face Morphing Attacks with Collaborative Representation of Steerable Features;enabling Text-Line Segmentation in Run-Length Encoded Handwritten Document Image Using Entropy-Driven Incremental Learning;fused Spectral Features in Kernel Weighted Collaborative Representation for Gender Classification Using Ocular Images;investigation on the Muzzle of a Pig as a Biometric for Breed Identification;integrated Semi-Supervised Model for Learning and Classification;robust Detection of Defective Parts Using pattern Matching for Online Machine Vision System;detection of Down Syndrome Using Deep Facial recognition;cosaliency Detection in Images Using Structured Matrix Decomposition and Objectness Prior;agriculture Parcel Boundary Detection from Remotely Sensed Images;a Reference Based Secure and Robust Zero Watermarking System;two-View Triangulation: A Novel Approach Using Sampson’s Distance;linear Regression Correlation Filter: An Application to Face recognition;human Head Pose and Eye State Based Driver Distraction Monitoring System;preface.

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Imagined Speech Decoding from EEG: The Winner of 3rd Iranian BCI Competition (iBCIC2020) 27

Imagined Speech Decoding from EEG: The Winner of 3rd Iranian...

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27th National and 5th international Iranian conference of Biomedical Engineering, ICBME 2020

作者： Hamedi, Nastaran Samiei, Susan Delrobaei, Mehdi Khadem, Ali K. N. Toosi University of Technology Department of Biomedical Engineering Tehran Iran

ISBN: (纸本)9781665419550

Brain-computer interface (BCI) is defined as the combination of machine and brain signals to control a device or computer to improve the quality of life, e.g., for people with paralysis. In this paper, we focus on people with speech disorders and investigate the capability of electroencephalogram (EEG) signals to discriminate four classes, including the speech imagination of three Persian words corresponding to the English words "rock,""paper,"and "scissors,"in addition to the resting state. We used the data available from the 3rd Iranian BCI competition (iBCIC2020), acquired from 10 healthy participants in a randomized study. Initially, the mutual information (MI) was used to find the optimum frequency band. Then, features were extracted from the data using the Common Spatial pattern (CSP) algorithm. Afterward, the most discriminative features were selected using the neighborhood component analysis (NCA). These features were then fed to a meta-classifier based on the stacking ensemble learning. The results show that working on an optimum frequency band will enhance the results compared with the fixed frequency band. It is also worth mentioning that the optimum frequency band is subject dependent;therefore, it is substantial to be selected accurately. Our method achieved an average classification accuracy of 51.90%±2.73 across all participants, which is promising compared with the results of previous studies in the field of imagined speech recognition in subject dependent BCI systems with randomized order of the stimuli. © 2020 IEEE.

关键词： Electroencephalography

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Hand detection and segmentation using multimodal information from Kinect 3

Hand detection and segmentation using multimodal information...

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3rd international conference on Multimedia Analysis and pattern recognition, MAPR 2020

作者： Pham, Van-Tien Le, Thi-Lan Tran, Thanh-Hai Nguyen, Thanh Phuong Modelling and Simulation Centre Viettel High Technology Industries Corporation Viet Nam School of Electronics and Telecommunications Hanoi University of Science and Technology Viet Nam International Research Institute Mica Hanoi University of Science and Technology Viet Nam University of Toulon Aix Marseille University Cnrs Lis Marseille France

ISBN: (数字)9781728165554

ISBN: (纸本)9781728165554

Nowadays, hand gestures are becoming one of the most natural and intuitive ways of communication between human and computer. To this end, a complex process including hand gesture acquisition, hand detection, gesture representation and recognition must be carried out. This paper presents a method that detects hand and segments hand regions from images captured by a Kinect sensor. As Kinect sensor provides not only RGB images as conventional camera, but also depth and skeleton, in our work, we incorporate multi-modal data from Kinect to deal with hand detection and segmentation. Specifically, we use skeleton to approximately determine hand palm. Then a skin based detector will be applied to discard non-skin pixels from the region of interest. Using depth data helps to limit the human body regions and remove false positive regions from the previous steps. Finally, morphological operations will be applied to fill holes in the hand region. The main advantage of this method is very easy to implement and it performs in real-time on an ordinary computer. We evaluate the proposed method on a dataset of hand gestures captured from different viewpoints. Experiment shows that it provides reasonable accuracy at very high frame rate. It also produces comparable performance in comparison with deep learning based methods. © 2020 IEEE.

关键词： Image segmentation

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A Pedestrian Detection Method Combining I-HOG and Sobel-ULBP Features 3

A Pedestrian Detection Method Combining I-HOG and Sobel-ULBP...

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3rd international conference on E-Business, Information Management and Computer Science, EBIMCS 2020

作者： Huang, Xiaoyong Zhang, Yi Zheng, Jing Wang, Zhenmei Cai, Xiaogang College of Information Science and Engineering Guilin University of Technology Guilin Guangxi541006 China

ISBN: (纸本)9781450389099

The previous Histogram of Oriented Gradient (HOG) and Local Binary pattern (LBP) feature fusion pedestrian detection method has disadvantages such as sensitivity to noise, serious spectral information loss, high feature dimension and much redundant information, etc. This paper proposes a pedestrian detection method based on the feature fusion of Improved Histogram of Oriented Gradient (I-HOG) and Sobel Uniform pattern LBP (Sobel-ULBP). By inserting Gaussian filtering algorithm to complete the pre-processing of the image to be extracted, the image transition is uniform and smooth, details are eliminated, and the noise generated by compression and transmission is removed. Subsequently, Sobel edge detection is performed on the image to reduce the influence of pedestrian background and clothing texture and other information, and highlight the pedestrian silhouette. Because the optimized I-HOG features and Sobel-ULBP texture feature obtained by calculation have higher feature dimensions. Therefore, the principal component analysis algorithm (PCA) is used to reduce the dimensions of the two features, and then the two features are fused in series, and finally the fused features are sent to the support vector machine (SVM) for training to obtain a pedestrian classifier. A simulation experiment was carried out on the INRIA pedestrian database, and the results show that the method has improved recall rate and pedestrian recognition rate. © 2020 ACM.

关键词： Local binary pattern

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A Queue Management Approach for Social Distancing and Contact Tracing 3

A Queue Management Approach for Social Distancing and Contac...

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3rd international conference on Artificial Intelligence for Industries (AI4I)

作者： Kyritsis, Athanasios, I Deriaz, Michel Univ Geneva Informat Sci Inst GSEM CUI Geneva Switzerland

ISBN: (纸本)9781728187013

Social distancing is necessary to prevent the rapid spread of a highly contagious disease, such as COVID-19, at least until a vaccine is found and mass-produced. By reducing the probability of an uninfected person coming close or in physical contact with an infected one, the disease transmission in the community can be suppressed. Although social distancing is simple to comprehend, it is not always easy to implement, mainly because not all public spaces are designed with this requirement in mind. In this paper, we present a queue management tool that can be used to allow people that wait for a service practice social distancing. In our approach, people are asked to join a virtual queue, in order to avoid crowds in physical waiting rooms or long waiting queues. Machine learning is used to predict the estimated waiting time of queuers, so they are called just in time to get served. We use past data and machine learning to predict how busy a location will be so that customers can pick the best time to visit the service. Finally, we present the method we use to monitor people taking a service at any time and implement contact tracing in a privacy-preserving manner.

关键词： AI COVID-19 machine learning pandemic pattern recognition queue system social distancing

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Face recognition Using Multi-local Descriptors-A Novel Approach 3rd

Face Recognition Using Multi-local Descriptors-A Novel Appro...

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3rd international conference on Smart Computing and Informatics (SCI)

作者： Mohanty, Lipika Saloni Satapathy, Suresh Chandra Kalinga Inst Ind Technol Sch Comp Engn Bhubaneswar Odisha India MAQ Software Hyderabad Telangana India

ISBN: (纸本)9789813296909;9789813296893

While face recognition has been a topic of interest for the researchers for quite some time now, most of the advancements and superior results have come out from the field of face recognition in controlled scenarios. As we shift away from the controlled environment like passport images, driver's license etc. to unconstrained environment like images taken from surveillance footage, images taken by bystanders etc. the recognition accuracy significantly decreases. This inconsistency is due to the fact that face images in unconstrained environment have vast variations in parameters like illumination, background detail, pose, expression, occlusion etc. At present times when important disciplines like security and forensics application depends on such systems it can prove to be very useful if a face recognition system in unconstrained environment can give comparable results to the systems in controlled environment. Our work focuses on face recognition using deep feature extraction by concatenating the features of different feature extractors to improve the recognition accuracy in unconstrained environment. We use multiple feature based methods (variants of LBP and LGS) to extract important features from the same image and combine them to form a single feature vector. For classification SVM is used and two face databases viz. ORL face database and LFWCrop (Labeled Faces in the Wild). The experimental results reveal that the proposed method improves the performance of the face recognition system.

关键词： Face recognition Local Binary pattern Local Graph Structure Concatenated feature vector

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2D Legendre wavelet Filter for Iris recognition Feature Extraction 19

2D Legendre wavelet Filter for Iris Recognition Feature Extr...

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3rd international conference on Cryptography, Security and Privacy (ICCSP) / 4th international conference on Multimedia and Image Processing (ICMIP)

作者： Muktar, Danlami Jamel, Sapiee Ramli, Sofia Najwa Deris, Mustafa Mat Univ Tun Hussein Onn Malaysia UTHM Batu Pahat Malaysia

ISBN: (纸本)9781450366182

In iris recognition system, the feature extraction is the most important stages where the iris unique feature is extracted. Several methods of achieving this have been proposed in order to extract the distinct features that are unique to each individual. The aim of this research is to proposed a new Legendre wavelet filter that will decompose the iris data and extract the unique pattern for the iris recognition system. The research considers using wavelet because wavelet are the most suitable means of image processing in general. The Legendre filter create show a very fine result of a decomposed iris image for classification.

关键词： Biometric Iris recognition pattern recognition Legendre Wavelet

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