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Look Closer to Touch Behavior-enabled Android pattern Locks: A Study in the Wild

Look Closer to Touch Behavior-enabled Android Pattern Locks:...

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IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Gergely Tuskó Weizhi Meng Brooke Lampe SPTAGE Lab DTU Compute Technical University of Denmark Denmark

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Android pattern lock is one of the most popular unlocking mechanisms on the Android platform. In order to enhance the security of Android’s unlocking functionality, a number of researchers have tried to combine touch biometrics with the pattern lock. Several studies have reported adequate— even excellent—authentication results, but, unlike fingerprint-and face recognition-based authentication, no biometrics-enabled pattern lock exists in the Android marketplace. Furthermore, most studies of biometrics-enabled pattern locks were conducted in controlled lab environments. As such, in this work, our goal is to investigate and validate the performance of touch behavior-enabled Android pattern locks in a more practical scenario, in which users have to download the application and learn to use it by themselves (as was often the case during the pandemic). During the course of our investigation, we collected substantial data, namely, the properties provided by the Android API for motion events, as well as measurements that could be extracted from the devices’ sensors. Our investigation found that touch-enabled Android pattern locks could achieve an average equal error ratio of 23.5% for user authentication—without changing the process from the user’s perspective. Next, we modified the user experience such that each user would train the authenticator with different fingers, similar to fingerprint-based authentication. With this modification, an average equal error ratio of 13.5% was achieved.

关键词： Privacy Pandemics Operating systems Face recognition Authentication Fingerprint recognition User experience

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A Lightweight Brain Tumor Segmentation Network Based on 3D Inverted Residual Modules 11

A Lightweight Brain Tumor Segmentation Network Based on 3D I...

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11th international conference on computing and pattern recognition, ICCPR 2022

作者： Liu, Yuchao Du, Xia Wang, Da-Han Zhu, Shunzhi Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9781450397056

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated segmentation methods for brain tumors include 2D and 3D convolution networks. The 3D networks give better results but lead to a significant increase in parameters and computational cost. In this paper, we propose a lightweight brain tumor segmentation network composed of 3D inverted residual modules, which can significantly reduce the computational complexity of 3D models. Based on a lightweight depthwise separable convolution, our 3D inverted residual module extracts high-dimensional brain tumor features through an intermediate expansion layer, thus improving performance. On the brain tumor dataset BraTS 2018, our network achieves dice scores of 80.8%, 90.7%, and 84.3% (for ET, WT, and TC, respectively) with only 0.68M parameters and 51.46G FLOPs. The results show that our method can significantly reduce the complexity of the 3D model and achieve very competitive performance. © 2022 ACM.

关键词： Semantics

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pattern recognition Algorithm to Detect Suspicious Activities

Pattern Recognition Algorithm to Detect Suspicious Activitie...

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Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： V C Mahavishnu S G Vikhas T Shyamganesh S Roopakumar S V Kavin Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India

Detecting suspicious activities in public places with higher people gathering and interaction has turned out to be an act with growing interest due to the increasing number of crime scenes and causalities happening in these days. Surveying and tracking of human activities are increasingly difficult owing to the random nature of human movements and actions. The reliability is greatly affected due to this randomness. Also a human operator cannot continuously monitor multiple screens efficiently in a consequent manner so an automated surveillance system deployment becomes a necessity. Currently, tracking individuals may be done remotely, and the analysis of the recorded images can be automated using object detection models, with the help of high resolution cameras and the development of machine learning techniques. This proposed system aims in identifying threats that are probable to occur in a public gathering or space which may be an explosion, accident or possession of armoury, etc. This proposed model takes advantage of the information from the image data to learn complex patterns and develop pattern recognition technique to identify the anomalies using high resolution camera and alert the monitoring authority in order to take the necessary actions. This proposed work compares various object detection techniques of machine learning algorithms and suggests the best model based on its performance metrics.

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Palm Vein Image Processing and Enhancement in Vein pattern recognition System on FPGA 2

Palm Vein Image Processing and Enhancement in Vein Pattern R...

引用

2nd international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2022

作者： Vats, Pranjal Tadepalli, Siva Kumar Electronics Engineering National Institute of Technology Pauri Uttarakhand Garhwal India

ISBN: (数字)9781665488754

ISBN: (纸本)9781665488754

Biometric recognition systems based on hand vein patterns are gaining popularity because they have qualities such as universality, uniqueness, and fraud resistance. Veins are not visible to the human eye since they are positioned beneath the skin, they constitute a strong barrier against deception. The hand's complicated vascular pattern allows for the estimation of a useful number of characteristics for identifying individuals. It has a sanitary advantage over other biometric authentication systems because it is contactless. In a real-world environment, image processing is critical. It's employed in a variety of applications, including security systems, artificial intelligence (AI), and so on. The Field Programmable Gate Array (FPGA) is the best-embedded platform for image processing applications. An FPGA's concurrent architecture allows it to do high-speed picture processing, allowing it to deliver timely warnings. Furthermore, modern FPGAs, particularly small-scale FPGAs, are very effi-cient in terms of cost and power usage. When compared to CPU execution, FPGA video/image computing delivers a processing speedup of ten times. This paper covered filtering approaches, contrast enhancement schemes and segmentation approaches. This paper demonstrates image enhancing techniques and then after results of these techniques are applied on vein images. We implemented an IP based embedded system because it is better in terms of power consumption, area and time. This paper has been work on reducing power as well as hardware utilization to design an optimized system since image processing requires extra hardware and power also. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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MB-ZZLBP: Multiscale Block ZigZag Local Binary pattern for Face recognition 2nd

MB-ZZLBP: Multiscale Block ZigZag Local Binary Pattern for F...

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2nd international conference on Machine Learning, Advances in computing,Renewable Energy and communication, MARC 2020

作者： Karanwal, Shekhar Diwakar, Manoj Dehradun Uttarakhand India

ISBN: (纸本)9789811623530

This work presents the LBP variant so-called MB-ZZLBP under illumination and expression variations. In MB-ZZLBP, initially mean is computed for all the square sub-blocks (size 2 × 2) of the 6 × 6 pixel window. After the mean computation, 3 × 3 window (pixel) is produced. Then ZigZag pixels are compared with each other, or in other words, the higher-order pixels are subtracted from the lower-order pixels. Differences of those which produce value ≥0 are allocated the label 1, else 0. After encoding each pixel position (binary pattern) eventually results in MB-ZZLBP transformed image. The MB-ZZLBP image is further divided into 3 × 3 sub-regions for extraction of histograms. The fused histogram is the feature size of MB-ZZLBP. The proposed FR approach attains remarkable results on EYB and Faces94 databases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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The Conglomerate Compression Approach for Visual pattern recognition Networks in Edge AI Applications

The Conglomerate Compression Approach for Visual Pattern Rec...

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IEEE Punecon

作者： Seema Bhalgaonkar Mousami Munot Alwin Anuse Dept of E&TC SCTR's Pune Institute of Computer Technology & PES's MCOE SPPU Pune India Dept of E&TC SCTR's Pune Institute of Computer Technology SPPU Pune India School of ECE Dr. Vishwanath Karad MIT-WPU Pune India

Deep Neural Networks have shown remarkable performance in various applications of Visual pattern recognition (VPR). This field continues to grow due to emergence of new architectures, availability of huge data, and powerful computing resources. However, because of their high computational complexity that subsequently demands higher memory requirements, and power consumption, the deployment of such DNN-based Visual pattern recognition Networks (VPRN) models in edgeAI applications is hindered. Such overheads necessitate the compression of VPRN models while retaining their performance. This paper explores multiple compression techniques which combines Knowledge Distillation (KD), pruning and quantization that are applied to DNNs to achieve higher compression ratio.

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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd international conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Identification of AI-Related Topic Evolution Based on Extraction of international conference Proceedings Titles 11

Identification of AI-Related Topic Evolution Based on Extrac...

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11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022

作者： Wibawa, Adhi Dharma Ramadhani, Prio Adi Utami, Ratna Purnama Technology Institute of Technology Sepuluh Nopember Deptartement of Medical Surabaya Indonesia

ISBN: (数字)9781665406482

ISBN: (纸本)9781665406482

international conference proceedings are a forum for researchers in various parts of the world to communicate their research findings to the scientific community and to the public in general. international conference proceedings published by globally recognized institutions are of unquestionable quality. Every conference must have a title that describes the theme or topic being discussed. This study wants to reveal the topic evolution by extracting conference titles. This study collects conference titles that contain terms related to artificial intelligence (AI) in them. Then the titles that have been collected are extracted to obtain a base noun phrase and filtered to eliminate uninformative terms. Of the 139,495 conference titles, it was obtained 8,133 titles containing topics related to AI. The results of the title extraction are 4,601 terms which are dominated by the exact term 'artificial intelligence', followed by the terms 'machine learning', 'applications', 'intelligent systems', 'semantic', 'pattern recognition' and other important related terms. Based on the glossary, frequency of occurrence, and year of publication, further identification of the evolution of a more specific topic is presented in this study. This study identified the early emergence and evolutionary phases of topics for specific terms such as natural language processing, cloud computing, and cybernetics or cyber security. Using the model proposed in this study, the process of identifying the evolution of specific topic can be extracted to get new insights. © 2022 IEEE.

关键词： Intelligent systems

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Utilizing Capsule Networks in Deep Learning for Traffic Sign Detection

Utilizing Capsule Networks in Deep Learning for Traffic Sign...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： R Giridharan P Mariush Rufin R A Navaneethan C Pranesh T Rahul Department of CSE Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Convolutional neural networks (CNNs) have been the predominant Current Advanced neural network techniques and Methods for Traffic Signal Classification. Despite their capabilities, image recognition models are restricted in their ability to capture aspects like pose, view, and orientation because of the limitations of the max pooling layer. A new approach has been introduced in this paper to identify German traffic signs utilizing capsule networks, which have displayed exceptional performance. Caps nets consist of capsules, which are clusters of neurons that encode object instantiation parameters such as position and alignment. This is achieved through algorithms for dynamic routing and agreement-based routing. In contrast to prior techniques that depended on manual feature extraction and the utilization of several complex neural networks with numerous parameters, our approach minimizes the necessity for manual parameter configuration and bolsters resilience against spatial variations. Moreover, CNNs are susceptible to various adversarial attacks, whereas capsule networks can effectively counter such intrusions, enhancing the dependability of traffic sign detection in autonomous vehicles.

关键词： Image recognition Heuristic algorithms Neural networks Neurons pattern classification Manuals Routing Vehicle dynamics Resilience Image classification

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Physical Reservoir computing for Interactive Estimation of Weight from Food Texture in 3D-Printed soft Matter in Picking Operations

Physical Reservoir Computing for Interactive Estimation of W...

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international conference on Information Fusion

作者： Rikuto Kawakami Moe Kakegawa Gianluca Inagawa Yorito Igeta Yuto Suzuki Jun Ogawa Hidemitsu Furukawa Dept. of Eng. Yamagata Univ. Yonezawa Japan Grad. Sch. of Sci. and Eng. Yamagata Univ. Yonezawa Japan

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper aims to improve the “Gel Biter,” a device that can simultaneously acquire chewing texture data from three different parts (the upper jaw, tongue, and lower jaw) composed of oral mimic end effectors with different softness (Young’s modulus), created using 3D printer technology, for use as a picking device. The Gel Biter utilizes physical reservoir computing, exploiting the deformation of the soft material in the oral model during chewing, to classify the texture of food materials with high accuracy. To apply this principle as a picking system, we examine whether it is possible to determine whether fried foods and fish roe have been cooked correctly and the extent of the weight being gripped based on texture. The results of the gripping object estimation experiments demonstrated that it is possible to distinguish between overcooked fried chicken and properly cooked fried chicken with $\mathbf{9 4 . 7 \%}$ accuracy, and to identify the weight difference of artificial salmon roe in $5 \mathrm{~g}, 10 \mathrm{~g}$, and 20 g increments with $\mathbf{9 4 . 5 \%}$ accuracy. These results suggest that the Gel Biter’s piezoelectric sensors through a physical reservoir computing system can determine information on weight and cooking status based on texture information.

关键词： Accuracy Tongue Three-dimensional displays Reservoir computing Estimation Sensor systems Safety Sensors Object recognition Standards

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