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Look Closer to Touch Behavior-enabled Android pattern Locks: A Study in the Wild 22

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

引用

IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Tusko, Gergely Meng, Weizhi Lampe, Brooke Tech Univ Denmark SPTAGE Lab DTU Compute Lyngby Denmark

ISBN: (纸本)9798350381993;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 fingerprintand 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： Touch Biometrics pattern Locks Wild Study Touch Dynamics User Authentication COVID-19

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ICCPR 2021 - Proceedings of 2021 10th international conference on computing and pattern recognition

ICCPR 2021 - Proceedings of 2021 10th International Conferen...

引用

10th international conference on computing and pattern recognition, ICCPR 2021

ISBN: (纸本)9781450390439

The proceedings contain 63 papers. The topics discussed include: younger face recognition by learning perceptual flows;lightweight facial expression recognition based on multi-scale dense convolutional neural network;imperceptible adversarial attack with entropy feature and segmentation-based constraint;brain connectivity feature mining of post-encephalitis epilepsy;unsupervised de-redundancy and multi-step fusion for continuous Chinese sign language recognition;multi-level relational knowledge distillation for low resolution image recognition;and a survey of person re-identification based on deep learning.

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Quantum computing in Music: Complex pattern recognition in Piano Music

Quantum Computing in Music: Complex Pattern Recognition in P...

引用

2024 international conference on Telecommunications and Power Electronics, TELEPE 2024

作者： He, Wei School of International Communication and Arts Hainan University Hainan Haikou570000 China

ISBN: (纸本)9798350369212

Music, as a form of art characterized by its complexity and diversity, holds significant cultural and emotional value for humanity. Piano music, being a prominent representative, demands efficient and precise recognition and analysis of its intricate harmonic, melodic, and rhythmic patterns. Traditional computational approaches often face limitations in handling such intricate patterns. However, quantum computing, emerging as a novel computational paradigm, presents new avenues for addressing these challenges. This paper aims to explore the potentials and methodologies of quantum computing in the realm of music, particularly focusing on the intricate pattern recognition of piano music. We introduce the fundamental principles and advantages of quantum computing. Subsequently, we delve into the application of quantum algorithms in piano music analysis, specifically addressing the recognition of harmonic structures and rhythmic patterns. Finally, we envision the future trends and challenges of quantum computing technology in the realm of music. Through this research endeavor, we aim to gain deeper insights into the potential of quantum computing in the field of music and offer novel ideas and methodologies for future investigations. © 2024 IEEE.

关键词： Quantum computers

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Design and Implementation of Anti-Peeping Screen Algorithm Based on soft-NMS YOLOv7 7

Design and Implementation of Anti-Peeping Screen Algorithm B...

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7th international conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Baining Chen, Hao Chen, Junlong Dai, Huawei School of Integrated Circuit Guangdong University of Technology Guangzhou China School of Computer Science and Engineering Huizhou University Huizhou China

ISBN: (纸本)9798350350890

The occurrence of privacy breaches through screen peeping has highlighted the increasing importance of anti-peeping screen algorithms. In the traditional YOLOv7 algorithm, it may not accurately detect faces when individuals are mutually obscured. In order to improve the success rate of face detection, we propose a new anti-peeping screen algorithm, soft-NMS-YOLOv7. Compared to the traditional NMS-YOLOv7, soft-NMS-YOLOv7 prevents face leakage by specially handling the scores of adjacent detection boxes. Therefore, soft-NMS-YOLOv7 exhibits better detection performance and robustness, increasing the accuracy of face detection in anti-peeping screen algorithms from 90.05% to 94.71%. Additionally, we have developed a physical model of soft-NMS-YOLOv7 based on Raspberry Pi 4B. © 2024 IEEE.

关键词： Face recognition

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A Brief Review of Unsupervised Learning Algorithms for Zero-Day Attacks in Intrusion Detection Systems 3

A Brief Review of Unsupervised Learning Algorithms for Zero-...

引用

3rd international conference on computing and Machine Intelligence (ICMI)

作者： Oluwadare, Sunkanmi ElSayed, Zag Adekoya, Oluwaseun Univ Cincinnati Dept Informat Technol Cincinnati OH 45221 USA Univ Cincinnati Dept Mech Engn Cincinnati OH 45221 USA

ISBN: (纸本)9798350372977;9798350372984

This research paper presents a brief review of ten popular unsupervised algorithms widely utilized in pattern recognition publications. The algorithms are assessed based on their popularity, strengths, limitations, and resource requirements. Considering these factors, we propose two most-preferred algorithms suitable for adoption in IDS (Intrusion Detection Systems) to address the problems associated with Zero Day exploits or attacks. Our review of the surveyed algorithms facilitated the recommendation of specific algorithms that can enhance IDS capabilities in detecting and mitigating Zero-Day attacks and anomalous intrusion attempts. These algorithms leverage unsupervised learning techniques to overcome the limitations of traditional signature-based approaches. By incorporating these algorithms, IDS can better handle sophisticated and evolving attacks that often evade detection. In conclusion, this research provides valuable insights into the strengths, limitations, and resource requirements of popular unsupervised algorithms used in pattern recognition. It highlights the potential of adopting these algorithms in IDS systems to bolster their ability to detect and respond to Zero-Day attacks. By recommending the integration of these algorithms, we contribute to the development of intelligent IDS solutions that can adapt to dynamic threat landscapes.

关键词： Intrusion Detection Zero-Day Attacks Machine Learning Unsupervised Learning pattern recognition

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Graph based Clustering Techniques 15

Graph based Clustering Techniques

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pawar, Vaishali Chandwadkar, Radhika MVPS's KBTCOE Computer Engineering Department Nashik India

ISBN: (纸本)9798331300579

Clustering is widely used technique to find hidden, meaningful patterns in the given dataset, called as clusters. It is grouping of data into groups of similar objects. The objective is that the objects within a group should be similar to one another and different from the objects in other groups. Clustering plays an excellent role in data mining applications, biology, science, pattern recognition, social networking, machine learning, web analysis, CRM, marketing, medical diagnostics, and many others. A variety of clustering algorithms have emerged that meet these requirements and were successfully applied to many data mining and pattern recognition problems. Due to their great representational power, Graphs can handle huge databases with large attributes. Hence Graphs play a vital role in clustering applications. © Grenze Scientific Society, 2024.

关键词： Clustering data mining machine learning pattern recognition social networking

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pattern recognition for Identifying a String Within a Text File Using Finite Automata 15

Pattern Recognition for Identifying a String Within a Text F...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Prajana, Mamidi Rajkumar, Harsha Sannidhi, Akepati Sai Vidyasri, Kammari Panda, Niharika Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350370249

pattern recognition is crucial across diverse domains, including retrieval of information, data mining, and bioinformatics. Numerous algorithms exist for string matching, and finite state machines (FSM) provide a robust framework for implementing these pattern recognition algorithms. This study introduces an efficient pattern recognition application utilizing a finite state machine, using the SSFSM library in Python. The SSFSM library facilitates the creation and management of FSMs, offering methods to define states and transitions efficiently. An algorithm is proposed, that identifies strings within a text file with the help of the SSFSM library in Python. It employs deterministic finite automata (DFA) for pattern recognition, scanning text files word by word and then character by character, and recording execution times to evaluate efficiency. The algorithm's effectiveness is demonstrated through a comparative analysis of its performance for the number of characters and size of the input file. Results demonstrate the algorithm's effectiveness in accurately identifying and quantifying specific string occurrences within large text files. It also shows that the search time of a string increases with an increase in the frequency of the character, size of the text file, and length of the search key, underscoring its potential for broader applications. Further, our results also show that there is approximately a 20% increase in search time when the search key length increases by 1. Future enhancements may include extended functionalities, support for additional document types, and improved complexity management. © 2024 IEEE.

关键词： Finite automata

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conference Proceedings of the IEEE Magnetics Society Skyrmionic synapse implementation for pattern recognition using convolutional neural network

Conference Proceedings of the IEEE Magnetics Society Skyrmio...

引用

IEEE international Magnetics conference (INTERMAG)

作者： Gupta, Saumya Vadde, Venkatesh Muralidharan, Bhaskaran Sharma, Abhishek Indian Inst Technol Dept Elect Engn Mumbai 400076 Maharashtra India Indian Inst Technol Ropar Dept Elect Engn Rupnagar 140001 Punjab India

ISBN: (纸本)9798350362213;9798350362220

Conventional neuromorphic computing faces the foreseeable limit of computational and power resources that can be mitigated by using spintronic-based devices. In this digest, we propose a 4-bit (16-state) skyrmion-based synaptic device for application in Convolutional Neural Network(CNN) consuming 0.971 fJ energy per weight update. Our proposed device offers compactness and scalability to higher bits. The skyrmionic synapse device is simulated using a micromagnetic simulation package(OOMMF) integrated with a software-based ReLU Max Pooled function to implement CNN for neuromorphic computing applications. We attained an accuracy of 98.15% for pattern recognition on the MNIST handwritten data set.

关键词： CNN Neural network Skyrmions Spintronics Synapse

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Leveraging Local Binary patterns with Transfer Learning for Face recognition in Low-light Conditions

Leveraging Local Binary Patterns with Transfer Learning for ...

引用

international conference on Multimedia computing, Networking and Applications (MCNA)

作者： Al Rayes, Lubana Haggag, Menatalla Alsmirat, Mohammad Univ Sharjah Comp Sci Dept Sharjah U Arab Emirates

ISBN: (纸本)9798350354744;9798350354737

The growing need for accurate facial recognition in low-light conditions continues to challenge existing technological frameworks. This research investigates the integration of Local Binary patterns (LBP) with transfer learning to enhance face recognition capabilities under sub-optimal lighting. By applying LBP as a preprocessing step, we utilize its robustness against lighting variations, which is crucial for environments such as poorly lit rooms. The study utilizes the Extended Yale B dataset. The dataset contains images of subjects under different and severe light conditions. Among the deep learning models tested, which include VGG16, ResNet50, DenseNet121, and InceptionV3, the VGG16 model demonstrated the best performance with validation accuracy of 98.37% and testing accuracy of 99.25%. This indicates significant potential for the integrated approach in practical applications.

关键词： Local binary pattern (LBP) Transfer learning Face recognition Low light conditions

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Multi-Modal Representation Learning with Text-Driven soft Masks

Multi-Modal Representation Learning with Text-Driven Soft Ma...

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IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Park, Jaeyoo Han, Bohyung Seoul Natl Univ Comp Vis Lab ECE Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350301298

We propose a visual-linguistic representation learning approach within a self-supervised learning framework by introducing a new operation, loss, and data augmentation strategy. First, we generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image, which are most relevant to a certain word in the corresponding caption, instead of completely removing them. Since our framework relies only on image-caption pairs with no fine-grained annotations, we identify the relevant regions to each word by computing the word-conditional visual attention using multi-modal encoder. Second, we encourage the model to focus more on hard but diverse examples by proposing a focal loss for the image-text contrastive learning (ITC) objective, which alleviates the inherent limitations of overfitting and bias issues. Last, we perform multi-modal data augmentations for self-supervised learning via mining various examples by masking texts and rendering distortions on images. We show that the combination of these three innovations is effective for learning a pretrained model, leading to outstanding performance on multiple vision-language downstream tasks.

关键词： language reasoning Vision

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