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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

ISBN: (纸本)9783031783463

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Crypto-Ransomware Detection Using Selective Hashing

Crypto-Ransomware Detection Using Selective Hashing

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2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022

作者： Almajali, Anas Qaffaf, Ahmad Alkayid, Natali Wadhawan, Yatin The Hashemite University Faculty of Engineering Department of Computer Engineering Zarqa Jordan Software Engineer SeattleWA United States

ISBN: (数字)9781665456005

ISBN: (纸本)9781665456005

Ransomware is a malicious software that attempts to encrypt the user's files and demand a ransom in exchange for decrypting the files. This malware may have catastrophic impacts on the availability of data and consequently on the services provided by the infected organizations and institutes. Ransomware detection has been a challenge for researchers in the past few years. In this paper, we propose a behavioral ransomware detection method that utilizes fast selective hashing techniques. By selective we mean that only few selected blocks from a file are used for similarity comparison. Our experimental results demonstrate the efficacy of the proposed method in ransonware detection in terms of detection time. For 1000 files of a total size of 20GB and a detection threshold set to five files, our proposed system is able to detect a ransomware on average within 2.76 seconds saving 99.5% of the total files without taking much of the system resources and affecting user experience. © 2022 IEEE.

关键词： Cybersecurity

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An software Defined Networking (SDN) Enhanced Edge Computing Framework for Internet of Healthcare Things (IoHT)

An Software Defined Networking (SDN) Enhanced Edge Computing...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Anh, Dang Van Chehri, Abdellah Quy, Nguyen Minh Hue, Chu Thi Minh Nguyen, Dinh C. Quy, Vu Khanh Hung Yen University of Technology and Education Faculty of Information Technology Hungyen Viet Nam Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada Fpt University Faculty of Software Engineering Hanoi Viet Nam The University of Alabama Department of Electrical and Computer Engineering HuntsvilleAL United States

ISBN: (纸本)9798350351255

The rapid proliferation of intelligent Internet of Health Things (IoHT) applications within the context of the COVID-19 pandemic has exerted significant strain on the backhaul network infrastructure. This paper aims to introduce a novel framework that leverages software-defined networking (SDN) to enhance edge computing capabilities. This framework will expect to facilitate dynamic and adaptable communication between edge and cloud servers, specifically designed to support real-time Internet of Health Things applications. Through the establishment of a connection between servers and the software-Defined Networking controller, the system is expected to facilitate load balancing, network optimization, and the utilization of resources in an efficient manner. This, in turn, enables the provision of real-time healthcare services. Ultimately, the efficacy of the suggested framework is assessed by analyzing its impact on service response time. The research results indicate that the proposed framework significantly benefits IoHT systems' service response times across various workloads and traffic. © 2024 IEEE.

关键词： Electronic health record

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Improved Time-Saving Face Detection in Video 9

Improved Time-Saving Face Detection in Video

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9th International engineering Conference on Sustainable Technology and Development, IEC 2023

作者： Qadir, Jihad Anwar Jalal, Shayan Ihsan Jameel, Samer Kais Al-Talabani, Abdulbasit University of Raparin College of Basic Education Department of Computer Science Kurdistan Region Rania Iraq Koya University Faculty of Engineering Department of Software Engineering Kurdistan Region Koya Iraq

ISBN: (纸本)9798350335064

Human face detection from video sequences is a difficult problem in computer vision. Face detection is the process of determining the location of a face or faces in each frame of a video. Face detection in real-time videos is an ongoing under-investigated research problem. This paper proposes a new method for face detection in videos that significantly reduces the time required to detect faces. The method targets a Search Area Boundary (SAB) in the next frames that are determined based on the position of the detected faces in the current frame. The model will look for the face in a box that is slightly larger than the SAB from the previous frame. This means that it will not search the entire video frame for the face, so it will take less time to find the face. After a few frames, the method will begin searching the entire frame for new faces. The results show that the proposed method can save up to 73.5% of the time, especially when the SAB is small, with an accuracy degradation of no more than 7% in the best case. © 2023 IEEE.

关键词： Face recognition

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A Secured and Optimized Clustering Approch in Vehicular Ad-hoc Network 4

A Secured and Optimized Clustering Approch in Vehicular Ad-h...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Khan, Imran Khan, Fazlullah Ali, Asad Alshawi, Bandar Alturki, Ryan Wedyan, Mohammad Abdul Wali Khan University Computer Science Department KPK Mardan Pakistan School of Computer Science University of Nottingham Ningbo China Faculty of Science and Engineering Zhejiang315104 China University of Engineering and Technology Department of Computer Science KPK Peshawar Pakistan College of Computing Umm Al-Qura University Department of Computer and Network Engineering Makkah Saudi Arabia College of Computing Umm Al-Qura University Department of Software Engineering Makkah Saudi Arabia Faculty of Information Technology and Computer Sciences Department of Computer Sciences Irbid21163 Jordan

ISBN: (纸本)9798350395914

Vehicular ad hoc networks (VANETs) take vital role in intelligent transportation systems, but they face challenges due to dynamic network topology, impacting communication efficiency, especially with increasing active nodes. Security and privacy concerns are paramount, necessitating authentication to ensure only legitimate vehicles access the network. Secure vehicular clustering offers a solution, yet the number of clusters affects network dependability and efficiency. To tackle this, we present a secure clustering mechanism using Bat optimization algorithm, mimicking echolocation behavior to optimize clusters. We introduce an authentication technique for cluster heads to admit approved members, enhancing vehicular authentication. Simulations using MATLAB evaluate our approach, showing improved performance in end-to-end delay, overhead, and cluster head count compared to similar methods. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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A Spectral Mask - Based on Method for Applying into Generalized Sidelobe Canceller Beamformer 12th

A Spectral Mask - Based on Method for Applying into Gener...

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Proceedings of the12th International Conference on Information Technology and its Applications, CITA 2023

作者： Huy, Nguyen Ba Anh, Pham Tuan The, Quan Trong Faculty of Control Systems and Robotics University ITMO St.Petersburg Russia Faculty of Software Engineering and Computer Systems University ITMO St.Petersburg Russia

ISBN: (纸本)9783031368851

The problem of signal processing is extracting the desired target speaker while suppressing the annoying background noise in complex recording situation, where exists third-party talker, transport vehicle or interference, noise. Speech quality or speech intelligibility are the major characteristics, which confirm the effectiveness of processed method. In speech applications, such as hearing-aids, hands-free telephony, teleconferencing, acoustic instruments, microphone array (MA) is applied for increasing the speech quality. MA uses the spatial response, the priori information about direction - of - arrival (DOA) of interest signal, the properties of recording environment to obtain speech components and remove noise. Generalized Sidelobe Canceller (GSC) is one of the most widely exploited in microphone array beamforming, due to its convenience and effective noise reduction. However, in several situations, the performance of GSC beamformer is often degraded, because of the imprecise DOA. In this paper, the authors suggested an additive spectral mask for improving the evaluation of GSC beamformer. The resulting experiment proves the received advantage of the author’s proposed algorithm to increase in the term of the signal-to-noise ratio (SNR). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Signal to noise ratio

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App 18

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Mueed, Iqra Arshad, Usama Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing 'GikiLenS', an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure. © 2023 IEEE.

关键词： Object detection

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EMARec: a sequential recommendation with exponential moving average

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Neural Computing and Applications 2024年第21期36卷 12917-12933页

作者： Chen, Rui Wang, Zonglin Tang, Cundong Zhang, Jianwei Li, Pu Kong, Xiangjie Huang, Min College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450001 China Henan Joint International Research Laboratory of Computer Animation Implementation Technologies Zhengzhou450001 China School of Information Science and Technology Northwest University Xi’an710127 China Faculty of Information and Engineering Xuchang Vocational Technical College Xuchang461000 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou310023 China

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model’s insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recommender systems

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks 18

Diving into Brain Complexity: Exploring Functional and Effec...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Siddique, Abu Bakar Khalid, Hassan Bin Ali, Raja Hashim University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan

ISBN: (纸本)9798350328172

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches. © 2023 IEEE.

关键词： Graph theory

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