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46th IEEE/ACM International Conference on Software Engineering, ICSE 2024

作者： Ma, Lipeng Jiang, Sihang Yang, Weidong Fei, Ben Xu, Bo Liang, Jiaqing Zhou, Mingjie Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China School of Computer Science and Technology Donghua University Shanghai China School of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Logs as semi-structured text are rich in semantic information, making their comprehensive understanding crucial for automated log analysis. With the recent success of pre-trained language models in natural language processing, many studies have leveraged these models to understand logs. Despite their successes, existing pre-trained language models still suffer from three weaknesses. Firstly, these models fail to understand domain-specific terminology, especially abbreviations. Secondly, these models struggle to adequately capture the complete log context information. Thirdly, these models have difficulty in obtaining universal representations of different styles of the same logs. To address these challenges, we introduce KnowLog, a knowledge-enhanced pre-trained language model for log understanding. Specifically, to solve the previous two challenges, we exploit abbreviations and natural language descriptions of logs from public documentation as local and global knowledge, respectively, and leverage this knowledge by designing novel pre-training tasks for enhancing the model. To solve the last challenge, we design a contrastive learning-based pre-training task to obtain universal representations. We evaluate KnowLog by fine-tuning it on six different log understanding tasks. Extensive experiments demonstrate that KnowLog significantly enhances log understanding and achieves state-of-the-art results compared to existing pre-trained language models without knowledge enhancement. Moreover, we conduct additional experiments in transfer learning and low-resource scenarios, showcasing the substantial advantages of KnowLog. Our source code and detailed experimental data are available at https://***/LeaperOvO/KnowLog. © 2024 IEEE computer Society. All rights reserved.

关键词： Computational linguistics

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Secured Provisioning of Multimedia Contents in Cloud Platform Using Cryptographic Approaches 1

Secured Provisioning of Multimedia Contents in Cloud Platfor...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Arul Mary, S.A. Sahaaya Nevgi, Akshaj Nair, Rhea Ranjith Sharukh Bhathena, Jehan Chaudhry, Angad Jessika, S. Sharon School of Computer Science and Engineering Vit India Computer Science and Engineering India Computer Science with Specialization in Data Science India

ISBN: (纸本)9798350341737

Cloud computing is a technology that allows the utilisation of a vast network of computers that are distributed and run in parallel with one another. The management of the multimedia files presents challenges and the significant of which is the prevalence of attackers and users with malicious intent. This article presents the conceptual foundation for a four-tiered data security paradigm that can be implemented in cloud computing. The proposed framework employs a DNA-computing-based password or secret key with a length of 1024 bits for the purpose of encrypting the user private multimedia file or correspondence. The DNABMS algorithm is applied in the cryptographic generation of digital identities as well as pseudorandom number generators. The results of the testing that was done on the model have validated both its reliability and its efficacy, demonstrating that the model that was developed is both reliable and effective. Experiments have demonstrated that the developed method is superior to other well-known existing strategies in terms of both effectiveness and efficiency. © 2023 IEEE.

关键词： Cryptography

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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

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Immersive video interaction system: a survey

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Fang, Xiaohan Chen, Peilin Wang, Meng Wang, Shiqi Department of Computer Science City University of Hong Kong Hong Kong School of Data Science Lingnan University Hong Kong

As communication technologies undergo rapid evolution, human interaction technologies have become increasingly efficient and accessible. Videoconferencing, for example, facilitates real-time, face-to-face communication worldwide, providing unprecedented convenience. Nevertheless, traditional two-dimensional (2D) videoconferencing systems frequently fall short of delivering a fully immersive experience, as they lack the ability to convey physical presence, depth perception, and natural interaction—key elements that are of particular significance in replicating the subtleties inherent in in-person communication. This disconnection leaves users yearning for the rich, multi-sensory experiences that real-life interactions can offer. In response, significant research efforts have been dedicated to the development of immersive video interaction systems (IVISs). This paper introduces the concept of IVISs and reviews a spectrum of systems advanced by researchers and industry innovators, each of whom contributes unique insights into the evolution of this field. A comparative analysis of the characteristics of current IVISs is provided, offering insights across multiple dimensions. This survey concludes with a discussion of potential future research directions and the challenges that remain in enhancing the IVIS technology. Through a comprehensive analysis, this work aims to foster further exploration and innovation in immersive communication technologies. © The Author(s) 2025.

关键词： 3D vision Immersive video interaction systems Survey Video communication

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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

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computers, Materials & Continua 2023年第10期77卷 647-664页

作者： Fayaz Muhammad Jahangir Khan Asad Ullah Fasee Ullah Razaullah Khan Inayat Khan Mohammed ElAffendi Gauhar Ali Department of Computer Science and Information Technology Sarhad University of Science and Information TechnologyPeshawar25000Pakistan Department of Computer Science University of Engineering and TechnologyMardan23200Pakistan EIAS Data Science and Blockchain Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

IIF(Indirect Immune Florescence)has gained much attention recently due to its importance in medical *** primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune *** use of IIF for detecting autoimmune diseases is widespread in different medical *** 80 different types of autoimmune diseases have existed in various body *** IIF has been used for image classification in both ways,manually and by using the computer-Aided Detection(CAD)*** data scientists conducted various research works using an automatic CAD system with low *** diseases in the human body can be detected with the help of Transfer Learning(TL),an advanced Convolutional Neural Network(CNN)*** baseline paper applied the manual classification to the MIVIA dataset of Human Epithelial cells(HEP)type II cells and the Sub Class Discriminant(SDA)analysis technique used to detect autoimmune *** technique yielded an accuracy of up to 90.03%,which was not reliable for detecting autoimmune disease in the mitotic cells of the *** the current research,the work has been performed on the MIVIA data set of HEP type II cells by using four well-known models of *** augmentation and normalization have been applied to the dataset to overcome the problem of overfitting and are also used to improve the performance of TL *** models are named Inception V3,Dens Net 121,VGG-16,and Mobile Net,and their performance can be calculated through parameters of the confusion matrix(accuracy,precision,recall,and F1 measures).The results show that the accuracy value of VGG-16 is 78.00%,Inception V3 is 92.00%,Dense Net 121 is 95.00%,and Mobile Net shows 88.00%accuracy,***,DenseNet-121 shows the highest performance with suitable analysis of autoimmune *** overall performance highlighted that TL is a suitable and enhanced technique compared to its ***,the proposed technique is used

关键词： Indirect immune fluorescence computer-aided diagnosis transfer learning confusion matrix

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Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

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computers, Materials & Continua 2024年第3期78卷 3021-3039页

作者： Shoukun Xu Lujun Zhang Guangqi Jiang Yining Hua Yi Liu School of Computer Science and Artificial Intelligence Aliyun School of Big Data and School of SoftwareChangzhou UniversityChangzhou213164China Department of Computer Science University of AberdeenKing’s CollegeAberdeenAB243FXUK

This paper focuses on the task of few-shot 3D point cloud semantic *** some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic *** tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding ***,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their ***,we designed a multi-prototype enhancement module to enhance the prototype ***,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich ***,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category *** studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic ***,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

关键词： Few-shot point cloud semantic segmentation CapsNets

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Sparse representation scheme with enhanced medium pixel intensity for face recognition

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

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StealthFace:Transfer Learning-Based Ensemble Model for Disguised Face Recognition using Skin-Segmented Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第4期52卷 1244-1260页

作者： Padmashree, G. Kotegar, Karunakar A Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Disguised face identification is challenging since people cover their identities by wearing masks, hats, sunglasses, or other disguises. These disguises dramatically modify face features, making identifying individuals a difficult task. In this study, we introduce StealthFace, an ensemble model that utilizes transfer learning and skin-segmented images to improve the recognition of disguised faces. To improve the accuracy and resilience of disguised face recognition, StealthFace leverages the power of deep learning algorithms, transfer learning, and ensemble methods. The framework employs pre-trained convolutional neural network models, such as ResNet50 and DenseNet121 for feature extraction and to learn discriminative features that are important in identifying individuals despite the disguises. The ensemble approach combines models’ predictions, utilizing their collective expertise and capturing multiple perspectives on disguised faces. Our ensemble model outperforms other state-of-the-art methods with an overall accuracy of 99.24% and contributes to enhancing security measures and advancing the development of face recognition systems. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Contrastive Learning

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Semënov Arithmetic, Affine VASS, and String Constraints 41

Semënov Arithmetic, Affine VASS, and String Constraints

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41st International Symposium on Theoretical Aspects of computer science, STACS 2024

作者： Draghici, Andrei Haase, Christoph Manea, Florin Department of Computer Science University of Oxford United Kingdom Computer Science Department Göttingen University Campus-Institut Data Science Germany

ISBN: (纸本)9783959773119

We study extensions of Semënov arithmetic, the first-order theory of the structure (N,+,2x). It is well-known that this theory becomes undecidable when extended with regular predicates over tuples of number strings, such as the Büchi V2-predicate. We therefore restrict ourselves to the existential theory of Semënov arithmetic and show that this theory is decidable in EXPSPACE when extended with arbitrary regular predicates over tuples of number strings. Our approach relies on a reduction to the language emptiness problem for a restricted class of affine vector addition systems with states, which we show decidable in EXPSPACE. As an application of our result, we settle an open problem from the literature and show decidability of a class of string constraints involving length constraints. © Andrei Draghici, Christoph Haase, and Florin Manea;licensed under Creative Commons License CC-BY 4.0.

关键词： Computability and decidability

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