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16th International Symposium on Electronics and Telecommunications, ISETC 2024

作者： Hazim, Layth Rafea Ata, Oguz Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Software Engineering Istanbul Turkey

ISBN: (纸本)9798350390865

AI-generated content impersonating human writing is an issue that has gained attention as AI spreads its wings. This particular study serves as a comparison between the existing Logistic Regression and Feedforward Neural Networks (FNNs) by employing sentence-BERT-appended models and RoBERTa in the determination of authenticity in a given text. A dataset that was balanced between human-written and AI-generated texts was utilized. Techniques like tokenization and normalization were first used, followed by feature extraction using transformer-based models. Cross-validation and confusion matrix analysis that used measures such as accuracy, precision, recall, F1 score, and ROC AUC were included to guarantee the models' robustness. The hybrid RoBERTa-FNN model that was deposed challengers was the most outstanding model in respect of precision and recall, and the highest accuracy (99.95%) was obtained as mentioned in the data. The improved performance serves as a proof of how effectively RoBERTa uses its embeddings to represent context on the fine-grained level required for this kind of text classification. This work is a stepping stone to the creation of strong AI text detection systems, besides our advancements to the knowledge of models and embedding performance with respect to text classification. The results lay emphasis on the selection of model configuration and the embedding technique, as they are the key factors in achieving the best results in practical applications. © 2024 IEEE.

关键词： Logistic regression

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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Surface Defect Detection Algorithm for Strip Steel Based on Improved YOLOv7 Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 308-316页

作者： Wang, Zhu Liu, Weisheng the College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China the College of Computer Science and Software Engineering the University of Science and Technology Liaoning CO Anshan114051 China

This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques inspired by the You Only Look Once version 7 (YOLOv7) detection method, resulting in significant enhancements in the model's accuracy, speed, and flexibility. The substitution of ELAN with Bottleneck Transformer 3 (BoT3) leads to improved accuracy and mean Average Precision (mAP) values, while also introducing a more lightweight network architecture. The incorporation of the Involution mechanism enhances the model's feature extraction capabilities, thereby improving its ability to recognize small targets through the utilization of local perceptual fields. The ASPP_CA architecture leverages a multi-scale feature fusion technique along with an attention mechanism to reduce model parameters and enhance inference speed. Furthermore, it extends the model's receptive field, allowing it to capture additional visual information. The enhanced algorithm, denoted as YOLOv7-IBA, demonstrates empirical results that underscore its superiority over the three current state-of-the-art detection techniques in identifying surface flaws on strip steel. The accuracy has been improved to 82.9%, representing a significant increase of 7.2% compared to the previous performance. Furthermore, the mean mAP value has experienced a 3.2% increase, reaching a total of 79.9%. Moreover, there has been a remarkable 8.8% improvement in efficiency. The adoption of this approach holds the potential to enhance both the precision and productivity of strip surface flaw detection, while also providing valuable methodological support for the advancement of other related disciplines. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

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UEBCS: software Development Technology Based on Component Selection 18th

UEBCS: Software Development Technology Based on Component S...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Yingnan Qi, Xuezhao Li, Jian Lu, Dan College of Computer Science and Technology Harbin Engineering University Harbin150009 China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (纸本)9789819998920

Current software development has moved away from the traditional manual workshop model and emphasizes improving software product quality. To address the issue of repetitive work, software reuse techniques can be adopted to continually enhance the quality and efficiency of software development. software reuse primarily involves reutilizing existing software knowledge during the software development process, effectively reducing maintenance costs incurred during development and controlling the overall software development expenses. software components are an effective form of supporting software product reuse and serve as the core technology for enabling software reuse. Component-based software engineering techniques emphasize the use of reusable software "components" to design and construct programs, aiming to assemble these components within a software architecture to achieve software reuse and improve the quality and productivity of software products. However, selecting the most suitable components from the results of component retrieval requires understanding the different usages of each component in the retrieval results. The existing methods suffer from excessive reliance on manual approaches and errors caused by inter-component relationships. Therefore, this paper proposes a component selection technique called UEBCS (Usage Example-Based Component Selection). This technique leverages steps such as clustering analysis and hierarchical classification to achieve optimal component selection. UEBCS has shown excellent results in terms of both efficiency and accuracy in selecting components. This method provides technical support for software developers in the software development process and holds significant practical significance for enhancing software development quality and efficiency, as well as promoting the deepening development of the software industry. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer software reusability

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Optimal Bounds for Distinct Quartics 51

Optimal Bounds for Distinct Quartics

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Charalampopoulos, Panagiotis Gawrychowski, Pawel Ghazawi, Samah School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom Institute of Computer Science University of Wroclaw Poland Department of Computer Science University of Haifa Israel Department of Software Engineering Braude College of Engineering Karmiel Israel

ISBN: (纸本)9783959773225

A fundamental concept related to strings is that of repetitions. It has been extensively studied in many versions, from both purely combinatorial and algorithmic angles. One of the most basic questions is how many distinct squares, i.e., distinct strings of the form UU, a string of length n can contain as fragments. It turns out that this is always O(n), and the bound cannot be improved to sublinear in n [Fraenkel and Simpson, JCTA 1998]. Several similar questions about repetitions in strings have been considered, and by now we seem to have a good understanding of their repetitive structure. For higher-dimensional strings, the basic concept of periodicity has been successfully extended and applied to design efficient algorithms – it is inherently more complex than for regular strings. Extending the notion of repetitions and understanding the repetitive structure of higher-dimensional strings is however far from complete. Quartics were introduced by Apostolico and Brimkov [TCS 2000] as analogues of squares in two dimensions. Charalampopoulos, Radoszewski, Rytter, Waleń, and Zuba [ESA 2020] proved that the number of distinct quartics in an n × n 2D string is O(n2 log2 n) and that they can be computed in O(n2 log2 n) time. Gawrychowski, Ghazawi, and Landau [SPIRE 2021] constructed an infinite family of n × n 2D strings with Ω(n2 log n) distinct quartics. This brings the challenge of determining asymptotically tight bounds. Here, we settle both the combinatorial and the algorithmic aspects of this question: the number of distinct quartics in an n × n 2D string is O(n2 log n) and they can be computed in the worst-case optimal O(n2 log n) time. As expected, our solution heavily exploits the periodic structure implied by occurrences of quartics. However, the two-dimensional nature of the problem introduces some technical challenges. Somewhat surprisingly, we overcome the final challenge for the combinatorial bound using a result of Marcus and Tardos [JCTA 2004] for permuta

关键词： Optimization

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Intrusion Detection Mechanism Using Deep Learning 5

Intrusion Detection Mechanism Using Deep Learning

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection Systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Using Blockchain to Overcome the Issues in Land Registry Management: A Systematic Review 1

Using Blockchain to Overcome the Issues in Land Registry Man...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Riaz, Ayesha Ikram, Muhammad Jawad Asadullah, Najmussaba University of Engineering and Technology Department of Computer Software Engineering Mardan Pakistan Jeddah International College Department of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

A Blockchain network contains a distributed ledger that is used to store a secure and permanent record of transactions among multiple parties. As the registries of land records are historically stored in the form of paper documentation where, there might arise the issue of losing, destroying, and modifying needed documents. This land registration process is complicated and time-consuming and required a lot of effort to transfer ownership of the land registry or change any information. For this purpose, various contributions have been made by different researchers to overcome the problem of the classical land registration process by using blockchain technology. In this survey, we shed light on some advantages (reduce time and effort, security, permanency, etc.) of blockchain to identify some early problems in the paper-based land registration process. © 2023 IEEE.

关键词： Blockchain

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Automatic Anomaly Monitoring in Public Surveillance Areas

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Intelligent Automation & Soft Computing 2023年第3期35卷 2655-2671页

作者： Mohammed Alarfaj Mahwish Pervaiz Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Bahria UniversityIslamabadPakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Air UniversityIslamabadPakistan Department of Computer Engineering Korea Polytechnic University237 Sangidaehak-ro Siheung-siGyeonggi-do15073Korea

With the dramatic increase in video surveillance applications and public safety measures,the need for an accurate and effective system for abnormal/sus-picious activity classiﬁcation also *** it has multiple applications,the problem is very *** this paper,a novel approach for detecting nor-mal/abnormal activity has been *** used the Gaussian Mixture Model(GMM)and Kalmanﬁlter to detect and track the objects,*** that,we performed shadow removal to segment an object and its *** object segmentation we performed occlusion detection method to detect occlusion between multiple human silhouettes and we implemented a novel method for region shrinking to isolate occluded *** c-mean is utilized to verify human silhouettes and motion based features including velocity and opticalﬂow are extracted for each identiﬁed *** Wolf Optimizer(GWO)is used to optimize feature set followed by abnormal event classiﬁcation that is performed using the XG-Boost classiﬁ*** system is applicable in any surveillance appli-cation used for event detection or anomaly *** of proposed system is evaluated using University of Minnesota(UMN)dataset and UBI(Uni-versity of Beira Interior)-Fight dataset,each having different type of *** mean accuracy for the UMN and UBI-Fight datasets is 90.14%and 76.9%*** results are more accurate as compared to other existing methods.

关键词： Abnormal event classiﬁcation gray wolf optimizer region shrinking xg-boost classiﬁer

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Research on Script-Based software Component Development 18th

Research on Script-Based Software Component Development

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Zhao, Yingnan Sun, Yang Fan, Bin Lu, Dan College of Computer Science and Technology Harbin Engineering University Harbin150009 China Harbin Institute of Technology Software Engineering Co. Ltd. Harbin China

ISBN: (纸本)9789819998920

As software demand proliferates and software size and complexity increase, traditional software development models face enormous challenges. As a result, new software development techniques are being explored to meet the requirements of software development. The development of new software development techniques has begun to meet the requirements of software development. Based on many years of experience in object-oriented and component-oriented software design methods, we propose an innovative four-factor scripted analysis and design method for state transfer using the knowledge of computer compilation principles on finite state machine (FSM) circuit module automation. It provides a methodological and empirical reference for programmers in the design and development of application systems using dynamic languages. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer software reusability

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PAIR: Protein-aptamer interaction prediction based on language models and contrastive learning framework

PAIR: Protein-aptamer interaction prediction based on langua...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Jun Yan, Zhiqiang Zeng, Hao Zhu, Zexuan Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions. © 2024 IEEE.

关键词： Contrastive Learning

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