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Simple, robust and systematic QCA clocking scheme for area-efficient nanocircuits

International Journal of Electronics Letters 2025年第2期13.0卷 210-218页

作者： Goswami, Mrinal Sharma, Tonmoy Jyoti Nath Boruah, Arpita Programme of Computer Science & Engineering Faculty of Engineering Assam Down Town University Assam Guwahati India

Quantum-dot cellular automata (QCA) represent an innovative forefront in nanotechnology, exploring the utilisation of quantum dots as carriers of data in computational systems. Temporal controls govern the coordination of information transmission and synchronisation within QCA circuits. Nevertheless, most research in QCA overlooks the crucial aspect of appropriately arranging the fundamental clocking mechanism. Consequently, QCA circuits often exhibit irregularities in their clocking regions, rendering them unsuitable for practical implementation. Adopting a consistent and scalable clocking framework is necessary to address this challenge and facilitate efficient design with optimal routing possibilities within clocking zones. Without a clearly defined and standardised clocking structure, QCA circuit designs remain incomplete and lack viability for fabrication. Numerous QCA clocking methodologies have been proposed, showcasing commendable ingenuity. Nonetheless, these approaches exhibit room for enhancement, particularly in tackling issues concerning design intricacies, such as establishing robust feedback loops and efficient area utilisation. A novel approach termed Simple, Robust and Systematic (SRS) scheme has been introduced in response to these challenges. Emphasising heightened feedback pathways, optimal spatial allocation and streamlined design, this scheme aims to overcome existing limitations. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Feedback path QCA symmetric clocking Qcadesigner quantum-dot cellular automata (QCA) wire-crossing

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A Novel Contour Tracing Algorithm for Object Shape Reconstruction Using Parametric Curves

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computers, Materials & Continua 2023年第4期75卷 331-350页

作者： Nihat Arslan Kali Gurkahraman Defence Technology Programme Sivas University of Science and TechnologySivas58070Turkey Department of Computer Engineering Sivas Cumhuriyet UniversitySivas58140Turkey

Parametric curves such as Bézier and B-splines, originally developedfor the design of automobile bodies, are now also used in image processing andcomputer vision. For example, reconstructing an object shape in an image,including different translations, scales, and orientations, can be performedusing these parametric curves. For this, Bézier and B-spline curves can be generatedusing a point set that belongs to the outer boundary of the object. Theresulting object shape can be used in computer vision fields, such as searchingand segmentation methods and training machine learning algorithms. Theprerequisite for reconstructing the shape with parametric curves is to obtainsequentially the points in the point set. In this study, a novel algorithm hasbeen developed that sequentially obtains the pixel locations constituting theouter boundary of the object. The proposed algorithm, unlike the methods inthe literature, is implemented using a filter containing weights and an outercircle surrounding the object. In a binary format image, the starting point ofthe tracing is determined using the outer circle, and the next tracing movementand the pixel to be labeled as the boundary point is found by the filter ***, control points that define the curve shape are selected by reducing thenumber of sequential points. Thus, the Bézier and B-spline curve equationsdescribing the shape are obtained using these points. In addition, differenttranslations, scales, and rotations of the object shape are easily provided bychanging the positions of the control points. It has also been shown that themissing part of the object can be completed thanks to the parametric curves.

关键词： Contour tracing algorithm bézier curve B-spline curve object shape reconstruction

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Hybrid Time Series Methods and Machine Learning for Seismic Analysis and Volcano Eruption Predict

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 104-122页

作者： Mandita, Fridy Ashari, Ahmad Wibowo, Moh. Edi Suryanto, Wiwit Department of Engineering Universitas 17 Agustus 1945 Surabaya Surabaya60118 Indonesia Programme of Computer Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Physics Universitas Gadjah Mada Yogyakarta55281 Indonesia

Volcanic eruption refers to a natural catastrophe on Earth that poses imminent danger to communities surrounding volcanoes. Therefore, ongoing monitoring of volcanic processes is crucial for effective analysis and observation of volcanic activities preceding an eruption. In response to this, the study presents a novel hybrid time series approach, integrated with machine learning techniques, to enhance the identification and classification of seismic events associated with volcanic eruptions. In this case, time series techniques, including STA/LTA, template matching, and autocorrelation, were implemented to facilitate the detection and classification process. The challenges, however, lie in addressing noise and ensuring accuracy in the analysis of seismic signals. To resolve this, a new hybrid time series method was proposed to improve signal analysis accuracy by integrating multiple time series techniques. In practice, the dataset was collected from Mount Merapi in Indonesia between 2019 and 2021, consisting of a compilation of seismic data categorized by event type, thus enhancing classification accuracy. On top of that, prior to implementing machine learning techniques for signal classification, the hybrid method was employed to efficiently remove noise, ensuring that genuine seismic events were clearly distinguished from spurious signals. Notably, the experimental learning rate was set at 0.01. The results demonstrated that the proposed hybrid method outperformed stand-alone time series techniques, achieving an accuracy of 0.93 to 0.95. This signifies the effectiveness of precise seismic event recognition and categorization, greatly enhancing the volcano monitoring system. Furthermore, the findings offer substantial improvements in the forecasting and risk mitigation associated with volcanic eruptions, hence, advancing reliable seismic analysis methodologies. Ultimately, the method enhances hybrid methods and machine learning for seismic event analysis and volcan

关键词： Volcanoes

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A Determination of the Level of Adoption of eGovernment-based Conversational Systems in Nigeria 23

A Determination of the Level of Adoption of eGovernment-base...

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7th International Conference on E-Commerce, E-Business and E-Government, ICEEG 2023

作者： Oyelami, Olufemi Moses Falana, Odunayo Thomas Erinfolami, Oluwaseyi Samuel Computer Science Programme College of Computing and Communication Studies Bowen University Iwo Nigeria

ISBN: (纸本)9798400708398

Towards reaping the benefits provided by conversational systems, conversational systems in form of chatbots have been deployed to provide different kinds of services in different countries of the world. However, government-based services to the citizens are limited. Consequently, there is a need to know the level of the adoption of this technology in Nigeria so as for the stakeholders to be guided as per the line of action to be taken in reaping the benefit of the technology being usable to bridge the digital divide prevalent in the country. This work therefore investigated the level of adoption of conversational systems - both text-based and voice-based by the various governments in Nigeria. In carrying out the investigations, the websites of the various state governments were visited and searched, for text-based and voice-based conversational systems as well as the availability of any chatting facilities. The results obtained show that even though the vast majority of the state governments have web presence, conversational systems are generally not available. Only three states have chatting facilities;two are text-based and one has a chatting interface that is really not a conversational system. None has voice-based conversational system and two states from the North Central zone have no web presence at all. Consequently, governments need to adopt the usage of conversational systems in order to make services more available in different modes for the benefits of the mases and to reach the unreached. The North central states specifically also need to fully have web presence like the others in the remaining geo-political zones. © 2023 ACM.

关键词： Interface states

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Detection of Cross-Site Scripting Attack Using Bagging Ensemble Learning 4th

Detection of Cross-Site Scripting Attack Using Bagging Ensem...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aier, Liaren Emani Bhowmick, Sreya Das, Barasha Boruah, Arpita Nath Goswami, Mrinal Programme of Computer Science and Engineering Faculty of Engineering Assam down town University Sankar Madhab Path Panikhaiti Gandhi Nagar Assam Guwahati India

ISBN: (纸本)9789819757022

In today’s digital age, the Internet is pivotal in bringing both convenience and security concerns. Individuals are vulnerable to unauthorized access and manipulation of their information by potential attackers. A notable cybersecurity threat revolves around injections, a method for introducing malicious code into web applications, creating vulnerabilities that attackers can exploit. Within the scope of this study, our attention has been directed toward a specific form of injection attack known as cross-site scripting (XSS). Following thorough research, our strategic direction involves adopting a bagging ensemble learning method for detecting XSS anomalies. The random forest classifier was employed as the chosen model in this approach. Considering all the essential comparison parameters, the proposed simulation results significantly improve the existing work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Stacking Ensemble Learning Based Detection of Cross-Site Scripting Attack 6th

Stacking Ensemble Learning Based Detection of Cross-Site Scr...

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6th International Conference on Information Systems and Management science, ISMS 2023

作者： Das, Barasha Bhowmick, Sreya Aier, Liaren Emani Boruah, Arpita Nath Goswami, Mrinal Programme of Computer Science and Engineering Faculty of Engineering Assam Down Town University Sankar Madhab Path Gandhi Nagar Panikhaiti Assam Guwahati India

ISBN: (纸本)9783031707889

In accordance with Cybersecurity statistics, a total average of 2,200 cyber-attacks occur per day. Cross-site Scripting (XSS) is one of the most notable weaknesses in web security. It belongs to the injection category. For an attacker to gain access to the user's systems the user has to interact with malicious links. This usually occurs when the victim visits any link from a website that may contain XSS payload or when visiting a vulnerable website that may trick the victim into sharing cookies resulting in a compromise of the security in the web application. In this regard, we concentrated on creating an advanced technique for identifying cross-site scripting attacks using the Stacking Ensemble Learning method is proposed. The model is implemented with a combination of different classifiers, namely Random Forest, Gaussian Naïve Bayes, SVM, and KNN, where Logistic Regression is used as the meta-model. This model has achieved a remarkable 99% accuracy rate while maintaining a high level of precision and Recall. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

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Traffic Congestion Prevention Using Ant Colony Optimization 8

Traffic Congestion Prevention Using Ant Colony Optimization

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8th International Engineering Conference on Renewable Energy and Sustainability, ieCRES 2023

作者： Abusamra, Aiman Ashour, Abeer Ghazal, Mai Aldahdooh, Jehad Abuarja, Raghad Islamic University of Gaza Computer Engineering Dept. Gaza Palestine University of Helsinki Doctoral Programme in Computer Science Helsinki Finland Islamic University of Gaza Computer Engineering Gaza Palestine

ISBN: (纸本)9798350300758

Managing traffic congestion is crucial for improving mobility, reducing fuel consumption, and mitigating environmental impacts in urban areas. To address this challenge, we present a novel framework named TCP-ACO for detecting traffic congestion that classifies congestion into three distinct types: expected, unexpected, and real-time. The framework utilizes data from various sources, including databases, Ant colony optimization (ACO) systems, and computer vision techniques, to precisely detect and handle traffic congestion. Expected congestion is identified by analyzing historical traffic data and scheduled events, while unexpected congestion is detected by leveraging real-time data from ACO systems. Real-time congestion is detected by employing computer vision techniques, such as analyzing video footage from cameras or drones. The proposed framework has the potential, by recognizing and managing various types of congestion, to improve traffic flow, shorten travel times, and decrease environmental impacts. Additionally, it also offers a precise and effective solution for traffic congestion detection, which is a crucial aspect of smart city traffic management systems. Our analysis shows that the ACO algorithm adapted in TCP-ACO is more effective in finding the shortest path between two cities (result obtained: 4.014) compared to the result obtained from the shortest path technique compounded with computer vision (which yields a score of 6.224 when the path is free). This indicates the effectiveness of the proposed framework in addressing the challenges of traffic congestion, offering a promising solution for smart city traffic management systems to improve mobility and reduce environmental impacts in urban areas. © 2023 IEEE.

关键词： Traffic congestion

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Language and Knowledge Representation A Stratified Approach

arXiv

引用

arXiv 2025年

作者： Bagchi, Mayukh Doctoral Programme in Information Engineering and Computer Science Department of Information Engineering and Computer Science University of Trento Italy

Semantic heterogeneity is conventionally understood as the existence of variance in the representation of the same target reality when computationally modelled by independent parties. It can have serious implications in critical application scenarios like that of Knowledge Graph-based multilingual data integration. In view of the above, the thesis argues that the current understanding of the problem of semantic heterogeneity as the ‘existence of variance’, while being crucially necessary, is not sufficient and under-characterized. There can be no variance without a prior notion of a unifying reference taken as the basis for computing the variance itself. To that end, the thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observers encode different representations of the same target reality in a stratified manner using different concepts, language and knowledge (as well as data). The thesis then advances a top-down solution approach to the above stratified problem of representation heterogeneity in terms of several solution components, namely: (i) a representation formalism stratified into concept level, language level, knowledge level and data level to accommodate representation heterogeneity, (ii) a top-down language representation using Universal Knowledge Core (UKC), UKC namespaces and domain languages to tackle the conceptual and language level heterogeneity, (iii) a top-down knowledge representation using the notions of language teleontology and knowledge teleontology to tackle the knowledge level heterogeneity, (iv) the usage and further development of the existing LiveKnowledge catalog for enforcing iterative reuse and sharing of language and knowledge representations, and, (v) the kTelos methodology integrating the solution components above to iteratively generate the language and knowledge representations absolving representation heterogeneit

关键词： Knowledge graph

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A cloud-native framework for globally distributed capture and analysis of Internet Background Radiation 18

A cloud-native framework for globally distributed capture an...

引用

18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Bortoluzzi, Fabricio Irwin, Barry Westphall, Carla Merkle Noroff University College Department of Applied Computing Kristiansand Norway Rhodes University Grahamstown Department of Computer Science South Africa Federal University of Santa Catarina Postgraduate Programme in Computer Science Florianópolis Brazil

ISBN: (纸本)9789893347928

Among the existing methods for analysing internet traffic, one focuses on unsolicited and often harmful packets, referred to as Internet Background Radiation (IBR). IBR data can be captured by using cloud-based computing instances to listen for incoming packets and recording the received headers and contents in files using the PCAP format. No services are run on these hosts so all traffic can be regarded as non-legitimate. This work presents the research plan for capturing a year-long sample of the IBR arriving to up to 1500 geographically distributed sensors across a major service cloud provider. In combination with collection, a data analysis pipeline will be constructed to enable the querying of most relevant quantitative and qualitative aspects of the resulting dataset. The ultimate goal is to answer 'how is the IBR characterised when captured within the context of cloud computing?'. Results will include packet distribution according to the properties of the network, transport, and application layers, together with an evaluation of traffic linking to botnet activity such as Mirai and Moobot. © 2023 ITMA.

关键词： Cloud computing

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Detection of Fake Reviews on Products Using Machine Learning 7th

Detection of Fake Reviews on Products Using Machine Learning

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7th International Conference on Information and Communication Technology for Competitive Strategies, ICTCS 2022

作者： Narayana Royal, M. Reddy, Rajula Pavan Kalyan Sangathya, Gokina Sri Sai Madesh Pretam, B. Kaliappan, Jayakumar Suganthan, C. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Institute of Industry and International Programme Vellore Institute of Technology Vellore India

ISBN: (纸本)9789811993039

It is hard to exaggerate how crucial user reviews are in figuring out an organization’s e-commerce income. Before purchasing any goods or service, online customers rely on product and service reviews. As a result, firms must consider how reliable Internet reviews are because they might directly affect their reputation and bottom line. Because of this, some businesses pay spammers to post false reviews. These false reviews profit from consumer buying choices. As a result, during the past twelve years, there has been substantial study into techniques for spotting false reviews. However, there is still a need for a survey that can analyze and summarize the diverse techniques. In this paper, we are going to put the SVM and Naive Bayesian machine learning model system into place that can spot false reviews with an accuracy of 0.801 and 0.687. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic commerce

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