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Software development processes in ocean system modeling

International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 47-69页

作者： Reiner Jung Sven Gundlach Wilhelm Hasselbring Software Engineering Group Department of Computer Science Kiel UniversityKielGermany

Scientific modeling provides mathematical abstractions of real-world systems and builds software as implementations of these mathematical *** science is a multidisciplinary discipline developing scientific models and simulations as ocean sys-tem models that are an essential research *** software engineering and information systems research,modeling is also an essential *** particular,business process modeling for business process management and systems engineering is the activity of representing processes of an enterprise,so that the current process may be analyzed,improved and *** this paper,we employ process modeling for analyzing sci-entific software development in ocean science to advance the state in engineering of ocean system models and to better understand how ocean system models are developed and maintained in ocean *** interviewed domain experts in semi-structured inter-views,analyzed the results via thematic analysis,and modeled the results via the Busi-ness Process Modeling Notation(BPMN).The processes modeled as a result describe an aspired state of software development in the domain,which are often not(yet)*** enables existing processes in simulation-based system engineering to be improved with the help of these process models.

关键词： Scientific modeling software development processes process modeling the-matic analysis

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High-Capacity DWDM Transmission System for Free-Space Optical Network 6

High-Capacity DWDM Transmission System for Free-Space Optica...

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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Rahman, M.T. Jahid, M.A. Anha, S.Y. Rahman, Md. Shohanur Azmi Orpy, F. Bakibillah, A.S.M. Department of Computer Science and Engineering Bangladesh Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo152-8552 Japan

ISBN: (纸本)9798350380040

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems. © 2024 IEEE.

关键词： Cost effectiveness

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Detection and Prevention of DoS Attack in VANET Using Artificial Neural Network 1

Detection and Prevention of DoS Attack in VANET Using Artifi...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Adil, Md Midha, Sugandhi Srivastav, V.K. Kavita Chandigarh University Department of Computer Science Engineering India Uttaranchal University Dehradun India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350373042

In the realm of Vehicular Ad Hoc Networks (VANETs), seamless communication between vehicles is crucial for the exchange of essential messages, such as road traffic updates and accident-related information. The dynamic nature of vehicular movement demands timely information dissemination to ensure effective traffic management and safety. However, the continuous availability of the network is imperative for the smooth functioning of VANETs. The vulnerability of VANETs to Denial-of-Service (DoS) attacks poses a significant threat, potentially rendering the network unavailable. Such attacks can disrupt communication among vehicles, leading to adverse consequences. This paper presents an enhanced approach for detecting DoS attacks, aiming to identify and mitigate them promptly. Leveraging neural networks, our proposed technique surpasses the efficacy of previous methods, ensuring improved results in the timely detection of DoS attacks within VANETs. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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IoT Network Security: NetFlow Traffic Analysis and Attack Classification Using Machine Learning Techniques 11

IoT Network Security: NetFlow Traffic Analysis and Attack Cl...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Tyagi, Kajal Ahlawat, Anil Chaudhary, Himanshi Kiet Group of Institutions Department. of Computer Science and Engineering Ghaziabad Delhi-NCR India

ISBN: (纸本)9798350350357

In the modern day, Internet of Things (IoT) has become critical since it permits seamless integration of physical objects and data-driven communication. IoT improves efficiency, automated processes, and real-time decision-making across the different sectors such as smart cities, transportation, agriculture, healthcare, etc. In IoT, machine learning is essential for advancing security, flexibility, and efficiency. Its real-time anomaly detection improves security by recognizing threats quickly. In this paper, we employed an IoT dataset called NF-ToN-IoT, a NetFlow-based dataset which has nine attack categories. On this dataset, we used SMOTE-ENN, a potent machine learning balancing technique created to solve the issue of class imbalance which provides best results after implementing Machine Learning(ML) models. Preprocessing was done on the dataset to eliminate extraneous or missing values. After that we applied four ML algorithms to classify attacks in NF-ToN-IoT dataset & these are Linear Discriminant Analysis (LDA), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbors (KNN) classifiers. These algorithms are quite good at identifying patterns and abnormalities in the dataset, which improves the efficiency of attack classification. The framework which we proposed achieved an accuracy rate of 60.6%, 98%, 98.3%, and 99% for LDA, RF, DT, and KNN respectively. Hence, we get the best results from KNN to classify those attacks present in NF-ToN-IoT dataset. © 2024 IEEE.

关键词： Internet of things

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Exploratory Data Analysis for Detecting of Iron Deficiency Using Machine Learning Techniques 7

Exploratory Data Analysis for Detecting of Iron Deficiency U...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Thakur, Dharmesh Kumar Mishra, Deepak Vns Group of Institutions Department of Computer Science Engineering M.P Bhopal India

ISBN: (纸本)9798350350067

Iron deficiency, a prevalent issue in global health, impacts a large number of individuals and can result in symptoms such as weariness, difficulty breathing, and other incapacitating effects. Although blood tests are considered the most reliable method for diagnosis, they can be inconvenient and intrusive. This dissertation explores the promising possibilities of machine learning in providing a non-invasive method for detecting iron *** caused by insufficient iron levels is a widespread global health issue. Conventional techniques for detection sometimes entail intrusive procedures such as blood testing. This dissertation investigates the capacity of machine learning methods to identify iron deficiency without the need for intrusive *** research will conduct a thorough examination of numerous data sources that may be associated with iron insufficiency using Exploratory Data Analysis (EDA). This may include physiological data such as heart rate and oxygen saturation, in addition to any accessible blood test findings. In addition, the inquiry will examine the potential use of image analysis from easily accessible places such as fingernails, palm, or the conjunctiva of the *** dissertation will utilise the knowledge obtained from exploratory data analysis (EDA) to assess and evaluate the effectiveness of several machine learning algorithms in detecting iron deficiency. This comparison research aims to identify the best precise and user-friendly method for non-invasive *** machine learning methods are being examined to determine their efficacy in detecting iron insufficiency. The dissertation evaluates the efficacy of different algorithms and determines the best viable method for precise and non-intrusive diagnosis of iron insufficiency. This study has the capacity to transform the process of identifying iron insufficiency. The findings might potentially lead to early diagnosis and therapies, which would improve patient he

关键词： Iron research

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The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models

The Generation Gap: Exploring Age Bias in the Value Systems ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Liu, Siyang Maturi, Trisha Yi, Bowen Shen, Siqi Mihalcea, Rada The LIT Group Department of Computer Science and Engineering University of Michigan Ann Arbor United States

ISBN: (纸本)9798891761643

We explore the alignment of values in Large Language Models (LLMs) with specific age groups, leveraging data from the World Value Survey across thirteen *** a diverse set of prompts tailored to ensure response robustness, we find a general inclination of LLM values towards younger demographics, especially when compared to the US *** a general inclination can be observed, we also found that this inclination toward younger groups can be different across different value ***, we explore the impact of incorporating age identity information in prompts and observe challenges in mitigating value discrepancies with different age *** findings highlight the age bias in LLMs and provide insights for future *** for our analysis are available at https://***/MichiganNLP/Age-Bias-In-LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs

PythonSaga: Redefining the Benchmark to Evaluate Code Genera...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yadav, Ankit Beniwal, Himanshu Singh, Mayank Department of Computer Science and Engineering Lingo Research Group Indian Institute of Technology Gandhinagar India

ISBN: (纸本)9798891761681

Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two popular benchmarks for Python code generation, analyzing their diversity and difficulty. Our findings unveil a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. Furthermore, we uncover a worrying prevalence of easy tasks that can inflate model performance estimations. To address these limitations, we propose a novel benchmark, PythonSaga, featuring 185 hand-crafted prompts in a balanced representation of 38 programming concepts across diverse difficulty levels. The robustness of our benchmark is demonstrated by the poor performance of existing Code-LLMs. The code and data set are openly available to the NLP community at https://***/PythonSaga/PythonSaga. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Innovative Educational group Recommendation with Knowledge-Aware Attentive Embedding Learning

Innovative Educational Group Recommendation with Knowledge-A...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Singh, Mahesh Kumar Bhardwaj, Mukesh Kumar Sharma, Nishita Dwivedi, Shudhanshu Ranjan Yadav, Prerna Mishra, Divyanshi Department of Computer Science and Engineering Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

This research presents an innovative method for education group recommendation systems by leveraging Knowledge-Aware Attentive Embedding Learning (KA-AEL), aimed at enhancing the collaborative learning experience. The core innovation lies in integrating domain-specific knowledge into the recommendation process, thereby tailoring educational content to both individual and group learning needs. The KA-AEL framework utilizes an attentive embedding mechanism that dynamically adjusts the influence of various knowledge aspects based on their relevance to the learners' current objectives and comprehension levels. The methodology encompasses two key phases: knowledge-aware embedding, which encodes both content and user profiles with domain knowledge infusion, and an attentive group recommendation engine that predicts optimal learning paths by assessing group compatibility and individual learning preferences in unison. This dual-phase approach enables the system to discern and adapt to the evolving educational goals of diverse groups, promoting a more engaged and effective learning experience. © 2024 IEEE.

关键词： Collaborative learning

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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&Development Chandigarh UniversityMohali140413India Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

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IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

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