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作者： Fleck, Martin Troya, Javier Wimmer, Manuel Business Informatics Group Institute of Software Technology and Interactive Systems WienTU Austria Department of Computer Languages and Systems University of Seville Seville Spain

Model transformations are an important cornerstone of model-driven engineering, a discipline which facilitates the abstraction of relevant information of a system as models. The success of the final system mainly depends on the optimization of these models through model transformations. Currently, the application of transformations is realized either by following the apply-as-long-as-possible strategy or by the provision of explicit rule orchestrations. This implies two main limitations. First, the optimization objectives are implicitly hidden in the transformation rules and their orchestration. Second, manually finding the best orchestration for a particular scenario is a major challenge due to the high number of possible combinations. To overcome these limitations, we present a novel framework that builds on the non-intrusive integration of optimization and model transformation technologies. In particular, we formulate the transformation orchestration task as an optimization problem, which allows for the efficient exploration of the transformation space and explication of the transformation objectives. Our generic framework provides several search algorithms and guides the user in providing a proper search configuration. We present different instantiations of our framework to demonstrate its feasibility, applicability, and benefits using several case studies. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： software engineering

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DRL-based Trajectory Planning and Sensor Task Scheduling for Edge Robotics 10

DRL-based Trajectory Planning and Sensor Task Scheduling for...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Bouhoula, Sirine Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and IT Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350373011

Mobile Edge Computing (MEC) and Edge Robotics have recently emerged as transformative technologies, revolutionizing industries by enabling real-time processing, decision-making, and automation at the network edge. However, the dynamicity induced by the system's conditions and specifically the mobility poses a challenge for optimally deciding where to execute a given computational task. As a response, we develop an intelligent algorithm for dynamic sensor task offloading tailored to the unique requirements of MEC-enabled robotic environments. Specifically, we first introduce the environmental dynamics including a sensor task's end-to-end delay and the robots' mobility and energy consumption and provide mathematical formulations to model these dynamics. Then, we mathematically formulate the optimization problem and its MDP counterpart and we propose a Deep Reinforcement Learning (DRL)-based computational offloading strategy to jointly optimize Quality of Service (QoS) and energy consumption through robot trajectory planning and fine-grained task allocation. Through hand-picked representative simulation scenarios, we demonstrate the superiority of our proposed mechanism in enhancing the overall system performance, specifically in optimizing task execution, reducing energy consumption, and mitigating transmission delays, compared to various baseline approaches. © 2024 IEEE.

关键词： Computation offloading

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Computational Offloading for the Industrial Internet of Things: A Performance Analysis 2

Computational Offloading for the Industrial Internet of Thin...

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2nd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2022

ISBN: (数字)9781665498258

ISBN: (纸本)9781665498258

The challenge of minimizing mission response times and the energy consumption of computationally-constrained IoT devices, is becoming increasingly important in Industry 4.0 environments, where mission-critical tasks require real-time and latency-sensitive processing. In this context, the computational offloading paradigm becomes an attractive alternative for utilising remote, computationally capable resources placed at the network Edge, to execute demanding applications. In this paper, an analysis of the main options for task execution (ondevice, Edge and Cloud) is performed, under different sensing and actuation scenarios. These results outline the need of an intelligent decision making mechanism to dynamically alternate between the available execution environments, as a response to the varying networking conditions. © 2022 IEEE.

关键词： Energy utilization

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Towards a component infrastructure for Cyber-Physical systems 16

Towards a component infrastructure for Cyber-Physical System...

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31st Annual ACM Symposium on Applied Computing, SAC 2016

作者： Maia, Marcio E. F. Andrade, Rossana M. C. Viana, Windson Group of Computer Networks Software Engineering and Systems Campus de Quixada Federal University of Ceara Brazil Group of Computer Networks Software Engineering and Systems Computer Science Department Federal University of Ceara Brazil Group of Computer Networks Software Engineering and Systems Instituto UFC Virtual Federal University of Ceara Brazil

ISBN: (纸本)9781450337397

The proximity between physical and cyber worlds is permitting the appearance of Cyber-Physical systems (CPS), formed by users and devices (physical) interacting with services, components and applications (cyber). In CPS, where unpredictability is an important challenge, two characteristics must be present in the developed systems: i) low coupling among interacting modules and ii) self-adaptive capabilities. In that direction, this paper describes an uncoupled sensor and actuator component-based infrastructure for self adaptive CPS. Its main contribution is two-fold: i) a lightweight and interoperable mechanism to specify and manage sensors and actuators, built on top of an ii) uncoupled communication and coordination mechanism for cyberphysical entities. © 2016 ACM.

关键词： Cyber Physical System

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A guide to agile development of interactive software with a "user Objectives"-driven methodology

A guide to agile development of interactive software with a ...

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作者： Losada, Begoña Urretavizcaya, Maite Fernández-Castro, Isabel Department of Computer Languages and Systems Faculty of Computer Engineering University of the Basque Country 20001 San Sebastián Spain

This paper presents the InterMod methodology. By combining the widely accepted Agile Methods, Model-Driven Developments and User-Centred Design it allows us to develop high-quality interactive applications. As a main characteristic, it plans and organises the software project as a series of iterations that are guided by the User Objectives in an agile and user-centred manner. At each iteration, the software development work can be distributed to different teams according to some developmental and integration activities. Each activity is driven by models that are validated by a multidisciplinary team composed of developers and users. The requirements are incrementally collected and formalised by means of models based on user-centred design. Besides, the Semantically Enriched Human-computer Interaction model is proposed to speed up project validation. This model enriches a human-computer interaction model with some visual characteristics and the application semantic. Thus, the enriched model provides enough information to generate prototypes so users and developers can easily validate this model. Diagram project is a real case study that is used to illustrate the application of the InterMod methodology through the whole paper. © 2012 Elsevier B.V. All rights reserved.

关键词： User centered design

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Security-Oriented Network Intent Placement using Particle Swarm Optimization 7

Security-Oriented Network Intent Placement using Particle Sw...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Landeau, Gabriel Avgeris, Marios Leivadeas, Aris Lambadaris, Ioannis Department of Software and It Engineering Montréal Canada Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350342871

As the network infrastructure grows, its configuration and service provisioning become a tedious process. Accordingly, new paradigms have emerged, such as the Intent-Based Networking (IBN), that envision the automation of the network configuration, while minimizing the human intervention. Specifically, IBN allows users to interact with the network through high-level and declarative requests, called intents, which later can be translated into low-level configurations. IBN can entail different scopes and target network infrastructures, while being domain specific, which can create several challenges in terms of the final activation of the requested intents. To this end, in this paper, we mainly focus on intents that are expressing security and Quality of Service (QoS) network services demands that can be translated into Service Function Chains (SFC) and automatically deployed over a campus network. Our work and depending on the security level expressed in the intent, tries to optimally decide the level of multi-tenancy or complete segregation of the users' services that can be achieved, while satisfying the network provider's objectives. In particular, an artificial intelligence inspired algorithm called Particle Swarm Optimization (PSO) is modeled that automatically tries to find the best placement of the intents, while satisfying the security and QoS requirements of the users issuing the intents. © 2023 IEEE.

关键词： Quality of service

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APFormer: Anti-Phishing Transformer for Website-Phishing Detection Via Joint Feature Learning

APFormer: Anti-Phishing Transformer for Website-Phishing Det...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Memon, Anam Manjotho, Ali Asghar National University of Modern Languages Department of Computer Science Hyderabad Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan

ISBN: (纸本)9798350349719

Phishing is a cybercrime activity where the perpe-Trator tricks users into believing that a fraudulent website is authentic, revealing sensitive information. Recently, the frequency of phishing attacks has increased, targeting both individuals and corporate sectors, thereby rendering traditional methods of detecting phishing websites less effective. These methods often produce high false positive rates due to the complexities involved in creating a strong correlation between brand identity and website features. Furthermore, sophisticated attacks, such as DOM element manipulation tricks, evade the existing methods and convince the user with visual and behavioural similarity. We propose a transformer-based approach for website phishing detection using joint feature learning. The approach jointly learns textual, visual, and heuristic multi-modal features to establish a stronger connection between brand identity and the webpage. The proposed model comprises a feature extraction module and a transformer network. The feature extraction module extracts textual, visual, and heuristic features from the DOM tree, while the transformer module classifies the input page as legitimate or phishing by learning the feature mapping to brand identity. Our method outperforms current state-of-The-Art methods on benchmark datasets by achieving a 3.5% increase in recall and a 5.9% reduction in false positive rate. © 2024 IEEE.

关键词： Websites

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software architecture documentation for developers: A survey

Software architecture documentation for developers: A survey

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software engineering 2014

作者： Rost, Dominik Naab, Matthias Lima, Crescendo Von Flach Chavez, Christina Fraunhofer Institute for Experimental Software Engineering Kaiserslautern Germany Fraunhofer Project Center on Software and Systems Engineering Software Engineering Laboratory Department of Computer Science Bahia Brazil

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

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computer Modeling in engineering & Sciences 2024年第11期141卷 1651-1671页

作者： Mohamed A.Mahdi Suliman Mohamed Fati Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

关键词： Cyberbullying offensive detection Bidirectional Encoder Representations from the Transformers(BERT) continuous bag of words Social Media natural language processing

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