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10th International Conference on Innovation and New Trends in Information Technology, INTIS 2022 and 11th International Conference on Innovation and New Trends in Information Technology, INTIS 2023

作者： El Karam, Bachira Abou Marghoubi, Rabia Khanboubi, Fadoua Allaki, Driss InnovatiVE REsearch on Software Systems and data INPT Rabat Morocco LIM Lab. IOS team Department of Computer Science FSTM Casablanca Morocco Smart Embedded Enterprise and Distributed Systems INPT Rabat Morocco

ISBN: (纸本)9783031473654

In a context marked by the rapid evolution of technologies and the omnipresence of IT at the heart of all activities, the digital transformation of companies is no longer a luxury but an absolute necessity. This technological transformation can lead to resistance from the employees affected by the change. In order to properly conduct the change management process, it is necessary to ensure the involvement of employees from the beginning of this process, which is composed of three phases: the change management strategy, the diagnosis, and the actions implementation. This paper focuses on the diagnostic phase and more specifically on the evaluation stage. This evaluation measures the employees’ hesitation to express their degree of resistance toward the digital initiatives launched in the company. However, this assessment is usually done subjectively, and employees express their hesitation in non-uniform manners. In this work, we propose a weighted generalized hesitating fuzzy approach based on the fusion of theoretical concepts of generalized hesitant fuzzy sets and formal concept analysis (FCA), to formalize the measurement of degree hesitation in change resistance. This approach is flexible and allows the top management of each organizational unit to correctly measure the resistance to change of their teams and then evaluate that measure at the global organizational level. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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Toward a reference architecture based science gateway framework with embedded e-learning support

Toward a reference architecture based science gateway framew...

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作者： Pierantoni, Gabriele Kiss, Tamas Bolotov, Alexander Kagialis, Dimitrios DesLauriers, James Ullah, Amjad Chen, Huankai Fee, David Chan You Dang, Hai-Van Kovacs, Jozsef Belehaki, Anna Herekakis, Themistocles Tsagouri, Ioanna Gesing, Sandra School of Computer Science and Engineering University of Westminster London United Kingdom Parallel and Distributed Systems Laboratory SZTAKI Budapest Hungary Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens Greece Department of Computer Science and Engineering University of Notre Dame Notre DameIN United States

science gateways have been widely utilized by a large number of user communities to simplify access to complex distributed computing infrastructures. While science gateways are still becoming increasingly popular and the number of user communities is growing, the fast and efficient creation of new science gateways and the flexibility to deploy these gateways on-demand on heterogeneous computational resources, remain a challenge. Additionally, the increase in the number of users, especially with very different backgrounds, requires intuitive embedded e-learning tools that support all stakeholders to find related learning material and to guide the learning process. This paper introduces a novel science gateway framework that addresses these challenges. The framework supports the creation, publication, selection, and deployment of cloud-based reference architectures that can be automatically instantiated and executed even by nontechnical users. The framework also incorporates a knowledge repository exchange and learning module that provides embedded e-learning support. To demonstrate the feasibility of the proposed solution, two scientific case studies are presented based on the requirements of the plasmasphere, ionosphere, and thermosphere research communities. © 2022 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Ionosphere

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Database Deadlock Diagnosis for Large-Scale ORM-Based Web Applications

Database Deadlock Diagnosis for Large-Scale ORM-Based Web Ap...

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International Conference on Data Engineering

作者： Zhiyuan Dong Zhaoguo Wang Chuanwei Yi Xian Xu Jinyuan Zhang Jinyang Li Haibo Chen Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Department of Computer Science New York University

Today, most database-backed web applications depend on the database to handle deadlocks. At runtime, the database monitors the progress of transaction execution to detect deadlocks and abort affected transactions. However, this common detect-and-recover strategy is costly to performance as aborted transactions waste CPU *** avoid deadlock-induced performance degradation, developers aim to reorganize the application code to remove deadlocks. Unfortunately, doing so is difficult for web applications. Not only do their implementations include hundreds of thousands of LoCs, but they also use third-party object-relational mapping (ORM) frameworks which hide database access details. Consequently, it is hard for developers to accurately diagnose *** propose WeSEER, a deadlock diagnosis tool for web applications. To overcome the opacity of ORMs, WeSEER performs concolic execution on unit tests to extract a web application’s transactions as a sequence of template statements with symbolic inputs as well as path conditions that enable the sequence. WeSEER then analyzes the extracted transactions based on fine-grained lock modeling to identify potential deadlocks and report the code locations that cause them. We implement WeSEER for Java-based (OpenJDK) web applications, and use it to analyze two popular open-source e-commerce applications, Broadleaf and Shopizer. WeSEER has successfully identified 18 potential deadlocks in Broadleaf and Shopizer. Eliminating these identified deadlocks can result in up to 39.5× and 4.5× throughput improvement for Broadleaf and Shopizer, respectively.

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Architecture of IoT-Based Collaborative Laboratory for Reconfigurable SoC: Review and Development

Architecture of IoT-Based Collaborative Laboratory for Recon...

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Education Conference (GeCon), IEEE German

作者： Rashed Al Amin Veit Wiese Mohammad Musfiqur Rahman Sven Jacobs Steffen Jaschke Roman Obermaisser Institute for Embedded Systems University of Siegen Siegen Germany Computer Science Education University of Siegen Siegen Germany

Reconfigurable System-on-Chip (SoC) is a computing architecture that allows users to program and reprogram the hardware to perform different tasks, making it highly flexible and adaptable. Due to its potential to significantly accelerate various applications, reconfigurable SoC has become a subject of much research for academicians and industry. On the other hand, the classical practice of experimenting in the laboratory has been changing radically with the advanced utilization of computers, electronic devices, and the internet. As a result, it has given birth to a new concept of laboratory practice called Internet-of-Things (IoT) based collaborative simulation laboratory, which enables students to access and use the laboratory equipment at their convenience, enabling them to learn at their own pace and comfort. First, this paper reviewed the state of the art of reconfigurable SoC, specially Field-Programmable Gate Arrays (FPGAs) laboratory architecture. Then, it developed an IoT-based collaborative simulation laboratory for reconfigurable SoC, allowing multiple students to work together to solve laboratory tasks remotely. Furthermore, this laboratory architecture is for reconfigurable FPGAs, which can manage several tasks automatically, such as logic synthesis, logic simulation, and FPGA run from multiple FPGAs or SoC. In addition to its benefits, the capability to virtualize user input/output interfaces and remotely measure the design's response further enhances its advantages. However, this proposed laboratory architecture is essential not only for doing laboratory tasks remotely but also simplifies the existing teaching methods and helping evaluate student performance in the examination.

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The Age and Updating Stability for a Communication Context in VANET

The Age and Updating Stability for a Communication Context i...

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Brazilian Symposium on Computing System Engineering (SBESC)

作者： Margarete De Sá Sérgio Gorender Distributed Systems Laboratory (LaSiD) Graduate Program in Mechatronics (PPGM) Computer Science Institute (IC) and Polytechnic School (EP) Federal University of Bahia (UFBA)

ISBN: (数字)9798331509002

ISBN: (纸本)9798331532857

VANETs make it possible to execute specific vehicular applications, in particular those with the aim of turning traffic safe and avoiding congestion. However, it is difficult to maintain communication between vehicles when these vehicles move dynamically at high speeds. Context-aware systems have been studied as a way to monitor context information from sensors, which can be used by systems to adapt to changes in their environment or in application requirements. This concept is used by VANET protocols and applications in different ways. Quality of Context (QoC) metrics are used to qualify the context information considering different characteristics of sensors and of the information, being a base for systems to decide if it is safe and interesting to use specific information. In this paper, we propose QoC metrics related to the stability of the updating process for a vehicular communication context. In addition, we also analyze the Age QoC metric and show how to calculate it for distinct context information. We simulate scenarios to verify the QoC metrics behavior. The results showed that the proposed QoC metrics can properly qualify the vehicular communication context.

关键词： Measurement Roads Vehicular ad hoc networks Sensor phenomena and characterization Turning systems engineering and theory Stability analysis Sensor systems Reliability Monitoring

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Cost-Effective Network Packet Manipulation and Error-Detection Device

Cost-Effective Network Packet Manipulation and Error-Detecti...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alexander F. K. Janus Steffen Moser Frank Slomka Faculty of Engineering Computer Science and Psychology Institute of Embedded System/Real-Time Systems School of Advanced Professional Studies Ulm University Ulm Germany

Real-time multimedia communication applications like video conferencing and screen sharing tools are an integral part of our daily communications. These time-critical services are handled via the Internet, a packet-switched and essentially best-effort network, not explicitly designed for robust real-time communication. From a video conferencing service provider's perspective, network issues cause a lot of user complaints due to various reasons: lossy WLAN channels, poorly configured home routers, or crowded carrier-grade network address translation devices. All of these issues have in common that they are often time-variant and non-deterministic. Furthermore, the end-to-end transmission in these highly inhomogeneous networks often suffers from a mixture of more than one error source. To enable a detailed and impartial analysis of multimedia service quality, it is necessary to analyze packets in defective online scenarios and introduce a broadly similar error pattern in an otherwise stable lab network. In this paper, we present SEDER, a hardware-software design of a layer two transparent Ethernet bridge that can be used for two purposes: error detection and error recreation. Implemented on low-cost hardware, the bridge can be easily deployed anywhere to allow debugging, even at a user's site. It is possible to parameterize our bridge to imitate almost all possible network effects, including complex combinations of more than one effect. In addition, we present two case studies, firstly emphasizing the capabilities of SEDER and secondly showing the need to use SEDER to harden open source Video Conferencing Tools against errors.

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