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Proceedings of the International Convention MIPRO

作者： Shahbaz Memon Johann F. Jadebeck Michael Osthege Anna Wendler David Kerkmann Henrik Zunker Wolfgang Wiechert Katharina Nöh Jens Henrik Göbbert Björn Hagemeier Morris Riedel Martin J. Kühn Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany Forschungszentrum Jülich GmbH Institute for Bio- and Geosciences IBG-1: Biotechnology Jülich Germany Computational Systems Biotechnology (AVT.CSB) RWTH Aachen University Aachen Germany Department for High-Performance Computing German Aerospace Center Institute for Software Technology Cologne Germany Helmholtz Centre for Infection Research Brunswick Germany ∥School of Engineering and Natural Sciences University of Iceland Reykjavik Iceland Life and Medical Sciences Institute University of Bonn Bonn Germany

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

When faced with the challenge of now- and forecasting infectious diseases, multiple data sources and state-of-the-art models have to be considered. Automatic aggregation, processing, and publishing to relevant data sinks is paramount to achieving consistent, reproducible, and timely results given daily-reported data. To facilitate scientific collaboration and reproducibility of workflows, open and extensible architectures for compute pipelines are *** this research, we devise an architecture realizing the seamless management and processing of reproducible pipelines. Our case-study is a daily pipeline for nowcasting the state of SARS-CoV-2 in Germany based on public data and state-of-the-art models implemented in the simulation software MEmilio. The results of our pipeline are pushed to ESID (Epidemiological Scenarios for Infectious Diseases), a user interface to epidemiological *** realize the given pipeline, a workflow management system is required to ensure pipeline processing and secure access to multiple heterogeneous data storages. For this purpose, we based our work on an open-source workflow management system - Apache Airflow, which provides the orchestration, coordination and management of complex connected tasks. S3 is utilized as an intermediate data storage service for sharing data between workflow steps and persisting experiment output. We provide a comprehensive view on our work on automated, end-to-end and reproducible pipelines, with detailed commentary on use case, and its realization.

关键词： Infectious diseases Pandemics Computational modeling Biological system modeling Memory Computer architecture Task analysis

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Non-intrusive MC/DC measurement based on traces 13

Non-intrusive MC/DC measurement based on traces

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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Ahishakiye, Faustin Jaksic, Svetlana Lange, Felix Dino Schmitz, Malte Stolz, Volker Thoma, Daniel Department of Computing Mathematics and Physics Western Norway University of Applied Sciences Bergen Norway Institute for Software Engineering and Programming Languages University of Lübeck Lübeck Germany

ISBN: (纸本)9781728133423

We present a novel, non-intrusive approach to MC/DC coverage measurement using modern processor-based tracing facilities. Our approach does not require recompilation or instrumentation of the software under test. Instead, we use the Intel Processor Trace (Intel PT) facility present on modern Intel CPUs. Our tooling consists of the following parts: a frontend that detects so-called decisions (Boolean expressions) that are used in conditionals in C source code, a mapping from conditional jumps in the object code back to those decisions, and an analysis that computes satisfaction of the MC/DC coverage relation on those decisions from an execution trace. This analysis takes as input a stream of instruction addresses decoded from Intel PT trace data, which was recorded while running the software under test. We describe our architecture and discuss limitations and future work. © 2019 IEEE.

关键词： software testing

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

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Procedia Computer Science 2021年 191卷 328-336页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications’ Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.

关键词： Cloud computing Computation offloading Edge computing Energy-efficiency Machine learning Queuing theory Support Vector Machine (SVM) Vehicular network

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A Raspberry Pi-based Traumatic Brain Injury detection system for single-channel Electroencephalogram

arXiv

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arXiv 2021年

作者： Dhillon, Navjodh Singh Sutandi, Agustinus Vishwanath, Manoj Lim, Miranda M. Cao, Hung Si, Dong Computing and Software Systems University of Washington BothellWA United States Department of Electrical Engineering and Computer Science University of California IrvineCA United States VA Portland Health Care System PortlandOR United States Department of Neurology Oregon Health & Science University PortlandOR United States Department of Biomedical Engineering University of California IrvineCA United States

Traumatic Brain Injury (TBI) is a common cause of death and disability. However, existing tools for TBI diagnosis are either subjective or require extensive clinical setup and expertise. The increasing affordability and reduction in the size of relatively high-performance computing systems combined with promising results from TBI related machine learning research make it possible to create compact and portable systems for early detection of TBI. This work describes a Raspberry Pi based portable, real-time data acquisition, and automated processing system that uses machine learning to efficiently identify TBI and automatically score sleep stages from a single-channel Electroencephalogram (EEG) signal. We discuss the design, implementation, and verification of the system that can digitize the EEG signal using an Analog to Digital Converter (ADC) and perform real-time signal classification to detect the presence of mild TBI (mTBI). We utilize Convolutional Neural Networks (CNN) and XGBoost based predictive models to evaluate the performance and demonstrate the versatility of the system to operate with multiple types of predictive models. We achieve a peak classification accuracy of more than 90% with a classification time of less than 1 s across 16 s - 64 s epochs for TBI vs control conditions. This work can enable the development of systems suitable for field use without requiring specialized medical equipment for early TBI detection applications and TBI research. Further, this work opens avenues to implement connected, real-time TBI related health and wellness monitoring systems. © 2021, CC BY.

关键词： Brain

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Spatial computing: Concept, Applications, Challenges and Future Directions

arXiv

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arXiv 2024年

作者： Yenduri, Gokul Ramalingam, M. Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Bandi, Ajay Chen, Junxin Wang, Wei Shirawalmath, Adarsh Arunkumar Ravishankar, Raghav Wang, Weizheng School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu 632014 India Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Computer Science and Information Systems Northwest Missouri State University 800 University Dr MaryvilleMO64468 United States School of Software Dalian University of Technology Dalian116621 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Vellore Institute of Technology TamilNadu 632014 India Department of Computer Science City University of Hong Kong Hong Kong

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2024, CC BY.

关键词： Augmented reality

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Designing Porthole Aluminium Extrusion Dies on the Basis of Explainable Artificial Intelligence

SSRN

引用

SSRN 2022年

作者： Llorca-Schenk, Juan Rico-Juan, Juan Ramón Sanchez-Lozano, Miguel Department of Graphic Expression Composition and Projects University of Alicante Carretera San Vicente del Raspeig s/n San Vicente del Raspeig Alicante03690 Spain Department of Software and Computing systems University of Alicante Alicante Spain Carretera San Vicente del Raspeig s/n San Vicente del Raspeig Alicante03690 Spain Department of Mechanical Engineering and Energy Miguel Hernandez University Avenida de la Universidad s/n. Elche Alicante03202 Spain

This paper shows the development of a tool with which to solve the most critical aspect of the porthole die design problem using a predictive model based on machine learning (ML). The model relies on a large amount of geometrical data regarding successful porthole die designs, information on which was obtained thanks to a collaboration with a leading extrusion company. In all cases, the dies were made of H-13 hot work steel and the billet material was 6063 aluminium alloy. The predictive model was chosen from a series of probes with different algorithms belonging to various ML families, which were applied to the analysis of geometrical data corresponding to 596 ports from 88 first trial dies. Algorithms based on the generation of multiple decision trees together with the boosting technique obtained the most promising results, the best by far being the CatBoost algorithm. The explainability of this model is based on a post-hoc approach using the SHAP (SHapley Additive exPlanations) tool. The results obtained with this ML-based model are notably better than those of a previous model based on linear regression as regards both the R2 metric and the results obtained with the application examples. An additional practical advantage is its explainability, which is a great help when deciding the best way in which to adjust an initial design to the predictive model. This ML-based model is, therefore, an optimal means to integrate the experience and know-how accumulated through many designs over time in order to apply it to new designs. It will also provide an aid in generating the starting point for the design of high-difficulty dies, in order to minimise the number of FEM (finite element method) simulation/correction iterations required until an optimal solution is achieved. It is not aimed to eliminate FEM simulation from the design tasks, but rather to help improve and accelerate the whole process of designing porthole dies. The work presented herein addresses a validation

关键词： Design

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Deep Reinforcement Learning-based Methods for Resource Scheduling in Cloud computing: A Review and Future Directions

arXiv

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arXiv 2021年

作者： Zhou, Guangyao Tian, Wenhong Buyya, Rajkumar Xue, Ruini Song, Liang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Laboratory Department of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China

With the acceleration of the Internet in Web 2.0, Cloud computing is a new paradigm to offer dynamic, reliable and elastic computing services. Efficient scheduling of resources or optimal allocation of requests is one of the prominent issues in emerging Cloud computing. Considering the growing complexity of Cloud computing, future Cloud systems will require more effective resource management methods. In some complex scenarios with difficulties in directly evaluating the performance of scheduling solutions, classic algorithms (such as heuristics and meta-heuristics) will fail to obtain an effective scheme. Deep reinforcement learning (DRL) is a novel method to solve scheduling problems. Due to the combination of deep learning (DL) and reinforcement learning (RL), DRL has achieved considerable performance in current studies. To focus on this direction and analyze the application prospect of DRL in Cloud scheduling, we provide a comprehensive review for DRL-based methods in resource scheduling of Cloud computing. Through the theoretical formulation of scheduling and analysis of RL frameworks, we discuss the advantages of DRL-based methods in Cloud scheduling. We also highlight different challenges and discuss the future directions existing in the DRL-based Cloud scheduling. © 2021, CC BY.

关键词： Deep reinforcement learning

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When Quantum Information Technologies Meet Blockchain in Web 3.0

arXiv

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arXiv 2022年

作者： Xu, Minrui Ren, Xiaoxu Niyato, Dusit Kang, Jiawen Qiu, Chao Xiong, Zehui Wang, Xiaofei Leung, Victor C.M. School of Computer Science and Engineering Nanyang Technological University Singapore College of Intelligence and Computing Tianjin University Tianjin300072 China Shenzhen518000 China School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

With the drive to create a decentralized digital economy, Web 3.0 has become a cornerstone of digital transformation, developed on the basis of computing-force networking, distributed data storage, and blockchain. With the rapid realization of quantum devices, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with the advantages of quantum computing and communication. Therefore, in this paper, we introduce a quantum blockchain-driven Web 3.0 framework that provides information-theoretic security for decentralized data transferring and payment transactions. First, we present the framework of quantum blockchain-driven Web 3.0 with future-proof security during the transmission of data and transaction information. Next, we discuss the potential applications and challenges of implementing quantum blockchain in Web 3.0. Finally, we describe a use case for quantum non-fungible tokens (NFTs) and propose a quantum deep learning-based optimal auction for NFT trading to maximize the achievable revenue for sufficient liquidity in Web 3.0. In this way, the proposed framework can achieve proven security and sustainability for the next-generation decentralized digital society. © 2022, CC BY.

关键词： Blockchain

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ER model Partitioning: Towards Trustworthy Automated systems Development

arXiv

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arXiv 2020年

作者： Pieris, Dhammika Wijegunesekera, M.C. Dias, N.G.J. Department of Human Resource Management Faculty of Commerce and Management Studies University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka Department of Computer Systems Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka

In database development, a conceptual model is created, in the form of an Entity-relationship(ER) model, and transformed to a relational database schema (RDS) to create the database. However, some important information represented on the ER model may not be transformed and represented on the RDS. This situation causes a loss of information during the transformation process. With a view to preserving information, in our previous study, we standardized the transformation process as a one-to-one and onto mapping from the ER model to the RDS. For this purpose, we modified the ER model and the transformation algorithm resolving some deficiencies existed in them. Since the mapping was established using a few real-world cases as a basis and for verification purposes, a formal-proof is necessary to validate the work. Thus, the ongoing research aiming to create a proof will show how a given ER model can be partitioned into a unique set of segments and use it to represent the ER model itself. How the findings can be used to complete the proof in the future will also be explained. Significance of the research on automating database development, teaching conceptual modeling, and using formal methods will also be discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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