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Asia-Pacific Conference on software engineering

作者： Mohd Hafeez Osman Stefan Kugele Sina Shafaei Department of Software Engineering & Information Systems University Putra Malaysia Serdang Malaysia Department of Computer Science Technical University of Munich Munich Germany

Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive electric and electronic architecture in the future. However, while such systems provide highly advanced functions, safety risk increases as AI-based systems may produce uncertain output and behaviour. In this paper, we devise a run-time safety monitoring framework for AI-based intelligence systems focusing on autonomous driving functions. In detail, this paper describes (i) the characteristics of a safety monitoring framework; (ii) the safety monitoring framework itself, and (iii) we develop a prototype and implement the framework for two critical driving functions: Lane detection and object detection. Through an implementation of the framework to a prototypic control environment, we show the possibility of this framework in the real context. Finally, we discuss the techniques used in developing the safety monitoring framework and describes the encountered challenges.

关键词： Monitoring Hazards software Anomaly detection ISO Standards Automotive engineering

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Correction to: Industrial-iot-hardware security-improvement using plan load optimization method in cloud

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International Journal of System Assurance engineering and Management 2021年第6期13卷 3108-3108页

作者： Basheer, Shakila Gopu, Magesh Mathew, Rincy Merlin Bivi, Maryam Aysha Prabu, M. Department of Information System College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Software and Systems Engineering Vellore Institute of Technology Vellore India Computer Science Department College of Science and Arts King Khalid University Khamis Mushayt Abha Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science Christ University Bangalore India

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Consumer product preference prediction towards online shopping

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IOP Conference Series: Materials science and engineering 2021年第1期1055卷

作者： S Deepa R Suguna P Sathishkumar C Jamunadevi E N Vidhya Assistant Professor Department of Computer Technology - PG Kongu Engineering College Perundurai Erode India Assistant Professor Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India Currently pursuing Master's degree programme in Software Systems Kongu Engineering College Perundurai India

Nowadays E-commerce plays a major role in a business organization. People prefer online shopping rather than offline shopping which helps them to purchase their product from anywhere around the world through mobile phones and laptop. Online shopping websites help people by saving time and product order can be done easily by clicking the product. Online shopping websites are built using the Single Page Application (SPA) framework and the objective of this research is to find the customer preference product prediction by tracking the frequent clicks of the product by the customer. By tracking the clicks of customer we can find their product choice and helps retailer to add the products according to user preference.

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Integrating simulation and numerical analysis in the evaluation of generalized stochastic petri nets

Integrating simulation and numerical analysis in the evaluat...

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作者： Zimmermann, Armin Hotz, Thomas Department of Computer Science and Automation Systems and Software Engineering Group Technische Universität Ilmenau Helmholtzplatz 5 Ilmenau98693 Germany Institute for Mathematics Group for Probability Theory and Mathematical Statistics Technische Universität Ilmenau Weimarer Straße 25 Ilmenau98693 Germany

The standard existing performance evaluation methods for discrete-state stochastic models such as Petri nets either generate the reachability graph followed by a numerical solution of equations or use some variant of simulation. Both methods have characteristic advantages and disadvantages depending on the size of the reachability graph and type of performance measure. This article proposes a hybrid performance evaluation algorithm for the steady-state solution of Generalized Stochastic Petri Nets that integrates elements of both methods. It automatically adapts its behavior depending on the available size of main memory and number of model states. As such, the algorithm unifies simulation and numerical analysis in a joint framework. It is proved to result in an unbiased estimator whose variance tends to zero with increasing simulation time. The article extends earlier results with an algorithm variant that starts with a small maximum number of particles and increases them during the run to increase the efficiency in cases that are rapidly solved by regular simulation. The algorithm's applicability is demonstrated through case studies, including an example where it outperforms the standard methods. © 2019 Copyright held by the owner/author(s).

关键词： Petri nets

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Scalability Evaluation of Big Data Processing Services in Clouds 1st

Scalability Evaluation of Big Data Processing Services in Cl...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Zhou, Xin Jiang, Congfeng Qiu, Yeliang Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

Currently, many cloud providers deploy their big data processing systems as cloud services, which helps users conveniently manage and process their data in clouds. Among different service providers’ big data processing services, how to evaluate and compare their scalability is an interesting and challenging work. Most traditional benchmark tools focus on performance evaluation of big data processing systems, such as aggregated throughput and IOPS, but fail to conduct a quantitative analysis of their scalability. In this paper, we propose a measurement methodology to quantify the scalability of big data processing services, which makes the cloud services scalability comparable. We conduct a group of comparative experiments on AliCloud E-MapReduce and Baidu MRS, and collect their respective scalability characteristics under Hadoop and Spark workloads. The scalability characteristics observed in our work could help cloud users choose the best cloud service platform to set up an optimized big data processing system to achieve their specific goals more successfully. © 2019, Springer Nature Switzerland AG.

关键词： Big data

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Big data analytics: A single window IoT-enabled climate variability system for all-year-round vegetable cultivation

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IOP Conference Series: Earth and Environmental science 2021年第1期655卷

作者： Wilson Nwankwo Kingsley Eghonghon Ukhurebor Software Engineering &Cyberphysical Systems Unit Department of Computer Science Edo University Iyamho Edo State Nigeria Climatic Environmental & Telecommunication Unit Department of Physics Edo University Iyamho Edo State Nigeria

Vegetables constitute a major food source with huge nutritional values as well as major source of income. The cultivation of vegetables is dictated by climate and seasonal changes across Nigeria. Edo State lies within the South of Nigeria and enjoys the two popular seasons (rainy and dry) like many other parts of the country. However, the variability in soil distribution and weather conditions across different locations is a determining factor as to the category of not just vegetables to grow but other crops. In this paper, Edo state is used as a flagship project for its diverse potentials and uniqueness in respect of known variability in soil and weather conditions. The State is divided into three geo-referenced agricultural districts. A prototype system is proposed to provide vegetable farmers with real-time information on vegetablefarming requirements. The proposed system is an Internet of things (IoT)-enabled climate variability system with interfaces to popular mobile networks, existing Geographical Information System (GIS) in the State, and remote sensing stations respectively. Each geo-referenced point is a nexus to areas with similar weather variability and soil distribution. Historical data is collected from the existing GIS and a provision is made to constantly enrich the historical data with new information from the geo-referenced points including crops grown, trends in cultivation, queries from farmers, etc. The information generated from the geo-referenced locations are routed via GPS to the central analytics server in the cloud and appropriate algorithms are used to carry out data analysis for real-time prediction and messages to farmers through the Internet and Short Message Services (SMS). With this system, it is submitted that subsistent and mechanized farmers would benefit through the guidance of an analytics system thereby boosting vegetablefarming regardless of the season of the year.

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Towards Self-Explainable Cyber-Physical systems

Towards Self-Explainable Cyber-Physical Systems

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ACM/IEEE International Conference on Model Driven engineering languages and systems Companion

作者： Mathias Blumreiter Joel Greenyer Francisco Javier Chiyah Garcia Verena Klos Maike Schwammberger Christoph Sommer Andreas Vogelsang Andreas Wortmann Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universitat Hannover Heriot-Watt University Software and Embedded Systems Engineering Technische Universitat Berlin Department of Computing Science University of Oldenburg Heinz Nixdorf Institute and Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universitat Berlin Software Engineering RWTH Aachen University

ISBN: (纸本)9781728151267

With the increasing complexity of Cyber-Physical systems, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.

关键词： Explainability Self-adaptive systems Cyber-physical systems Adaptive systems Cyber-physical systems MONITOR Runtime Buildings

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Power Characterization of Memory Intensive Applications: Analysis and Implications 1st

Power Characterization of Memory Intensive Applications: Ana...

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Qiu, Yeliang Jiang, Congfeng Fan, Tiantian Wang, Yumei Zhang, Liangbin Wan, Jian Shi, Weisong Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou Dianzi University Hangzhou310037 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310037 China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science Wayne State University DetroitMI48202 United States

ISBN: (纸本)9783030328122

DRAM is a significant source of server power consumption especially when the server runs memory intensive applications. Current power aware scheduling assumes that DRAM is as energy proportional as other components. However, the non-energy proportionality of DRAM significantly affects the power and energy consumption of the whole server system when running memory intensive applications. Thus good knowledge of server power characterization under memory intensive workloads can help better workload placement with power reduction. In this paper, we investigate the power characteristics of memory intensive applications on real rack servers of different generations. Through comprehensive analysis we find that (1) Server power consumption changes with workload intensity and concurrent execution threads. However, fully utilized memory systems are not the most energy efficient. (2) Powered memory modules of installed memory capacity, i.e. the memory capacity per processor core has significant impact on the application’s performance and server power consumption even if the memory system is not fully utilized. (3) Memory utilization is not always a good indicator for server power consumption when it is running memory intensive applications. Our experiments show that hardware configuration, workload types, as well as concurrently running threads have significant impact on a server’s energy efficiency when running memory intensive applications. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy efficiency aware job scheduling and power reductions. © 2019, Springer Nature Switzerland AG.

关键词： Energy efficiency

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Dynamic solution probability acceptance within the flower pollination algorithm for t-way test suite generation

arXiv

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arXiv 2019年

作者： Nasser, Abdullah B. Zamli, Kamal Z. Ahmed, Bestoun S. Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan Pahang26300 Malaysia Faculty of Electrical Engineering Department of Computer Science Czech Technical University Czech Republic

Flower Pollination Algorithm (FPA) is the new breed of metaheuristic for general optimization problem. In this paper, an improved algorithm based on Flower Pollination Algorithm (FPA), called imFPA, has been proposed. In imFPA, the static selection probability is replaced by the dynamic solution selection probability in order to enhance the diversification and intensification of the overall search process. Experimental adoptions on combinatorial tway test suite generation problem (where t indicates the interaction strength) show that imFPA produces very competitive results as compared to existing strategies. Copyright © 2019, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Large language models surpass human experts in predicting neuroscience results

arXiv

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arXiv 2024年

作者： Luo, Xiaoliang Rechardt, Akilles Sun, Guangzhi Nejad, Kevin K. Yáñez, Felipe Yilmaz, Bati Lee, Kangjoo Cohen, Alexandra O. Borghesani, Valentina Pashkov, Anton Marinazzo, Daniele Nicholas, Jonathan Salatiello, Alessandro Sucholutsky, Ilia Minervini, Pasquale Razavi, Sepehr Rocca, Roberta Yusifov, Elkhan Okalova, Tereza Gu, Nianlong Ferianc, Martin Khona, Mikail Patil, Kaustubh R. Lee, Pui-Shee Mata, Rui Myers, Nicholas E. Bizley, Jennifer K. Musslick, Sebastian Bilgin, Isil Poyraz Niso, Guiomar Ales, Justin M. Gaebler, Michael Murty, N. Apurva Ratan Loued-Khenissi, Leyla Behler, Anna Hall, Chloe M. Dafflon, Jessica Bao, Sherry Dongqi Love, Bradley C. Department of Experimental Psychology University College London London United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Physiology Anatomy & Genetics University of Oxford Oxford United Kingdom Max Planck Institute for Neurobiology of Behavior – caesar Bonn Germany Bilkent University Ankara Turkey Department of Computer Science University of Bristol Bristol United Kingdom The Alan Turing Institute London United Kingdom Department of Psychiatry Yale University New Haven United States Psychology Emory University Atlanta United States Faculty of Psychology and Educational Sciences Université de Genève Genève Switzerland Neurosurgery Novosibirsk State Medical University Novosibirsk Russia Federal Center of Neurosurgery FSBI Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Analysis Ghent University Ghent Belgium Psychology New York University New York United States Department of Cognitive Neurology University of Tübingen Tübingen Germany Computer Science Princeton University Princeton United States ILCC University of Edinburgh Edinburgh United Kingdom Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh United Kingdom Department of Culture Cognition and Computation Aarhus University Aarhus Denmark Department of Molecular Life Sciences University of Zurich Zurich Switzerland Bioengineering University of Pennsylvania Philadelphia United States Linguistic Research Infrastructure University of Zurich Zurich Switzerland Electronic and Electrical Engineering University College London London United Kingdom Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Institute of Neuroscience and Medicine INM-7: Brain and Behaviour Research Centre Jülich Jülich Germany Medical Faculty Institute of Systems Neuroscience Heinrich Hein

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors. © 2024, CC BY.

关键词： Prediction models

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