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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Kai-Kristian Kemell Atte Elonen Mari Suoranta Anh Nguyen-Duc Juan Garbajosa Rafael Chanin Jorge Melegati Usman Rafiq Abdullah Aldaeej Nana Assyne Afonso Sales Sami Hyrynsalmi Juhani Risku Henry Edison Pekka Abrahamsson Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Business and Economics University of Jyväskylä Jyvaskyla Finland Department of Business and IT University of Southeast Norway Bø Norway Universidad Politécnica de Madrid Madrid Spain School of Technology PUCRS Porto Alegre Brazil Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Systems University of Maryland Baltimore Country Baltimore United States Department of Software Engineering LUT University Lahti Finland Lero NUI Galway Galway Ireland

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team- related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups.

关键词： Business software Interviews Tools Task analysis software engineering Ontologies

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FairSearch: A tool for fairness in ranked search results

arXiv

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arXiv 2019年

作者： Zehlike, Meike Castillo, Carlos Sühr, Tom Kitanovski, Ivan Humboldt Universität zu Berlin Max Planck Inst. for Software Systems Germany Universitat Pompeu Fabra Spain Technische Universität Berlin Germany Faculty of Computer Science and Engineering University Saint Cyril and Methodius Macedonia

Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine business opportunities, education, access to benefits, and even social success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features. In this paper we present FairSearch, the first fair open source search API to provide fairness notions in ranked search results. We implement two well-known algorithms from the literature, namely FA*IR (Zehlike et al., [2017]) and DELTR (Zehlike and Castillo, [2018]) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, a well-known search engine API based on Apache Lucene. The interfaces use the aforementioned Java libraries and enable search engine developers who wish to ensure fair search results of different styles to easily integrate DELTR and FA*IR into their existing Elasticsearch environment. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Green Energy Sources: Issues and Challenges

Green Energy Sources: Issues and Challenges

引用

Roedunet International Conference (RoEduNet)

作者： Mostafa Abdulghafoor Mohammed Inas Adnan Mohammed Raed.A. Hasan Nicolae Ţăpuş Ahmed Hussien Ali Omar A. Hammood Faculty of Automatic Control and Computers University Polytechnic of Bucharest/Romania The Great Emam University College Baghdad University of Information Technology and Communications / college of Engineering/ Baghdad Iraq Faculty of Al-dour Technical institute Northern Technical University Mosel IRAQ Faculty of Automatic Control and Computers University Polytechnic of Bucharest/Romania Computer Science Department AL Salam University College Baghdad Iraq Faculty of Computer Systems and Software Engineering UniversityMalaysia Pahang Malaysia

Mobile offloading is a platform that facilitates the distribution of computationally intensive tasks from mobile devices to the cloud or other devices in order to conserve energy and improve performance. The concept is practically based on the idea of exchanging relatively low communication energy for high computation power utilization. Efforts have been channeled towards energy conservation by clusters using Dynamic Power Management (DPM). This paper is a review for green energy sources for reducing energy and using energy consumption to manage sources by stating the latest studies of energy consumption and energy consumptions.

关键词： Energy consumption Task analysis Servers Energy efficiency Power system management Voltage control Cloud computing

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Fuzzy Adaptive Teaching Learning-based Optimization Strategy for GUI Functional Test Cases Generation

Fuzzy Adaptive Teaching Learning-based Optimization Strategy...

引用

2018年第七届软件与计算机应用国际会议

作者： Fakhrud Din Kamal Z.Zamli Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Department of Computer Science & IT University of Malakand KPK

Graphical User Interface(GUI) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with GUIs. Unlike command based interaction, GUI uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. GUI testing is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, GUI functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive Teaching Learning-based Optimization(ATLBO) algorithm, a variant of the basic Teaching Learning-based Optimization(TLBO) algorithm, for GUI functional testing is proposed. ATLBO utilizes Event-Interaction Graph(EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.

关键词： GUI Functional Testing Teaching Learning-based Optimization Mamdani Fuzzy Inference System Model-based testing

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A Flexible Keyphrase Extraction Technique for Academic Literature 3

A Flexible Keyphrase Extraction Technique for Academic Liter...

引用

3rd International Conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Rabby, Gollam Azad, Saiful Mufti, Mahmud Zamli, Kamal Z. Mostafizur Rahman, Mohammed Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gambang Kuantan Malaysia IBM Centre of Excellence Gambang Kuantan Malaysia Department of Computing and Technology Nottingham Trent University Nottingham United Kingdom Dhaka Bangladesh

A keyphrase extraction technique endeavors to extract quality keyphrases from a given document, which provide a high-level summary of that document. Except statistical keyphrase extraction approaches, all other approaches are either domain-dependent or require a sufficient amount of training data, which are rare at present. Therefore, in this paper, a new tree-based automatic keyphrase extraction technique is proposed, which is domain-independent and employs nominal statistical knowledge;but no train data are required. The proposed technique extracts a quality keyphrase through forming a tree from a candidate keyphrase;and later, it is expanded or shrunk or remained in the same state depending on other similar candidate keyphrases. At the end, keyphrases are extracted from the resultant trees based on a value, μ (which is the Maturity Index (MI) of a node in the tree), which enables flexibility in this process. A small μ value would yield many and/or lengthy keyphrases (greedy approach);whereas, a large μ value would yield lower and/or abbreviated keyphrases (conservative approach). Thereby, a user can extract his/her desired-level of keyphrases through tuning μ value. The effectiveness of the proposed technique is evaluated on an actual corpus, and compared with Rapid Automatic Keyphrase Extraction (RAKE) technique. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Extraction

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Lockout-Tagout Ransomware: A Detection Method for Ransomware using Fuzzy Hashing and Clustering

Lockout-Tagout Ransomware: A Detection Method for Ransomware...

引用

IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Nitin Naik Paul Jenkins Jonathan Gillett Haralambos Mouratidis Kshirasagar Naik Jingping Song School of Computing University of Portsmouth United Kingdom Faculty of Science and Technology Bournemouth University United Kingdom Centre for Secure Intelligent and Usable Systems University of Brighton United Kingdom Department of Electrical and Computer Engineering University of Waterloo Canada Software College Northeastern University China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Ransomware attacks are a prevalent cybersecurity threat to every user and enterprise today. This is attributed to their polymorphic behaviour and dispersion of inexhaustible versions due to the same ransomware family or threat actor. A certain ransomware family or threat actor repeatedly utilises nearly the same style or codebase to create a vast number of ransomware versions. Therefore, it is essential for users and enterprises to keep well-informed about this threat landscape and adopt proactive prevention strategies to minimise its spread and affects. This requires a technique to detect ransomware samples to determine the similarity and link with the known ransomware family or threat actor. Therefore, this paper presents a detection method for ransomware by employing a combination of a similarity preserving hashing method called fuzzy hashing and a clustering method. This detection method is applied on the collected WannaCry/WannaCryptor ransomware samples utilising a range of fuzzy hashing and clustering methods. The clustering results of various clustering methods are evaluated through the use of the internal evaluation indexes to determine the accuracy and consistency of their clustering results, thus the effective combination of fuzzy hashing and clustering method as applied to the particular ransomware corpus. The proposed detection method is a static analysis method, which requires fewer computational overheads and performs rapid comparative analysis with respect to other static analysis methods.

关键词： Ransomware Clustering methods Indexes Cryptography Engines Syntactics

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Simulation and Modelling the Human Crowd Evacuation

引用

IOP Conference Series: Materials Science and engineering 2019年第1期551卷

作者： Noor Akma Abu Bakar Mazlina Abdul Majid Khalid Adam Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Faculty of Electric and Electronic Engineering Universiti Malaysia Pahang

The operational research (OR) become one of emerging areas and significance and its relevance to be used in the simulation and modelling. To simulating and modelling the crowd evacuation, the most important elements to have in a realistic model is the appropriate simulation technique. To simulate the evacuee's movement in crowd is still in research and challenge because of the emergence and become a complex task and dangerous for the real and actual case. The computational simulation technique is required in order to model the crowd evacuation as one part of OR and become a solution to represent the fire crowd evacuation in the closed space e.g. Building relates to human movement and its states. The theories and concept of computational method allows for creating, analysing and experimentation. The techniques; Agent-Based Simulation (ABS), Social Force Model (SFM) and the hybrid SFM/ABS has been proposed for this research work. SFM is the well-known and popular technique for crowd evacuation while ABS is best-known, intelligent and appropriate to imitate the human movement. This paper provides a review of this research work from an OR perspective and the outcomes of a review of the computational simulation techniques literature are presented, using a proposed conceptual model will be valuable for future researchers, and modellers alike.

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Modeling and Simulation of Emergency Evacuation from Industry Buildings under Toxic Cloud Release

Modeling and Simulation of Emergency Evacuation from Industr...

引用

Applied Mathematics and engineering (BICAME), Borneo International Conference on

作者： Hocine Benseghir Mohd Fadzil Mohd Idris Abdullah Bin Ibrahim Muhammad Nomani Kabir Faculty of Engineering Technology University Malaysia Pahang Malaysia Kuantan Malaysia Faculty of Computer Systems & Software Engineering University Malaysia Pahang Kuantan Malaysia

ISBN: (数字)9781538677254

ISBN: (纸本)9781538677261

Toxic cloud caused by accidents in the industries is extremely dangerous for the occupants. Thus, in case of such accidents, the occupants must be immediately evacuated from industry buildings. In this paper, we explore modeling and simulation of occupant-evacuation during a toxic cloud release. The proposed model evaluates the toxic cloud concentrations in terms of probability values and then the exit route of each occupant is computed such that the toxic effect on the occupants is minimized. We integrated A* search algorithm with the toxic cloud model to find the safest routes for occupants to exit through the doors from the current positions of the occupants. To find the best route, the model considers the distance to egress, the obstacles and the probability of toxic concentration. We presented the simulation results of the model to demonstrate the plausibility of the model. This model can be used by evacuation management to guide the industry occupants to exit the industry building through safest routes during an accident scenario.

关键词： Mathematical model Accidents Buildings Computational modeling Hazards

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Multi-objective model optimisation using genetic algorithms for pleurotus sp. cultivation

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IOP Conference Series: Materials Science and engineering 2020年第1期863卷

作者： N Zainol A S Fakharudin N A Dzulkefli M F A Bakar CARIFF Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia. Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia. Faculty of Chemical & Natural Resources Engineering Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia.

Malaysia is the largest exporter of Elaeis Guineensis (Palm oil) in the international market. Oil palm cultivation generates a significant amount of lignocellulosic biomass derived from empty fruit bunches (EFB) as waste product. This research focused on optimizing the mycelium growth in Pleurotus sp. cultivation by using EFB as a culture medium. The EFB was cut into the range of size of substrate (S) from 1.5 cm to 3.0 cm, soaked in water for overnight, applied steam treatment and incubated at the selected range of temperature (T) from 29 °C to 32 °C. The responses were mycelium extension rate (M) and nitrogen concentration in mycelium (N). The multi-objective optimisation of M and N requires the objective functions which represent both processes. For this type of problem, multi-objective genetic algorithm was chosen as the methodology, specifically using NSGA-II algorithm. Through the implementation of selected multi-objective genetic algorithm, it was able to produce the pareto front for optimising both nitrogen concentration and the extension rate of the mycelium. The highest nitrogen concentration and mycelium extension rate was from the result with crossover and mutation probability of 0.5 and 0.2. It produced 388.45 mg/L of nitrogen concentration and 0.370 cm/day of mycelium growth.

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