Article
Computer Science, Hardware & Architecture
Messaoud Abbas, Renaud Rioboo, Choukri-Bey Ben-Yelles, Colin F. Snook
Summary: This study formalizes UML activity diagrams using FoCaLiZe for detection of inconsistencies and proof of properties, while also supporting action constraints, activity partitions, and communication between structural and dynamic aspects of UML models.
JOURNAL OF SYSTEMS ARCHITECTURE
(2021)
Article
Computer Science, Artificial Intelligence
Mehrnoosh Askarpour, Livia Lestingi, Samuele Longoni, Niccolo Iannacci, Matteo Rossi, Federico Vicentini
Summary: Developing Human Robot Collaborative systems requires adaptability and safety, with model-based design and reconfiguration as key techniques.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Liang Jiang, Zhen Peng, Yimin Liang, Zheng-Bin Tang, Kejiang Liang, Jiali Liu, Zhichang Liu
Summary: This study presents a molecular-strain engineering approach to facilitate consecutive [1,2]-aryl shifts in molecular bows, enabling the seemingly impractical [1,3]-aryl shift and offering a promising direction for developing new tools and strategies in synthetic chemistry.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2023)
Article
Engineering, Civil
Xiaohong Chen, Juan Zhang, Zhi Jin, Min Zhang, Tong Li, Xiang Chen, Tingliang Zhou
Summary: This article proposes an approach to empower domain experts with formal methods for verifying safety requirements' consistency, aiming to enhance time efficiency and productivity. The approach involves transforming natural requirements into formal models and using formal methods for verification. The effectiveness and time-saving benefits of this approach are validated through two practical case studies.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Aerospace
Pierre de Saqui-Sannes, Ludovic Apvrille, Rob Vingerhoeds
Summary: This paper discusses the transition from document-centric approaches in systems engineering to model-based systems engineering (MBSE), introducing the methodology and tools support associated with SysML. Through sharing experiences in real-time systems modeling, it focuses on methodological issues and verification related to safety and security properties.
JOURNAL OF AEROSPACE INFORMATION SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Lina Bisikirskiene, Lina Ceponiene, Mantas Jurgelaitis, Linas Ablonskis, Egle Grigonyte
Summary: Inadequate early scope estimation is a common problem in software projects and can lead to project failures. This paper presents a methodology for improving the accuracy of early scope estimation by collecting requirements information in various forms. The methodology involves compiling and reconciling requirements from different sources, as well as estimating the project scope using story points.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Farzana Zahid, Awais Tanveer, Matthew M. Y. Kuo, Roopak Sinha
Summary: The requirements engineering of Industrial Cyber-Physical Systems is challenging due to system complexity, stakeholder diversity, and continuous evolution. Formal and semi-formal methods can provide assistance in eliciting, specifying, analyzing, verifying, and maintaining requirements. Current research lacks a comprehensive landscape study, with gaps found in the use of semi-formal notations, key phases like requirements elicitation and management, and addressing privacy and trust requirements. Further research is needed to address these gaps and advance the field.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Computer Science, Artificial Intelligence
Maria-Jose Escalona, Nora Koch, Laura Garcia-Borgonon
Summary: This article presents a low-cost mechanism for automating requirements traceability based on the model-driven paradigm. It extends an existing development methodology and has been successfully applied in real software development projects, demonstrating the effectiveness of this approach. Future work can focus on the systematic evaluation of traceability management in industrial projects.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Software Engineering
Tong Ye, Yi Zhuang, Gongzhe Qiao
Summary: This paper presents a model-based approach for identifying privacy violations from software requirements. It proposes a method to automatically verify privacy properties in UML requirement models, and demonstrates its high accuracy and efficiency on software systems from various domains.
SOFTWARE AND SYSTEMS MODELING
(2023)
Article
Computer Science, Information Systems
Yuvaraj Selvaraj, Ashfaq Farooqui, Ghazaleh Panahandeh, Wolfgang Ahrendt, Martin Fabian
Summary: This article emphasizes the importance of correctness in autonomous driving software and discusses the challenges of widespread industrial adoption of formal methods. It demonstrates the feasibility of automated learning for automotive industrial use by applying active learning techniques to obtain a formal model of an autonomous driving software module. The article also addresses practical challenges and possible directions for integrating automata learning into the automotive software development workflow.
Article
Chemistry, Analytical
Feng Luo, Yifan Jiang, Jiajia Wang, Zhihao Li, Xiaoxian Zhang
Summary: The rapid development of intelligent connected vehicles has increased the complexity and attack surface of vehicle systems. However, existing methods in the automotive domain cannot accurately describe and identify threats for new features while also quickly matching appropriate security requirements. This article proposes a cybersecurity requirements management system (CRMS) framework to address this issue and assist in comprehensive automated threat analysis and risk assessment.
Article
Computer Science, Software Engineering
David Ameller, Xavier Franch, Cristina Gomez, Silverio Martinez-Fernandez, Joao Araujo, Stefan Biffl, Jordi Cabot, Vittorio Cortellessa, Daniel Mendez Fernandez, Ana Moreira, Henry Muccini, Antonio Vallecillo, Manuel Wimmer, Vasco Amaral, Wolfgang Bohm, Hugo Bruneliere, Loli Burgueno, Miguel Goulao, Sabine Teufl, Luca Berardinelli
Summary: Practitioners perceive managing NFRs in MDD as complex with little tool support. Productivity and maintainability are expected to be supported types of NFRs when MDD is adopted. Companies adapt MDD to deal with NFRs, but manual changes to generated code compromise maintainability. Despite this, practitioners believe the benefits of MDD outweigh the extra effort required for manual adaptations. Further research and conceptual work is needed to lower the barrier of integrating a broad spectrum of NFRs in practice.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Hao Wen, Jinzhao Wu, Jianmin Jiang, Guofu Tang, Zhong Hong
Summary: Consistency is crucial for measuring the correctness of a software system, but inconsistencies often occur during the software development process. Developers detect and repair inconsistencies, but maintaining consistency during evolution is challenging. Our work provides a formal framework for consistency management in multi-view model-driven software development, including a Structure model and consistency preservation strategies.
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
(2023)
Article
Chemistry, Multidisciplinary
Yepeng Ding, Hiroyuki Sato
Summary: Formal methods play a crucial role in program specification and verification. However, their applicability is limited due to the requirement of mathematical knowledge. To address this issue, we propose formalism-driven development (FDD), which guides developers to adopt formal methods throughout the development process. We introduce system graphs and model-checking techniques in FDD, and present the Seniz framework for practicalizing and automating FDD.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Luigi De Simone, Mario Di Mauro, Roberto Natella, Fabio Postiglione
Summary: Nowadays, most telecommunication services implement network functions via software using the Service Function Chain (SFC) paradigm. Container virtualization is commonly used to deploy network functions and achieve resource slicing among tenants. Evaluating the steady-state availability and latency is important for the complex infrastructure of containers implementing different SFC functionalities and shared by multiple tenants. This paper proposes a latency-driven availability assessment for multi-tenant service chains implemented via Containerized Network Functions (CNFs), using a multi-state system model and queueing formalism to compute availability and solving an optimization problem to minimize SFC cost.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Aniello De Santo, Antonino Ferraro, Vincenzo Moscato, Giancarlo Sperli
Summary: This paper proposes a diffusion algorithm based on user-to-content relationships and an action-reaction paradigm, integrating different cross-disciplinary theories to characterize how users influence each other in OSNs. The approach is evaluated using the Yahoo Flickr Creative Commons 100 Million dataset, demonstrating its superior efficiency and effectiveness compared to existing methods.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Chemistry, Analytical
Jaspreet Singh Bajaj, Naveen Kumar, Rajesh Kumar Kaushal, H. L. Gururaj, Francesco Flammini, Rajesh Natarajan
Summary: The number of road accidents caused by driver drowsiness is a major global challenge. This paper proposes a hybrid model that combines non-intrusive and intrusive approaches to detect driver drowsiness. The model shows efficacy of 91% in identifying the transition from awake to a drowsy state in all conditions.
Article
Engineering, Civil
L. L. L. Starace, F. Rocco Di Torrepadula, S. Di Martino, N. Mazzocca
Summary: This study investigates the feasibility of using different-sized fleets of taxis for vehicular crowdsensing in the historical cities of Porto and Rome. The results show that even with as few as 50 vehicles, a relevant part of the road network can be visited within the considered time frame. However, recruiting more vehicles and/or devising specialized routing/incentivization mechanisms might be necessary for more comprehensive coverage.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
Article
Computer Science, Information Systems
Vincenzo Moscato, Marco Postiglione, Carlo Sansone, Giancarlo Sperli
Summary: In Biomedical Named Entity Recognition (BioNER), the lack of publicly available annotated datasets hampers the use of cutting-edge deep learning-based methods like BERT and GPT-3. Annotating multiple entity types poses challenges as most existing datasets only provide annotations for a single entity type. To address this, the proposed TaughtNet framework leverages knowledge distillation to fine-tune a single multi-task student model using both ground truth and single-task teachers. Experimental results demonstrate the effectiveness of TaughtNet in recognizing mentions of diseases, chemical compounds, and genes, outperforming state-of-the-art baselines in terms of precision, recall, and F1 scores. TaughtNet also enables the training of smaller and lighter student models, making them suitable for real-world scenarios with limited-memory hardware devices and fast inferences, while also showing potential for explainability. The code and multi-task model have been made publicly available on GitHub (1) and the huggingface repository (2).
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Green & Sustainable Science & Technology
S. Subash Chandra Bose, Rajesh Natarajan, H. L. Gururaj, Francesco Flammini, P. V. Praveen Sundar
Summary: A tumor is an abnormal growth of cells in the body, and early diagnosis is crucial for treatment. To address the issues of low accuracy and time consumption in tumor detection, an IRPS-BAC method is introduced for early and accurate detection of brain tumors. It includes feature selection and classification processes, improving tumor detection accuracy and efficiency.
Article
Computer Science, Information Systems
Radha Raman Chandan, Awatef Balobaid, Naga Lakshmi Sowjanya Cherukupalli, H. L. Gururaj, Francesco Flammini, Rajesh Natarajan
Summary: The study introduces a blockchain-based method, Transaction Verification Denied conflict with spurious node (TVDCSN), for wireless communication technologies to detect malicious nodes and prevent attacks. This method aims to protect the security and confidentiality of sensitive information in modern wireless communication networks (MWCNs) by removing malicious nodes and preventing intrusion.
Article
Agronomy
Nithin Kumar, Nagarathna, Francesco Flammini
Summary: Insects exhibit remarkable diversity, abundance, spread, and adaptability in the field of biology. Insect recognition is fundamental for insect study and pest management, yet it heavily relies on a limited number of taxonomic experts. With advancements in computer technology, accurate insect differentiation can now be achieved using computers instead of professionals.
Article
Computer Science, Software Engineering
Simona Bernardi, Rail Javierre, Jose Merseguer
Summary: This paper introduces tegdet, a new Python library for anomaly detection in unsupervised approaches. It identifies anomalous epochs based on differences in observations compared to a baseline distribution. The library implements 28 dissimilarity metrics and has a user-friendly API for carrying out the anomaly detection. tegdet is the first publicly available library based on time evolving graphs for anomaly detection in time series, and it shows promising results in terms of accuracy and speed.
Article
Automation & Control Systems
Lorenzo De Donato, Stefano Marrone, Francesco Flammini, Carlo Sansone, Valeria Vittorini, Roberto Nardone, Claudio Mazzariello, Frederic Bernaudin
Summary: This paper focuses on the intelligent detection of anomalies in warning bells through non-intrusive acoustic monitoring and proposes a solution combining deep learning and transfer learning for warning bell detection.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
P. V. Kumaraguru, Vidyavathi Kamalakkannan, H. L. Gururaj, Francesco Flammini, Badria Sulaiman Alfurhood, Rajesh Natarajan
Summary: The article introduces a method for analyzing big data in social networks using distributed multi-agent technology. The HDAO-PFEC method is developed to analyze large amounts of data accurately and efficiently. It utilizes the Hessian Mutual Distributed Ant Optimization MapReduce model and the Hadoop platform for distributed computing to estimate the Eigen Vector Centrality value for each social network member. Extensive experimental learning demonstrates the accuracy and efficiency of the HDAO-PFEC method.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Proceedings Paper
Computer Science, Software Engineering
Farid Edrisi, Mehdi Saman Azari
Summary: Digital Twin is an emerging technology that is used for monitoring, optimization, prediction, and more in real-world applications. While the manufacturing industry has embraced digital twin technology to enhance sustainability, there is a lack of systematic review on its contribution to sustainability assessment and policy evaluation. This paper aims to fill this gap by conducting a literature review and identifying the potential benefits of integrating digital twin with sustainable assessment and policy evaluation approaches.
2023 IEEE/ACM 7TH INTERNATIONAL WORKSHOP ON GREEN AND SUSTAINABLE SOFTWARE, GREENS
(2023)
Article
Computer Science, Information Systems
H. L. Gururaj, N. Manju, A. Nagarjun, V. N. Manjunath Aradhya, Francesco Flammini
Summary: Skin cancer is a rapidly spreading illness and early detection is crucial. Deep learning, especially Convolutional Neural Networks (CNN), has been effective in detecting and classifying skin cancer. The experiment used the MNIST: HAM10000 dataset and employed data pre-processing techniques and transfer learning with DenseNet169 and Resnet 50.
Article
Social Sciences, Interdisciplinary
Nithin Kumar, Nagarathna, L. Vijay Kumar, Francesco Flammini
Summary: Agriculture is crucial to the Indian economy, contributing 17% to the GDP. However, farmers face challenges, such as insect pests, in crop growth. Computational Entomology applies data mining techniques to assist farmers in overcoming these challenges by using sensors, methodologies, and pest classification for timely pesticide application. This study used machine learning and deep learning algorithms to classify insects, finding that a proposed CNN-based model achieved a classification accuracy of 94.6% in insect categorization. Applying precise insect classification techniques using machine learning and deep learning algorithms has significant implications for entomological research.
INTERDISCIPLINARY DESCRIPTION OF COMPLEX SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
H. L. Gururaj, Rajesh Natarajan, Nouf Abdullah Almujally, Francesco Flammini, Sujatha Krishna, Shashi Kant Gupta
Summary: This article proposes a collaborative energy-efficient routing protocol (CEEPR) for improving the energy efficiency and system lifespan of 5G/6G wireless sensor networks (WSNs). The network nodes are clustered using reinforcement learning technique and a residual energy-based cluster head selection algorithm is employed for better data transmission. By using a multi-objective improved seagull algorithm for optimization, the proposed approach saves 50% energy while improving network lifespan and energy efficiency.
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY
(2023)
Article
Computer Science, Information Systems
Subaselvi Sundarraj, R. Vijaya Kumar Reddy, Mahesh Babu Basam, Gururaj Harinahalli Lokesh, Francesco Flammini, Rajesh Natarajan
Summary: An autonomous robotic vehicle is a self-driving vehicle that uses advanced technologies to navigate without human intervention. Route planning and management are important for efficient and safe travel. The combination of particle swarm optimization and Dijkstra algorithm can provide a reliable solution for route planning. The study shows that the suggested method performs well.
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
