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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Ananthi, P. Ramya, T.E. Janani, R. Kongu Engineering College MSc Software Systems India

ISBN: (纸本)9798350333602

An Intrusion Detection System (IDS) is a security-based mechanism used for finding malicious activities and unauthorized access in a computer network or system. IDS solutions are now more essential than ever because of the rising use of Internet of Things (IoT) devices. This study proposes an approach to build IDS for IoT devices is to use Recursive Feature Elimination (REE) with KDD 99 dataset. REE is a feature selection algorithm that removes irrelevant or redundant features recursively until the optimal feature subset are obtained. The KDD 99 dataset contains network traffic data that a proposed learning model can be trained on to classify normal and malicious network traffic. A deep neural network is deployed for classification after the necessary features have been selected via RER A deep neural network can be trained on the selected features to categorise network traffic as legitimate or malicious using the KDD99 dataset. The proposed IDS performance is assessed by using various evaluation metrics such as recall, precision, F1-score and accuracy, The Hyper parameter tuning and ensemble learning are used to improve the performance representations of IDS. © 2023 IEEE.

关键词： Intrusion detection

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Machine Learning-assisted Test Records Analysis during the Real-Time Validation of Automotive software systems based on HIL Simulation 58

Machine Learning-assisted Test Records Analysis during the R...

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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Abboush, Mohammad Knieke, Christoph Rausch, Andreas Technische Universität Clausthal Institute for Software and Systems Engineering Germany

ISBN: (纸本)9780998133188

In the automotive industry, according to ISO 26262, comprehensive testing is conducted to ensure software systems quality over various phases of the V-model. However, at the system integration and testing phase, a significant amount of time series data is analyzed manually based on expert knowledge to identify the nature of the failure occurring, which is a costly process in terms of time, effort, and difficulty. This study proposes a novel Machine Learning (ML)-assisted failure analysis approach for the real-time validation of automotive software systems (ASSs). Specifically, based on a representative critical faults dataset, intelligent data-driven ML-based Fault Detection and Diagnosis (FDD) models are developed, including LSTM and Denoising Autoencoder (DAE) + k-means techniques for known and unknown sensor-related faults, respectively. The novelty of these models lies in their ability to identify single and simultaneous faults in noisy conditions. To assess the efficacy of the developed model, a dataset from a virtual test drive platform with a high-fidelity entire vehicle model was employed. The evaluation outcomes illustrate the superiority of the proposed model for known faults classification in comparison to other state-of-the-art methods, with an average F1-score of 91.85%. Furthermore, the integration of DAE with k-means exhibited a high clustering performance against noise with a low MSE and DB, i.e., 0.044 and 0.68, respectively. Consequently, the proposed approach facilitates the real-time validation of the ASSs in an efficient manner enhancing the safety and reliability. © 2025 IEEE Computer Society. All rights reserved.

关键词： software testing

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WIP: An Engaging Undergraduate Intro to Model Checking in software engineering Using TLA+

arXiv

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arXiv 2024年

作者： Läufer, Konstantin Mertin, Gunda Thiruvathukal, George K. Software and Systems Laboratory Department of Computer Science Loyola University Chicago United States Institute for Software Engineering and Programming Languages University of Lübeck Germany

Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computer science education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computer science programs, (2) construct level-appropriate examples that could be included midway into one’s undergraduate studies, (3) demonstrate how to address successive "failures" through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and

关键词： Students

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Phase Re-Service in Reinforcement Learning Traffic Signal Control 27

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Zhang, Zhiyao Gunter, George Quinones-Grueiro, Marcos Zhang, Yuhang Barbour, William Biswas, Gautam Work, Daniel Institute for Software Integrated Systems Vanderbilt University Department of Civil and Environmental Engineering United States Institute for Software Integrated Systems Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University Department of Computer Science United States

ISBN: (纸本)9798331505929

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation. © 2024 IEEE.

关键词： Markov processes

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Towards Feature-based Versioning for Musicological Research 24

Towards Feature-based Versioning for Musicological Research

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18th International Working Conference on Variability Modelling of software-Intensive systems, VaMoS 2024

作者： Grünbacher, Paul Neuwirth, Markus Institute of Software Systems Engineering Johannes Kepler University Linz Linz4040 Austria

ISBN: (纸本)9798400708770

This paper discusses the management of revisions and variants of musical works for the context of musicological research. Domain-specific languages (DSLs) are a fundamental tool in music notation and analysis, as they enable the notation of music and also support music analysis when investigating particular structural properties of melody, harmony, rhythm, or form. However, the fields of music philology and music analysis still lack a systematic approach for uniformly managing revisions and variants of musical compositions. This research-in-progress paper proposes the use of feature-based versioning to streamline the management of revisions and variants in music artifacts. We introduce an illustrative example and present research challenges regarding variability and feature-based versioning for musicological research. We present a preliminary approach which involves mapping features to specific parts of musical works and musical analyses, thereby facilitating the composition of new variants based on selected features, a prerequisite for enhanced research in music philology and music analysis. © 2024 Owner/Author.

关键词： Music

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Towards A software Factory for Developing the Chatbots in Smart Tourism Mobile Applications 14

Towards A Software Factory for Developing the Chatbots in Sm...

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14th International Conference on Emerging Ubiquitous systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2023

作者： Benaddi, Lamya Ouaddi, Charaf Jakimi, Abdeslam Ouchao, Brahim Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Today, the tourism industry is significantly impacted by mobile applications leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) to enhance tourists' experiences before, during, and after their visits. This technological convergence has given rise to Smart Tourism Destinations (STDs). However, efficiently integrating these functionalities into mobile apps poses a major challenge, leading to the emergence of chatbots. Companies like IBM, Google, Microsoft, and Amazon offer tools such as Watson, Bot Framework, Dialogflow, and Amazon Lex for their development. Nevertheless, creating chatbots remains intricate, demanding expertise in software development and AI, along with associated costs related to NLP service providers. Additionally, employing an appropriate modeling language is crucial for designing a chatbot. At this juncture, the concepts of software factories and Domain-Specific Languages (DSLs) become indispensable. These innovative approaches provide solutions for addressing these complex challenges. software factories automate the development process, expediting chatbot creation while ensuring consistency. Simultaneously, DSLs furnish tools for accurately modeling and articulating the specific requirements of smart tourism, simplifying the development of tailor-made chatbots suited to this domain. This article introduces a model-driven approach for a DSL aimed at offering an abstract, high-level representation of the various aspects of chatbots within the context of an intelligent tourism mobile application. © 2024 Elsevier B.V.. All rights reserved.

关键词： Problem oriented languages

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Architecture, Tools, and DSLs for Developing Conversational Agents: An Overview 14

Architecture, Tools, and DSLs for Developing Conversational ...

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作者： Ouaddi, Charaf Benaddi, Lamya Jakimi, Abdeslam Software Engineering and Information Systems Engineering Team FST Errachidia University of Moulay Ismail Meknes50050 Morocco

Conversational agents (CA) are software programs that can converse with users using natural language. They are now widely used in various domains, such as tourism, healthcare, and others, to perform tasks and provide permanent assistance to users by interacting with them in natural language. The development of such applications is a task that requires expertise in several fields, such as software engineering, machine learning, deep learning, and natural language processing (NLP). However, several platforms and frameworks on the market facilitate the building of CA, such as Dialogflow, Rasa, and others. Recently, several research studies have proposed solutions to reduce the workload of developers and designers by offering their model-driven development approaches using domain-specific languages (DSLs), which facilitate the automation of the development of CA. This work aims to provide an Overview of CA to identify and describe their architecture and the details of its key components. and discuss the tools and technologies for their development. At the same time, discover the research topics that focus on using DSLs for model-driven development to automate and speed up the creation of these agents and discover approaches and technologies employed to implement each of these DSLs. © 2024 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Multi Agent Path Finding Under Obstacle Uncertainty 33

Multi Agent Path Finding Under Obstacle Uncertainty

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33rd International Conference on Automated Planning and Scheduling, ICAPS 2023

作者： Shofer, Bar Shani, Guy Stern, Roni Software and Information Systems Engineering Ben Gurion University Israel

ISBN: (纸本)1577358813

In multi-agent path finding (MAPF), agents must move from their current positions to their target positions without colliding. Prior work on MAPF commonly assumed perfect knowledge of the environment. We consider a MAPF setting where this is not the case, and the planner does not know a-priori whether some positions are blocked or not. To sense whether such a position is traversable, an agent must move close to it and adapt its behavior accordingly. In this work we focus on solving this type of MAPF problem, for cases where planning is centralized but cannot be done during execution. In this setting, a solution can be formulated as a plan tree for each agent, branching on the observations. We propose algorithms for finding such plans trees for two modes of executions: centralized, where the agents share information concerning observed obstacles during execution, a decentralized, where such communication is not allowed. The proposed algorithms are complete and can be configured to optimize solution cost, measured for either the best case or the worst case. We implemented these algorithms and provide experimental results demonstrating how our approach scales with respect to the number of agents and the number of positions we are uncertain about. The results show that our algorithms can solve non-trivial problems, but also highlight that this type of MAPF problems is significantly harder than classical MAPF. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi agent systems

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An adaptive synthetic sampling and batch generation-oriented hybrid approach for addressing class imbalance problem in software defect prediction

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Soft Computing 2024年第23期28卷 13595-13614页

作者： Taskeen, Anam Khan, Saif Ur Rehman Mashkoor, Atif Islamabad45550 Pakistan Islamabad45550 Pakistan Institute of Software Systems Engineering Johannes Kepler University Altenbergerstraße 69 Linz4040 Austria

Learning classifiers with uneven class distribution datasets poses a significant challenge in software defect prediction. This problem arises when the number of samples representing one class is significantly smaller than the others, leading to weak classification performance, particularly for minority class instances. Traditional classification models assuming equal class instances can result in low prediction accuracy and decision-making precision for minority class instances, raising concerns about identifying such instances accurately. To overcome this issue, this research proposes a hybrid technique that combines the Adaptive Synthetic Sampling (ADASYN) approach with a batch generator named the HADAB technique. ADASYN generates synthetic samples for the minority class, balancing the dataset and improving prediction accuracy. Conversely, the batch generator feeds data to the model in batches, enhancing training efficiency. The Multi-Layer Perceptron (MLP) serves as the base classifier in this study. The proposed HADAB technique significantly improves prediction accuracy and training efficiency without requiring additional parameter tuning, algorithm modification, or increasing complexity. We validate the performance of HADAB using publicly available NASA datasets encompassing diverse types. The results demonstrate the superiority of HADAB over traditional prediction accuracy methods. In conclusion, the proposed HADAB technique offers a practical and effective solution for handling class imbalance in software defect prediction, leading to improved prediction accuracy. © The Author(s) 2024.

关键词： Prediction models

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GoLive A modular Mixed Reality Simulation for Semantic Plug and Play 23

GoLive A modular Mixed Reality Simulation for Semantic Plug ...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Wanninger, Constantin Badem, Thomas Schorner, Martin Eymueller, Christian Poeppel, Alexander Reif, Wolfgang Institute for Software and Systems Engineering University of Augsburg Augsburg Germany

ISBN: (纸本)9788993215274

In the field of mobile robotics, the interaction of robots with their environment plays a key role. Simulations serve to simulate a part of the environment so that the partly complex hardware is not damaged in extensive field tests. However, the transition from simulation to reality can be difficult. This paper presents a modular simulation and visualization with mixed reality mechanisms that can be reconfigured at runtime using a new concept called MR devices. A MR device is a physical device that registers with the simulation through self-description mechanisms and can interact with real objects. MR devices themselves can be used as tangible user interfaces in mixed reality visualization, e.g. to influence the composition of the environment. This concept enables the stepwise transition from simulation to reality. The advantages of these intermediate steps are presented by means of three scenarios, on which drones, sensors, infrastructure and also gas emitters are used. GoLive builds on the self-description mechanisms resulting from the own preliminary work: Semantic Plug and Play, in which capabilities and properties can be described by Semantic Web mechanisms and linked to the execution logic of the hardware at runtime. © 2023 ICROS.

关键词： Robotics

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