To react to unforeseen circumstances or amend abnormal situations in communication-centric systems, programmers are in charge of "undoing" the interactions which led to an undesired state. To assist this tas...
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To react to unforeseen circumstances or amend abnormal situations in communication-centric systems, programmers are in charge of "undoing" the interactions which led to an undesired state. To assist this task, session-based languages can be endowed with reversibility mechanisms. In this paper we propose a language enriched with programming facilities to commit session interactions, to roll back the computation to a previous commit point, and to abort the session. Rollbacks in our language always bring the system to previous visited states and a rollback cannot bring the system back to a point prior to the last commit. Programmers are relieved from the burden of ensuring that a rollback never restores a checkpoint imposed by a session participant different from the rollback requester. Such undesired situations are prevented at design-time (statically) by relying on a decidable compliance check at the type level, implemented in MAUDE. We show that the language satisfies error-freedom and progress of a session.
Program synthesis is an important challenge that has attracted significant research interest, especially in recent years with advancements in Large Language Models (LLMs). Although LLMs have demonstrated success in pr...
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Program synthesis is an important challenge that has attracted significant research interest, especially in recent years with advancements in Large Language Models (LLMs). Although LLMs have demonstrated success in program synthesis, there remains a lack of trust in the generated code due to documented risks (e.g., code with known and risky vulnerabilities). Therefore, it is important to restrict the search space and avoid bad programs. In this work, pre-defined restricted Backus-Naur Form (BNF) grammars are utilised, which are considered 'safe', and the focus is on identifying the most effective technique for grammar-obeying program synthesis, where the generated code must be correct and conform to the predefined grammar. It is shown that while LLMs perform well in generating correct programs, they often fail to produce code that adheres to the grammar. To address this, a novel Similarity-Based Many-Objective Grammar Guided Genetic programming (SBMaOG3P) approach is proposed, leveraging the programs generated by LLMs in two ways: (i) as seeds following a grammar mapping process and (ii) as targets for similarity measure objectives. Experiments on a well-known and widely used program synthesis dataset indicate that the proposed approach successfully improves the rate of grammar-obeying program synthesis compared to various LLMs and the state-of-theart Grammar-Guided Genetic programming. Additionally, the proposed approach significantly improved the solution in terms of the best fitness value of each run for 21 out of 28 problems compared to G3P.
Typically,a computer has infectivity as soon as it is *** is a reality that no antivirus programming can identify and eliminate all kinds of viruses,suggesting that infections would persevere on the *** understand the...
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Typically,a computer has infectivity as soon as it is *** is a reality that no antivirus programming can identify and eliminate all kinds of viruses,suggesting that infections would persevere on the *** understand the dynamics of the virus propagation in a better way,a computer virus spread model with fuzzy parameters is presented in this *** is assumed that all infected computers do not have the same contribution to the virus transmission process and each computer has a different degree of infectivity,which depends on the quantity of *** this,the parametersβandγbeing functions of the computer virus load,are considered fuzzy *** fuzzy theory helps us understand the spread of computer viruses more realistically as these parameters have fixed values in classical *** essential features of the model,like reproduction number and equilibrium analysis,are discussed in fuzzy ***,with fuzziness,two numerical methods,the forward Euler technique,and a nonstandard finite difference(NSFD)scheme,respectively,are developed and *** the evidence of the numerical simulations,the proposed NSFD method preserves the main features of the dynamic *** can be considered a reliable tool to predict such types of solutions.
Multi-label neural networks are important in various tasks, including safety-critical tasks. Several works show that these networks are susceptible to adversarial attacks, which can remove a target label from the pred...
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ISBN:
(纸本)9783031747755;9783031747762
Multi-label neural networks are important in various tasks, including safety-critical tasks. Several works show that these networks are susceptible to adversarial attacks, which can remove a target label from the predicted label list or add a target label to this list. To date, no deterministic verifier determines the list of labels for which a multilabel neural network is locally robust. The main challenge is that the complexity of the analysis increases by a factor exponential in the multiplication of the number of labels and the number of predicted labels. We propose MuLLoC, a sound and complete robustness verifier for multi-label image classifiers that determines the robust labels in a given neighborhood of inputs. To scale the analysis, MuLLoC relies on fast optimistic queries to the network or to a constraint solver. Its queries include sampling and pair-wise relation analysis via numerical optimization and mixed-integer linear programming (MILP). For the remaining unclassified labels, MuLLoC performs an exact analysis by a novel mixed-integer programming (MIP) encoding for multi-label classifiers. We evaluate MuLLoC on convolutional networks for three multi-label image datasets. Our results show that MuLLoC classifies all labels as robust or not within 23.22 min on average and that our fast optimistic queries classify 96.84% of the labels.
This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains,including scientific writing,mathematics,education,programming,and *** explore the potential of ChatGPT to enhance produ...
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This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains,including scientific writing,mathematics,education,programming,and *** explore the potential of ChatGPT to enhance productivity,streamline problem-solving processes,and improve writing ***,we highlight the potential risks associated with excessive reliance on ChatGPT in these *** limitations encompass factors like incorrect and fictitious responses,inaccuracies in code,limited logical reasoning abilities,overconfidence,and critical ethical concerns of copyright and privacy *** outline areas and objectives where ChatGPT proves beneficial,applications where it should be used judiciously,and scenarios where its reliability may be *** light of observed limitations,and given that the tool's fundamental errors may pose a special challenge for non-experts,ChatGPT should be used with a strategic *** drawing from comprehensive experimental studies,we offer methods and flowcharts for effectively using *** recommendations emphasize iterative interaction with ChatGPT and independent verification of its *** the importance of utilizing ChatGPT judiciously and with expertise,we recommend its usage for experts who are well-versed in the respective domains.
Teaching text-based programming poses significant challenges in both school and university contexts. This study explores the potential of ChatGPT as a sustainable didactic tool to support students, freshmen, and teach...
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ISBN:
(纸本)9783031448997;9783031449000
Teaching text-based programming poses significant challenges in both school and university contexts. This study explores the potential of ChatGPT as a sustainable didactic tool to support students, freshmen, and teachers. By focusing on a beginner's course with examples also relevant to vocational schools, we investigated three research questions. First, the extent to which ChatGPT assists students in solving and understanding initial examples;secondly, the feasibility of teachers utilizing the chatbot for grading student solutions;and finally, the additional support ChatGPT provides in terms of teaching. Our findings demonstrate that ChatGPT offers valuable guidance for teachers in terms of assessment and grading and aids students in understanding and optimizing their solutions.
Electric buses serve as a key leverage in mitigating the transportation sector's carbon footprint. However, they pose a challenge, requiring transit agencies to adapt to a new operational approach. In particular, ...
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ISBN:
(数字)9783031605970
ISBN:
(纸本)9783031605963;9783031605970
Electric buses serve as a key leverage in mitigating the transportation sector's carbon footprint. However, they pose a challenge, requiring transit agencies to adapt to a new operational approach. In particular, the assignment of buses to trips is more complex because it must consider the planning of the recharging activities. Unlike diesel buses, electric buses have less autonomy and take longer to refuel. In this paper, we address the assignment of electric buses to trips and the scheduling of charging events, taking into account parking constraints at the depot (a novelty in the literature). These constraints are particularly relevant in countries such as Canada where the buses are parked indoors to shelter them from harsh winter conditions. This problem, called the electric Bus Assignment Problem with Parking Constraints (eBAP-PC), is a feasibility problem. We propose a Constraint programming model to solve it and compare it to mixed-integer linear programming approaches. In particular, we show its benefits for solving this problem with a one-day horizon and minimum end-of-day charge level constraints.
Much of the recent work investigating large language models and AI Code Generation tools in computing education has focused on assessing their capabilities for solving typical programming problems and for generating r...
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ISBN:
(纸本)9798400704239
Much of the recent work investigating large language models and AI Code Generation tools in computing education has focused on assessing their capabilities for solving typical programming problems and for generating resources such as code explanations and exercises. If progress is to be made toward the inevitable lasting pedagogical change, there is a need for research that explores the instructor voice, seeking to understand how instructors with a range of experiences plan to adapt. In this paper, we report the results of an interview study involving 12 instructors from Australia, Finland and New Zealand, in which we investigate educators' current practices, concerns, and planned adaptations relating to these tools. Through this empirical study, our goal is to prompt dialogue between researchers and educators to inform new pedagogical strategies in response to the rapidly evolving landscape of AI code generation tools.
In the development of ethernet passive optical networks (EPONs), quality of service (QoS) support and fairness per optical network unit (ONU) are crucial issues. However, making an elaborate analysis of the existing p...
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The problem of peer selection in peer-to-peer (P2P) video content distribution network is significant to solve since it affects the performance and efficiency of the network widely. In this article, a novel framework ...
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The problem of peer selection in peer-to-peer (P2P) video content distribution network is significant to solve since it affects the performance and efficiency of the network widely. In this article, a novel framework is introduced that uses fuzzy linear programming (FLP) to address the inherent uncertainties in peer selection. The primary motivation for the use of FLP lies in its capability to handle the imprecision and vagueness that are characteristic of dynamic P2P environments. Factors such as peer reliability, bandwidth, and proximity are often uncertain in this environment. By using fuzzy logic, the proposed framework models these criteria as fuzzy sets and then integrates uncertainty into the decision-making process. FLP is then applied to optimize peer selection, improving download speed, reducing download time, and enhancing peer reliability. The proposed method is evaluated and analyzed using extensive simulation with SciPy. The result reveals that proposed technique works better compared to some of the traditional methods in terms of download time, download speed and also reliability measure. It also exhibits approximately 20% of increase in download speed as well as a 15% decrease in download time compared to traditional approaches. It leads to faster content retrieval and enhanced the efficiency in content distribution. Also, in selection of reliable peers for content distribution, there is a notable 20% of increase in peer reliability with result of enhanced robustness. The proposed method provides efficient and robust solution to the problem of peer selection. It can be implemented in a broad range of P2P content distribution networks.
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