Hasil untuk "Systems engineering"

Edward A. Lee, S. Seshia

P. Mali, K. Esvelt, G. Church

J. Abrial

B. Nuseibeh, S. Easterbrook

Jun S. Liu, Rong Chen

S. Strogatz

R. Barnes, E. Kreyszig

S. R. S. Kalpakjian

Nicola Guarino

E. Chikofsky, J. Cross

S. Furber

Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. This early work influenced others to carry developments forward, and advances in VLSI technology supported steady growth in the scale and capability of neuromorphic devices. Recently, a number of large-scale neuromorphic projects have emerged, taking the approach to unprecedented scales and capabilities. These large-scale projects are associated with major new funding initiatives for brain-related research, creating a sense that the time and circumstances are right for progress in our understanding of information processing in the brain. In this review we present a brief history of neuromorphic engineering then focus on some of the principal current large-scale projects, their main features, how their approaches are complementary and distinct, their advantages and drawbacks, and highlight the sorts of capabilities that each can deliver to neural modellers.

C. Eliasmith, C. Anderson

J. Bongard, Hod Lipson

Judith Becker, O. Zelder, S. Häfner et al.

G. Fortino, Claudio Savaglio, G. Spezzano et al.

The Internet of Things (IoT) is the latest example of the System of Systems (SoS), demanding for both innovative and evolutionary approaches to tame its multifaceted aspects. Over the years, different IoT methodologies, frameworks, platforms, and tools have been proposed by industry and academia, but the jumbled abundance of such development products have resulted into a high (and disheartening) entry-barrier to IoT system engineering. In this survey, we steer IoT developers by: 1) providing baseline definitions to identify the most suitable class of development products—methodologies, frameworks, platforms, and tools–for their purposes and 2) reviewing seventy relevant products through a comparative and practical approach, based on general SoS engineering features revised in the light of main IoT systems desiderata (i.e., interoperability, scalability, smartness, and autonomy). Indeed, we aim to lessen the confusion related to IoT methodologies, frameworks, platforms, and tools as well as to freeze their current state, for eventually easing the approach towards IoT system engineering.

T. Insperger, G. Stépán

Sehyeok Kang, Minu Kim, Jihwan Oh et al.

Designing effective reward functions is fundamental challenging in reinforcement learning, especially in complex multi-agent systems with intricate credit assignment. Preference-based reinforcement learning (PbRL) offers an alternative to manual reward engineering by learning from preferences. However, the prevalent approach in PbRL, which involves pairwise trajectory comparisons, encounters difficulties when applied to multi-agent systems due to exponentially large state-action spaces and temporal credit assignment problem. To address these challenges, we introduce Dual Preference-based Multi-Agent Reinforcement Learning (DPM), which uniquely employs dual preferences: trajectory-level and, crucially, transition-level agent contribution comparisons. This allows to distinguish cooperative and non-cooperative actions, enhancing reward learning efficiency. Furthermore, DPM leverages Large Language Models to generate dual preferences, significantly reducing reliance on costly human feedback and the potential for human error. Experiments on the StarCraft Multi-Agent Challenge, SMACv2, and Google Research Football show significant performance gains over baselines, demonstrating DPM effectiveness in optimizing rewards and improving performance in multi-agent systems.

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

Orges Cico, M. L. Jaccheri, Anh Nguyen-Duc et al.

Abstract Context: Software has become ubiquitous in every corner of modern societies. During the last five decades, software engineering has also changed significantly to advance the development of various types and scales of software products. In this context, Software Engineering Education plays an important role in keeping students updated with software technologies, processes, and practices that are popular in industries. Objective: We investigate from literature the extent Software Engineering Education addresses major Software Engineering Trends in the academic setting. Method: We conducted a systematic mapping study about teaching major Software Engineering Trends in project courses. We classified 126 papers based on their investigated Software Engineering Trends, specifically Software Engineering processes and practices, teaching approaches, and the evolution of Software Engineering Trends over time. Results: We reveal that Agile Software Development is the major trend. The other Trends, i.e., Software Implementation, Usability and Value, Global Software Engineering, and Lean Software Startup, are relatively small in the academic setting, but continuously growing in the last five years. System of Systems is the least investigated among all Trends. Conclusions: The study points out the possible gaps between Software Industry and Education, which implies actionable insights for researchers, educators, and practitioners.

Abdulaziz Khalid Alsharidi, Ahmet Bekir

The paper consists of various types of wave solutions for the truncated M-fractional Bateman–Burgers equation, a significant mathematical physics equation. This model describes the nonlinear waves and solitons in different physical fields such as optical fibers, plasma physics, fluid dynamics, traffic flow, etc. Through the application of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mo form="prefix">exp</mo><mi>a</mi></msub></semantics></math></inline-formula> function method and the modified simplest equation method, we are able to obtain exact series of soliton solutions. The results differ from the current solutions of the Bateman–Burgers model because of the fractional derivative. The achieved results could be helpful in various engineering and scientific domains. The Mathematica software is used to assist in obtaining and verifying the exact solutions and to obtain contour plots of the solutions in two and three dimensions. To ensure that the model in question is stable, a stability analysis is also carried out using the modulation instability method. Future research on the system in question and related systems will benefit from the findings. The methods used are simple and effective.