Search for: AI-driven aggregator for chain oracles

Open source software (OSS) underpins modern software infrastructure, yet many projects struggle with long- term sustainability. We introduce OSSPREY, an AI-powered platform that can predict the sustainability of any GitHub- hosted project. OSSPREY collects longitudinal socio-technical data, such as: commits, issues, and contributor interactions, and uses a transformer-based model to generate month-by-month sustainability forecasts. When project downturns are detected, it recommends evidence-based interventions drawn from published software engineering studies. OSSPREY integrates scraping, forecasting, and actionable guidance into an interactive dash- board, enabling maintainers to monitor project health, anticipate decline, and respond with targeted strategies. By connecting real- time project data with research-backed insights, OSSPREY offers a practical tool for sustaining OSS projects at scale. The codebase is linked to the project website at: https: //oss-prey.github.io/OSSPREY-Website/ The screencast is available at: https://www.youtube.com/ watch?v=N7a0v4hPylU 

We report that a dielectric polymer chain, constrained at both ends, sharply collapses when exposed to a high electric field. The chain collapse is driven by nonlocal dipolar interactions and anisotropic polarization of monomers, a characteristic of real polymers that prior theories were unable to incorporate. Once collapsed, a large number of chain monomers accumulate at the center location between the chain ends, locally increasing the electric field and polarization by orders of magnitude. The chain collapse is sensitive to the orientation of the applied electric field and chain stretch. Our findings not only offer new ways for rapid actuation and sensing but also provide a pathway to discover the critical physics behind instabilities and electrical breakdown in dielectric polymers. 

As cyber threats grow in both frequency and sophistication, traditional cybersecurity measures struggle to keep pace with evolving attack methods. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing threat detection, prevention, and response. AI-driven security systems offer the ability to analyze vast amounts of data in real-time, recognize subtle patterns indicative of cyber threats, and adapt to new attack strategies more efficiently than conventional approaches. However, despite AI’s potential, challenges remain regarding its effectiveness, ethical implications, and risks of adversarial manipulation. This research investigates the strengths and limitations of AI-driven cybersecurity by comparing AI-based security tools with traditional methods, identifying key advantages and vulnerabilities, and exploring ethical considerations. Additionally, a survey of cybersecurity professionals was conducted to assess expert opinions on AI’s role, effectiveness, and potential risks. By combining these insights with experimental testing and a comprehensive review of existing literature, this study provides a nuanced understanding of AI’s impact on cybersecurity and offers recommendations for optimizing its integration into modern security infrastructures. 

Artificial intelligence (AI), including large language models and generative AI, is emerging as a significant force in software development, offering developers powerful tools that span the entire development lifecycle. Although software engineering research has extensively studied AI tools in software development, the specific types of interactions between developers and these AI-powered tools have only recently begun to receive attention. Understanding and improving these interactions has the potential to enhance productivity, trust, and efficiency in AI-driven workflows. In this paper, we propose a taxonomy of interaction types between developers and AI tools, identifying eleven distinct interaction types, such as auto-complete code suggestions, command-driven actions, and conversational assistance. Building on this taxonomy, we outline a research agenda focused on optimizing AI interactions, improving developer control, and addressing trust and usability challenges in AI-assisted development. By establishing a structured foundation for studying developer-AI interactions, this paper aims to stimulate research on creating more effective, adaptive AI tools for software development. 

(TSFAM) model, an adaptive human-AI teaming framework designed to enhance hard-to-place kidney acceptance decision-making by integrating transplant surgeons’ individualized expertise with advanced AI analytics (Figure 1). Methods: TSFAM is an innovative solution for complex issues in kidney transplant decision-making support. It employs fuzzy associative memory to capture and codify unique decision-making rules of transplant surgeons. Using the Deceased Donor Organ Assessment (DDOA) and Final Acceptance AI models designed to evaluate hard-to-place kidneys, TSFAM integrates fuzzy logic with deep learning techniques to manage inherent uncertainties in donor organ assessments. Surgeon-specifi c ontologies and membership functions are extracted through interviews. Similar to how a pain scale is used for understanding patients, an ontology ambiguity scale is used to develop surgeon rules (Figure 2). Fuzzy logic captures ambiguity and enables the model to adapt to evolving clinical, environmental, and policy conditions. The structured incorporation of human expertise ensures decision support remains closely aligned with local clinical practices and global best evidence. Results: This novel framework incorporates human expertise into AI decisionmaking tools to support donor organ acceptance in transplantation. Integrating surgeon-defi ned criteria into a robust decision-support tool enhances accuracy and transparency of organ allocation decision-making support. TSFAM bridges the gap between data-driven models and nuanced judgment required in complex clinical scenarios, fostering trust and promoting responsible AI adoption. Conclusions: TSFAM fuses deep learning analytics with subtleties of human expertise for a promising pathway to improve decision-making support in transplant surgery. The framework enhances clinical assessment and sets a precedent for future systems prioritizing human-AI collaboration. Prospective studies will focus on clinical implementation with dynamic interfaces for a more patient-centered, evidencebased model in organ transplantation. The intent is for this approach to be adaptable to individual case scenarios and the diverse needs of key transplant team members 

Scientific discovery is a complex cognitive process that has driven human knowledge and technological progress for centuries. While artificial intelligence (AI) has made significant advances in automating aspects of scientific reasoning, simulation, and experimentation, we still lack integrated AI systems capable of performing autonomous long-term scientific research and discovery. This paper examines the current state of AI for scientific discovery, highlighting recent progress in large language models and other AI techniques applied to scientific tasks. We then outline key challenges and promising research directions toward developing more comprehensive AI systems for scientific discovery, including the need for science-focused AI agents, improved benchmarks and evaluation metrics, multimodal scientific representations, and unified frameworks combining reasoning, theorem proving, and data-driven modeling. Addressing these challenges could lead to transformative AI tools to accelerate progress across disciplines towards scientific discovery. 

Abstract Recent advances in AI culminate a shift in science and engineering away from strong reliance on algorithmic and symbolic knowledge towards new data-driven approaches. How does the emerging intelligent data-centric world impact research on real-time and embedded computing? We argue for two effects: (1) new challenges in embedded system contexts, and (2) new opportunities for community expansion beyond the embedded domain. First,on the embedded system side, the shifting nature of computing towardsdata-centricityaffects the types of bottlenecks that arise. At training time, the bottlenecks are generallydata-related. Embedded computing relies onscarcesensor data modalities, unlike those commonly addressed in mainstream AI, necessitating solutions forefficient learningfrom scarce sensor data. At inference time, the bottlenecks areresource-related, calling forimproved resource economyandnovel scheduling policies. Further ahead, the convergence of AI around large language models (LLMs) introduces additionalmodel-relatedchallenges in embedded contexts. Second,on the domain expansion side, we argue that community expertise in handling resource bottlenecks is becoming increasingly relevant to a new domain: thecloudenvironment, driven by AI needs. The paper discusses the novel research directions that arise in the data-centric world of AI, covering data-, resource-, and model-related challenges in embedded systems as well as new opportunities in the cloud domain.