Abstract

A method and system based on machine learning to create self-adapting analog circuits adapted to change their internal components on-the-fly in response to changes in process, voltage, and temperature to re-tune the electrical characteristics back to nominal specified values is disclose. The method and system herein comprise of designing the analog circuit, generating simulation data for machine learning, creating a full query database, creating and training, using simulation results, a machine learning (ML) model of the circuit and applying the ML model to infer the required changes to internal components of the analog circuit in response to changes in P, V, and T conditions. With this method and system, evaluation of the adverse effects of PVT changes, decision on internal circuit changes, and realization of requisite design changes are performed by the computer system solely within a ML data domain

Abstract

We address the problem of network connectivity inference by observing interacting time-series signals at the nodes. The evolving nodal signals depend on the connectivity structure of the network thus allowing us to successfully invert for it. Our approach is demonstrated using the example of heat diffusion dynamics. To learn the graph, we combine our knowledge of the dynamical equation and the observed graph signals. Specifically, we perform approximate numerical discretization of the dynamical equation leading to a system of linear equations in the unknown graph topology. Simulations indicate feasibility of this method for accurate network inference.

Abstract

In this article, we propose a new practical synchronization protocol for multiple Euler–Lagrange systems without structural linear-in-the-parameters (LIP) knowledge of the uncertainty and where the agents can be interconnected before control design by unknown state-dependent interconnection terms. This setting is meant to overcome two standard a priori assumptions in the literature concerning uncertainty with LIP structure and absence of interaction among agents before designing the synchronization protocol. To overcome these assumptions, we propose an adaptive distributed control mechanism having the purpose of estimating the coefficients of the resulting state-dependent uncertainty structure.

Abstract

Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.

Abstract

Local activities have a significant impact on the air quality in every region. A study for two consecutive years (2021-2022) has been conducted in the Gachibowli region of Hyderabad, India, which employs spatially distributed low-cost IoT-based air quality monitors across residential and at traffic junctions and main roads measuring particulate matter (PM), temperature, and humidity. The study shows the observations during three seasons, aiming to establish correlations between the PM spikes and specific events that triggered these spikes. Detailed discussions focus on the variations in PM levels linked to traffic patterns over six months and the Diwali festival in 2021 and 2022. PM concentrations increased 2-3 times the normal range during Diwali and decreased post-Diwali. In the case of traffic-related pollution, 1.5 times higher PM levels were observed during morning and evening peak traffic hours, with significant reductions in afternoons across all months with variations in range depending on the season.

Abstract

This report documents the program and outcomes of Dagstuhl Seminar 23302, "Software Architecture and Machine Learning". We summarize the goals and format of the seminar, results from the breakout groups, key definitions relevant to machine learning-enabled systems that were discussed, and the research roadmap that emerged from the discussions during the seminar. The report also includes the abstracts of the talks presented at the seminar and summaries of open discussions.

Abstract

Microservice architectures have gained enormous popularity due to their ability to be dynamically added/removed, replicated, and updated according to run-time needs. However, the dynamic nature of microservices introduces uncertainty, which in turn can affect the provided Quality of Service (QoS). This calls for novel service discovery mechanisms able to adapt to the variability of the QoS attributes and further perform effective service discovery and selection. To this end, this paper combines machine learning and self-adaptation techniques to perform service discovery and selection by trading off different QoS attributes. The results of our validation on a state-of-the-art microservices exemplar show that our ML-enabled approach can perform service discovery with 35% higher effectiveness with respect to existing baselines.

Abstract

Urbanization, driven by technological advancements, has brought about improved connectivity and efficiency, especially with the rise of Internet of Things (IoT) devices. Smart cities use these innovations to manage resources better and enhance resident’s quality of life. However, implementing smart city projects comes with challenges like monitoring, maintaining, and testing urban infrastructure. Digital Twin(DT) entails the connection of physical facilities or devices with their digital counterparts, facilitating real-time monitoring, manipulation, and predictive analysis of their behavior. This innovative concept offers a virtual replica of assets, processes and systems, enabling insights into their real-time performance and predictive behaviors. By simulating real-world scenarios, DT aids in planning maintenance activities and conducting comprehensive testing, thereby enhancing the resilience and efficiency of Smart City Systems. Particularly in the context of managing water networks, DT technology holds significant promise. Visualization capabilities provide intuitive insights, while actuation enables control actions based on predictive analytics and optimization algorithms. To this end, in this paper, we present the architecture of WaterTwin, a DT we developed for water quality networks in smart city systems. We demonstrate our approach through the use case of a water quality network in the smart city living lab of the IIIT Hyderabad campus.

Abstract

While documenting Architectural Knowledge (AK) is crucial, it is frequently neglected in many projects, and existing manual tools are underutilized. Although undocumented, Archi-tecture Knowledge (AK) is dispersed across various sources such as source code, documentation, and runtime logs. To address this, automated tools for efficient AK extraction and documentation are essential. Even after generating AK, navigating through vast the Architectural Records can be overwhelming. Building on that, we propose an automated Architectural Knowledge Management (AKM) System using Information Extraction and Generative AI, which generates AK from various source for a given system and answers architectural queries with respect to the given system. The development of an efficient Architectural Knowledge Management (AKM) system, which is both effective and user-friendly, entails the resolution of numerous challenges. It requires consolidating diverse AK data sources scattered across code, dia-grams, repository commits, and online platforms. The integration of Multimodal AI for AK extraction, incorporation of global AK, and leveraging Generative AI for AK documentation further compounds the problem. Moreover, generating contextually appropriate query responses adds another layer of complexity. To this end, we performed an initial exploratory study on generating Architectural Design Decisions using generative Large Language Models (LLM) in the context of Architecture Decision Records (ADR). Our initial results have been promising indicating the potential impact of GenAI for architectural knowledge management.

Abstract

Text summarization is a well-established task within the natural language processing (NLP) community. However, the focus on controllable summarization tailored to user requirements is gaining traction only recently. While several efforts explore controllability in text summarization, the investigation of Multi-Attribute Controllable Summarization (MACS) remains limited. This work addresses this gap by examining the MACS task through the lens of large language models (LLMs), using various learning paradigms, particularly low-rank adapters. We experiment with different popular adapter fine-tuning strategies to assess the effectiveness of the resulting models in retaining cues and patterns associated with multiple controllable attributes. Additionally, we propose and evaluate a novel hierarchical adapter fusion technique to integrate learnings from two distinct controllable attributes. Subsquently, we present our findings, discuss the challenges encountered, and suggest potential avenues for advancing the MACS task.