Abstract

Breast cancer remains a significant global health concern, and machine learning algorithms and computer-aided detection systems have shown great promise in enhancing the accuracy and efficiency of mammography image analysis. However, there is a critical need for large, benchmark datasets for training deep learning models for breast cancer detection. In this work we developed Mammo-Bench, a large-scale benchmark dataset of mammography images, by collating data from seven well-curated resources, viz., INbreast, Mini-DDSM, KAU-BCMD, CMMD, CDD-CESM, DMID, and RSNA Screening Dataset. To ensure consistency across images from diverse sources while preserving clinically relevant features, all the images underwent a preprocessing pipeline that includes breast segmentation, pectoral muscle removal, and intelligent cropping. The dataset consists of 71,844 high-quality mammographic images from 26,500 patients across 8 countries and is one of the largest open-source mammography databases to the best of our knowledge. To show the utility of Mammo-Bench, ResNet101 architecture was used for classifying the images into Normal, Benign and Malignant classes. Performance of ResNet101 was evaluated on the proposed dataset and the results compared with a few member datasets and an external dataset, VinDr-Mammo. We show that training on the larger, proposed benchmark dataset is more reliable compared to when trained on other smaller datasets. An accuracy of 78.8% (with data augmentation of the minority classes) and 77.8% (without data augmentation) was achieved on the proposed benchmark dataset, compared to the other datasets for which the accuracy varied from 25 – 69%. Most striking was the improved prediction of the minority classes using the Mammo-Bench. These results establish baseline performance and demonstrate Mammo-Bench's utility as a comprehensive resource for developing and evaluating mammography analysis systems.

Abstract

Stuttering constitutes a pervasive speech disorder with a global impact on individuals. Detecting stuttering in speech would aid speech pathologists in monitoring fluency over time and enhance the quality of life for those with atypical speech patterns. Presently, the available public datasets tailored for stuttering identification are confined to the utterance level, with labels ascribed to individual utterances. However, in real-time situations, a considerable proportion of individuals grappling with stuttering manifest dysfluency at the syllable level. This limitation markedly constrains the effectiveness and applicability of dysfluency detection systems. In an effort to address this lacuna in knowledge, this study introduces a novel dataset comprising audio recordings directly obtained from native Kannada-speaking individuals afflicted with the stuttering disorder at the syllable level and also provides detailed information on the dataset's creation, including patient selection criteria and clip labelling. Additionally, benchmark models trained on this distinctive dataset are presented as part of this research endeavor.

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.