Publications

2024

1. ACM DGOV

   Generative AI Literacy: Twelve Defining Competencies

   Ravinithesh Annapureddy, Alessandro Fornaroli, and Daniel Gatica-Perez

   Digit. Gov.: Res. Pract. Aug 2024

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   This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly. Embedding these competencies into educational programs and professional training initiatives can equip individuals to become responsible and informed users and creators of generative AI. The competencies follow a logical progression and serve as a roadmap for individuals seeking to get familiar with generative AI and for researchers and policymakers to develop assessments, educational programs, guidelines, and regulations.

2022

1. Master Thesis

   Searching for visual patterns in a children’s drawings collection

   Ravinithesh Annapureddy

   Aug 2022

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   The success of large-scale digitization projects at museums, archives, and libraries is pushing other cultural institutions to embrace digitization to preserve their collections. By juxtaposing digital tools with digitized collections, it is now possible to study these cultural objects at a previously unknown scale. This thesis is the first attempt to explore a recently digitized children’s drawings collection while developing a system to identify patterns in them linked with popular cultural objects. Artists, as young as three and as old as 25, created nearly 90,000 drawings in the span of three decades from most countries in the world. The preliminary examination unveils that these drawings mirror a solid cultural ethos by using specific iconographic subjects, objects, and colors, and the distinction between children of different parts of the globe is visible in their works. These factors not only make the dataset distinct from other sketch datasets but place it distantly from them in terms of size and multifariousness of creations and the creators. The essential and another dimension of the project is matching the drawings and the popular cultural objects they represent. A deep learning model that learns a metric to rank the visual similarity between the images is used to identify the drawing-artwork pairs. Though the networks developed for image classification perform inadequately for the matching task, networks used for pattern matching in paintings show good performance. Fine-tuning the models increases the performance drastically. The primary outcomes of this work are (1) systems trained with a few methodically chosen examples perform comparably to the systems trained on thousands of generic samples and (2) using drawings enriched by adding generic effects of watercolor, oil painting, pencil sketch, and texturizing mitigates the situation of network learning examples by heart.

2018

1. SSCI

   Adaptive Critic Design for Extreme Learning Machines applied to noisy and drifting industrial processes

   Ravinithesh Annapureddy, Arya K. Bhattacharya, and Niranjan Reddy M

   Aug 2018

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   Natural and manmade continuous-time dynamic systems are susceptible to adverse digressions, i.e. periods of rapid deterioration of performance. Conceptually, digressions of a given type are engendered when specific parameters of the system get into definitive combinatorial relationships. Machine Learning (ML) techniques can in principle identify in real time the acquisition (formation) of such relationships and release warnings that consequently command actions that can return the system to normality. Here a manufacturing system, namely the continuous casting process of steel making, is the object of study and both Artificial Neural Networks (ANNs) and Extreme Learning Machines (ELMs) have been shown to perform the above function. A specific characteristic of industrial systems isprocess drift; in such processes the drift is induced into the inter-parameter data relationship learnt by the ML mechanism and the latter has to adapt to the evolving relationship else lose out on accuracy. Adaptive-Critic techniques can function as enablers for the ML mechanism to adapt to this drift. In this work two such Adaptive-Critic techniques are developed, the first for ANNs and then for ELMs, which are demonstrated to work successfully for the industrial process of interest. Importantly, this is the first development in public domain of an Adaptive-Critic technique using ELMs for adaptation to industrial drift. The techniques are generic and amenable for drifting processes in any industrial environment.

2. ACODS

   Advance Predictions of critical digressions in a noisy industrial process- performance of Extreme Learning Machines versus Artificial Neural Networks

   Ravinithesh Annapureddy, Arya K. Bhattacharya, and G. Rishita

   IFAC-PapersOnLine Aug 2018

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   Manmade continuous-time systems like vehicles, grids and industrial processes are susceptible to adverse digressions in performance which can result in losses to severe breakdowns. Traditionally, emergence of faults in systems was detected by algorithms based on specific sensory signals responding to the incipience of the fault. However, the fault itself is engendered by the fact that certain specific system state parameters acquire a specific combinatorial relationship preceding the fault, thus if the acquisition of this relationship can be detected immediately on formation, neutralization of the fault can be effected early - something particularly relevant to critical systems. The advent of the industrial IoT has made real time systemic data (state parameters) across a chain of processes available at computing platforms, and potentially enabled data-based algorithms - specifically Artificial Neural Networks - to predict such adverse digressions before actual initiation. However, most manmade systems are subject to drift, due to which ANNs trained on data acquired over a certain operational period lose accuracy going forward. Adaptive Critic systems are designed to enable ANNs to neutralize this drift effect, but these need frequent retraining hitting the constraint on computational time. Extreme Learning Machines have emerged as alternatives to ANNs with training times less by orders of magnitude, but their accuracy has to be tested against real noisy industrial data. This work investigates the accuracy of ELM performance versus that of ANNs for such data, and synthesizes ensembles of ELMs to provide accuracy at similar levels as ANNs. This facilitates the incorporation of ELM ensembles into Adaptive Critic frameworks for accurate pre-initiation prediction of faults and related control functions.

2017

1. INDICON

   Extreme Learning Machines with frequency based noise filtering for prediction of critical digressions in a noisy industrial process

   Aditya Gupta, PLN Manikumar, Ravinithesh Annapureddy, and 1 more author

   Dec 2017

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   Many systems that are continuous in time are also susceptible to adverse digressions that can result in severe losses. Conventional algorithms tend to use signals from sensors and detect faults after incipience, consequent to which corrective measures can be taken for mitigation. From an alternate perspective, it may be visualized that when some of the state parameters of the system combine to form specific relationships, such adverse digressions are engendered. Therefore, identifying these combinatorial relationships immediately upon formation can help in mitigation of such adverse effects. The advent of IoT in industry helped bring real time data to computing platforms where Machine Learning techniques can potentially be developed to detect formation of such relationships. Traditional Neural Networks (NN) can be used as a Machine Learning tool, but they require lot of time for training, and moreover, because the industrial data comes with a significant noise component, further processing of the NN outputs are needed before they can be used for such prediction purposes. Extreme Learning Machines (ELM) require about two orders of magnitude less training time, but are even more susceptible to noise corruption than NN due to reasons discussed in this paper. In this work, a method is developed to first filter out noise from industrial sensory data in real time from its spectral content, and then its downstream use in ELMs designed in ensemble pattern is found to generate outputs for predicting adverse digressions that are at least as accurate as traditional neural networks but with significantly reduced training time.

2016

1. IOP Science

   Magnetic Metamaterials: A comparative study of resonator geometry and metal conductivity

   Shashank Rangu, Kamireddy Sreekar, Ravinithesh Annapureddy, and 3 more authors

   Journal of Physics: Conference Series Oct 2016

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   In this work, split ring resonators based metamaterials are studied for microwave, terahertz and infrared frequency regimes. Two different geometries, circular and rectangular split ring resonators based metamaterials are investigated numerically for different frequency regimes. Our study indicates that the effect of metal conductivity and resonator geometry shows very little impact on the fundamental resonance mode. However the higher order modes go through significant frequency tuning because of the change in resonator geometry. We have further shown that the metal conductivity is an important parameter for the metamaterials employed in infrared domains.