Shrisha Rao

International Institute of Information Technology Bangalore | IIITB · Department of Computer Science

We present a cloud resource procurement approach which not only automates the selection of an appropriate cloud vendor but also implements dynamic pricing. Three possible mechanisms are suggested for cloud resource procurement: C-DSIC, C-BIC and C-OPT. C-DSIC is dominant strategy incentive compatible, based on the VCG mechanism, and is a low-bid Vi...

Motivation: Accurate prediction of binding between a major histocompatibility complex (MHC) allele and a peptide plays a major role in the synthesis of personalized cancer vaccines. The immune system struggles to distinguish between a cancerous and a healthy cell. In a patient suffering from cancer who has a particular MHC allele, only those pepti...

Social media are extensively used in today's world, and facilitate quick and easy sharing of information, which makes them a good way to advertize products. Influencers of a social media network, owing to their massive popularity, provide a huge potential customer base. However, it is not straightforward to decide which influencers should be select...

Researchers worldwide have become increasingly interested in developing computational approaches to handle challenges facing electric vehicles (EVs) in recent years. This paper examines the challenges and future potential of computational approaches for problems such as EV routing, EV charging scheduling, EV charging station (CS) placement, CS sizi...

The rise in the penetration of the internet across the world has led to a rapid increase in the consumption of energy at the data centers established by leading cloud data service providers. High power consumption by these data centers [DCs] leads to high operational costs and high carbon emissions into the environment. From a sustainability point...

Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear c...

In the context of increasingly complex environmental challenges, effective pollution control mechanisms are crucial. By extending the state of the art auction mechanisms, we aim to develop an efficient approach for allocating pollution abatement resources in a multi-pollutant setting with pollutants affecting each other's reduction costs. We modify...

Extracting relevant information from legal documents is a challenging task due to the technical complexity and volume of their content. These factors also increase the costs of annotating large datasets, which are required to train state-of-the-art summarization systems. To address these challenges, we introduce CivilSum, a collection of 23,350 leg...

Management of power is a crucial problem in computing systems where power is finite, processor performance and energy needs are high, and thermal constraints have to be respected. The trade-off between performance and energy expenditure is well recognized. To satisfy these conflicting requirements, in this paper, a dynamic system framework is adopt...

We propose a multi-agent system that enables groups of agents to collaborate and work autonomously to execute tasks. Groups can work in a decentralized manner and can adapt to dynamic changes in the environment. Groups of agents solve assigned tasks by exploring the solution space cooperatively based on the highest reward first. The tasks have a de...

In the context of rising greenhouse gas emissions and climate change, we propose a pollution control system through the use of the Vickrey-Clarke-Groves (VCG) auction mechanism. Agents bid on pollution permits that grant them a right to pollute a single unit of a pollutant. This auction algorithm efficiently allocates the pollution permits for mult...

Accurate prediction of the phages that target a bacterial host plays an important role in combating anti-microbial resistance. Our work explores the power of deep neural networks, convolutional neural networks, and pre-trained large DNA/protein language models to predict the host for a given phage. This work mainly uses the data provided by Gonzale...

We propose a multi-agent system that enables groups of agents to collaborate and work autonomously to execute tasks. Groups can work in a decentralized manner and can adapt to dynamic changes in the environment. Groups of agents solve assigned tasks by exploring the solution space cooperatively based on the highest reward first. The tasks have a de...

Existing studies on prejudice, which is important in multi-group dynamics in societies, focus on the social-psychological knowledge behind the processes involving prejudice and its propagation. We instead create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on the prosperity of individuals as w...

We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space, make inferences, as well as query for information under a limited budget. Re-exploration of the solution space takes place by an agent when an older solution expires and is thus able to adap...

Modelling the engaging behaviour of humans using multimodal data collected during human-robot interactions has attracted much research interest. Most methods that have been proposed previously predict engaging behaviour directly from multimodal features, and do not incorporate personality inferences or any theories of interpersonal behaviour in hum...

We develop an iterative differentially private algorithm for client selection in federated settings. We consider a federated network wherein clients coordinate with a central server to complete a task; however, the clients decide whether to participate or not at a time step based on their preferences—local computation and probabilistic intent. The...

A finite-time resilient consensus protocol (RCP) is developed for a connected network of agents, where communication between agents occurs locally, a few of the agents are malicious (MA), and the non-malicious or cooperating (CO) agents do not know the locations of the MA ones. Networks with a single leader and several followers as well as leaderle...

Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the research community. In this context, agents demand resources based on their local computation. Due to t...

(See https://doi.org/10.1109/ACCESS.2023.3283503 for journal version.) Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the research community. In this con...

This paper proposes a Robust Gradient Classification Framework (RGCF) for Byzantine fault tolerance in distributed stochastic gradient descent. The framework consists of a pattern recognition filter which we train to be able to classify individual gradients as Byzantine by using their direction alone. This filter is robust to an arbitrary number of...

In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained. However, in robotics applications such as industrial systems or autonomous vehicles, this cannot be guaranteed. A robot can be pushed out of its training space by some unforeseen perturbation, which may...

With the advent of Electric Vehicles (EVs), issues connected to the electric vehicle charging scheduling (EVCS) problem, which is NP-hard, have become important. In previous studies, EVCS has been mainly formulated as a constrained shortest-path problem; however, such formulations have not involved variables such as the charging rates, traffic cong...

As usage of artificial intelligence (AI) technologies across industries increases, there is a growing need for creating large marketplaces to host and transact good quality data sets to train AI algorithms. Our study analyses the characteristics of such an oligopsony crowdsourced AI Marketplace (AIM) that has sets of large number of producers and f...

We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space, make inferences, as well as query for information under a limited budget. Re-exploration of the solution space takes place by an agent when an older solution expires and is thus able to adap...

Charging station (CS) planning for electric vehicles (EVs) for a region has become an important concern for urban planners and the public alike to improve the adoption of EVs. Two major problems comprising this research area are: (i) EV charging station placement (EVCSP) problem; and (ii) CS need estimation problem for a region. In this work, diffe...

Existing studies on prejudice, which is important in multi-group dynamics in societies, focus on the social-psychological knowledge behind the processes involving prejudice and its propagation. We instead create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on the prosperity of individuals as w...

The effects of the Peter Principle (PP) on a hierarchical firm have been extensively studied, but existing firm models fail to capture real-world firm dynamics such as employee motivation and CEO characteristics. We thus extend an existing firm model to introduce the notion of employee motivation and a CEO agent with parameters for leadership and m...

Algorithms are not merely tools and procedural abstractions that define the manner in which computers work—they now have a large cultural context. Computational systems are everywhere in modern society, and algorithms drive them. Perhaps on account of scientism in common thinking, algorithmic processes, and the systems that enable them, are seen as...

This is a consolidated look at computational techniques for sustainability, and their limits and possibilities. Sustainability is already well established as a concern and a topic of study and practice, given the alarming increase of environmental degradation, pollution, and other adverse effects of industrialization and urbanization. Computational...

Developing a framework for the locomotion of a six-legged robot or a hexapod is a complex task that has extensive hardware and computational requirements. In this paper, we present a bio-inspired framework for the locomotion of a hexapod. Our locomotion model draws inspiration from the structure of a cockroach, with its fairly simple central nervou...

Data-centric models of COVID-19 have been attempted, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease and the real situatio...

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even sl...

These are 1000 individual queries, one per line, which can be used to test the efficacy of a search engine, tool, or front end. Each line indicates a query and the complexity of the same (the complexity being a natural number, 1 or greater). Common search engines presently do not handle queries of complexity greater than 2. The queries are legitim...

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even sl...

In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained. However, in robotics applications such as industrial systems or autonomous vehicles, this cannot be guaranteed. A robot can be pushed out of its training space by some unforeseen perturbation, which may...

In toy environments like video games, a reinforcement learning agent is deployed and operates within the same state space in which it was trained. However, in robotics applications such as industrial systems or autonomous vehicles, this cannot be guaranteed. A robot can be pushed out of its training space by some unforeseen perturbation, which may...

Social media are extensively used in today's world, and facilitate quick and easy sharing of information, which makes them a good way to advertise products. Influencers of a social media network, owing to their massive popularity, provide a huge potential customer base. However, it is not straightforward to decide which influencers should be select...

A consensus protocol is designed for a connected network of cooperative (CO) and Byzantine (BZ) agents. An agent is considered cooperative if it plays by the rules, and Byzantine if it does not apply the same consensus protocol as the cooperative agents, sends different/misleading state values to its neighbors, or forges signatures of cooperative a...

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking---a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interle...

With increased requirements of processing power in many computing applications, and the consequent huge increases in resource costs including electrical power consumption, there is a push to find alternative computing architectures that achieve high speeds but require less resources. One direction that shows promise is brain-inspired neuromorphic c...

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal scheduling and present theoretical foundations of the same. We derive results concerning energy-minimal scheduling...

The telecommunications industry has evolved from voice-centric to provisioning of broadband data services. As witnessed in countries around the world, the industry has an oligopoly market structure with a few operators providing services. The services offered by the operators differ in both price and quality of service. On the other hand, consumers...

Realistic models of decision-making and social interactions, considering the nature of memory and biases, continue to be an area of immense interest. Emotion and mood are a couple of key factors that play a major role in decisions, nature of social interactions, size of the social network, and the level of engagement. Most of the prior work in this...

This paper presents an integrated model that approximates pedestrian behavior in case of a fire emergency, and its consequences. We have modeled a confined fire with a variable spread rate, based on the existing literature pertaining to the field. Fire has both psychological and physical impacts on the state of the agents. The model also incorporat...

The "Thinking, Fast and Slow" paradigm of Kahneman proposes that we use two different styles of thinking -- a fast and intuitive System 1 for certain tasks, along with a slower but more analytical System 2 for others. While the idea of using this two-system style of thinking is gaining popularity in AI and robotics, our work considers how to interl...

Bundling is a technique e-commerce companies have adopted from traditional retail stores to increase the average order size. It has been observed that bargaining helps increase customer satisfaction while increasing the average order revenue for retailers. We propose a mathematical framework to incorporate bargaining capabilities with the product b...

Data-centric models of COVID-19 have been tried, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease, and the real situation (...

Neuromorphic computing describes the use of VLSI systems to mimic neuro-biological architectures and is also looked at as a promising alternative to the traditional von Neumann architecture. Any new computing architecture would need a system that can perform floating-point arithmetic. In this paper, we describe a neuromorphic system that performs I...

Modelling ethics is critical to understanding and analysing social phenomena. However, prior literature either incorporates ethics into agent strategies or uses it for evaluation of agent behaviour. This work proposes a framework that models both, ethical decision making as well as evaluation using virtue ethics and utilitarianism. In an iteration...

The problem of distributed election majority-winner monitoring for checkpoint-based protocols has been of interest for some time now, and the approach that most of the major algorithms take on this is to employ a center-based pull from all voting sites in conjunction with a count tracker, to continually monitor the information about the incoming vo...

See the full version of this paper at https://www.nature.com/articles/s41598-020-77715-6

Social influence is an essential aspect of human social interaction. Influence propagation modeling has been extensively studied and applied in various research fields, such as the maximization of product adoption, the spread of infectious diseases, etc. The primary sources of information in any social network are the influential nodes that propaga...

Many countries are implementing lockdown measures to slow the COVID-19 pandemic, putting more than a third of the world's population under restrictions. The scale of such lockdowns is unprecedented, and while some effects of lockdowns are readily apparent, it is less clear what effects they may have on outbreaks of serious communica-ble diseases. W...

A problem currently faced by autonomous systems, learning agents, multi-agent systems and other cognitive systems is the extreme susceptibility to bad information and malicious misdirection. In this paper, we propose a strategy to identify trusted agents in a multi-agent system, so that a system can eliminate the knowledge provided by malicious or...

Neuromorphic computing is looked at as one of the promising alternatives to the traditional von Neumann architecture. In this paper, we consider the problem of doing arithmetic on neuromorphic systems and propose an architecture for doing IEEE 754 compliant addition on a neuromorphic system. A novel encoding scheme is also proposed for reducing the...

Drug-drug interaction causes potential impact on patients when a second drug is administered during the duration of action of the first. It may result in the delay or decrease in the absorption of rate of drugs or enhance their absorption. This also in turn may affect the action of drugs or induce adverse effects on patients. There exists a need to...

Modeling social interactions based on individual behavior has always been an area of interest, but prior literature generally presumes rational behavior. Thus, such models may miss out on capturing the effects of biases humans are susceptible to. This work presents a method to model egocentric bias, the real-life tendency to emphasize one's own opi...

Modeling social interactions based on individual behavior has always been an area of interest, but prior literature generally presumes rational behavior. Thus, such models may miss out on capturing the effects of biases humans are susceptible to. This work presents a method to model egocentric bias, the real-life tendency to emphasize one's own opi...

Vaccine development is a laborious and time-consuming process and can benefit from statistical machine learning techniques, which can produce general outcomes based on the patterns observed in the limited available empirical data. In this paper, we show how limited gene expression data from a small sample of subjects can be used to predict the outc...

We propose a novel general topological model for semantic image retrieval using relevance feedback. We use point-set topology to develop mathematical constructs for modeling the semantic retrieval. We also develop an image retrieval algorithm based on the Ant Sleeping Model and extend our topological model to analyze it. Through experiments we show...

In hybrid cloud computing, cloud users have the ability to procure resources from multiple cloud vendors, and furthermore also the option of selecting different combinations of resources. The problem of procuring a single resource from one of many cloud vendors can be modeled as a standard winner determination problem, and there are mechanisms for...

We present ASD (Action, Sequence, and Divide), a new framework for Hierarchical Reinforcement Learning (HRL). Present HRL methods construct the task hierarchies but fail to avoid exploration when tasks are to be performed in a particular sequence, resulting in the agent needlessly exploring all permutations of the tasks. When the task hierarchies a...

High sparsity and the problem of overspecialization are challenges faced by collaborative filtering (CF) algorithms in recommender systems. In this paper, we design an approach that efficiently tackles the above problems. We address the first issue of high sparsity in CF by modifying the popular parallel seeding technique proposed by Bahmani \etal...

Achieving a balance of supply and demand in a multi-agent system with many individual self-interested and rational agents that act as suppliers and consumers is a natural problem in a variety of real-life domains---smart power grids, data centers, and others. In this paper, we address the profit-maximization problem for a group of distributed suppl...

Permanent memory, and biases that humans have such as confirmation bias, are not modeled in some of the leading strategies in Iterated Prisoner's Dilemma. This reduces their effectiveness at modeling actual human behaviors. As a solution to this problem we have proposed a framework to model egoistic agents using trust. This framework accounts for s...

Automated negotiation is an important class of problems that has wide reaching application in the real world. While a lot of work has been done in Agent-Agent negotiations, Human-Agent negotiations have been relatively unexplored. Human-Agent multi issue bilateral negotiations deals with autonomous agents negotiating with humans over more than one...

Achieving a balance of supply and demand in a multi-agent system with many individual self-interested and rational agents that act as suppliers and consumers is a natural problem in a variety of real-life domains---smart power grids, data centers, and others. In this paper, we address the profit-maximization problem for a group of distributed suppl...

The relationship between tax rates and revenue guides governments in making policy decisions regarding tax reforms. The Laffer curve suggests the possibility of an inverse relationship between tax rates and revenue. There are conflicting views and opinions regarding this relationship and the shape of the Laffer curve. Since the behavioral response...

Astronomical datasets are typically very large, and manually classifying the data in them is effectively impossible. We use machine learning algorithms to provide classifications (as stars, quasars and galaxies) for more than one billion objects given photometrically in the Third Data Release of the Sloan Digital Sky Survey (SDSS III). We have used...

How cloud computing and data analytics come together.

This is the classified data obtained from the SDSS data set as described in the paper.

We present a new $4$-approximation algorithm for the Combinatorial Motion Planning problem which runs in $\mathcal{O}(n^2\alpha(n^2,n))$ time, where $\alpha$ is the functional inverse of the Ackermann function, and a fully distributed version for the same in asynchronous message passing systems, which runs in $\mathcal{O}(n\log_2n)$ time with a mes...