arXiv subjects

Bhaskar Krishnamachari

Publications and source records attributed to Bhaskar Krishnamachari.

At least 73 records · Page 4Linked to original sources

arXiv2024

Modeling and Analysis of Crypto-Backed Over-Collateralized Stable Derivatives in DeFi

In decentralized finance (DeFi), stablecoins like DAI are designed to offer a stable value amidst the fluctuating nature of cryptocurrencies. We examine the class of crypto-backed stable derivatives, with a focus on mechanisms for price stabilization, which is exemplified by the well-known stablecoin DAI from MakerDAO. For simplicity, we focus on a single-collateral setting. We introduce a belief parameter to the simulation model of DAI in a previous work (DAISIM), reflecting market sentiments about the value and stability of DAI, and show that it better matches the expected behavior when this parameter is set to a sufficiently high value. We also propose a simple mathematical model of DAI price to explain its stability and dependency on ETH price. Finally, we analyze possible risk factors associated with these stable derivatives to provide valuable insights for stakeholders in the DeFi ecosystem.

q-fin.RM↗

arXiv2024

CAREForMe: Contextual Multi-Armed Bandit Recommendation Framework for Mental Health

The COVID-19 pandemic has intensified the urgency for effective and accessible mental health interventions in people's daily lives. Mobile Health (mHealth) solutions, such as AI Chatbots and Mindfulness Apps, have gained traction as they expand beyond traditional clinical settings to support daily life. However, the effectiveness of current mHealth solutions is impeded by the lack of context-awareness, personalization, and modularity to foster their reusability. This paper introduces CAREForMe, a contextual multi-armed bandit (CMAB) recommendation framework for mental health. Designed with context-awareness, personalization, and modularity at its core, CAREForMe harnesses mobile sensing and integrates online learning algorithms with user clustering capability to deliver timely, personalized recommendations. With its modular design, CAREForMe serves as both a customizable recommendation framework to guide future research, and a collaborative platform to facilitate interdisciplinary contributions in mHealth research. We showcase CAREForMe's versatility through its implementation across various platforms (e.g., Discord, Telegram) and its customization to diverse recommendation features.

cs.AI↗

arXiv2024

Forecasting Cryptocurrency Staking Rewards

This research explores a relatively unexplored area of predicting cryptocurrency staking rewards, offering potential insights to researchers and investors. We investigate two predictive methodologies: a) a straightforward sliding-window average, and b) linear regression models predicated on historical data. The findings reveal that ETH staking rewards can be forecasted with an RMSE within 0.7% and 1.1% of the mean value for 1-day and 7-day look-aheads respectively, using a 7-day sliding-window average approach. Additionally, we discern diverse prediction accuracies across various cryptocurrencies, including SOL, XTZ, ATOM, and MATIC. Linear regression is identified as superior to the moving-window average for perdicting in the short term for XTZ and ATOM. The results underscore the generally stable and predictable nature of staking rewards for most assets, with MATIC presenting a noteworthy exception.

q-fin.ST↗

arXiv2023

Reinforcement Learning for Safe Occupancy Strategies in Educational Spaces during an Epidemic

Epidemic modeling, encompassing deterministic and stochastic approaches, is vital for understanding infectious diseases and informing public health strategies. This research adopts a prescriptive approach, focusing on reinforcement learning (RL) to develop strategies that balance minimizing infections with maximizing in-person interactions in educational settings. We introduce SafeCampus , a novel tool that simulates infection spread and facilitates the exploration of various RL algorithms in response to epidemic challenges. SafeCampus incorporates a custom RL environment, informed by stochastic epidemic models, to realistically represent university campus dynamics during epidemics. We evaluate Q-learning for a discretized state space which resulted in a policy matrix that not only guides occupancy decisions under varying epidemic conditions but also illustrates the inherent trade-off in epidemic management. This trade-off is characterized by the dilemma between stricter measures, which may effectively reduce infections but impose less educational benefit (more in-person interactions), and more lenient policies, which could lead to higher infection rates.

cs.AI↗

arXiv2023

ILB: Graph Neural Network Enabled Emergency Demand Response Program For Electricity

Demand Response (DR) programs have become a crucial component of smart electricity grids as they shift the flexibility of electricity consumption from supply to demand in response to the ever-growing demand for electricity. In particular, in times of crisis, an emergency DR program is required to manage unexpected spikes in energy demand. In this paper, we propose the Incentive-Driven Load Balancer (ILB), a program designed to efficiently manage demand and response during crisis situations. By offering incentives to flexible households likely to reduce demand, the ILB facilitates effective demand reduction and prepares them for unexpected events. To enable ILB, we introduce a two-step machine learning-based framework for participant selection, which employs a graph-based approach to identify households capable of easily adjusting their electricity consumption. This framework utilizes two Graph Neural Networks (GNNs): one for pattern recognition and another for household selection. Through extensive experiments on household-level electricity consumption in California, Michigan, and Texas, we demonstrate the ILB program's significant effectiveness in supporting communities during emergencies.

cs.CY↗

arXiv2023

Incentivizing Private Data Sharing in Vehicular Networks: A Game-Theoretic Approach

In the context of evolving smart cities and autonomous transportation systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV) are growing in significance. Vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data to make driving safer and more convenient. However, this advancement ushers in complex issues concerning the centralized structure of traditional vehicular networks and the privacy and security concerns around vehicular data. This paper offers a novel, game-theoretic network architecture to address these challenges. Our approach decentralizes data collection through distributed servers across the network, aggregating vehicular data into spatio-temporal maps via secure multi-party computation (SMPC). This strategy effectively reduces the chances of adversaries reconstructing a vehicle's complete path, increasing privacy. We also introduce an economic model grounded in game theory that incentivizes vehicle owners to participate in the network, balancing the owners' privacy concerns with the monetary benefits of data sharing. This model aims to maximize the data consumer's utility from the gathered sensor data by determining the most suitable payment to participating vehicles, the frequency in which these vehicles share their data, and the total number of servers in the network. We explore the interdependencies among these parameters and present our findings accordingly. To define meaningful utility and loss functions for our study, we utilize a real dataset of vehicular movement traces.

eess.SY↗

arXiv2023

Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning

With recent advancements in the field of communications and the Internet of Things, vehicles are becoming more aware of their environment and are evolving towards full autonomy. Vehicular communication opens up the possibility for vehicle-to-infrastructure interaction, where vehicles could share information with components such as cameras, traffic lights, and signage that support a countrys road system. As a result, vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data used to make driving safer and more convenient. With 5G cellular networks and beyond, there is going to be more data bandwidth available on our roads, but it may be heterogeneous because of limitations like line of sight, infrastructure, and heterogeneous traffic on the road. This paper addresses the problem of route planning for autonomous vehicles in urban areas accounting for both driving time and data transfer needs. We propose a novel reinforcement learning solution that prioritizes high bandwidth roads to meet a vehicles data transfer requirement, while also minimizing driving time. We compare this approach to traffic-unaware and bandwidth-unaware baselines to show how much better it performs under heterogeneous traffic. This solution could be used as a starting point to understand what good policies look like, which could potentially yield faster, more efficient heuristics in the future.

cs.LG↗

arXiv2023

Holistic Survey of Privacy and Fairness in Machine Learning

Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has been independently studied in the literature with the aim of reducing utility loss in achieving them. Despite the significant interest attracted from both academia and industry, there remains an immediate demand for more in-depth research to unravel how these two objectives can be simultaneously integrated into ML models. As opposed to well-accepted trade-offs, i.e., privacy-utility and fairness-utility, the interrelation between privacy and fairness is not well-understood. While some works suggest a trade-off between the two objective functions, there are others that demonstrate the alignment of these functions in certain scenarios. To fill this research gap, we provide a thorough review of privacy and fairness in ML, including supervised, unsupervised, semi-supervised, and reinforcement learning. After examining and consolidating the literature on both objectives, we present a holistic survey on the impact of privacy on fairness, the impact of fairness on privacy, existing architectures, their interaction in application domains, and algorithms that aim to achieve both objectives while minimizing the utility sacrificed. Finally, we identify research challenges in achieving privacy and fairness concurrently in ML, particularly focusing on large language models.

cs.LG↗

arXiv2023

DARSAN: A Decentralized Review System Suitable for NFT Marketplaces

We introduce DARSAN, a decentralized review system designed for Non-Fungible Token (NFT) marketplaces, to address the challenge of verifying the quality of highly resalable products with few verified buyers by incentivizing unbiased reviews. DARSAN works by iteratively selecting a group of reviewers (called ``experts'') who are likely to both accurately predict the objective popularity and assess some subjective quality of the assets uniquely associated with NFTs. The system consists of a two-phased review process: a ``pre-listing'' phase where only experts can review the product, and a ``pre-sale'' phase where any reviewer on the system can review the product. Upon completion of the sale, DARSAN distributes incentives to the participants and selects the next generation of experts based on the performance of both experts and non-expert reviewers. We evaluate DARSAN through simulation and show that, once bootstrapped with an initial set of appropriately chosen experts, DARSAN favors honest reviewers and improves the quality of the expert pool over time without any external intervention even in the presence of potentially malicious participants.

cs.CY↗

arXiv2023

Partitioning and Deployment of Deep Neural Networks on Edge Clusters

Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet no system exists to split a DNN across these clusters while maximizing the inference throughput of the system. Additionally, no production-ready orchestration system exists for deploying said models over such edge networks which adopts the robustness and scalability of the cloud. We present an algorithm which partitions DNNs and distributes them across a set of edge devices with the goal of minimizing the bottleneck latency and therefore maximizing inference throughput. The system scales well to systems of different node memory capacities and numbers of nodes, while being node fault-tolerant. We find that we can reduce the bottleneck latency by 10x over a random algorithm and 35% over a greedy joint partitioning-placement algorithm, although the joint-partitioning algorithm outperforms our algorithm in most practical use-cases. Furthermore we find empirically that for the set of representative models we tested, the algorithm produces results within 9.2% of the optimal bottleneck latency. We then developed a standalone cluster network emulator on which we tested configurations of up to 20 nodes and found a steady increase in throughput and decrease in end-to-end latency as the cluster size scales. In these tests, we observed that our system has multi-node fault-tolerance as well as network and system IO fault-tolerance. We have implemented our framework in open-source software that is publicly available to the research community at https://github.com/ANRGUSC/SEIFER.

cs.NI↗

arXiv2023

Correlation-Aware Neural Networks for DDoS Attack Detection In IoT Systems

We present a comprehensive study on applying machine learning to detect distributed Denial of service (DDoS) attacks using large-scale Internet of Things (IoT) systems. While prior works and existing DDoS attacks have largely focused on individual nodes transmitting packets at a high volume, we investigate more sophisticated futuristic attacks that use large numbers of IoT devices and camouflage their attack by having each node transmit at a volume typical of benign traffic. We introduce new correlation-aware architectures that take into account the correlation of traffic across IoT nodes, and we also compare the effectiveness of centralized and distributed detection models. We extensively analyze the proposed architectures by evaluating five different neural network models trained on a dataset derived from a 4060-node real-world IoT system. We observe that long short-term memory (LSTM) and a transformer-based model, in conjunction with the architectures that use correlation information of the IoT nodes, provide higher performance (in terms of F1 score and binary accuracy) than the other models and architectures, especially when the attacker camouflages itself by following benign traffic distribution on each transmitting node. For instance, by using the LSTM model, the distributed correlation-aware architecture gives 81% F1 score for the attacker that camouflages their attack with benign traffic as compared to 35% for the architecture that does not use correlation information. We also investigate the performance of heuristics for selecting a subset of nodes to share their data for correlation-aware architectures to meet resource constraints.

cs.CR↗

arXiv2023

SMILE: Robust Network Localization via Sparse and Low-Rank Matrix Decomposition

Motivated by collaborative localization in robotic sensor networks, we consider the problem of large-scale network localization where location estimates are derived from inter-node radio signals. Well-established methods for network localization commonly assume that all radio links are line-of-sight and subject to Gaussian noise. However, the presence of obstacles which cause non-line-of-sight attenuation present distinct challenges. To enable robust network localization, we present Sparse Matrix Inference and Linear Embedding (SMILE), a novel approach which draws on both the well-known Locally Linear Embedding (LLE) algorithm and recent advances in sparse plus low-rank matrix decomposition. We demonstrate that our approach is robust to noisy signal propagation, severe attenuation due to non-line-of-sight, and missing pairwise measurements. Our experiments include simulated large-scale networks, an 11-node sensor network, and an 18-node network of mobile robots and static anchor radios in a GPS-denied limestone mine. Our findings indicate that SMILE outperforms classical multidimensional scaling (MDS) which ignores the effect of non-line of sight (NLOS), as well as outperforming state-of-the-art robust network localization algorithms that do account for NLOS attenuation including a graph convolutional network-based approach. We demonstrate that this improved accuracy is not at the cost of complexity, as SMILE sees reduced computation time for very large networks which is important for position estimation updates in a dynamic setting, e.g for mobile robots.

eess.SP↗

arXiv2022

a survey on GPT-3

This paper provides an introductory survey to GPT-3. We cover some of the historical development behind this technology, some of the key features of GPT-3, and discuss the machine learning model and the datasets used. We survey both academic and commercial efforts applying GPT-3 in diverse domains such as developing conversational AI chatbots, software development, creative work, domain knowledge, and business productivity. We discuss some of the challenges that GPT-3 faces such as the problems of training complexity, bias, and hallucination/incorrect answers. We also discuss the future research opportunities in this area.

cs.CL↗

arXiv2022

QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols

Automated Market Makers (AMMs) have cemented themselves as an integral part of the decentralized finance (DeFi) space. AMMs are a type of exchange that allows users to trade assets without the need for a centralized exchange. They form the foundation for numerous decentralized exchanges (DEXs), which help facilitate the quick and efficient exchange of on-chain tokens. All present-day popular DEXs are static protocols, with fixed parameters controlling the fee and the curvature - they suffer from invariance and cannot adapt to quickly changing market conditions. This characteristic may cause traders to stay away during high slippage conditions brought about by intractable market movements. We propose an RL framework to optimize the fees collected on an AMM protocol. In particular, we develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol and maximizes the expected fee collected under a range of different market conditions. We show that QLAMMP is consistently able to outperform its static counterparts under all the simulated test conditions.

cs.LG↗

arXiv2022

CoMMA Protocol: Towards Complete Mitigation of Maximal Extractable Value (MEV) Attacks

MEV attacks have been an omnipresent evil in the blockchain world, an implicit tax that uninformed users pay for using the service. The problem arises from the miners' ability to reorder and insert arbitrary transactions in the blocks they mine. This paper proposes a 2-phased transaction protocol to eliminate MEV attacks. The user requests an interaction token from the on-chain counter-party. This token serves as a blind preemption for the counter-party and prevents the reordering of transactions at lower levels in the blockchain framework. We prove the correctness of the CoMMA protocol and demonstrate its efficacy against MEV attacks.

cs.CR↗

arXiv2022

SEIFER: Scalable Edge Inference for Deep Neural Networks

Edge inference is becoming ever prevalent through its applications from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet there is no production-ready orchestration system for deploying deep learning models over such edge networks which adopts the robustness and scalability of the cloud. We present SEIFER, a framework utilizing a standalone Kubernetes cluster to partition a given DNN and place these partitions in a distributed manner across an edge network, with the goal of maximizing inference throughput. The system is node fault-tolerant and automatically updates deployments based on updates to the model's version. We provide a preliminary evaluation of a partitioning and placement algorithm that works within this framework, and show that we can improve the inference pipeline throughput by 200% by utilizing sufficient numbers of resource-constrained nodes. We have implemented SEIFER in open-source software that is publicly available to the research community.

cs.NI↗

arXiv2022

Partitioning and Placement of Deep Neural Networks on Distributed Edge Devices to Maximize Inference Throughput

cs.NI↗

arXiv2022

Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning

In Multi-Agent Reinforcement Learning, communication is critical to encourage cooperation among agents. Communication in realistic wireless networks can be highly unreliable due to network conditions varying with agents' mobility, and stochasticity in the transmission process. We propose a framework to learn practical communication strategies by addressing three fundamental questions: (1) When: Agents learn the timing of communication based on not only message importance but also wireless channel conditions. (2) What: Agents augment message contents with wireless network measurements to better select the game and communication actions. (3) How: Agents use a novel neural message encoder to preserve all information from received messages, regardless of the number and order of messages. Simulating standard benchmarks under realistic wireless network settings, we show significant improvements in game performance, convergence speed and communication efficiency compared with state-of-the-art.

cs.AI↗