Abstract

Proof-of-Work is a consensus algorithm where miners solve cryptographic puzzles to mine blocks and obtain a reward through some Block Reward Mechanism (BRM). PoW blockchain faces the problem of centralization due to the formation of mining pools, where miners mine blocks as a group and distribute rewards. The rationale is to reduce the risk (variance) in reward while obtaining the same expected block reward. In this work, we address the problem of centralization due to mining pools in PoW blockchain. We propose a two-player game between the new miner joining the system and the PoW blockchain system. We model the utility for the incoming miner as a combination of (i) expected block reward, (ii) risk, and (iii) cost of switching between different mining pools. With this utility structure, we analyze the equilibrium strategy of the incoming miner for different BRMs: (a) memoryless -- block reward is history independent (e.g., Bitcoin) (b) retentive: block reward is history-dependent (e.g., Fruitchains). For memoryless BRMs, we show that depending on the coefficient of switching cost c, the protocol is decentralized when c=0 and centralized when c>c–. In addition, we show the impossibility of constructing a memoryless BRM where solo mining gives a higher payoff than forming/joining mining pools. While retentive BRM in Fruitchains reduces risk in solo mining, the equilibrium strategy for incoming miners is still to join mining pools, leading to centralization. We then propose our novel retentive BRM -- Decent-BRM. We show that under Decent-BRM, incoming miners obtain higher utility in solo mining than joining mining pools. Therefore, no mining pools are formed, and the Pow blockchain using Decent-BRM is decentralized.

Abstract

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (μg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2 ), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2 ), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Abstract

The evolving landscape of gig labor, particularly within food delivery platforms, has been influenced by a shift towards algorithmic management, emphasizing performance metrics as a daily burden to be carried through delivery work. Through a study of Indian food delivery agents, our paper suggests an emerging gamification culture between delivery platforms and workers. Agents gradually yet consistently adapt to stringent algorithmic management to overcome everyday work's precarity. Despite the challenges of performance pressure on food delivery labor, we capture the daily rhythms and routines of delivery work punctuated by flashes of agenting, revealing small capacities to make choices and influence work outcomes. By employing a qualitative research framework, we uncover the mechanisms through which delivery platforms manipulate labor, simultaneously exploring delivery agents' tactics to extract agency and exploit the platforms' rules. We develop the notion of 'agency’ to disentangle the idea of `gamification by agents’ as a socio-technical derivative governing food delivery agents in urban India. The findings highlight the potential of platform work empowering, albeit with limits, delivery agents with agency and decision-making authority.

Abstract

This work tackles the important task of understanding out-of-distribution behavior in two prominent types of generative models, i.e., GANs and Diffusion models. Understanding this behavior is crucial in understanding their broader utility and risks as these systems are increasingly deployed in our daily lives. Our first contribution is demonstrating that diffusion spaces outperform GANs' latent spaces in inverting high-quality OOD images. We also provide a theoretical analysis attributing this to the lack of prior holes in diffusion spaces. Our second significant contribution is to provide a theoretical hypothesis that diffusion spaces can be projected onto a bounded hypersphere, enabling image manipulation through geodesic traversal between inverted images. Our analysis shows that different geodesics share common attributes for the same manipulation, which we leverage to perform various image manipulations. We conduct thorough empirical evaluations to support and validate our claims. Finally, our third and final contribution introduces a novel approach to the few-shot sampling for out-of-distribution data by inverting a few images to sample from the cluster formed by the inverted latents. The proposed technique achieves state-of-the-art results for the few-shot generation task in terms of image quality. The effectiveness of the generated samples is further validated when used to augment data for underrepresented classes in a classification task, leading to improved performance. Our research underscores the promise of diffusion spaces in out-of-distribution imaging and offers avenues for further exploration.

Abstract

The Patent Office. Government of India Dance Teaching Tool Design No.: 413513-001 Date: 12/04/2024 Certified that the design of which a copy is annexed hereto has been registered as of the number and date given above in class 19-07 in respect of the application of such design to DANCE TEACHING TOOL in the name of 1. International Institute of Information Technology 2. Prof. Vinoo Alluri 3. S Jayachandran In pursuance of and subject to the provisions of the Designs Act, 2000 and the Designs Rule, 2001. Date of Issue: 10/07/2024

Abstract

Accurately recognizing a revisited place is crucial for embodied agents to localize and navigate. This requires visual representations to be distinct, despite strong variations in camera viewpoint and scene appearance. Existing visual place recognition pipelines encode the whole image and search for matches. This poses a fundamental challenge in matching two images of the same place captured from different camera viewpoints: the similarity of what overlaps can be dominated by the dissimilarity of what does not overlap. We address this by encoding and searching for image segments instead of the whole images. We propose to use open-set image segmentation to decompose an image into `meaningful' entities (i.e., things and stuff). This enables us to create a novel image representation as a collection of multiple overlapping subgraphs connecting a segment with its neighboring segments, dubbed SuperSegment. Furthermore, to efficiently encode these SuperSegments into compact vector representations, we propose a novel factorized representation of feature aggregation. We show that retrieving these partial representations leads to significantly higher recognition recall than the typical whole image based retrieval. Our segments-based approach, dubbed SegVLAD, sets a new state-of-the-art in place recognition on a diverse selection of benchmark datasets, while being applicable to both generic and task-specialized image encoders. Finally, we demonstrate the potential of our method to revisit anything by evaluating our method on an object instance retrieval task, which bridges the two disparate areas of research: visual place recognition and object-goal navigation, through their common aim of recognizing goal objects specific to a place.

Abstract

Current algorithms for Maximal Frequent Subgraph (MFS) mining do not scale to large databases with more than 300k graphs. Frequency computation is commonly done using numerous subgraph isomorphism operations, which are computationally expensive. This paper explores a partition-based secondary memory algorithm, PMCS, that can make MFS mining for large databases viable. PMCS intelligently avoids redundant computations to perform frequency computation operations optimally. Its intermediate results are also small enough to fit in the main memory. Our results demonstrate that PMCS scales to databases of up to 1000k graphs with an average of 25-30 edges per graph.

Abstract

The recently proposed Transaction Fee Mechanism (TFM) literature studies the strategic interaction between the miner of a block and the transaction creators (or users) in a blockchain. In a TFM, the miner includes transactions that maximize its utility while users submit fees for a slot in the block. The existing TFM literature focuses on satisfying standard incentive properties – which may limit widespread adoption. We argue that a TFM is “fair" to the transaction creators if it satisfies specific notions, namely Zero-fee Transaction Inclusion and Monotonicity. First, we prove that one generally cannot ensure both these properties and prevent a miner’s strategic manipulation. We also show that existing TFMs either do not satisfy these notions or do so at a high cost to the miners’ utility. As such, we introduce a novel TFM using on-chain randomness – rTFM. We prove that rTFM guarantees incentive compatibility for miners and users while satisfying our novel fairness constraints.

Abstract

This work presents a novel approach to identifying possible early accident-prone zones in a large city-scale road network using geo-tagged collision alert data from a vehicle fleet. The alert data has been collected for a year from 200 city buses installed with the Advanced Driver Assistance System (ADAS). To the best of our knowledge, no research paper has used ADAS alerts to identify the early accident prone zones. A nonparametric technique called Kernel Density Estimation (KDE) is employed to model the distribution of alert data across stratified time intervals. A novel recall-based measure is introduced to assess the degree of support provided by our density-based approach for existing, manually determined accident-prone zones (‘blackspots’) provided by civic authorities. This shows that our KDE approach significantly outperforms existing approaches in terms of the recall-based measure. Introducing a novel linear assignment Earth Mover Distance based measure to predict previously unidentified accident-prone zones. The results and findings support the feasibility of utilizing alert data from vehicle fleets to aid civic planners in assessing accident-zone trends and deploying traffic calming measures, thereby improving overall road safety and saving lives.

Abstract

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily focused on semantic similarity, often within the English language context, we instead investigate the broader phenomenon of semantic relatedness. In this paper, we present SemRel, a new semantic relatedness dataset collection annotated by native speakers across 13 languages: Afrikaans, Algerian Arabic, Amharic, English, Hausa, Hindi, Indonesian, Kinyarwanda, Marathi, Moroccan Arabic, Modern Standard Arabic, Spanish, and Telugu. These languages originate from five distinct language families and are predominantly spoken in Africa and Asia – regions characterised by a relatively limited availability of NLP resources. Each instance in the SemRel datasets is a sentence pair associated with a score that represents the degree of semantic textual relatedness between the two sentences. The scores are obtained using a comparative annotation framework. We describe the data collection and annotation processes, challenges when building the datasets, baseline experiments, and their impact and utility in NLP.