Abstract

Automated segmentation of medical image volumes promises to reduce costly medical experts’ time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. This paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72×. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64×. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

Abstract

This paper explores the concept of solving implicature in Natural Language Processing (NLP), highlighting its significance in understanding indirect communication. Drawing on foundational theories by Austin, Searle, and Grice, we discuss how implicature extends beyond literal language to convey nuanced meanings. We review existing datasets, including the Pragmatic Understanding Benchmark (PUB), that assess models’ capabilities in recognizing and interpreting implicatures. Despite recent advances in large language models (LLMs), challenges remain in effectively processing implicature due to limitations in training data and the complexities of contextual interpretation. We propose future directions for research, including the enhancement of datasets and the integration of pragmatic reasoning tasks, to improve LLMs’ understanding of implicature and facilitate better human-computer interaction.

Abstract

This research utilises the capabilities of Google Earth Engine, a cloud-based computing platform, to conduct a comprehensive spatiotemporal assessment of the Coringa mangrove. Situated within the Coringa Wildlife Sanctuary in Andhra Pradesh, India, this mangrove – the country's second largest – is evaluated across various timeframes: 1999, 2002, 2007, 2013, 2017, and 2022. ML algorithms – Random Forest and Support Vector Machine with an RBF kernel – are employed. These algorithms analyze band composites derived from Landsat-7 ETM+ (1999, 2002, 2007), Landsat-8 (2013, 2017), and Sentinel-2 MSI (2022) satellite images. This analysis incorporates key spectral indices and utilises two spectral index thresholds for mangrove classification: one derived from established literature that identifies common thresholds at which mangroves typically occur (standard threshold), and the other is a customised threshold obtained from individual spectral index maps, tailored specifically to delineate mangrove areas in the study area (customized threshold). The results show that RF, particularly with the CT, outperforms all other methods, including RF with the ST and SVM with both thresholds, in terms of training and testing accuracy. These findings affirm the effectiveness of RF with the CT approach in accurately differentiating mangrove areas, emphasizing the critical role of threshold selection in enhancing the accuracy and competence of classification methods for mapping mangrove ecosystems.

Abstract

Understanding what makes a video memorable has important applications in advertising or education technology. Towards this goal, we investigate spatio-temporal attention mechanisms underlying video memorability. Different from previous works that fuse multiple features, we adopt a simple CNN+Transformer architecture that enables analysis of spatio-temporal attention while matching state-of-the-art (SoTA) performance on video memorability prediction. We compare model attention against human gaze fixations collected through a small-scale eye-tracking study where humans perform the video memory task. We uncover the following insights: (i) Quantitative saliency metrics show that our model, trained only to predict a memorability score, exhibits similar spatial attention patterns to human gaze, especially for more memorable videos. (ii) The model assigns greater importance to initial frames in a video, mimicking human attention patterns. (iii) Panoptic segmentation reveals that both (model and humans) assign a greater share of attention to things and less attention to stuff as compared to their occurrence probability.

Abstract

Existing research suggests that mountain and plain societies differ due to geographical factors and this study tests this hypothesis by examining the political dynamics of these two types of societies. We perform a quantitative analysis, followed by an exploration of possible qualitative explanations. Duverger’s Law states that single-member plurality electoral systems typically result in two-party dominance. However, India is a well-known exception in the literature due to strong regional parties as it deviates from this trend. For the quantitative analysis, central election data from the Election Commission of India from 1977 to 2014 is used to conduct a temporal analysis. Laakso and Taagepera’s Effective Number of Parties (ENP) metric is calculated by squaring each party’s share of votes or seats, summing these squares and taking the reciprocal. It is used to find the effective political parties that exist in a district considering both their number and vote share. According to Duverger’s Law, the ENP should ideally converge to 2 over time. This analysis reveals that in plains the ENP values are increasing over time (suggesting divergence) whereas mountain regions are gradually converging towards a two-party system. We conduct a one-way ANOVA for each election by grouping the ENP values for mountain and plain districts separately. A statistically significant difference between the ENP values of mountain and plain districts across all elections. For qualitative analysis, James Scott’s concept of Zomia along with different ideas of Zomia presented by Schendel, Schneiderman are discussed and how they might offer an explanation for the observation. Ultimately, the study sheds light on the socio-political distinctions between mountainous and plain regions in India, supporting historical and sociological theories that suggest mountain societies preserve unique political traits.

Abstract

The rapid evolution of Large Language Models (LLMs) have opened new possibilities in automating tasks across the software developing life cycle, including test case generation This paper presents a comparative analysis of six LLMs in the context of generating test cases for technical requirements written in natural language (in this case English). We compare publicly available general purpose LLMs viz., BARD, ChatGPT3.5, Claude, Gemini, ChatGPT4.o (Omni) and Llama3. The generated test cases are tested against a Simulink model created for the corresponding set of requirements. The coverage metrics thus generated are used for a quantitative comparison of the LLMs.

Abstract

Evaluating machine translation (MT) systems for low-resource languages has long been a challenge due to the limited availability of evaluation metrics and resources. As a result, researchers in this space have relied primarily on lexical-based metrics like BLEU, TER, and ChrF, which lack semantic evaluation. In this first-of-its-kind work, we propose a novel pivot-based evaluation framework that addresses these limitations; after translating low-resource language outputs into a related high-resource language, we leverage advanced neural and embedding-based metrics for more meaningful evaluation. Through a series of experiments using five low-resource languages: Assamese, Manipuri, Kannada, Bhojpuri, and Nepali, we demonstrate how this method extends the coverage of both lexical-based and embedding-based metrics, even for languages not directly supported by advanced metrics. Our results show that the differences between direct and pivot-based evaluation scores are minimal, proving that this approach is a viable and effective solution for evaluating translations in endangered and low-resource languages. This work paves the way for more inclusive, accurate, and scalable MT evaluation for underrepresented languages, marking a significant step forward in this under-explored area of research.

Abstract

The significant improvements in point cloud representation learning have increased its applicability in many real-life applications, resulting in the need for lightweight, better-performing models. One widely proposed efficient method is knowledge distillation, where a lightweight model uses knowledge from large models. Very few works exist on distilling the knowledge for point clouds. Most of the work focuses on cross-modal-based approaches that make the method expensive to train. This paper proposes PointKAD, an adversarial knowledge distillation framework for point cloud-based tasks. PointKAD includes adversarial feature distillation and response distillation with the help of discriminators to extract and distill the representation of feature maps and logits. We conduct extensive experimental studies on both synthetic (ModelNet40) and real (ScanObjectNN) datasets to show that PointKAD achieves state-of-the-art results compared to the existing knowledge distillation methods for point cloud classification. Additionally, we present results on the part segmentation task, highlighting the efficacy of the PointKAD framework. Our experiments further reveal that PointKAD is capable of transferring knowledge across different tasks and datasets, showcasing its versatility. Furthermore, we demonstrate that PointKAD can be applied to a cross-modal training setup, achieving competitive performance with cross-modal-based point cloud methods for classification.

Abstract

Tornado Cash is a decentralized application (dApp) that runs on Ethereum Virtual Machine (EVM) compatible networks to enhance users’ privacy in terms of user transaction history over the blockchain. This dApp achieves its goal by enabling users to deposit currencies into designated pools and subsequently withdraw them, severing the link between depositor and withdrawer addresses. At deposit time, Tornado Cash communicates to users the level of privacy they will benefit from (anonymity set) by depositing currencies into one of its pools. Existing analyses have indicated discrepancies between the claimed anonymity set and the actual level of privacy provided, primarily attributed to users’ incorrect utilization of the dApp. This paper explores the road towards a new way to challenge the dApp’s proposed anonymity set by examining wallet fingerprints, a factor not directly related to user behavior within the application. The findings of this research shed light on the potential for creating links between clusters of users in TC according to the new proposed approach and raise a privacy concern within the Ethereum network, resulting in 13203 transactions, over 66948, linkable to the wallet used to initialize them.

Abstract

A Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is one of the primary barriers between notorious bots and legitimate human users. However, advancements in Artificial Intelligence (AI) have enabled malicious bots to circumvent CAPTCHA challenges effectively. As a result, several types of CAPTCHA have been rendered ineffective. In this work, we introduce Swiss Cheese CAPTCHA, a novel sensor-based solution designed to be easily solvable by humans while presenting multiple obstructions for bots (similar to the Swiss Cheese Model) even when the sensor outputs can be predicted and interfered with. We leverage a range of human cognitive abilities and Generic Sensor API in modern devices to provide robust protection against automated attacks by making it more computationally expensive for bots to produce a valid answer within a stipulated time. We conducted two user studies to assess our proposal’s effectiveness: one involving 116 participants to assess the likability and improvise the design, and the other, with 107 participants, to investigate the impact of improvised design changes on cognitive abilities. Our results from these studies show an average completion time of 4.76 seconds and 6.12 seconds, with a success rate of 90.3% and 83.25%, respectively. By analyzing the 2141 resultant trajectories from both user studies, we assess the learnability, error recovery rate, efficiency, and satisfaction of users using the scheme. Finally, we devise an automated attack against our proposal to analyze its security in the real world; we find the probability of attack success is low. We also make our dataset available for further research.