Abstract

There is growing excitement about the potential of Language Models (LMs) to accelerate scientific discovery. Falsifying hypotheses is key to scientific progress, as it allows claims to be iteratively refined over time. This process requires significant researcher effort, reasoning, and ingenuity. Yet current benchmarks for LMs predominantly assess their ability to generate solutions rather than challenge them. We advocate for developing benchmarks that evaluate this inverse capability — creating counterexamples for subtly incorrect solutions. To demonstrate this approach, we start with the domain of algorithmic problem solving, where counterexamples can be evaluated automatically using code execution. Specifically, we introduce REFUTE, a dynamically updating benchmark that includes recent problems and incorrect submissions from programming competitions, where human experts successfully identified counterexamples. Our analysis finds that the best reasoning agents, even OpenAI o3-mini (high) with code execution feedback, can create counterexamples for only < 9% of incorrect solutions in REFUTE, even though ratings indicate its ability to solve up to 48% of these problems from scratch. We hope our work spurs progress in evaluating and enhancing LMs’ ability to falsify incorrect solutions — a capability that is crucial for both accelerating research and making models self-improve through reliable reflective reasoning.

Abstract

Understanding user emotions to identify user opinion, sentiment, stance, and preferences has become a hot topic of research in the last few years. Many studies and datasets are designed for user emotion analysis including news websites, blogs, and user tweets. However, there is little exploration of political emotions in the Indian context for multi-label emotion detection. This paper presents a PoliEMO dataset—a novel benchmark corpus of political tweets in a multi-label setup for Indian elections, consisting of over 3512 tweets manually annotated. In this work, 6792 labels were generated for six emotion categories: anger, insult, joy, neutral, sadness, and shameful. Next, PoliEMO dataset is used to understand emotions in a multi-label context using state-of-the-art machine learning algorithms with multi-label classifier (binary relevance (BR), label powerset (LP), classifier chain (CC), and multi-label k-nearest neighbors (MkNN)) and deep learning models like convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and transfer learning model, i.e., bidirectional encoder representations from transformers (BERT). Experiments and results show Bi-LSTM performs better with micro-averaged F1 score of 0.81, macro-averaged F1 score of 0.78, and accuracy 0.68 as compared to state-of-the-art approaches.

Abstract

In covering elections, journalists often use conflict frames which depict events and issues as adversarial, often highlighting confronta- tions between opposing parties. Although conflict frames result in more citizen engagement, they may distract from substantive policy discussion. In this work, we analyze the use of conflict frames in online English-language news articles by seven major news outlets in the 2014 and 2019 Indian general elections. We find that the use of conflict frames is not linked to the news outlets’ ideological biases but is associated with TV-based (rather than print-based) media. Further, the majority of news outlets do not exhibit ideolog- ical biases in portraying parties as aggressors or targets in articles with conflict frames. Finally, comparing news articles reporting on political speeches to their original speech transcripts, we find that, on average, news outlets tend to consistently report on attacks on the opposition party in the speeches but under-report on more substantive electoral issues covered in the speeches such as farmers’ issues and infrastructure.

Abstract

Engaging in political discussions is crucial in democratic societies, yet many individuals remain politically disinclined due to various factors such as perceived knowledge gaps, conflict avoidance, or a sense of disconnection from the political system. In this paper, we explore the potential of personal narratives—short, first-person ac- counts emphasizing personal experiences—as a means to empower these individuals to participate in online political discussions. Using a text classifier that identifies personal narratives, we conducted a large-scale computational analysis to evaluate the relationship between the use of personal narratives and participation in po- litical discussions on Reddit. We find that politically disinclined individuals (PDIs) are more likely to use personal narratives than more politically active users. Personal narratives are more likely to attract and retain politically disinclined individuals in political discussions than other comments. Importantly, personal narratives posted by politically disinclined individuals are received more posi- tively than their other comments in political communities. These results emphasize the value of personal narratives in promoting inclusive political discourse.

Abstract

This study investigates the landscape of environmental litigation in India, analyzing how environmental disputes unfold within the judiciary. It aims to identify significant patterns in case subject matter, types of appellants, and regional distribution of such cases. By examining the broader socio-legal context, the research explores how various stakeholders interact with the legal system to address environmental issues. The methodology involves assembling a dataset of legal cases and applying computational techniques to extract metadata and detect trends across multiple dimensions. Statistical analysis was used to assess correlations between case topics and appellant categories, while spatial data provided insights into regional variations in environmental disputes. Additionally, a qualitative review of relevant legislation and policies cited in key rulings helped contextualize the legal reasoning behind environmental judgments. Through a computational lens, this study uncovers the institutional dynamics that shape environmental justice in India, emphasizing the value of data-driven approaches in understanding the interplay between law, governance, and civic engagement in environmental policymaking.

Abstract

We present TabGuard, a privacy-preserving framework for an end-to-end secure Table Structure Recognition. Tab-Guard masks all the contents of the table locally and utilizes the masked table image for structure recognition. Our method is simple yet effective for detecting table cells while preserving the inherent table alignment characteristics to reconstruct tables. Our approach benefits from inductive bias, expressed through an approximated table grid which helps alleviate challenges in the detection of cells that are small or have extreme aspect ratios. Experimental results demonstrate that our solution not only establishes a new state-of-the-art on several benchmark datasets but also effectively addresses long-standing challenges associated with dense tables having complex layouts.

Abstract

The demand for contactless heartbeat monitoring systems is rapidly increasing, particularly in healthcare settings where minimizing physical contact is crucial. Conventional methods often require subjects to remain stationary to ensure accurate measurements, which restricts their use in dynamic or realworld scenarios. In this paper, we present a novel system that utilizes Texas Instruments’ 77 GHz AWR1843BOOST FMCW radar technology to circumvent this constraint. By mitigating the effects of motion artifacts, the system enables accurate heartbeat detection even when the subject is in motion. Implemented on the PYNQ-Z2 system-on-chip (SOC) platform, the system achieves an accuracy of 82.78%, providing a more robust and portable embedded solution for real-time, non-invasive heart monitoring in a dynamic environments.

Abstract

In this paper, we propose an FPGA based system for non-contact monitoring of respiration rates (RR) using a mmWave frequency modulated continuous wave (FMCW) radar. The novel FPGA based hardware architecture presented in this paper classifies normal and abnormal RR with a Support Vector Machine (SVM) model using mmWave radar-based sensing. SVM with various kernels are evaluated in order to identify the most effective approach for respiratory rate (RR) classification. SVM with a quadratic polynomial kernel shows a high classification accuracy of 95%. While the accuracy was comparable to other kernels, the quadratic polynomial kernel achieved a substantial reduction in the number of support vectors and other parameters. This optimisation results in lower resource utilisation and power consumption, making it highly efficient for FPGA implementa- tion.

Abstract

This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (VGG) 16 model. We then assessed four machine learning models for object detection and found that the support vector machine (SVM) model and logistic regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

Abstract

Wearable heating pads are widely used as therapeutic warmers for tending to multiple health conditions. Precise temperature control is the key to ensuring the safety and effectiveness of the device due to its near-skin application. The paper presents a comparative analysis of temperature control using Proportional-Integral (PI) and Proportional-Integral-Derivative(PID) controllers based on time domain response parameters. The system utilizes an Arduino microcontroller to perform the error calculation between the set point and real-time temperature and accordingly generate PWM pulses to control the actuator driving the heating pad. The device features three different modes of operation based on the analog output voltage from an external driving circuit. Alongside, characterization data, system design and implementation of controllers, a comparative time domain response parameter analysis based on experimental data is also presented.