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@inproceedings{bib_Free_2025, AUTHOR = {Hruday, Kavuri Vivek and Vysishtya, Karanam and Jahnavi, Venkamsetty Venkata and Kriti, Madumadukala and Balaji, Darur Lakshmipathi and Kumaraguru, Ponnurangam }, TITLE = {Freeze and Reveal: Exposing Modality Bias in Vision-Language Models}, BOOKTITLE = {Recent advance in Natural language Processing}. YEAR = {2025}}

Vision-Language Models (VLMs) achieve impressive multimodal performance but often inherit gender biases from their training data. This bias might be coming from both the vision and text modalities. In this work, we dissect the contributions of vision and text backbones to these biases by applying targeted debiasing—Counterfactual Data Augmentation (CDA) and Task Vector methods. Inspired by data-efficient approaches in hate speech classification, we introduce a novel metric, Degree of Stereotypicality (DoS), and a corresponding debiasing method, Data Augmentation Using DoS (DAUDoS), to reduce bias with minimal computational cost. We curate a gender-annotated dataset and evaluate all methods on the VisoGender benchmark to quantify improvements and identify the dominant source of bias. Our results show that CDA reduces the gender gap by 6% and DAUDoS by 3% but using only one-third the data. Both methods also improve the model’s ability to correctly identify gender in images by 3%, with DAUDoS achieving this improvement using only almost one-third of training data. From our experiments, we observed that CLIP’s vision encoder is more biased whereas PaliGemma2’s text encoder is more biased. By identifying whether the bias stems more from the vision or text encoders, our work enables more targeted and effective bias mitigation strategies in future multi-modal systems. We release our code public at https://github.com/ vivekhruday05/VLM_bias

@inproceedings{bib_SPIR_2025, AUTHOR = {Mishra, Aditya and Ravindra, T and Iyengar, Srinivasan and Kalyanaraman, Shivkumar and Kumaraguru, Ponnurangam }, TITLE = {SPIRIT: Short-Term Prediction of Solar IRradIance for Transfer Learning Using Foundation Models}, BOOKTITLE = {Association for the Advancement of Artificial Intelligence Workshop}. YEAR = {2025}}

Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms. As renewable energy is highly intermittent, building accurate solar irradiance forecasting systems that are data-efficient is essential for efficient grid management and enabling the ongoing proliferation of solar energy, which is crucial to achieve the United Nations' net zero goals. In this work, we propose SPIRIT, a novel framework leveraging foundation models for solar irradiance forecasting, making it applicable to newer solar installations. Our approach outperforms state-of-the-art models in zero-shot transfer learning by upto 70%, enabling effective performance at new locations without relying on any past data. Further improvements in performance are achieved through fine-tuning, as more location-specific data becomes available. These findings are supported by statistical significance, further validating our approach. By dramatically reducing the forecasting setup timeline, SPIRIT accelerates solar farm deployment in all potential global sites, most of which lack historical data, thereby democratizing access to clean energy and enabling participation in the renewable energy transition.

@inproceedings{bib_LABE_2025, AUTHOR = {Ganguly, Debargha and Kumar, Sumit and Balappanawar, Ishwar B and Chen, Weicong and Kambhatla, Shashank and Iyengar, Srinivasan and Kalyanaraman, Shivkumar and Kumaraguru, Ponnurangam and Chaudhary, Vipin }, TITLE = {LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision}, BOOKTITLE = {IEEE Transactions on Big Data}. YEAR = {2025}}

Curating high-quality, domain-specific datasets is a major bottleneck for deploying robust vision systems, requiring complex trade-offs between data quality, diversity, and cost when researching vast, unlabeled data lakes. We introduce Labeling Copilot, the first data curation deep research agent for computer vision. A central orchestrator agent, powered by a large multimodal language model, uses multi-step reasoning to execute specialized tools across three core capabilities: (1) Calibrated Discovery sources relevant, in-distribution data from large repositories; (2) Controllable Synthesis generates novel data for rare scenarios with robust filtering; and (3) Consensus Annotation produces accurate labels by orchestrating multiple foundation models via a novel consensus mechanism incorporating non-maximum suppression and voting. Our large-scale validation proves the effectiveness of Labeling Copilot's components. The Consensus Annotation module excels at object discovery: on the dense COCO dataset, it averages 14.2 candidate proposals per image-nearly double the 7.4 ground-truth objects-achieving a final annotation mAP of 37.1%. On the web-scale Open Images dataset, it navigated extreme class imbalance to discover 903 new bounding box categories, expanding its capability to over 1500 total. Concurrently, our Calibrated Discovery tool, tested at a 10-million sample scale, features an active learning strategy that is up to 40x more computationally efficient than alternatives with equivalent sample efficiency. These experiments validate that an agentic workflow with optimized, scalable tools provides a robust foundation for curating industrial-scale datasets.

@inproceedings{bib_Do_L_2025, AUTHOR = {Mohammadi, Ali and Hanuma, Vedula Bhaskara and Lamba, Hemank and Raff, Edward and Kumaraguru, Ponnurangam and Ferraro, Francis and Gaur, Manas }, TITLE = {Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions}, BOOKTITLE = {Conference on Empirical Methods in Natural Language Processing}. YEAR = {2025}}

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlleD experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-curated, LLM- generated, perturbed, and swapped definitions. Our results reveal that while explicit label definitions can enhance accuracy and explainability, their integration into an LLM’s task-solvinG processes is neither guaranteed nor consistent, suggesting reliance on internalized representations in many cases. Models often default to their internal representations, particularly in general tasks, whereas domain-specific tasks benefit more from explicit definitions. These findings underscore the need for a deeper understanding of how LLMs process external knowledge alongside their pre-existing capabilities.

@inproceedings{bib_SEMM_2025, AUTHOR = {A, Arvindh and Kumar, Sumit and Nayyeri, Mojtaba and Xiong, Bo and Kumaraguru, Ponnurangam and Vergari, Antonio and Staab, Steffen }, TITLE = {SEMMA: A Semantic Aware Knowledge Graph Foundation Model}, BOOKTITLE = {Conference on Empirical Methods in Natural Language Processing}. YEAR = {2025}}

Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely solely on graph structure, overlooking the rich semantic signals encoded in textual attributes. We introduce SEMMA, a dual-module KGFM that systematically integrates transferable textual semantics alongside structure. SEMMA leverages Large Language Models (LLMs) to enrich relation identifiers, generating semantic embeddings that subsequently form a textual relation graph, which is fused with the structural component. Across 54 diverse KGs, SEMMA outperforms purely structural baselines like ULTRA in fully inductive link prediction. Crucially, we show that in more challenging generalization settings, where the test-time relation vocabulary is entirely unseen, structural methods collapse while SEMMA is 2x more effective. Our findings demonstrate that textual semantics are critical for generalization in settings where structure alone fails, highlighting the need for foundation models that unify structural and linguistic signals in knowledge reasoning.

@inproceedings{bib_Enha_2025, AUTHOR = {Swaroop, G Satya and Vipparla, Sreeram and Singh, Harpreet and Goel, Shashwat and Kumaraguru, Ponnurangam }, TITLE = {Enhancing AI Safety Through the Fusion of Low Rank Adapters}, BOOKTITLE = {Research and Applications of Foundation Models for Data Mining and Affective Computing Workshop}. YEAR = {2025}}

Instruction fine-tuning of large language models (LLMs) is a powerful method for improving task-specific performance, but it can inadvertently lead to a phenomenon where models generate harmful responses when faced with malicious prompts. In this paper, we explore Low-Rank Adapter Fusion (LoRA) as a means to mitigate these risks while preserving the model’s ability to handle diverse instructions effectively. Through an extensive comparative analysis against established baselines using recognized benchmark datasets, we demonstrate a 42% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter, the latter of which is specifically trained on our safety dataset. In addition, we made noteworthy observations related to exaggerated safety behavior, where the model rejects safe prompts that closely resemble unsafe ones.

@inproceedings{bib_TAMA_2025, AUTHOR = {Kishorkumar, Kavathekar Ishan and Ashok, Jain Hemang and Rathod, Ameya Sandesh and Kumaraguru, Ponnurangam and Ganu, Tanuja }, TITLE = {TAMAS: A Dataset for Investigating Security Risks in Multi-Agent LLM Systems}, BOOKTITLE = {International Conference on Machine Learning}. YEAR = {2025}}

Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents through tool use, planning, and decision-making abilities, leading to their widespread adoption across diverse tasks. As task complexity grows, multi-agent LLM systems are increasingly used to collaboratively solve problems. However, safety and security of these multi-agent systems remains largely unexplored. Existing benchmarks and datasets predominantly focus on single-agent settings, failing to capture the unique vulnerabilities of multi-agent dynamics and co-ordination. To address this gap, we introduce textbf{T}hreats and textbf{A}ttacks in textbf{M}ulti-textbf{A}gent textbf{S}ystems (textbf{TAMAS}), a dataset designed to evaluate the robustness and security of multi-agent LLM systems. TAMAS includes five distinct scenarios comprising 250 adversarial instances across five attack types and 163 different normal and attack tools, along with 100 harmless tasks. We assess system performance across 5 backbone LLMs and 3 agent interaction configurations from Autogen framework, highlighting critical challenges and failure modes in current multi-agent deployments. Our findings show that multi-agent systems are highly vulnerable to adversarial attacks, with Impersonation reaching a 73% success rate and other attacks ranging from 27% to 67%, underscoring the need for stronger defenses.

@inproceedings{bib_Just_2025, AUTHOR = {Garg, Rahul and Ashok, Jain Hemang and Kumaraguru, Ponnurangam and Kursuncu, Ugur }, TITLE = {Just KIDDIN: Knowledge Infusion and Distillation for Detection of INdecent Memes}, BOOKTITLE = {Association for Computational Linguistics - Findings}. YEAR = {2025}}

Toxicity identification in online multimodal environments remains a challenging task due to the complexity of contextual connections across modalities (e.g., textual and visual). In this paper, we propose a novel framework that integrates Knowledge Distillation (KD) from Large Visual Language Models (LVLMs) and knowledge infusion to enhance the performance of toxicity detection in hateful memes. Our approach extracts sub-knowledge graphs from ConceptNet, a large-scale commonsense Knowledge Graph (KG) to be infused within a compact VLM framework. The relational context between toxic phrases in captions and memes, as well as visual concepts in memes enhance the model's reasoning capabilities. Experimental results from our study on two hate speech benchmark datasets demonstrate superior performance over the state-of-the-art baselines across AU-ROC, F1, and Recall with improvements of 1.1%, 7%, and 35%, respectively. Given the contextual complexity of the toxicity detection task, our approach showcases the significance of learning from both explicit (i.e. KG) as well as implicit (i.e. LVLMs) contextual cues incorporated through a hybrid neurosymbolic approach. This is crucial for real-world applications where accurate and scalable recognition of toxic content is critical for creating safer online environments.

@inproceedings{bib_From_2025, AUTHOR = {Prashant, Kodali and Goel, Anmol and Asapu, Likhith and Bonagiri, Vamshi Krishna and Govil, Anirudh and Choudhury, Monojit and Kumaraguru, Ponnurangam and Shrivastava, Manish }, TITLE = {From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences}, BOOKTITLE = {ACM Trasactions on Asian and Low Resource Language Information Processing}. YEAR = {2025}}

Current computational approaches for analysing or generating code-mixed sentences do not explicitly model ``naturalness'' or ``acceptability'' of code-mixed sentences, but rely on training corpora to reflect distribution of acceptable code-mixed sentences. Modelling human judgement for the acceptability of code-mixed text can help in distinguishing natural code-mixed text and enable quality-controlled generation of code-mixed text. To this end, we construct Cline - a dataset containing human acceptability judgements for English-Hindi~(en-hi) code-mixed text. Cline is the largest of its kind with 16,642 sentences, consisting of samples sourced from two sources: synthetically generated code-mixed text and samples collected from online social media. Our analysis establishes that popular code-mixing metrics such as CMI, Number of Switch Points, Burstines, which are used to filter/curate/compare code-mixed corpora have low correlation with human acceptability judgements, underlining the necessity of our dataset. Experiments using Cline demonstrate that simple Multilayer Perceptron (MLP) models when trained solely using code-mixing metrics as features are outperformed by fine-tuned pre-trained Multilingual Large Language Models (MLLMs). Specifically, among Encoder models XLM-Roberta and Bernice outperform IndicBERT across different configurations. Among Encoder-Decoder models, mBART performs better than mT5, however Encoder-Decoder models are not able to outperform Encoder-only models. Decoder-only models perform the best when compared to all other MLLMS, with Llama 3.2 - 3B models outperforming similarly sized Qwen, Phi models. Comparison with zero and fewshot capabilitites of ChatGPT show that MLLMs fine-tuned on larger data outperform ChatGPT, providing scope for improvement in code-mixed tasks. Zero-shot transfer from En-Hi to En-Te acceptability judgments are better than random baselines.

@inproceedings{bib_A_sh_2025, AUTHOR = {Kolipaka, Varshita and Sinha, Akshit and Mishra, Debangan and Kumar, Sumit and A, Arvindh and Goel, Shashwat and Kumaraguru, Ponnurangam }, TITLE = {A shot of Cognac to forget bad memories: Corrective Unlearning in GNNs}, BOOKTITLE = {International Conference on Machine Learning}. YEAR = {2025}}

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. Because graph data does not follow the independently and identically distributed i.i.d. assumption, adversarial manipulations or incorrect data can propagate to other data points through message passing, which deteriorates the model's performance. To allow model developers to remove the adverse effects of manipulated entities from a trained GNN, we study the recently formulated problem of Corrective Unlearning. We find that current graph unlearning methods fail to unlearn the effect of manipulations even when the whole manipulated set is known. We introduce a new graph unlearning method,Cognac, which can unlearn the effect of the manipulation set even when only % of it is identified. It recovers most of the performance of a strong oracle with fully corrected training data, even beating retraining from scratch without the deletion set, and is 8x more efficient while also scaling to large datasets. We hope our work assists GNN developers in mitigating harmful effects caused by issues in real-world data, post-training.

@inproceedings{bib_Can__2025, AUTHOR = {Sinha, Shiven and Goel, Shashwat and Kumaraguru, Ponnurangam and Geiping, Jonas and Bethge, Matthias and Prabhu, Ameya }, TITLE = {Can Language Models Falsify? Evaluating Algorithmic Reasoning with Counterexample Creation}, BOOKTITLE = {workshop on International Conference on Learning Representations}. YEAR = {2025}}

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.

@inproceedings{bib_Deep_2025, AUTHOR = {Gupta, Anuradha and Mehta, Shikha and Kumaraguru, Ponnurangam }, TITLE = {Deep learning and transfer learning to understand emotions: a PoliEMO dataset and multi-label classification in Indian elections.}, BOOKTITLE = {International Journal of Data Science and Analytics}. YEAR = {2025}}

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.

@inproceedings{bib_Fram_2025, AUTHOR = {Tejasvi, C S Ramakrishna and Modepalle, Rohan Chowdary V and Vardhan, Nemani Harsha and Kumaraguru, Ponnurangam and Rajadesingan, Ashwin }, TITLE = {Framing the Fray: Conflict Framing in Indian Election News Coverage}, BOOKTITLE = {ACM Web Science Conference}. YEAR = {2025}}

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.

@inproceedings{bib_Pers_2025, AUTHOR = {Tejasvi, C S Ramakrishna and Kumaraguru, Ponnurangam and Rajadesingan, Ashwin }, TITLE = {Personal Narratives Empower Politically Disinclined Individuals to Engage in Political Discussions}, BOOKTITLE = {ACM Web Science Conference}. YEAR = {2025}}

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.

@inproceedings{bib_COBI_2025, AUTHOR = {Govil, Priyanshul and Ashok, Jain Hemang and Bonagiri, Vamshi Krishna and Chadha, Aman and Kumaraguru, Ponnurangam and Gaur, Manas and Dey, Sanorita }, TITLE = {COBIAS: Assessing the Contextual Reliability of Bias Benchmarks for Language Models}, BOOKTITLE = {ACM Web Science Conference}. YEAR = {2025}}

Large Language Models (LLMs) often inherit biases from the web data they are trained on, which contains stereotypes and prejudices. Current methods for evaluating and mitigating these biases rely on bias-benchmark datasets. These benchmarks measure bias by observing an LLM’s behavior on biased statements. However, these statements lack contextual considerations of the situations they try to present. To address this, we introduce a contextual reliability framework, which evaluates model robustness to biased statements by considering the various contexts in which they may appear. We develop the Context-Oriented Bias Indicator and Assessment Score (COBIAS) to measure a biased statement’s reliability in detecting bias, based on the variance in model behavior across different contexts. To evaluate the metric, we augmented 2,291 stereotyped statements from two existing benchmark datasets by adding contextual information. We show that COBIAS aligns with human judgment on the contextual reliability of biased statements (Spearman’s 𝜌 = 0.65, 𝑝 = 3.4 ∗ 10−60) and can be used to create reliable benchmarks, which would assist bias mitigation works.

@inproceedings{bib_High_2025, AUTHOR = {Sinha, Akshit and Vennam, Sreeram Reddy and Sharma, Charu and Kumaraguru, Ponnurangam }, TITLE = {Higher Order Structures For Graph Explanations}, BOOKTITLE = {AAAI Conference on Artificial Intelligence}. YEAR = {2025}}

Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data, demonstrating remarkable performance across various tasks. Recognising their importance, there has been extensive research focused on explaining GNN predictions, aiming to enhance their interpretability and trustworthiness. However, GNNs and their explainers face a notable challenge: graphs are primarily designed to model pair-wise relationships between nodes, which can make it tough to capture higher-order, multi-node interactions. This characteristic can pose difficulties for existing explainers in fully representing multi-node relationships. To address this gap, we present Framework For Higher-Order Representations In Graph Explanations (FORGE), a framework that enables graph explainers to capture such interactions by incorporating higher-order structures, resulting in more accurate and faithful explanations. Extensive evaluation shows that on average real-world datasets from the GraphXAI benchmark and synthetic datasets across various graph explainers, FORGE improves average explanation accuracy by 1.9x and 2.25x, respectively. We perform ablation studies to confirm the importance of higher-order relations in improving explanations, while our scalability analysis demonstrates FORGE's efficacy on large graphs.

@inproceedings{bib_http_2024, AUTHOR = {Goel, Shashwat and Prabhu, Ameya and Torr, Philip and Kumaraguru, Ponnurangam and Sanyal, Amartya }, TITLE = {https://openreview.net/pdf/cf959d04ea5f33179a7208c85c5bce756b1bcf3f.pdf}, BOOKTITLE = {Transactions in Machine Learning Research}. YEAR = {2024}}

Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects including vulnerability to backdoored samples, systemic biases, and reduced accuracy on certain input domains. Realistically, all manipulated training samples cannot be identified, and only a small, representative subset of the affected data can be flagged. We formalize ``Corrective Machine Unlearning'' as the problem of mitigating the impact of data affected by unknown manipulations on a trained model, only having identified a subset of the corrupted data. We demonstrate that the problem of corrective unlearning has significantly different requirements from traditional privacy-oriented unlearning. We find most existing unlearning methods, including retraining-from-scratch without the deletion set, require most of the manipulated data to be identified for effective corrective unlearning. However, one approach, Selective Synaptic Dampening, achieves limited success, unlearning adverse effects with just a small portion of the manipulated samples in our setting, which shows encouraging signs for future progress. We hope our work spurs research towards developing better methods for corrective unlearning and offers practitioners a new strategy to handle data integrity challenges arising from web-scale training.

@inproceedings{bib_Effe_2024, AUTHOR = {Phutela, Nishtha and Gabrani, Goldie and Kumaraguru, Ponnurangam and Relan, Devanjali }, TITLE = {Effectiveness of Higuchi fractal dimension in differentiating subgroups of stressed and non-stressed individuals.}, BOOKTITLE = {Multimedia Tools and Applications}. YEAR = {2024}}

Stress has a significant mental health problem of the 21st century. The number of people suffering from stress is increasing rapidly. Thus, easy-to-use, inexpensive, and accurate biomarkers are needed to detect stress during its inception. Early detection of stress-related diseases allows people to access health-care services and leads to the development of new therapies. Thus, for the early detection of stress, a biomarker would be beneficial. We aim to find if there are significant differences between stressed and non-stressed groups of participants and which brain region gets impacted during stress. We conducted experiments to acquire EEG signals to identify the most significant brain region that gets affected due to stress. This research investigates if Higuchi Fractal Dimension (HFD) extracted from EEG signals can act as potential stress biomarkers. A gamified mobile application, known as Color Word and Memory Test (CWMT), was developed, inspired by the well-known Stroop Test, to elicit mental stress at two different difficulty levels. A MUSE headband with four EEG sensors (TP9, AF7, AF8, TP10) was used to collect the EEG signals at the two difficulty levels. The Higuchi Fractal Dimension (HFD) was extracted from EEG signals acquired from 32 participants while they were exposed to stress stimuli (using the proposed CWMT application) and we then performed three experimental analyses, i.e., Analysis I, II and III. Analysis I was performed on all the 32 participants performing low and high difficulty tasks on CWMT application. Analysis II was performed on sub-groups of participants. These sub-groups were made based on score obtained by participants while using CWMT application. Analysis II aimed to find the most significantly impacted brain region during stress. For Analysis III, we used the same sub-groups as Analysis II and it aimed to identify any differences between the left and right hemispheres during stress. We conducted the statistical analysis, and p-values were calculated between the two groups (non-stressed and stressed) to detect EEG channels and brain frequency significantly associated with stress. We performed three experimental analyses (Analysis I and II are intra-hemisphere, and analysis III is inter-hemisphere). In Analysis I, we inferred that beta and alpha frequencies from the AF8 region of the brain are affected during stress. In Analysis II, we inferred that beta waves from the AF8 region are a characteristic indicator of stress. In Analysis III, we identified significant differences between the left and right parts of the brain during stress. We found a significant difference (p<0.05) between HFD value in stress and non-stressed groups in the AF8 region. Our results indicated that stressed patients have a significantly higher value of HFD in the frontal areas.

@inproceedings{bib_Effi_2024, AUTHOR = {Goyal, Nidhi and Goel, Anmol and Garg, Tanuj and Sachdeva, Niharika and Kumaraguru, Ponnurangam }, TITLE = {Efficient Knowledge Graph Embeddings via Kernelized Random Projections}, BOOKTITLE = {Big Data Analytics in Astronomy, Science, and Engineering}. YEAR = {2024}}

Knowledge Graph Completion (KGC) aims to predict missing entities or relations in knowledge graph but it becomes computationally expensive as KG scales. Existing research focuses on bilinear pooling-based factorization methods (LowFER, TuckER) to solve this problem. These approaches introduce too many trainable parameters which obstruct the deployment of these techniques in many real-world scenarios. In this paper, we introduce a novel parameter-efficient framework, KGRP which a) approximates bilinear pooling using Kernelized Random Projection matrix b) employs CNN for the better fusion of entities and relations to infer missing links. Our experimental results show that KGRP has 73% fewer parameters as compared to the state-of-theart approaches (LowFER, TuckER) for the knowledge graph completion task while retaining 88% performance for the best baseline. Furthermore, we also provide novel insights on the interpretability of relation embeddings. We also test the effectiveness of KGRP on a large-scale recruitment knowledge graph of 0.25 M entities.

@inproceedings{bib_Sani_2024, AUTHOR = {Kolipaka, Varshita and Sinha, Akshit and Mishra, Debangan and Kumar, Sumit and A, Arvindh and Goel, Shashwat and Kumaraguru, Ponnurangam }, TITLE = {Sanity Checks for Evaluating Graph Unlearning}, BOOKTITLE = {Conference on Lifelong Learning Agents. PMLR}. YEAR = {2024}}

Graph neural networks (GNNs) are increasingly being used on sensitive graph-structured data, necessitating techniques for handling unlearning requests on the trained models, particularly node unlearning. However, unlearning nodes on GNNs is challenging due to the interdependence between the nodes in a graph. We compare MEGU, a state-of-the-art graph unlearning method, and SCRUB, a general unlearning method for classification, to investigate the efficacy of graph unlearning methods over traditional unlearning methods. Surprisingly, we find that SCRUB performs comparably or better than MEGU on random node removal and on removing an adversarial node injection attack. Our results suggest that 1) graph unlearning studies should incorporate general unlearning methods like SCRUB as baselines, and 2) there is a need for more rigorous behavioral evaluations that reveal the differential advantages of proposed graph unlearning methods. Our work, therefore, motivates future research into more comprehensive evaluations for assessing the true utility of graph unlearning algorithms.

@inproceedings{bib_Towa_2024, AUTHOR = {Balappanawar, Ishwar B and Chamoli, Ashmit and Wickramarachchi, Ruwan and Mishra, Aditya and Kumaraguru, Ponnurangam and Sheth, Amit }, TITLE = {Towards Infusing Auxiliary Knowledge for Distracted Driver Detection}, BOOKTITLE = {KNOWLEDGE DISCOVERY AND DATA MINING WORKSHOPS}. YEAR = {2024}}

Distracted driving is a leading cause of road accidents globally. Identifying distracted driving involves reliably detecting and classifying various forms of driver distraction (e.g., texting, eating, or using in-car devices) from in-vehicle camera feeds to enhance road safety. This task is challenging due to the need for robust models that can generalize to a diverse set of driver behaviors without requiring extensive annotated datasets. In this paper, we propose KiD3, a novel method for distracted driver detection (DDD) that infuses auxiliary knowledge about semantic relations between entities in a scene and the structural configuration of the driver’s pose. Specifically, we construct a unified framework that integrates scene graphs and driver pose information with visual cues from video frames to create a holistic representation of the driver’s actions. Our results indicate that KiD3 achieves a 13.64% accuracy improvement over the vision-only baseline by incorporating auxiliary knowledge with visual information.

@inproceedings{bib_InSa_2024, AUTHOR = {Tripathi, Yogesh and Kumaraguru, Ponnurangam and Donakanti, Raghav and Girhepuje, Sahil and Kishorkumar, Kavathekar Ishan and Hanuma, Vedula Bhaskara and Krishnan, Gokul S and Goyal, Shreya and Goel, Anmol and Ravindran, Balaraman }, TITLE = {InSaAF: Incorporating Safety Through Accuracy and Fairness - Are LLMs Ready for the Indian Legal Domain?}, BOOKTITLE = {International Conference on Legal Knowledge and Information Systems}. YEAR = {2024}}

Large Language Models (LLMs) have emerged as powerful tools to per- form various tasks in the legal domain, ranging from generating summaries to pre- dicting judgments. Despite their immense potential, these models have been proven to learn and exhibit societal biases and make unfair predictions. Hence, it is es- sential to evaluate these models prior to deployment. In this study, we explore the ability of LLMs to perform Binary Statutory Reasoning in the Indian legal land- scape across various societal disparities. We present a novel metric, β -weighted Le- gal Safety Score (LSSβ ), to evaluate the legal usability of the LLMs. Additionally, we propose a ﬁnetuning pipeline, utilising specialised legal datasets, as a potential method to reduce bias. Our proposed pipeline effectively reduces bias in the model, as indicated by improved LSSβ . This highlights the potential of our approach to enhance fairness in LLMs, making them more reliable for legal tasks in socially diverse contexts.

@inproceedings{bib_Put__2024, AUTHOR = {Jangra, Hitkul and Shah, Rajiv Ratn and Kumaraguru, Ponnurangam }, TITLE = {Put Your Money Where Your Mouth Is: Dataset and Analysis of Real World Habit Building Attempts}, BOOKTITLE = {International Conference on Web and Social Media}. YEAR = {2024}}

The pursuit of habit building is challenging, and most people struggle with it. Research on successful habit formation is mainly based on small human trials focusing on the same habit for all the participants as conducting long-term heterogonous habit studies can be logistically expensive. With the advent of self-help, there has been an increase in online communities and applications that are centered around habit building and logging. Habit building applications can provide large-scale data on real-world habit building attempts and unveil the commonalities among successful ones. We collect public data on stickk.com, which allows users to track progress on habit building attempts called commitments. A commitment can have an external referee, regular check-ins about the progress, and a monetary stake in case of failure. Our data consists of 742,923 users and 397,456 commitments. In addition to the dataset, rooted in theories like Fresh Start Effect, Accountablity, and Loss Aversion, we ask questions about how commitment properties like start date, external accountability, monitory stake, and pursuing multiple habits together affects the odds of success. We found that people tend to start habits on temporal landmarks, but that does not affect the probability of their success. Practices like accountability and stakes are not often used but are strong determents of success. Commitments of 6 to 8 weeks in length, weekly reporting with an external referee, and a monetary amount at stake tend to be most successful. Finally, around 40% of all commitments are attempted simultaneously with other goals. Simultaneous attempts of pursuing commitments may fail early, but if pursued through the initial phase, they are statistically more successful than building one habit at a time.

@inproceedings{bib_The__2024, AUTHOR = {Li, Nathaniel and Mukobi, Gabriel and Helm-Burger, Nathan and Lababidi, Rassin and Justen, Lennart and Liu, Andrew Bo and Kumaraguru, Ponnurangam and Pan, Alexander and Gopal, Anjali and Yue, Summer and Berrios, Daniel and Gatti, Alice and Li, Justin D. and Dombrowski, Ann-Kathrin and Goel, Shashwat }, TITLE = {The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning}, BOOKTITLE = {International Conference on Machine Learning}. YEAR = {2024}}

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai

@inproceedings{bib_Game_2024, AUTHOR = {Dhawan, Mudit and Sharma, Shakshi and Sharma, Rajesh and Santosh, Kadam Aditya and Kumaraguru, Ponnurangam }, TITLE = {Game-on: graph attention network based multimodal fusion for fake news detection}, BOOKTITLE = {Social Network Analysis and Mining}. YEAR = {2024}}

Fake news being spread on social media platforms has a disruptive and damaging impact on our lives. Multimedia content improves the visibility of posts more than text data but is also being used for creating fake news. Previous multimodal works have tried to address the problem of modeling heterogeneous modalities in identifying fake news. However, these works have the following limitations: (1) inefficient encoding of inter-modal relations by utilizing a simple concatenation operator on the modalities at a later stage in a model, which might result in information loss; (2) training very deep neural networks with a disproportionate number of parameters on small multimodal datasets result in higher chances of overfitting. To address these limitations, we propose GAME-ON, a Graph Neural Network based end-to-end trainable framework that allows granular interactions within and across different modalities to learn more robust data representations for multimodal fake news detection. We use two publicly available fake news datasets, Twitter and Weibo, for evaluations. GAME-ON outperforms on Twitter by an average of 11% and achieves state-of-the-art performance on Weibo while using 91% fewer parameters than the best comparable state-of-the-art baseline. For deployment in real-world applications, GAME-ON can be used as a lightweight model (less memory and latency requirements), which makes it more feasible than previous state-of-the-art models.

@inproceedings{bib_Coun_2024, AUTHOR = {Kishorkumar, Kavathekar Ishan and Rani, Anku and Chamoli, Ashmit and Kumaraguru, Ponnurangam and Sheth, Amit and Das, Amitava }, TITLE = {Counter Turing Test (CT^2): Investigating AI-Generated Text Detection for Hindi - Ranking LLMs based on Hindi AI Detectability Index (ADI_hi)}, BOOKTITLE = {Empirical Methods in Natural Language Processing-Findings}. YEAR = {2024}}

The widespread adoption of Large Language Models (LLMs) and awareness around multilingual LLMs have raised concerns regarding the potential risks and repercussions linked to the misapplication of AI-generated text, necessitating increased vigilance. While these models are primarily trained for English, their extensive training on vast datasets covering almost the entire web, equips them with capabilities to perform well in numerous other languages. AI-Generated Text Detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by the emergence of techniques to bypass detection. In this paper, we report our investigation on AGTD for an indic language Hindi. Our major contributions are in four folds: i) examined 26 LLMs to evaluate their proficiency in generating Hindi text, ii) introducing the AI-generated news article in Hindi (AG_hi) dataset, iii) evaluated the effectiveness of five recently proposed AGTD techniques: ConDA, J-Guard, RADAR, RAIDAR and Intrinsic Dimension Estimation for detecting AI-generated Hindi text, iv) proposed Hindi AI Detectability Index (ADI_hi) which shows a spectrum to understand the evolving landscape of eloquence of AI-generated text in Hindi. The code and dataset is available at https://github.com/ishank31/Counter_Turing_Test

@inproceedings{bib_Impr_2024, AUTHOR = {Balaji, Darur Lakshmipathi and Keerthi, Gouravarapu Shanmukha Sai and Goel, Shashwat and Kumaraguru, Ponnurangam }, TITLE = {Improving Bias Metrics in Vision-Language Models by Addressing Inherent Model Disabilities}, BOOKTITLE = {Neural Information Processing Systems Workshops}. YEAR = {2024}}

The integration of Vision-Language Models (VLMs) into various applications has highlighted the importance of evaluating these models for inherent biases, especially along gender and racial lines. Traditional bias assessment methods in VLMs typically rely on accuracy metrics, assessing disparities in performance across different demographic groups. These methods, however, often overlook the impact of the model’s disabilities, like lack spatial reasoning, which may skew the bias assessment. In this work, we propose an approach that systematically examines how current bias evaluation metrics account for the model’s limitations. We introduce two methods that circumvent these disabilities by integrating spatial guidance from textual and visual modalities. Our experiments aim to refine bias quantification by effectively mitigating the impact of spatial reasoning limitations, offering a more accurate assessment of biases in VLMs.

@inproceedings{bib_Expo_2024, AUTHOR = {Krishna, and Gujar, Shreyash Narendra and Chaudhari, Sachin and Kumaraguru, Ponnurangam }, TITLE = {Exposing Privacy Risks in Indoor Air Pollution Monitoring Systems}, BOOKTITLE = {International Conference on Environment Pollution and Prevention}. YEAR = {2024}}

Indoor air pollution monitoring has been a region of interest in recent times. Multiple Internet of Things (IoT) enabled devices are available for this purpose. With the growing number of sensors in our daily environment, huge amounts of data are being collected and pushed to the servers through the Internet. This study aims to reveal that seemingly trivial indoor air pollution data containing particulate matter, carbon dioxide, and temperature can reveal complex insights about an individual’s lifestyle. Data was collected over a period of four months in a real-world environment. The study demonstrates the inference of cooking activities by using machine learning and deep learning techniques. The study further demonstrates that different food items and culinary practices have different air pollution signatures, which can be identified and distinguished with great accuracy (>90%). In the practice of inferential analysis, it is not necessary to rely on data characterised by high frequency or granularity. Less detailed data like hourly averages, can be used to make meaningful conclusions that might intrude on an individual’s privacy. With the rapid advancement in machine learning and deep learning, a proactive approach to privacy is needed to ensure that the collected data and its usage do not intentionally or unintentionally breach individual privacy.

@inproceedings{bib_Emer_2024, AUTHOR = {Vennam, Sreeram Reddy and Singh, Shashwat and Govil, Anirudh and Kumaraguru, Ponnurangam }, TITLE = {Emergence of Text Semantics in CLIP Image Encoders}, BOOKTITLE = {Neural Information Processing Systems Workshops}. YEAR = {2024}}

Certain self-supervised approaches to train image encoders, like CLIP, align images with their text captions. However, these approaches do not have an a priori incentive to learn to associate text inside the image with the semantics of the text. Humans process text visually; our work studies the semantics of text rendered in images. We show that the semantic information captured by image representations can decisively classify the sentiment of sentences and is robust against visual attributes like font and not based on simple character frequency associations.

@inproceedings{bib_LLM__2024, AUTHOR = {Vennam, Sreeram Reddy and Joishy, Anish R and Kumaraguru, Ponnurangam }, TITLE = {LLM Vocabulary Compression for Low-Compute Environments}, BOOKTITLE = {Neural Information Processing Systems Workshops}. YEAR = {2024}}

We present a method to compress the final linear layer of language models, reducing memory usage by up to 3.4 x without significant performance loss. By grouping tokens based on Byte Pair Encoding (BPE) merges, we prevent materialisation of the memory-intensive logits tensor. Evaluations on the TinyStories dataset show that our method performs on par with GPT-Neo and GPT2 while significantly improving throughput by up to 3x, making it suitable for low-compute environments.

@inproceedings{bib_Rand_2024, AUTHOR = {Prabhu, Ameya and Sinha, Shiven and Kumaraguru, Ponnurangam and Torr, Philip H.S. and Sener, Ozan and Dokania, Puneet K. }, TITLE = {Random Representations Outperform Online Continually Learned Representations}, BOOKTITLE = {Neural Information Processing Systems}. YEAR = {2024}}

Continual learning has primarily focused on the issue of catastrophic forgetting and the associated stability-plasticity tradeoffs. However, little attention has been paid to the efficacy of continually learned representations, as representations are learned alongside classifiers throughout the learning process. Our primary contribution is empirically demonstrating that existing online continually trained deep networks produce inferior representations compared to a simple pre-defined random transforms. Our approach embeds raw pixels using a fixed random transform, approximating an RBF-Kernel initialized before any data is seen. We then train a simple linear classifier on top without storing any exemplars, processing one sample at a time in an online continual learning setting. This method, called RanDumb, significantly outperforms state-of-the-art continually learned representations across all standard online continual learning benchmarks. Our study reveals the significant limitations of representation learning, particularly in low-exemplar and online continual learning scenarios. Extending our investigation to popular exemplar-free scenarios with pretrained models, we find that training only a linear classifier on top of pretrained representations surpasses most continual fine-tuning and prompt-tuning strategies. Overall, our investigation challenges the prevailing assumptions about effective representation learning in online continual learning.

@inproceedings{bib_Tele_2024, AUTHOR = {Agarwal, Anmol and Priyadarshi, Pratyush and Sinha, Shiven and Gupta, Shrey and Jangra, Hitkul and Kumaraguru, Ponnurangam and Garimella, Kiran }, TITLE = {Television Discourse Decoded: Comprehensive Multimodal Analytics at Scale}, BOOKTITLE = {ACM International Conference on Knowledge Discovery and Data Mining}. YEAR = {2024}}

In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimedia essence of these debates [27]. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and speech-to-text techniques for large-scale multimedia analysis. Utilizing state-of-the-art computer vision algorithms and speech-to-text methods, we transcribe, diarize, and analyze thousands of YouTube videos of prime-time television debates in India. These debates are a central part of Indian media but have been criticized for compromised journalistic integrity and excessive dramatization [18]. Our toolkit provides concrete metrics to assess bias and incivility, capturing a comprehensive multimedia perspective that includes text, audio utterances, and video frames. Our findings reveal significant biases in topic selection and panelist representation, along with alarming levels of incivility. This work offers a scalable, automated approach for future research in multimedia analysis, with profound implications for the quality of public discourse and democratic debate. We will make our data analysis pipeline and collected data publicly available to catalyze further research in this domain.

@inproceedings{bib_Repr_2024, AUTHOR = {Singh, Shashwat and Ravfogel, Shauli and Cotterell, Ryan and Herzig, onathan and Aharoni, Roee and Kumaraguru, Ponnurangam }, TITLE = {Representation Surgery: Theory and Practice of Affine Steering}, BOOKTITLE = {International Conference on Machine Learning}. YEAR = {2024}}

Language models often exhibit undesirable behavior, e.g., generating toxic or gender-biased text. In the case of neural language models, an encoding of the undesirable behavior is often present in the model's representations. Thus, one natural (and common) approach to prevent the model from exhibiting undesirable behavior is to steer the model's representations in a manner that reduces the probability of it generating undesirable text. This paper investigates the formal and empirical properties of steering functions, i.e., transformation of the neural language model's representations that alter its behavior. First, we derive two optimal, in the least-squares sense, affine steering functions under different constraints. Our theory provides justification for existing approaches and offers a novel, improved steering approach. Second, we offer a series of experiments that demonstrate the empirical effectiveness of the methods in mitigating bias and reducing toxic generation.

@inproceedings{bib_Tigh_2024, AUTHOR = {Jaglan, Kshitijaa and Pindiprolu, Meher Chaitanya and Sharma, Triansh and Singam, Abhijeeth Reddy and Goyal, Nidhi and Kumaraguru, Ponnurangam and Brandes, Ulrik }, TITLE = {Tight Sampling in Unbounded Networks}, BOOKTITLE = {International Conference on Web and Social Media}. YEAR = {2024}}

The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to prioritize the inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired

@inproceedings{bib_Corr_2024, AUTHOR = {Goel, Shashwat and Prabhu, Ameya and Torr, Philip and Kumaraguru, Ponnurangam and Sanyal, Amartya }, TITLE = {Corrective Machine Unlearning}, BOOKTITLE = {Transactions on Machine Learning Research}. YEAR = {2024}}

@inproceedings{bib_SaGE_2024, AUTHOR = {Bonagiri, Vamshi Krishna and Vennam, Sreeram Reddy and Govil, Priyanshul and Kumaraguru, Ponnurangam and Gaur, Manas }, TITLE = {SaGE: Evaluating Moral Consistency in Large Language Models}, BOOKTITLE = {International Conference on Computational Linguistics}. YEAR = {2024}}

Despite recent advancements showcasing the impressive capabilities of Large Language Models (LLMs) in conversational systems, we show that even state-of-the-art LLMs are morally inconsistent in their generations, questioning their reliability (and trustworthiness in general). Prior works in LLM evaluation focus on developing ground-truth data to measure accuracy on specific tasks. However, for moral scenarios that often lack universally agreed-upon answers, consistency in model responses becomes crucial for their reliability. To address this issue, we propose an information-theoretic measure called Semantic Graph Entropy (SaGE), grounded in the concept of "Rules of Thumb" (RoTs) to measure a model's moral consistency. RoTs are abstract principles learned by a model and can help explain their decision-making strategies effectively. To this extent, we construct the Moral Consistency Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and the RoTs that these models followed. Furthermore, to illustrate the generalizability of SaGE, we use it to investigate LLM consistency on two popular datasets -- TruthfulQA and HellaSwag. Our results reveal that task-accuracy and consistency are independent problems, and there is a dire need to investigate these issues further.

@inproceedings{bib_Meas_2024, AUTHOR = {Bonagiri, Vamshi Krishna and Vennam, Sreeram Reddy and Gaur, Manas and Kumaraguru, Ponnurangam }, TITLE = {Measuring Moral Inconsistencies in Large Language Models}, BOOKTITLE = {EMNLP Workshop}. YEAR = {2024}}

A Large Language Model (LLM) is considered consistent if semantically equivalent prompts produce semantically equivalent responses. Despite recent advancements showcasing the impressive capabilities of LLMs in conversational systems, we show that even state-of-the-art LLMs are highly inconsistent in their generations, questioning their reliability. Prior research has tried to measure this with task-specific accuracy. However, this approach is unsuitable for moral scenarios, such as the trolley problem, with no "correct" answer. To address this issue, we propose a novel information-theoretic measure called Semantic Graph Entropy (SGE) to measure the consistency of an LLM in moral scenarios. We leverage "Rules of Thumb" (RoTs) to explain a model's decision-making strategies and further enhance our metric. Compared to existing consistency metrics, SGE correlates better with human judgments across five LLMs. In the future, we aim to investigate the root causes of LLM inconsistencies and propose improvements.

@inproceedings{bib_CAFI_2023, AUTHOR = {A, Arvindh and Aanegola, Aakash and Agrawal, Amul and Narayanam, Ramasuri and Kumaraguru, Ponnurangam }, TITLE = {CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs}, BOOKTITLE = {European Conference on Artificial Intelligence}. YEAR = {2023}}

Unsupervised Representation Learning on graphs is gaining traction due to the increasing abundance of unlabelled network data and the compactness, richness, and usefulness of the representations generated. In this context, the need to consider fairness and bias constraints while generating the representations has been well-motivated and studied to some extent in prior works. One major limitation of most of the prior works in this setting is that they do not aim to address the bias generated due to connectivity patterns in the graphs, such as varied node centrality, which leads to a disproportionate performance across nodes. In our work, we aim to address this issue of mitigating bias due to inherent graph structure in an unsupervised setting. To this end, we propose CAFIN, a centrality-aware fairness-inducing framework that leverages the structural information of graphs to tune the representations generated by existing frameworks. We deploy it on GraphSAGE (a popular framework in this domain) and showcase its efficacy on two downstream tasks – Node Classification and Link Prediction. Empirically, CAFIN consistently reduces the performance disparity across popular datasets (varying from 18 to 80% reduction in performance disparity) from various domains while incurring only a minimal cost of fairness.

@inproceedings{bib_Expl_2023, AUTHOR = {Mishra, Ritwik and Yadav, Ajeet and Shah, Rajiv Ratn and Kumaraguru, Ponnurangam }, TITLE = {Explaining Finetuned Transformers on Hate Speech Predictions Using Layerwise Relevance Propagation}, BOOKTITLE = {International Conference on Big Data Analytics}. YEAR = {2023}}

@inproceedings{bib_Towa_2023, AUTHOR = {Goel, Shashwat and Prabhu, Ameya and Sanyal, Amartya and Lim, Ser-Nam and Torr, Phillip and Kumaraguru, Ponnurangam }, TITLE = {Towards Adversarial Evaluations of Inexact Machine Unlearning}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias. Machine Unlearning can address these by allowing post-hoc deletion of affected training data from a learned model. Achieving this task exactly is computationally expensive; consequently, recent works have proposed inexact unlearning algorithms to solve this approximately as well as evaluation methods to test the effectiveness of these algorithms. In this work, we first outline some necessary criteria for evaluation methods and show no existing evaluation satisfies them all. Then, we design a stronger black-box evaluation method called the Interclass Confusion (IC) test which adversarially manipulates data during training to detect the insufficiency of unlearning procedures. We also propose two analytically motivated baseline methods~(EU-k and CF-k) which outperform several popular inexact unlearning methods. Overall, we demonstrate how adversarial evaluation strategies can help in analyzing various unlearning phenomena which can guide the development of stronger unlearning algorithms.

@inproceedings{bib_Prob_2023, AUTHOR = {Singh, Shashwat and Goel, Shashwat and Vaduguru, Saujas Srinivasa and Kumaraguru, Ponnurangam }, TITLE = {Probing Negation in Language Models}, BOOKTITLE = {Conference of the Association for Computational Linguistics Workshops}. YEAR = {2023}}

Prior work has shown that pretrained language models often make incorrect predictions for negated inputs. The reason for this behaviour has remained unclear. It has been argued that since language models (LMs) don’t change their predictions about factual propositions under negation, they might not detect negation. We show encoder LMs do detect negation as their representations across layers reliably distinguish negated inputs from non-negated inputs, and when negation leads to contradictions. However, probing experiments show that these LMs indeed don’t use negation when evaluating whether a factual statement is true, even when fine-tuned with the objective of changing outputs on negated sentences (Hosseini et al., 2021). We hypothesize about why pretrained LMs are inconsistent under negation: when the statement could refer to multiple ground entities with conflicting properties, negation may not entail a change in output. This means negation minimal pairs in different training samples can have the same completion in pretraining corpora. We argue pretraining may not provide enough signal to learn the distribution of ground referents a token could have, confusing the LM on how to handle negation.

@inproceedings{bib_Blin_2023, AUTHOR = {Chaudhary, Tanishq and Malhotra, Pulak and Mamidi, Radhika and Kumaraguru, Ponnurangam }, TITLE = {Blind Leading the Blind: A Social-Media Analysis of the Tech Industry}, BOOKTITLE = {International Conference on Natural Language Processing.}. YEAR = {2023}}

@inproceedings{bib_Repr_2023, AUTHOR = {Govil, Priyanshul and Bonagiri, Vamshi Krishna and Garg, Muskan and Kumaraguru, Ponnurangam }, TITLE = {Representation Learning for Identifying Depression Causes in Social Media}, BOOKTITLE = {KNOWLEDGE DISCOVERY AND DATA MINING WORKSHOPS}. YEAR = {2023}}

Social media provides a supportive and anonymous environment for discussing mental health issues, including depression. Existing research on identifying the cause of depression focuses primarily on improving classifier models, while neglecting the importance of learning better data representations. To address this gap, we introduce an architecture that enhances the identification of the cause of depression by learning improved data representations. Our work enables a deeper interpretation of the cause of depression in social media contexts, emphasizing the significance of effective rep- resentation learning for this task. Our work can act as a foundation for self-help applications in the field of mental health.

@inproceedings{bib_Tigh_2023, AUTHOR = {Jaglan, Kshitijaa and Chaitanya, Meher and Sharma, Triansh and Singam, Abhijeeth Reddy and Goyal, Nidhi and Kumaraguru, Ponnurangam and Brandes, Ulrik }, TITLE = {Tight Sampling in Unbounded Networks}, BOOKTITLE = {Computing Research Repository}. YEAR = {2023}}

The default approach to deal with the enormous size and lim- ited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnet- works will crucially depend on the sampling scheme. Mo- tivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to priori- tize inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.

@inproceedings{bib_Expl_2023, AUTHOR = {Khatri, Mann and Yusuf, Mirza and Shah, Rajiv Ratn and Kumaraguru, Ponnurangam }, TITLE = {Exploring Graph Neural Networks for Indian Legal Judgment Prediction}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial process, the proposition of an automated system capable of suggesting case outcomes based on factual evidence and precedent from past cases gains significance. This research paper centres on developing a graph neural network-based model to address the Legal Judgment Prediction (LJP) problem, recognizing the intrinsic graph structure of judicial cases and making it a binary node classification problem. We explored various embeddings as model features, while nodes such as time nodes and judicial acts were added and pruned to evaluate the model's performance. The study is done while considering the ethical dimension of fairness in these predictions, considering gender and name biases. A link prediction task is also conducted to assess the model's proficiency in anticipating connections between two specified nodes. By harnessing the capabilities of graph neural networks and incorporating fairness analyses, this research aims to contribute insights towards streamlining the adjudication process, enhancing judicial efficiency, and fostering a more equitable legal landscape, ultimately alleviating the strain imposed by mounting case backlogs. Our best-performing model with XLNet pre-trained embeddings as its features gives the macro F1 score of 75% for the LJP task. For link prediction, the same set of features is the best performing giving ROC of more than 80%

@inproceedings{bib_Towa_2023, AUTHOR = {Agarwal, Anmol and Gupta, Shrey and Bonagiri, Vamshi Krishna and Gaur, Manas and Reagle, Joseph and Kumaraguru, Ponnurangam }, TITLE = {Towards Effective Paraphrasing for Information Disguise}, BOOKTITLE = {European Conference on Information Retrieval}. YEAR = {2023}}

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors’ posts on the Internet. Research on ID becomes important when authors’ written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or

@inproceedings{bib_Are__2023, AUTHOR = {Girhepuje, Sahil and Goel, Anmol and Krishnan, Gokul S and Goyal, Shreya and Pandey, Satyendra and Kumaraguru, Ponnurangam and Ravindran, Balaraman }, TITLE = {Are Models Trained on Indian Legal Data Fair?}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

Recent advances and applications of language technology and artificial intelligence have enabled much success across multiple domains like law, medical and mental health. AI-based Language Models, like Judgement Prediction, have recently been proposed for the legal sector. However, these models are strife with encoded social biases picked up from the training data. While bias and fairness have been studied across NLP, most studies primarily locate themselves within a Western context. In this work, we present an initial investigation of fairness from the Indian perspective in the legal domain. We highlight the propagation of learnt algorithmic biases in the bail prediction task for models trained on Hindi legal documents. We evaluate the fairness gap using demographic parity and show that a decision tree model trained for the bail prediction task has an overall fairness disparity of 0.237 between input features associated with Hindus and Muslims. Additionally, we highlight the need for further research and studies in the avenues of fairness/bias in applying AI in the legal sector with a specific focus on the Indian context.

@inproceedings{bib_Towa_2023, AUTHOR = {Agarwal, Anmol and Gupta, Shrey and Bonagiri, Vamshi Krishna and Gaur, Manas and Reagle, Joseph and Kumaraguru, Ponnurangam }, TITLE = {Towards Effective Paraphrasing for Information Disguise}, BOOKTITLE = {European Conference on Information Retrieval}. YEAR = {2023}}

Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors’ posts on the Internet. Research on ID becomes important when authors’ written online communication pertains to sensitive domains, e.g., mental health. Over time, researchers have utilized AI-based automated word spinners (e.g., SpinRewriter, WordAI) for paraphrasing content. However, these tools fail to satisfy the purpose of ID as their paraphrased content still leads to the source when queried on search engines. There is limited prior work on judging the effectiveness of paraphrasing methods for ID on search engines or their proxies, neural retriever (NeurIR) models. We propose a framework where, for a given sentence from an author’s post, we perform iterative perturbation on the sentence in the direction of paraphrasing with an attempt to confuse the search mechanism of a NeurIR system when the sentence is queried on it. Our experiments involve the subreddit “r/AmItheAsshole” as the source of public content and Dense Passage Retriever as a NeurIR system-based proxy for search engines. Our work introduces a novel method of phrase-importance rankings using perplexity scores and involves multi-level phrase substitutions via beam search. Our multi-phrase substitution scheme succeeds in disguising sentences 82% of the time and hence takes an essential step towards enabling researchers to disguise sensitive content effectively before making it public. We also release the code of our approach

@inproceedings{bib_Soci_2023, AUTHOR = {Jangra, Hitkul and Abinaya.K, and SAHA, SOHAM and Banerjee, Satyajit and Chelliah, Muthusamy and Kumaraguru, Ponnurangam }, TITLE = {Social Re-Identification Assisted RTO Detection for E-Commerce}, BOOKTITLE = {Companion Proceedings of the ACM Web Conference}. YEAR = {2023}}

E-commerce features like easy cancellations, returns, and refunds can be exploited by bad actors or uninformed customers, leading to revenue loss for organization. One such problem faced by e-commerce platforms is Return To Origin (RTO), where the user cancels an order while it is in transit for delivery. In such a scenario platform faces logistics and opportunity costs. Traditionally, models trained on historical trends are used to predict the propensity of an order becoming RTO. Sociology literature has highlighted clear correlations between socio-economic indicators and users’ tendency to exploit systems to gain financial advantage. Social media profiles have information about location, education, and profession which have been shown to be an estimator of socio-economic condition. We believe combining social media data with e-commerce information can lead to improvements in a variety of tasks like RTO …

@inproceedings{bib_Cite_2023, AUTHOR = {Khatri, Mann and Khatri, Mann and Satija, Gitansh and Sheik, Reshma and Kumar, Yaman and Shah, Rajiv Ratn and Kumaraguru, Ponnurangam }, TITLE = {CiteCaseLAW: Citation Worthiness Detection in Caselaw for Legal Assistive Writing}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

In legal document writing, one of the key elements is properly citing the case laws and other sources to substantiate claims and arguments. Understanding the legal domain and identifying appropriate citation context or cite-worthy sentences are challenging tasks that demand expensive manual annotation. The presence of jargon, language semantics, and high domain specificity makes legal language complex, making any associated legal task hard for automation. The current work focuses on the problem of citation-worthiness identification. It is designed as the initial step in today's citation recommendation systems to lighten the burden of extracting an adequate set of citation contexts. To accomplish this, we introduce a labeled dataset of 178M sentences for citation-worthiness detection in the legal domain from the Caselaw Access Project (CAP). The performance of various deep learning models was examined on this novel dataset. The domain-specific pre-trained model tends to outperform other models, with an 88% F1-score for the citation-worthiness detection task.

@inproceedings{bib_Effe_2023, AUTHOR = {Jangra, Hitkul and Shah, Rajiv and Kumaraguru, Ponnurangam }, TITLE = {Effect of Feedback on Drug Consumption Disclosures on Social Media}, BOOKTITLE = {International Conference on Web and Social Media}. YEAR = {2023}}

Deaths due to drug overdose in the US have doubled in the last decade. Drug-related content on social media has also exploded in the same time frame. The pseudo-anonymous nature of social media platforms enables users to discourse about taboo and sometimes illegal topics like drug consumption. User-generated content (UGC) about drugs on social media can be used as an online proxy to detect offline drug consumption. UGC also gets exposed to the praise and criticism of the community. Law of effect proposes that positive reinforcement on an experience can incentivize the users to engage in the experience repeatedly. Therefore, we hypothesize that positive community feedback on a user's online drug consumption disclosure will increase the probability of the user doing an online drug consumption disclosure post again. To this end, we collect data from 10 drug-related subreddits. First, we build a deep learning model to classify UGC as indicative of drug consumption offline or not, and analyze the extent of such activities. Further, we use matching-based causal inference techniques to unravel community feedback's effect on users' future drug consumption behavior. We discover that 84% of posts and 55% comments on drug-related subreddits indicate real-life drug consumption. Users who get positive feedback generate up to two times more drugs consumption content in the future. Finally, we conducted an anonymous user study on drug-related subreddits to compare members' opinions with our experimental findings and show that user tends to underestimate the effect community peers can have on their decision to interact with drugs

@inproceedings{bib_X-Ri_2023, AUTHOR = {Moradshahi, Mehrad and Shen, Tianhao and Bali, Kalika and Choudhury, Monojit and Chalendar, Gaël De and Goel, Anmol and Prashant, Kodali and Kumaraguru, Ponnurangam and Shrivastava, Manish }, TITLE = {X-RiSAWOZ: High-Quality End-to-End Multilingual Dialogue Datasets and Few-shot Agents}, BOOKTITLE = {Technical Report}. YEAR = {2023}}

Task-oriented dialogue research has mainly focused on a few popular languages like English and Chinese, due to the high dataset creation cost for a new language. To reduce the cost, we apply manual editing to automatically translated data. We create a new multilingual benchmark, X-RiSAWOZ, by translating the Chinese RiSAWOZ to 4 languages: English, French, Hindi, Korean; and a code-mixed EnglishHindi language. X-RiSAWOZ has more than 18,000 human-verified dialogue utterances for each language, and unlike most multilingual prior work, is an end-to-end dataset for building fully-functioning agents. The many difficulties we encountered in creating X-RiSAWOZ led us to develop a toolset to accelerate the post-editing of a new language dataset after translation. This toolset improves machine translation with a hybrid entity alignment technique that combines neural with

@inproceedings{bib_Prec_2023, AUTHOR = {Hanuma, Vedula Bhaskara and Prashant, Kodali and Shrivastava, Manish and Kumaraguru, Ponnurangam }, TITLE = {PrecogIIITH@WASSA2023: Emotion Detection for Urdu-English Code-mixed Text}, BOOKTITLE = {Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis}. YEAR = {2023}}

Code-mixing refers to the phenomenon of using two or more languages interchangeably within a speech or discourse context. This practice is particularly prevalent on social media platforms, and determining the embedded affects in a code-mixed sentence remains as a challenging problem. In this submission we describe our system for WASSA 2023 Shared Task on Emotion Detection in English-Urdu code-mixed text. In our system we implement a multiclass emotion detection model with label space of 11 emotions. Samples are code-mixed English-Urdu text, where Urdu is written in romanised form. Our submission is limited to one of the subtasks - Multi Class classification and we leverage transformer-based Multilingual Large Language Models (MLLMs), XLM-RoBERTa and Indic-BERT. We fine-tune MLLMs on the released data splits, with and without pre-processing steps (translation to english), for classifying texts into the appropriate emotion category. Our methods did not surpass the baseline, and our submission is ranked sixth overall.

@inproceedings{bib_“H_2023, AUTHOR = {Singh, Bhavyajeet and Vaswani, Kunal Mukesh and Sreeharsha, Paruchuri Venkata Surya and Saarikallio, Suvi and Kumaraguru, Ponnurangam and R, Vinoo A }, TITLE = {“Help! I need some music!”: Analysing music discourse & depression on Reddit}, BOOKTITLE = {Plos One}. YEAR = {2023}}

Individuals choose varying music listening strategies to fulfill particular mood-regulation goals. However, ineffective musical choices and a lack of cognizance of the effects thereof can be detrimental to their well-being and may lead to adverse outcomes like anxiety or depression. In our study, we use the social media platform Reddit to perform a large-scale analysis to unearth the several music-mediated mood-regulation goals that individuals opt for in the context of depression. A mixed-methods approach involving natural language processing techniques followed by qualitative analysis was performed on all music-related posts to identify the various music-listening strategies and group them into healthy and unhealthy associations. Analysis of the music content (acoustic features and lyrical themes) accompanying healthy and unhealthy associations showed significant differences. Individuals resorting to unhealthy strategies gravitate towards low-valence tracks. Moreover, lyrical themes associated with unhealthy strategies incorporated tracks with low optimism, high blame, and high self-reference. Our findings demonstrate that being mindful of the objectives of using music, the subsequent effects thereof, and aligning both for well-being outcomes is imperative for comprehensive understanding of the effectiveness of music

@inproceedings{bib_JobX_2023, AUTHOR = {Goyal, Nidhi and Kalra, Jushaan Singh and Sharma, Charu and Mutharaju, Raghava and Sachdeva, Niharika and Kumaraguru, Ponnurangam }, TITLE = {JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks}, BOOKTITLE = {Conference of the European Chapter of the Association for Computational Linguistics (EACL)}. YEAR = {2023}}

Writing a good job description is an important step in the online recruitment process to hire the best candidates. Most recruiters forget to include some relevant skills in the job description. These missing skills affect the performance of recruitment tasks such as job suggestions, job search, candidate recommendations, etc. Existing approaches are limited to contextual modelling, do not exploit inter-relational structures like job-job and job-skill relationships, and are not scalable. In this paper, we exploit these structural relationships using a graph-based approach. We propose a novel skill prediction framework called JobXMLC, which uses graph neural networks with skill attention to predict missing skills using job descriptions. JobXMLC enables joint learning over a job-skill graph consisting of 22.8K entities (jobs and skills) and 650K relationships. We experiment with real-world recruitment datasets to evaluate our proposed approach. We train JobXMLC on 20, 298 jobs and 2, 548 skills within 30 minutes on a single GPU machine. JobXMLC outperforms the state-of-the-art approaches by 6% on precision and 3% on recall. JobXMLC is 18X faster for training tasks and up to 634X faster in skill prediction on benchmark datasets enabling JobXMLC to scale up on larger datasets. We have made our code and dataset public at https://precog.iiit.ac.in/resources.html.

@inproceedings{bib_Warn_2023, AUTHOR = {Goyal, Nidhi and Sachdeva, Niharika and Mamidi, Radhika and Kumaraguru, Ponnurangam }, TITLE = {Warning: It’sa scam!! Towards understanding the Employment Scams using Knowledge Graphs}, BOOKTITLE = {Joint International Conference on Data Science & Management of Data}. YEAR = {2023}}

Employment scams, such as scapegoat positions, clickbait and non-existing jobs, etc., are among the top five scams registered over online platforms.1 Generally, scam complaints contain heterogeneous information (money, location, employment type, organization, email, and phone number), which can provide critical insights for appropriate interventions to avoid scams. Despite substantial efforts to analyze employment scams, integrating relevant scam-related information in structured form remains unexplored. In this work, we extract this information and construct a large-scale Employment Scam Knowledge Graph consisting of 0.1M entities and 0.2M relationships. Our findings include discovering different modes of employment scams, entities, and relationships among entities to alert job seekers. We plan to extend this work by utilizing a knowledge graph to identify and avoid potential scams in the future

@inproceedings{bib_A_Su_2023, AUTHOR = {Bhasin, Drishti and Kumaraguru, Ponnurangam and Shah, Rajiv Ratn }, TITLE = {A Suspect Identification Framework Using Contrastive Relevance Feedback}, BOOKTITLE = {Winter Conference on Applications of Computer Vision}. YEAR = {2023}}

Suspect Identification is one of the most pivotal aspects of a forensic and criminal investigation. A significant amount of time and skill is devoted to creating sketches for it and requires a fair amount of recollections from the witness to provide a useful sketch. We devise a method that aims to automate the process of suspect identification and model this problem by iteratively retrieving images from feedback provided by the user. Compared to standard image retrieval tasks, interactive facial image retrieval is specifically more challenging due to the high subjectivity involved in describing a person’s facial attributes and appropriately evolving with the preferences put forward by the user. Our method uses a relatively simpler form of supervision by utilizing the user’s feedback to label images as either similar or dissimilar to their mental image of the suspect based on which we propose a loss function using the contrastive learning paradigm that is optimized in an online fashion. We validate the efficacy of our proposed approach using a carefully designed testbed to simulate user feedback and a large-scale user study. We empirically show that our method iteratively improves personalization, leading to faster convergence and enhanced recommendation relevance, thereby, improving user satisfaction. Our proposed framework is being designed for real-time use in the metropolitan crime investigation department, and thus is also equipped with a user-friendly web interface with a real-time experience for suspect retrieval.

@inproceedings{bib_EEG__2022, AUTHOR = {Phutela, Nishtha and Relan, Devanjali and Gabrani, Goldie and Kumaraguru, Ponnurangam }, TITLE = {EEG Based Stress Classification in Response to Stress Stimulus}, BOOKTITLE = {International Conference on Artificial Intelligence and Speech Technology}. YEAR = {2022}}

Stress, either physical or mental, is experienced by almost every person at some point in his lifetime. Stress is one of the leading causes of various diseases and burdens society globally. Stress badly affects an individual's well-being. Thus, stress-related study is an emerging field, and in the past decade, a lot of attention has been given to the detection and classification of stress. The estimation of stress in the individual helps in stress management before it invades the human mind and body. In this paper, we proposed a system for the detection and classification of stress. We compared the various machine learning algorithms for stress classification using EEG signal recordings. Interaxon Muse device having four dry electrodes

@inproceedings{bib_Stre_2022, AUTHOR = {Phutela, Nishtha and Relan, Devanjali and Gabrani, Goldie and Kumaraguru, Ponnurangam and Samuel, Mesay }, TITLE = {Stress Classification Using Brain Signals Based on LSTM Network}, BOOKTITLE = {Computational Intelligence and Neuroscience}. YEAR = {2022}}

The early diagnosis of stress symptoms is essential for preventing various mental disorder such as depression. Electroencephalography (EEG) signals are frequently employed in stress detection research and are both inexpensive and noninvasive modality. This paper proposes a stress classification system by utilizing an EEG signal. EEG signals from thirty-five volunteers were analysed which were acquired using four EEG sensors using a commercially available 4-electrode Muse EEG headband. Four movie clips were chosen as stress elicitation material. Two clips were selected to induce stress as it contains emotionally inductive scenes. The other two clips were chosen that do not induce stress as it has many comedy scenes. The recorded signals were then used to build the stress classification model. We compared the Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) for classifying stress and nonstress group. The maximum classification accuracy of 93.17% was achieved using two-layer LSTM architecture.

@inproceedings{bib_Ask__2022, AUTHOR = {Arora, Udit and Goyal, Nidhi and Goel, Anmol and Sachdeva, Niharika and Kumaraguru, Ponnurangam }, TITLE = {Ask It Right! Identifying Low-Quality questions on Community Question Answering Services}, BOOKTITLE = {International Joint Conference on Neural Networks}. YEAR = {2022}}

—Stack Overflow is a Community Question Answering service that attracts millions of users to seek answers to their questions. Maintaining high-quality content is necessary for relevant question retrieval, question recommendation, and enhancing the user experience. Manually removing low-quality content from the platform is time-consuming and challenging for site moderators. Thus, it is imperative to assess the content quality by automatically detecting and ‘closing’ the low-quality questions. Previous works have explored lexical, communitybased, vote-based, and style-based features to detect low-quality questions. These approaches are limited to writing styles, textual, and handcrafted features. However, these features fall short in understanding semantic features and capturing the implicit relationships between tags and questions. In contrast, we propose LQuaD (Low-Quality Question Detection), a multi-tier hybrid framework that, a) incorporates semantic information of questions associated with each post using transformers, b) includes the question and tag information that enables learning via a graph convolutional network. LQuaD outperforms the state-of-the-art methods by a 21% higher F1-score on the dataset of 2.8 million questions. Furthermore, we apply survival analysis which acts as a proactive intervention to reduce the number of questions closed by informing users to take appropriate action. We find that the timeframe between the stages from the question’s creation till it gets ‘closed’ varies significantly for tags and different ‘closing’ reasons for these questions. Index Terms—Stack Overflow, Community Question Answering, Low-quality questions

@inproceedings{bib_“T_2022, AUTHOR = {Kamble, Tanvi and Desur, Pooja Govind and Krause, Amanda and Kumaraguru, Ponnurangam and R, Vinoo A }, TITLE = {“The Times They Are-a-Changin”: The Effect of the Covid-19 Pandemic on Online Music Sharing in India}, BOOKTITLE = {International Conference on Social Informatics}. YEAR = {2022}}

Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.

@inproceedings{bib_Leve_2022, AUTHOR = {Singhal, Shivangi and Pandey, Tanisha and Mrig, Saksham and Shah, Rajiv Ratn and Kumaraguru, Ponnurangam }, TITLE = {Leveraging Intra and Inter Modality Relationship for Multimodal Fake News Detection}, BOOKTITLE = {International Conference on World wide web}. YEAR = {2022}}

Recent years have witnessed a massive growth in the proliferation of fake news online. User-generated content is a blend of text and visual information leading to producing different variants of fake news. As a result, researchers started targeting multimodal methods for fake news detection. Existing methods capture high-level information from different modalities and jointly model them to decide. Given multiple input modalities, we hypothesize that not all modalities may be equally responsible for decision-making. Hence, this paper presents a novel architecture that effectively identifies and suppresses information from weaker modalities and extracts relevant information from the strong modality on a per-sample basis. We also establish intra-modality relationship by extracting fine-grained image and text features. We conduct extensive experiments on real-world datasets to show that our approach outperforms the state-of-the-art by an average of 3.05% and 4.525% on accuracy and F1-score, respectively. We also release the code, implementation details, and model checkpoints for the community’s interest.1

@inproceedings{bib_Twee_2022, AUTHOR = {Kapoor, Arnav and Guhathakurta, Dipanwita and Mathur, Mehul and Yadav, Rupanshu and Gupta, Manish and Kumaraguru, Ponnurangam }, TITLE = {TweetBoost: Influence of Social Media on NFT Valuation}, BOOKTITLE = {International Conference on World wide web}. YEAR = {2022}}

NFT or Non-Fungible Token is a token that certifies a digital asset to be unique. A wide range of assets including, digital art, music, tweets, memes, are being sold as NFTs. NFT-related content has been widely shared on social media sites such as Twitter. We aim to understand the dominant factors that influence NFT asset valuation. Towards this objective, we create a first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT marketplace) to capture social media profiles and linked NFT assets. Our dataset contains 245,159 tweets posted by 17,155 unique users, directly linking 62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset public. We analyze the growth of NFTs, characterize the Twitter users promoting NFT assets, and gauge the impact of Twitter features on the virality of an NFT. Further, we investigate the effectiveness of different social media and NFT platform features by experimenting with multiple machine learning and deep learning models to predict an asset's value. Our results show that social media features improve the accuracy by 6% over baseline models that use only NFT platform features. Among social media features, count of user membership lists, number of likes and retweets are important features.

@inproceedings{bib_Cont_2022, AUTHOR = {Gupta, Devansh and Bhasin, Drishti and Bhagat, Sarthak and Uppal, Shagun and Kumaraguru, Ponnurangam and Shah, Rajiv Ratn }, TITLE = {Contrastive Personalization Approach to Suspect Identification (Student Abstract)}, BOOKTITLE = {AAAI Conference on Artificial Intelligence}. YEAR = {2022}}

Targeted image retrieval has long been a challenging problem since each person has a different perception of different features leading to inconsistency among users in describing the details of a particular image. Due to this, each user needs a system personalized according to the way they have structured the image in their mind. One important application of this task is suspect identifcation in forensic investigations where a witness needs to identify the suspect from an existing criminal database. Existing methods require the attributes for each image or suffer from poor latency during training and inference. We propose a new approach to tackle this problem through explicit relevance feedback by introducing a novel loss function and a corresponding scoring function. For this, we leverage contrastive learning on the user feedback to generate the next set of suggested images while improving the level of personalization with each user feedback iteration.

@inproceedings{bib_Hash_2022, AUTHOR = {Prashant, Kodali and Bhatnagar, Akshala and Ahuja, Naman and Shrivastava, Manish and Kumaraguru, Ponnurangam }, TITLE = {HashSet - A Dataset For Hashtag Segmentation}, BOOKTITLE = {International Conference on Language Resources and Evaluation}. YEAR = {2022}}

Hashtag segmentation is the task of breaking a hashtag into its constituent tokens. Hashtags often encode the essence of usergenerated posts, along with information like topic and sentiment, which are useful in downstream tasks. Hashtags prioritize brevity and are written in unique ways - transliterating and mixing languages, spelling variations, creative named entities. Benchmark datasets used for the hashtag segmentation task - STAN, BOUN - are small in size and extracted from a single set of tweets. However, datasets should reflect the variations in writing styles of hashtags and also account for domain and language specificity, failing which the results will misrepresent model performance. We argue that model performance should be assessed on a wider variety of hashtags, and datasets should be carefully curated. To this end, we propose HashSet, a dataset comprising of: a) 1.9k manually annotated dataset; b) 3.3M loosely supervised dataset. HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models. We show that the performance of SOTA models for Hashtag Segmentation drops substantially on proposed dataset, indicating that the proposed dataset provides an alternate set of hashtags to train and assess models. Datasets and results are released publicly and can be accessed from https://github.com/prashantkodali/HashSet

@inproceedings{bib_SyMC_2022, AUTHOR = {Prashant, Kodali and Goel, Anmol and Choudhury, Monojit and Shrivastava, Manish and Kumaraguru, Ponnurangam }, TITLE = {SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing}, BOOKTITLE = {Findings of the Association for Computational Linguistics}. YEAR = {2022}}

Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations. Recent work on code-mixing in computational settings has leveraged social media code mixed texts to train NLP models. For capturing the variety of code mixing in, and across corpus, Language ID (LID) tags based measures (CMI) have been proposed. Syntactical variety/patterns of code-mixing and their relationship vis-a-vis computational model’s performance is under explored. In this work, we investigate a collection of English(en)-Hindi(hi) code-mixed datasets from a syntactic lens to propose, SyMCoM, an indicator of syntactic variety in code-mixed text, with intuitive theoretical bounds. We train SoTA en-hi PoS tagger, accuracy of 93.4%, to reliably compute PoS tags on a corpus, and demonstrate the utility of SyMCoM by applying it on various syntactical categories on a collection of datasets, and compare datasets using the measure.