Large Language Models (LLMs) have demonstrated impressive performance in natural language processing tasks by leveraging chain of thought (CoT) that enables step-by-step thinking. Extending LLMs with multimodal capabilities is the recent interest, but incurs computational cost and requires substantial hardware resources. To address these challenges, we propose KAM-CoT a framework that integrates CoT reasoning, Knowledge Graphs (KGs), and multiple modalities for a comprehensive understanding of multimodal tasks. KAM-CoT adopts a two-stage training process with KG grounding to generate effective rationales and answers. By incorporating external knowledge from KGs during reasoning, the model gains a deeper contextual understanding reducing hallucinations and enhancing the quality of answers. This knowledge-augmented CoT reasoning empowers the model to handle questions requiring external context, providing more informed answers. Experimental findings show KAM-CoT outperforms the state-of-the-art methods. On the ScienceQA dataset, we achieve an average accuracy of 93.87%, surpassing GPT-3.5 (75.17%) by 18% and GPT-4 (83.99%) by 10%. Remarkably, KAM-CoT achieves these results with only 280M trainable parameters at a time, demonstrating its cost-efficiency and effectiveness.

(Sumbitted to EDBT 2024)In this demo we introduce Headwork, an open-source academic platform for the crowdsourcing of complex tasks. Besides classical crowdsourcing features, Headwork eases the development of crowdsourcing campaigns through a full relational abstraction of relevant concepts (participants, skills, tasks, current answers, decision procedures, GUI, etc.). It allows in particular the orchestration of complex dynamic tasks using so-called tuple artifacts (i.e. finite-state automata which transition guards and actions are SQL-defined, on an evolving database). The demo will illustrates these key features, both from the participant and developer point of view.

Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In this paper, we focus on automatically constructing such a dataset of Support and Attack relations between sentences in a court judgment with reasonable accuracy. We propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. The first rule set is based on multiple discourse connectors, the second rule set is based on common semantic structures between argumentative sentences in a close neighbourhood, and the third rule set uses the information about the source of the argument. We also explore a BERT-based sentence pair classification model which is trained on this dataset. We release the dataset of 20506 sentence pairs - 10746 Support (precision 77.3%) and 9760 Attack (precision 65.8%). We believe that this dataset and the ideas explored in designing the linguistic rules and will boost the argumentation mining research for legal arguments.

In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method. Our feature selection model is diversity optimized and cost-sensitive over conditional likelihood of the training and validation examples that maximizes classification model performance. We employ multi-stage hybrid feature selection process involving first level filter method and second level wrapper method. We achieve 85% detection accuracy by using publicly available MIT-Physionet challenge 2016 datasets consisting of more than 3000 annotated PCG signals.

Feature subset selection and identification of appropriate classification method plays an important role to optimize the predictive performance of supervised machine learning system. Current literature makes isolated attempts to optimize the feature selection and classifier identification. However, feature set has an intrinsic relationship with classification technique and together they form a `model' for classification task. In this paper, we propose AutoModeling that finds optimal learning model and jointly optimize the feature and hypothesis space to maximize performance measure objective function. It is an automated framework of selecting the ensemble model {selected feature subset, selected classifier} from a given superset of features and classifiers learned from given training dataset in a computational efficient manner. We introduce novel relax-greedy search with our proposed patience function as a wrapper feature selection that maximizes the predictive performance and eliminates the classical nesting effect. We perform extensive experimentations on different types of publicly available datasets and AutoModeling demonstrates superior performance over relevant state-of-the-art methods, expert-driven manual methods and deep neural networks.

Remote cardiac health management is an important healthcare application. We have developed Heartmate that enables basic screening of cardiac health using low cost sensors or smartphone-inbuilt sensors without manual intervention. It consists of robust denoising algorithm along with effective anomaly analytics for physiological signals. Heartmate identifies and eliminates signal corruption as well as detects cardiac anomaly condition from physiological cardiac signals like heart sound or phonocardiogram (PCG) and photoplethysmogram (PPG).

Sensors play a vital role for realizing the vision of connected smart universe. In this paper, we present a novel sensor agnostic model SensIPro to perform robust unsupervised analysis of sensor data to support scalable analytics, a prime need for Internet of things (IoT). In the context of sensor analytics, outliers contain most delicate information. Analysis of anomaly or outlier is mostly dependent on the application domain as well as signal characteristics. Our proposed sensor analytics model SensIPro automates analysis of outliers based on inferring signal characteristics of diverse sensors from different IoT applications like healthcare, smart energy, smart transport. We apply relevant time-series algorithms using statistical analysis, information theoretic measure for sensor data analytics. We measure similarity/dissimilarity of the time series sensor data and correlate with detected outliers. Our algorithm does not require any prior knowledge of sensor data type and metadata. We present results and analysis based on real life heterogeneous sensor data sets. Obtained results further prove efficacy of the proposed mechanism.

Detection of normal and anomalous events from sensor signal is a key necessity in today's smart world. Here, we propose a novel mechanism to classify normal and anomalous phenomena by using self-learning of signal, i.e., by discovering its pattern. This is the first step in the long drawn out analysis of signals. We demonstrate a prototype of our proposed method by using a real field quasi-periodic photoplethysmogram (PPG) signal with (or without) motion artifacts, which has an immense impact on cardiac health monitoring, stress, blood pressure, and SPO2 measurement. We have achieved more than 90% accuracy to detect anomalous phenomena in the signal.

Sensors are one of the primary building blocks of IoT. Owing to close proximity of physical world, sensors often collect sensitive information. Invariably, sensor data has rich information content. Here we propose a novel solution IAS: Information Analytics for Sensors to unlock massive potential of sensor data through information analytics and demonstrate an alerting mechanism based on criticality of sensor information. ECG anomaly detection for healthcare, unusual appliance operation detection from smart energy meter data, bad road condition as well as activity detection from accelerometer data are typical use-case scenarios. We use robust statistical and information theoretic approaches. Our approach is unsupervised and is completely sensor agnostic. This abstract provides overview of design and implementation of our tool IAS along with obtained results tested on publicly available datasets. Last but not the least, IAS validates that outliers contain most delicate information.