Abstract

Facilitating the discovery of drugs by combining diverse compounds is becoming prevalent, especially for treating complex diseases like cancers and HIV. A drug is a chemical compound structure and any sub-structure of a chemical compound is designated as a fragment. A chemical compound or a fragment can be modeled as a graph structure. Given a set of chemical compounds and their corresponding large set of fragments modeled as graph structures, we address the problem of identifying potential combinations of diverse chemical compounds, which cover a certain percentage of the set of fragments. In this regard, the key contributions of this work are three-fold: First, we introduce the notion of Graph Transactional Coverage Patterns (GTCPs) for any given graph transactional dataset. Second, we propose an efficient model and framework for extracting GTCPs from a given graph transactional dataset. Third, we conduct an extensive performance study using three real datasets to demonstrate that it is indeed feasible to efficiently extract GTCPs using our proposed GTCP-extraction framework. We also demonstrate the effectiveness of the GTCP-extraction framework through a case study in computer-aided drug design. Index Terms—Graph mining, Graph transactions, Coverage patterns, Drug discovery

Abstract

—Mobile robots play a crucial role in cleaning, maintenance, and surveillance applications. This article advocates for the use of a novel robust output feedback based path following controller, for a class of selfreconfigurable mobile robot under actuator saturation. The reconfigurability property of such platforms is captured via an uncertain Euler–Lagrange dynamics. The proposed control framework estimates the unmeasurable states and the uncertain dynamics terms through two extended high gain observers, whereas the actuator limits are honored via a fast dynamic compensator. The closed-loop stability is analyzed via contraction theory, which, compared to the conventional Lyapunov based approaches, avoids the requirement of arbitrarily large controller and observer gains. Such a feature is of particular interest in view of actuator saturation. The experimental results with PANTHERA self-reconfigurable robot validate the effectiveness of the proposed technique over the state of the art.

Abstract

This paper describes SimpleNER, a model developed for the sentence simplification task at GEM-2021. Our system is a monolingual Seq2Seq Transformer architecture that uses control tokens pre-pended to the data, allowing the model to shape the generated simplifications according to user desired attributes. Additionally, we show that NER-tagging the training data before use helps stabilize the effect of the control tokens and significantly improves the overall performance of the system. We also employ pretrained embeddings to reduce data sparsity and allow the model to produce more generalizable outputs.

Abstract

Aspect extraction is not a well-explored topic in Hindi, with only one corpus having been developed for the task. In this paper, we discuss the merits of the existing corpus in terms of quality, size, sparsity, and performance in aspect extraction tasks using established models. To provide a better baseline corpus for aspect extraction, we translate the SemEval 2014 aspect-based sentiment analysis dataset and annotate the aspects in that data. We provide rigorous guidelines and a replicable methodology for this task. We quantitatively evaluate the translations and annotations using inter-annotator agreement scores. We also evaluate our dataset using state-of-the-art neural aspect extraction models in both monolingual and multilingual settings and show that the models perform far better on our corpus than on the existing Hindi dataset. With this, we establish our corpus as the gold-standard aspect extraction dataset in Hindi.

Abstract

The extensive rise in consumption of online social media (OSMs) by a large number of people poses a critical problem of curbing the spread of hateful content on these platforms. With the growing usage of OSMs in multiple languages, the task of detecting and characterizing hate becomes more complex. The subtle variations of code-mixed texts along with switching scripts only add to the complexity. This paper presents a solution for the HASOC 2021 Multilingual Twitter Hate-Speech Detection challenge by team PreCog IIIT Hyderabad. We adopt a multilingual transformer based approach and describe our architecture for all 6 sub-tasks as part of the challenge. Out of the 6 teams that participated in all the sub tasks, our submissions rank 3rd overall.

Abstract

Social media platforms have democratized the publication process resulting into easy and viral propagation of information. Oftentimes this misinformation is accompanied by misleading or doctored images that quickly circulate across the internet and reach many unsuspecting users. Several manual as well as automated efforts have been undertaken in the past to solve this critical problem. While manual efforts cannot keep up with the rate at which this content is churned out, many automated approaches only leverage concatenation (of the image and text representations) thereby failing to build effective crossmodal embeddings. Architectures like this fail in many cases because the text or image doesn’t need to be false for the corresponding text, image pair to be misinformation. While some recent work attempts to use attention techniques to compute a crossmodal representation using pretrained text and image embeddings, we show a more effective approach towards utilizing such pretrained embeddings to build richer representations that can be classified better. This involves several challenges like how to handle text variations on Twitter and Weibo, how to encode the image information and how to leverage the text and image encodings together effectively. Our architecture, SCATE (Shared Cross Attention Transformer Encoders), leverages deep convolutional neural networks and transformer-based methods to encode image and text information utilizing crossmodal attention and shared layers for the two modalities. Our experiments with three popular benchmark datasets (Twitter, WeiboA and WeiboB) show that our proposed methods outperform the state-of-the-art meth

Abstract

Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model that leverages the multiple-channel information available in the standard 12-channel ECG recordings and learns patterns at the beat, rhythm, and channel level. The experimental results show that our model achieved a macro-averaged ROC-AUC score of 0.9216, mean accuracy of 88.85% and a maximum F1 score of 0.8057 on the PTB-XL dataset. The attention visualization results from the interpretable model are compared against the cardiologist’s guidelines to validate the correctness and usability

Abstract

Long standing challenges in adaptive bipedal walking control (i.e. control taking care of unknown robot parameters) were to unify the control design instead of designing multiple controllers for different walking phases as well as to bypass computing constraint forces, since it often leads to complex designs. A few attempts to design a single controller for all walking phases ignored or oversimplified the constraint forces. However, these forces are state-dependent and may lead to conservative performance or instability if not countered properly. This work proposes an innovative adaptive control method, based on artificial time delay control, which covers the entire bipedal walking phase and provides robustness against state-dependent unmodelled dynamics such as constraint forces and external impulsive forces arising during walking. Studies using a high fidelity simulator under various forms of disturbances show the effectiveness of the proposed design over the state of the art.

Abstract

A crucial challenge in maintaining formation in an autonomous vehicle platoon rests on designing a suitable control scheme that can tackle external disturbances and uncertain system parameters, especially the friction forces between wheel and ground, which vary with change in road surface, wear in tires and speed of the vehicle. State-of-the-art adaptive controller negotiating such uncertainties can typically handle a priori bounded (or non state-dependent) uncertainties. However, besides the difficulty in accurate modelling and identification of frictional forces, in general, these forces are state-dependent and cannot be a priori bounded (e.g., viscous friction). This paper proposes an adaptive sliding mode controller for nonholonomic wheeled mobile robot based vehicle platoon which can handle unknown complex behaviour of frictional forces without a priori knowledge of its parameters and structures. The effectiveness of the proposed controller is verified in Gazebo simulation in comparison with the state of the art.

Abstract

This paper focuses on designing a low-cost and robust IoT-based TDS measurement system for the smart campus. The objective of this low-cost design problem is to find a solution that guarantees precise and uninterrupted output data. The dynamic reading of data, storage capacity, and calibration errors of sensors are the major challenges for IoT-based TDS measurement systems. These challenges are combated in the proposed design using a non-invasive mechanism for data collection, wireless connectivity to the data server, and machine learning calibration of sensor nodes. The TDS data of various water stations located inside the campus is used for the experimental study to develop a regression model for temperature compensation and calibration. The value of TDS sensor voltage variation against temperature is analyzed. The evaluation of the model was performed based on the R 2 and the root mean square error. By using 3 rd degree polynomial regression, we have obtained an R 2 value of 93.96 % and an RMSE of 27.93