Abstract

Clickbait headlines have become a nudge in social media and news websites. The methods to identify clickbaits are largely being developed for English. There is a need for the same in other languages as well with the increase in the usage of social media platforms in different languages. In this work, we present an annotated clickbait dataset of 112,657 headlines that can be used for building an automated clickbait detection system for Telugu, a resource-poor language. Our contribution in this paper includes (i) generation of the latest pre-trained language models, including RoBERTa, ALBERT, and ELECTRA trained on a large Telugu corpora of 8,015,588 sentences that we had collected, (ii) data analysis and benchmarking the performance of different approaches ranging from hand-crafted features to state-of-the-art models. We show that the pre-trained language models trained on Telugu outperform the existing pre-trained models viz. BERT-Mulingual-Case [1], XLM-MLM [2], and XLM-R [3] on clickbait task. On a large Telugu clickbait dataset of 112,657 samples, the Light Gradient Boosted Machines (LGBM) model achieves an F1- score of 0.94 for clickbait headlines. For Non-Clickbait headlines, F1-score of 0.93 is obtained which is similar to that of Clickbait class. We open-source our dataset, pre-trained models, and code1

Abstract

— Humans have a natural ability to effortlessly comprehend linguistic commands such as “park next to the yellow sedan” and instinctively know which region of the road the vehicle should navigate. Extending this ability to autonomous vehicles is the next step towards creating fully autonomous agents that respond and act according to human commands. To this end, we propose the novel task of Referring Navigable Regions (RNR), i.e., grounding regions of interest for navigation based on the linguistic command. RNR is different from Referring Image Segmentation (RIS), which focuses on grounding an object referred to by the natural language expression instead of grounding a navigable region. For example, for a command “park next to the yellow sedan,” RIS will aim to segment the referred sedan, and RNR aims to segment the suggested parking region on the road. We introduce a new dataset, Talk2Car-RegSeg, which extends the existing Talk2car [1] dataset with segmentation masks for the regions described by the linguistic commands. A separate test split with concise manoeuvre-oriented commands is provided to assess the practicality of our dataset. We benchmark the proposed dataset using a novel transformer-based architecture. We present extensive ablations and show superior performance over baselines on multiple evaluation metrics. A downstream path planner generating trajectories based on RNR outputs confirms the efficacy of the proposed framework.

Abstract

Media bias is a predominant phenomenon present in most forms of print and electronic media such as news articles, blogs, tweets, etc. Since media plays a pivotal role in shaping public opinion towards political happenings, both political parties and media houses often use such sources as outlets to propagate their own prejudices to the public. There has been some research on detecting political bias in news articles. However, none of it attempts to analyse the nature of bias or quantify the magnitude of the bias in a given text. This paper presents a political bias annotated corpus viz. PoBiCo-21, which is annotated using a schema specifically designed with 10 labels to capture various techniques used to create political bias in news. We create a ranking of these techniques based on their contribution to bias. After validating the ranking, we propose methods to use it to quantify the magnitude of bias in political news articles.

Abstract

For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.

Abstract

The ACM Hypertext and Social Media Conference is a premium venue for high quality peerreviewed research on hypertext theory, systems and applications. It is concerned with all aspects of modern hypertext research including social media, semantic web, dynamic and computed hypertext and hypermedia as well as narrative systems and applications. ACM Hypertext 2021, the 32nd ACM Conference on Hypertext and Social Media was organized as a virtual event, hosted by the ADAPT Centre and Trinity College Dublin, Ireland. The theme of HT'21 is: "Hypertext in a Multimodal World". The hypertext paradigm has transformed the way we store and transfer knowledge and how we think about information and access it. We access the same sources via multiple devices, ranging from smart watches and smart phones to laptops. Information and transactions have become ever more visual, with video increasingly replacing traditional text-based web pages. Particularly, we use interactive hypertext, news feeds and videos for getting things done: planning a trip, managing our finances, interacting with colleagues, friends and family, being productive and creative. The Covid-19 situation has put a sharp focus on the interwoven disciplines needed to realise such rich applications. The research of the Hypertext community has never been more relevant. The main tracks for ACM Hypertext 2021 are: Adaptive Web and Recommender Systems, Social Web, Semantic Web and NLP, Human-information Interaction, Search and Retrieval, and Digital Humanities, Games, and Culture. In addition, the conference features a Late Breaking Results track, in which visionary Blue Sky papers were also invited, and a Doctoral Consortium.

Abstract

Quantum states that possess negative conditional von Neumann entropy provide quantum advantage in several information-theoretic protocols including superdense coding, state merging, distributed private randomness distillation and one-way entanglement distillation. While entanglement is an important resource, only a subset of entangled states have negative conditional von Neumann entropy. In this work, we characterize the class of density matrices having non-negative conditional von Neumann entropy as convex and compact. This allows us to prove the existence of a Hermitian operator (a witness) for the detection of states having negative conditional entropy for bipartite systems in arbitrary dimensions. We show two constructions of such witnesses. For one of the constructions, the expectation value of the witness in a state is an upper bound to the conditional entropy of the state. We pose the problem of obtaining a tight upper bound to the set of conditional entropies of states in which an operator gives the same expectation value. We solve this convex optimization problem numerically for a two qubit case and find that this enhances the usefulness of our witnesses. We also find that for a particular witness, the estimated tight upper bound matches the value of conditional entropy for Werner states. We explicate the utility of our work in the detection of useful states in several protocols.

Abstract

Localizing structured layout components such as tables is an important task in document image analysis. Numerous layout datasets with document images from various domains exist. However, healthcare and medical documents represent a crucial domain that has not been included so far. To address this gap, we contribute MediTables, a new dataset of 200 diverse medical document images with multi-category table annotations. Meditables contains a wide range of medical document images with variety in capture quality, layouts, skew, occlusion and illumination. The dataset images include pathology, diagnostic and hospital-related reports. In addition to document diversity, the dataset includes implicitly structured tables that are typically not present in other datasets. We benchmark state of the art table localization approaches on the MediTables dataset and introduce a custom-designed U-Net which exhibits robust performance while being drastically smaller in size compared to strong baselines. Our annotated dataset and models represent a useful first step towards the development of focused systems for medical document image analytics, a domain that mandates robust systems for reliable information retrieval. The dataset and models can be accessed at https://github.com/atmacvit/meditables

Abstract

The performance for many document-based problems (OCR, Document Layout Segmentation, etc.) is typically studied in terms of a single aggregate performance measure (Intersection-Over-Union, Character Error Rate, etc.). While useful, the aggregation is a trade-off between instance-level analysis of predictions which may shed better light on a particular approach’s biases and performance characteristics. To enable a systematic understanding of instance-level predictions, we introduce DocVisor - a web-based multi-purpose visualization tool for analyzing the data and predictions related to various document image understanding problems. DocVisor provides support for visualizing data sorted using custom-specified performance metrics and display styles. It also supports the visualization of intermediate outputs (e.g., attention maps, coarse predictions) of the processing pipelines. This paper describes the appealing features of DocVisor and showcases its multi-purpose nature and general utility. We illustrate DocVisor’s functionality for four popular document understanding tasks – document region layout segmentation, tabular data detection, weakly-supervised document region segmentation and optical character recognition. DocVisor is available as a documented public repository for use by the communit

Abstract

Reservoirs are essential infrastructures in human life. It provides water supply, flood control, hydroelectric power supply, navigations, irrigation, recreation, and other functionalities. To provide these services and resources from the reservoir, it’s necessary to know the reservoir system's inflow. The Machine Learning (ML) techniques are widely acknowledged to forecast the inflow into the reservoir system. In this paper, the popular ML technique, Gradient Boosting Regressor (GBR), is used to predict the reservoir system's inflow. This technique has been applied to the Bhadra reservoir of India at a daily time scale. In this study, the effect and complex relationship of climate phenomenon indices with inflow has been considered. The considered climate phenomenon indices are (1) Arctic Oscillation (AO), (2) East Pacific/North Pacific Oscillation (EPO), (3) North Atlantic Oscillation (NAO), (4) Extreme Eastern Tropical Pacific SST (NINO1+2), (5) Eastern Tropical Pacific SST (NINO3), (6) Central Tropical Pacific SST (NINO4), (7) East Central Tropical pacific SST (NINO34), (8) Pacific North American Index (PNA), (9) Southern Oscillation Index (SOI), (10) Western Pacific Index (WP), (11) Seasonality. In this paper, different parameter settings have been discussed on the models’ performances. The analysis of the GBR method for the Bhadra reservoir includes the number of estimators, maximum depth. The results indicate that the GBR model can capture the inflow's peaks and droughts into the reservoir systems. The study demonstrates how ML methods can be used to generate accurate reservoir inflow predictions.

Abstract

Datasets for training crowd counting deep networks are typically heavy-tailed in count distribution and exhibit discontinuities across the count range. As a result, the de facto statistical measures (MSE, MAE) exhibit large variance and tend to be unreliable indicators of performance across the count range. To address these concerns in a holistic manner, we revise processes at various stages of the standard crowd counting pipeline. To enable principled and balanced