Abstract

Opinion spam on online restaurant review sites are a major problem as the reviews influence the users’ choice to visit or not to a restaurant. In this paper, we address the problem of detecting genuine and fake reviews in restaurant online reviews. We propose a fake review detection technique comprising data preprocessing, detection and ensemble learning that learns the reviews and their features to filter out the fake reviews. Initially, we preprocess to obtain the refined reviews and employ two independent classifiers using deep machine learning and feature-based machine learning techniques for detection. These classifiers tackle the problem in two aspects, i.e., the deep machine learning model learns the word distributions and the feature-based machine learning model extracts the relevant features from the reviews. Finally, a hybrid ensemble model from the two classifiers are built to detect the genuine and fake reviews. The experimental analysis of the proposed approach on Yelp datasets outperforms the existing state-of-the-art methods.

Abstract

This paper presents a bio-inspired intelligent controller to enhance the performance of a nonlinear system. The controller is designed by capturing the emotional intelligence of mammalian brain mediated by the limbic system in which certain parts are responsible for generating emotions and these can be combined together as brain affective system inspired control architecture (BASIC). This controller has been used to cope with nonlinearities present in control applications. In this paper brain inspired control architecture is proposed while incorporating the sensory cortex explicitly in the architecture and the computational equations of various modules are suitably developed. The performance of the proposed controller is analyzed on a typical nonlinear system, namely, Permanent magnet synchronous motor for speed control, harmonics in stator phase currents and ripples produced in electromagnetic torque at different operating conditions. Its performance is compared with the existing control techniques in offline simulations as well as in real-time Hardware-in-loop environment. The results establish the computational efficiency, accuracy and robustness of the proposed controller.

Abstract

Fraudulent jobs are an emerging threat over online recruitment platforms such as LinkedIn, Glassdoor. Fraudulent job postings affect the platform’s trustworthiness and have a negative impact on user experience. Therefore, these platforms need to detect and remove these fraudulent jobs. Generally, fraudulent job postings contain untenable facts about domain-specific entities such as mismatch in skills, industries, offered compensation, etc. However, existing approaches focus on studying writing styles, linguistics, and context-based features, and ignore the relationships among domain-specific entities. To bridge this gap, we propose an approach based on the Knowledge Graph (KG) of domain-specific entities to detect fraudulent jobs. In this paper, we present a multi-tier novel end-to-end framework called FRaudulent Jobs Detection (FRJD) Engine, which considers a) fact validation module using KGs, b) contextual module using deep neural networks c) meta-data module to capture the semantics of job postings. We conduct our experiments using a fact validation dataset containing 4 million facts extracted from job postings. Extensive evaluation shows that FRJD yields a 0.96 F1-score on the curated dataset of 157,880 job postings. Finally, we provide insights on the performance of different fact-checking algorithms on recruitment domain

Abstract

Billboard advertisement is among the dominant modes of outdoor advertisements. The billboard operator has an opportunity to improve its revenue by satisfying the advertising demands of an increased number of clients by means of exploiting the user trajectory data. Hence, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue, and propose an efficient user trajectory-based transactional framework using coverage pattern mining. Our experiments validate the effectiveness of our framework.

Abstract

Placement of items on the shelf space of retail stores signifcantly impacts the revenue of the retailer. Given the prevalence and popularity of medium-to-large-size retail stores, several research efforts have been made towards facilitating item/itemset placement in retail stores for improving retailer revenue. However, they do not consider the issue of urgency of sale of individual items. Hence, they cannot efficiently index, retrieve and place high-revenue itemsets in retail store slots in an urgency-aware manner. Our key contributions are two-fold. First, we introduce the notion of urgency for retail itemset placement. Second, we propose the urgency-aware URI index for efficiently retrieving high-revenue and urgent itemsets of different sizes. We discuss the URIP itemset placement scheme, which exploits URI for improving retailer revenue. We also conduct a performance evaluation with two real datasets to demonstrate that URIP is indeed effective in improving retailer revenue w.r.t. existing schemes.

Abstract

Handwritten documents are often characterized by dense and uneven layout. Despite advances, standard deep network based approaches for semantic layout segmentation are not robust to complex deformations seen across semantic regions. This phenomenon is especially pronounced for the low-resource Indic palm-leaf manuscript domain. To address the issue, we first introduce Indiscapes2, a new large-scale diverse dataset of Indic manuscripts with semantic layout annotations. Indiscapes2 contains documents from four different historical collections and is larger than its predecessor, Indiscapes. We also propose a novel deep network Palmira for robust, deformation-aware instance segmentation of regions in handwritten manuscripts. We also report Hausdorff distance and its variants as a boundary-aware performance measure. Our experiments demonstrate that Palmira provides robust layouts, outperforms strong baseline approaches and ablative variants. We also include qualitative results on Arabic, South-East Asian and Hebrew historical manuscripts to showcase the generalization capability of Palmira.

Abstract

We present a CATALIST model that ‘tames’ the attention (heads) of an attention-based scene text recognition model. We provide supervision to the attention masks at multiple levels, i.e., line, word, and character levels while training the multi-head attention model. We demonstrate that such supervision improves training performance and testing accuracy. To train CATALIST and its attention masks, we also present a synthetic data generator ALCHEMIST that enables the synthetic creation of large scene-text video datasets, along with mask information at character, word, and line levels. We release a real scene-text dataset of 2k videos, CATALISTd with videos of real scenes that potentially contain scene-text in a combination of three different languages, namely, English, Hindi, and Marathi. We record these videos using 5 types of camera transformations - (i) translation, (ii) roll, (iii) tilt, (iv) pan, and (v) zoom to create transformed videos. The dataset and other useful resources are available as a documented public repository for use by the community.

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

Abstract

Sexism, a prejudice that causes enormous suffering, mani- fests in blatant as well as subtle ways. As sexist content towards women is increasingly spread on social networks, the automatic detection and categorization of these tweets/posts can help social scientists and policy- makers in research, thereby combating sexism. In this paper, we explore the problem of detecting whether a Twitter/Gab post is sexist or not. We further discriminate the detected sexist post into one of the fine-grained sexism categories. We propose a neural model for this sexism detec- tion and classification that can combine representations obtained using RoBERTa model and linguistic features such as Empath, Hurtlex, and Perspective API by involving recurrent components. We also leverage the unlabeled sexism data to infuse the domain-specific transformer model into our framework. Our proposed framework also features a knowledge module comprised of emoticon and hashtag representations to infuse the external knowledge-specific features into the learning process. Several proposed methods outperform various baselines across several standard metrics

Abstract

This paper describes the system submitted to the Humor Analysis based on Human Annotation (HAHA) task at IberLEF 2021. This system achieves the winning F1 score of 0.8850 in the main task of binary classification (Task 1) utilizing an ensemble of a pre-trained multilingual BERT, pre-trained Spanish BERT (BETO), RoBERTa, and a naive Bayes classifier. We also achieve second place with macro F1 Scores of 0.2916 and 0.3578 in Multi-class Classification and Multi-label Classification tasks, respectively, and third place with an RMSE score of 0.6295 in the Regression task.