Abstract

Billboard advertisement is one of the dominant modes of traditional outdoor advertisements. A billboard operator manages the ad slots of a set of billboards. Normally, a user traversal is exposed to multiple billboards. Given a set of billboards, there is an opportunity to improve the revenue of the billboard operator by satisfying the advertising demands of an increased number of clients and ensuring that a user gets exposed to different ads on the billboards during the traversal. In this paper, we propose a framework to improve the revenue of the billboard operator by employing transactional modeling in conjunction with pattern mining. Our main contributions are three-fold. First, we introduce the problem of billboard advertisement allocation for improving the billboard operator revenue. Second, we propose an efficient user trajectory-based transactional framework using coverage pattern mining for improving the revenue of the billboard operator. Third, we conduct a performance study with a real dataset to demonstrate the effectiveness of our proposed framework.

Abstract

Given a coarse satellite image and a fine satellite image of a particular location taken at the same time, the high-resolution spatiotemporal image fusion technique involves understanding the spatial correlation between the pixels of both images and using it to generate a finer image for a given coarse (or test) image taken at a later time. This technique is extensively used for monitoring agricultural land cover, forest cover, etc. The two key issues in this technique are: (i) handling missing pixel data and (ii) improving the prediction accuracy of the fine image generated from the given test coarse image. This paper tackles these two issues by proposing an efficient method consisting of the following three basic steps: (i) imputation of missing pixels using neighborhood information, (ii) cross-scale matching to adjust both the Point Spread Functions Effect (PSF) and geo-registration errors between the course and high-resolution images, and (iii) error-based modulation, which uses pixel-based multiplicative factors and residuals to fix the error caused due to modulation of temporal changes. The experimental results on the real-world satellite imagery datasets demonstrate that the proposed model outperforms the state-of-art by accurately producing the high-resolution satellite images closer to the ground truth.

Abstract

It is a well-established fact in the retail industry that the placement of products on the shelves of the retail store has a significant impact on the revenue of the retailer. Given that customers tend to purchase sets of items together (i.e., itemsets) instead of individual items, it becomes a necessity to strategically place itemsets on the shelves of the retail store for improving retailer revenue. Furthermore, in practice, customers belong to different market segments based on factors such as purchasing power, demographics and customer behaviour. Existing research efforts do not address the issue of market segmentation w.r.t. itemset placement in retail stores. Consequently, they fail to efficiently index, retrieve and place high-utility itemsets in the retail slots in a market segmentation aware manner. In this work, we introduce the problem of market segmentation aware itemset placement for retail stores. Moreover, we propose a market segmentation aware retail itemset placement framework, which takes high-utility itemsets as input. Our performance evaluation with two real datasets demonstrates that our proposed framework is indeed effective in improving retailer revenue w.r.t. existing schemes.

Abstract

Air pollution is a major threat to human health in India. More than three-quarters of the people in India are exposed to pollution levels higher than the limits recommended by the National Ambient Air Quality Standards in India and significantly higher than those recommended by the World Health Organization. Despite the poor air quality, the monitoring of air pollution levels is limited even in large urban areas in India and virtually absent in small towns and rural areas. The lack of data results in a minimal understanding of spatial and temporal patterns of air pollutants at local and regional levels. This paper is the second in a planned series of papers presenting particulate air pollution trends monitored in small cities and towns in India. The findings presented here are important for framing state and regional level policies for addressing air pollution problems in urban areas, and achieve the sustainable development goals (SDGs) linked to public health, reduction in the adverse environmental impact of cities, and adaptation to climate change, as indicated by SDGs 3.9, 11.6 and 11.b.

Abstract

Exposure to ambient Particulate Matter (PM) of air pollution is a leading risk factor for morbidity and mortality. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which are expensive to acquire as well as to maintain. Moreover, such stations are typically sparsely deployed, thereby resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative for improving the granularity of monitoring. We are making an effort to explore the framework for air quality data collection by employing low-cost sensors. In this paper, we have reported our initial experiences and observations concerning PM data collection for four months starting from October 2021 in the city of Hyderabad in India.

Abstract

“Rare item problem” is a fundamental problem in pattern mining. It represents the inability of a pattern mining model to discover the knowledge about frequent and rare items in a database. In the literature, researchers advocated the usage of null-invariant measures as they disclose genuine correlations without being influenced by the object co-absence in the database. Since the existing null-invariant measures consider only an item’s frequency and disregard its temporal occurrence information, they are inadequate to address the rare item problem faced by the partial periodic pattern model. This paper proposes a novel null-invariant measure, called relative periodic-support, to find the patterns containing both frequent and rare items in non-uniform temporal databases. We also introduce an efficient pattern-growth algorithm to find all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient.

Abstract

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.

Abstract

Body shaming, a criticism based on the body’s shape, size, or appearance, has become a dangerous act on social media. With a rise in the reporting of body shaming experiences on the web, automated monitoring of body shaming posts will help rescue individuals, especially adolescents, from the emotional anguish they experience. To the best of our knowledge, this is the first work on body shaming detection, and we contribute the dataset in which the posts are tagged as body shaming or non-body shaming. We use transformer-based language models to detect body shaming posts. Further, we leverage unlabeled data in a semi-supervised manner using the GAN-BERT model, as it was developed for tasks where labeled data is scarce and unlabeled data is abundant. The findings of the experiments reveal that the algorithm learns valuable knowledge from the unlabeled dataset and outperforms many deep learning and conventional machine learning baselines.

Abstract

Coherence is an important aspect of text quality and is crucial for ensuring its readability. It is essential for outputs from text generation systems like summarization, question answering, machine translation, question generation, table-to-text, etc. An automated coherence scoring model is also helpful in essay scoring or providing writing feedback. A large body of previous work has leveraged entity-based methods, syntactic patterns, discourse relations, and traditional deep learning architectures for text coherence assessment. However, these approaches do not consider factual information present in the documents. The transitions of facts associated with entities across sentences could help capture the essence of textual coherence better. We hypothesize that coherence assessment is a cognitively complex task that requires deeper fact-aware models and can benefit from other related tasks. In this work, we propose a novel deep learning model that fuses document-level information with factual information to improve coherence modeling. We further enhance the model efficacy by training it simultaneously with Natural Language Inference task in multi-task learning setting, taking advantage of inductive transfer between the two tasks. Our experiments with popular benchmark datasets across multiple domains demonstrate that the proposed model achieves state-of-the-art results on a synthetic coherence evaluation task and two real-world tasks involving prediction of varying degrees of coherence.

Abstract

Parkinson’s disease (PD) is a speech disorder caused by the effect of neurological damage on motor control activity of speech production. Due to the noninvasive nature, detection of PD from the speech is remarkable. This study proposes the use of Long-Term Average Spectral (LTAS) feature to detect PD from speech using different filterbank techniques and classifiers on the PC-GITA dataset. The fact that PD can be perceived from long-temporal information has motivated us to explore the LTAS features for PD detection. Performance of LTAS features is compared to baseline features, namely statistical and openSMILE features (ComParE, GeMAPS, and eGeMAPS). The performance of these features is validated using five different classifiers. The LTAS features, which are obtained with auditory filterbanks (Constant Q Transform and Gammatone), showed better performance as compared to the statistical features (MFCC_Stat and LPCC_Stat) and openSMILE features (ComParE, GeMAPS, and eGeMAPS). The combination of the Gammatone-based LTAS feature with eGeMAPS further improved the detection accuracy of the PD detection system on the PC-GITA database.