Abstract

In this paper, we study the role of nodes and edges in a complex network in dictating the robustness of a community structure towards structural perturbations. Specifically, we attempt to identify all vital nodes, which, when removed, would lead to a large change in the underlying community structure of the network. This problem is critical because the community structure of a network allows us to explore deep underlying insights into how the function and topology of the network affect each other. Moreover, it even provides a way to condense large net- works into smaller modules where each community acts as a meta node and aids in more straightforward network analysis. If the community structure were to be compromised by either accidental or intentional per- turbations to the network, that would make such analysis difficult. Since identifying such vital nodes is computationally intractable, we propose a suite of heuristics that allow to find solutions close to the optimality. To show the effectiveness of our approach, we first test these heuristics on small networks and then move to more extensive networks to show that we achieve similar results. Further analysis reveals that the pro- posed approaches are useful to analyze the vulnerability of communities in networks irrespective of their size and scale. Additionally, we show the performance through an extrinsic evaluation framework – we employ two tasks, i.e., link prediction and information diffusion, and show that the effect of our algorithms on these tasks is higher than the other baselines.

Abstract

ackground: The adoption of nonpharmaceutical interventions and their surveillance are critical for detecting and stopping possible transmission routes of COVID-19. A study of the effects of these interventions can help shape public health decisions. The efficacy of nonpharmaceutical interventions can be affected by public behaviors in events, such as protests. We examined mask use and mask fit in the United States, from social media images, especially during the Black Lives Matter (BLM) protests, representing the first large-scale public gatherings in the pandemic. Objective: This study assessed the use and fit of face masks and social distancing in the United States and events of large physical gatherings through public social media images from 6 cities and BLM protests. Methods: We collected and analyzed 2.04 million public social media images from New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis between February 1, 2020, and May 31, 2020. We evaluated correlations between online mask usage trends and COVID-19 cases. We looked for significant changes in mask use patterns and group posting around important policy decisions. For BLM protests, we analyzed 195,452 posts from New York and Minneapolis from May 25, 2020, to July 15, 2020. We looked at differences in adopting the preventive measures in the BLM protests through the mask fit score. Results: The average percentage of group pictures dropped from 8.05% to 4.65% after the lockdown week. New York City, Dallas, Seattle, New Orleans, Boston, and Minneapolis observed increases of 5.0%, 7.4%, 7.4%, 6.5%, 5.6%, and 7.1%, respectively, in mask use between February 2020 and May 2020. Boston and Minneapolis observed significant increases of 3.0% and 7.4%, respectively, in mask use after the mask mandates. Differences of 6.2% and 8.3% were found in group pictures between BLM posts and non-BLM posts for New York City and Minneapolis, respectively. In contrast, the differences in the percentage of masked faces in group pictures between BLM and non-BLM posts were 29.0% and 20.1% for New York City and Minneapolis, respectively. Across protests, 35% of individuals wore a mask with a fit score greater than 80%. Conclusions: The study found a significant drop in group posting when the stay-at-home laws were applied and a significant increase in mask use for 2 of 3 cities where masks were mandated. Although a positive trend toward mask use and social distancing was observed, a high percentage of posts showed disregard for the guidelines. BLM-related posts captured the lack of seriousness to safety measures, with a high percentage of group pictures and low mask fit scores. Thus, the methodology provides a directional indication of how government policies can be indirectly monitored through social media

Abstract

Twitter is an extensively used micro-blogging site for publishing user’s views on recent happenings. This wide reachability of messages over large audience poses a threat, as the degree of personally identifiable information disclosed might lead to user regrets. The Tweet-Scan-Post system scans the tweets contextually for sensitive messages. The tweet repository was generated using cyber- keywords for personal, professional and health tweets. The Rules of Sensitivity and Contextuality was defined based on standards established by various national regulatory bodies. The naive sensitivity regression function uses the Bag-of-Words model built from short text messages. The imbalanced classes in dataset result in misclassification with 25% of sensitive and 75% of insensitive tweets. The system opted stacked classification to combat the problem of imbalanced classes. The system initially applied various state-of-art algorithms and predicted 26% of the tweets to be sensitive. The proposed stacked classification approach increased the overall proportion of sensitive tweets to 35%. The system contributes a vocabulary set of 201 Sensitive Privacy Keyword using the boosting approach for three tweet categories. Finally, the system formulates a sensitivity scaling called TSP’s Tweet Sensitivity Scale based on Senti-Cyber features composed of Sensitive Privacy Keywords, Cyber-keywords with Non-Sensitive Privacy Keywords and Non-Cyber-keywords to detect the degree of disclosed sensitive information.

Abstract

The COVID-19 pandemic has so far accounted for reported 5.5𝑀 deaths worldwide, with 8.7% of these coming from India. The pandemic exacerbated the weakness of the Indian healthcare system. As of January 20, 2022, India is the second worst affected country with 38.2𝑀 reported cases and 487𝐾 deaths. According to epidemiologists, vaccines are an essential tool to prevent the spread of the pandemic. India’s vaccination drive began on January 16, 2021 with governmental policies being introduced to prioritize different populations of the society. Through the course of the vaccination drive, multiple new policies were also introduced to ensure that vaccines are readily available and vaccination coverage is increased. However, at the same time, some of the government policies introduced led to unintended inequities in the populations being targeted. In this report, we enumerate and analyze the inequities that existed in India’s vaccination policy drive, and also compute the effect of the new policies that were introduced. We analyze these potential inequities not only qualitatively but also quantitatively by leveraging the data that was made available through the government portals. Specifically, (a) we discover inequities that might exist in the policies, (b) we quantify the effect of new policies introduced to increase vaccination coverage, and (c) we also point the data discrepancies that exist across different data sources

Abstract

Social media in present times has a significant and growing influence. Fake news being spread on these platforms have a disruptive and damaging impact on our lives. Furthermore, as multimedia content improves the visibility of posts more than text data, it has been observed that often multi- media is being used for creating fake content. A plethora of previous multimodal-based work has tried to address the problem of modeling heteroge- neous modalities in identifying fake content. How- ever, these works have the following limitations: (1) inefficient encoding of inter-modal relations by utilizing a simple concatenation operator on the modalities at a later stage in a model, which might result in information loss; (2) training very deep neural networks with a disproportionate number of parameters on small but complex real-life multi- modal datasets result in higher chances of over- fitting. To address these limitations, we propose GAME-ON, a Graph Neural Network based end- to-end trainable framework that allows granular in- teractions within and across different modalities to learn more robust data representations for multi- modal fake news detection. We use two publicly available fake news datasets, Twitter and Weibo, for evaluations. Our model outperforms on Twitter by an average of 11% and keeps competitive perfor- mance on Weibo, within a 2.6% margin, while us- ing 65% fewer parameters than the best comparable state-of-the-art baseline.

Abstract

Data has become an integral part of day-to-day human life. Users leave behind a trail of digital footprint that includes their personal and non-personal information. A normal user puts 1.7 megabytes of data every second into the hand of service providers and trusts them to keep it safe. However, researchers have found out that in the name of improving the quality of service, the service providers, knowing or accidentally, put users’ personal information at risk of getting into the hands of an adversary. The service providers usually apply masking or anonymization before releasing the users’ data. Anonymization techniques do not guarantee privacy preservation and are proven to be prone to cross-linking attacks. In the past, researchers were able to successfully cross-link multiple datasets to leak the sensitive information of various users. Cross-linking attacks are always possible on anonymized datasets, and therefore, service providers must use a technique that guarantees privacy preservation. Differential privacy is superior for publishing sensitive information while pro- tecting privacy. It provides mathematical guarantees and prevents background knowledge attacks such that infor- mation remains private regardless of whatever information an adversary might have. This paper discusses how differential privacy can help achieve privacy guarantees for the release of sensitive heterogeneous datasets while pre- serving its utility.

Abstract

Alongside revenue maximization, retailers seek to offer a diverse range of items to facilitate sustainable revenue generation in the long run. Moreover, customers typically buy sets of items, i.e., itemsets, as opposed to individual items. Therefore, strategic placement of diversified and high-revenue itemsets is a priority for the retailer. Research efforts made towards the extraction and placement of high-revenue itemsets in retail stores do not consider the notion of diversification. Further, candidate itemsets generated using existing utility mining schemes usually explode; which can cause memory and retrieval- time issues. This work makes three key contributions. First, we propose an efficient framework for retrieval of high-revenue itemsets with a varying size and a varying degree of diversification. A higher degree of diversification is indicative of fewer repetitive items in the top-revenue itemsets. Second, we propose the kUI (k Utility Itemset) index for quick and efficient retrieval of diverse top-λ high-revenue itemsets. We also propose the HUDIP (High-Utility and Diversified Itemset Placement) scheme, which exploits our proposed kUI index for placement of high-revenue and diversified itemsets. Third, our extensive performance study with both real and synthetic datasets demonstrates the effectiveness of our proposed HUDIP scheme in efficiently determining high-revenue and diversified itemsets.

Abstract

Anatomical variabilities seen in longitudinal data or inter- subject data is usually described by the underlying deformation, cap- tured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for reg- istration. However, these methods cover only a limited degree of defor- mations. We address this limitation and define an approximate metric space for the manifold of diffeomorphisms G. We propose a method to break down the large deformation into finite set of small sequential defor- mations. This results in a broken geodesic path on G and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the proposed method show that it can capture large and complex de- formations while producing qualitatively better results than state-of-the- art methods. The results also demonstrate that the proposed registration metric is a good indicator of the degree of deformation.

Abstract

Self-supervised methods have shown promising results in denoising and dehazing tasks, where the collection of the paired dataset is challenging and expensive. However, we find that these methods fail to remove the rain streaks when applied for image deraining tasks. The method’s poor per- formance is due to the explicit assumptions: (i) the distribu- tion of noise or haze is uniform and (ii) the value of a noisy or hazy pixel is independent of its neighbors. The rainy pix- els are non-uniformly distributed, and it is not necessarily dependant on its neighboring pixels. Hence, we conclude that the self-supervised method needs to have some prior knowledge about rain distribution to perform the deraining task. To provide this knowledge, we hypothesize a network trained with minimal supervision to estimate the likelihood of rainy pixels. This leads us to our proposed method called FLUID: Few Shot Self-Supervised Image Deraining. We perform extensive experiments and comparisons with existing image deraining and few-shot image-to-image translation methods on Rain 100L and DDN-SIRR datasets containing real and synthetic rainy images. In addition, we use the Rainy Cityscapes dataset to show that our method trained in a few-shot setting can improve semantic segmen- tation and object detection in rainy conditions. Our ap- proach obtains a mIoU gain of 51.20 over the current best- performing deraining method.

Abstract

Restoring images degraded due to atmospheric turbulence is challenging as it consists of several distortions. Several deep learning methods have been proposed to minimize atmospheric distortions that consist of a single-stage deep net- work. However, we find that a single-stage deep network is insuffi- cient to remove the mixture of distortions caused by atmospheric turbulence. We propose a two-stage deep adversarial network that minimizes atmospheric turbulence to mitigate this. The first stage reduces the geometrical distortion and the second stage minimizes the image blur. We improve our network by adding channel attention and a proposed sub-pixel mechanism, which utilizes the information between the channels and further reduces the atmospheric turbulence at the finer level. Unlike previous methods, our approach neither uses any prior knowledge about atmospheric turbulence conditions at inference time nor requires the fusion of multiple images to get a single restored image. Our final restoration models DT-GAN+ and DTD-GAN+ outperform the general state-of-the-art image-to-image translation models and baseline restoration models. We synthesize turbulent image datasets to train the restoration models. Additionally, we also curate a natural turbulent dataset from YouTube to show the generalisability of the proposed model. We perform extensive experiments on restored images by utilizing them for downstream tasks such as classification, pose estimation, semantic keypoint estimation, and depth estimation. We observe that our restored images outperform turbulent images in downstream tasks by a significant margin demonstrating the restoration model’s applica- bility in real-world problems.