Abstract

Twitter has emerged as a prominent social media platform for activism and counterpublic narratives. The counterpublics leverage hashtags to build a diverse support network and share content on a global platform that counters the dominant narrative. This paper applies the framework of connective action on the counter-narrative campaign over the cause of death of #SushantSinghRajput. We combine descriptive network, modularity, and hashtag based topical analysis to identify three major mechanisms underlying the campaign: generative role taking, hashtag-based narratives and formation of alignment network towards a common cause. Using the case study of #SushantSinghRajput, we highlight how connective action framework can be used to identify different strategies adopted by counterpublics for the emergence of connective action. Index Terms—Social Computing, Data Mining, Online Social Media, Computational Social Science, Social Media Campaign, Counterpublics, Network Analysis, Sushant Singh Rajput

Abstract

Online social networks like Twitter actively monitor their platform to identify accounts that go against their rules. Twitter enforces account level moderation, i.e. suspension of a Twitter account in severe cases of platform abuse. A point of note is that these suspensions are sometimes temporary and even incorrect. Twitter provides a redressal mechanism to 'restore' suspended accounts. We refer to all suspended accounts who later have their suspension reversed as 'restored accounts'. In this paper, we release the firstever dataset and methodology 1 to identify restored accounts. We inspect account properties and tweets of these restored accounts to get key insights into the effects of suspension.We build a prediction model to classify an account into normal, suspended or restored. We use SHAP values to interpret this model and identify important features. SHAP (SHapley Additive exPlanations) is a method to explain individual predictions. We show that profile features like date of account creation and the ratio of retweets to total tweets are more important than content-based features like sentiment scores and Ekman emotion scores when it comes to classification of an account as normal, suspended or restored. We investigate restored accounts further in the pre-suspension and post-restoration phases. We see that the number of tweets per account drop by 53.95% in the post-restoration phase, signifying less 'spammy' behaviour after reversal of suspension. However, there was no substantial difference in the content of the tweets posted in the pre-suspension and post-restoration phases.

Abstract

Construction crane vessels make use of dynamic positioning (DP) systems during the installation and removal of offshore structures to maintain the vessel’s position. Studies have reported cases of instability of DP systems during offshore operation caused by uncertainties, such as mooring forces. DP “robustification” for heavy lift operations, i.e., handling such uncertainties systematically and with stability guarantees, is a long-standing challenge in DP design. A new DP method, composed by an observer and a controller, is proposed to address this challenge, with stability guarantees in the presence of uncertainties. We test the proposed method on an integrated cranevessel simulation environment, where the integration of several subsystems (winch dynamics, crane forces, thruster dynamics, fuel injection system etc.) allow a realistic validation under a wide set of uncertainties.

Abstract

The paradigm shift in the consumption of news via online platforms has cultivated the growth of digital journalism. Contrary to traditional media, lowering entry barriers and enabling everyone to be part of content creation have disabled the concept of centralized gatekeeping in digital journalism. This in turn has triggered the production of fake news. Current studies have made a significant effort towards multimodal fake news detection with less emphasis on exploring the discordance between the different multimedia present in a news article. We hypothesize that fabrication of either modality will lead to dissonance between the modalities, and resulting in misrepresented, misinterpreted and misleading news. In this paper, we inspect the authenticity of news coming from online media outlets by exploiting relationship (discordance) between the textual and multiple visual cues. We develop an inter-modality discordance based fake news detection framework to achieve the goal. The modal-specific discriminative features are learned, employing the cross-entropy loss and a modified version of contrastive loss that explores the inter-modality discordance. To the best of our knowledge, this is the first work that leverages information from different components of the news article (i.e., headline, body, and multiple images) for multimodal fake news detection. We conduct extensive experiments on the real-world datasets to show that our approach outperforms the state-of-the-art by an average F1-score of 6.3%.

Abstract

High utility itemset mining is an important model in data mining. It involves discovering all itemsets in a quantitative transactional database that satisfy a user-specified minimum utility (minUtil) constraint. MinUtil controls the minimum value that an itemset must maintain in a database. Since the model evaluates an itemset’s interestingness using only the minUtil constraint, it implicitly assumes that all items in the database have similar utility values. However, some items have high utility, while others may have relatively low utility in a database. If minUtil is set too high, the user will miss all itemsets containing low utility items. To find itemsets that involve both high and low utility items, minUtil has to be set very low. However, this may cause a combinatorial explosion as the items with high utility may combine with others in all possible ways. This dilemma is called the low utility item problem. This paper proposes a flexible …

Abstract

Spatial High Utility Itemset Mining (SHUIM) is an important knowledge discovery technique with many real-world applications. It involves discovering all itemsets that satisfy the user-specified m inimum u tility (minUtil) i n a q uantitative spatiotemporal database. The popular adoption and the successful industrial application of this technique have been hindered by the following two limitations: (i) Since the rationale of SHUIM is to find all itemsets that satisfy the minUtil constraint, it often produces too many patterns, most of which may be redundant or uninteresting to the user. (ii) Specifying a right minUtil value is an open research problem in SHUIM. This paper tackles these two problems by proposing a novel model of top-k spatial high utility itemsets that may exist in a database. A new constraint, called dynamic minimum utility (dMinUtil), was explored to reduce the search space effectively. This constraint is based on …

Abstract

This paper presents a novel passive three-output differential with three degrees of freedom (3DOF), that translates motion and torque from a single input to three outputs. The proposed ThreeOutput Open Differential is designed such that its functioning is analogous to the functioning of a traditional two-output open differential. That is, the differential translates equal motion and torque to all its three outputs when the outputs are unconstrained or are subjected to equivalent load conditions. The introduced design is the first differential with three outputs to realise this outcome. The differential action between the three outputs is realised passively by a symmetric arrangement of three two-output open differentials and three two-input open differentials. The resulting differential mechanism achieves the novel result of equivalent input to output angular velocity and torque relations for all its three outputs. Furthermore, Three-Output Open Differential achieves the novel result for differentials with more than two outputs where each of its outputs shares equivalent angular velocity and torque relations with all the other outputs. The kinematics and dynamics of the Three-Output Open Differential are derived using the bond graph method. In addition, the merits of the differential mechanism along with its current and potential applications are presented.

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.