Abstract

Recent revision of Indian standard code on earthquake resistant design suggested certain recommendations for inclusion of effects of vertical ground acceleration for design of buildings. Clause 6.3.2.1 of IS 1893 (Part 1) suggests that for non-orthogonal frames, load combinations should include 30% of earthquake load in other directions along with the direction considered and Clause 6.3.2.1.1 suggests that for orthogonal frames, full load has to be applied. Besides, clause 6.3.3 mentions that all the buildings present in seismic zone IV and V have to consider vertical ground motion effects. This results in 73 load combinations for calculating the design forces in structural members. However, the necessity to consider the effects of vertical ground motions for regular buildings, although present in seismic zones IV and V, is not verified. To verify the above clauses for regular buildings, a case study is performed on a 5 storied regular framed RC building. Using, linear static analysis, design forces in structural members for the contradicting clauses on load combinations are compared. From the study, it is concluded that 3-dimensional ground motion effects are not necessary for every building even if the building is located in seismic zone IV or V.

Abstract

We introduce a novel model for future/next page prediction in on- line user journeys that uses a combination of doc2vec webpage representations with an LSTM-based neural network to mine pat- terns from users’ online navigational paths combined with their content preferences. Empirical explorations show promise towards creating customized user experiences leveraging this work.

Abstract

In this paper, the problem of path planning through tight spaces, for the task of spatial payload transportation, using a formation of mobile manipulators is addressed. Due to the high dimensional configuration space of the system, efficient and geometrically stable path planning through tight spaces is challenging. We resolve this by planning the path for the system in two phases. First, an obstacle-free trajectory in R 3 for the payload being transported is determined using RRT. Next, near-energy optimal and quasi-statically stable paths are planned for the formation of robots along this trajectory using non-linear multi-objective optimization. We validate the proposed approach in simulation experiments and compare different multi-objective optimization algorithms to find energy optimal and geometrically stable robot path plans.

Abstract

In the field of hyperspectral remote sensing, the spectral data is derived from hyperspectral images and are matched with the spectral library. Till date there are no studies that have matched with the hand–held spectrum collected by spectroradiometer at temporal scale. Creating a spectral library for the same species at various regions is a challenge, as acquiring spectrum at temporal sets are practically difficult and expensive. The absorption features are the unique key print for the identification of the two different samples of same species and also helps to discriminate two different species. The intensity and position of strong absorption region helps to match the spectrum accurately. In the current study probability based matching technique is used where the dips of a species of a season is matched to the other within a predefined abundance window. This study helps to know the seasonal changes of the tree due to its environmental factors, stress and deficiency.

Abstract

In this work we introduce the task of item recommendation in an aggregated marketplace. An aggregated market place is a single place where items from multiple e-commerce sites can be viewed and compared. Our main objective while recommending items is to provide users with alternatives across different e-commerce sites. To accomplish this, we embed items in a vector space and apply retrofitting, a method used in language processing to inject relationships into word embeddings. Due to recent attention towards item recommendation using only click data without any user feedback and our particular constraints, we test our model on real click data derived from Samsung Mall App.

Abstract

Social network and publishing platforms, such as Twitter, support the concept of verification. Verified accounts are deemed worthy of platform-wide public interest and are separately authenticated by the platform itself. There have been repeated assertions by these platforms about verification not being tantamount to endorsement. However, a significant body of prior work suggests that possessing a verified status symbolizes enhanced credibility in the eyes of the platform audience. As a result, such a status is highly coveted among public figures and influencers. Hence, we attempt to characterize the network of verified users on Twitter and compare the results to similar analysis performed for the entire Twitter network. We extracted the entire network of verified users on Twitter (as of July 2018) and obtained 231,246 English user profiles and 79,213,811 connections. Subsequently, in the network analysis, we found that the sub-graph of verified users mirrors the full Twitter users graph in some aspects such as possessing a short diameter. However, our findings contrast with earlier findings on multiple aspects, such as the possession of a power law out- degree distribution, slight dissortativity, and a significantly higher reciprocity rate, as elucidated in the paper. Moreover, we attempt to gauge the presence of salient components within this sub-graph and detect the absence of homophily with respect to popularity, which again is in stark contrast to the full Twitter graph. Finally, we demonstrate stationarity in the time series of verified user activity levels. To the best of our knowledge, this work represents the first quantitative attempt at characterizing verified users on Twitter. Index Terms—Twitter, Social Influence, Centrality, Network Analysis, Online User Characterization, User Categorization

Abstract

Stack Overflow, a Q&A site on programming, awards reputation points and badges (game elements) to users on performing various actions. Situating our work in Digital Signaling Theory, we investigate the role of these game elements in characterizing social qualities (specifically, popularity and impact) of its users. We operationalize these attributes using common metrics and apply statistical modeling to empirically quantify and validate the strength of these signals. Our results are based on a rich dataset of 3,831,147 users and their activities spanning nearly a decade since the site's inception in 2008. We present evidence that certain non-trivial badges, reputation scores and age of the user on the site positively correlate with popularity and impact. Further, we find that the presence of costly to earn and hard to observe signals qualitatively differentiates highly impactful users from highly popular users.

Abstract

Social network and publishing platforms, such as Twitter, support the concept of a secret proprietary verification process, for han- dles they deem worthy of platform-wide public interest. In line with significant prior work which suggests that possessing such a status symbolizes enhanced credibility in the eyes of the platform audience, a verified badge is clearly coveted among public figures and brands. What are less obvious are the inner workings of the verification process and what being verified represents. This lack of clarity, coupled with the flak that Twitter received by extend- ing aforementioned status to political extremists in 2017, backed Twitter into publicly admitting that the process and what the status represented needed to be rethought. With this in mind, we seek to unravel the aspects of a user’s profile which likely engender or preclude verification. The aim of the paper is two-fold: First, we test if discerning the verification status of a handle from profile metadata and content features is feasible. Second, we unravel the features which have the greatest bearing on a handle’s verification status. We collected a dataset con- sisting of profile metadata of all 231,235 verified English-speaking users (as of July 2018), a control sample of 175,930 non-verified English-speaking users and all their 494 million tweets over a one year collection period. Our proposed models are able to reliably identify verification status (Area under curve AUC > 99%). We show that number of public list memberships, presence of neutral sen- timent in tweets and an authoritative language style are the most pertinent predictors of verification status. To the best of our knowledge, this work represents the first attempt at discerning and classifying verification worthy users on

Abstract

Narratives of the gig economy are largely perspectives from the global North and the nature of their ride-hailing market. Freelancing or gigging offer contrasting connotations depending on the market and the labour it is situated in. India, with its demographic dividend and employment challenges, view opportunities arising out of the gig- economy differently. To investigate the impact of ride-hailing apps in the Indian market, and the challenges and opportunities it has brought in, we offer learnings from an ethnographic study of Uber drivers in India.

Abstract

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority Over Sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) finding that SMOTE provides a higher performance increase. Overall, this research contains implications for researchers interested in inferring users' cognitive states from eye-tracking data.