Abstract

Spatial High Utility Itemset Mining (SHUIM) aims to discover all itemsets in a spatiotemporal database that satisfy the userspecified minimum utility (minU til) and maximum distance (maxDist) constraints. The popular adoption and successful industrial application of SHUIM suffers from the following two limitations: (i) Since SHUIM determines the interestingness of an itemset without taking into account its support with in the data, it is sensitive to the random noise and length of the transactions within the data. Consequently, SHUIM facilitates sporadic itemsets with high utility to be generated as SHUIs. For instance, items in long transactions can combine with each other and be generated as SHUIs. (ii) SHUIM is a computationally expensive process because the generated itemsets do not satisfy the downward closure property. This paper introduces Spatial High Utility Frequent Itemset Mining (SHUFIM) to address these two issues. A SHUI in a spatiotemporal database is said to be a SHUFI if and only if its support is no less than the user-specified minimum support (minSup) constraint. The usage of minSup not only facilitates the proposed model to be tolerant to the random noise within the data, but also facilitate us to employ additional pruning techniques to reduce the computational cost. A single scan fast algorithm has also been proposed to discover all SHUFIs in a spatiotemporal database. Experimental results demonstrate that the proposed algorithm is efficient. We also demonstrate the usefulness of the proposed model with two real-world applications.

Abstract

This note offers a perspective on the informal markets as an assortment of human players and networks contributing to a socio‐business culture of economic survival. The note also offers a few thoughts on specific segments of the informal workforce in India based on ethnographic evidence and a sociological assessment. The author begins with an appraisal of the socio‐economic phenomenon of informality including an anthropological framing of the “informal.” An important focus is the IT‐based businesses and the variety of players ranging from middle‐range businesses to floating or freelancing actors. The note ends with a spotlight on the internet fuelled gig economy where ICTs move beyond being a product or a service and take over as the business platform ushering a new era of informal or gig economy.

Abstract

In this work, we consider a multi-wheeled payload transport system. Each of the wheels can be selectively actuated.When they are not actuated, wheels are free moving and do not consume battery power. The payload transport system is modeled as an actuated multi-agent system, with each wheelmotor pair as an agent. Kinematic and dynamic models are developed to ensure that the payload transport system moves as desired. We design optimization formulations to decide on the number of wheels to be active and which of the wheels to be active so that the battery is conserved and the wear on the motors is reduced. Our multi-level control framework over the agents ensures that near-optimal number of agents is active for the payload transport system to function. Through simulation studies we show that our solution ensures energy efficient operation and increases the distance traveled by the payload transport system, for the same battery power. We have built the payload transport system and provide results for preliminary experimental validation.

Abstract

Financial transactions are intimately bound with social interactions and woven into everyday economic lives. In this paper we focus on PayTM, a digital wallet, and a specific set of users of PayTM, street vendors in urban India. Through an ethnographic investigation we offer to unpack two questions: 1. Can digital forms of money create financial inclusion by opening up access to the marginalized 2. Can digital platforms amplify socio-economic capacities of low literate users enhancing financial literacy? We argue that digital and financial literacy are an immersive component of digital wallet use acquiring ‘everyday life’ in specific socio-economic ecosystems. Our study captures daily practices of digital money staking a claim in advancing the understanding of financial inclusion as a lived process accumulating habits, practices and stakes to expand socio-economic capabilities.

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 Indi

Abstract

The present disclosure discloses methods and systems for enhancing shopping experience in physical stores. The method includes receiving at least one persona associated with a user based on one or more of: ethnographic data obtained from a user, demographic data associated with the user, buying behavioral data associated with the user, and social networking data associated with the user. After this, one or more historical activities of the user inside one or more physical stores are received. Also, one or more constraints associated with the user are received. Once received, the at least one persona, the one or more constraints, and the one or more historical activities are analyzed to generate a pre-defined number of personalized recommendations. Finally, the personalized recommendations are displayed to the user within a window of a user interface.

Abstract

Understanding people's attitudes towards and uses of technology is an essential aspect of a successful design process. Ethnography is a proven method for acquiring this understanding. However, there are challenges to incorporating fieldwork, most notably the time factor, considered by some as the greatest barrier. This is especially true for many technology companies whose turn-around time from concept to design to implementation is accelerated. We propose a solution to the dilemma between acquiring the benefits of fieldwork with the compressed timescales of many technology projects by using focused field trips as a method for gaining rich insights into peoples' uses of and attitudes towards technologies in real-world settings. In a short amount of time (one or two days), field trips sensitize design teams to the priorities of stakeholder groups. We outline a systematic approach to incorporating field trips as a …

Abstract

Within the social system of science, citation practices characterize social functions like the conferral of recognition upon the work of others as well as the acknowledgement of one’s intellectual debt. However, the structure of intellectual influence is misrepresented when only the immediate citations and their cardinality are taken into consideration. Thus, in order to better understand the associative dissemination of influence and approximately construe the anatomy of this structure, complex interactions in the convoluted network of authors and papers need to be probed. Our study aims at understanding these heterogeneous complex interactions. For the bibliographic dataset of authors and publications, we define proxy scores that attempt to determine the associative influence of the cited author over the citing author. In order to harness structural connectivity of the network, we generate author vector representations using these influence scores. Furthermore, with a view to assess the competence of our proposed scores, we evaluate these representations and provide an empirical study of the results obtained with our algorithm against the baseline and also present a qualitative analysis.

Abstract

The brain is the most complex biological system that exists. Timbre, in its very nature, is a multidimensional concept with several levels of abstraction thus rendering the investigation of its processing in the brain extremely challenging. Timbre processing can be discussed in relation to levels of abstraction. Low- to midlevel representations can be associated with the neural representation of acoustic structure while high-level abstractions correspond to the neural representation of sound source properties. Furthermore, neural correlates of timbre can be broadly classified based on three stimulus categories, that is, those pertaining to music, speech, and environmental sounds. This chapter summarizes studies that have attempted to uncover neural correlates of varying levels of timbre abstractions. Finally, developments in methodological approaches are described, including the shift from univariate to multivariate statistical models, the employment of more naturalistic stimuli, and brain measurement paradigms from hitherto controlled auditory paradigms.

Abstract

Owing to the stigmatization of mental illnesses such as depression in India [1], there is a need for indirect unsuspecting ways to identify risk for depression and provide timely intervention. Healthy-Unhealthy Music scale (HUMS) [2] is one such assessment tool developed on Australian population that uses music engagement as an indicator of anxiety levels and potential high-risk for depression as assessed by Kessler’s Psychological Distress Scale (K10). The current study aims to ascertain its validity in an Indian setting followed by applying machine learning approaches to predict mental well-being from music associations. A diverse group comprising Indian adult population was assessed using HUMS and mental well-being and proneness to depression measures. HUMS structure investigated via Exploratory factor analyses, and concurrent validity tested with correlations to depression risk and wellbeing revealed high external validity and applicability of HUMS in Indian adult population.Furthermore, very low in-sample error for models like Support Vector Machines (SVM) with nonlinear kernels suggests an underlying pattern between HUMS responses and K10 score. Finally, a two-class model resulted in out of sample accuracy of 81%. To conclude, HUMS demonstrates high generalizability and hence applicability in Indian adult population and potential for employing ML models to capture the underlying pattern.