Abstract

This paper proposes the use of Shewhart test to reduce the number of data-transmissions in IoT networks. It is shown to outperform the widely-used least mean square (LMS) based data reduction method in terms of the number of data-transmissions, implementation complexity and mean square error (MSE) in prediction of time-series data at the sink node based on the partial transmissions of the measured time-series data from the sensor node. The paper also proposes the use of piggybacking and interpolation to further reduce the MSE of the estimated time-series data at the sink node without increasing the number of packet transmissions. The time-series data used for the comparison of data reduction algorithms is a set of measured temperature values in indoor and outdoor scenarios for four days using custom-designed wireless sensor nodes. To express the effectiveness of the piggybacked transmissions on battery lifetime, the total current consumption of the sensor node is measured for different number of piggybacks and corresponding battery lifetime is estimated. It is shown that the proposed piggyback approach significantly reduces the MSE at the cost of slight decrease in battery-lifetime. Published in: 2019 IEE

Abstract

This paper describes the Neural Machine Translation systems used by IIIT Hyderabad (CVIT-MT) for the translation tasks part of WAT-2019. We participated in tasks pertaining to Indian languages and submitted results for English-Hindi, Hindi-English, English-Tamil and Tamil-English language pairs. We employ Transformer architecture experimenting with multilingual models and methods for lowresource languages.

Abstract

The Earth’s climate is not static; it changes according to the natural and anthropogenic climate variability. Anthropogenic forcing due to increase of greenhouse gases in the atmosphere has driven changes in climate variables globally. Changes in climatological variables have severe impact on global hydrological cycle affecting the severity and occurrence of natural hazards such as floods and droughts. Estimation of projections under climate signals with statistical and dynamic downscaling models and integration with water resource management models for the impact assessment have gained much attention. The fine-resolution climate change predictions of dynamic regional climate model (RCM) outputs, which include regional parameterization, have been widely applied in the hydrological impact assessment studies. Advancement of the Coordinated Regional Downscaling Experiment (CORDEX) program has enabled the use of RCMs in regional impact assessment which has progressed in recent years. CORDEX model outputs were considered to be valuable in terms of establishing large ensembles of climate projections based on regional climate downscaling all over the world. However, the simulations of RCM outputs have to be evaluated to check the reliability in reproducing the observed climate variability over a region. The present study demonstrates the use of bias-corrected CORDEX model simulation in analyzing the regional-scale climatology at river basin scale, Krishna river basin (KRB), India. The precipitation and temperature simulations from CORDEX models with RCP 4.5 were evaluated for the historical data for the period of 1965 to 2014 with India Meteorological Department (IMD) gridded rainfall and temperature data sets cropped over the basin. The projected increase

Abstract

This is a short report on the Tutorials and Tech Briefings session of the 12th Innovations in Software Engineering (ISEC 2019) confer- ence held on 14th February 2019 in Pune, India. The tutorials and tech briefings at ISEC have been popular with the participants because they offer a gentle and friendly introduc- tion to cutting edge topics and research at the frontiers of the discipline of software engineering. This year’s track attracted a total of 11 submissions (four tutorial and seven tech briefings). The five submissions that were selected reflect the current interests and directions of the field of software engineering

Abstract

Air-conditioned buildings are conventionally designed and operated to maintain homogeneous thermal conditions. Maintaining the occupied and unoccupied zones at the same thermal conditions leads to higher energy consumption. More importantly, homogeneous thermal conditions do not address the need for individual thermal comfort preferences. A personal comfort system (PCS) allows the occupants to create desired localized thermal conditions around workstations in the office environment. Using computational fluid dynamic (CFD) and multi-node thermoregulation models, this paper evaluates the feasibility of a PCS with a radiantly cooled partition panel system to achieve thermal comfort. All input parameters for the model were derived from reallife measurements, including thermal characteristics of the room, work desk with radiantly cooled partition, and HVAC systems. A combination of scSTREAM™ and scTETRA™ was used to model the room and the human body (Cradle MSC Software, 2017). The simulation model had a mannequin in a seated position having summer clothing values and office activity metabolic rate. Combinations of three ambient room air temperatures and five panel surface temperatures were investigated to estimate the impact of radiant panels on overall thermal comfort and various body parts of the mannequin. The body parts like the thighs, chest, back, and pelvis showed a low thermal variation in the range of 0.9-1.2°C. The parts such as the head, neck, shoulders, arms, and legs showed a thermal variation in the range of 1.6-2.7°C, while the body parts farthest from the warm torso - the feet, experienced the highest variation in the range of 4.4- 4.5°C. It was observed that the back side of the body was distinctly warmer than the front side of the body throughout the studied cases due to the action of frontplaced radiant panels. It also indicates that at a given room air temperature with an increase in the difference between surface and air temperature, from 0°C to 8°C, all the body

Abstract

The development of building codes for energy efficiency depends on climate zones. National Building Codes of India prescribes five climate zones in India. This classification does not consider fluctuations of outdoor conditions and its effect on indoor comfort conditions. The indoor comfort conditions can be incorporated by using Heating Degree Day (HDD) and Cooling Degree Day (CDD) analysis. Additionally, this classification used mean monthly temperature which cannot capture extreme conditions of the month while the degree day can account for fluctuations in the outdoor temperature and eliminate those periods when heating or cooling systems do not need to operate for a day. This study proposes a new climate zones classification based on hierarchical cluster analysis on 60 Indian locations. The analysis uses climate indices such as HDD, CDD and annual mean relative humidity as variables for clustering analysis. The 60 locations are grouped into 8 climate zones. Three climate zones have only one city as they are distinct from the other location in terms of climate. This updated climate classification may improve the accuracy of the energy conservation codes and building design

Abstract

Road safety mapping using satellite images is a cost-effective but a challenging problem for smart city planning. The scarcity of labeled data, misalignment and ambiguity makes it hard to learn efficient embeddings in order to classify between safe and dangerous road segments. In this paper, we address the challenges using a region guided attention network. In our model, we extract global features from a base network and augment it with local features obtained using the region guided attention network. In addition, we perform domain adaptation for unlabeled target data. In order to bridge the gap between safe samples and dangerous samples from source and target respectively, we propose a loss function based on within and between class covariance matrices. We conduct experiments on a public dataset of London to show that the algorithm achieves significant results with the classification accuracy of 86.21%. We obtain an increase of 4% accuracy for NYC using domain adaptation network.

Abstract

Livestreaming platforms enable content producers, or streamers, to broadcast creative content to a potentially large viewer base. Chatrooms form an integral part of such platforms, enabling viewers to interact both with the streamer, and amongst themselves. Streams with high engagement (many viewers and active chatters) are typically considered engaging, and often promoted to end users by means of recommendation algorithms, and exposed to better monetization opportunities via revenue share from platform advertising, viewer donations, and third- party sponsorships. Given such incentives, some streamers make use of fraudulent means to increase perceived engagement by simulating chatter via fake “chatbots” which can be purchased from shady online marketplaces. This inauthentic engagement can negatively influence recommendation, hurt streamer and viewer trust in the platform, and harm monetization for hon- est streamers. In this paper, we tackle the novel problem of automating detection of chatbots on livestreaming platforms. To this end, we first formalize the livestreaming chatbot detection problem and characterize differences between botted and gen- uine chatter behavior observed from a real-world livestream- ing chatter dataset collected from Twitch.tv. We then propose S HERLOCK , which posits a two-stage approach of detecting chatbotted streams, and subsequently detecting the constituent chatbots. Finally, we demonstrate effectiveness on both real and synthetic data: to this end, we propose a novel strategy for collecting labeled, synthetic chatter dataset (typically unavailable) from such platforms, enabling evaluation of proposed detection approaches against chatbot behaviors with varying signatures. Our approach achieves .97 precision/recall on the real-world dataset, and .80+ F1 scores across most simulated attack settings.

Abstract

Massively Multiplayer Online Role-Playing Games (MMORPGs) are persistent virtual environments where millions of players interact in an online manner. We study the problem of player churn and social contagion using MMORPG game logs by analyzing the impact of a node’s churn behavior on its immediate neighborhood or group. The two key research questions in this paper are - When an active node, ego, becomes dormant, what is the impact on the activity behavior of ego’s immediate neighbor, alter, 1) based on ego’s characteristics and ego’s relationship with alter and 2) based on the activity behavior of alter’s remaining neighbors. We use a supervised learning framework to study the impact of player churn and social contagion. Experimental results show that the classification models perform substantially better than random for both the research problems. Finally, we use a data-driven approach to propose a player typology based on degree of socialization and analyze churn behavior among these player types. Experimental results show that the loner player type is much more likely to churn than the socializer player types and as the degree of socialization decreases among socializers, the propensity to churn increases. Index Terms—churn, social contagion, supervised learning, clustering, player typology

Abstract

Social dynamics are based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time 2) making choice between a large number of weak ties or few strong social ties. 3) finding a social group. In general, these are the major determinants of an individual’s social life. This paper looks into the phenomenon of social exploration in an activity based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player’s social behavior over time? 3) Are certain social behaviors more stable than the others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient. We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties. Index Terms—Social Exploration, Social Behavior Typology, MMORPG, Clusterin