Abstract

Sexism, a permeate form of oppression, causes profound sufering through various manifestations. Given the increasing number of experiences of sexism shared online, categorizing these recollections automatically can support the battle against sexism, since it can promote successful evaluations by gender studies researchers and government representatives engaged in policy making. In this paper, we examine the fne-grained, multi-label classifcation of accounts (reports) of sexism. To the best of our knowledge, we consider substantially more categories of sexism than any related prior work through our 23-class problem formulation. Moreover, we present the frst semi-supervised work for the multi-label classifcation of accounts describing any type(s) of sexism. We devise self-training-based techniques tailor-made for the multi-label nature of the problem to utilize unlabeled samples for augmenting the labeled set. We identify high textual diversity with respect to the existing labeled set as a desirable quality for candidate unlabeled instances and develop methods for incorporating it into our approach. We also explore ways of infusing class imbalance alleviation for multi-label classifcation into our semisupervised learning, independently and in conjunction with the method involving diversity. In addition to data augmentation methods, we develop a neural model which combines biLSTM and attention with a domain-adapted BERT model in an endto-end trainable manner. Further, we formulate a multi-level training approach in which models are sequentially trained using

Abstract

Residential electricity consumption (REC) in India has tripled in the past two decades accounting for 24% of the overall electricity consumption during 2018-19. Residential air conditioning (AC) usage is responsible for about 20%-40% of REC in India. This paper investigates the relationship of residential AC use with indoor temperature and relative humidity (RH) using concurrent time-series monitoring data gathered in eight dwellings during summer and monsoon seasons. Contextual data about the dwelling (physical) and household characteristics were gathered using face-to-face interview based surveys. The dwellings were located in Hyderabad representing the composite climate of India. The mean daily electricity consumption was found to be higher in summer (11.5kWh) possibly due to the higher usage of AC (because of higher ambient conditions) as compared to 6.5kWh/day during monsoon season. Binary logistic regression identified the trigger indoor temperature and RH at which AC was likely to be switched on in the summer as 29 °C - 31.9 °C for indoor temperature and 36%-38.9% RH. In the monsoon season AC was predicted to come on sooner at 26°C-28.9°C but at higher RH range of 59%-61.9%. These empirical findings can be used to reduce residential cooling energy demand through smart management of ACs in Indian dwellings

Abstract

A smart city is a collection of a variety of smart devices representing several disparate infrastructural verticals. The data gathered by these smart devices helps bring a strong integration of human, collective, and artificial intelligence within the city operation. With the advent and proliferation of electric vehicles (EVs), the EV charge point is a relatively new addition to this plethora of smart devices. We believe that the EV charge point can acquire enhanced functionality if it can be seamlessly integrated with the rest of the smart city infrastructure. For example, charging services can be stopped if smart grid or smart fire alarms sense an electrical overload or fire hazard in the vicinity. However, the interaction of an EV charge point with the smart city infrastructure requires seamless information exchange across various verticals. This horizontal flow of information is enabled by making the charge point complaint with the oneM2M platform. To this end, we present the design and fabrication of an EV charge point based on the OCPP communication standard and complaint with the oneM2M platform. Further, we discuss various use cases showing an increased functional capability of the EV charger due to access to data from other IoT devices.

Abstract

Natural Language Understanding has made recent advancements where context-aware token representation and word disambiguation have become possible to a large extent. In this scenario, comprehension of phrasal semantics particularly in the context of multi word expressions (MWE) and idioms, is the subsequent task to be addressed. Word level metaphor detection is unable to handle phrases or MWE(s) which occur in both literal and idiomatic context. State of the art transformer architectures can be useful in this context, but the absence of a large comprehensive dataset is a bottleneck. In this paper, we present a labelled EPIE dataset containing 3136 occurrences for 358 formal idioms. To prove the efficacy of our dataset, we also train a sequence classification model effectively and perform cross-dataset evaluation on three independent datasets. Our method achieves good results on all datasets with F1 score of 96% on our test data, and 82%, 74% and 76% F1 score on SemEval All Words, SemEval Lex Sample, and PIE Corpus datasets respectively.

Abstract

Conventional approaches for addressing road safety rely on manual interventions or immobile CCTV infras-tructure. Such methods are expensive in enforcing compliance to traffic rules and do not scale to large road networks. This paper proposes a simple mobile imaging setup to address several common problems in road safety at scale. We use recent computer vision techniques to identify possible irregularities on roads, the absence of street lights, and defective traffic signs using videos from a moving camera-mounted vehicle. Beyond the inspection of static road infrastructure, we also demonstrate the mobile imaging solution’s applicability to spot traffic violations. Before deploying our system in the real-world, we investigate the strengths and shortcomings of computer vi- sion techniques on thirteen condition-based hierarchical labels. These conditions include different timings, road type, traffic density, and state of road damage. Our demonstrations are then carried out on 2000 km of unconstrained road scenes, captured across an entire city. Through this, we quantitatively measure the overall safety of roads in the city through carefully constructed metrics. We also show an interactive dashboard for visually inspecting and initiating action in a time, labor and cost-efficient manner. Code, models, and datasets used in this work will be publicly released

Abstract

3D reconstruction of human body model from a monocular image is an under-constrained and challenging yet desired research problem in computer vision. Recently proposed multi-layered shape repre-sentation called PeeledHuman attempted a sparse non-parametric 2D representation that can handle severe self-occlusion. However, the key limitation of their PeeledHuman model is that the predicted depth maps of self-occluded parts are sparse and noisy, and hence after back-projection lead to distorted body parts and sometimes with discontinuity between them. In this work, proposed to intro- duce Peeled Segmentation map representation in a coarse-to-fine refinement framework which consist of a cascade three networks namely, PeelGAN, PSegGAN and RefGAN. At first, we use original PeeledHuman as baseline model to predict initial coarse estimation of peeled depth maps from input RGB image. These peeled maps are subsequently fed as input along with monocular RGB image to our novel PSegGAN which predict Peeled Segmentation maps in a gen- erative fashion. Finally, we feed these peeled segmentation maps as additional context along with monocular input image to our Ref-GAN which predicts the refined peeled RBG and Depth maps. This also provides an additional output of 3D semantic segmentation of the reconstructed shape. We perform thorough empirical evaluation over four publicly available datasets to demonstrate superiority of our model.

Abstract

This paper presents a learning assistant that tests one’s knowledge and gives feedback that helps a person learn at a faster pace. A learning assistant (based on automated question generation) has extensive uses in education, information websites, self-assessment, FAQs, testing ML agents, research, etc. Multiple researchers, and companies have worked on Virtual Assistance, but majorly in English. We built our learning assistant for Telugu language to help with teaching in the mother tongue, which is the most efficient way of learning. Our system is built primarily based on Question Generation in Telugu. Many experiments were conducted on Question Generation in English in multiple ways. We have built the first hybrid machine learning and rule-based solution in Telugu, which proves efficient for short stories or short passages in children’s books. Our work covers the fundamental question forms with question types: adjective, yes/no, adverb, verb, when, where, whose, quotative, and quantitative (how many/how much). We constructed rules for question generation using Part of Speech (POS) tags and Universal Dependency (UD) tags along with linguistic information of the surrounding relevant context of the word. We used keyword matching, multilingual sentence embedding to evaluate the answer. Our system is primarily built on question generation in Telugu, and is also capable of evaluating the user’s answers to the generated questions.

Abstract

Centrality measures on graphs have found appli- cations in a large number of domains including modeling the spread of an infection/disease, social network analysis, and transportation networks. As a result, parallel algorithms for computing various centrality metrics on graphs are gaining significant research attention in recent years. In this paper, we study parallel algorithms for the percolation centrality measure which extends the betweenness-centrality measure by incorporating a time dependent state variable with every node. We present parallel algorithms that compute the source-based and source-destination variants of the percolation centrality values of nodes in a network. Our algorithms extend the algorithm of Brandes, introduce optimizations aimed at exploiting the structural properties of graphs, and extend the algorithmic techniques introduced by Sariyuce et al. [26] in the context of centrality computation. Experimental studies of our algorithms on an Intel Xeon(R) Silver 4116 CPU and an Nvidia Tesla V100 GPU on a collection of 12 real-world graphs indicate that our algorithmic techniques offer a significant speedup.

Abstract

Quantum walks [1–6] are quantum analogs of random walks on graphs and have widespread applications in several areas of quantum information processing [7]. In particular, they are a universal model for quantum computation and are crucial to the design of quantum algorithms for a plethora of problems such as element distinctness [8,9], searching for a marked state in graphs [10–16], matrix product verification [17], triangle finding [18], group commutativity [19], and many others [20–24]. In fact, for some oracular problems, quantum walks exhibit an exponential speedup over their classical counterparts [21,22]. For several of these algorithms, the runtime strongly depends on the time required for the quantum walk on a graph to localize at a vertex inside a marked set of vertices of interest. In the context of classical random walks, the hitting time is defined as the expected time required to hit a marked vertex. As such, the quantum hitting time characterizes the running time of the underlying quantum algorithm [14,15,20,21,25,26]. For continuous-time quantum walk (CTQW), the Hamiltonian governing the dynamics of the walk encodes the (vertex or edge) connectivity of the underlying graph. The dynamics involves evolving some initial state according to this Hamiltonian for time T , followed by a measurement in the basis spanned by the vertices of the graph, thus finding a target state with a high probability. However, Hamiltonian evolutions are unitary, and therefore, unlike classical random walks, quantum walks never converge to a fixed state with time. One method to address this is to evolve the system by H

Abstract

Indian English (IE), on the surface, seems quite similar to standard English. However, closer observation shows that it has actually been influenced by the surrounding vernacu- lar languages at several levels from phonology to vocabulary and syntax. Due to this, automatic speech recognition (ASR) systems developed for American or British varieties of English result in poor performance on Indian English data. The most prominent feature of Indian English is the characteristic pronunciation of the speakers. The systems are unable to learn these acoustic variations while modelling and cannot parse the non-standard articulation of non-native speakers. For this purpose, we propose a new phone dictionary de- veloped based on the Indian language Com- mon Phone Set (CPS). The dictionary maps the phone set of American English to existing Indian phones based on perceptual similarity. This dictionary is named Indian English Com- mon Phone Set (IE-CPS). Using this, we build an Indian English ASR system and compare its performance with an American English ASR system on speech data of both varieties of En- glish. Our experiments on the IE-CPS show that it is quite effective at modelling the pro- nunciation of the average speaker of Indian En- glish. ASR systems trained on Indian English data perform much better when modelled us- ing IE-CPS, achieving a reduction in the word error rate (WER) of upto 3.95% when used in place of CMUdict. This shows the need for a different lexicon for Indian English.