Abstract

Is painting space fundamentally perspectival? In the European Renaissance (14th to the 17th century), the painting space was thought of as having an interior of perspective where one could place an object. It took many years after the Renaissance for European art to come out of this optical or geometrical perspective and realise that the space of painting is fundamentally non-perspectival. Historically in Europe, impressionists (1860) painters are the ones who tried to break away from this optical or single-point perspective and create paintings according to 'lived perspective'. Optical perspective is one of the visual dogmas which are believed till today; thus, it is tough to appreciate non-perspectival paintings. This paper aims to give technical reasons why painting space is fundamentally not perspectival; the first section of the paper will deal with the question 'what kind of space is painting space?', and in the second section, we will compare method of photograph and drawing to find the differences between mechanism of camera and human perception . In the last section of the paper we will use Indian folk paintings, to demonstrate how cognitive or alternative/multiple perspectives open new possibilities in painting space.

Abstract

Around 40% of the world’s population is currently without access to the Internet. The digital divide is due to the high cost of provisioning these services and the low return on investment for network operators. We propose using 5G network slicing with multi-tenancy (also known as neutral host networks (NHN)) for macro-cells and small cells in rural areas to reduce the costs. This paper investigates the techno-economic feasibility of using rural 5G NHN to minimise the digital divide. A generic model is developed to analyse the techno-economic analysis of 5G NHN deployment around the world, with a special focus on rural areas where no MNO is interested in providing services. To understand the application, it is applied to the Indian scenario. First, a discussion on existing infrastructure, competition and statistics for Indian telecommunications is presented. Next, the technical requirements are analysed using the key performance indicators (KPI) required for the rural 5G NHN for the Indian scenario. The study also analyses the relationship between coverage, investment in the network, the number of subscribers, investment time, demand, the investment per user and sensitivity analysis to understand the feasibility of the proposed solution for Indian villages with different input conditions. Later, a case study is carried out based on the proposed approach, along with coverage modelling for a few Indian villages having different topologies. The results show that 5G NHN using network slicing can significantly reduce the total investment required for providing 5G services in rural areas. Furthermore, the study shows that rural 5G NHN is a viable investment and a key enabler for Internet connectivity for villages with 10-year investment, having a subscribers’ base as low as 100 with a customer growth rate of 7%.

Abstract

In this paper, we present a novel approach for domain adaptation in Neural Machine Translation which aims to improve the translation quality over a new domain. Adapting new domains is a highly challenging task for Neural Machine Translation on limited data, it becomes even more difficult for technical domains such as Chemistry and Artificial Intelligence due to specific terminology, etc. We propose Domain Specific Back Translation method which uses available monolingual data and generates synthetic data in a different way. This approach uses Out Of Domain words. The approach is very generic and can be applied to any language pair for any domain. We conduct our experiments on Chemistry and Artificial Intelligence domains for Hindi and Telugu in both directions. It has been observed that the usage of synthetic data created by the proposed algorithm improves the BLEU scores significantly.

Abstract

This paper describes the participation of team oneNLP (LTRC, IIIT-Hyderabad) for the WMT 2021 task, similar language translation1 . We experimented with transformer based Neural Machine Translation and explored the use of language similarity for Tamil-Telugu and Telugu-Tamil. We incorporated use of different subword configurations, script conversion and single model training for both directions as exploratory experiments.

Abstract

Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.

Abstract

We present findings from a first in-depth postediting effort estimation study in the EnglishHindi direction along multiple effort indicators. We conduct a controlled experiment involving professional translators, who complete assigned tasks alternately, in a translation from scratch and a post-edit condition. We find that post-editing reduces translation time (by 63%), utilizes fewer keystrokes (by 59%), and decreases the number of pauses (by 63%) when compared to translating from scratch. We further verify the quality of translations thus produced via a human evaluation task in which we do not detect any discernible quality differences.

Abstract

Data is expanding at an unimaginable rate, and with this development comes the responsibility of the quality of data. Data Quality refers to the relevance of the information present and helps in various operations like decision making and planning in a particular organization. Mostly data quality is measured on an ad-hoc basis, and hence none of the developed concepts provide any practical application.

Abstract

Continuous-time quantum walks have proven to be an extremely useful framework for the design of several quantum algorithms. Often, the running time of quantum algorithms in this framework is characterized by the quantum hitting time: the time required by the quantum walk to find a vertex of interest with a high probability. In this article, we provide improved upper bounds for the quantum hitting time that can be applied to several CTQW-based quantum algorithms. In particular, we apply our techniques to the glued-trees problem, improving their hitting time upper bound by a polynomial factor: from O(n 5 ) to O(n 2 log n). Furthermore, our methods also help to exponentially improve the dependence on precision of the continuous-time quantum walk based algorithm to find a marked node on any ergodic, reversible Markov chain by Chakraborty et al. [PRA 102, 022227 (2020)].

Abstract

The social media technologies are open to users who are intended in creating a community and publishing their opinions of recent incidents. The participants of the online social networking sites remain ignorant of the criticality of disclosing personal data to the public audience. The private data of users are at high risk leading to many adverse effects like cyberbullying, identity theft, and job loss. This research work aims to define the user entities or data like phone number, email address, family details, health-related information as user’s sensitive private data (SPD) in a social media platform. The proposed system, Tweet-Scan-Post (TSP), is mainly focused on identifying the presence of SPD in user’s posts under personal, professional, and health domains. The TSP framework is built based on the standards and privacy regulations established by social networking sites and organizations like NIST, DHS …

Abstract

We describe our participation in all the subtasks of the Germeval 2021 shared task on the identification of Toxic, Engaging, and FactClaiming Comments. Our system is an ensemble of state-of-the-art pre-trained models finetuned with carefully engineered features. We show that feature engineering and data augmentation can be helpful when the training data is sparse. We achieve an F1 score of 66.87, 68.93, and 73.91 in Toxic, Engaging, and Fact-Claiming comment identification subtasks.