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.

Abstract

Earthquake safety of buildings is one of the primary concerns of structural engineers throughout the world. The performance of buildings during past earthquakes has brought out the lacunas in design, analysis and execution of these structures. The constant endeavour for uniqueness has led to the distribution of irregularities along the plan and height of buildings. One of such attempts is the development of Open Ground Storey (OGS) buildings. It is the dominant parameter having vertical irregularity, if not rectified, may often lead to the collapse of structure. Reinforced concrete building with OGS; absence of infill wall in a ground storey, is prevailing construction practice in most places in India. It causes a sudden decrease in lateral stiffness (K) and strength (V), from adjacent upper to ground storey, results in accumulation of stresses due to large lateral displacement in ground storey columns to cause soft storey effect. The problem can be solved by increasing K and V of the ground storey relative to the adjacent upper storey. Seismic codes specify the design process for buildings with OGS by relative K and V of ground and adjacent upper storey. Quantifying design criteria for such structural configurations based on Design Amplification Factor (DAF) for ground storey columns is preferable. K and V of storey depends on masonry strength, central opening (OP) in infills and interaction between wall infill and frame. In the current study, DAF is proposed to overcome OGS effect considering the above parameters. Additional parameters like Stiffness factor (KF) and strength factor (VF), ratios of elastic lateral stiffness (KE) and maximum

Abstract

The advent of the cracked moment of inertia (MoI) in Indian code provisions has sparked many discussions and confusions regarding what MoI is to be used in the design of structures in service load case and ultimate load case. The intent of this study is to understand the uncracked and cracked moment of inertia, what code provisions recommend, their applicability and the possible reasoning behind the recommendations. Solved examples are presented along with this review study to clear the usage of cracked MoI and u n d e r s t a n d t h e s i g n i  c a n c e o f s u c h recommendations.

Abstract

An effective earthquake (Mw 7.9) struck Alaska on 3 November 2002. This earthquake ruptured 340 km along Susitna Glacier, Denali, and Totschunda faults in Central Alaska. The peak ground acceleration (PGA) was recorded about 0.32 g at station PS10, which was located 3 km from the fault rupture. The PGA would have recorded a high value if more instruments had been installed in the region. A numerical study has been conducted to find out the possible ground motion record that could occur at maximum horizontal slip during the Denali earthquake. The current study overcomes the limitation of number of elementsto model the Denali fault. These numerical results are compared with observed ground motions. It is observed that the ground motions obtained through numerical analysis are in good agreement with observed ground motions. From numerical results, it is observed that the possible expected PGA is 0.62 g at maximum horizontal slip of Denali fault.