Abstract

Requirements Engineering in the industry is expertise-driven, heavily manual, and centered around various types of requirement specification documents being prepared and maintained. These specification documents are in diverse formats and vary depending on whether it is a business requirement document, functional specification, interface specification, client specification, and so on. These diverse specification documents embed crucial product knowledge such as functional decomposition of the domain into features, feature hierarchy, feature types and their specific feature characteristics, dependencies, business context, etc. Moreover, in a product development scenario, thousands of pages of requirement specification documentation is created over the years. Comprehending functionality and its associated context from large volumes of specification documents is a highly complex task. To address this problem, we propose to digitalize the requirement specification documents into processable models. This paper discusses the salient aspects involved in the digitalization of requirements knowledge from diverse requirement specification documents. It proposes an AI engine for the automatic transformation of diverse text-based requirement specifications into machine-processable models using NLP techniques and the generation of context-sensitive user stories. The paper describes the key requirement abstractions and concepts essential in an industrial scenario, the conceptual meta-model, and DizReq engine (AI engine for digitalizing requirements) implementation for automatically transforming diverse requirement specifications into user stories embedding the business context. The evaluation results from digitalizing specifications of an IT product suite are discussed: mean feature extraction efficiency is 40 features/file, mean user story extraction efficiency is 71 user stories/file, feature extraction accuracy is 94%, and requirement extraction accuracy is 98%. Keywords: NLP, NLP4RE, model extraction, requirements digitalization, requireme

Abstract

RF bio-sensing is often utilized for the detection of bio-samples by using the concept of shift in resonant frequency. It is widely reported that the shift in resonant frequency is narrower compared to the bandwidth of the device used for measuring this shift. As a result, wideband Vector Network Analyzer (VNA) proves to be an overkill and instead a narrowband measurement setup can fulfill this requirement. Therefore, in this paper, an integrated solution for the detection of materials having varying permittivities is presented. A DualBand Six Port Reflectometer (DBSPR) is proposed and designed using planer microstrip line technology for the purpose of characterizing the sensor using reflection and transmission coefficient. A Complimentary Split Ring Resonator (CSRR) based sensor having an unloaded resonance frequency of 2.3 GHz is utilized as a test set. The designed DBSPR operates at 1.12 GHz and 2.26 GHz with a 10dB bandwidth of 254 MHz and 215 MHz respectively. The DBSPR and CSRR sensor are integrated using a microstrip line. The integrated device is tested for two different materials having a permittivity of 2.3 and 4.3. A shift of 118 MHz is observed between these materials and thus shows the potential usefulness of the proposed setup.

Abstract

L-Lysine is an essential amino acid and bio-sample observing major significance in food processing, pharmaceutical and agricultural industries. Conventional sensing techniques require longer pre-processing times and are sensitive to ambient conditions. However, radio frequency (RF) sensing based on Complementary Split Ring Resonator (CSRR) exhibits a significant shift in the resonant frequency and is highly desirable for the L-Lysine’s quantitative analysis. The frequency shifts of 498.4 MHz, 482.9 MHz, 471.4 MHz, 459.9 MHz and 452.3 MHz are obtained through varying concentrations from 0 mg/ml to 40 mg/ml in step size 10 mg/ml of L-Lysine solution. Therefore, experimental results and analysis presented in this work indicate the proposed radio frequency sensor’s linearity in the abovereported concentration range.

Abstract

Problems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission to the Sentimix Hindi-English task involving sentiment classification of code-mixed texts, and with an F1 score of 67.1%, we demonstrate that simple convolution and attention may well produce reasonable results.

Abstract

Resolution of POF based pressure sensor was improved by suppressing voltage fluctuation in LED-fiber-Photodiode setup using time windowing technique. This reduced the standard deviation by 98%, thereby showing significant improvement in gait analysis images

Abstract

With the development in the field of quantum physics, several methods for building a quantum computer have emerged. These differ in qubit technologies, interaction topologies, and noise characteristics. In this article, insights are given into the circuit‐ centric architecture design of Noisy Intermediate‐Scale Quantum (NISQ) devices. The dependence of the circuit size, circuit depth on the interaction and connection between different qubits present in quantum hardware are discussed. A noise‐aware procedure is presented which helps in determining the optimal interactions between different qubits of a quantum chip to execute a given circuit in the most efficient way possible. In this article, the 5‐qubit hardware in a noiseless setting is illustrated with an example. Also, a benchmark‐driven analysis is performed to show the importance of noise adaptivity in determining the hardware reliability. It is concluded that a generalized and flexible procedure such as this approach can aid in determining the design of hardware accurately for which the circuit runs efficiently, that is, with the least number of clock cycles, the lowest gate operations, and noise‐based errors

Abstract

A majority of the real world applications involve mapping the task at hand to an optimization problem, whose solutions are either fully known or can be approximated by relaxing some of its constraints. In general, the increment in problem size or augmentation of additional constraints can increase the hardness of the problem. This means that the computational resources required in solving them scale-up exponentially, making them computationally intractable. Hence, researchers worldwide have been working on developing efficient tools and techniques for solving these problems efficiently with the available computing power. With the recent d

Abstract

An earthquake Mw7.8 occurred on 25 April 2015 in a seismically dominant area of Nepal. No major earthquakes (>Mw7.0) were recorded in the past two decades, though Nepal is one of the active seismic regions in the world. The combined effect of the main event and the aftershocks, which are temporally distributed over 40 days, has led to a huge devastation, killing thousands of people in the epi-central area. A complete seismic catalog and influencing parameters such as type of fault, epi-central/hypo-central distance, type of soil are required for a region to assess seismic hazard scenario. The seismic hazard can be represented in terms of Peak Ground Acceleration (PGA), which is the most widely used parameter in strong motion studies. The main objective of this paper is to understand the characteristics of ground motion of the 2015 Nepal earthquake. Since the available ground motion records are limited, an attempt has been made to generate ground motion records using a modified semi-empirical approach. Contour maps of PGA and intensity have also been generated. It is observed that around 22% of buildings experienced an intensity of VI on MMI scale at Bageswari mandal. A few buildings have experienced an intensity of VII in Thankot, Seuchatar, Satikhel, Mahadevathan, Indrayani, Chhaimale, and Balambu.

Abstract

UN World Conference on Disaster Risk Reduction held in 2015 at Sendai, Japan, reiterated the need for substantial reduction in loss of life and property. Thus, assessing disaster risk and identifying key items for risk mitigation are in focus. Assessment of disaster risk is a multidisciplinary effort, which includes expected tangible physical loss, such as collapse and damage to built environment. In countries with limited resources, prioritizing risk reduction effort across the different parts of the country needs a quantitative (yet simple) approach to bring objectivity into the decision-making process. This paper presents a simple method, called Earthquake Disaster Risk Index (EDRI), for estimating relative earthquake risk across the different regions in a country; it uses three major constituents of risk, namely Hazard, Exposure and Vulnerability. Hazard is taken as the acceleration hazard specified in the national earthquake design standard, Exposure as per the permitted occupancy in the Local Municipal Byelaws, and Vulnerability through a Level 2 Detailed Qualitative Assessment of buildings built in the Town or City (with penalty points for missing features of earthquake resistance compared to an Ideal Building). First, the EDRI of a Town or City is estimated for each typology of building in the Town or City, and then the net EDRI of the Town or City is obtained as a weighted average of the EDRI of each typology of buildings using the number of buildings of each typology present in the city. The EDRI of a city so estimated can be compared with that of another city. Also, disaggregation of risk through its three major constituents of EDRI will increase awareness of the factors which contribute to risk – from expected intensity of earthquake ground shaking, to exposure of people in each building type, to vulnerability of each building typology. The exercise can be undertaken at a large scale across the country. Policy Makers of a prefecture or a state in a nation will find the results of EDRI to be a useful tool for prioritising allocation of earthquake risk mitigation resources and effort across Towns and Cities.

Abstract

India is a country which is prone to earthquakes. Many great earthquakes of the world have occurred in India, but still safe earthquake construction practices are not popular yet. This is mainly due to lack of awareness regarding earthquake problems and also earthquake safety features of buildings. On the other hand, India is a country with many diversified building technologies where there is a large variation in selection of building materials and building technologies. Over the ages, building materials and technologies were linked to the geo-climatic conditions of the area and the same is evident from census data of the country. However, during past earthquakes, many buildings collapsed due to the lack of earthquake resistant features in these buildings, resulting in huge life loss. This paper is an attempt to identify the critical earthquake resistant elements in each building typology which is popular in a given region. The same is demonstrated by taking two building typologies i.e., framed buildings and load bearing structures in four different regions i.e., hilly region, plain region, snowfall region and heavy rainfall region. From the above, it is clear that each housing typology can be made earthquake resistant by understanding the role of critical elements and by not deviating from standard design which is safe as well as compatible with geo-climatic conditions of the region.