Abstract

This paper aims to establish the formal theory of painting. We are looking into two fundamental elements which are crucial to build a theory of painting, and those are strokes and Shapes, the central claim of this research is that the ‘Painting Exists between Stroke and Shape’, and the space of the painting is dynamic in nature, it exists not entirely in the matter (canvas/paper) nor in the mind (Imagination), it is in-between, it is like a hologram which is more than 2D less than 3D. We are using Indian Folk Paintings as a case to demonstrate our theory because of their uniqueness.

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

Measurement of the electric field radiations by ultra-wideband antenna is a challenging task. In general, all the antennas are measured for the radiation pattern and return loss to estimate their overall performance. However, when it comes to the antennas that are fed by very high voltages (HV), in the order of few hundreds of kilo volts and the input is not continuous, the measurements become complex. There arises an ambiguity in deciding about the peak value of the high electric fields generated, especially when the HV electric discharge mechanism is involved in the radiation of the antenna. The electric discharges are, in general, not repetitive. This paper proposes a measurement technique that is well suited for measuring the high electric fields, especially in the order of hundreds of kV/m. The proposed measurement procedure is verified and validated by experiments. The repeatability and reliability of the proposed measurement procedure are checked and presented here.

Abstract

We introduce PeeledHuman - a novel shape representation of the human body that is robust to self-occlusions. PeeledHuman encodes the human body as a set of Peeled Depth and RGB maps in 2D, obtained by performing raytracing on the 3D body model and extending each ray beyond its first intersection. This formulation allows us to handle self-occlusions efficiently compared to other representations. Given a monocular RGB image, we learn these Peeled maps in an end-to-end generative adversarial fashion using our novel framework - PeelGAN. We train PeelGAN using a 3D Chamfer loss and other 2D losses to generate multiple depth values per-pixel and a corresponding RGB field per-vertex in a dual-branch setup. In our simple non-parametric solution, the generated Peeled Depth maps are back-projected to 3D space to obtain a complete textured 3D shape. The corresponding RGB maps provide vertex-level texture details. We compare our method with current parametric and non-parametric methods in 3D reconstruction and find that we achieve state-of-the-art-results. We demonstrate the effectiveness of our representation on publicly available BUFF and MonoPerfCap datasets as well as loose clothing data collected by our calibrated multi-Kinect setup.