METHOD, SYSTEM, AND COMPUTER PROGRAM PRODUCT FOR AUTOMATICALLY GENERATING A RECOMMENDED ORDER

A method for automatically generating a recommended order is disclosed. The method may include receiving transaction data associated with a plurality of transactions of a user. The transaction data may include line-item data associated with at least one item for each transaction of the plurality of transactions. Merchant inventory data associated with at least one merchant may be received. A recommended order for the user may be generated based on the transaction data and the merchant inventory data. The recommended order may include a plurality of items from the at least one merchant. The recommended order may be communicated to a user device associated with the user. A system and computer program product are also disclosed

STEFANN: Scene Text Editor using Font Adaptive Neural Network

Textual information in a captured scene plays an important role in scene interpretation and decision making. Though there exist methods that can successfully detect and interpret complex text regions present in a scene, to the best of our knowledge, there is no significant prior work that aims to modify the textual information in an image. The ability to edit text directly on images has several advantages including error correction, text restoration and image reusability. In this paper, we propose a method to modify text in an image at character-level. We approach the problem in two stages. At first, the unobserved character (target) is generated from an observed character (source) being modified. We propose two different neural network architectures - (a) FANnet to achieve structural consistency with source font and (b) Colornet to preserve source color. Next, we replace the source character with the generated character maintaining both geometric and visual consistency with neighboring characters. Our method works as a unified platform for modifying text in images. We present the effectiveness of our method on COCO-Text and ICDAR datasets both qualitatively and quantitatively.Comment: Accepted in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 202

A CNN Based Framework for Unistroke Numeral Recognition in Air-Writing

Air-writing refers to virtually writing linguistic characters through hand gestures in three-dimensional space with six degrees of freedom. This paper proposes a generic video camera-aided convolutional neural network (CNN) based air-writing framework. Gestures are performed using a marker of fixed color in front of a generic video camera, followed by color-based segmentation to identify the marker and track the trajectory of the marker tip. A pre-trained CNN is then used to classify the gesture. The recognition accuracy is further improved using transfer learning with the newly acquired data. The performance of the system varies significantly on the illumination condition due to color-based segmentation. In a less fluctuating illumination condition, the system is able to recognize isolated unistroke numerals of multiple languages. The proposed framework has achieved 97.7%, 95.4% and 93.7% recognition rates in person independent evaluations on English, Bengali and Devanagari numerals, respectively.Comment: Accepted in The International Conference on Frontiers of Handwriting Recognition (ICFHR) 201

Work-Related Musculoskeletal Disorder an Increasing Concern in Garment Industry

  Background:  Musculoskeletal Disorders (MSDs)-related occupational health concerns are becoming a growing problem for the clothing industry, which is influenced by the repetitive tasks, prolonged static postures and poor ergonomic conditions that characterise the nature of work in this industry. This study aims to provide a thorough assessment of the numerous musculoskeletal problems that are prevalent in the garment industry and to recommend useful intervention strategies to mitigate their consequences. Methods:  One hundred thirty textile workers aged between 25 to 45 years were taken randomly from five different textile manufacturing sites of Baranagar, West Bengal. A modified Nordic musculoskeletal questionnaire was applied to evaluate the postural stress of the garment’s workers. The discomfort/pain intensity in different body parts were evaluated by Body Part Discomfort (BPD) scale . Appropriate statistical tests were applied. Results: Discomfort or pain in hip and lower back were found to be maximum among workers. Conclusion: Pain, discomfort and postural stress among various body parts like upper back, lower back and hip are verry much common for the workers. Despite efforts to deal with these problems, more thorough research and efficient intervention strategies are required to reduce musculoskeletal ailments in the garment industry.

Prevalence Of Work-Related Musculoskeletal Disorders Among Construction Workers

Construction workers frequently have work-related musculoskeletal disorders (WRMSDs) with significant adverse health and financial effects. The aim of this study is to look at the reasons, effects, and prevention measures for WRMSDs among construction workers.Methods: In the study, one hundred thirty-six male construction workers between the ages of 30 and 56 participated. The postural stress experienced by the construction workers was assessed using a modified Nordic musculoskeletal questionnaire. The Body Part Discomfort (BPD) scale was used to assess the severity of the discomfort/pain in various body areas.Results: Discomfort or pain in neck portion was found to be maximum percentage of workers. BPD scaling revealed that neck, lower back and shoulder are among three most susceptible body parts in relation to pain sensation.Conclusion: Manual construction workers in the construction industry are particularly vulnerable to developing work-related musculoskeletal disorders (WRMSDs), working hours and MSD symptoms, particularly in the lower limb, are significantly correlated

Validating a novel angular power spectrum estimator using simulated low frequency radio-interferometric data

The "Tapered Gridded Estimator" (TGE) is a novel way to directly estimate the angular power spectrum from radio-interferometric visibility data that reduces the computation by efficiently gridding the data, consistently removes the noise bias, and suppresses the foreground contamination to a large extent by tapering the primary beam response through an appropriate convolution in the visibility domain. Here we demonstrate the effectiveness of TGE in recovering the diffuse emission power spectrum through numerical simulations. We present details of the simulation used to generate low frequency visibility data for sky model with extragalactic compact radio sources and diffuse Galactic synchrotron emission. We then use different imaging strategies to identify the most effective option of point source subtraction and to study the underlying diffuse emission. Finally, we apply TGE to the residual data to measure the angular power spectrum, and assess the impact of incomplete point source subtraction in recovering the input power spectrum $C_{\ell}$ of the synchrotron emission. This estimator is found to successfully recovers the $C_{\ell}$ of input model from the residual visibility data. These results are relevant for measuring the diffuse emission like the Galactic synchrotron emission. It is also an important step towards characterizing and removing both diffuse and compact foreground emission in order to detect the redshifted $21\, {\rm cm}$ signal from the Epoch of Reionization.Comment: 18 pages, 1 table, 9 figures, Accepted for publication in New Astronom

Parameter Enhancement and Size Reduction using DGS of L Band Antenna

Proposed research is the outcome of the detailed literature review and intensive research carried out in the field of parameter improvement of antenna using defected ground structure. In this paper a patch antenna is proposed with the introduction of a slit ring DGS structure to modify its parameters and reduce the size of antenna. Antenna was designed and simulated at 1.95GHz initially but after implementing DGS its radiation efficiency is shifted to 1.58GHz which theoretically a sign of size reduction of antenna. DGS is actually a cut made in the ground plane of the antenna which create a disturbance in radiating power, this disturbance in the ground basically distributes the radiating frequency and make antenna more efficient than ever before

Effects of Degradations on Deep Neural Network Architectures

Recently, image classification methods based on capsules (groups of neurons) and a novel dynamic routing protocol are proposed. The methods show promising performances than the state-of-the-art CNN-based models in some of the existing datasets. However, the behavior of capsule-based models and CNN-based models are largely unknown in presence of noise. So it is important to study the performance of these models under various noises. In this paper, we demonstrate the effect of image degradations on deep neural network architectures for image classification task. We select six widely used CNN architectures to analyse their performances for image classification task on datasets of various distortions. Our work has three main contributions: 1) we observe the effects of degradations on different CNN models; 2) accordingly, we propose a network setup that can enhance the robustness of any CNN architecture for certain degradations, and 3) we propose a new capsule network that achieves high recognition accuracy. To the best of our knowledge, this is the first study on the performance of CapsuleNet (CapsNet) and other state-of-the-art CNN architectures under different types of image degradations. Also, our datasets and source code are available publicly to the researchers.Comment: Journa