386 research outputs found
Online Collaborative Editor
“Online collaborative editor” is a node.js based browser application that provides real time collaborative editing of files and improves pair programming. Current real time editors fail to provide simultaneous viewing and editing of files within the server and results in a complex version controlling system. Such systems are also vulnerable to deadlocks and race conditions. This project provides a platform for real time collaborative editors, which can support simultaneous editing and viewing of files and handle concurrency problems by using locking mechanism. The experiment results showed that node.js platform provides good performance for collaborative editing
Efficient Reduced BIAS Genetic Algorithm for Generic Community Detection Objectives
The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by introducing redundancies. They also utilize a crossover operator which imposes a linear ordering that is not suitable for community detection.
The algorithm presented here is a framework to detect communities for complex biological networks that removes both redundancies and linearity. We also introduce a novel operator, named Gene Repair. This algorithm is unique as it is a flexible community detection technique aimed at maximizing the value of any given mathematical objective for the network. We reduce the memory requirements by representing chromosomes as a 3-dimensional bit array. Furthermore, in order to increase diversity while retaining promising chromosomes, we use natural selection process based on tournament selection with elitism. Additionally, our approach doesn’t require prior information about the number of true communities in the network. We apply our novel algorithm to benchmark datasets and also to a network representing a large cohort of AD cases and controls.
By utilizing this efficient and flexible implementation that is cognizant of characteristics for networks representing complex disease genetics, we sift out communities representing patterns of interacting genetic variants that are associated with this enigmatic disease
Plantation and Harvesting Autonomous Locomotive (PHAL)
Agriculture has seen quite a good growth due to the latest machinery being used to maximize yield and minimize cost. People around the world are starting to understand the inherent potential and scope of automation and robotics in agriculture. However, there are many problems which continue to prevail like the non-availability of labour, poor and costly machinery, etc. So there is a need to address these existing problems. This project addresses the inherent difficulties in the agricultural field. It tries to provide a remarkable solution to many of the existing problems. This project, named PHAL is a rover type bot which canperform all the basic activities included in farming. It is fully autonomous, ecofriendly machine which can perform many tasks like ploughing, sowing, irrigation, harvesting etc. Using this machine, farmers can get rid of majority of the problems, all this at a very low cost
The Nobodies of Corporate Media: Postmedia's Purchase of Irving Media is Not Good News for New Brunswick's Working Class
Flavour Enhanced Food Recommendation
We propose a mechanism to use the features of flavour to enhance the quality
of food recommendations. An empirical method to determine the flavour of food
is incorporated into a recommendation engine based on major gustatory nerves.
Such a system has advantages of suggesting food items that the user is more
likely to enjoy based upon matching with their flavour profile through use of
the taste biological domain knowledge. This preliminary intends to spark more
robust mechanisms by which flavour of food is taken into consideration as a
major feature set into food recommendation systems. Our long term vision is to
integrate this with health factors to recommend healthy and tasty food to users
to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted
Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page
Regularized Neural Detection for One-Bit Massive MIMO Communication Systems
Detection for one-bit massive MIMO systems presents several challenges
especially for higher order constellations. Recent advances in both model-based
analysis and deep learning frameworks have resulted in several robust one-bit
detector designs. Our work builds on the current state-of-the-art gradient
descent (GD)-based detector. We introduce two novel contributions in our
detector design: (i) We augment each GD iteration with a deep learning-aided
regularization step, and (ii) We introduce a novel constellation-based loss
function for our regularized DNN detector. This one-bit detection strategy is
applied to two different DNN architectures based on algorithm unrolling,
namely, a deep unfolded neural network and a deep recurrent neural network.
Being trained on multiple randomly sampled channel matrices, these networks are
developed as general one-bit detectors. The numerical results show that the
combination of the DNN-augmented regularized GD and constellation-based loss
function improve the quality of our one-bit detector, especially for higher
order M-QAM constellations.Comment: Initially submitted to IEEE TMLCN in October 202
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