Nonnegative Matrix Factorization Under Adversarial Noise
Peter Ballen, Department of Computer and Information Science, University of Pennsylvania, Philadelphia, USA
Nonnegative Matrix Factorization (NMF) is a popular tool to estimate the missing entries of a dataset under the assumption that the true data has a low-dimensional factorization. One example of such a matrix is found in movie recommendation settings, where NMF corresponds to predicting how a user would rate a movie. Traditional NMF algorithms assume the input data is generated from the underlying representation plus mean-zero independent Gaussian noise. However, this simplistic assumption does not hold in real-world settings that contain more complex or adversarial noise. We provide a new NMF algorithm that is more robust towards these nonstandard noise patterns. Our algorithm outperforms
existing algorithms on movie rating datasets, where adversarial noise corresponds to a group of adversarial users attempting to review-bomb a movie.
Nonnegative Matrix Factorization, Adversarial Noise, Recommendation
Data Model for Bigdeepexaminator
Janusz Bobulski and Mariusz Kubanek, Department of Computer Science, Czestochowa University of Technology, Poland
Big Data is a term used for such data sets, which at the same time are characterized by high volume, diversity, real-time stream inflow, variability, complexity, as well as require the use of innovative technologies, tools and methods in order to extracting new and useful knowledge from them. Big Data is a new challenge and information possibilities. The effective acquisition and processing of data will play
a key role in the global and local economy as well as social policy and large corporations. The article is a continuation of research and development works on the design of the data analysis system using artificial intelligence, in which we present a data model for this system.
Big data, intelligent systems, data processing, multi-data processing
Using LoRa Technology to Determine the Location of a Bus in Real-Time
Ronald Tumuhairwe, Department of Engineering, Ashesi university, Eastern region, Ghana
Travelers without prior information about the bus they are waiting for, often waste time at bus stations which affects their daily activity plan. GPS receivers are used to address this problem by providing real-time information about the location of the bus, however, it is quite expensive and power consuming to use and every bus needs to have a GSM/GPRS module to be able to send the information to the internet. This paper suggests a low-cost and less power consuming approach of using LoRa technology, geometry methods to determine the location of the bus and only one GSM module for multiple buses to transmit the data to the internet. Results in the project show promising achievement of determining the location of the bus in realtime.
SLoRa, Trilateration, Real-Time, Tracking, Geometry
An efficient algorithm to find the height of a text line and overcome overlapped and broken line problem during segmentation
Sanjibani Pattanayak, Sateesh Kumar Pradhan, Ramesh Chandra mallick, Utkal University, India
Line segmentation is one of the critical phases of the character recognition process that separates the individual lines from the image document. The accuracy rate of the character recognition is directly proportional to the line segmentation accuracy which is followed by word/character segmentation. Here, an algorithm, named height_based_segmentation algorithm is proposed for the text line segmentation of printed Odia documents. This algorithm finds the average height of a text line based on which it minimizes the overlapped text line cases. A post-processing step is included in the algorithm that combines the modifier zone with the base zone that has been separated during the segmentation process, with the base zone. The performance of the algorithm is evaluated with the ground truth and also by comparing it with the existing segmentation approaches. A database has been built with the segmented lines which will be helpful for researchers who work in word or character segmentation field.
Document Image Analysis, Line segmentation, word segmentation, Database creation, printed Odia document
Good Neighbour Alpha Stable Fusion in Wavelet Decomposition and Laplacian Pyramid
Rachid Sabre1 and Ias Wahyuni2, 1Laboratory Biogéosciences CNRS, University of Burgundy/Agrosup Dijon, France and 2Universitas Gunadarma, J1. Margonda Raya No 100 Depok 16424 Indonesia
In this paper, a new multifocus image fusion method is proposed; combining Laplacian pyramid, wavelet decomposition and using alpha stable distance as selector rule. First, using Laplacian pyramid, we decompose the multifocus images into several levels of pyramid. Then we apply wavelet decomposition at each level of pyramid. We fuse the wavelet images at each level by using alpha stable distance as select rule. To get the final fused image we reconstruct the combined image at every level of pyramid. This method is compared to other methods and give good result.
Image fusion, Laplacian pyramid, Wavelet decomposition