Classification of Test Documents Based on Handwritten Student ID's Characteristics

AbstractThe bag of words (BoW) model is an efficient image representation technique for image categorization and annotation tasks. Building good feature vocabularies from automatically extracted image feature vectors produces discriminative feature words, which can improve the accuracy of image categorization tasks. In this paper we use feature vocabularies based biometric characteristic for identification on student ID and classification of students’ papers and various exam documents used at the University of Mostar. We demonstrated an experiment in which we used OpenCV as an image processing tool and tool for feature extraction. As regards to classification method, we used Neural Network for Recognition of Handwritten Digits (student ID). We tested out proposed method on MNIST test database and achieved recognition rate of 94,76% accuracy. The model is tested on digits which are extracted from the handwritten student exams and the accuracy of 82% is achieved (92% correctly classified digits)

STATE-OF-THE-ART OF MESSAGING FOR DISTRIBUTED COMPUTING SYSTEMS

Modern software applications rarely live in isolation and nowadays it is common practice to rely on services or consume information provided by remote entities. In such a distributed architecture, integration is key. Messaging, for more than a decade, is the reference solution to tackle challenges of a distributed nature, such as network unreliability, strong-coupling of producers and consumers and the heterogeneity of applications. Thanks to a strong community and a common effort towards standards and consolidation, message brokers are today the transport layer building blocks in many projects and services, both within the physics community and outside. Moreover, in recent years, a new generation of messaging services has appeared, with a focus on low-latency and high-performance use cases, pushing the boundaries of messaging applications. This paper will present messaging solutions for distributed applications going through an overview of the main concepts, technologies and services

Next Generation Data Flow and Storage Solution in ALICE Experiment

LHC Run3 and Run4 represent an unprecedented challenge for HEP computing in terms of both data flow and data volume. New approaches are needed for how data is collected and filtered, processed, moved, stored and analyzed if these challenges are to be met with a realistic budget. This paper gives the innovative technologies that are currently being explored by CERN and discusses the long-term strategies that are pursued by the LHC communities with the help of industry in closing the technological gap in networking and storage needs expected in Run3 and Run4. The EOS storage system with the bandwidth of the external network (LHCOPN, LHCONE) is promising solution for these requirements