The Role of Modern Technology to Improve Education in Bangladesh

Modern technology in education is regularly developing day by day To realize the effects of modern technology is indeed significant for educational institutions Technology affects all the aspects of education Technology helps the instructors and learners to be more motivated to learn something very clearly Study background is discussed to understand the real perspective of modern technology and education By terms the points- significant of technology in education objective of the study literature review technological challenges of education the benefits of technology in education digital technologies in education the impact of technology in education technological transforming in education sector the impact of technology on the students traditional teaching versus virtual teaching challenges in implementing technology in the schools and colleges the importance of eLearning the ways to improve education based on technology limitations of technology in education are delineated in a straight forward way so that everyone can decipher the purpose of this articl

An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge. Our primary classification framework constitutes a convolutional neural network with 1D-CNN time-convolution (tConv) layers, which uses features transferred from a model trained on the 2016 Physionet Heart Sound Database. We also employ a Representation Learning (RL) approach to generate features in an unsupervised manner using Deep Recurrent Autoencoders and use Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. Finally, we utilize an SVM classifier on a high-dimensional segment-level feature extracted using various functionals on short-term acoustic features, i.e., Low-Level Descriptors (LLD). An ensemble of the three different approaches provides a relative improvement of 11.13% compared to our best single sub-system in terms of the Unweighted Average Recall (UAR) performance metric on the evaluation dataset.Comment: 5 pages, 5 figures, Interspeech 2018 accepted manuscrip

Review of intelligence for additive and subtractive manufacturing: current status and future prospects

Additive manufacturing (AM), an enabler of Industry 4.0, recently opened limitless possibilities in various sectors covering personal, industrial, medical, aviation and even extra-terrestrial applications. Although significant research thrust is prevalent on this topic, a detailed review covering the impact, status, and prospects of artificial intelligence (AI) in the manufacturing sector has been ignored in the literature. Therefore, this review provides comprehensive information on smart mechanisms and systems emphasizing additive, subtractive and/or hybrid manufacturing processes in a collaborative, predictive, decisive, and intelligent environment. Relevant electronic databases were searched, and 248 articles were selected for qualitative synthesis. Our review suggests that significant improvements are required in connectivity, data sensing, and collection to enhance both subtractive and additive technologies, though the pervasive use of AI by machines and software helps to automate processes. An intelligent system is highly recommended in both conventional and non-conventional subtractive manufacturing (SM) methods to monitor and inspect the workpiece conditions for defect detection and to control the machining strategies in response to instantaneous output. Similarly, AM product quality can be improved through the online monitoring of melt pool and defect formation using suitable sensing devices followed by process control using machine learning (ML) algorithms. Challenges in implementing intelligent additive and subtractive manufacturing systems are also discussed in the article. The challenges comprise difficulty in self-optimizing CNC systems considering real-time material property and tool condition, defect detections by in-situ AM process monitoring, issues of overfitting and underfitting data in ML models and expensive and complicated set-ups in hybrid manufacturing processes

Fighting Pollution Attacks in P2P Streaming

In recent years, the demand for multimedia streaming over the Internet is soaring. Due to the lack of a centralized point of administration, Peer-to-Peer (P2P) streaming systems are vulnerable to pollution attacks, in which video segments might be altered by any peer before being shared. Among existing proposals, reputation-based defense mechanisms are the most effective and practical solutions. In this thesis, we perform a measurement study on the effectiveness of this class of solutions. We simulate a framework that allows us to simulate different variations of the reputation rating systems, from the centralized global approach to the decentralized local approach, under different parameter settings and pollution models. In order to ensure that the framework and the simulated solution is representative enough, we dissect existing proposals and simulate a flexible defense mechanism, in which different components may be enabled and disabled by simply tuning certain parameters. Our experimental results reveal that global knowledge of the reputation rating is necessary to provide the best defense against the attack. But it is often susceptible under collaborative attacks, like collusion. We also find that expelling misbehaving peers is often more useful to prevent attacks than limiting their likelihood to be connected, although this can lead to poor playback quality. Based on these key observations, we propose DRank, a fully distributed rank-based reputation system, which decentralizes the global ranking system and combines it with Bayesian reputation rating systems. Experimental results show that this technique is more flexible and robust in fighting pollution attacks