Interactive Transcription Techniques for Interaction Analysis

Interaction analysis is a valuable method and approach to study knowledge in use in the learning sciences and CSCL communities. Central to interaction analysis is the creation of transcripts to selectively encode and represent audio and video data. However, current transcription techniques used in interaction analysis, including multimodal transcription techniques, have yet to explore the strengths and weaknesses of interactive visualization to selectively encode and represent people’s interaction in context. Drawing from our recent efforts to amplify, not automate, transcription in qualitative research, this paper interactively visualizes one video dataset in five different ways using contemporary interactive visualization techniques. Findings and discussion characterize these visualizations as interactive transcripts that demonstrate techniques valuable to interaction analysis, but also highlight the need to expand how people, things, and context are represented through visualization mediums such as visualization programming languages to align with work more meaningfully in the learning sciences and CSCL communities

Internet of Things and its enhanced data security

The Internet of Things (IoT), an emerging global Internet-based technical architecture facilitating the exchange of information, goods and services in the internet world has an impact on the security and privacy of the involved stakeholders. Measures ensuring the architectures resilience to attacks, data authentication, and access control and client privacy need to be established. This paper includes a survey of IoT and various security issues related to it. Furthermore, out of all security issues, concern over data authentication and transfer is taken into consideration. Here we will discuss the idea for two levels of security in form of two different approaches i.e. Advance Encryption Standards (AES) and the Steganography approach via an image and the simulating of these two logics in the MATLAB

Generation of Electricity using Wind Energy Produced due to the Motion of Trains

The aim of this work is to generate free electricity for general use, through wind energy which is created due to the motion of trains. This is achieved by using a low friction ball bearing sensitive dynamo (22V, 100mA) with adjustments such that it can support a fan and using it practically as a small wind turbine. By creating a closely developed arrangements of many such dynamos around tracks, supported by feeding the outputs of all these dynamos systematically to a central electrical transmission line we can feed all the energy produced to a battery for further use. Keywords: Energy, Free, Electricity, Wind, Train

Accelerating Newton optimization for log-linear models through feature redundancy

Log-linear models are widely used for labeling feature vectors and graphical models, typically to estimate robust conditional distributions in presence of a large number of potentially redundant features. Limited-memory quasi-Newton methods like LBFGS or BLMVM are optimization workhorses for such applications, and most of the training time is spent computing the objective and gradient for the optimizer. We propose a simple technique to speed up the training optimization by clustering features dynamically, and interleaving the standard optimizer with another, coarse-grained, faster optimizer that uses far fewer variables. Experiments with logistic regression training for text classification and conditional random field (CRF) training for information extraction show promising speed-ups between 2× and 9× without any systematic or significant degradation in the quality of the estimated models.© IEE

A Building Permit System for Smart Cities: A Cloud-based Framework

In this paper we propose a novel, cloud-based framework to support citizens and city officials in the building permit process. The proposed framework is efficient, user-friendly, and transparent with a quick turn-around time for homeowners. Compared to existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of both the end user experience and the permitting and urban planning processes. This is enabled through a data mining-powered permit recommendation engine as well as a data analytics process that allow a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on their location, time, and type of request. The novelty of the proposed framework lies in the integration of a pre-permit processing front-end with permit processing and data analytics & mining modules, along with utilization of techniques for extracting knowledge from the data generated through the use of the system. The proposed framework is completely cloud-based, such that any city can deploy it with lower initial as well as maintenance costs. We also present a proof-of-concept use case, using real permit data from New York City