Abstract

Recently, there has been research on what factors influence a user’s password setting practices, which include various types of emotions such as anger, risk-taking tendencies, etc. How- ever, research has shown that factors such as memorability and perceived memorability have a greater influence on password choice. Some recent research has shown a negative correlation between the perceived memorability and the perceived secu- rity of passwords, particularly passphrases (that are technically more secure). However, it is unclear whether this effect can be extended to groups with good experiences with digital spaces (IT professionals, entrepreneurs, etc.). Furthermore, it has not been determined whether random, uncommonly-worded, or complex structure passphrases would also maintain the cor- relation, as opposed to relatively less secure, common/simple passphrases. This study examines this problem using a diverse demographic and different categories of passphrases. Keywords: Metacognition; Passwords; Perception; Memora- bility; Security

Abstract

Congestion in a network can cause several undesirable conditions such as packet loss, retransmission of packets, etc. There have been many variations of TCP for appropriate congestion control. These variations manage the sending rate of the users based upon the feedback they get from the network. In like manner, there are congestion control methods that take leverage of the Software Defined Networking and manage congestion from the controller. In this work we aim to manage congestion by coupling the activity of both the users and network controller in an SDN. We provide a utility based framework for a joint proactive and reactive mechanism for congestion control. We propose a system optimization function and show that our user and network congestion control methods optimize the system utility. Further, we simulate our model and show that our model significantly improves the system utility.

Abstract

This chapter provides a behind-the-scenes account of the birth of a naturalistic approach to the neuroscience of the musical aesthetic experience. The story starts from a lab talk giving the inspiration to translate the naturalistic paradigm initially applied to neuroimaging studies of the visual domain into music research. The circumstantial co-presence of neuroscientists and computational musicologists at the same center did the trick, permitting the identification of controlled variables for brain signal processing from the automatic extraction of the acoustic features of real music. This approach is now well accepted by the music neuroscience community while still waiting for full exploitation by aesthetic research.

Abstract

Organizational and management theories generally focus on obtaining a conceptual and operational understanding of a particular facet of enterprises. In contrast, systems engineers focus on obtaining a logical integrated understanding of the structure and functioning of the systems they design and work with. This chapter presents such an approach to capturing the nature of enterprises and their evolution over time, suitable for creating software models and analysis tools, based on systems science and systems engineering concepts and principles. Specifically, we present an understanding of enterprises in terms of a morphogenetic structure: a series of models with derivation-based consistency relationships among them. We also discuss how the epigenesis of enterprises, i.e., their change and evolution over time, can be understood relative to this structure. These models are influenced by the fact that they were developed within an information technology organization, concerned with the information, processes, and knowledge that enable the functioning of business systems. This enables all the information about the enterprise, relating to different facets and hierarchical levels, to be captured in a unified modelling structure, so that various views can be derived from it for analysis purposes. As we go through the approach, we indicate how the representation can enable the application of various management theories – the models provide the basic structure for representing the information, from which views can be

Abstract

The study focuses on the extraction of cardiac sound components using a multi-channel micro-electromechanical system (MEMS) microphone-based phonocardiography system. The proposed multi-channel phonocardiography system classifies the cardiac sound components using artificial neural networks (ANNs) and synaptic weights that are calculated using the inverse delayed (ID) function model of the neuron. The proposed ANN model was simulated in MATLABR and implemented in a field-programmable gate array (FPGA). The proposed system examined both abnormal and normal samples collected from 30 patients. Experimental

Abstract

The study presents a novel design method that improves system availability using fault-tolerant features in a non-invasive medical diagnostic system. This approach addresses the effective detection of functional faults, improves the uninterruptible system operating period with reduced false alarms, and provides an authentic measure of vital cardiac signs using diverse multimodal sensing elements like the photoplethysmogram (PPG) and the ECG. Most systems rely on a 1oo1 (one-out-of-one) design method, which inherently limits accuracy in existing practice. In this proposed approach, the quality of segregated authentic vital sign measured values could tremendously benefi

Abstract

A major problem in Acoustic Scene Classification (ASC) is a representation of an acoustic scene, which serves to be an important task for ASC. This study used Linear Prediction Cepstral Coefficients (LPCC) and Spectral Centroid Magnitude Cepstral Coefficients (SCMC) features along with log-Mel band energies for the representation of an acoustic scene. Deep Neural Networks (DNN) is being used to model the Acoustic Scene Classification (ASC). LPCCs are used to capture the changes in the auditory spectrum with time and SCMCs are used to capture the weighted average magnitude finely for a given acoustic scene subband. log-Mel band energies are used to capture the spectral envelopes of audio frame. The DNN architecture is used for audio track level classification. We have experimented on Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 development dataset and DCASE 2017 dataset. We carried out experiments with individual feature sets, and also performed decision level DNN score fusions for improving the performance.

Abstract

An Electric Vehicle (EV) charger, also called Electric Vehicle Charging Station (EVCS) is an infrastructure element that supplies electrical energy for recharging EVs. The paper focuses on the design and fabrication of two-wheeler (escooter) electric vehicle charging equipment. Considering the future scenario of mass privatization of EV two-wheelers on Indian roads, the outline discusses the product design based on fabricability, affordability, and ability to mass manufacture. The proposed architecture follows the Level 2 charging standards (240 Volts), and is based on the open charge point protocol (OCPP). The suggested EVCS is constructed considering the safety prerequisites of system administrators, installers, consumers, government agencies and others. The design of EVCS links to three industries: equipment manufacturers, software industry and electric power networks. This paper presents design considerations by elaborating the hardware, software, and protocols followed to design the Level 2 charging standard EVCS.

Abstract

Accelerometers are among the most mature sensor technologies with a broad range of applications in multiple fields and industries. They represent the most widely used microelectromechanical system (MEMS) devices with excellent and reliable performance. MEMS acceleration sensors established dominance mainly in navigation and control applications. In recent years, however, recent technologies and materials have emerged that introduce novel sensing mechanisms, improve performance, enable customization, reduce cost, and reduce fabrication complexity. Herein, the recent advances in accelerometers based on MEMS and recent emerging technologies are reviewed. This work provides a comprehensive review of accelerometers’ sensing mechanisms and the main characteristics and features of each type of sensor, material, and fabrication strategies used to fabricate them. From the aspect of sensor application, this work focuses on reviewing applications that demonstrate the use of accelerometers manufactured using unconventional technologies and materials in prevailing fields such as healthcare monitoring, automotive industry, navigation, building, and structural monitoring. Moreover, challenges and future efforts needed to be addressed in this field are summarized

Abstract

Point clouds, being the simple and compact representation of surface geometry of 3D objects, have gained increasing popularity with the evolution of deep learning networks for classification and segmentation tasks. Unlike human, teaching the machine to analyze the segments of an object is a challenging task and quite essential in various machine vision applications. In this paper, we address the problem of segmentation and labelling of the 3D point clouds by proposing a inception based deep network architecture called PIG-Net, that effectively characterizes the local and global geometric details of the point clouds. In PIG-Net, the local features are extracted from the transformed input points using the proposed inception layers and then aligned by feature transform. These local features are aggregated using the global average pooling layer to obtain the global features. Finally, feed the concatenated local and global features to the convolution layers for segmenting the 3D point clouds. We perform an exhaustive experimental analysis of the PIG-Net architecture on two state-of-the-art datasets, namely, ShapeNet [1] and PartNet [2]. We evaluate the effectiveness of our network by performing ablation study.