Abstract

The question of how the human brain represents information, and if insights from this knowledge help us in formulating better deep learning algorithms, is still open. This paper attempts to address these questions by investigating the brain responses when participants viewed images of objects belonging to a variety of categories. We adopt the representational similarity analysis (RSA) framework wherein the brain responses are projected into a representational dissimilarity (RD) space so that responses from multiple subjects can be assessed on a common basis. We performed RSA of the brain responses of 15 participants on a set of 92 images (from the Algonauts challenge data set) Cichy et al. (The Algonauts project: a platform for communication between the sciences of biological and artificial intelligence 2019 [4]). The results reveal that human brain responses exhibit appropriate qualitative similarities observed in the image space. We then evaluated various deep networks such as VGGnet, AlexNet, and Inception in Siamese configuration with RD-loss function and identified hidden layers that had maximal similarity to human RD matrices (RDM). The best layers then were subjected to further analysis by looking at the image pairs corresponding to high and

Abstract

There has been increasing interest in building deep hierarchy-aware classifiers that aim to quantify and reduce the severity of mistakes, and not just reduce the number of errors. The idea is to exploit the label hierarchy (e.g., the WordNet ontology) and consider graph distances as a proxy for mistake severity. Surprisingly, on examining mistake-severity distributions of the top-1 prediction, we find that current state-of-the-art hierarchy-aware deep classifiers do not always show practical improvement over the standard cross-entropy baseline in making better mistakes. The reason for the reduction in average mistake-severity can be attributed to the increase in low-severity mistakes, which may also explain the noticeable drop in their accuracy. To this end, we use the classical Conditional Risk Minimization (CRM) framework for hierarchy-aware classification. Given a cost matrix and a reliable estimate of likelihoods (obtained from a trained network), CRM simply amends mistakes at inference time; it needs no extra hyperparameters and requires adding just a few lines of code to the standard cross-entropy baseline. It significantly outperforms the state-of-the-art and consistently obtains large reductions in the average hierarchical distance of top- predictions across datasets, with very little loss in accuracy. CRM, because of its simplicity, can be used with any off-the-shelf trained model that provides reliable likelihood estimates.

Abstract

Online abuse and offensive language on social media have become widespread problems in today’s digital age. In this paper, we contribute a Reddit-based dataset, consisting of 68,159 insults and 51,102 compliments targeted at individuals instead of targeting a particular community or race. Secondly, we benchmark multiple existing state-of-the-art models for both classification and unsupervised style transfer on the dataset. Finally, we analyse the experimental results and conclude that the transfer task is challenging, requiring the models to understand the high degree of creativity exhibited in the data.

Abstract

Text simplification is the process of splitting and rephrasing a sentence to a sequence of sentences making it easier to read and un- derstand while preserving the content and ap- proximating the original meaning. Text sim- plification has been exploited in NLP appli- cations like machine translation, summariza- tion, semantic role labeling, and information extraction, opening a broad avenue for its ex- ploitation in comprehension-based question- answering downstream tasks. In this work, we investigate the effect of text simplification in the task of question-answering using a compre- hension context. We release Simple-SQuAD, a simplified version of the widely-used SQuAD dataset. Firstly, we outline each step in the dataset cre- ation pipeline, including style transfer, thresh- olding of sentences showing correct transfer, and offset finding for each answer. Secondly, we verify the quality of the transferred sen- tences through various methodologies involv- ing both automated and human evaluation. Thirdly, we benchmark the newly created cor- pus and perform an ablation study for ex- amining the effect of the simplification pro- cess in the SQuAD-based question answering task. Our experiments show that simplification

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