Reviewerly/exHarmony huggingface dataset:
Single-Shot Analysis of Refractive Shape Using Convolutional Neural Networks

The appearance of a transparent object is determined by a combination of refraction and reflection, as governed by a complex function of its shape as well as the surrounding environment.Prior works on 3D reconstruction have largely ignored transparent objects due to this challenge, yet they occur frequently in real-world scenes.This paper presents an approach to estimate depths and normals for transparent objects using a single image acquired under a distant but otherwise arbitrary environment map.In particular, we use a deep convolutional neural network (CNN) for this task.Unlike opaque objects, it is challenging to acquire ground truth training data for refractive objects, thus, we propose to use a large-scale synthetic dataset.To accurately capture the image formation process, we use a physically-based renderer.We demonstrate that a CNN trained on our dataset learns to reconstruct shape and estimate segmentation boundaries for transparent objects using a single image, while also achieving generalization to real images at test time.In experiments, we extensively study the properties of our dataset and compare to baselines demonstrating its utility.
Taste for democracy: A critique of the mechanical paradigm in education

In this article we argue for the need to rethink the crisis of democracy both within and beyond education (Toft and Rüsselbæk Hansen, 2017). This crisis can be explained variously, depending on how it is understood and on what basis. From our point of view, and with inspiration from thinkers as Nietzsche, Arendt, Agamben and Rancière, we argue that the crisis of democracy in today’s schools is related in part to the weak form of authority ascribed to certain individual and collective aesthetic experiences. The scientific experience has been assigned a strong form of ‘evidenced based authority’. As a result of a powerful belief that not all forms of experiences, for instance the aesthetical ones, which are not based on a mechanical logic, are worth paying attention to and are of value, scientific experience has gained hegemonic status. Illustrated by several examples, which stem from both Danish and Canadian educational contexts, we show how aesthetic experiences have the potential to revitalize democratic practices and undermine tyrannical regimes of technocracy in education. We recommend that the concept of Bildung (self-cultivation), which is orientated towards vertical (Apollonian) as well as horizontal (Dionysian) transcending processes, must be given renewed attention. Opportunities for students to play with and suspend the social order, even temporarily, are important if students are to experience themselves, others and the world in new ways. By engaging students’ aesthetic sensibilities in multiple ways, playful schools both produce and provoke the dominant social order thereby fostering students’ taste for democracy.
Inertial-Based Gesture Recognition for Artificial Intelligent Cockpit Control using Hidden Markov Models

Driving a car is no longer just a means to get around quickly. Currently, driving itself has become an entertainment factor with steadily increasing occupant safety. This is ensured by a multitude of safety and comfort features, such as various driver assistance systems and innovative control options within the cockpit. The content of this paper introduces a new way to cockpit operation, which contributes to the increase of user comfort. For gesture recognition of the occupant low-cost inertial sensors on a bracelet are used. Due to the relatively poor sensor performance, complex analysis algorithms are needed. For this purpose, the implementation of Hidden Markov models for the establishment of an artificially intelligent data analysis system is suitable. The present work is based on past research results performed at the CCASS, which provided a framework for reference-less human motion analysis and validation using low-cost inertial motion sensors. The developed algorithms are based on the theory of Hidden Markov Models for the stochastic modelling of human motion using Markov chains. In the present work the algorithm will be adapted to a concept of gesture recognition for cockpit control.
Advanced encryption standard based on key dependent S‐Box cube

The advanced encryption standard (AES) is the most encryption algorithm used around the world. This fact made it coveted by several cryptanalysis attacks. Some of them succeed to reveal certain weakness, due to its static parameters known by the public mass. The substitution box (S-Box) is among the most sensitive parameters of this standard, which plays a crucial role in the protection against these attacks. In this study, a new version of AES is introduced, which uses a set of AES-like S-Boxes instead of the original one. These S-Boxes, generated from different irreducible polynomials, are dynamically chosen, depending on the encryption key. This introduction is conducted by the design and the implementation of a new block cipher version of AES, based on a key dependent S-Box cube. Where a cube of six interchangeable S-Boxes key-dependently generated is constructed. This cube determines the active S-Box for a given round, through its random rotations guided by the round key. Furthermore, the improvement of the cryptographic robustness, provided by this method is demonstrated. This is accomplished through a comparison with the original AES, in terms of statistical and cryptographic randomness properties.
Learning Generator Networks for Dynamic Patterns

We address the problem of learning dynamic patterns from unlabeled video sequences, either in the form of generating new video sequences, or recovering incomplete video sequences. This problem is challenging because the appearances and motions in the video sequences can be very complex. We propose to use the alternating back-propagation algorithm to learn the generator network with the spatial-temporal convolutional architecture. The proposed method is efficient and flexible. It can not only generate realistic video sequences, but can also recover the incomplete video sequences in the testing stage or even in the learning stage. The proposed algorithm can be further improved by using learned initialization which is useful for the recovery tasks. Further, the proposed algorithm can naturally help to learn the shared representation between different modalities. Our experiments show that our method is competitive with the existing state of the art methods both qualitatively and quantitatively.
Iterative Privileged Learning

While in the learning using privileged information paradigm, privileged information may not be as informative as example features in the context of making accurate label predictions, it may be able to provide some effective comments (e.g., the values of the auxiliary function) like a human teacher on the efficacy of the learned model. In a departure from conventional static manipulations of privileged information within the support vector machine framework, this paper investigates iterative privileged learning within the context of gradient boosted decision trees (GBDTs). As the learned model evolves, the comments learned from privileged information to assess the model should also be actively upgraded instead of remaining static and passive. During the learning phase of the GBDT method, new DTs are discovered to enhance the performance of the model, and iteratively update the comments generated from the privileged information to accurately assess and coach the up-to-date model. The resulting objective function can be efficiently solved within the gradient boosting framework. Experimental results on real-world data sets demonstrate the benefits of studying privileged information in an iterative manner, as well as the effectiveness of the proposed algorithm.
Automatic Classification of Radiological Report for Intracranial Hemorrhage

Deep learning algorithms, in particular long short-term memory (LSTM), have become an increasingly popular choice for natural language processing for a variety of applications such as sentiment analysis and text analysis. In this study, we propose a fully automated deep learning algorithm which learns to classify radiological reports for the presence of intracranial hemorrhage (ICH) diagnosis. The proposed automated deep learning architecture consists of 1D convolution neural networks (CNN), long short-term memory (LSTM) units and a logistic function which was trained and tested on the large dataset of 12,852 head computed tomography (CT) radiological reports. The architecture extracts semantically co-located features using 1D CNNs, the sequential structure of features using LSTM, and finally detects ICH using a logistic function. The receiver operator characteristic (ROC) curve is generated as a metric to test the classification performance of the architecture. The model achieved an area under the curve (AUC) of the ROC curve of 0.94. The promising results suggest that modern deep learning based algorithms are capable of extracting diagnosis information from unstructured medical text. The purpose of this paper is to label 27,148 radiological reports automatically to reduce human error, cost, and time.
WindyWall

Wind simulations are typically one-off implementations for specific applications. We introduce WindyWall, a platform for creative design and exploration of wind simulations. WindyWall is a three-panel 90-fan array that encapsulates users with 270? of wind coverage. We describe the design and implementation of the array panels, discussing how the panels can be re-arranged, where various wind simulations can be realized as simple effects. To understand how people perceive "wind" generated from WindyWall, we conducted a pilot study of wind magnitude perception using different wind activation patterns from WindyWall. Our findings suggest that: horizontal wind activations are perceived more readily than vertical ones, and that people's perceptions of wind are highly variable-most individuals will rate airflow differently in subsequent exposures. Based on our findings, we discuss the importance of developing a method for characterizing wind simulations, and provide design directions for others using fan arrays to simulate wind.
Towards the Enactment of Learning Situations Connecting Formal and Non-Formal Learning in SLEs

Smart Learning Environments hold promise of adapting learning processes to the individual context of students and connecting formal with non-formal learning. To do so, SLEs need to know the current context of the students, regardless of the physical or virtual space where learning takes place. This paper presents an architecture that assists in the deployment and enactment of learning situations across-spaces, able to sense and react to changes in the students’ context in order to adapt the learning process.
MoBeTrack: A Toolkit to Analyze User Experience of Mobile Apps in the Wild

MoBeTrack (Mobile Behaviour Tracking) is a toolkit for automated collection of data necessary to support User Experience (UX) assessment of mobile applications. In contrast to existing frameworks, it is able to collect user demographic information (i.e., age and gender), trace any user interaction and recognize user's emotions during the use of an application. An SDK for iOS allows to easily embedding the toolkit in every mobile application in a flexible and scalable way.
Do Nonprofessional Investors Care About How and When Data Breaches are Disclosed?

ABSTRACT While prior research suggests that the market responds negatively to data breach disclosures, how nonprofessional investors assess factors surrounding these disclosures has only been assessed anecdotally. We examine whether investor judgments are influenced by whether a breached company is the first to disclose a data breach and whether a significant amount of time has lapsed between the breach and disclosure. We find evidence that investors respond to a company originating disclosure with lower investment judgments than if disclosure comes from an external source, without consistent regard to the timing of disclosure. We also find that investors make the least favorable investment judgments when the breached company initiates the data breach disclosure and when there is a significant delay between the data breach and initial public disclosure. Our study provides a greater understanding of one consequence of data breaches, that is, how timing and disclosure initiative influence nonprofessional investors' judgments. JEL Classifications: G41; M41.
Baby Tango

We describe two prototypes from the Baby Tango project: electronic textile toys that enable soft, tangible, full-body interaction. It presents interaction techniques that bridge the physical, the digital, and the social, as well as a case study in constructing interactive composite textiles. Given that the softness of the toy is a central design constraint, most of the circuit, including the sensors, is embroidered directly on the surface of the artifact using technical threads (with varying electro-mechanical properties) and a digital embroidery/laying machine. This submission includes design and technical details, as well as initial interaction design scenarios. The next steps of this project will explore how these toys could support the development of empathy in toddlers through embodied play. Further work is needed in order to develop background research, collaborations with early childhood researchers, as well as empirical studies. Future work will include the development of these studies; iterating aspects of interaction and play through participatory design; and improving technical design to focus on reliability, robustness, and durability.
Data Mining for Generalizable Pre-admission Prediction of Elective Length of Stay

An elective admission involves an hospital admission for which a doctor schedules the day and time of the visit in advance. That is, it is not an emergency or urgent admission. Factors in the scheduling of such admissions and also resource impacts include bed availability and utilization, and as such it is of benefit to be able to predict in advance the length of stay of patients. In this work we report on the use of data mining on a large, cross-provider dataset to develop potentially generalizable machine learning models to predict all- condition, high length of stay for elective admissions, prior to the time of admission based upon patient, admitting condition, hospital and locale attributes. The best of these models achieves an AUC score of 0.900. We also carry out evaluations to consider the generalizability of such models via measuring their predictive performance on a dataset from a different time period, and the models demonstrate that they maintain equivalent predictive levels on data from a different period.
Modification of the Viola-Jones approach for the detection of the government seal stamp of the Russian Federation

In this paper we present modification of the Viola-Jones approach for solving government seal stamp of the Russian Federation detection problem. The main contributions of the proposed modification are combining brightness and edge features as well as using L<sub>1</sub> norm of the gradient of the image for calculating edge features. This modification allows to build classifiers which are more robust to noise, absence of a characteristic structure of contrasts and object's boundaries. The modification is experimentally compared to original Viola-Jones algorithm and showing better quality on different testing sets.
Towards the Development of a Low-Cost Irradiance Nowcasting Sky Imager

Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants’ location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.
Dynamic handwritten signature and machine learning based identity verification for keyless cryptocurrency transactions

In this paper we propose a novel system for identity verification by amalgamating online signature verification, machine learning, IOT and blockchain to garner their potentials to cope up and to contain this risk of identity theft specifically in the case of online transactions. In this system signals of roll, pitch and yaw values retrieved from MPU6050 sensor (Inertial Measurement Unit) are analysed using Digital Time Wrapping to obtain DTW minimum distance to verify the identity of the user. In case of cryptocurrencies, we propose a system where private key is not stored anywhere but the same unique private key, assigned to the user by Blockchain, is generated every time with the help of method incorporating biometrics and machine learning. The required data will then be sent to blockchain with the help of IOT system to complete the transaction.
The role of domain knowledge in document selection from search results

It is a frequently seen scenario that when people are not familiar with their search topics, they use a simple keyword search, which leads to a large amount of search results in multiple pages. This makes it difficult for users to pick relevant documents, especially given that they are not knowledgeable of the topics. To explore how systems can better help users find relevant documents from search results, the current research analyzed document selection behaviors of users with different levels of domain knowledge (DK). Data were collected in a laboratory study with 35 participants each searching on four tasks in the genomics domain. The results show that users with high and low DK levels selected different sets of documents to view; those high in DK read more documents and gave higher relevance ratings for the viewed documents than those low in DK did. Users with low DK tended to select documents ranking toward the top of the search result lists, and those with high in DK tended to also select documents ranking down the search result lists. The findings help design search systems that can personalize search results to users with different levels of DK.
A new construction of rotation symmetric Boolean functions with optimal algebraic immunity and higher nonlinearity

In this paper, we present two new kinds of theoretical construction of rotation symmetric Boolean functions with optimal algebraic immunity both on odd variables and on even variables based on ordered integer partitions. Our rotation symmetric Boolean functions are of much better nonlinearity than all the existing theoretical constructions of rotation symmetric Boolean functions with optimal algebraic immunity. Further, the algebraic degrees and the fast algebraic immunities of our rotation symmetric Boolean functions are also high enough in some cases.
Using planning CTs to enhance CNN-based bladder segmentation on cone beam CT

For prostate cancer patients, large organ deformations occurring between the sessions of a fractionated radiotherapy treatment lead to uncertainties in the doses delivered to the tumour and the surrounding organs at risk. The segmentation of those structures in cone beam CT (CBCT) volumes acquired before every treatment session is desired to reduce those uncertainties. In this work, we perform a fully automatic bladder segmentation of CBCT volumes with u-net, a 3D fully convolutional neural network (FCN). Since annotations are hard to collect for CBCT volumes, we consider augmenting the training dataset with annotated CT volumes and show that it improves the segmentation performance. Our network is trained and tested on 48 annotated CBCT volumes using a 6-fold cross-validation scheme. The network reaches a mean Dice similarity coefficient (DSC) of 0:801 &plusmn; 0:137 with 32 training CBCT volumes. This result improves to 0:848 &plusmn; 0:085 when the training set is augmented with 64 CT volumes. The segmentation accuracy increases both with the number of CBCT and CT volumes in the training set. As a comparison, the state-of-the-art deformable image registration (DIR) contour propagation between planning CT and daily CBCT available in RayStation reaches a DSC of 0:744 &plusmn; 0:144 on the same dataset, which is below our FCN result.
Respondent preferences in travel survey design: An initial comparison of narrative, structured and technology-based travel survey instruments

Respondent satisfaction with a travel survey instrument, and broader engagement with the survey topic, is critical to data quality. This research provides an initial investigation into respondent satisfaction of two different travel survey instruments—a paper-based survey and a broadly equivalent smartphone survey—and explores potential cascading effects on data quality. A third instrument, a free-form description of travel, was also requested, to provide an unstructured sketch of how people understand their own travel experience. The findings indicate that different survey instruments will deliver different descriptions of travel, even for the same travel day. While each survey has the potential to collect accurate data, reporting of travel is entirely dependent on the motivation and conscientiousness of respondents. In this research, the effort of completing a traditional paper-based travel survey was identified as problematic. This is contrasted with smartphone survey data that, while perceived by users to be more accurate, was ultimately difficult for participants to validate. The narrative approach of describing travel in personal terms could usefully accompany more structured survey designs.
Network Security Approaches in Distributed Environment

Distributed computing is one of the thrust areas in the field of computer science, but when we are concerned about security a question arises, “Can it be secure?” From this note, the authors start this chapter. In the distributed environment, when the system is connected to a network, and the operating system firewall is active, it will take care of all the authentication and access control requests. There are several traditional cryptographic approaches which implement authentication and access control. The encryption algorithms such as Rijndael, RSA, A3, and A5 is used for providing data secrecy. Some of the key distribution techniques have been discussed such as Diffie Hellman key exchange for symmetric key, and random key generation (LCG) technique is used in red-black tree traversal which provides the security of the digital contents. The chapter deals with the advanced versions of the network security techniques and cryptographic algorithms for the security of multimedia contents over the internet.