Reviewerly/exHarmony huggingface dataset:
Selection of a Stopping Criterion for Anisotropic Diffusion Filtering in Ultrasound Images

Ultrasound imaging is a safe and cost-effective diagnostic tool, but the quality of the images is affected by speckle noise and artifacts. Anisotropic diffusion filters can be used to reduce noise and preserve the edges in the image. However, this technique is very sensitive to the number of iterations selected. This paper proposes a stopping criterion for effective noise removal without blurring the edges, based on the relative variance between the estimated denoised image and the original one. Different quality metrics were evaluated in 25 test images. The results suggest that the proposed stopping criterion can be implemented efficiently and aids in the process of automation of the filter.
Big Data technologies to process spatial and attribute data when designing and operating mine-engineering systems

A myriad of spatial and attribute data are in use when designing and operating mine-engineering systems. These data are hosted on various servers of a company and are diverse in structure of data and units of measurement and have different subject areas related to each other in a complex way. There is no conformity between conventional systems for managing and storing data and tools for analysing incoming data. Big Data paradigm that implies methods for processing distributed data is utilised in ‘big data’ processing. A MapReduce method involves two procedures: map procedure that applies a proper function to each element of the list and reduce procedure, integrating the map procedure results. Such conventional database management system methods as integration and indexation, graph search and other methods are used to cluster big data. These methods should be used within MapReduce. Employing standard equipment and means to control a distributed Hadoop and Apache Hadoop Distributed File System (HDFS) file system, data storages in petabytes can be implemented. To make a more in-depth analysis, Data Mining methods are utilised along with network analysis, predictive analytics, etc. The articlecovers various methods applied to collect and analyse ‘big data’ to do a feasibility study and design mine and engineering systems.
A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.
Recognizing User-Defined Subsequences in Human Motion Data

Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal multimedia data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. In this paper, we focus on an important task of recognition of a user-defined motion, based on a collection of labelled actions known in advance. We utilize current advances in deep feature learning and scalable similarity retrieval to build an effective and efficient k-nearest-neighbor recognition technique for 3D human motion data. The properties of the technique are demonstrated by a web application which allows a user to browse long motion sequences and specify any subsequence as the input for probabilistic recognition based on 130 predefined classes.
VizEmo

Current wearable technologies in the classroom do not properly support tracking children's emotions with regards to their progress in learning. Interactive, learning ambience connected with wearables to track and visualize children's emotion, is one approach that may allow for better communication of emotional state with teachers and peers alike. We introduce VizEmo by probing into connectivity and interactivity of wearables to capture children's emotional feedback, wireless technology and sensors to detect the wearable device, and visual representations such as lights and pictures to display children's captured emotions. Lifelogging helps informal tracking and visualization of children's real-time physiological signals, such as heart-rate and electrodermal activities connected to their emotions progressing in their education. Supported by our prior study, this work aims at advancing children's emotion communication technology while improving their educational outcomes. It further augments connectivity and interactivity of wearables to learning ambience when communicating with the classroom.
Towards Explanations of Anti-Recommender Content in Public Radio

Other than private broadcasters, publicly financed broadcasters have to fulfil a public service remit. Individual playouts in public radio, therefore, consist not only of recommender content but also of 'anti-recommender content" that matches public interests. Such anti-recommender content in individual playouts may be unexpected for users and may need explanation. To find out what explanations might look like in public radio, we elicit the requirements of the public service remit for an example country. Based on these requirements, we propose an approach for designing explanations of recommendations that align with the public service remit.
Humanitarian Education as a Resource for Sustainable Development

Abstract This article studies the role of humanitarian education in providing conditions for sustained economic growth. The study is based on the analysis of contemporary tendencies of economical, social and ecological development. Sustainable development in the current context creates a need for the new type of engineers and workers with such qualities as responsibility, creativity, openness to innovations. The author maid the conclusion that it’s impossible to achieve new employee’s qualities, demanded by modern economic growth, only by means of professional education because they depend of human development which is a product of humanitarian education. Thus the study of philosophy and other humanitarian disciplines should be an important part of specialist’s education in modern world.
Feature Correlation Loss in Convolutional Neural Networks for Image Classification

Feature maps in Convolutional neural networks are extracted automatically with some initialization methods and training strategies, which greatly economizes the cost of feature engineering. However, correlation between feature maps are not considered in common networks, resulting in the increase of redundant feature maps with the networks becoming more complicated. In this work, we proposed the correlation layer and designed the correlation loss, which can compute the correlation coefficient matrix of the feature maps in the last convolutional layer and optimize the weights distribution respectively. In the training phase, 2 strategies, namely the supervision and initialization are studied with Gaussian and He initialization methods for the baseline. The experimental results on CIFAR-10 dataset demonstrated that the supervision strategy for the multi-task training could efficiently reduce the correlation between the feature maps learned and increase the classification accuracy from 0.39% to 1.14% on the test set.
Waste Not: Meta-Embedding of Word and Context Vectors

The word2vec and fastText models train two vectors per word: a word and a context vector. Typically the context vectors are discarded after training, even though they may contain useful information for different NLP tasks. Therefore we combine word and context vectors in the framework of meta-embeddings. Our experiments show performance increases at several NLP tasks such as text classification, semantic similarity, and analogy. In conclusion, this approach can be used to increase performance at downstream tasks while requiring minimal additional computational resources.
Conformity of Higher Education with Requirements of Digital Natives

The paper explores stages that teaching followed in its revolutionizing transitions from Education 1.0 to Education 4.0. The study presents a detailed analysis of the scientific approaches, which economists used in examining the influence of digital technologies on the behaviour of young people and on identifying a generation of children as “digital natives” who grew up in the era of informational changes. An assessment of traits inherent in the new generation of young people has been used as a basis for carrying out a comparative analysis of the features of “digital natives” with the distinctiveness of “digital immigrants.” The study identified factors that contributed to a growing number of “digital immigrants” in developing and least developed countries. The notions of “digital cowboys” and “digital nomads” are considered in terms of their recent appearance in the academic market. Countries with the highest share of “digital natives” comprise the high-income and above-average income countries, the countries with very high levels of general Internet penetration, the countries with top ICT Development Index (IDI), and the countries with a relatively high proportion of young people. Solutions are proposed to support “digital natives” in their educational aspirations and narrow the gap between them and “digital immigrants”.
Bayesian Estimation of Panel Count Data Models: Dynamics, Latent Heterogeneity, Serial Error Correlation, and Nonparametric Structures

In this chapter we discuss how Bayesian techniques can be used to estimate the Poisson model with exponential conditional mean, a well-known panel count data model. In particular, we focus on the implementation of Markov Chain Monte Carlo methods to various specifications of this model that allow for dynamics, latent heterogeneity and/or serial error correlation. The latent heterogeneity distribution is assigned a nonparametric structure, which is based on the Dirichlet process prior. The initial conditions problem also is addressed. For each resulting model specification, we provide the associated inferential algorithm for conducting posterior simulation. Finally, relevant computer codes are posted as an online supplement.
Towards an Architecture for Electronic Notifications Services on the Cloud: A Model Driven Approach

Cloud computing technology is being adopted by organizations driven by their need to reduce operational costs, and to deliver scalable and flexible services. Solutions deployed on the cloud are a great point of interest in the commercial and governmental area for e-commerce transactions. Here, electronic invoicing applications play a key role in commercial activities, being a branch of vital importance for the business activities. In order to accomplish certain tasks (e.g., collection of overdue invoices, notifications about payment plans) technological solutions have been proposed. Then, it is important to establish architectures that support developers in creating notification solutions in order to send messages about issues of interest for organizations in a proper manner. Moreover, due to the constant changes of laws, a model driven approach can help as a flexible and maintainable option for these solutions. Therefore, this paper proposes an architecture for the creation of electronic notifications, which allows a good communication between organizations and their clients. In this context, a case study is presented in order to demonstrate the instantiation of the proposed architecture in a real scenario.
A Reliable Security Alternative: Quantum Cryptography

Once the different alternatives of the classical order are addressed, it is necessary to assume that the security systems are exposed to vulnerabilities that can cause a critical collapse in the world's economic and financial activities, therefore, it becomes a challenge to raise the research into a new field such as quantum cryptography that does not depend on the computer capacity of the adversary but is absolutely guaranteed by the laws of quantum physics, although it is in an initial stage, it is necessary to motivate the work for research purposes in the academic world, industry and society in general is taken as a solid alternative of security.
Optimized Design of Accompanying Smart Car for the Elderly Based on Humanization Design Methods

The aim of this paper is to design accompanying smart car for elderly people, it is not just a tool to assist them more appropriate to travel but also reducing their psychological burden. This paper uses the method of user-friendly theory and human factor engineering theory. Through observation, interviews and literature analysis, this paper analysis the travel needs and characteristics of studied group deeply. Through market research, the advantages and disadvantages of related product is obtained. As result, considering the design orientation mainly from four aspects of shape, structure, color and ergonomics. In conclusion, this paper introduces the design of companion intelligent vehicle of short distance, and discusses the connotation of elderly-friendliness in product design and its relationship.
Memorization Approach to Quantum Associative Memory Inspired by the Natural Phenomenon of Brain

An attempt to present the functionality of quantum associative memory, mainly memorization operation to mimic the natural phenomenon of the brain (more remembering of frequently observed patterns and gradual forgetting the non-recalled patterns) is done. Quantum neural network has features such as understanding, awareness, and consciousness. Quantum Neural Networks use content-addressable quantum associative memory for its “memorize” and “recall” operations. We attempt to mimic the memorization process to that of the natural phenomenon of the brain, wherein the memorization of patterns and their storage is based on the time and the frequency of its recall.
LSVS: Link Specification Verbalization and Summarization

An increasing number and size of datasets abiding by the Linked Data paradigm are published everyday. Discovering links between these datasets is thus central to achieve the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users, particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we address this problem by proposing a generic approach that allows a LS to be verbalized, i.e., converted into understandable natural language. We propose a summarization approach to the verbalized LS based on the selectivity of the underlying LS. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users.
LightNets: The Concept of Weakening Layers

Deep neural networks generally use the information fusion at the front and back layers because the traditional convolutional networks that stack convolutional layers have limited ability to extract the effective information or effective information cannot be passed to the back layer. These networks, which incorporate the information of the previous layer, are attributed to the improvement of the accuracy achieved to facilitate the propagation of gradients and incorporate various techniques for adjusting parameters. We have explored the relationship between the residual networks and dense networks and found that they have a great degree of similarity in certain situations and can combine them to exert their respective advantages. We propose a lightweight network (LightNets) architecture with few parameters. LightNets has faster training and inference speeds because it has fewer parameters. The main way to explore is to change the flow and integration of information. This paper compares various network architectures and regularization techniques to analyze the generalization capabilities of the LightNets.
ComMute—Towards a Computational Musical Theory of Everything

This paper draws future perspectives of music as a comprising cultural achievement of humans. We discuss the role of music for mathematics and physics from Pythagoras to String Theory, its global human presence, transcending specific fields of knowledge in its synthetical force that unifies distant fields of knowledge and action in the concrete and abstract realms.
Improving Usability of Distributed Neural Network Training

The recent success of Deep Neural Networks (DNN) has triggered a race to build increasingly large DNNs; however, training speed is a known limitation. To solve this limitation, distributed neural network training has become a large area of research. Usability, the complexity for a user to implement distributed neural network training, is an aspect rarely considered, yet critical. The impact of complexity on software development has been studied extensively and evidence shows increased complexity has a direct correlation on development effort, maintainability, and fault-proneness of software. In this work, we investigate if automation can greatly reduce the implementation complexity of distributing neural network training across multiple devices without loss of computational efficiency when compared to manual implementations. Experiments were conducted using convolution and fully-connected neural network layers to perform image classification on a four-node NVIDIA Jetson TX1 cluster. Our module, Torch Automatic Distributed Neural Network (TorchAD-NN) reduced the implementation complexity of data parallel neural network training by more than 90% and provided components to easily parallelize fully-connected neural layers.
Application of Ellipsometry to the Identification of Ore Minerals

The preceding article on amethyst (Kile 2019) includes descriptions of ore minerals from the Thunder Bay (Ontario) area. Marcasite from the Blue Points mine has nontypical polarization colors, as o...
Learning Mobile App Embeddings Using Multi-task Neural Network

Last few years have seen a consistent increase in the availability and usage of mobile application (apps). Mobile operating systems have dedicated stores to host these apps and make them easily discoverable. Also, app developers depict their core features in textual descriptions while consumers share their opinions in form of user reviews. Apart from these inputs, applications hosted on app stores also contain indicators such as category, app ratings, and age ratings which affect the retrieval mechanisms and discoverability of these applications. An attempt is made in this paper to jointly model app descriptions and reviews to evaluate their use in predicting other indicators like app category and ratings. A multi-task neural architecture is proposed to learn and analyze the influence of application's textual data to predict other categorical parameters. During the training process, the neural architecture also learns generic app-embeddings, which aid in other unsupervised tasks like nearest neighbor analysis and app clustering. Various qualitative and quantitative experiments are performed on these learned embeddings to achieve promising results.
PROMAR: Practical Reference Object-based Multi-user Augmented Reality (poster)

Mobile Augmented Reality (MAR) represents an emerging category of applications that bring users interactive experiences with the physical experiencesnvironment. In such applications, users can place virtual objects in real-world space, and view them through a camera view. In this work, we develop a framework that supports multi-users interactions for MAR apps, where one user places a virtual object that can be recognized by other users.
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning

We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of products that attract them. This not only benefits merchants and the platform, but also improves user experience. The problem is challenging due to the following reasons: (1) We need to carefully manipulate the ad content without affecting user search experience. (2) It is difficult to obtain users' explicit feedback of their preference in product features. (3) Nowadays, a great portion of the search traffic in E-Commerce platforms is from their mobile apps (e.g., nearly 90% in Taobao). The situation would get worse in the mobile setting due to limited space. We are focused on the mobile setting and propose to manipulate ad titles by adding a few selling point keywords (SPs) to attract query users. We model it as a personalized attractive SP prediction problem and carry out both large-scale offline evaluation and online A/B tests in Taobao. The contributions include: (1) We explore various exhibition schemes of SPs. (2) We propose a surrogate of user explicit feedback for SP preference. (3) We also explore multi-task learning and various additional features to boost the performance. A variant of our best model has already been deployed in Taobao, leading to a 2% increase in revenue per thousand impressions and an opt-out rate of merchants less than 4%.
Performance evaluation of destriping algorithms: a test procedure based on simulated images

Striping noise is intrinsic to the process of image acquisition via scanning systems. Although it can be mitigated by radiometric calibration, the residual noise can jeopardize the quantitative analysis of the images and the extraction of physical parameters. This paper presents a new approach for evaluating destriping algorithms by means of a synergic use of: (1) distortion measurements, (2) quality indexes and relevant benchmark values calculated from simulated data, and (3) evaluation of effects on parameters extraction. The proposed procedure is here used to evaluate the performance of three destriping algorithms: Destriping Algorithm based on Statistics and Filtering (DASF), ENVI® algorithm and Wavelet-Fourier Filtering (WFF), on two different scenarios of hyperspectral push-broom data. The study pointed out the need of an approach based on the analysis of several aspects in order to identify the best performing algorithm. In addition, the results showed a good performance of both DASF and WFF that preserved the original radiometry without introducing spectral distortion.