Reviewerly/exHarmony huggingface dataset:
Adaptive Denoising Approach for High-Rate GNSS Seismic Waveform Preservation: Application to the 2010 EI Mayor-Cucapah Earthquake and 2012 Brawley Seismic Swarm

The high noise levels of high-rate Global Navigation Satellite System (GNSS) solutions limit their seismological applications, including capturing earthquake-induced coseismic displacements. In this study, we developed a new adaptive denoising approach for high-rate GNSS observations to improve the precision of seismic displacements and preserve seismic waveforms in GNSS coseismic signals. The performance of the proposed method was evaluated using high-rate (5 Hz) GNSS data acquired from the moderate EI Mayor-Cucapah earthquake (M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> 7.2) on 4 April 2010 and the small Brawley seismic swarm (M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> 4.6-5.5) on 26 August 2012. The performance of the proposed method was compared with those of modified sidereal filtering, Stacking filtering, and MSF plus Stacking. The comparison showed that the proposed method is more precise than other widely used method. It can significantly remove high-frequency white noise and low-frequency colored noise caused by CME, multipath errors, and/or other unmodeled systematic errors in high-rate GNSS displacements. The results were also compared with collocated strong motion data (50 and 200 Hz). The high precision of the proposed method was mainly afforded by the high performance of complete ensemble empirical mode decomposition, which was used to decompose the GNSS signal into different frequency modes. However, the normalized autocorrelation function and correlation coefficients used to determine the noise-dominated high-frequency modes and the “wavelet-like” soft-threshold used for direct denoising of the noise-dominated high-frequency modes also contributed. Despite the high noise levels of GNSS solutions, especially regarding the vertical displacement components, some small-amplitude details, which are usually only detectable by seismic instruments, could be observed in the denoised displacements in this study. The results reported herein indicate that the proposed method significantly improves the precision and reliability of GNSS displacements and the effectiveness of seismic signal detection, which is particularly critical for the measurement of earthquake-induced coseismic displacements.
Fault Attacks Resistant AES Hardware Implementation

The protection of the symmetric cryptographic algorithm, specially the Advanced Encryption Standard (AES), against fault injection attacks is very inportant to guarantee the security of transmitted data. In this paper, we proposed a new fault detection scheme based information redundancy for the AES. We analysis the detection scheme robustness against the fault injection attacks. The simulation fault attacks results prove that the fault coverage reaches 71.43%. In addition, the original and the protected AES hardware implementation have been implemented on the Xilinx Virtex-5 FPGA. We confirm that the protected AES is very effective while keeping the frequency overhead very low.
Re-using Enterprise Architecture Repositories for Agile Threat Modeling

Digitization has increased exposure and opened up for more cyber threats and attacks. To proactively handle this issue, enterprise modeling needs to include threat management during the design phase that considers antagonists, attack vectors, and damage domains. Agile methods are commonly adopted to efficiently develop and manage software and systems. This paper proposes to use an enterprise architecture repository to analyze not only shipped components but the overall architecture, to improve the traditional designs represented by legacy systems in the situated IT-landscape. It shows how the hidden structure method (with Design Structure Matrices) can be used to evaluate the enterprise architecture, and how it can contribute to agile development. Our case study uses an architectural descriptive language called ArchiMate for architecture modeling and shows how to predict the ripple effect in a damaging domain if an attacker's malicious components are operating within the network.
Solution for the problem of the parameters identification for autoregressions with multiple roots of characteristic equations

When describing a real image using a mathematical model, the problem of model parameters identification is of importance. In this case the identification itself is easier to perform when a particular type of model is known. In other words, if there is a number of models characterized by different properties, then if there is a correspondence with the type of suitable images, then the model to be used can be determined in advance. Therefore, in this paper, we do not consider the criteria for model selection, but perform the identification of parameters for autoregressive models, including those with multiple roots of characteristic equations. This is due to the fact that the effectiveness of identification is verified by the images generated by this model. However, even using this approach where the model is known, one must first determine the order of the model. In this regard, on the basis of YuleWalker equations, an algorithm for determining the order of the model is investigated, and the optimal parameters of the model are also found. In this case the proposed algorithm can be used when processing real images.
Vietnamese Question Classification based on Deep Learning for Educational Support System

Question classification has an important role in many applications. This task is predicting the entity type of the answering sentence for a given question in natural language. In this research we propose using pre-train word embeddings in Bidirectional LSTM model for Vietnamese question classification and integrate automated question answering systems into Educational Support System. Experiment results both on Vietnamese Question Classification and automated question answering systems show that our proposed method archives the better results.
The use of Quizlet to enhance vocabulary in the English language classroom

This research focuses on vocabulary acquisition in foreign language learning. The latest trends of teaching as well as the huge advance of technology allow teachers to utilize online and mobile applications through diverse apps. This quasi-experimental design research investigated Quizlet, in its mobile version, on vocabulary acquisition in English as a Foreign Language (EFL). One group of A2 (according to the Common European Framework of Reference for Languages) EFL learners at a state secondary school in Valencia (Spain) (N=24) participated in this study. Learners from the Treatment group (henceforth, T-group) underwent a pre-test and a post-test to assess their acquisition of the assigned vocabulary lessons which were extracted from the course syllabus. After utilizing Quizlet for vocabulary learning for the academic year 2017-2018, the results proved that these learners improved significantly their level of EFL vocabulary at the post-test. Accordingly, this research recommends utilizing Quizlet in its mobile version at secondary education.
Approximate MMSE and MAP estimation using continuous-time particle filter

The algorithm is proposed for solving approximately the optimal filtering problem for nonlinear continuous-time stochastic observation systems that provides two estimates for the state. These estimates are the minimum mean squared error estimate and the maximum a posteriori estimate. The proposed algorithm is based on the continuous-time particle filter, which corresponds to the Duncan–Mortensen–Zakai equation. To find the mode of the conditional distribution approximately, the Edgeworth series is used for the conditional probability density function expansion. This approach allows one to significantly reduce the computation time in contrast to finding the mode by estimating the conditional probability density function, for example, by the histogram or the kernel density estimation.
The Application of AlphaZero to Wargaming

In this paper, we explore the process of automatically learning to play wargames using AlphaZero deep reinforcement learning. We consider a simple wargame, Coral Sea, which is a turn-based game played on a hexagonal grid between two players. We explore the differences between Coral Sea and traditional board games, where the successful use of AlphaZero has been demonstrated. Key differences include: problem representation, wargame asymmetry, limited strategic depth, and the requirement for significant hardware resources. We demonstrate how bootstrapping AlphaZero with supervised learning can overcome these challenges. In the context of Coral Sea, this enables AlphaZero to learn optimal play and outperform the supervised examples on which it was trained.
Exploring the domestication of thermal imaging

Recent work demonstrated the opportunities of thermal imaging in the development of novel interactive systems. However, the exploration is limited to controlled lab setups. Hence, little we know about how thermal imaging could be useful for a broader range of daily applications by novice users. To investigate the potential of domestication of thermal imaging, we conducted an exploration with a technology-cultural probe. Ten households (26 individuals) used a mobile thermal camera in their daily life. We collected thermal photos taken by the participants and conducted interviews after using the camera. We found that the users were excited about using thermal cameras in their everyday lives and found many practical uses for them. Our study provides insights into how novice users wish to use thermal imaging technology to augment their vision in daily setups, as well as identifying and classifying common thermal imaging use cases. Our work contributes implications for designing thermal imaging devices targeted towards novice users.
Promoting Personhood for People with Dementia Through Shared Social Touchscreen Interactions

In this chapter we describe an approach aimed to promote personhood in technology via touchscreen interactions. The approach we propose employs co-design methods aimed at giving people living with dementia a strong voice to overcome usage barriers. Secondly, we propose interest-based design to engage and create meaningful social interactions. We illustrate the approach through two case studies demonstrating its successful application within playing games (two player games) and music (small collaborative ensembles). The chapter concludes with six recommendations for the design of creative and shared interactions for touchscreen technologies.
User values of smart home energy management system

Ubiquitous computing continues to transform our lives, including our homes and leisure activities. Smart home energy management system (SHEMS) are one example of such a technology. It connects homes to a smart grid and may increase the use of renewable energy by directing the demand to off-peak hours and reducing the overall energy demand. User values of such a technology may be critical in the acquisition, adoption and assimilation of the technology. This research fills the gap of understanding user values of SHEMS users. We studied new, potential and experienced users of SHEMS and their values. Sensory ethnography interview method was applied in the value sensitive design empirical investigation to elicit key user values of SHEMS in 28 families. The users relate to SHEMS values such as economic gains, environmental sustainability, comfort and security. Some SHEMS users' values such as stimulation, creativity, and autonomy, can be in conflict with the values of other family members, and with those which are currently built in the SHEMS technologies. The recognized values of SHEMS stakeholders act as an input for the design of smart grid and smart home services and products. In addition, the research contributes to the theory-building of smart home technology user research.
Joint-oriented Features for Skeleton-based Action Recognition

In this paper, we propose joint-oriented features for skeleton-based action recognition, which aims to decrease the influence of ambiguous joints in the skeleton sequences. When doing skeleton-based action recognition, we noticed that if the skeleton data contains noisy joints, the result would be influenced by the noise. Since the selections of disambiguous joints might be impossible, it would be a hard task for humans to distinguish whether the joint in the frame is correct. To deal with this situation, we propose joint-oriented features to train joint-oriented models. If some joints are noise in the frame, the corresponding joint-oriented models would not perform well on the case. As we could not preFigure the noise in the data, we apply ensemble modeling with our joint-oriented models to let the disambiguous joints correct the ambiguous ones. To demonstrate the effectiveness of our proposed method, we conducted the experiments on three benchmark skeleton-based action datasets, including the large-scale challenging NTU-RGBD, and our approach achieves competitive performance over the state-of-the-art.
Multi-label Feature Selection Using Particle Swarm Optimization: Novel Initialization Mechanisms

In standard single-label classification, feature selection is an important but challenging task due to its large and complex search space. However, feature selection for multi-label classification is even more challenging since it needs to consider not only the feature interactions but also the label interactions. Particle Swarm Optimization (PSO) has been widely applied to select features for single-label classification, but its potential has not been investigated in multi-label classification. Therefore, this work proposes PSO-based multi-label feature selection algorithms to investigate the importance of population initialization in multi-label feature selection. Particularly, the discriminative information is utilized to let the swarm start with more promising feature combinations. Results on eight real-world datasets show that the new strategies can reduce the number of features and improve classification performance over using all features and standard PSO-based multi-label feature selection.
An Extension of the QUAMOCO Quality Model to Specify and Evaluate Feature-Dependent Non-Functional Requirements

Features in a software system usually must satisfy different quality expectations originating from the various stakeholders of a software system, ranging from direct users to the software manufacturer. As an example, the design quality of the source code will likely be more important if the associated feature is frequently used by customers or if it has strategic value for the software manufacturer. In order to effectively approach these qualitative subtleties, we need a means to specify non-functional requirements on the level of individual software features. Fine-grained specification of non-functional requirements on feature level respects their individual relevance for a feature and facilitates deriving suitable constraints for them. Particularly in DevOps-driven software projects with operational data the fulfillment of non-functional requirements for individual features can then continuously be evaluated. This guides software engineers in meeting these quality expectations on a much finer level than if treated uniformly across the software system. In this paper we present an extension of the QUAMOCO meta quality model for specifying and evaluating non-functional requirements on feature level. While the existing meta model focused on static quality measures, the extension of the meta model now also captures dynamic measures which accumulate during feature execution. An exploratory case study based on the results from an interview study shows the completeness of the approach for specifying and evaluating feature-dependent non-functional requirements.
SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structural Similarity

Adversarial examples form a major threat to incorporating machine learning (ML) models in critical applications. The existence and generalisation of these attacks have been attributed to the linear nature of ML models, deep neural network models in particular, in the high dimensional space. This paper presents a new nonlinear computational layer to the deep convolutional neural network architectures. This layer performs a set of comprehensive convolution operations that mimics the overall function of the human visual system (HVS) via focusing on learning structural information. The core of its computations is evaluating the components of the structural similarity metric (SSIM) in a setting that allows the kernels to learn to match structural information. The proposed SSIMLayer is inherently nonlinear. Experiments conducted on CIFAR-10 benchmark demonstrate that the SSIMLayer provides high learning capacity and shows more robustness against adversarial attacks.
DFQA: Deep Face Image Quality Assessment

A face image with high quality can be extracted dependable features for further evaluation, however, the one with low quality can't. Different from the quality assessment algorithms for general images, the face image quality assessment need to consider more practical factors that directly affect the accuracy of face recognition, face verifcation, etc. In this paper, we present a two-stream convolutional neural network (CNN) named Deep Face Quality Assessment (DFQA) specifically for face image quality assessment. DFQA is able to predict the quality score of an input face image quickly and accurately. Specifcally, we design a network with two-stream for increasing the diversity and improving the accuracy of evaluation. Compared with other CNN network architectures and quality assessment methods for similar tasks, our model is smaller in size and faster in speed. In addition, we build a new dataset containing 3000 face images manually marked with objective quality scores. Experiments show that the performance of face recognition is improved by introducing our face image quality assessment algorithm.
Probabilistic Shared Control for a Smart Wheelchair: A Stochastic Model-Based Framework

This article presents progress made towards implementing a shared control framework for a smart wheelchair based upon stochastic dynamic programming (a model-based control design). First, we describe the mechanical, electrical and software design process of our instrumented wheelchair platform. Then, we detail a deterministic control-oriented model of the wheelchair motion dynamics using Euler-Lagrange equations. Finally, we discuss the development of a stochastic model of the human driver’s intention in view of using Markov chain.
A Survey on Recent Learning Approaches in School Education Using Edmodo

Edmodo is a platform combining social networking and learning. It provides functionality addressed to tutors, students, and parents of young students. It attempts to exploit the popularity of social networking and tailor it to the needs of education. Access to Edmodo functionality is freely available. Open educational resources involving various subjects and different languages are available within Edmodo. Edmodo users may search for and retrieve such resources. Many learning approaches using Edmodo have been implemented in school education. The results are positive in various aspects. This chapter surveys approaches using Edmodo in school education. The survey focuses on the most recent approaches. The research results are analyzed. An important result that has been derived from several approaches is the improvement in learning. The surveyed approaches generally demonstrate the usefulness of Edmodo in school education. Furthermore, social learning platforms may be used to disseminate open educational resources and integrate them in school education practices.
Evaluation of Varying Visual Intensity and Position of a Recommendation in a Recommending Interface Towards Reducing Habituation and Improving Sales

The abundance of advertising in e-commerce results in limited user attention to marketing-related content on websites. As far as recommender systems are concerned, presenting recommendation items in a particular manner becomes equally relevant as the underlying product selection algorithms. To enhance content presentation effectiveness, marketers experiment with layout and visual intensity of website elements. The presented research investigates those aspects for a recommending interface. It uses a quantitative research methodology involving gaze tracking for implicit monitoring of human-website interaction in an experiment instrumented for a simple-structure recommending interface. The experimental results are discussed from the perspective of the attention attracted by recommended items in various areas of the website and with varying intensity, while the main goal is to provide advice on the most viable solutions.
Video-Based Person Re-Identification using Refined Attention Networks

We consider the problem of video-based person reidentification. The goal is to identify a person from videos captured under different cameras. In this paper, we propose an efficient attention based model for person re-identifying from videos. Our method generates an attention score for each frame based on frame-level features. The attention scores of all frames in a video are used to produce a weighted feature vector for the input video. This video-level feature vector is refined iteratively for re-identifying persons from videos. Unlike most existing deep learning methods that use global or spatial representation, our approach focuses on attention scores. Extensive experiments on three benchmark datasets demonstrate that our method achieves the state-of-the-art performance.
Integrating Episodic Future Thinking into Virtual Reality to Mitigate Substance Use Disorders: A Theoretical Framework

Substance use disorders present major health risks and economic burdens, while current healthcare systems face insufficient resources and accessibility deficiencies that challenge long term treatment for chronic diseases. This paper presents a theoretical approach of integrating the concept of episodic future thinking into immersive virtual reality to treat substance use disorders. Episodic future thinking can extend the temporal window over which an individual values rewards, rebalancing the impulsive and executive decision systems and thereby reducing the valuation of substances of abuse. In theory, virtual reality can enhance episodic future thinking by providing realistic cues and contexts. We propose using schema theory to guide the design of virtual reality for facilitating episodic future thinking. Future work should focus on developing and evaluating a proof-of-concept of this theoretical framework.
Clustering for multi-dimensional data set: a case study on educational data

Abstract This paper present two-step cluster method to handle data sets in educational data mining. It can handle multi-dimentional metric data points especially in complex data sets. For a case study, we use tracer study data in order to assign a clustering of alumni based on the profile. The result of clustering will help the school to evaluate and improve the quality of its graduates.