Reviewerly/exHarmony huggingface dataset:
This Land AR: an Australian Music and Sound XR Installation

This demonstration presents a development of an Augmented Reality (AR) Indigenous music and sound installation, an extended reality (XR) interactive audible experiential approach for augmenting audible elements in a public exhibition setting. It is a transmedia initiative as part of a music project, This Land. The project connected contributors and musicians, involving traditional to contemporary vocal and instrumental sounds. This Land project embraces cultural and social perspectives and related contemporary discourses within the Australia context. As augmented reality was being explored as an on-going study for the project, a number of conventional printed wall design (posters and photograph exhibits) were enhanced with augmented musical and sound elements. This Land project commenced as artistic performative event built around many years of collaboration between staff and students from the School of Creative Industries and the Wollotuka Institute at University of Newcastle (UON). Its vision embraces issues of Indigenisation, decolonisation, reciprocity and language revitalisation. A portable version of This Land AR will be used for the demonstration where users could experience features of the prototype system in the public setting.
Can We Predict Susceptibility to Cybersickness?

This study investigated whether individual differences in postural stability/activity can be used to predict who will become sick when exposed to head-mounted display (HMD) based virtual reality (VR). We found that participants who reported feeling sick after at least one exposure to VR displayed different postural activity than those who remained well. Importantly these differences were present in their sway data before they even donned the HMD. These results are inline with the postural instability theory of motion sickness and suggest that we can identify individuals who are more susceptible HMD-based cybersickness based on their spontaneous postural sway.
Facilitation and the focus on process

Systems Research and Behavioral ScienceVolume 38, Issue 1 p. 173-176 RESEARCH NOTE Facilitation and the focus on process Michael Hogan, Corresponding Author Michael Hogan michael.hogan@nuigalway.ie orcid.org/0000-0001-6227-0530 School of Psychology, National University of Ireland Galway, Galway, Ireland Correspondence Michael J. Hogan, PhD, School of Psychology, National University of Ireland Galway, Galway H91 TK33, Ireland. Email: michael.hogan@nuigalway.ieSearch for more papers by this authorBenjamin Broome, Benjamin Broome School of Communication, Arizona State University, Tempe, ArizonaSearch for more papers by this author Michael Hogan, Corresponding Author Michael Hogan michael.hogan@nuigalway.ie orcid.org/0000-0001-6227-0530 School of Psychology, National University of Ireland Galway, Galway, Ireland Correspondence Michael J. Hogan, PhD, School of Psychology, National University of Ireland Galway, Galway H91 TK33, Ireland. Email: michael.hogan@nuigalway.ieSearch for more papers by this authorBenjamin Broome, Benjamin Broome School of Communication, Arizona State University, Tempe, ArizonaSearch for more papers by this author First published: 12 November 2019 https://doi.org/10.1002/sres.2639Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume38, Issue1January/February 2021Pages 173-176 RelatedInformation
Envisioning SLO-driven Service Selection in Multi-cloud Applications

The current large selection of cloud instances that are functionally equivalent makes selecting the right cloud service a challenging decision. We envision a model driven engineering (MDE) approach to raise the level of abstraction for cloud service selection. One way to achieve this is through a domain specific language (DSL) for modelling the service level objectives (SLOs) and a brokerage system that utilises the SLO model to select services. However, this demands an understanding of the provider SLAs and the capabilities of the current cloud modelling languages (CMLs). This paper investigates the state-of-the-art for SLO support in both cloud providers SLAs and CMLs in order to identify the gaps for SLO support. We then outline research directions towards achieving the MDE-based cloud brokerage.
Multi-Source and multi-Scale Imaging-Data Integration to boost Mineral Mapping

We propose to develop an efficient and integrated exploration workflow that includes remote sensing data obtained by multiple types of sensors at different altitudes, a combination that has been identified as potentially disruptive technology for the mineral exploration sector. The fusion of multi-source and multi-temporal data is, therefore, a key challenge for a successful data integration. Ultimately, the objective is to boost the competitiveness, growth, sustainability, and attractiveness of the raw material sector.
A Sampling-Based System for Approximate Big Data Analysis on Computing Clusters

To break the in-memory bottleneck and facilitate online sampling in cluster computing frameworks, we propose a new sampling-based system for approximate big data analysis on computing clusters. We address both computational and statistical aspects of big data across the main layers of cluster computing frameworks: big data storage, big data management, big data online sampling, big data processing, and big data exploration and analysis. We use the new Random Sample Partition (RSP) distributed data model to store a big data set as a set of ready-to-use random sample data blocks in Hadoop Distributed File System (HDFS), called RSP blocks. With this system, only a few RSP blocks are selected and processed using a sequential algorithm in a distributed data-parallel manner to produce approximate results for the entire data set. In this paper, we present a prototype RSP-based system and demonstrate its advantages. Our experiments show that RSP blocks can be used to get approximate models and summary statistics as well as estimate the proportions of inconsistent values without computing the entire data or running expensive online sampling operations. This new system enables big data exploration and analysis where the entire data set cannot be computed.
Prediction of MVP Attribution in NBA Regular Match Based on BP Neural Network Model

The prediction of MVP (Most Valuable Player) in NBA regular season is still on the surface impression. Even though the application of high-order data (such as efficiency value, victory contribution value, etc.) is very popular, the forecasters will not combine macroscopically. Aiming at the main selection criteria of MVP in NBA regular season, this paper constructs a BP neural network model. In this paper, through the prediction performance of various high-order data, the composition and proportion of the model are constantly changed. Finally, a rule algorithm generated by the trained neural network is obtained. Then, the BP neural network model is trained (including the 18-19 season that has not yet ended) by using the historical data of the NBA in the past ten years. Then, the established model is mainly used in the 18-19 season (at the time of publication). The results are still unknown) NBA regular season MVP prediction. An improved BP neural network model is presented by experiment and data display.
Automatic Generation of Chinese Vector Fonts via Deep Layout Inferring

Designing a high-quality Chinese vector font library which can be directly used in real applications is very time-consuming, since the font library typically consists of large amounts of glyphs. To address this problem, we propose a data-driven system in which only a small number (about 10%) of glyphs need to be designed. Specifically, the system first automatically decomposes those input glyphs into vectorized components. Then, a layout prediction module based on deep neural network is applied to learn the layout and structure information of input characters. Finally, proper components are selected to assemble each character based on the predicted layout to build the font library that can be directly used in computers and smart mobile devices. Experimental results demonstrate that our system synthesizes high-quality glyphs and significantly enhances the producing efficiency of Chinese vector fonts.
An Overview of Blockchain-based Applications for Consumer Electronics

A high number of research initiatives investigated the advantages of blockchain technology in different sectors. The objective of this work is to present an overview of blockchain-based applications in the consumer electronics field. In particular this work reports around fifty blockchain initiatives in the contexts of healthcare, smart homes, smart cities, automotive and smart devices, and discusses advantages/limitations of this technology.
Weighted Group Sparsity Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Restoration

Total variation (TV) regularization has been widely used in the hyperspectral image (HSI) mixed noise removal problem by utilizing ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm to constrain the spatial difference image and promote the piecewise smooth structure. Unfortunately, it cannot depict the group sparse structure of spatial difference image along the spectral dimension. This paper proposes a new HSI restoration method using weighted group sparsity regularized low-rank tensor decomposition (LRTDGS). Specifically, we use a weighted group sparsity regularization which is denoted by ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm to explore the group structure of spatial difference image along the spectral dimension. Moreover, the spatial-spectral correlation from three directions of HSI is depicted by low-rank Tucker decomposition. We use efficient augmented Lagrange multiplier method to optimize the proposed LRTDGS model, and a series of experimental results are presented to demonstrate the effectiveness of the proposed method.
3-D Path-Searching for Uavs Using Geographical Spatial Information

Efficient path-planning is necessary to ensure safety and reliability for UAV's flights. However, existing path-searching algorithms are difficult to meet the high-efficiency-requirement for path planning in three-dimension situation. Therefore, this paper proposed a new three-dimensional path-planning method using improved Ant Colony Optimization (ACO) algorithm and fine hierarchical stratification. Firstly, we constructed a spatial gridding map for path-searching using grid discrete method based on high-precision geospatial data. Secondly, we searched for the optimal two-dimensional path on the minimum safe flight level for UAVs by improved algorithm. Lastly, paths were extended to three-dimensional space by introducing prominent mountains. To demonstrate the algorithm, we applied this method in a simulated environment established by MATLAB and compared outcomes with that of the traditional method. It is concluded that the method has higher convergence speed, better stability and higher computational efficiency.
Automatic segmentation of facial soft tissue in MRI data based on non-rigid normalization in application to soft tissue thickness measurement

For measuring the thickness of soft tissue in magnetic resonance (MRI) images, precise borders between skull and face surfaces should be known. We present an algorithm for segmentation of the human head in T1-weighted MRI images that generates smooth, complete segments of head tissues for further landmarks definition and measurements of the soft tissue thickness of the human head. As a segmentation tool we use an algorithm based on nonlinear normalization of the MRI template to MRI data and application of transform matrix to the head model. The algorithm uses preprocessed subject MRI data and a head model with separate tissue segments. The head model is obtained using a hybrid algorithm and consists of four segments: soft tissue, skull, brain and air. To assess the precision of segmentation, specificity, sensitivity, Dice and Jaccard Similarity Coefficients were computed. The algorithm was tested on MRI images from 10 Caucasian adults from free public database IXI. Specificity of 93% and 98% and sensitivity of 87% and 93% was achieved for soft tissue and brain segment, respectively. Specificity of 67% and 72% and sensitivity of 83% and 62% was achieved for the skull and air segments, respectively.
Battlebot : Logic Learning Based on Visual Programming Implementation in Multiplayer Game Online

Logic is one of the multiple intelligence expressed by howard gardener. The problem is that many people are still having difficulty in knowing their problems, moreover find solutions to these problems. Along with the development of computer technology and communication. Learning through software and games is commonplace. Efforts to learn logic through the game media have already begun. One of the most popular is robocode. This game invites the player to design their own artificial intelligence to control the characters in the game. But the method used to make artificial intelligence is to use conventional programming languages. This certainly becomes an obstacle for ordinary people, because programming languages are not generally known. Especially the code syntax in it. Visual programming is a solutions for this problem, where people often use certain symbols in communication.
Micro-blog Retweeting Prediction Based on Combined-Features and Random Forest

Aiming at the issue of incomplete features selection in micro-blog retweeting prediction researches, a micro-blog retweeting prediction method based on comprehensive features and random forest was proposed. Firstly, macroscopically, we analyzed the distinguishing features between high-retweeted micro-blog and low-retweeted micro-blog, between high-retweeted users and low-retweeted users, between high-retweeting users and low-retweeting users, and extracted nineteen features as the characteristics of micro-blog retweeting prediction. From the micro perspective, we extracted three local features which included the user’s activity, the user’s interest to the followee, the user’s retweeting interest to the micro-blog, as microscopic features to predict micro-blog retweeting. And then we defined the computed methods of the features. Finally, combined with the features, a micro-blog retweeting prediction method based on Random Forest was proposed. The experimental results on Sina micro-blog datasets showed that the proposed method was superior to the classical classification prediction algorithms such as Logistic, BayesNet and Support Vector Machine (SMO), and had good stability on datasets of different scales.
Instance Selection Techniques for Multiple Instance Classification

As the amount of data increases, fully supervised learning methods relying on dense annotations often become impractical, and are substituted by weakly supervised methods, that exploit data with a variable content in respect to size and semantics. In such schemes the volume of irrelevant information might be critically high impacting negatively the modeling performance and increasing considerably the memory and computational cost. Data reduction or selection are necessary to mitigate these effects. In this paper we propose and compare three different instance selection techniques for the Multiple Instance Learning (MIL) paradigm. The techniques are assessed for the problem of image classification using features from standard benchmark MIL datasets, as well as recently proposed features based on tensor decomposition. As implementation paradigm we exploit the widely accepted JC2MIL algorithm that performs joint clustering and classification. Two of the proposed instance selection techniques are based on Shannon entropy in image and feature space respectively, while one technique is based on a clustering evaluation metric, the silhouette score, that is introduced internally in the iterative joint clustering and classification algorithm. The enrichment of the MIL framework with the instance selection step showed to outperform the original algorithm providing state-of-the-art results in the vast majority of the performed experiments.
A Survey on the Application of Safety, Security, and Privacy Standards for Dependable Systems

Safety-critical systems are required to comply with safety standards. These systems are increasingly digitized and networked to an extent where they need to also comply with security and privacy standards. This paper aims to provide insights into how practitioners apply the standards on safety, security or privacy (Sa/Se/Pr), as well as how they employ Sa/Se/Pr analysis methodologies and software tools to meet such criteria. To this end, we conducted a questionnaire-based survey within the participants of an EU project SECREDAS and obtained 21 responses.
Kaizen: Building a Performant Blockchain System Verified for Consensus and Integrity

We report on the development of a blockchain system that is significantly verified and performant, detailing the design, proof, and system development based on a process of continuous refinement. We instantiate this framework to build, to the best of our knowledge, the first blockchain (Kaizen) that is performant and verified to a large degree, and a cryptocurrency protocol (KznCoin) over it. We experimentally compare its performance against the stock Bitcoin implementation.
e-SAAD system

In the first generation of interventions and home support, the needs were triggered generally by phone call and they are described orally. In most cases, there is no registered information about the patient's conditions or medical history. The interveners are either from the private or state health sector. Despite the fact that there are people who can intervene quicker than others, yet they have not been recruited, or work as professional liberal. With the development of technology, systems based on data mining or artificial intelligence have been developed to focus on the intervention's time for example. Although intervention and home-based care are the subject of many studies [1], the resolution of the overall decision-making problem is not sufficiently developed. On the one hand, the state of health presupposes the definition of a patient. There is a set of parameters characterizing the habits of daily life of the person analyzed in parallel with the evolution of physiological and environment. On the other hand, it is necessary to take into consideration the medical Core, his location, his profile not only his professional status but also his abilities and skills that are not explicitly described in his curriculum. Different studies and systems exist in the literature [2]. Each of his studies tackles only a part of the parameters. Indeed, these studies consider either the monitoring of daily activities, the monitoring of physiological data or other environmental parts. Either they consider the specificities of the medical Core's profile, or these systems use a probabilistic data mining that involves many interactions with the experts to interpret the data, either wise an expert system based on the inference rules defined by the medical experts. In addition, most systems do not use controlled vocabulary that provides semantics needed. This complicates information sharing and collaborative work. The objective of the e-SAAD project is to propose a methodological process to facilitate the analysis and procedure of intervention systems and home support. The process should identify the generic and specific aspects of each part. The patient's data set, profile, history, its environment and location should be taken into consideration. As well as the service providers, their profiles, their skills and essentially their availability and locations. These models must be open to be adapted to new data sources.
A correlation analysis of the sentiment analysis scores and numerical ratings of the students in the faculty evaluation

This paper aims to analyze the relationship between the students' numerical rating and the qualitative measure of the students' written comments in the faculty evaluation using sentiment analysis. The dataset which consists of the numerical ratings and students' feedback obtained from the faculty evaluation system was used in the experiment. An ensemble model which consists of five machine learning algorithms was used to analyze and identify the polarity of the written comments of the students. The overall sentiment score was computed for each faculty and was compared to the numerical score using the statistical technique, Pearson's correlation coefficient. The result indicates that there is significance but very small relationship between the numerical rating and the overall sentiment scores. Based on the result, universities and colleges should exploit written comments since it is rich with observations and insights about the performance and effectiveness of a teacher. Moreover, sentiment analysis technique can be used to identify students' feeling towards teaching.
Deep neural model with enhanced embeddings for pharmaceutical and chemical entities recognition in Spanish clinical text

In this work, we introduce a Deep Learning architecture for pharmaceutical and chemical Named Entity Recognition in Spanish clinical cases texts.We propose a hybrid model approach based on two Bidirectional Long Short-Term Memory (Bi-LSTM) network and Conditional Random Field (CRF) network using character, word, concept and sense embeddings to deal with the extraction of semantic, syntactic and morphological features.The approach was evaluated on the Pharma-CoNER Corpus obtaining an F-measure of 85.24% for subtask 1 and 49.36% for sub-task2.These results prove that deep learning methods with specific domain embedding representations can outperform the state-of-theart approaches.
Turnstile Jumping Detection in Real-Time Video Surveillance

Turnstile jumping, a common action happening on a daily basis at high volume pedestrian areas, causes various problems for society. This study proposes a novel framework in detecting tunrstile jumping with no GPU necessary. The proposed model is a combination of a YOLO v2 based human detector, a Kernelized Correlation Filters (KCF) tracker and a Motion History Image (MHI)-based Convolutional Neural Network (CNN) classifier. Experimental results show that the developed model is not only capable of operating in real-time but can also detect suspicious human actions with an accuracy rate of 91.69%
Reliable Classification of FAQs with Spelling Errors Using an Encoder-Decoder Neural Network in Korean

To resolve lexical disagreement problems between queries and frequently asked questions (FAQs), we propose a reliable sentence classification model based on an encoder-decoder neural network. The proposed model uses three types of word embeddings; fixed word embeddings for representing domain-independent meanings of words, fined-tuned word embeddings for representing domain-specific meanings of words, and character-level word embeddings for bridging lexical gaps caused by spelling errors. It also uses class embeddings to represent domain knowledge associated with each category. In the experiments with an FAQ dataset about online banking, the proposed embedding methods contributed to an improved performance of the sentence classification. In addition, the proposed model showed better performance (with an accuracy of 0.810 in the classification of 411 categories) than that of the comparison model.