Reviewerly/exHarmony huggingface dataset:
Conference Paper Program Chairs Message

It is with great pleasure that we present the Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR) 2018 Conference Papers track. As the next evolution of the IEEE Symposium on 3D User Interfaces, the IEEE VR Conference Papers track is now a fully integrated part of IEEE VR. This has resulted in a broader scope of submissions than ever before, from many different disciplines, including virtual, augmented, and mixed reality, human-computer interaction, mobile computing, computer graphics, cognitive and perceptual psychology, games, education, and the social sciences. This breadth of submissions also reflects the recent explosion of interest in VR and 3DUI technologies; never before has VR/3DUI technology been so available to so many people. There are therefore many open research questions in VR, and more new technologies under examination.
Extracting Social Networks From E-Government By Sentiment Analysis of Users Comments

Nowadays, the improvement of governance, ensurance the security and the timely detection of propaganda against the government are major problems of e-government. Extraction of hidden social networks is one of the most actual problems in the term of government security. The extraction of hidden social networks operating against the state in e-government is one of the key factors to ensure the security in e-government. In the paper, a method has been proposed for extracting hidden social networks to improve management in e-government, prevent promotion against the government and ensure the security. In this approach, hidden social networks are extracted through the analysis of user's comments via opinion and text mining technologies.
Entanglement Measure of the Planar-Transverse Classical Light Field

Classical light fields are considered physical examples of nonquantum entanglement\cite{Eberly2011}. We apply concurrence and Schmidt approach to evaluate the degree of entanglement for a generalized polarization state that Qian and Eberly suggested, and we obtained an analytic form of the general entanglement state for planar polarization states of classical light.
Methodology for Developing a Usability Assessment Questionnaire in Spanish as a Bilingual Strategy for Software Improvement

Usability is the characteristic of a software product of being effective and efficient and producing satisfaction for users and traditionally is assessed through questionnaires but most of them are only available in English. A software usability assessment questionnaire (SUAQ) is proposed in two languages: Spanish and English. The methodology comprises four stages: 1) questionnaire development, 2) administration, 3) statistical validation, and 4) sample size determination. Twenty items were evaluated in terms of clarity, consistency, and relevancy. Then, the SUAQ was administered to 95 respondents. Overall, reliability values were acceptable in Spanish and English version, respectively. The factor analysis was feasible since the KMO index, and the Bartlett sphericity test was statistically significant. Both versions of SUAQ were tested to determine their validity. The findings show that the proposed methodology is an effective usability assessment instrument and thus an effective software improvement tool from a bilingual approach.
Towards Language Agnostic Universal Representations

When a bilingual student learns to solve word problems in math, we expect the student to be able to solve these problem in both languages the student is fluent in, even if the math lessons were only taught in one language.However, current representations in machine learning are language dependent.In this work, we present a method to decouple the language from the problem by learning language agnostic representations and therefore allowing training a model in one language and applying to a different one in a zero shot fashion.We learn these representations by taking inspiration from linguistics, specifically the Universal Grammar hypothesis and learn universal latent representations that are language agnostic (Chomsky, 2014;Montague, 1970).We demonstrate the capabilities of these representations by showing that models trained on a single language using language agnostic representations achieve very similar accuracies in other languages.
Dynamic texture classification using Gumbel mixtures in the complex wavelet domain

Dynamic texture (DT) classification has attracted extensive attention in the field of image sequence analysis. The probability distribution model, which has been used to analysis DT, can describe well the distribution property of signals. Here, the authors introduce the finite mixtures of Gumbel distributions (MoGD) and the corresponding parameter estimation method based on expectation–maximisation algorithm. Then, the authors propose the DT features based on MoGD model for DT classification. Specifically, after decomposing DTs with the dual-tree complex wavelet transform (DT-CWT), the median values of complex wavelet coefficient magnitudes of non-overlapping blocks in detail subbands are modelled with MoGDs. The model parameters are accumulated into a feature vector to describe DT. During the classification, a variational approximation version of the Kullback–Leibler divergence is used to measure the similarity between different DTs. The experimental evaluations on two popular benchmark DT data sets (UCLA and DynTex++) demonstrate the effectiveness of the proposed approach.
KERJA SAMA DI ANTARA SDM PERPUSTAKAAN UNTUK MENUJU LAYANAN PRIMA DAN UNGGUL

&lt;p&gt;Internal problems among the human resources in each library must exist. To achieve service excellence and superior library requires cooperation. Cooperation among individuals there is a need of every library. Solid team work is the key to success in managing the library. The practice of cooperation among individuals in the library can be assessed based on aspects of social relations and the exchange of knowledge.&lt;/p&gt;&lt;p&gt;Keywords: team work, cooperation, human resources, library&lt;/p&gt;
Geochemistry and evolution of the ultrabasic Blue Hills intrusive complex (Namibia)

Kurszlaukis et al., in prep.) is part of the Gibeon Kimberlite Province in southern Namibia and is located about 5 km south of the Gross Brukkaros Mountain.Gross Brukkaros is surrounded by a number of magnesio-and silicocarbonatitic dykes and vents (Janse, 1969;Kurszlaukis, 1994), and the Blue Hills are located within the range of these volcanic rocks.The Blue Hills form an ovally elongated, laccolithic body with a maximum length of 800 m and a thickness of about 50 m within contact metamorphosed shales and quartzites of the Precambrian to Cambrian Nama Group.Judging from the surrounding geology, the maximum intrusion depth of the laccolith did not exceed 400 m.Six different types of magmatic rocks were distinguished from their mineralogical and chemical composition.Four of these six types are highly undersaturated silicate rocks forming a fractionation trend towards increasing carbonate contents.A late carbonatite, probably derived by liquid immiscibility, and a pegmatite -both with very low REE patterns -are the vaining expressions of the Blue Hills magmatism.Our age determinations yield an age of 75.1 +/-0.5 Ma (isochrone) (Kurszlaukis 1994, Kurszlaukis & Smith, in prep.) for the pegmatite.This is consistent with the results of Spriggs (1988) on the monticellite picrite.The magmatic rocks may be characterised as follows: The base of the Blue Hills is formed by a layer of monticellite-picrite (MP) with a thickness of 20 m.This rock consists of olivine phenocrysts in a matrix of mostly monticellite and subordinate phlogopite, carbonate, magnetite, and perovskite.The MP is overlain by an up to 45 m thick, strongly altered mica" olivine-carbonatite (MOC).Compared to MP carbonate and phlogopite are increased while monticellite is absent.In the upper portions pillow-like structures of MP within MOC can be observed.In the southern part of the Blue Hills, a discrete body of phlogopite-carbonatepicrite (PCP) is located within the MOC.It consists of olivine and phlogopite phenocrysts in a groundmass of carbonate, perovskite, and magnetite.In addition to these main intrusive bodies, several phlogopife-carbonate-sills (PCS) occur.Carbonatite sills and dykes (C) also occur, the latter cutting through all other magmatic bodies.The carbonatites mainly consist of carbonate and to a lesser amount of phlogopite and apatite.Most remarkable is the presence of a small carbonate-phlogopite-apatite-pegmatite (P), which forms veins with thicknesses up to 25 cm in the host rock.Although different in their modal mineral content, the MP, MOC, PCP and PCS are remarkably similar in their trace element abundances.Their main differences mostly lie in their variable contents of the volatiles H20 and C02.MgO/CaO ratios are 1-1.3 (Fig. 1) which indicates melting in the dolomite stability field at a depth of 80-100 km (Brey, 1978).Spinel-olivine geothermometry points to temperatures of 1100-1250°C for the parental PCP.
Bironchigram processing method for quantitative evaluation of optical focusing mirrors

Since its proposal the Ronchi “ruling” test has been considered as the most remarkable technique of the Schlieren family. Despite being based on fringe pattern generation and commonly interpreted in a qualitative way, it was capable of achieving outstanding results in the detection of aberrations in optical surfaces. Given its theoretical straightforwardness, experimental simplicity and economic affordability in comparison to other methodologies in the field, in the present work is addressed the lingering issue of how to extract quantitative information from digital captures of this test. Such task was accomplished taking advantage of the improved Ronchi test with a square grid, from which it was possible to develop a mathematical model with foundation in the Fourier theory to describe its observations. Thereupon, it was devised a novel iterative algorithm grounded in Fourier phase measuring able to compute fairly accurate wavefront gradient estimations from a single “bironchigram” sample with grid defocus. This procedure is complemented by a mandatory integration algorithm that is based on the regularization theory. The overall proposed methodology was put to test in a large aspherical mirror, estimating a surface aberration profile with a P–V (peak to valley) ratio of 155 nm, value well in accord with the one reported by the commercial interferometric system used for validation.
Convergence in Probability on a Big Class of Time-Variant Evolutionary Algorithms

Motivated by the growing popularity of time-variant evolutionary algorithms (EAs) in solving practical problems, this paper uses spectral analyses to study convergence in probability for a general class of time-variant EAs which can be asymptotically described by reducible Markov chains with multiple aperiodic recurrent classes, covering many existing concrete case studies as specific instantiations. We provide a universal yet easily checkable characteristic for time-variant EAs satisfying global convergence, by introducing the asymptotical elitism and asymptotical monotonicity. To illustrate the effectiveness of our result, we consider four specific EAs with distinct asymptotical behavior, and recover, under even mild conditions, the state-of-the-art result as simple applications of our general theorem. Besides, simulation experiments further verify these results.
A Pilot Study Exploring Augmented Reality to Increase Motivation of Chinese College Students Learning English

Abstract A Pilot Study Exploring Augmented Reality to Increase Motivation of Chinese College Students Learning EnglishWith the advent and accelerated development of augmented reality (AR), an increasing numberof studies have been conducted to test the effectiveness of this technique in education. Few,however, have investigated how AR might influence students’ motivation toward learning of asecond language. The goal of this research was to address this gap in the literature. We used acombination of convenience sampling and criterion sampling to select five Chinese collegestudents to evaluate an English vocabulary learning application built upon augmented realitytechnology. To assess student motivation, the ARCS motivational model was adopted. A semi-structured interview with open-ended questions was used to collect data. Participants indicatedthat though they were attracted by this tool at the beginning, their motivation level decreasedtoward the end of the study. An interpretation of our observations in the context of the ARCSmodel suggests three motivational issues. First, predefined AR materials failed to establishrelevance to subjects’ personal interests and previous experiences. Secondly, subjects’confidence seemed to have been negatively influenced due to their difficulty in achieving thestated learning objectives. Lastly, technical issues delayed the computer quickly identifying thetriggering image and thus resulted in a noticeable lack of system responsiveness. It seems thisdelay decreased subjects’ satisfaction and distracted their attention from the learning task. Thesefactors seemed most determinative in compromising AR’s effectiveness as a tool to increasestudent motivation toward English vocabulary learning. It must be stressed that this study is apilot with too low number of subjects from which to make any binding generalizations.Nonetheless, these findings should provide useful insights toward the successful application ofAR in the educational realm. The authors recommend further study with a larger number ofsubjects and a more powerful computer capable of more quickly identifying the trigger image.
Automatic Summarization of Real World Events Using Twitter

Microblogging sites, such as Twitter, have become increasingly popular in recent years for reporting details of real world events via the Web. Smartphone apps enable people to communicate with a global audience to express their opinion and commentate on ongoing situations - often while geographically proximal to the event. Due to the heterogeneity and scale of the data and the fact that some messages are more salient than others for the purposes of understanding any risk to human safety and managing any disruption caused by events, automatic summarization of event-related microblogs is a non-trivial and important problem. In this paper we tackle the task of automatic summarization of Twitter posts, and present three methods that produce summaries by selecting the most representative posts from real-world tweet-event clusters. To evaluate our approaches, we compare them to the state-of-the-art summarization systems and human generated summaries. Our results show that our proposed methods outperform all the other summarization systems for English and non-English corpora.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si20.gif" overflow="scroll"><mml:mrow><mml:mi>κ</mml:mi></mml:mrow></mml:math>-Lookback random-based text encryption technique

Although many encryption methods are available, there is always an ongoing need for more to resist the adversaries’ ever-growing analytical skills and techniques. We propose in this paper an innovative method for text encryption. We devise a random number generation function that creates sequences of signed random numbers that depend on both plaintext and key. The random numbers support the functionality of four random operations: random mutation, random cyclic shifting, random permutation, and dirty symbol random insertion. These operations ensure the data security by steadily melting the statistical structure of plaintext and relationships to a key. The experiments with our prototype implementation showed that our method has high effectiveness (in terms of diffusion, confusion, avalanche) and high efficiency with respect to the computation demands.
MDA: A Reconfigurable Memristor-Based Distance Accelerator for Time Series Mining on Data Centers

The rapid development of Internet-of-Things is yielding a huge volume of time series data, the real-time mining of which becomes a major load for data centers. The computation bottleneck in time series data mining is distance function, which is the fundamental element of many high data mining tasks. Recently various software optimization and hardware acceleration techniques have been proposed to tackle the challenge. However, each of these techniques is only designed or optimized for a specific distance function. To address this problem, in this paper we propose MDA, a high-throughput reconfigurable memristor-based distance accelerator for real-time and energy-efficient data mining with time series in data centers. Common circuit structure is extracted for efficiency, and the circuit can be configured to any specific distance functions. Particularly, we adopt the emerging device memristor for the design of MDA. Comprehensive experiments are presented with public available datasets to evaluate the performance of the proposed MDA. Experimental results show that compared with existing works, MDA has achieved a speedup of 3.5×-376× on performance and an improvement of 1-3 orders of magnitude on energy efficiency with little accuracy loss.
Indoor object recognition in RGBD images with complex-valued neural networks for visually-impaired people

We present a new multi-modal technique for assisting visually-impaired people in recognizing objects in public indoor environment. Unlike common methods which aim to solve the problem of multi-class object recognition in a traditional single-label strategy, a comprehensive approach is developed here allowing samples to take more than one label at a time. We jointly use appearance and depth cues, specifically RGBD images, to overcome issues of traditional vision systems using a new complex-valued representation. Inspired by complex-valued neural networks (CVNNs) and multi-label learning techniques, we propose two methods in order to associate each input RGBD image to a set of labels corresponding to the object categories recognized at once. The first one, ML-CVNN, is formalized as a ranking strategy where we make use of a fully complex-valued RBF network and extend it to be able to solve multi-label problems using an adaptive clustering method. The second method, L-CVNNs, deals with problem transformation strategy where instead of using a single network to formalize the classification problem as a ranking solution for the whole label set, we propose to construct one CVNN for each label where the predicted labels will be later aggregated to construct the resulting multi-label vector. Extensive experiments have been carried on two newly collected multi-labeled RGBD datasets prove the efficiency of the proposed techniques.
Continuous User Authentication by Contactless Wireless Sensing

This paper presents BodyPIN, which is a continuous user authentication system by contactless wireless sensing using commodity Wi-Fi. BodyPIN can track the current user's legal identity throughout a computer system's execution. In case the authentication fails, the consequent accesses will be denied to protect the system. The recent rich wireless-based user identification designs cannot be applied to BodyPIN directly, because they identify a user's various activities, rather than the user herself. The enforced to be performed activities can thus interrupt the user's operations on the system, highly inconvenient and not user-friendly. In this paper, we leverage the bio-electromagnetics domain human model for quantifying the impact of human body on the bypassing Wi-Fi signals and deriving the component that indicates a user's identity. Then, we extract suitable Wi-Fi signal features to fully represent such an identity component, based on which we fulfill the continuous user authentication design. We implement a BodyPIN prototype by commodity Wi-Fi NICs without any extra or dedicated wireless hardware. We show that BodyPIN achieves promising authentication performances, which is also lightweight and robust under various practical settings.
Collaborative immersive authoring tool for real-time creation of multisensory VR experiences

With the appearance of innovative virtual reality (VR) technologies, the need to create immersive content arose. Although there are already some non-immersive solutions to address immersive audio-visual content, there are no solutions that allow the creation of immersive multisensory content. This work proposes a novel architecture for a collaborative immersive tool that allows the creation of multisensory VR experiences in real-time, thus promoting the expeditious development, adoption, and use of immersive systems and enabling the building of custom-solutions that can be used in an intuitive manner to support organizations' business initiatives. To validate the presented proposal, two approaches for the authoring tools (Desktop interface and Immersive interface) were subjected to a set of tests and evaluations consisting of a usability study that demonstrated not only the participants' acceptance of the authoring tool but also the importance of using immersive interfaces for the creation of such VR experiences.
Deep Learning and Its Applications to Natural Language Processing

Natural language processing (NLP), utilizing computer programs to process large amounts of language data, is a key research area in artificial intelligence and computer science. Deep learning technologies have been well developed and applied in this area. However, the literature still lacks a succinct survey, which would allow readers to get a quick understanding of (1) how the deep learning technologies apply to NLP and (2) what the promising applications are. In this survey, we try to investigate the recent developments of NLP, centered around natural language understanding, to answer these two questions. First, we explore the newly developed word embedding or word representation methods. Then, we describe two powerful learning models, Recurrent Neural Networks and Convolutional Neural Networks. Next, we outline five key NLP applications, including (1) part-of-speech tagging and named entity recognition, two fundamental NLP applications; (2) machine translation and automatic English grammatical error correction, two applications with prominent commercial value; and (3) image description, an application requiring technologies of both computer vision and NLP. Moreover, we present a series of benchmark datasets which would be useful for researchers to evaluate the performance of models in the related applications.