Reviewerly/exHarmony huggingface dataset:
Reliability of the Thai Version of the Motion Sickness Susceptibility Questionnaire Short-Form

INTRODUCTION: Motion sickness is a feeling of unwellness and introduces a significant safety risk. If a specific questionnaire to screen for motion sickness susceptibility is available, it would have benefit for susceptible people. This aimed at examining test-retest reliability and internal consistency of the Thai, non-English version Motion Sickness Susceptibility Questionnaire Short-Form (MSSQ-Short).METHODS: The original English-version MSSQ-Short was translated into Thai and then had a backward translation into English. The process of forward and backward translation was repeated until fulfillment of content and language equivalence between the backward translation and the original questionnaire. The Thai-version MSSQ-Short then was applied to people with history of motion sickness for two occasions with a 2-wk interval. The two questionnaires were examined for test-retest reliability and the first occasion was examined for internal consistency. MSSQ total raw score and percentile score between the first and second occasions were tested for correlation.RESULTS: The questionnaires were completed by 30 subjects. The weighted kappa values between the two occasions for the 18 subitems of the MSSQ ranged from 0.38 to 0.86, with most being in the higher range. The Pearson's correlation coefficient of the MSSQ raw score between the two occasions was 0.94. The internal consistency was 0.80.CONCLUSION: The Thai, non-English version MSSQ-Short had an acceptable value for reliability. Further studies should be conducted with a larger sample size and with other populations.Asawavichienjinda T, Patarapak S. Reliability of the Thai version of the Motion Sickness Susceptibility Questionnaire Short-Form. Aerosp Med Hum Perform. 2019; 90(1):26-31.
Augmented reality application for location finder guidance

&lt;p&gt;Finding directions to a specific location can be troublesome especially when we are not familiar with a new area. Conventionally, we may want to ask people around or possibly we use Global Positioning System (GPS) navigator. However, using GPS navigator may not be the best solution if the address is not entered accurately. Therefore, this paper presents an augmented reality (AR) application for location finder guidance. Instead, a user is only required to scan the address indicated on a surface such as card or flyer using smart phone camera.The proposed application has utilized various components of AR technology including multiple image target, virtual button and markerless features. The development of the AR application follows phases of activities in Multimedia Mobile Content Development (MMCD) model. The proposed application is found to be very interactive and convenient in finding directions to specific location.&lt;/p&gt;
A Data Services Composition Approach for Continuous Query on Social Media Streams

We witness a rapid increase in the number of social media streams due to development of Web2.0, IoT and Cloud Computing technology. These sources include both traditional relational databases and streaming data from messaging infrastructure. We would like to use multiple social media streams to answer complex queries to enable information sharing and intelligence gathering for better collaboration. For this purpose, we adopt data services as the basic abstraction for both traditional relational databases and data streams retrieval. A flexible continuous data service model with continuous query as service operation is proposed. Service operation instance is modeled as a view defined on data streams. In the view, data part and time synchronization part are separated from each other. Based on the continuous data service model, we proposed a continuous data service composition algorithm for answering queries across data streams and relational data. The main idea is to find the contained rewriting of user query on views satisfying both data part and time synchronization part containment relationship. We also present use case and experimental studies that indicate that the approach is effective and efficient.
XRD: Scalable Messaging System with Cryptographic Privacy

Even as end-to-end encrypted communication becomes more popular, private messaging remains a challenging problem due to metadata leakages, such as who is communicating with whom. Most existing systems that hide communication metadata either (1) do not scale easily, (2) incur significant overheads, or (3) provide weaker guarantees than cryptographic privacy, such as differential privacy or heuristic privacy. This paper presents XRD (short for Crossroads), a metadata private messaging system that provides cryptographic privacy, while scaling easily to support more users by adding more servers. At a high level, XRD uses multiple mix networks in parallel with several techniques, including a novel technique we call aggregate hybrid shuffle. As a result, XRD can support 2 million users with 251 seconds of latency with 100 servers. This is 12x and 3.7x faster than Atom and Pung, respectively, which are prior scalable messaging systems with cryptographic privacy.
Comparative Expressiveness of Product Line Calculus of Communicating Systems and 1-Selecting Modal Transition Systems

Product line calculus of communicating systems (PL-CCSs) is a process calculus proposed to model the behavior of software product lines. Modal transition systems (MTSs) are also used to model variability in behavioral models. MTSs are known to be strictly less expressive than PL-CCS. In this paper, we show that the extension of MTSs with hyper transitions by Fecher and Schmidt, called 1-selecting modal transition systems (1MTSs), closes this expressiveness gap. To this end, we propose a novel notion of refinement for 1MTSs that makes them more suitable for specifying variability for software product lines and prove its various essential properties.
Impossible Differential Cryptanalysis of SPECK

The security of SPECK under impossible differential cryptanalysis is studied. Based on multiple 6-round impossible differential distinguishers and combined with the divide-and-conquer attack and time-and-memory tradeoff, the improved 10-round impossible differential cryptanalysis of SPECK32/64 and SPECK48/96 is presented by adding one round forward and three rounds backward. In addition, by further analyzing the properties of the differential diffusion of addition, many 7-round impossible differential distinguishers of SPECK families of block ciphers are constructed. And the 11-round impossible differential cryptanalysis of SPECK32/64 and SPECK48/96 is presented by adding one round forward and three rounds backward.
ANALISIS HASIL TES EVALUASI PENDIDIKAN PADA MAHASISWA DITINJAU DARI PERBEDAAN GENDER

Analysis of test results is one of the materials that must be mastered by students in educational evaluation courses. In this analysis students are also required to be able to make questions well and be able to evaluate them. The purpose of this study is to describe the results of the calculation of the scores that have been obtained from the evaluation data that has been carried out in terms of gender differences. The approach used is quantitative and qualitative approaches. The research subjects were IKIP Budi Utomo Malang students in the 2016 class B even semester 2017/2018. The instrument used in this study is a test question. From the calculation of the data obtained it can be seen that the percentage level of difficulty of male students is 32.84%, while for the calculation of the level of difficulty of female students is 42.95%. The percentage of differentiation for male students is 29.26%, and the percentage of female students is 38%. For the percentage of male students the answer pattern is 39.16%, while the percentage pattern of answers for female students is 51.59%. Based on the data it can be concluded that female students better understand the educational evaluation material which consists of the level of difficulty, the different power of the questions, and the answer pattern of the questions. It is expected that the education evaluation courses between male and female students have a balance in understanding the material and not only being dominated by female students. For further research can be developed how to design learning that is able to accommodate both reasoning and accuracy, so that between male and female students have the same opportunity to understand educational evaluation material.
Static Analysis for Asynchronous JavaScript Programs

Asynchrony has become an inherent element of JavaScript, as an effort to improve the scalability and performance of modern web applications. To this end, JavaScript provides programmers with a wide range of constructs and features for developing code that performs asynchronous computations, including but not limited to timers, promises, and non-blocking I/O. However, the data flow imposed by asynchrony is implicit, and not always well-understood by the developers who introduce many asynchrony-related bugs to their programs. Worse, there are few tools and techniques available for analyzing and reasoning about such asynchronous applications. In this work, we address this issue by designing and implementing one of the first static analysis schemes capable of dealing with almost all the asynchronous primitives of JavaScript up to the 7th edition of the ECMAScript specification. Specifically, we introduce the callback graph, a representation for capturing data flow between asynchronous code. We exploit the callback graph for designing a more precise analysis that respects the execution order between different asynchronous functions. We parameterize our analysis with one novel context-sensitivity flavor, and we end up with multiple analysis variations for building callback graph. We performed a number of experiments on a set of hand-written and real-world JavaScript programs. Our results show that our analysis can be applied to medium-sized programs achieving 79% precision on average. The findings further suggest that analysis sensitivity is beneficial for the vast majority of the benchmarks. Specifically, it is able to improve precision by up to 28.5%, while it achieves an 88% precision on average without highly sacrificing performance.
Hand Sign to Bangla Speech: A Deep Learning in Vision based system for Recognizing Hand Sign Digits and Generating Bangla Speech.

Recent advancements in the field of computer vision with the help of deep neural networks have led us to explore and develop many existing challenges that were once unattended due to the lack of necessary technologies. Hand Sign/Gesture Recognition is one of the significant areas where the deep neural network is making a substantial impact. In the last few years, a large number of researches has been conducted to recognize hand signs and hand gestures, which we aim to extend to our mother-tongue, Bangla (also known as Bengali). The primary goal of our work is to make an automated tool to aid the people who are unable to speak. We developed a system that automatically detects hand sign based digits and speaks out the result in Bangla language. According to the report of the World Health Organization (WHO), 15% of people in the world live with some kind of disabilities. Among them, individuals with communication impairment such as speech disabilities experience substantial barrier in social interaction. The proposed system can be invaluable to mitigate such a barrier. The core of the system is built with a deep learning model which is based on convolutional neural networks (CNN). The model classifies hand sign based digits with 92% accuracy over validation data which ensures it a highly trustworthy system. Upon classification of the digits, the resulting output is fed to the text to speech engine and the translator unit eventually which generates audio output in Bangla language. A web application to demonstrate our tool is available at this http URL.
Comparison of the value of superficial mark guided localization and hook‐wire guided localization techniques for non‐palpable breast microcalcifications: A retrospective clinical research

Abstract Aim The present study compares the effect and accuracy of the superficial mark guided localization (SGL) and hook‐wire guided localization (WGL) techniques for non‐palpable breast microcalcifications. Methods This retrospective study was conducted to compare SGL and WGL techniques. These techniques were performed on 51 patients with non‐palpable breast microcalcifications from January 2015 to May 2016. Results Among these 51 patients, 25 (49.01%) patients were subjected to WGL and 26 patients (50.99%) were subjected to SGL. The SGL technique had a higher rate of malignant cancer detection (WGL = 12.0% and SGL = 23.0%). Furthermore, no significant differences were found with regard to average age, the rate of a second excision and the diameter of the excised tissue. Moreover, no complications were observed in the SGL group, while four (16.0%) patients in the WGL group experienced problems. Conclusion The SGL technique is as accurate as the WGL technique. Furthermore, the procedure has advantages of being less expensive and causing less complications.
The Typology Of Modern Students’ Family Self-Determination

The family institution transformation, blurring of social norms, the absence of the courses aimed at getting young people prepared for marriage and family life in the school, college and university curriculum have resulted in placing young men and women in the conditions of independent family self-determination, understanding and choosing the system of family values on their own. On this background, the problem of substantial and structural characteristics and types of family self-determination study, detection of social and psychological factors that determine family values’ development among young people is becoming particularly urgent. The goal of the research is to construct a model of family self-determination and empirically identify typological groups of respondents. To achieve this goal, a set of complementary research methods was used: theoretical and methodological literature analysis; a questionnaire survey, psychodiagnostic tests; mathematical-statistical methods of data analysis, including calculating descriptive statistics, factor analysis by the means of Principal components method, reliability analysis, and k-means cluster analysis. The sample comprised 1109 students aged between 15 to 22 years old.The article provides a two-factorial model of young men and women’s family self-determination. Typological groups of respondents that vary in the extent of emotional appeal and dynamism (the factor of value and activity), influence (the factor of force) of ideas about family and marriage identified at levels statistical significance. The types (predetermined, declared, diffusive, realized and reached) and psychological mechanisms of family self-determination of students’ youth are described and analyzed.
Distributed and personalised social network privacy protection

Considering the privacy issues on social network, a variety of anonymous techniques have been proposed, but these techniques neglect some differences among individuals in their demand for privacy protection. With the development of internet technology, the number of social network individuals increases yearly, and network data are poised for a massive change in trends. Motivated by this, we specify three levels of privacy information for victim individuals and propose a personalised k-degree-m-label (PKDML) anonymity model. Furthermore, we design and implement a distributed and personalised k-degree-m-lable (DPKDML) anonymisation algorithm, which takes advantage of the 'vertex-centric' GraphX programming model to complete the entire anonymous process by multiple message passing and node value updating. Finally, we conduct experiments on real social network datasets to evaluate the DPKDML, The experimental results show that our methods may overcome the shortcomings of traditional methods in processing massive data, and reduce anonymous costs and increase data utility.
Sentiment and Sarcasm Classification with Multitask Learning.

Sentiment classification and sarcasm detection are both important natural language processing (NLP) tasks. Sentiment is always coupled with sarcasm where intensive emotion is expressed. Nevertheless, most literature considers them as two separate tasks. We argue that knowledge in sarcasm detection can also be beneficial to sentiment classification and vice versa. We show that these two tasks are correlated, and present a multi-task learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multi-task learning setting. Our method outperforms the state of the art by 3-4% in the benchmark dataset.
The Pocket Laboratory: The Blowpipe in Eighteenth-Century Swedish Chemistry

This article examines the role of the blowpipe in the discovery process of the disproportionately large number of new elements found by Swedish chemists during the eighteenth century and into the nineteenth. While individual chemists abroad used versions of the tool, in Sweden alone its use was ubiquitous across the chemical and mineralogical research community, and its consistently simple handheld design made basic dry chemical analysis quick, cheap, and portable. This shared use of the tool was crucial to the development of the mineral analysis projects that uncovered new substances, first by enabling the adoption of a system defining minerals by their chemical components and mineralogy by chemical analysis, and second by providing a simple and practical method for that analysis that facilitated collaboration across institutions, physical distance, and time.
CNN and RNN Using PyTorch

Probability and random variables are an integral part of computation in a graph-computing platform like PyTorch. Understanding probability and the associated concepts are essential. This chapter covers probability distributions and implementation using PyTorch, as well as how to interpret the results of a test. In probability and statistics, a random variable is also known as a stochastic variable, whose outcome is dependent on a purely stochastic phenomenon, or random phenomenon. There are different types of probability distribution, including normal distribution, binomial distribution, multinomial distribution, and the Bernoulli distribution. Each statistical distribution has its own properties.
Image Dehazing Technique Based On DWT Decomposition and Intensity Retinex Algorithm

Conventional designs use multiple image or single image to deal with haze removal. The presented paper uses median filer with modified co-efficient (16 adjacent pixel median) and estimate the transmission map and remove haze from a single input image. The median filter prior(co-efficient) is developed based on the idea that the outdoor visibility of images taken under hazy weather conditions seriously reduced when the distance increases. The thickness of the haze can be estimated effectively and a haze-free image can be recovered by adopting the median filter prior and the new haze imaging model. Our method is stable to image local regions containing objects in different depths. Our experiments showed that the proposed method achieved better results than several state-of-the-art methods, and it can be implemented very quickly. Our method due to its fast speed and the good visual effect is suitable for real-time applications. This work confirms that estimating the transmission map using the distance information instead the color information is a crucial point in image enhancement and especially single image haze removal.
Lie Group Auto-Encoder.

In this paper, we propose an auto-encoder based generative neural network model whose encoder compresses the inputs into vectors in the tangent space of a special Lie group manifold: upper triangular positive definite affine transform matrices (UTDATs). UTDATs are representations of Gaussian distributions and can straightforwardly generate Gaussian distributed samples. Therefore, the encoder is trained together with a decoder (generator) which takes Gaussian distributed latent vectors as input. Compared with related generative models such as variational auto-encoder, the proposed model incorporates the information on geometric properties of Gaussian distributions. As a special case, we derive an exponential mapping layer for diagonal Gaussian UTDATs which eliminates matrix exponential operator compared with general exponential mapping in Lie group theory. Moreover, we derive an intrinsic loss for UTDAT Lie group which can be calculated as l-2 loss in the tangent space. Furthermore, inspired by the Lie group theory, we propose to use the Lie algebra vectors rather than the raw parameters (e.g. mean) of Gaussian distributions as compressed representations of original inputs. Experimental results verity the effectiveness of the proposed new generative model and the benefits gained from the Lie group structural information of UTDATs.
Demosaicking with adaptive reference range selection

Image demosaicking is a method of reconstructing an RGB image from a Bayer pattern, which is required when color information is lacking because a single charge-coupled device is used during the image extraction process of a digital camera. There are many restrictions on the reconstruction of a Bayer image to an RGB image. Given that each pixel contains only one-color information, artifacts, such as false color or the zipper effect, may occur at the edges, which can arise as a result of significant differences in brightness and color change. We propose a demosaicking method for adaptively selecting the reference range of color difference to obtain reliable information from texture regions and reconstructing into the RGB image. In particular, we determine the adaptive weight and reference range for four directions, east (E), west (W), south (S), and north (N), to improve the reliability of the color pixel obtained by a color difference estimation using guided filtering applied on residuals. In our experiment, we compare the results of the proposed method for the Kodak and IMAX datasets with those of nine demosaicking methods. The proposed method shows similar or improved results in terms of the color peak signal-to-noise ratio. In addition, compared to other methods, the visual quality improved by reducing residual artifacts.
Disappearing Diamonds: Fitch-Like Results in Bimodal Logic

Augment the propositional language with two modal operators: □ and ■. Define $\blacklozenge $ to be the dual of ■, i.e. $\blacklozenge \equiv \neg \blacksquare \neg $. Whenever (X) is of the form φ → ψ, let (X$^{\blacklozenge } $ ) be $\varphi \rightarrow \blacklozenge \psi $. (X$^{\blacklozenge } $ ) can be thought of as the modally qualified counterpart of (X)—for instance, under the metaphysical interpretation of $\blacklozenge $, where (X) says φ implies ψ, (X$^{\blacklozenge } $ ) says φ implies possiblyψ. This paper shows that for various interesting instances of (X), fairly weak assumptions suffice for (X$^{\blacklozenge } $) to imply (X)—so, the modally qualified principle is as strong as its unqualified counterpart. These results have surprising and interesting implications for issues spanning many areas of philosophy.