nils-herrmann/clean_scirepeval huggingface dataset:
Implementation of the Standard I vector System for the Kaldi Speech Recognition Toolkit

This report describes implementation of the standard i vector PLDA framework for the Kaldi speech recognition toolkit. The current existing speaker recognition system implementation is based on the Subspace Gaussian Mixture Model (SGMM) technique although it shares many similarities with the standard implementation. In our implementation, we modified the code so that it mimics the standard algorithms in the ivector based speaker recognition system. The implementation is compared with the existing Kaldi recipe for speaker recognition on the NIST SRE 2008 evaluation set. The entire implementation is made available at https:/github.com/idiap/kaldi ivector under the Apache 2.0 license.
How to Add Word Classes to the Kaldi Speech Recognition Toolkit
Venue: TSD

The paper explains and illustrates how the concept of word classes can be added to the widely used open source speech recognition toolkit Kaldi. The suggested extensions to existing Kaldi recipes are limited to the word level grammar (G) and the pronunciation lexicon (L) models. The implementation to modify the weighted finite state transducers employed in Kaldi makes use of the OpenFST library. In experiments on small and mid sized corpora with vocabulary sizes of 1.5 K and 5.5 K respectively a slight improvement of the word error rate is observed when the approach is tested with (hand crafted) word classes. Furthermore it is shown that the introduction of sub word unit models for open word classes can help to robustly detect and classify out of vocabulary words without impairing word recognition accuracy.
Highly transparent AlON ceramics sintered from powder synthesized by carbothermal reduction nitridation

Aluminum oxynitride (AlON) powders were synthesized by the carbothermal reduction and nitridation process using commercial g Al 2 O 3 and carbon black powders as starting materials. And AlON transparent ceramics were fabricated by pressureless sintering under nitrogen atmosphere. The effects of ball milling time on morphology and particle size distribution of the AlON powders, as well as the microstructure and optical property of AlON transparent ceramics were investigated. It is found that single phase AlON powder was obtained by calcining the g Al 2 O 3 /C mixture at 1550 degC for 1 h and a following heat treatment at 1750 degC for 2 h. The AlON powder ball milled for 24 h showed smaller particles and narrower particle size distribution compared with the 12 h one, which was benefit for the improvement of optical property of AlON transparent ceramics. With the sintering aids of 0.25 wt% MgO and 0.04 wt% Y 2 O 3 highly transparent AlON ceramics with in line transmittance above 80% from visible to infrared range were obtained through pressureless sintering at 1850 degC for 6 h.
Fast densification mechanism of bimodal powder during pressureless sintering of transparent AlON ceramics

Both bimodal and unimodal AlON powders were pressureless sintered and their phase assemblages and microstructure evolutions at 1400 1900 degC were monitored to reveal the fast densification mechanism of bimodal powder. Phase assemblages analysis of samples sintered at 1400 degC suggests that fine AlON particles were transformed into hexagonal a Al2O3 and AlN, while coarse AlON particles were transformed into cubic e Al2O3 and AlN. Correspondingly, only a small fraction of the unimodal mixture was e Al2O3, while the relatively unstable e Al2O3 kept dominating the bimodal samples and most of them were directly reformed into AlON at a lower temperature of 1600 1700 degC for their similarity in crystal structure. As a result, a bimodal grain size distribution was kept in the whole sintering process of the bimodal sample, which led to its fast densification. Using the bimodal AlON powder, highly transparent AlON ceramics were fully pressureless sintered at 1820 degC after holding for 2.5 h.
narratives of choice: marriage, choosing right and the responsibility of agency in urban middle class Sri Lanka

The shift to companionate marriage in South Asia and elsewhere is widely read as a move from 'tradition' to 'modernity' resulting in an expansion of individual agency, especially for women. This paper critically examines the narratives of urban middle class women in Sri Lanka spanning three generations to illustrate that rather than indicating a radical shift in the way they negotiated between individual desires and social norms, the emphasis on 'choice' signals a shift in the narrative devices used in the presentation of the 'self' The paper illustrates how young women's narratives about marriage appear to suggest 'modernity' as inevitable that its processes are reconstituting the person who, less constrained by 'tradition' and collective expectations, is now experiencing greater freedom in the domain of marriage. However, it also shows how urban middle class families in Sri Lanka have collectively invested in the narrative of choice through which 'a choosing person' is consciously created as a mark of the family's modernity and progress. Rather than signalling freedom, these narratives about choice reveal how women are often burdened with the risks and responsibility of agency. The paper illustrates that the 'choosing person' is produced through narratives that emphasise agency as a responsibility that must be exercised with caution because women are expected by and obligated to their families to make the 'right' choices. Hence, a closer look at the individualised 'choosing person' reveals a less unitary, relational self with permeable boundaries embedded within and accountable to family and kinship.
Characterization of spatial temporal patterns in dynamic speckle sequences using principal component analysis

Speckle is being used as a characterization tool for the analysis of the dynamics of slow varying phenomena occurring in biological and industrial samples at the surface or near surface regions. The retrieved data take the form of a sequence of speckle images. These images contain information about the inner dynamics of the biological or physical process taking place in the sample. Principal component analysis (PCA) is able to split the original data set into a collection of classes. These classes are related to processes showing different dynamics. In addition, statistical descriptors of speckle images are used to retrieve information on the characteristics of the sample. These statistical descriptors can be calculated in almost real time and provide a fast monitoring of the sample. On the other hand, PCA requires a longer computation time, but the results contain more information related to spatial temporal patterns associated to the process under analysis. This contribution merges both descriptions and uses PCA as a preprocessing tool to obtain a collection of filtered images, where statistical descriptors are evaluated on each of them. The method applies to slow varying biological and industrial processes.
Breast cancer statistics, 2015: Convergence of incidence rates between black and white women
Venue: CA: a cancer journal for clinicians

In this article, the American Cancer Society provides an overview of female breast cancer statistics in the United States, including data on incidence, mortality, survival, and screening. Approximately 231,840 new cases of invasive breast cancer and 40,290 breast cancer deaths are expected to occur among US women in 2015. Breast cancer incidence rates increased among non Hispanic black (black) and Asian/Pacific Islander women and were stable among non Hispanic white (white) Hispanic, and American Indian/Alaska Native women from 2008 to 2012. Although white women have historically had higher incidence rates than black women, in 2012, the rates converged. Notably, during 2008 through 2012, incidence rates were significantly higher in black women compared with white women in 7 states, primarily located in the South. From 1989 to 2012, breast cancer death rates decreased by 36% which translates to 249,000 breast cancer deaths averted in the United States over this period. This decrease in death rates was evident in all racial/ethnic groups except American Indians/Alaska Natives. However, the mortality disparity between black and white women nationwide has continued to widen; and, by 2012, death rates were 42% higher in black women than in white women. During 2003 through 2012, breast cancer death rates declined for white women in all 50 states; but, for black women, declines occurred in 27 of 30 states that had sufficient data to analyze trends. In 3 states (Mississippi, Oklahoma, and Wisconsin) breast cancer death rates in black women were stable during 2003 through 2012. Widening racial disparities in breast cancer mortality are likely to continue, at least in the short term, in view of the increasing trends in breast cancer incidence rates in black women. CA Cancer J Clin 2016;31 42.
Characterization of H7N2 Avian Influenza Virus in Wild Birds and Pikas in Qinghai Tibet Plateau Area
Venue: Scientific reports

Qinghai Lake is a major migrating bird breeding site that has experienced several recent highly pathogenic avian influenza virus (HPAIV) epizootics. From 2006 to 2009 we studied Qinghai's wild birds and pikas for evidence of AIV infections. We sampled 941 healthy wild animals and isolated seventeen H7N2 viruses (eight from pikas and nine from wild birds) The H7N2 viruses were phylogenetically closely related to each other and to viruses isolated in Hong Kong in the 1970s. We determined the pathogenicity of the H7N2 viruses by infecting chickens and mice. Our results suggest that pikas might play an important role in the ecology of AIVs, acting as intermediate hosts in which viruses become more adapted to mammals. Our findings of AI infection in pikas are consistent with previous observations and raise the possibility that pikas might play a previously unrecognized role in the ecology of AIVs peridomestic aquatic environments.
Using Drilling to Teach Pronunciation to the Seventh Graders

The role of pronunciation is quite significant in speaking skill as a measure for language proficiency. Most teachers understand the theories of pronunciation, but in terms of teaching, they often give little material about it because of the lacks of knowledge to apply how to teach pronunciation effectively. To teach pronunciation to students, there are a lot of strategies and supports for effective pronunciation teaching. One of those techniques is Drilling. This paper describes how to teach pronunciation effectively through drilling technique and supported by some supporting strategies.
Audio Event Detection using Weakly Labeled Data
Venue: ACM Multimedia

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.
Fall Detection Using Smartphone Audio Features
Venue: IEEE Journal of Biomedical and Health Informatics

An automated fall detection system based on smartphone audio features is developed. The spectrogram, mel frequency cepstral coefficents (MFCCs) linear predictive coding (LPC) and matching pursuit (MP) features of different fall and no fall sound events are extracted from experimental data. Based on the extracted audio features, four different machine learning classifiers: k nearest neighbor classifier (k NN) support vector machine (SVM) least squares method (LSM) and artificial neural network (ANN) are investigated for distinguishing between fall and no fall events. For each audio feature, the performance of each classifier in terms of sensitivity, specificity, accuracy, and computational complexity is evaluated. The best performance is achieved using spectrogram features with ANN classifier with sensitivity, specificity, and accuracy all above 98% The classifier also has acceptable computational requirement for training and testing. The system is applicable in home environments where the phone is placed in the vicinity of the user.
How student behavior and reflective learning impact grades in online business courses

Many universities now offer courses online using learning management systems (LMS) Numerous studies have been conducted to assess the effectiveness of the LMS but few studies have examined how student online behavior within the course, or what they think about the online course, are related to their actual learning outcomes. The paper aims to discuss this issue.  In this study, student activity in an online business course was captured though learning analytics and assignments to determine if online activity and reflective learning impact final grade. A post positivist ideology was employed. The dependent variable was the grade resulting from five assignments assessed using rubrics. Correlation, t tests, multiple regression, surface response regression, General Linear Model (GLM)/F tests, text analytics, analysis of means (ANOM) and cluster analysis were used to test the hypotheses.  Four statistically significant predictors of online student learning performance were identified: course logins, lesson reading, lesson quiz activity, and lesson quiz scores. This four factor model captured 78 percent of variance on course grade which is a strong effect and larger than comparative studies using learning analytics with online courses. Text analytics and ANOM conducted on student essays identified 17 reflective learning keywords that were grouped into five clusters to explain online student behavior.  First, from a pedagogy standpoint, encouraging students to complete more online lessons including quizzes, generally promotes learning, resulting in higher grades, which is a win:win for students and for the university. Second, from an IT perspective, the student pre and post testing resulted in statistically significant increase of IT course knowledge, which puts students on a solid foundation to begin an online business course. Additionally, the link between students voicing IT problems but nonetheless scoring very well on the course certainly implies the development of IT self efficacy, developed partly through the pre and post testing process. A clear link was established between course learning objectives and student learning performance by using a unique text analytics procedure.  The mixed methods research design started with hypothesis testing using parametric and nonparametric techniques. Once a statistically significant predictive GLM was developed, qualitative data were collected from what the students thought as expressed in their last essay assignment. Text analytics was used to identify and statistically weight the 17 most frequent reflective learning keywords from student essays. A visual word cloud was presented. Parametric statistics were then used to partition the reflective learning keywords into grade boundaries. Nonparametric cluster analysis was used to group similar reflective keyword grade associations together to form five clusters. The five clusters helped to explain student online behavior.
Gig Work, Online Selling and Home Sharing

Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping America and the world. It does not take policy positions. The center conducts public opinion polling, demographic research, content analysis and other data driven social science research. It studies U.S. politics and policy; journalism and media; internet, science and technology; religion and public life; Hispanic trends; global attitudes and trends; and U.S. social and demographic trends. All of the Center's reports are available at www.pewresearch.org. Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. From neighborhood handymen to freelance computer programmers, Americans have long taken on piecemeal work in lieu of (or in addition to) traditional salaried employment. But today a variety of apps and online platforms are making it easier than ever for people to connect with customers who might like to hire them to do any number of jobs from performing various types of online tasks to driving for ride hailing services or cleaning someone's home. These platforms also allow users to earn money in a range of other ways, such as sharing their possessions with others or selling their used goods or personal creations. Proponents of these digital earning platforms argue that they offer important benefits, such as the freedom and flexibility to work at a time and place of one's choosing or the ability to turn a hobby or pastime into a source of income. But others worry that this emerging " gig economy " represents a troubling shift in which workers face increased financial instability and are required to shoulder more of the burden for ensuring their own pay and benefits. Against this backdrop, a new Pew Research Center survey of U.S. adults finds that a relatively substantial share of the public has earned money recently from a digital commerce platform. In the context of gig employment, nearly one in ten Americans (8% have earned money in the last year using digital platforms to take on a job or task. Meanwhile, nearly one in five Americans (18% have earned money in the last year by selling something online, while 1% have rented out their properties on a home sharing site. Adding up everyone who has performed at least one of these three activities, some 24% of American adults have earned money in the " platform economy " over the last year.
Demand Growth in Services: A Discrete Choice Analysis of Customer Preferences and Online Selling
Venue: Decis. Sci.

The selling of perishable services (e.g. hotel rooms, airline seats, and rental cars) online is increasingly popular with both retailers and consumers. Among the innovative approaches to online sales is opaque selling. First popularized by Priceline.com's name your own price model, opaque selling hides some attributes of the service (notably, brand and specific location) until after the purchase decision, in exchange for a discounted price. This means that a branded "product" is being sold as somewhat of a commodity, but the brand "name" is protected by the opaque model. The attraction of this model for retailers is that they are presumably able to increase their revenue stream, albeit at a lower rate, by selling rooms that otherwise would remain in inventory. In this article, we outline the development and analysis of an online choice survey to understand consumer preferences among three types of online distribution channels: regular full information sales channels, and opaque sales channels with or without consumer bidding. A Multinomial Logit model is employed to analyze the data and measure the consumer trade offs between price and other attributes of the product. We use the estimated model to calculate the incremental demand and revenue created by using an opaque channel simultaneously with regular full information channels. On balance, we find that correctly priced opaque channels can add to hotels revenue streams without undue cannibalization of regular room sales.
Laboratory investigation of nonlinear flow characteristics in rough fractures during shear process

To understand the influence of shear behavior on the transporting properties of fluid through a single fracture, splitting fractures were made in the laboratory and shear flow tests were carried out under constant normal load conditions. The applied normal stress is in the range of 0.5 3.0 MPa. Before the physical test, the fracture's morphology is measured for identification of the roughness. At each shear step, we performed 5 8 high precise hydraulic tests with different hydraulic gradient. The relationship between pressure gradient and volume flow rate demonstrates to be nonlinear and fits very well with Forchheimer's and Izbash's laws. The linear and nonlinear coefficients in Forchheimer's law are quite sensitive to shear deformation (closure or dilation) experienced 1 2 and 1 3 orders of magnitude reduction during shear, respectively. An empirical equation is proposed to quantify the relationship between linear coefficient and nonlinear coefficient based on the experimental observations. The two coefficients in Izbash's law are quantified. The m value is in the range between 1.06 and 1.41 and the l value experiences a reduction of 1 2 orders of magnitude during shear. In addition, the studied critical Reynolds number exhibits a decreasing and increasing variation corresponding to shear contraction and shear dilation of rock fracture. For all the cases in this study, the critical Reynolds number ranges between 1.5 and 13.0.
Recurrent Spiking Networks Solve Planning Tasks
Venue: Scientific reports

A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate injected input this dynamics is shaped to generate high reward state trajectories. A general class of reward modulated plasticity rules for these afferent synapses is presented. The updates optimize the likelihood of getting a reward through a variant of an Expectation Maximization algorithm and learning is guaranteed to convergence to a local maximum. We find that the network dynamics are qualitatively similar to transient firing patterns during planning and foraging in the hippocampus of awake behaving rats. The model extends classical attractor models and provides a testable prediction on identifying modulating contextual information. In a real robot arm reaching and obstacle avoidance task the ability to represent multiple task solutions is investigated. The neural planning method with its local update rules provides the basis for future neuromorphic hardware implementations with promising potentials like large data processing abilities and early initiation of strategies to avoid dangerous situations in robot co worker scenarios.
Ransomware attacks: detection, prevention and cure
Venue: Netw. Secur.

Over the past three years, ransomware has become one of the biggest cyber scams to hit businesses. Indeed, the FBI estimates that losses incurred in 2016 due to ransomware will top $1bn. Ransomware is malicious software that allows a hacker to restrict access to an individual's or company's vital information in some way and then demand some form of payment to lift the restriction. The most common form of restriction today is encryption of important data on the computer or network, which essentially lets the attacker hold user data or a system hostage.Over the past three years, ransomware has become one of the biggest scams to hit businesses. Indeed, the FBI estimates that losses in 2016 will top $1bn.The ramifications of a successful attack are far more extensive than just the cost of the ransom. Organisations can suffer lost productivity, loss of business, inconvenience to customers and potentially the permanent loss of data. Ross Brewer of LogRhythm details the five phases of a ransomware attack and the five steps you can take to prepare for, handle and recover from it.