Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Better Policy and Practice Using Real-Time Data

Can Search Engines (SE) queries and Tweets help predict economic activity? Can Social Networking Services (SNS) help understand time use and help predict online activity? Clearly, those two questions address different issues, but both emphasize the use of (near) real-time data to refine and improve the comprehension of our Society. Indeed, in a fast changing world such as the one we live in, it appears more and more crucial for policy and practice to be aware of what is going on in the very present in view of proposing responsive and relevant solutions in the near future. One very innovative and promising way to achieve this is by using online real-time data. Real-time data denotes information that is delivered immediately after collection, meaning there is no delay or extremely small delay in the time-line of information provided. For example, thanks to the Twitter API , it is nowadays possible to easily gather data on what people are doing/thinking right now. Besides, tools like Google Trends provide a very simple way to collect hourly, daily or weekly data on the volume of queries related to various keywords. Now, those tools enable researchers to get a fantastic amount of data on an extremely large set of topics. For instance, an economist could use SE queries related to a specific industry (e.g. cars) in a specific part of the world (e.g. France) to refine his seasonal auto-regressive model for this industry in this country. Similarly, it could be interesting to analyze SE queries and tweets making reference to ‘job interviews’ or to online job platforms such as ‘monster.com’ to help predict global and local unemployment rates. Finally, data gathering on the individuals’ online activity, that is, for example, the number of tweets and re-tweets per units of time (e.g. minutes) or the number of Facebook status updates per units of time could help understand when people are the most active online and, performed together with a contents’ semantic analysis, could help understand for what purpose. This short paper aims at proposing methods to enhance current policy and practice. The first section is interested in enhancing current forecasting methods for macroeconomic activity by taking into account real-time data. The second section proposes and discusses methods using real time data to examine the time use of individuals or groups of individuals. Finally, conclusion takes place in the last section.
From prosodic structure to acoustic saliency: an fMRI investigation of speech rate, clarity, and emphasis

Acoustic variability in fluent speech can arise at many stages in speech production planning and execution. For example, at the phonological encoding stage, the grouping of phonemes into syllables determines which segments are coarticulated and, by consequence, segment-level acoustic variation. Likewise phonetic encoding, which determines the spatiotemporal extent of articulatory gestures, will affect the acoustic detail of segments. Functional magnetic resonance imaging (fMRI) was used to measure brain activity of fluent adult speakers in four speaking conditions: fast, normal, clear, and emphatic (or stressed) speech. These speech manner changes typically result in acoustic variations that do not change the lexical or semantic identity of productions but do affect the acoustic saliency of phonemes, syllables and/or words. Acoustic responses recorded inside the scanner were assessed quantitatively using eight acoustic measures and sentence duration was used as a covariate of non-interest in the neuroimaging analysis.
Reading in the digital age: A case study in faculty and librarian collaboration

ABSTRACT This article describes a collaborative partnership of two librarians and an English professor who created a unit for a senior capstone seminar on “The Digital Turn in English Studies. ” After reading Charlotte Perkins Gilman's “The Yellow Wallpaper” in both print and digitized versions, students completed a survey on reading preferences and provided feedback on an experimental eTextReader allowing for annotation. Students also did blog postings providing a fuller picture of attitudes toward digital texts. We situate quantitative and qualitative findings in three contexts: the field of digital humanities, reading in the digital age, and tool development.
Scalable Methods for the Inference of Identity by Descent

Scalable Methods for the Inference of Identity by Descent Fiona Grimson Chair of the Supervisory Committee: Professor Elizabeth Thompson Statistics Identity by descent (IBD) describes the shared inheritance of DNA and underlies genetic similarity between individuals. Estimated IBD graphs describing the IBD relationships among individuals have many uses in statistical genetics. An important application is to detect, through linkage analysis, the location of genes that cause genetic diseases. Accurate estimation of IBD graphs among large groups of individuals is essential. IBD is typically estimated either among individuals in small family pedigrees or among distantly related individuals sampled from a population with an unknown pedigree relationship. Both pedigree and population approaches require different modeling assumptions and are applied to different study designs. In this thesis, scalable methods of estimating IBD that combine both pedigree and population estimation methods are developed for family based studies. IBD is estimated between the founders of the family pedigrees to incorporate more information about shared inheritance without additional data collection. A combined IBD model is developed for sib-pair studies, and is demonstrated on siblings from a 50 generation simulated population. A merging algorithm is also developed to combine pedigree and population IBD estimates for family based studies with larger component pedigrees. The merging method is demonstated on pedigrees from the simulated population and on an Alzheimer’s disease family study. Combined IBD models are shown to increase power and resolution for locating genes that cause genetic diseases.
An Extension to the Delone and Mclean Information Systems Success Model and Validation in the Internet Banking Context

The modern internet era opened a plethora of opportunities for doing business online. Internet banking (IB) is one such innovation that made great strides from its humble beginnings in the mid-1990s. It is important for bank practitioners to know the factors contributing to the success of new technology by customers to enable them to be better placed in the competitive segment. This study proposes an extension to the Delone and Mclean IS success model to evaluate information systems(IS) success in the context of internet banking, with security as a new dimension. Data was collected from 312 respondents. The results found that security is a key factor for IB success. Furthermore, the study found that the dimension system quality has not had any substantial effect on IB user satisfaction; however, security and information quality were found to influence user satisfaction. Based on the findings, some implications for research and practice were prescribed, in addition to directions for future researchers of IS success in the IB context.
Reinforced backpropagation for deep neural network learning

Standard error backpropagation is used in almost all modern neural network learnings for minimizing training errors with respect to network parameters. However, it typically suffers from proliferation of saddle points in the high-dimensional parameter space. Therefore, it is highly desirable to design an efficient algorithm to escape from these saddle points and reach a parameter region of better generalization capabilities. Here, we propose a simple extension of the backpropagation, namely reinforced backpropagation, which simply adds previous first-order gradients in a stochastic manner with a probability that increases with learning time. As verified in a simple synthetic dataset, this method significantly accelerates learning compared to standard backpropagation. Surprisingly, it dramatically reduces over-fitting effects, even compared to state-of-the-art adaptive learning algorithm—Adam. For a benchmark handwritten dataset, the learning performance is comparable to Adam, yet with an extra advantage of requiring one-fold less computer memory. Overall, our method introduces stochastic memory into gradients, which may be an important starting point to understand how gradient-based training algorithms can work and its relationship with the generalization abilities of deep networks.
Adversarial Inversion: Inverse Graphics with Adversarial Priors

We propose adversarial inversion, a weakly supervised neural network model that combines inverse rendering with adversarial networks. Given a set of images, our inverse rendering encoder predicts a set of latent factors (e.g., depth, camera pose), which a renderer then projects to reconstruct (part of) the visual input. Inversion is often ambiguous, e.g., many compositions of 3D shape and camera pose give rise to the same 2D projection. To address this ambiguity, we impose priors on the predicted latent factors, through an adversarial discriminator network trained to discriminate between predicted factors and ground-truth ones. Training adversarial inversion does not require input-output paired annotations, but merely a collection of ground-truth factors, unrelated (unpaired) to the current input. Our model can thus be self-supervised by unlabelled image data, by minimizing a joint reconstruction and adversarial loss, complementing any direct supervision provided by paired annotations. We apply adversarial inversion to 3D human pose estimation and 3D structure and egomotion estimation, and outperform models supervised by only paired annotations, and/or reconstruction losses, that do not use adversarial priors. Applying adversarial inversion to super-resolution and inpainting results in automated “visual plastic surgery”. In adversarial superresolution, when the discriminator is provided with young, old, female, male, or Tom Cruise faces as ground-truth, our model renders the input face image towards its young, old, feminine, masculine or Tom Cruise-like equivalent. In adversarial inpainting, when the discriminator is provided with faces with big lips or big noses as ground-truth, it creates visual lip or nose augmentations. Humans imagine far more than they see. In Figure 1 (a), we imagine missing arms and legs of the sitting woman. In Figure 1 (b), we imagine forward motion of the camera, as opposed to the road drifting backwards underneath the cam∗equal contribution Generator Discriminator
STUDENT ATTITUDES TOWARD STATISTICS FROM A RANDOMIZATION-BASED CURRICULUM

Recently, a large national (US) sample was used to evaluate attitudes toward statistics among undergraduate students. The majority of the courses in the sample used a similar (AP Statistics) curriculum. Recent interest in the use of randomization-based methods in introductory statistics raises substantial questions about the conceptual effectiveness and attitudes of students in such courses. To begin to better understand the advantages and disadvantages of a randomizationbased curriculum, we evaluated student attitudes in courses using randomization curricula and compared these to students using a traditional curriculum. Overall, there were only small, statistically and practically insignificant, differences in student attitudes between the two samples. While randomization approaches remain new, our analysis suggests that these curricula may not be harming nor improving students’ attitudes toward statistics relative to traditional courses in undergraduate courses.
Repetitive Thought about an Aversive Learning Experience Maintains Conditioned Responding

Clinical anxiety is often believed to be based on fear conditioning, a procedure of pairing an originally neutral stimulus (conditioned stimulus, CS) with an aversive unconditioned stimulus (US), which results in the CS eliciting a conditioned fear response in the absence of the US. Recently, it was shown that repeatedly thinking about an aversive conditioning event maintains subsequent fear responding. A crucial question is how such effects should be interpreted. Does rehearsal of a CS-US-contingency result in a strengthened memory trace of the conditioning experience or in changes in the US-representation (e.g., US-inflation or increased US-coherence)? The current study was set up to investigate the underlying mechanism of this rehearsal effect. After acquisition training with two CS+s paired with the same US, participants rehearsed one of both CS-US-contingencies. The CS+ that was part of the rehearsed contingency elicited more US-expectancy than the CS+ that was not rehearsed. As changes in the US-representation could not explain our data, we suggest that a strengthening of the memory trace of the CS-US-association underlies this effect. Repetitive thought about a conditioning experience seems to result in sustained conditioned responding due to a strengthened CS-US-association. We propose that the repetitive nature of thought processes such as worry might partly explain its unconstructive role in anxiety.
The use of academic vocabulary and words collocation in undergraduates' written assignments

This study aims to determine the use of academic words and their collocation in UNIMAS undergraduates’ academic written texts. Coxhead’s (1998) AWL was used in identifying the academic words. There are three objectives of this study which are 1) determine the percentage of AW used in the undergraduates’ academic writing, 2) identify the words that collocate with the AW identified in the undergraduates’ academic writing 3) analyze the type of words (content words or functional) used by science background undergraduate as collocation of the AW identified in the undergraduates’ academic writing. The corpus comprised of academic texts written by EPP students which are reports based on their own mini research. The texts were retrievedfrom the repository and analyzed using a web-based analysis tool ‘VocabProfile’ andconcordance software, TextSTAT. The results showed that the use of academic words among the undergraduates still needs to be improved in order for them to produce a better academic text. However, as L2 learners, their repertoires of word collocation area bit restricted and they tend use the same pattern over and over again to avoid errors. The percentage of functional words used is relatively high compared to content words. It affects the quality of the academic texts by making the text sound less formal. Nevertheless, the undergraduates are aware of some of the stylistic of academic text such as the use of more passive voice compared to active voice.
eXtensible Multi Security: Infrastruktura bezpiecze ´ nstwa dla platformy .NET

Modern computer systems depend on many pieces of software gathered together to perform certain activities. That is why various forms of distributed systems are being developed where individual software components come from different developers. Such distributed systems bring a lot of freedom and convenience but when misused they can do a lot of damage. Thus, there is a deep need for various kinds of safety and security at different levels of a software life-cycle. In this work we investigate some notions of safety taking the safety based on contracts into special consideration. We build eXtensible Multi Security a framework based on the notion of Proof Carrying Code which is a powerful and coherent platform able to unify various notions of safety. We also show how XMS forms a certification framework for Microsoft Intermediate Language and other programming languages of the . NET Platform.
A Study on Wireless Body Area Network of Intelligent Motion Sensors for Computer Assisted Physical Rehabilitation

: In the recent generation aged/young people are suffering from various health issues like respiratory problem, blood sugar, blood pressure, heart related diseases, the current medical techniques fails to overcome these situations. Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN). This is a study on the applications of Wireless Body Area Network in physical rehabilitation and to study the existence of Wireless Body Area Network in and around the city.
Impact of self-heating and thermal coupling on analog circuits in SOI CMOS

This paper examines the influence of the static and dynamic electrothermal behavior of silicon-on-insulator (SOI) CMOS transistors on a range of primitive analog circuit cells. In addition to the more well-known self-heating close-range thermal coupling effects are also examined. Particular emphasis is given to the impact of these effects on drain current mismatch due to localized temperature differences. Dynamic electrothermal behavior in the time and frequency domains is also considered, measurements and analyses are presented for a simple amplifier stage, current mirrors, a current output D/A converter, and ring oscillators fabricated in a 0.7-/spl mu/m SOI CMOS process. It is shown that circuits which rely strongly on matching, such as the current mirrors or D/A converter, are significantly affected by self-heating and thermal coupling. Anomalies due to self-heating are also clearly visible in the small-signal characteristics of the amplifier stage. Self-heating effects are less significant for fast switching circuits. The paper demonstrates how circuit-level simulations can be used to predict undesirable nonisothermal operating conditions during the design stage.
A SURVEY ON SECURITY FRAMEWORKS FOR WIRELESS SENSOR NETWORKS

— Wireless Sensor Networks (WSN) is an emerging technology now-a-days and has a wide range of applications such as battlefield surveillance, traffic surveillance, forest fire detection, flood detection etc. But wireless sensor networks are susceptible to a variety of potential attacks which obstructs the normal operation of the network. The wireless sensor network nature of communication is unsafe and unprotected because of deployment in hostile environment, limited resources, an automated nature and untrusted broadcast transmission media. The most of security techniques are not sufficient in WSN network and security is a vital requirement for network. The intent of this paper is to investigate the security related frameworks. The introductory section gives brief information on the WSN components and its architecture. Then it deals with some of the major security issues and challenges over wireless sensor networks (WSNs).
Architecture of a programmable systolic array

The architecture of a simple but programmable linear systolic array tuned to support a variety of symbolic computations is presented. The system, the Brown Systolic Array (B-SYS) is currently being implemented in CMOS. B-SYS demonstrates that programmable processor arrays may be made fully systolic with no need for local program memory or global instruction broadcasting. Any hazards introduced by the systolic instruction stream can be avoided using a processing phase concept. The application of these ideas results in a basic cell that is both simple and flexible, making it possible to build massively parallel, programmable systolic arrays. <<ETX>>
L2 Learners’ Retrieval of Pre-learned Word Meanings While Reading in a New Context

This study investigated how L2 learners comprehend newly learned words in a sentential context. Theoretically, learners’ experiences of recalling pre-learned word meanings and applying this knowledge to new contexts may enhance future lexical performance. In the experiment, 84 undergraduate students learned low-frequency words in English with example sentences; half were highly imageable (i.e., it was easy to evoke a mental image while reading) for learners. After the learning phase, the participants were presented with a list of new sentences and asked to interpret the pre-learned words in context. After a one-week delay, the participants were given a recall task that tested their memory of both the target words and example sentences. The results suggested some difficulty in meaning retrieval when learners encountered words in a new context. Further analysis showed that correctly interpreted words were retained better. In addition, the retention of pre-learned word meanings was affected by memory of example sentences, suggesting the effectiveness of giving more easily remembered imageable example sentences. Research findings are discussed in terms of transfer-appropriate processing and other related theoretical frameworks.
Peer Assisted Learning in a Large Class Format

Introduction: Most of the research around peer assisted learning (PAL) has been directed towards the contribution of PAL in small groups for both knowledge and skill gains. Although PAL has been used for didactic sessions like lectures or presentations in tutorials, the dynamics of PAL in these sessions are not well researched. We therefore designed this study to explore the acceptance and perceived usefulness of didactic PAL sessions in a large class format. Method: Peer assisted learning was incorporated in a Urology module for 4th year MBBS students. Students were assigned topics in groups to prepare and present in front of the whole class. The presentations were followed by question answer sessions by students in the presence of the teacher. The teacher summed up the session at the end. The perception of students was assessed using an electronically distributed questionnaire regarding acceptance and usefulness of PAL. The usefulness was also assessed by rating student presentations for content, explanation of concepts, and number of questions asked by the peers. A focus group (FGD) was also conducted to explore student perceptions on engagement in this didactic large class PAL in depth. Results: Seventy-three students participated in the survey (Response Rate = 73.7%). Students were neutral for both acceptability (Mean= 3.08, SD= 0.72) and usefulness (Mean= 3.20, SD= 0.54) of PAL in the given setting. Only 25% of students demonstrated content adequacy in their primary eﬀort for presentation, 10% explained all concepts, 40% explained at least major concepts while 40% just read the slides. Students asked more questions from peer tutors than teachers (Mean= 1.14, SD= 0.80 Teacher feedback and background collaborative learning remain the most valued aspects of this format.
Identifying Multisubject Cortical Activation in Functional MRI: A Frequency Domain Approach

Functional magnetic resonance imaging (fMRI) has, since its de- scription fifteen years ago, become the most common in-vivo neuroimaging technique. FMRI allows the identification of brain areas which are related to specific tasks, by statistical analysis of the BOLD (blood oxigenation level dependent) signal. Classically, the observed BOLD signal is compared to an expected haemodynamic response function (HRF) using a general linear model (GLM). However, the results of GLM rely on the HRF specification, which is usually determined in an ad hoc fashion. For periodic experimental designs, we propose a multisubject frequency domain brain mapping, which requires only the stimulation frequency, and consequently avoids subjective choices of HRF. We present some computational simulations, which demon- strate a good performance of the proposed approach in short length time series. In addition, an application to real fMRI datasets is also presented.