Miracle-dz/UniFAR-FTUs huggingface dataset:
Your attention please: designing security-decision UIs to make genuine risks harder to ignore

We designed and tested attractors for computer security dialogs: user-interface modifications used to draw users' attention to the most important information for making decisions. Some of these modifications were purely visual, while others temporarily inhibited potentially-dangerous behaviors to redirect users' attention to salient information. We conducted three between-subjects experiments to test the effectiveness of the attractors. In the first two experiments, we sent participants to perform a task on what appeared to be a third-party site that required installation of a browser plugin. We presented them with what appeared to be an installation dialog from their operating system. Participants who saw dialogs that employed inhibitive attractors were significantly less likely than those in the control group to ignore clues that installing this software might be harmful. In the third experiment, we attempted to habituate participants to dialogs that they knew were part of the experiment. We used attractors to highlight a field that was of no value during habituation trials and contained critical information after the habituation period. Participants exposed to inhibitive attractors were two to three times more likely to make an informed decision than those in the control condition.
A Comparative Study of Graphical and Alphanumeric Passwords for Mobile Device Authentication

Mobile devices such as smartphones and tablets are widely used to perform security critical and privacy sensitive activities, such as mobile banking, mobile health care, mobile shopping, etc. Screen locks are used in mobile devices to protect sensitive information. Graphical password and alphanumeric password are two common types of screen locking schemes. The alphanumeric password scheme has shown some security and usability drawbacks. For example, a user may pick an easy to remember alphanumeric password that may also be easy to guess. On the contrary, if as user picks a password that is hard to guess it may also be hard to remember. Several alternative password mechanisms have been introduced. Graphical password is one of them, and it is based on pictures or patterns. However, graphical password is also vulnerable to certain types of attack. In this paper, we study an alphanumeric password method (i.e., PIN) and a graphical password method (i.e., pattern) in order to unravel security and usability issues related to mobile device authentication. The study uses observation and survey data to compare these two authentication methods on following criteria: creation time, memorability, and login time and login success rate. In addition, we also measure how the screen size of a mobile device affects usability and security aspects of screen locks by measuring differences on creation time, memorability, login time, login success rate for Android smartphone and tablet.
Age and Gender Identification in Social Media

This paper describes the submission of the University of Washington's Center for Data Science to the PAN 2014 author profiling task. We examine the predictive quality in terms of age and gender of several sets of features extracted from various genres of online social media. Through comparison, we establish a feature set which maximizes accuracy of gender and age prediction across all genres examined. We report accuracies obtained by two approaches to the multi-label classification problem of predicting both age and gender; a model wherein the multi-label problem is reduced to a single-label problem using powerset transformation, and a chained classifier approach wherein the output of a dedicated classifier for gender is used as input for a classifier for age.
A multi-centre evaluation of the RIFLE criteria for early acute kidney injury in critically ill patients.

BACKGROUND The Acute Dialysis Quality Initiative Working Group recently developed the RIFLE criteria, a consensus definition for acute kidney injury (AKI). We sought to evaluate the RIFLE criteria on the day of ICU admission in a large heterogenous population of critically ill patients. METHODS Retrospective interrogation of prospectively collected data from the Australian New Zealand Intensive Care Society Adult Patient Database. We evaluated 120 123 patients admitted for >/=24 h from 1 January 2000 to 31 December 2005 from 57 ICUs across Australia. RESULTS The median (IQR) age was 64.3 (50.8-75.4) years, 59.5% were male, 28.6% had co-morbid disease, 50.3% were medical admissions and the initial mean (+/-SD) APACHEII score was 16.9 (+/-7.7). According to the RIFLE criteria, on the day of admission, AKI occurred in 36.1%, with a maximum RIFLE category of Risk in 16.3%, Injury in 13.6%, and Failure 6.3%. AKI, defined by any RIFLE category, was associated with an increase in hospital mortality (OR 3.29, 95% CI 3.19-3.41, P < 0.0001). The crude hospital mortality stratified by RIFLE category was 17.9% for Risk, 27.7% for Injury and 33.2% for Failure. By multivariable analysis, each RIFLE category was independently associated with hospital mortality (OR: Risk 1.58, Injury 2.54 and Failure 3.22). CONCLUSION In a large heterogenous cohort of critically ill patients, the RIFLE criteria classified >36% with AKI on the day of admission. For successive increases in severity of RIFLE category, there were increases in hospital mortality. The RIFLE criteria represent a simple tool for the detection and classification of AKI and for correlation with clinical outcomes.
Migration and the Welfare State: A Dynamic Political-Economy Theory

We develop a dynamic politico-economic theory of welfare state and immigration policies, featuring three groups of voters: skilled workers, unskilled workers, and old retirees. The welfare-state is modeled by a proportional tax on labor income to finance a demogrant in a balanced-budget manner to capture the essence of inter- and intra- generational redistribution of a typical welfare system. Migrants arrive when young and their birth rate exceeds the native-born birth rate. We characterize political-economic equilibrium policy rules consisting of the tax rate, the skill composition of migrants, and the total number of migrants, in terms of demographic and labor productivity characteristics. We find that political coalitions will form among skilled and unskilled voters or among unskilled and old voters in order to block the other group from coming into power. As a consequence, the ideal policies of the unskilled voters are featured more often in the political economy equilibria than any other groups regardless of the size of unskilled voters.
MicroRNA‐29b modulates Japanese encephalitis virus‐induced microglia activation by targeting tumor necrosis factor alpha‐induced protein 3

Japanese encephalitis virus (JEV), a single‐stranded RNA (ssRNA) virus, is the leading cause of encephalitis in Asia. Microglial activation is one of the key events in JEV‐induced neuroinflammation. Although the various microRNAs (miRNAs) has been shown to regulate microglia activation during pathological conditions including neuroviral infections, till date, the involvement of miRNAs in JEV infection has not been evaluated. Hence, we sought to evaluate the possible role of miRNAs in mediating JEV‐induced microglia activation. Initial screening revealed significant up‐regulation of miR‐29b in JEV‐infected mouse microglial cell line (BV‐2) and primary microglial cells. Furthermore, using bioinformatics tools, we identified tumor necrosis factor alpha‐induced protein 3, a negative regulator of nuclear factor‐kappa B signaling as a potential target of miR‐29b. Interestingly, in vitro knockdown of miR‐29b resulted in significant over‐expression of tumor necrosis factor alpha‐induced protein 3, and subsequent decrease in nuclear translocation of pNF‐κB. JEV infection in BV‐2 cell line elevated inducible nitric oxide synthase, cyclooxygenase‐2, and pro‐inflammatory cytokine expression levels, which diminished after miR‐29b knockdown. Collectively, our study demonstrates involvement of miR‐29b in regulating JEV‐ induced microglial activation.
On morphological relatedness

Abstract In this paper, we discuss the results of a new unsupervised and computationally lightweight scoring of how two words are morphologically related to each other. This measure is meant to be an alternative to stemming, radicals (root) extraction, and morphological analysis in a wide range of applications; especially information extraction related ones. Compared to light stemming, which seems to be the most convenient approach for systems with efficiency concerns, our measure does not neglect unconditionally a prefix or a suffix as the light stemming does. Instead, our measure takes into account all letters of the word but with different weights. This prevents the missing of a significant letter. Compared to heavy stemming, morphological analysis, or radicals extraction, which rely on dictionaries and compatibility databases, our measure does not rely on any language-specific morphology knowledge. This makes our approach unsupervised and theoretically language independent and computationally much lighter. Our tests targeted Arabic: a Semitic language recognized to have a complex morphology due to its highly inflectional lexicon.
Learning from Subsampled Data: Active and Randomized Strategies

Author(s): Wauthier, Fabian Lutz-Frank | Advisor(s): Jordan, Michael I | Abstract: In modern statistical applications, we are often faced with situationswhere there is either too little or too much data. Both extremes canbe troublesome: Interesting models can only be learnt when sufficientamounts of data are available, yet these models tend to becomeintractable when data is abundant. An important thread of researchaddresses these difficulties by subsampling the data prior to learninga model. Subsampling can be active (i.e. active learning) orrandomized. While both of these techniques have a long history, adirect application to novel situations is in many casesproblematic. This dissertation addresses some of these issues.We begin with an active learning strategy for spectral clustering whenthe cost of assessing individual similarities is substantial orprohibitive. We give an active spectral clustering algorithm whichiteratively adds similarities based on information gleaned from apartial clustering and which improves over common alternatives.Next, we consider active learning in Bayesian models. Complex Bayesianmodels often require an MCMC-based method for inference, which makes anaive application of common active learning strategiesintractable. We propose an approximate active learning method whichreuses samples from an existing MCMC chain in order to speed up thecomputations.Our third contribution looks at the effects of randomized subsamplingon Gaussian process models that make predictions about outliers andrare events. Randomized subsampling risks making outliers evenrarer, which, in the context of Gaussian process models, can lead tooverfitting. We show that Heavy-tailed stochastic processes can beused to improve robustness of regression and classification estimatorsto such outliers by selectively shrinking them more strongly in sparseregions than in dense regions.Finally, we turn to a theoretical evaluation of randomized subsamplingfor the purpose of inferring rankings of objects. We present twosimple algorithms that predict a total order over n objects from arandomized subsample of binary comparisons. In expectation, thealgorithms match an aOmega(n) lower bound on the sample complexityfor predicting a permutation with fixed expected Kendall taudistance. Furthermore, we show that given O(nlog(n)) samples, onealgorithm recovers the true ranking with uniform quality, while theother predicts the ranking more accurately near the top than thebottom. Due to their simple form, the algorithms can be easilyextended to online and distributed settings.
First UK survey of paediatric type 2 diabetes and MODY

Aims: To estimate the UK prevalence of childhood type 2 diabetes and maturity onset diabetes of the young (MODY), and distinguish them from each other and from type 1 diabetes. Methods: The British Society for Paediatric Endocrinology and Diabetes Clinical Trials/Audit Group undertook a cross-sectional questionnaire survey of all paediatric diabetes centres during 2000, collecting data on all children with non-type 1 diabetes. Results: Of 112 children reported to the survey, 25 had type 2 diabetes and 20 had MODY. In contrast to type 1, type 2 patients presented later (12.8 v 9.3 years), were usually female, overweight, or obese (92% v 28%), and a greater proportion were of ethnic minority origin (56% v 22%). In contrast to type 2, MODY patients were younger (10.8 years), less likely to be overweight or obese (50% v 92%), and none were from ethnic minority groups. The crude minimum UK prevalence of type 2 diabetes under 16 years is 0.21/100 000, and of MODY is 0.17/100 000. South Asian children have a relative risk of type 2 diabetes of 13.7 compared to white UK children. Conclusions: UK children still have a low prevalence of type 2 diabetes. Children from ethnic minorities are at significantly higher risk, but in white UK children with non-type 1 diabetes a diagnosis of MODY is as likely as type 2 diabetes. Childhood type 2 diabetes is characterised by insulin resistance, and is distinct from both type 1 and MODY.
Cathepsin K inhibitors for osteoporosis and potential off-target effects

Cathepsin K is a highly potent collagenase and the predominant papain-like cysteine protease expressed in osteoclasts. Cathepsin K deficiencies in humans and mice have underlined the central role of this protease in bone resorption and, thus, have rendered the enzyme as an attractive target for anti-resorptive osteoporosis therapy. In the past decade, a lot of efforts have been made in developing highly potent, selective and orally applicable cathepsin K inhibitors. Some of these inhibitors have passed preclinical studies and are presently in clinical trials at different stages of advancement. The development of the inhibitors and preliminary results of the clinical trials revealed problems and lessons concerning the in situ specificity of the compounds and their tissue targeting. In this review, we briefly summarize the history of cathepsin K research and discuss the current development of cathepsin K inhibitors as novel anti-resorptives for the treatment of osteoporosis. We also discuss potential off-target effects of cathepsin K inhibition and alternative applications of cathepsin K inhibitors in arthritis, atherosclerosis, blood pressure regulation, obesity and cancer.
Molecular characterization of breast cancer cell lines by clinical immunohistochemical markers

Breast cancer is the leading cause of cancer mortality in females worldwide. Studies based on gene expression profiles have identified different breast cancer molecular subtypes, such as luminal A and B cells, cancer cells that are estrogen receptor (ER) and/or progesterone receptor (PR) positive, human epidermal growth factor 2 (HER2)-enriched cells, cancer cells that exhibit an overexpression of the oncogene HER2, and triple-negative cells, cancer cells that are negative for ER, PR and HER2 expression. Immunohistochemistry is the most common type of method used for the identification of these molecular subtypes, through the identification of specific cell receptors. The present study aimed to evaluate the ER, PR and HER2 receptor expression in human breast cancer cell lines, and to classify the corresponding molecular subtype comparing two alternative methods. In the present study, a panel of human mammary carcinoma cell lines: BT-20; Hs578T; MCF-7; MCF-7/AZ; MDA-MB-231; MDA-MB-468; SKBR3; and T47D were used. Immunohistochemical and immunocytochemistry assays were used to characterize the breast cancer subtypes of these cell lines according to the expression of ER, PR and HER2 receptors. The results revealed the molecular characterization of this panel of breast cancer cell lines, using the differential expression of classical and clinically used markers in concordance with previous studies. In addition, these data are important for additional in vitro studies of these specific receptors.
Compound MQA, a Caffeoylquinic Acid Derivative, Protects Against NMDA‐Induced Neurotoxicity and Potential Mechanisms In Vitro

Compound MQA (1,5‐O‐dicaffeoyl‐3‐O‐[4‐malic acid methyl ester]‐quinic acid) is a natural derivative of caffeoylquinic acid isolated from Arctium lappa L. roots. However, we know little about the effects of MQA on the central nervous system. This study aims to investigate the neuroprotective effects and underlying mechanisms of MQA against the neurotoxicity of N‐methyl‐d‐aspartate (NMDA).
Early Expression of Yeast Genes Affected by Chemical Stress

ABSTRACT The variety of environmental stresses is probably the major challenge imposed on transcription activators and the transcriptional machinery. To precisely describe the very early genomic response developed by yeast to accommodate a chemical stress, we performed time course analyses of the modifications of the yeast gene expression program which immediately follows the addition of the antimitotic drug benomyl. Similar analyses were conducted with different isogenic yeast strains in which genes coding for relevant transcription factors were deleted and coupled with efficient bioinformatics tools. Yap1 and Pdr1, two well-known key mediators of stress tolerance, appeared to be responsible for the very rapid establishment of a transient transcriptional response encompassing 119 genes. Yap1, which plays a predominant role in this response, binds, in vivo, promoters of genes which are not automatically up-regulated. We proposed that Yap1 nuclear localization and DNA binding are necessary but not sufficient to elicit the specificity of the chemical stress response.
Analysis of the terrestrial locomotion of a salamander robot

Salamanders propel themselves by proper coordination of limb movements and body undulations. This type of locomotion is interesting for robotics to design robots capable of locomotion on water and land. In this work we identify the control and structural parameters that contribute to forward terrestrial locomotion. We introduce a kinematic model of Salamandra robotica II, a new salamander robot, to explore how the stride length varies with different limb sizes and different types of body oscillations. We also perform systematic tests using a dynamic model built in a physics-based simulator to analyze the locomotion performance in terms of forward speed and power consumption. The results show that it is beneficial to use body undulations with variable curvature along the body, and that the tail can serve as a fifth limb to provide thrust on ground. Experiments using the real robot validate the simulation results and the contribution of the proposed control strategies.
Activation of the Epidermal Growth Factor Receptor by Respiratory Syncytial Virus Results in Increased Inflammation and Delayed Apoptosis*

Respiratory syncytial virus (RSV) preferentially infects lung epithelial cells. Infection by RSV leads to an extended inflammatory response, characterized by the release of interleukin-8 (IL-8). Activation of ERK MAP kinase is required for both RSV-induced inflammation and the extended survival of infected cells. In this study, we analyzed the role of the epidermal growth factor receptor (EGFR) in RSV activation of ERK. We demonstrate for the first time that RSV activates EGFR in lung epithelial cells. Activation of EGFR results in increased ERK activity, contributing to both the inflammatory response (IL-8 release) and prolonging the survival of RSV-infected cells. Inhibition of EGFR with siRNA decreased both ERK activation and IL-8 production after RSV. In analyzing the effect of EGFR activation on survival of RSV-infected cells, we found that EGFR activation by RSV resulted in ERK-dependent alterations in the balance of pro- versus anti-apoptotic Bcl2 proteins. RSV altered the balance between pro- and anti-apoptotic Bcl2 proteins (increased BclxL and decreased BimEL) increasing the relative amount of pro-survival proteins. This occurred in an EGFR-dependent manner. This study supports an important role for EGFR activity in the lifespan and inflammatory potential of RSV-infected epithelial cells.
Effects of chemical restraint on electroretinograms recorded sequentially in awake, sedated, and anesthetized dogs.

OBJECTIVE To quantitatively and qualitatively compare electroretinography (ERG) recordings in awake, sedated, and anesthetized dogs. ANIMALS Six 6-month-old Beagles. PROCEDURES A brief ERG protocol for dogs was used. Following 1-minute and subsequent 5-minute dark adaptation, mixed rod-cone responses were recorded bilaterally with a handheld multispecies ERG device with dogs in each of 3 states of consciousness: awake, sedated (dexmedetomidine and butorphanol), and anesthetized (atropine and hydromorphone, followed by propofol and midazolam and anesthetic maintenance with isoflurane). Low- and high-frequency noise levels were quantified via Fourier analysis, and the effect of consciousness state on signal amplitude, implicit time, and noise was analyzed via repeated-measures ANOVA. In addition, 13 veterinary ophthalmologists who were unaware of the dogs' consciousness states subjectively graded the ERG recording quality, and scores for each tracing were compared. RESULTS ERG amplitudes were highest in awake dogs and lowest in anesthetized dogs. Implicit times were shortest in awake dogs and longest in anesthetized dogs. Differences in b-wave amplitudes and a-wave implicit times were significant. Neither low- nor high-frequency noise levels differed significantly among consciousness states. Furthermore, no significant differences were identified among observers' scores assigned to ERG tracings. CONCLUSIONS AND CLINICAL RELEVANCE Anesthesia and sedation resulted in significant attenuation and delay of ERG responses in dogs. Chemical restraint of dogs had no consistently significant effect on low- or high-frequency noise levels or on observer perception of signal quality.
A novel cloud gaming framework using joint video and graphics streaming

As the popularity of smart phones and tablets, users have an increasing desire to enjoy ubiquitous game playing. The emerging cloud gaming turns this desire into reality, enabling users to play games at anywhere on any devices. However, due to the huge amount of data transmission, it is challenging to provide a high quality game experience under the limited bandwidth capacity. In this paper, we propose a novel cloud gaming framework, in which we introduce two synchronized graphics buffers at both the server and the client sides. The server not only streams the compressed frames captured from game scenes, but also progressively transmits graphics data. The received graphics data is used to generate reference frames. When compressing the next frame, the cloud server will choose the reference frame with a lower residual error, from the previous frame and the current frame rendered from the graphics buffer. With the accumulation of graphics data, the frame rendered from the graphics buffer is close to the captured frame, which greatly reduces the transmission bit rates. Based on the proposed framework, we study the rate allocation problem, in which we optimize the allocated bit rates between the compressed frame and the graphics data to minimize the total distortion under the bandwidth constraint. Experimental results demonstrate that the proposed framework can optimally allocate bit rates to achieve a minimal distortion for cloud gaming compared to the traditional video streaming and graphics streaming approaches.