nils-herrmann/clean_scirepeval huggingface dataset:
PriVaricator: Deceiving Fingerprinters with Little White Lies

Researchers have shown that, in recent years, unwanted web tracking is on the rise, with browser-based fingerprinting being adopted by more and more websites as a viable alternative to third-party cookies. In this paper we propose PriVaricator, a solution to the problem of browser-based fingerprinting. A key insight is that when it comes to web tracking, the real problem with fingerprinting is not uniqueness of a fingerprint, it is linkability, i.e., the ability to connect the same fingerprint across multiple visits. Thus, making fingerprints non-deterministic also makes them hard to link across browsing sessions. In PriVaricator we use the power of randomization to "break" linkability by exploring a space of parameterized randomization policies. We evaluate our techniques in terms of being able to prevent fingerprinting and not breaking existing (benign) sites. The best of our randomization policies renders all the fingerprinters we tested ineffective, while causing minimal damage on a set of 1000 Alexa sites on which we tested, with no noticeable performance overhead.
Impact of reducing STI-induced stress on layout dependence of MOSFET characteristics

Active-area layout dependence of MOSFET parametric characteristics and its reduction by reducing shallow trench isolation (STI)-induced mechanical stress were investigated. Threshold voltages (V/sub th/) and saturation drain currents (I/sub ds/) become sensitive to the active-area layout of MOSFET in scaled-down technology. This phenomenon is the effect of mechanical stress from STI edge, which reduces impurity diffusion in channel region and enhances carrier mobility. To reduce the STI-induced stress, we examined STI-wall-oxide nitridation and STI gap-fill-oxide densifying in pure N/sub 2/ ambient. These processes reduced the reoxidation of the STI wall oxide, therefore reduced the STI-induced stress. According to the new STI process, the active-area layout dependence of V/sub th/ and I/sub ds/ were reduced successfully.
How visual fatigue and discomfort impact 3D-TV quality of experience: a comprehensive review of technological, psychophysical, and psychological factors

The Quality of Experience (QoE) of 3D contents is usually considered to be the combination of the perceived visual quality, the perceived depth quality, and lastly the visual fatigue and comfort. When either fatigue or discomfort are induced, studies tend to show that observers prefer to experience a 2D version of the contents. For this reason, providing a comfortable experience is a prerequisite for observers to actually consider the depth effect as a visualization improvement. In this paper, we propose a comprehensive review on visual fatigue and discomfort induced by the visualization of 3D stereoscopic contents, in the light of physiological and psychological processes enabling depth perception. First, we review the multitude of manifestations of visual fatigue and discomfort (near triad disorders, symptoms for discomfort), as well as means for detection and evaluation. We then discuss how, in 3D displays, ocular and cognitive conflicts with real world experience may cause fatigue and discomfort; these includes the accommodation vergence conflict, the inadequacy between presented stimuli and observers depth of focus, and the cognitive integration of conflicting depth cues. We also discuss some limits for stereopsis that constrain our ability to perceive depth, and in particular the perception of planar and in-depth motion, the limited fusion range and various stereopsis disorders. Finally, this paper discusses how the different aspects of fatigue and discomfort apply to 3D technolo̊ Corresponding author Matthieu Urvoy ̊  ̈ Marcus Barkowsky  ̈ Patrick Le Callet LUNAM Université, Université de Nantes, IRCCyN UMR CNRS 6597, Institut de Recherche en Communications et Cybernétique de Nantes, Polytech Nantes, rue Christian Pauc BP 50609 44306 Nantes Cedex 3 E-mail: {matthieu.urvoy, marcus.barkowsky, patrick.lecallet} @univ-nantes.fr gies and contents. We notably highlight the need for respecting a comfort zone and avoiding camera and rendering artifacts. We also discuss the influence of visual attention, exposure duration and training. Conclusions provide guidance for best practices and future research.
ECG measurement on a chair without conductive contact

For the purpose of long-term, everyday electrocardiogram (ECG) monitoring, we present a convenient method of ECG measurement without direct conductive contact with the skin while subjects sat on a chair wearing normal clothes. Measurements were made using electrodes attached to the back of a chair, high-input-impedance amplifiers mounted on the electrodes, and a large ground-plane placed on the chair seat. ECGs were obtained by the presented method for several types of clothing and compared to ECGs obtained from conventional measurement using Ag-AgCl electrodes. Motion artifacts caused by usual desk works were investigated. This study shows the feasibility of the method for long-term, convenient, everyday use
Facial performance sensing head-mounted display

There are currently no solutions for enabling direct face-to-face interaction between virtual reality (VR) users wearing head-mounted displays (HMDs). The main challenge is that the headset obstructs a significant portion of a user's face, preventing effective facial capture with traditional techniques. To advance virtual reality as a next-generation communication platform, we develop a novel HMD that enables 3D facial performance-driven animation in real-time. Our wearable system uses ultra-thin flexible electronic materials that are mounted on the foam liner of the headset to measure surface strain signals corresponding to upper face expressions. These strain signals are combined with a head-mounted RGB-D camera to enhance the tracking in the mouth region and to account for inaccurate HMD placement. To map the input signals to a 3D face model, we perform a single-instance offline training session for each person. For reusable and accurate online operation, we propose a short calibration step to readjust the Gaussian mixture distribution of the mapping before each use. The resulting animations are visually on par with cutting-edge depth sensor-driven facial performance capture systems and hence, are suitable for social interactions in virtual worlds.
Salient region detection for stereoscopic images

In this paper, we propose an effective saliency model, which combines region-level depth, color and spatial information, to detect salient regions in stereoscopic images. Based on region segmentation results of stereoscopic images, depth contrast, depth weighted color contrast, and spatial compactness of color distribution are measured for each region, and combined to generate the region-level saliency map. Experimental results on a public stereoscopic image dataset with ground truths of salient objects demonstrate that the proposed saliency model outperforms the state-of-the-art saliency models.
Providing Context-Aware Security for IoT Environments Through Context Sharing Feature

Nowadays security and privacy in Internet of Things (IoT) environments is a real issue. Traditional security mechanisms use a non-aware approach, in which static parameters are used to provide secure decisions. IoT is a dynamic environment. Thus a non-static approach for security provision becomes mandatory. Context-aware security appears as a viable choice for this kind of processing. It uses the context information of IoT environments thus providing dynamic security. When together with context sharing feature, it can add new dimensions to the IoT security. Context sharing allows the use of off-domain context information to the security provision. This paper defines an EdgeCentric Context Sharing Architecture that provides context-aware security by using shared context information. Moreover, we discuss the challenges in the context-aware security area.
Interaction effects of perceived gaze direction and dynamic facial expression : Evidence for appraisal theories of emotion

Appraisal theorists suggest that the face expresses cognitive processes involved both in the orienting of attention (primarily gaze direction) and in the evaluation of emotion-eliciting events. Contrary to the assumption of direct emotion recognition by basic emotions theorists, this implies an interaction effect between ‘‘perceived gaze direction’’ and ‘‘perceived facial expression’’ in inferring emotion from the face. These two theoretical perspectives were comparatively tested by requesting participants to decode dynamic synthetic facial expressions of emotion presented with either an averted or a direct gaze. Confirming the interaction predicted by appraisal theories, the perceived specificity and intensity of fear and anger depended on gaze direction (direct gaze for anger and averted gaze for fear).
Credit Scoring Techniques : A Survey

Credit scoring is a numerical expression of the credit worthiness of an individual. A Value with a specific creditworthiness associated is assigned to an individual. Overall objective is to determine the creditworthiness of an individual. Ability of an individual to repay is determined in the credit scoring process. The credit scoring process looks at specific criteria such as income, credit history and many others. All this is done with the intent to reduce the overall default rate thereby decreasing the overall risk of financial institutions such as banks and micro lending institutions. Several credit scoring methodologies have been proposed and implemented and are varied from statistical based methods to Artificial Intelligence based techniques.
Unsupervised Learning of Sequence Representations by Autoencoders

Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation. The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style). One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.
Simulating water and smoke with an octree data structure

We present a method for simulating water and smoke on an unrestricted octree data structure exploiting mesh refinement techniques to capture the small scale visual detail. We propose a new technique for discretizing the Poisson equation on this octree grid. The resulting linear system is symmetric positive definite enabling the use of fast solution methods such as preconditioned conjugate gradients, whereas the standard approximation to the Poisson equation on an octree grid results in a non-symmetric linear system which is more computationally challenging to invert. The semi-Lagrangian characteristic tracing technique is used to advect the velocity, smoke density, and even the level set making implementation on an octree straightforward. In the case of smoke, we have multiple refinement criteria including object boundaries, optical depth, and vorticity concentration. In the case of water, we refine near the interface as determined by the zero isocontour of the level set function.
Do Online Reviews Reflect a Product's True Perceived Quality? - An Investigation of Online Movie Reviews Across Cultures

When does the reported average of online ratings match perceived average assessment? We apply behavioral theory to capture intentions in rating online movie reviews in two dissimilar countries - China and the United States. We argue that consumers' rating behaviors are affected by cultural influences. Based on data collected from imdb.com and douban.com, we found significant differences across raters from these two different cultures. Additionally, we examined how cultural elements influence rating behavior for a hybrid culture - Singapore. Under-reporting bias occurs when consumers with extreme opinions are more likely to report their opinions than consumers with moderate reviews, resulting in reviews that may be biased estimators of quality and certainly have higher variance. An experimental study shows that under-reporting is more prevalent among U.S. online social network, and thus that online reviews are more reflective of a movie's true perceived quality in Chinese and Singapore than in the U.S.
The Finite Element Method with Anisotropic Mesh Grading for Elliptic Problems in Domains with Corners and Edges

This paper is concerned with a specific finite element strategy for solving elliptic boundary value problems in domains with corners and edges. First, the anisotropic singular behaviour of the solution is described. Then the finite element method with anisotropic, graded meshes and piecewise linear shape functions is investigated for such problems; the schemes exhibit optimal convergence rates with decreasing mesh size. For the proof, new local interpolation error estimates for functions from anisotropically weighted spaces are derived. Finally, a numerical experiment is described, that shows a good agreement of the calculated approximation orders with the theoretically predicted ones. ( 1998 B. G. Teubner Stuttgart—John Wiley & Sons Ltd.
A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders

Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoc techniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial datasets have been tested, and a more thorough statistical foundation is required. We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate firstinitial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN. The advantage of the approach is the relative ease and flexibility with which these probabilistic models can be developed.
Habanero-Java: the new adventures of old X10

In this paper, we present the Habanero-Java (HJ) language developed at Rice University as an extension to the original Java-based definition of the X10 language. HJ includes a powerful set of task-parallel programming constructs that can be added as simple extensions to standard Java programs to take advantage of today's multi-core and heterogeneous architectures. The language puts a particular emphasis on the usability and safety of parallel constructs. For example, no HJ program using async, finish, isolated, and phaser constructs can create a logical deadlock cycle. In addition, the future and data-driven task variants of the async construct facilitate a functional approach to parallel programming. Finally, any HJ program written with async, finish, and phaser constructs that is data-race free is guaranteed to also be deterministic.
 HJ also features two key enhancements that address well known limitations in the use of Java in scientific computing --- the inclusion of complex numbers as a primitive data type, and the inclusion of array-views that support multidimensional views of one-dimensional arrays. The HJ compiler generates standard Java class-files that can run on any JVM for Java 5 or higher. The HJ runtime is responsible for orchestrating the creation, execution, and termination of HJ tasks, and features both work-sharing and work-stealing schedulers. HJ is used at Rice University as an introductory parallel programming language for second-year undergraduate students. A wide variety of benchmarks have been ported to HJ, including a full application that was originally written in Fortran 90. HJ has a rich development and runtime environment that includes integration with DrJava, the addition of a data race detection tool, and service as a target platform for the Intel Concurrent Collections coordination language
Image Super-Resolution Based on Dual Path Network

Recently, Convolutional Neural Network (CNN) ba-sed methods have demonstrated high-quality reconstruction for Single Image Super-Resolution (SISR). Particularly, resi-dual learning shows improved performance. For the SR recon-struction performance of the CNN-based models, the design of model architecture is very important. In this paper, we present SRDPN, a super-resolution network based on Dual Path Net-work (DPN). The DPN combines the advantages of the state-of-the-art Residual Network (ResNet) and Dense Convolu-tional Network (DenseNet), which enjoys lower computational cost, lower memory consumption and higher parameter effici-ency. Extensive qualitative and quantitative evaluations on benchmark datasets show that our proposed method performs better than the state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR) and image quality.
Strong Injection Locking in Low- $Q$ LC Oscillators: Modeling and Application in a Forwarded-Clock I/O Receiver

A general model for injection-locked LC oscillators (LC-ILOs) is presented that is valid for any tank quality factor and injection strength. Important properties of an ILO such as lock-range, phase shift, bandwidth and response to input jitter are described. An LC-ILO together with a half-rate data sampler is implemented as a forwarded-clock I/O receiver in 45-nm CMOS. A strongly-injected low-Q LC oscillator enables clock deskew across 1UI and rejects high-frequency clock jitter. The complete 27 Gb/s ILO-based data receiver has an overall power efficiency of 1.6 mW/Gb/s.
Clinical and radiological pictures of two newborn babies with manifestations of chondrodysplasia punctata and review of available literature

Chondrodysplasia punctata (CDP) is a rare, heterogeneous congenital skeletal dysplasia, characterized by punctate or dot-like calcium deposits in cartilage observed on neonatal radiograms. A number of inborn metabolic diseases are associated with CDP, including peroxisomal and cholesterol biosynthesis dysfunction and other inborn errors of metabolism such as: mucolipidosis type II, mucopolysacharidosis type III, GM1 gangliosidosis. CDP is also related to disruption of vitamin K-dependent metabolism, causing secondary effects on the embryo, as well as fetal alcohol syndrome (FAS), chromosomal abnormalities that include trisomies 18 and 21, Turner syndrome.



This article presents clinical data and diagnostic imaging findings of two newborn babies with chondrodysplasia punctata. Children presented with skeletal and cartilage anomalies, dysmorphic facial feature, muscles tone abnormalities, skin changes and breathing difficulties. One of the patients demonstrated critical stenosis of spinal canal with anterior subluxation of C1 vertebra relative to C2. The aim of this article is to present cases and briefly describe current knowledge on etiopathogenesis as well as radiological and clinical symptoms of diseases coexisting with CDP.



Radiological diagnostic imaging allows for visualization of punctate focal mineralization in bone epiphyses during neonatal age and infancy. Determining the etiology of chondrodysplasia punctata requires performing various basic as well as additional examinations, including genetic studies.
A Corpus-based Approach to Finding Happiness

What are the sources of happiness and sadness in everyday life? In this paper, we employ ‘linguistic ethnography’ to seek out where happiness lies in our everyday lives by considering a corpus of blogposts from the LiveJournal community annotated with happy and sad moods. By analyzing this corpus, we derive lists of happy and sad words and phrases annotated by their ‘happiness factor.’ Various semantic analyses performed with this wordlist reveal the happiness trajectory of a 24-day (3am and 9-10p are most happy), and a 7-day week (Wednesdays are saddest), and compare the socialness and humancenteredness of happy descriptions versus sad descriptions. We evaluate our corpus-based approach in a classification task and contrast our wordlist with emotionally-annotated wordlists produced by experimental focus groups. Having located happiness temporally and semantically within this corpus of everyday life, the paper concludes by offering a corpus-inspired livable recipe for happiness.
Detection of ascending stairs using stereo vision

Environment perception is an important task in computer vision for many applications in robotics. Especially for robots navigating through different levels of a building, stair detection constitutes an important perception task. In this paper, we propose a stair detection algorithm using range data. Firstly, we introduce a parameter, which describes local surface orientations w.r.t. a global reference. Secondly, a matched filter is used to detect relevant edges in the orientation data. Afterwards, line segments are determined using these edge data which are further used to estimate stairs. The proposed method is invariant against rotations of the sensor. We show that the system can handle typical outdoor stair types and outperforms the accuracy of state-of-the-art stair detection methods. Moreover, the method is used in real time to assist visually impaired people who wear the camera system on a helmet.