nils-herrmann/clean_scirepeval huggingface dataset:
The state of the art in automating usability evaluation of user interfaces

Usability evaluation is an increasingly important part of the user interface design process. However, usability evaluation can be expensive in terms of time and human resources, and automation is therefore a promising way to augment existing approaches. This article presents an extensive survey of usability evaluation methods, organized according to a new taxonomy that emphasizes the role of automation. The survey analyzes existing techniques, identifies which aspects of usability evaluation automation are likely to be of use in future research, and suggests new ways to expand existing approaches to better support usability evaluation.
MonoCap: Monocular Human Motion Capture using a CNN Coupled with a Geometric Prior

Recovering 3D full-body human pose is a challenging problem with many applications. It has been successfully addressed by motion capture systems with body worn markers and multiple cameras. In this paper, we address the more challenging case of not only using a single camera but also not leveraging markers: going directly from 2D appearance to 3D geometry. Deep learning approaches have shown remarkable abilities to discriminatively learn 2D appearance features. The missing piece is how to integrate 2D, 3D, and temporal information to recover 3D geometry and account for the uncertainties arising from the discriminative model. We introduce a novel approach that treats 2D joint locations as latent variables whose uncertainty distributions are given by a deep fully convolutional neural network. The unknown 3D poses are modeled by a sparse representation and the 3D parameter estimates are realized via an Expectation-Maximization algorithm, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Extensive evaluation on benchmark datasets shows that the proposed approach achieves greater accuracy over state-of-the-art baselines. Notably, the proposed approach does not require synchronized 2D-3D data for training and is applicable to “in-the-wild” images, which is demonstrated with the MPII dataset.
Improved Analytical Modeling of Axial Flux Machine With a Double-Sided Permanent Magnet Rotor and Slotless Stator Based on an Analytical Method

This paper presents the results of an experimental verification and electromagnetic field analysis for an axial flux permanent magnet (PM) synchronous generator (AFPMSG) with a double-sided PM rotor and slotless stator windings based on analytical field computation to offer improved analytical modeling method. An analytical approach to analyze the magnetic field distribution and estimate the equivalent circuit parameters is presented that dramatically reduces analysis time while maintaining high reliability. Since the flux leakage phenomenon is dominant in the inner and outer radii of this type of AFPMSG, it is specifically considered. In addition, an actual device was fabricated on the basis of one of the analysis models, and used to experimentally verify all of the analysis results.
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series

Gaussian Processes (GPs) provide a general and analytically tractable way of modeling complex time-varying, nonparametric functions. The Automatic Bayesian Covariance Discovery (ABCD) system constructs natural-language description of time-series data by treating unknown timeseries data nonparametrically using GP with a composite covariance kernel function. Unfortunately, learning a composite covariance kernel with a single time-series data set often results in less informative kernel that may not give qualitative, distinctive descriptions of data. We address this challenge by proposing two relational kernel learning methods which can model multiple time-series data sets by finding common, shared causes of changes. We show that the relational kernel learning methods find more accurate models for regression problems on several real-world data sets; US stock data, US house price index data and currency exchange rate data.
Knowing me, knowing you: the accuracy and unique predictive validity of self-ratings and other-ratings of daily behavior.

Many people assume that they know themselves better than anyone else knows them. Recent research on inaccuracies in self-perception, however, suggests that self-knowledge may be more limited than people typically assume. In this article, the authors examine the possibility that people may know a person as well as (or better than) that person knows himself or herself. In Study 1, the authors document the strength of laypeople's beliefs that the self is the best expert. In Study 2, the authors provide a direct test of self- and other-accuracy using an objective and representative behavioral criterion. To do this, the authors compared self- and other-ratings of daily behavior to real-life measures of act frequencies assessed unobtrusively over 4 days. Our results show that close others are as accurate as the self in predicting daily behavior. Furthermore, accuracy varies across behaviors for both the self and for others, and the two perspectives often independently predict behavior. These findings suggest that there is no single perspective from which a person is known best and that both the self and others possess unique insight into how a person typically behaves.
OntoRama: Browsing RDF Ontologies Using a Hyperbolic-Style Browser

This paper presents a Java-based hyperbolic-style browser designed to render RDF files as structured ontological maps. The program was motivated by the need to browse the content of a web-accessible ontology server: WEBKB-2. The ontology server contains descriptions of over 74,500 object types derived from the WORDNET 1.7 lexical database and can be accessed using RDF syntax. Such a structure creates complications for hyperbolic-style displays. In WEBKB-2 there are 140 stable ontology link types and a hyperbolic display needs to filter and iconify the view so different link relations can be distinguished in multi-link views. Our browsing tool, ONTORAMA, is therefore motivated by two possibly interfering aims: the first to display up to 10 times the number of nodes in a hyperbolicstyle view than using a conventional graphics display; secondly, to render the ontology with multiple links comprehensible in that view.
Uncorrelated Patient Similarity Learning

Patient similarity learning aims to derive a clinically meaningful similarity metric to measure the similarity between a pair of patients according to their historical clinical information, which could help to predict the clinical outcomes of the patient of interest. However, the patient clinical data are usually complex, and contain much irrelevant and redundant information, which makes it difficult to learn the similarity metric with high accuracy. Although some methods have been proposed to address the complex nature of patient data, they overemphasize sparsity-based relevant feature selection and fail to take into consideration the redundant features that are highly correlated with each other, and this heavily degrades the accuracy of the learned results. To address the above challenges, we propose a novel uncorrelated patient similarity learning approach, which can not only select the most relevant features for the learning task, but also guarantee that the selected features have low correlations with each other. Additionally, to address the scenarios where the patient data are distributed across different sites, we extend the proposed approach and design a distributed mechanism, based on which the similarity metric can be accurately learned without directly accessing the raw patient data at each site. The desirable performance of the proposed methods are verified through extensive experiments conducted on both real-world and synthetic datasets.
A case study-based comparison of web testing techniques applied to AJAX web applications

Asynchronous Javascript And XML (AJAX) is a recent technology used to develop rich and dynamic Web applications. Different from traditional Web applications, AJAX applications consist of a single page whose elements are updated dynamically in response to callbacks activated asynchronously by the user or by a server message. On the one hand, AJAX improves the responsiveness and usability of a Web application, but on the other hand, it makes the testing phase more difficult. In this paper, our state-based testing technique, developed to test AJAX-based applications, is compared to existing Web testing techniques, such as white-box and black-box ones. To this aim, an experiment based on two case studies has been conducted to evaluate effectiveness and test effort involved in the compared Web testing techniques. In particular, the capability of each technique to reveal injected faults of different fault categories is analyzed in detail. The associated effort was also measured. The results show that state-based testing is complementary to the existing Web testing techniques and can reveal faults otherwise unnoticed or hard to reveal with the other techniques.
Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition

Deep Neural Network Hidden Markov Models, or DNN-HMMs, are recently very promising acoustic models achieving good speech recognition results over Gaussian mixture model based HMMs (GMM-HMMs). In this paper, for emotion recognition from speech, we investigate DNN-HMMs with restricted Boltzmann Machine (RBM) based unsupervised pre-training, and DNN-HMMs with discriminative pre-training. Emotion recognition experiments are carried out on these two models on the eNTERFACE'05 database and Berlin database, respectively, and results are compared with those from the GMM-HMMs, the shallow-NN-HMMs with two layers, as well as the Multi-layer Perceptrons HMMs (MLP-HMMs). Experimental results show that when the numbers of the hidden layers as well hidden units are properly set, the DNN could extend the labeling ability of GMM-HMM. Among all the models, the DNN-HMMs with discriminative pre-training obtain the best results. For example, for the eNTERFACE'05 database, the recognition accuracy improves 12.22% from the DNN-HMMs with unsupervised pre-training, 11.67% from the GMM-HMMs, 10.56% from the MLP-HMMs, and even 17.22% from the shallow-NN-HMMs, respectively.
Empirical analysis and statistical modeling of attack processes based on honeypots

Honeypots are more and more used to collect data on malicious activities on the Internet and to better understand the strategies and techniques used by attackers to compromise target systems. Analysis and modeling methodologies are needed to support the characterization of attack processes based on the data collected from the honeypots. This paper presents some empirical analyses based on the data collected from the Leurré.com honeypot platforms deployed on the Internet and presents some preliminary modeling studies aimed at fulfilling such objectives.
Deep Knowledge Tracing

Knowledge tracing—where a machine models the knowledge of a student as they interact with coursework—is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neural Networks (RNNs) to model student learning. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge. Using neural networks results in substantial improvements in prediction performance on a range of knowledge tracing datasets. Moreover the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks. These results suggest a promising new line of research for knowledge tracing and an exemplary application task for RNNs.
A Methodology for Formalizing Model-Inversion Attacks

Confidentiality of training data induced by releasing machine-learning models, and has recently received increasing attention. Motivated by existing MI attacks and other previous attacks that turn out to be MI "in disguise," this paper initiates a formal study of MI attacks by presenting a game-based methodology. Our methodology uncovers a number of subtle issues, and devising a rigorous game-based definition, analogous to those in cryptography, is an interesting avenue for future work. We describe methodologies for two types of attacks. The first is for black-box attacks, which consider an adversary who infers sensitive values with only oracle access to a model. The second methodology targets the white-box scenario where an adversary has some additional knowledge about the structure of a model. For the restricted class of Boolean models and black-box attacks, we characterize model invertibility using the concept of influence from Boolean analysis in the noiseless case, and connect model invertibility with stable influence in the noisy case. Interestingly, we also discovered an intriguing phenomenon, which we call "invertibility interference," where a highly invertible model quickly becomes highly non-invertible by adding little noise. For the white-box case, we consider a common phenomenon in machine-learning models where the model is a sequential composition of several sub-models. We show, quantitatively, that even very restricted communication between layers could leak a significant amount of information. Perhaps more importantly, our study also unveils unexpected computational power of these restricted communication channels, which, to the best of our knowledge, were not previously known.
An organizing framework for collective identity: articulation and significance of multidimensionality.

The authors offer a framework for conceptualizing collective identity that aims to clarify and make distinctions among dimensions of identification that have not always been clearly articulated. Elements of collective identification included in this framework are self-categorization, evaluation, importance, attachment and sense of interdependence, social embeddedness, behavioral involvement, and content and meaning. For each element, the authors take note of different labels that have been used to identify what appear to be conceptually equivalent constructs, provide examples of studies that illustrate the concept, and suggest measurement approaches. Further, they discuss the potential links between elements and outcomes and how context moderates these relationships. The authors illustrate the utility of the multidimensional organizing framework by analyzing the different configuration of elements in 4 major theories of identification.
User interest and social influence based emotion prediction for individuals

Emotions are playing significant roles in daily life, making emotion prediction important. To date, most of state-of-the-art methods make emotion prediction for the masses which are invalid for individuals. In this paper, we propose a novel emotion prediction method for individuals based on user interest and social influence. To balance user interest and social influence, we further propose a simple yet efficient weight learning method in which the weights are obtained from users' behaviors. We perform experiments in real social media network, with 4,257 users and 2,152,037 microblogs. The experimental results demonstrate that our method outperforms traditional methods with significant performance gains.
The LilyPad Arduino: Toward Wearable Engineering for Everyone

Electronic textiles, or e-textiles, are an increasingly important part of wearable computing, helping to make pervasive devices truly wearable. These soft, fabric-based computers can function as lovely embodiments of Mark Weiser's vision of ubiquitous computing: providing useful functionality while disappearing discreetly into the fabric of our clothing. E-textiles also give new, expressive materials to fashion designers, textile designers, and artists, and garments stemming from these disciplines usually employ technology in visible and dramatic style. Integrating computer science, electrical engineering, textile design, and fashion design, e-textiles cross unusual boundaries, appeal to a broad spectrum of people, and provide novel opportunities for creative experimentation both in engineering and design. Moreover, e-textiles are cutting- edge technologies that capture people's imagination in unusual ways. (What other emerging pervasive technology has Vogue magazine featured?) Our work aims to capitalize on these unique features by providing a toolkit that empowers novices to design, engineer, and build their own e-textiles.
The eyes have it: the neuroethology, function and evolution of social gaze

Gaze is an important component of social interaction. The function, evolution and neurobiology of gaze processing are therefore of interest to a number of researchers. This review discusses the evolutionary role of social gaze in vertebrates (focusing on primates), and a hypothesis that this role has changed substantially for primates compared to other animals. This change may have been driven by morphological changes to the face and eyes of primates, limitations in the facial anatomy of other vertebrates, changes in the ecology of the environment in which primates live, and a necessity to communicate information about the environment, emotional and mental states. The eyes represent different levels of signal value depending on the status, disposition and emotional state of the sender and receiver of such signals. There are regions in the monkey and human brain which contain neurons that respond selectively to faces, bodies and eye gaze. The ability to follow another individual's gaze direction is affected in individuals with autism and other psychopathological disorders, and after particular localized brain lesions. The hypothesis that gaze following is "hard-wired" in the brain, and may be localized within a circuit linking the superior temporal sulcus, amygdala and orbitofrontal cortex is discussed.
Non-Negative Tensor Factorization with RESCAL

Non-negative data is generated by a broad selection of applications today, e.g in gene expression analysis or imaging. Many factorization techniques have been extended to account for this natural constraint and have become very popular due to their decomposition into interpretable latent factors. Generally relational data like protein interaction networks or social network data can also be seen as naturally non-negative. In this work, we extend the RESCAL tensor factorization, which has shown state-of-the-art results for multi-relational learning, to account for non-negativity by employing multiplicative update rules. We study the performance via these approaches on various benchmark datasets and show that a non-negativity constraint can be introduced by losing only little in terms of predictive quality in most of the cases but simultaneously increasing the sparsity of the factors significantly compared to the original RESCAL algorithm.
MILES: Multiple-Instance Learning via Embedded Instance Selection

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (multiple-instance learning via embedded instance selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty
Continuous monitoring of top-k queries over sliding windows

Given a dataset P and a preference function f, a top-k query retrieves the k tuples in P with the highest scores according to f. Even though the problem is well-studied in conventional databases, the existing methods are inapplicable to highly dynamic environments involving numerous long-running queries. This paper studies continuous monitoring of top-k queries over a fixed-size window W of the most recent data. The window size can be expressed either in terms of the number of active tuples or time units. We propose a general methodology for top-k monitoring that restricts processing to the sub-domains of the workspace that influence the result of some query. To cope with high stream rates and provide fast answers in an on-line fashion, the data in W reside in main memory. The valid records are indexed by a grid structure, which also maintains book-keeping information. We present two processing techniques: the first one computes the new answer of a query whenever some of the current top-k points expire; the second one partially pre-computes the future changes in the result, achieving better running time at the expense of slightly higher space requirements. We analyze the performance of both algorithms and evaluate their efficiency through extensive experiments. Finally, we extend the proposed framework to other query types and a different data stream model.
ScreenPass: secure password entry on touchscreen devices

Users routinely access cloud services through third-party apps on smartphones by giving apps login credentials (i.e., a username and password). Unfortunately, users have no assurance that their apps will properly handle this sensitive information. In this paper, we describe the design and implementation of ScreenPass, which significantly improves the security of passwords on touchscreen devices. ScreenPass secures passwords by ensuring that they are entered securely, and uses taint-tracking to monitor where apps send password data. The primary technical challenge addressed by ScreenPass is guaranteeing that trusted code is always aware of when a user is entering a password. ScreenPass provides this guarantee through two techniques. First, ScreenPass includes a trusted software keyboard that encourages users to specify their passwords' domains as they are entered (i.e., to tag their passwords). Second, ScreenPass performs optical character recognition (OCR) on a device's screenbuffer to ensure that passwords are entered only through the trusted software keyboard. We have evaluated ScreenPass through experiments with a prototype implementation, two in-situ user studies, and a small app study. Our prototype detected a wide range of dynamic and static keyboard-spoofing attacks and generated zero false positives. As long as a screen is off, not updated, or not tapped, our prototype consumes zero additional energy; in the worst case, when a highly interactive app rapidly updates the screen, our prototype under a typical configuration introduces only 12% energy overhead. Participants in our user studies tagged their passwords at a high rate and reported that tagging imposed no additional burden. Finally, a study of malicious and non-malicious apps running under ScreenPass revealed several cases of password mishandling.