nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating Causal Models by Comparing Interventional Distributions

The predominant method for evaluating the quality of causal models is to measure the graphical accuracy of the learned model structure. We present an alternative method for evaluating causal models that directly measures the accuracy of estimated interventional distributions. We contrast such distributional measures with structural measures, such as structural Hamming distance and structural intervention distance, showing that structural measures often correspond poorly to the accuracy of estimated interventional distributions. We use a number of real and synthetic datasets to illustrate various scenarios in which structural measures provide misleading results with respect to algorithm selection and parameter tuning, and we recommend that distributional measures become the new standard for evaluating causal models.
Text-Mining and Pattern-Matching based Prediction Models for Detecting Vulnerable Files in Web Applications

The proliferation of technology has empowered the web applications. At the same time, the presences of Cross-Site Scripting (XSS) vulnerabilities in web applications have become a major concern for all. Despite the many current detection and prevention approaches, attackers are exploiting XSS vulnerabilities continuously and causing significant harm to the web users. In this paper, we formulate the detection of XSS vulnerabilities as a prediction model based classification problem. A novel approach based on text-mining and pattern-matching techniques is proposed to extract a set of features from source code files. The extracted features are used to build prediction models, which can discriminate the vulnerable code files from the benign ones. The efficiency of the developed models is evaluated on a publicly available labeled dataset that contains 9408 PHP labeled (i.e. safe, unsafe) source code files. The experimental results depict the superiority of the proposed approach over existing ones.
Implementation of a Complex Event Engine for the Web

One of the key aspects in the implementation of reactive behaviour in the Web and, most importantly, in the semantic Web is the development of event detection engines. An event engine detects events occurring in a system and notifies their occurrences to its clients. Although primitive events are useful for modelling a good number of applications, certain other applications require the combination of primitive events in order to support reactive behaviour. This paper presents the implementation of an event detection engine that detects composite events specified by expressions of an illustrative sublanguage of the SNOOP event algebra
3D Deformable Face Tracking with a Commodity Depth Camera

Recently, there has been an increasing number of depth cameras available at commodity prices. These cameras can usually capture both color and depth images in real-time, with limited resolution and accuracy. In this paper, we study the problem of 3D deformable face tracking with such commodity depth cameras. A regularized maximum likelihood deformable model fitting (DMF) algorithm is developed, with special emphasis on handling the noisy input depth data. In particular, we present a maximum likelihood solution that can accommodate sensor noise represented by an arbitrary covariance matrix, which allows more elaborate modeling of the sensor’s accuracy. Furthermore, an 1 regularization scheme is proposed based on the semantics of the deformable face model, which is shown to be very effective in improving the tracking results. To track facial movement in subsequent frames, feature points in the texture images are matched across frames and integrated into the DMF framework seamlessly. The effectiveness of the proposed method is demonstrated with multiple sequences with ground truth information.
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models

We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects – counting, locating and classifying the elements of a scene – without any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network at unprecedented speed. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.
Automatic registration of LiDAR and optical imagery using depth map stereo

Automatic fusion of aerial optical imagery and untextured LiDAR data has been of significant interest for generating photo-realistic 3D urban models in recent years. However, unsupervised, robust registration still remains a challenge. This paper presents a new registration method that does not require priori knowledge such as GPS/INS information. The proposed algorithm is based on feature correspondence between a LiDAR depth map and a depth map from an optical image. Each optical depth map is generated from edge-preserving dense correspondence between the image and another optical image, followed by ground plane estimation and alignment for depth consistency. Our two-pass RANSAC with Maximum Likelihood estimation incorporates 2D-2D and 2D-3D correspondences to yield robust camera pose estimation. Experiments with a LiDAR-optical imagery dataset show promising results, without using initial pose information.
Defining a Digital Earth System

In a 1998 speech before the California Science Center in Los Angeles, then US VicePresident Al Gore called for a global undertaking to build a multi-faceted computing system for education and research, which he termed “Digital Earth.” The vision was that of a system providing access to what is known about the planet and its inhabitants’ activities – currently and for any time in history – via responses to queries and exploratory tools. Furthermore, it would accommodate modeling extensions for predicting future conditions. Organized efforts towards realizing that vision have diminished significantly since 2001, but progress on key requisites has been made. As the 10 year anniversary of that influential speech approaches, we re-examine it from the perspective of a systematic software design process and find the envisioned system to be in many respects inclusive of concepts of distributed geolibraries and digital atlases. A preliminary definition for a particular digital earth system as: “a comprehensive, distributed geographic information and knowledge organization system,” is offered and discussed. We suggest that resumption of earlier design and focused research efforts can and should be undertaken, and may prove a worthwhile “Grand Challenge” for the GIScience community.
Limb Salvage for Osteosarcoma: Current Status with a Review of Literature

In the last 30 years a sea of change has occurred in the outlook for these cancers. Chemotherapy has allowed better local and systemic control5, 6. Better imaging with CT and MRI has allowed the surgeon to accurately define the extent and therefore plan tumor resection. Advances in bioengineering have provided exciting options for reconstruction and the world has moved from amputation to limb salvage. In osteosarcoma, survival improved from dismal 10-20% to 50-70%7, 8. Long term studies showed that limb salvage operations, performed with wide margins and chemotherapy did not compromise the survival or local control compared to an amputation9-14.
PD-1 regulates germinal center B cell survival and the formation and affinity of long-lived plasma cells

Memory B and plasma cells (PCs) are generated in the germinal center (GC). Because follicular helper T cells (TFH cells) have high expression of the immunoinhibitory receptor PD-1, we investigated the role of PD-1 signaling in the humoral response. We found that the PD-1 ligands PD-L1 and PD-L2 were upregulated on GC B cells. Mice deficient in PD-L2 (Pdcd1lg2−/−), PD-L1 and PD-L2 (Cd274−/−Pdcd1lg2−/−) or PD-1 (Pdcd1−/−) had fewer long-lived PCs. The mechanism involved more GC cell death and less TFH cell cytokine production in the absence of PD-1; the effect was selective, as remaining PCs had greater affinity for antigen. PD-1 expression on T cells and PD-L2 expression on B cells controlled TFH cell and PC numbers. Thus, PD-1 regulates selection and survival in the GC, affecting the quantity and quality of long-lived PCs.
Score test variable screening.

Variable screening has emerged as a crucial first step in the analysis of high-throughput data, but existing procedures can be computationally cumbersome, difficult to justify theoretically, or inapplicable to certain types of analyses. Motivated by a high-dimensional censored quantile regression problem in multiple myeloma genomics, this article makes three contributions. First, we establish a score test-based screening framework, which is widely applicable, extremely computationally efficient, and relatively simple to justify. Secondly, we propose a resampling-based procedure for selecting the number of variables to retain after screening according to the principle of reproducibility. Finally, we propose a new iterative score test screening method which is closely related to sparse regression. In simulations we apply our methods to four different regression models and show that they can outperform existing procedures. We also apply score test screening to an analysis of gene expression data from multiple myeloma patients using a censored quantile regression model to identify high-risk genes.
Vegetative Propagation of Amaryllis ( Hippeastrum × johnsonii ) by Different Cutting Methods

Department of Horticultural Science and Landscape, Faculty of Agriculture, Ferdowsi University of Mashhad, 91779-48974, Mashhad, Iran Department of Ornamental Plant Biotechnology, Iranian Academic Center for Education, Culture and Research, Branch of Mashhad, 91779-49367, Mashhad, Iran Department of Plant Biotechnology and Breeding, Faculty of Agriculture, Ferdowsi University of Mashhad, 91779-48974, Mashhad, Iran
Wi-Fi based indoor localization and tracking using sigma-point Kalman filtering methods

Estimating the location of people and tracking them in an indoor environment poses a fundamental challenge in ubiquitous computing. The accuracy of explicit positioning sensors such as GPS is often limited for indoor environments. In this study, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. At the core of our system is a novel location and tracking algorithm using a sigma-point Kalman smoother (SPKS) based Bayesian inference approach. The proposed SPKS fuses a predictive model of human walking with a number of low-cost sensors to track 2D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infrared (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau Inc. The superior accuracy of our approach over a number of trials is demonstrated.
Probabilistic (logic) programming concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.
The Sharing Economy in Computing: A Systematic Literature Review

The sharing economy has quickly become a very prominent subject of research in the broader computing literature and the in human--computer interaction (HCI) literature more specifically. When other computing research areas have experienced similarly rapid growth (e.g. human computation, eco-feedback technology), early stage literature reviews have proved useful and influential by identifying trends and gaps in the literature of interest and by providing key directions for short- and long-term future work. In this paper, we seek to provide the same benefits with respect to computing research on the sharing economy. Specifically, following the suggested approach of prior computing literature reviews, we conducted a systematic review of sharing economy articles published in the Association for Computing Machinery Digital Library to investigate the state of sharing economy research in computing. We performed this review with two simultaneous foci: a broad focus toward the computing literature more generally and a narrow focus specifically on HCI literature. We collected a total of 112 sharing economy articles published between 2008 and 2017 and through our analysis of these papers, we make two core contributions: (1) an understanding of the computing community's contributions to our knowledge about the sharing economy, and specifically the role of the HCI community in these contributions (i.e.what has been done) and (2) a discussion of under-explored and unexplored aspects of the sharing economy that can serve as a partial research agenda moving forward (i.e.what is next to do).
A Survey of Visualization Systems for Malware Analysis

Due to the increasing threat from malicious software (malware), monitoring of vulnerable systems is becoming increasingly important. The need to log and analyze activity encompasses networks, individual computers, as well as mobile devices. While there are various automatic approaches and techniques available to detect, identify, or capture malware, the actual analysis of the ever-increasing number of suspicious samples is a time-consuming process for malware analysts. The use of visualization and highly interactive visual analytics systems can help to support this analysis process with respect to investigation, comparison, and summarization of malware samples. Currently, there is no survey available that reviews available visualization systems supporting this important and emerging field. We provide a systematic overview and categorization of malware visualization systems from the perspective of visual analytics. Additionally, we identify and evaluate data providers and commercial tools that produce meaningful input data for the reviewed malware visualization systems. This helps to reveal data types that are currently underrepresented, enabling new research opportunities in the visualization community.
Die neue Realität: Erweiterung des Data Warehouse um Hadoop, NoSQL & Co

Durch die immer starker wachsenden Datenberge stößt der klassische Data Warehouse-Ansatz an seine Grenzen, weil er in Punkto Schnelligkeit, Datenvolumen und Auswertungsmöglichkeiten nicht mehr mithalten kann. Neue Big Data-Technologien wie analytische Datenbanken, NoSQL-Datenbanken oder Hadoop versprechen Abhilfe, haben aber einige Nachteile: Während sich analytische Datenbanken nur unzureichend mit anderen Datenquellen integrieren lassen, reichen die Abfragesprachen von NoSQL-Datenbanken nicht an die Möglichkeiten von SQL heran. Die Einführung von Hadoop erfordert wiederum den aufwändigen Aufbau von Knowhow im Unternehmen. Durch eine geschickte Kombination des Data Warehouse-Konzepts mit modernen Big Data-Technologien lassen sich diese Schwierigkeiten überwinden: Die Data Marts, auf die analytische Datenbanken zugreifen, können aus dem Data Warehouse gespeist werden. Die Vorteile von NoSQL lassen sich in den Applikationsdatenbanken nutzen, während die Daten für die Analysen in das Data Warehouse geladen werden, wo die relationalen Datenbanken ihre Stärken ausspielen. Die Ergebnisse von Hadoop-Transaktionen schließlich lassen sich sehr gut in einem Data Warehouse oder in Data Marts ablegen, wo sie einfach über eine Data-Warehouse-Plattform ausgewertet werden können, während die Rohdaten weiterhin bei Hadoop verbleiben. Zudem unterstützt Hadoop auch Werkzeuge fur einen performanten SQL-Zugriff. Der Artikel beschreibt, wie aus altem Data Warehouse-Konzept und modernen Technologien die „neue Realität“ entsteht und illustriert dies an verschiedenen Einsatzszenarien.
Multilinear Dynamical Systems for Tensor Time Series

Data in the sciences frequently occur as sequences of multidimensional arrays called tensors. How can hidden, evolving trends in such data be extracted while preserving the tensor structure? The model that is traditionally used is the linear dynamical system (LDS) with Gaussian noise, which treats the latent state and observation at each time slice as a vector. We present the multilinear dynamical system (MLDS) for modeling tensor time series and an expectation–maximization (EM) algorithm to estimate the parameters. The MLDS models each tensor observation in the time series as the multilinear projection of the corresponding member of a sequence of latent tensors. The latent tensors are again evolving with respect to a multilinear projection. Compared to the LDS with an equal number of parameters, the MLDS achieves higher prediction accuracy and marginal likelihood for both artificial and real datasets.
Action-video-game experience alters the spatial resolution of vision.

Playing action video games enhances several different aspects of visual processing; however, the mechanisms underlying this improvement remain unclear. Here we show that playing action video games can alter fundamental characteristics of the visual system, such as the spatial resolution of visual processing across the visual field. To determine the spatial resolution of visual processing, we measured the smallest distance a distractor could be from a target without compromising target identification. This approach exploits the fact that visual processing is hindered as distractors are brought close to the target, a phenomenon known as crowding. Compared with nonplayers, action-video-game players could tolerate smaller target-distractor distances. Thus, the spatial resolution of visual processing is enhanced in this population. Critically, similar effects were observed in non-video-game players who were trained on an action video game; this result verifies a causative relationship between video-game play and augmented spatial resolution.
Evaluation of the ARTutor augmented reality educational platform in tertiary education

Augmented Reality (AR) has seen a significant increase in applications in a number of fields in recent years. One of the areas in which AR has been applied to is education, most commonly by means of augmenting educational books. The present paper builds on previous work by using an AR authoring environment and a mobile application, developed by the authors, in undergraduate courses at the Eastern Macedonia & Thrace Institute of Technology. Using the authoring tool, the students were able to enhance existing secondary education textbooks by adding digital content to them, and, using the mobile application, view the digital content and retrieve information from the textual content of the books by asking questions in natural language form. At the end of the semester, the students were asked to evaluate the ARTutor platform by means of questionnaires and the SECTIONS framework. This study presents and discusses the results of this evaluation exercise and proposes new directions of the research.
Comparative Study on Frequency and Time Domain Analyses for Seismic Site Response

In this paper, a comparative study on frequency and time domain analyses for the evaluation of the seismic response of subsoil to the earthquake shaking is presented. After some remarks on the solutions given by the linear elasticity theory for this type of problem, the use of some widespread numerical codes is illustrated and the results are compared with the available theoretical predictions. Bedrock elasticity, viscous and hysteretic damping, stress-dependency of the stiffness and nonlinear behaviour of the soil are taken into account. A series of comparisons between the results obtained by the different computer programs is shown.
THE PURCHASING POWER PARITY DEBATE

The Purchasing Power Parity Debate* Originally propounded by the 16th-century scholars of the University of Salamanca, the concept of purchasing power parity (PPP) was revived in the interwar period in the context of the debate concerning the appropriate level at which to re-establish international exchange rate parities. Broadly accepted as a long-run equilibrium condition in the post-war period, it first was advocated as a short-run equilibrium by many international economists in the first few years following the breakdown of the Bretton Woods system in the early 1970s and then increasingly came under attack on both theoretical and empirical grounds from the late 1970s to the mid 1990s. Accordingly, over the last three decades, a large literature has built up that examines how much the data deviated from theory, and the fruits of this research have provided a deeper understanding of how well PPP applies in both the short run and the long run. Since the mid 1990s, larger datasets and nonlinear econometric methods, in particular, have improved estimation. As deviations narrowed between real exchange rates and PPP, so did the gap narrow between theory and data, and some degree of confidence in long-run PPP began to emerge again. In this respect, the idea of long-run PPP now enjoys perhaps its strongest support in more than 30 years, a distinct reversion in economic thought.