nils-herrmann/clean_scirepeval huggingface dataset:
Single point active alignment method (SPAAM) for optical see-through HMD calibration for AR

Augmented reality (AR) is a technology in which a user’s view of the real world is enhanced or augmented with additional information generated from a computer model. In order to have a working AR system, the see-through display system must be calibrated so that the graphics is properly rendered. The optical see-through systems present an additional challenge because we do not have access to the image data directly as in video see-through systems. This paper reports on a method we developed for optical see-through head-mounted displays. The method integrates the measurements for the camera and the magnetic tracker which is attached to the camera in order to do the calibration. The calibration is based on the alignment of image points with a single 3D point in the world coordinate system from various viewpoints. The user interaction to do the calibration is extremely easy compared to prior methods, and there is no requirement for keeping the head static while doing the calibration.
Breast disorders in pregnant and lactating women.

The breast undergoes extensive changes during pregnancy and lactation that can create diagnostic challenges. This article reviews the anatomy of the breast, breast changes associated with pregnancy and lactation, and breast imaging techniques for pregnant and lactating women. Various benign breast conditions in this patient population also are discussed, such as lactating adenomas, galactoceles, and granulomatous mastitis. Finally, pregnancy-associated breast cancer is presented, including its epidemiology, diagnosis, staging, treatment, and prognosis.
Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

The success of opinion mining for automatically processing vast amounts of opinionated content available on the Internet has been demonstrated as a less expensive and lower latency solution for gathering public opinion. In this paper, we investigate whether it is possible to predict variations in vote intention based on sentiment time series extracted from news comments, using three Brazilian elections as case study. The contributions of this case study are: a) the comparison of two approaches for opinion mining in user-generated content in Brazilian Portuguese, b) the proposition of two types of features to represent sentiment behavior towards political candidates that can be used for prediction, c) an approach to predict polls vote intention variations that is adequate for scenarios of sparse data. We developed experiments to assess the influence on the forecasting accuracy of the proposed features, and their respective preparation. Our results display an accuracy of 70% in predicting positive and negative variations. These are important contributions towards a more general framework that is able to blend opinions from several different sources to find representativeness of the target population, and make more reliable predictions.
Empirical analysis of programming language adoption

Some programming languages become widely popular while others fail to grow beyond their niche or disappear altogether. This paper uses survey methodology to identify the factors that lead to language adoption. We analyze large datasets, including over 200,000 SourceForge projects, 590,000 projects tracked by Ohloh, and multiple surveys of 1,000-13,000 programmers.
 We report several prominent findings. First, language adoption follows a power law; a small number of languages account for most language use, but the programming market supports many languages with niche user bases. Second, intrinsic features have only secondary importance in adoption. Open source libraries, existing code, and experience strongly influence developers when selecting a language for a project. Language features such as performance, reliability, and simple semantics do not. Third, developers will steadily learn and forget languages. The overall number of languages developers are familiar with is independent of age. Finally, when considering intrinsic aspects of languages, developers prioritize expressivity over correctness. They perceive static types as primarily helping with the latter, hence partly explaining the popularity of dynamic languages.
From Vision to NLP : A Merge

The study of artificial intelligence can be simplified into one goal: trying to mimic/enhance human senses. This paper attempts to combine computer vision and natural language processing to create a question answer system. This system takes a question and an image as input and outputs a response to the answer based on how the RCNN understands the question asked. The system correlates the question with the image by leveraging attention and memory mechanisms.  Arun Chaganty
Versatile rigid-fluid coupling for incompressible SPH

We propose a momentum-conserving two-way coupling method of SPH fluids and arbitrary rigid objects based on hydrodynamic forces. Our approach samples the surface of rigid bodies with boundary particles that interact with the fluid, preventing deficiency issues and both spatial and temporal discontinuities. The problem of inhomogeneous boundary sampling is addressed by considering the relative contribution of a boundary particle to a physical quantity. This facilitates not only the initialization process but also allows the simulation of multiple dynamic objects. Thin structures consisting of only one layer or one line of boundary particles, and also non-manifold geometries can be handled without any additional treatment. We have integrated our approach into WCSPH and PCISPH, and demonstrate its stability and flexibility with several scenarios including multiphase flow.
Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Collaborative ltering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, including techniques based on correlation coefcients, vector-based similarity calculations, and statistical Bayesian methods. We compare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evaluation metrics. The rst characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second estimates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommendation in an ordered list. Experiments were run for datasets associated with 3 application areas, 4 experimental protocols, and the 2 evaluation metrics for the various algorithms. Results indicate that for a wide range of conditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vectorsimilarity methods. Between correlation and Bayesian networks, the preferred method depends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other considerations include the size of database, speed of predictions, and learning time. Appears in Proceedings of the Fourteenth Conference on Uncertainty in Arti cial Intelligence, Madison, WI, July, 1998. Morgan Kaufmann Publisher.
Intrusion Detection in Computer Networks based on Machine Learning Algorithms

Network security technology has become crucial in protecting government and industry computing infrastructure. Modern intrusion detection applications face complex requirements; they need to be reliable, extensible, easy to manage, and have low maintenance cost. In recent years, machine learning-based intrusion detection systems have demonstrated high accuracy, good generalization to novel types of intrusion, and robust behavior in a changing environment. This work aims to compare efficiency of machine learning methods in intrusion detection system, including artificial neural networks and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in machine learning-based intrusion detectors, we propose to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compare the accuracy, detection rate, false alarm rate for 4 attack types. The extensive experimental results on the KDD-cup intrusion detection benchmark dataset demonstrate that the proposed approach produces higher performance than KDD Winner, especially for U2R and U2L type attacks.
MP-Index: A Multi-predicate Publish/Subscribe Mechanism for Internet of Things

The IoT (Internet of Things) is a network ofphysical devices (i.e. objects like Vehicles, Machines, Buildings, Furniture, and so on) that helps achieve better service andvalue. Large amounts of data is generated while connectingthese devices and communicating between them. Faster capturing, processing and forwarding of this data certainly requiresan efficient technique during each stage of communication. The Publish/Subscribe communication paradigm is a powerfulmedium for IoT devices to connect and communicate with eachother. It offers anonymous, many-to-many and asynchronouscommunication which plays a very significant role in designinghighly scalable distributed systems. Event matching is oneof the key aspects of Publish/Subscribe paradigm where anevent that has occurred is matched against the large volumeof subscriptions to find the appropriate subscribers that needto be notified. The existing event matching solutions are based on the assumptionthat an assignment (group of events) will be generatedand matched against a large number of subscriptions. In thereal world, events do not get generated together and henceconsidering them as an assignment is not the best way to handlethem. We are proposing a solution based on the temporal, causal and spatial properties of events, which overcomes theassumption of assignment generation. Our proposed solution, MP-Index (Multi-Predicate Index) allows the Publish/Subscribesystem to match individual events occurring at different timestamps against a large volume of subscriptions. The proposedsolution was implemented and its results were compared withtwo of the existing algorithms. The results of the comparisonshow that the proposed Multi-Predicate Index algorithmoutperforms the other two algorithms in terms of matchingtime. We have also implemented the proposed solution inMosquitto which is an open source implementation of theMQTT (Message Queuing Telemetry Transport) protocol. Theresult of this implementation clearly confirms the support formulti-predicate subscription match against events occurring atdifferent time stamps.
Temporal Convolutional Neural Networks for Diagnosis from Lab Tests

Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases’ onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K individuals over 8 years, 18 common lab measurements, and 171 diseases show that the temporal signatures learned via convolution are significantly more predictive than baselines commonly used for early disease diagnosis.
From random walks to distances on unweighted graphs

The spatial graph model depends on the following latent quantities: • p(x): latent probability density in compact connected smooth domain D ⊂ Rd; •Xn = {x1, . . . , xn}: coordinate points drawn i.i.d. from p(x); •  Xn → R>0: local scale function (may depend on Xn); • h : R≥0 → [0, 1]: connectivity kernel with h(x) = 0 for x > 1, h(1) > 0, and h left-continuous at 1. Definition 1: Spatial graph The spatial graph Gn corresponding to εn and h is the random graph with: • vertex set Xn; • a directed edge from xi to xj with probability h(|xi − xj|εn(xi)).
MazeBase: A Sandbox for Learning from Games

This paper introduces an environment for simple 2D maze games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple games based on algorithmic tasks (e.g. embodying simple if-then statements). We deploy a range of neural models (fully connected, convolutional network, memory network) on these games, with and without a procedurally generated curriculum. We show that these architectures can be trained with reinforcement to respectable performance on these tasks, but are still far from optimal, despite their simplicity. We also apply these models to games involving combat, including StarCraft, demonstrating their ability to learn non-trivial tactics which enable them to consistently beat the in-game AI.
Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens

We design a field experiment to explore the use of social comparison to increase contributions to an online community. We find that, after receiving behavioral information about the median user’s total number of movie ratings, users below the median demonstrate a 530% increase in the number of monthly movie ratings, while those above the median do not necessarily decrease their ratings. When given outcome information about the average user’s net benefit score, above-average users mainly engage in activities that help others. Our findings suggest that effective personalized social information can increase the level of public goods
Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions

This document provides additional details about the experiments described in (Heilman and Smith, 2010). Note that while this document provides information about the datasets and experimental methods, it does not provide further results. If you have any further questions, please feel free to contact the first author. The preprocessed datasets (i.e., tagged and parsed) will be made available for research purposes upon request.
Discoverer: Automatic Protocol Reverse Engineering from Network Traces

Application-level protocol specifications are useful for many security applications, including intrusion prevention and detection that performs deep packet inspection and traffic normalization, and penetration testing that generates network inputs to an application to uncover potential vulnerabilities. However, current practice in deriving protocol specifications is mostly manual. In this paper, we present Discoverer, a tool for automatically reverse engineering the protocol message formats of an application from its network trace. A key property of Discoverer is that it operates in a protocol-independent fashion by inferring protocol idioms commonly seen in message formats of many application-level protocols. We evaluated the efficacy of Discoverer over one text protocol (HTTP) and two binary protocols (RPC and CIFS/SMB) by comparing our inferred formats with true formats obtained from Ethereal . For all three protocols, more than 90% of our inferred formats correspond to exactly one true format; one true format is reflected in five inferred formats on average; our inferred formats cover over 95% of messages, which belong to 30-40% of true formats observed in the trace.
The classification of inherited epidermolysis bullosa (EB): Report of the Third International Consensus Meeting on Diagnosis and Classification of EB.

Since publication in 2000 of the Second International Consensus Report on Diagnosis and Classification of Epidermolysis Bullosa, many advances have been made to our understanding of this group of diseases, both clinically and molecularly. At the same time, new epidermolysis bullosa (EB) subtypes have been described and similarities with some other diseases have been identified.



We sought to arrive at a new consensus of the classification of EB subtypes.



We now present a revised classification system that takes into account the new advances, as well as encompassing other inherited diseases that should also be included within the EB spectrum, based on the presence of blistering and mechanical fragility. Current recommendations are made on the use of specific diagnostic tests, with updates on the findings known to occur within each of the major EB subtypes. Electronic links are also provided to informational and laboratory resources of particular benefit to clinicians and their patients.



As more becomes known about this disease, future modifications may be needed. The classification system has been designed with sufficient flexibility for these modifications.



This revised classification system should assist clinicians in accurately diagnosing and subclassifying patients with EB.
Personalized Reliability Prediction of Web Services

Service Oriented Architecture (SOA) is a business-centric IT architectural approach for building distributed systems. Reliability of service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet connections. Designing efficient and effective reliability prediction approaches of Web services has become an important research issue. In this article, we propose two personalized reliability prediction approaches of Web services, that is, neighborhood-based approach and model-based approach. The neighborhood-based approach employs past failure data of similar neighbors (either service users or Web services) to predict the Web service reliability. On the other hand, the model-based approach fits a factor model based on the available Web service failure data and use this factor model to make further reliability prediction. Extensive experiments are conducted with our real-world Web service datasets, which include about 23 millions invocation results on more than 3,000 real-world Web services. The experimental results show that our proposed reliability prediction approaches obtain better reliability prediction accuracy than other competing approaches.
High-performance web crawling

High-performance web crawlers are an important component of many web services. For example, search services use web crawlers to populate their indices, comparison shopping engines use them to collect product and pricing information from online vendors, and the Internet Archive uses them to record a history of the Internet. The design of a high-performance crawler poses many challenges, both technical and social, primarily due to the large scale of the web. The web crawler must be able to download pages at a very high rate, yet it must not overwhelm any particular web server. Moreover, it must maintain data structures far too large to fit in main memory, yet it must be able to access and update them efficiently. This chapter describes our experience building and operating such a high-performance crawler.
One-Class SVMs for Document Classification

We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one-class SVM based on identifying “outlier” data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called “Hadamard” representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on “bottleneck” compression generated filters. The SVM approach as represented by Schölkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.
A Sound Type System for Secure Flow Analysis

Ensuring secure information ow within programs in the context of multiple sensitivity levels has been widely studied. Especially noteworthy is Denning's work in secure ow analysis and the lattice model [6][7]. Until now, however, the soundness of Denning's analysis has not been established satisfactorily. We formulate Denning's approach as a type system and present a notion of soundness for the system that can be viewed as a form of noninterference. Soundness is established by proving, with respect to a standard programming language semantics, that all well-typed programs have this noninterference property.
Beyond the Wall: Near-Data Processing for Databases

The continuous growth of main memory size allows modern data systems to process entire large scale datasets in memory. The increase in memory capacity, however, is not matched by proportional decrease in memory latency, causing a mismatch for in-memory processing. As a result, data movement through the memory hierarchy is now one of the main performance bottlenecks for main memory data systems. Database systems researchers have proposed several innovative solutions to minimize data movement and to make data access patterns hardware-aware. Nevertheless, all relevant rows and columns for a given query have to be moved through the memory hierarchy; hence, movement of large data sets is on the critical path.
 In this paper, we present JAFAR, a Near-Data Processing (NDP) accelerator for pushing selects down to memory in modern column-stores. JAFAR implements the select operator and allows only qualifying data to travel up the memory hierarchy. Through a detailed simulation of JAFAR hardware we show that it has the potential to provide 9x improvement for selects in column-stores. In addition, we discuss both hardware and software challenges for using NDP in database systems as well as opportunities for further NDP accelerators to boost additional relational operators.
Skin Detection Using Color, Texture and Space Information

Among various skin detection methods, Skin Probability Map (SPM) method is an effective one. Though SPM method possesses high true acceptance rate (TAR), its false acceptance rate (FAR) is unacceptable in some cases. The reason is that SPM method only use pixel-level color information. This paper proposes an improved skin detection method that integrates color, texture and space information. After color filter, a texture filter is constructed based on texture features extracted form Gabor wavelet transform. Texture filter will further filter non-skin pixels, meanwhile, it may also filter some skin pixel. To compensate the loss, after texture filter, a marker driven watershed transform is then used to grow already obtained skin regions. Experimental results show that the proposed method can achieve better performance than that of SPM.