nils-herrmann/clean_scirepeval huggingface dataset:
Semi-supervised ranking on very large graphs with rich metadata

Graph ranking plays an important role in many applications, such as page ranking on web graphs and entity ranking on social networks. In applications, besides graph structure, rich information on nodes and edges and explicit or implicit human supervision are often available. In contrast, conventional algorithms (e.g., PageRank and HITS) compute ranking scores by only resorting to graph structure information. A natural question arises here, that is, how to effectively and efficiently leverage all the information to more accurately calculate graph ranking scores than the conventional algorithms, assuming that the graph is also very large. Previous work only partially tackled the problem, and the proposed solutions are also not satisfying. This paper addresses the problem and proposes a general framework as well as an efficient algorithm for graph ranking. Specifically, we define a semi-supervised learning framework for ranking of nodes on a very large graph and derive within our proposed framework an efficient algorithm called Semi-Supervised PageRank. In the algorithm, the objective function is defined based upon a Markov random walk on the graph. The transition probability and the reset probability of the Markov model are defined as parametric models based on features on nodes and edges. By minimizing the objective function, subject to a number of constraints derived from supervision information, we simultaneously learn the optimal parameters of the model and the optimal ranking scores of the nodes. Finally, we show that it is possible to make the algorithm efficient to handle a billion-node graph by taking advantage of the sparsity of the graph and implement it in the MapReduce logic. Experiments on real data from a commercial search engine show that the proposed algorithm can outperform previous algorithms on several tasks.
Boom analytics: exploring data-centric, declarative programming for the cloud

Building and debugging distributed software remains extremely difficult. We conjecture that by adopting a data-centric approach to system design and by employing declarative programming languages, a broad range of distributed software can be recast naturally in a data-parallel programming model. Our hope is that this model can significantly raise the level of abstraction for programmers, improving code simplicity, speed of development, ease of software evolution, and program correctness.
 This paper presents our experience with an initial large-scale experiment in this direction. First, we used the Overlog language to implement a "Big Data" analytics stack that is API-compatible with Hadoop and HDFS and provides comparable performance. Second, we extended the system with complex distributed features not yet available in Hadoop, including high availability, scalability, and unique monitoring and debugging facilities. We present both quantitative and anecdotal results from our experience, providing some concrete evidence that both data-centric design and declarative languages can substantially simplify distributed systems programming.
Why does social exclusion hurt? The relationship between social and physical pain.

The authors forward the hypothesis that social exclusion is experienced as painful because reactions to rejection are mediated by aspects of the physical pain system. The authors begin by presenting the theory that overlap between social and physical pain was an evolutionary development to aid social animals in responding to threats to inclusion. The authors then review evidence showing that humans demonstrate convergence between the 2 types of pain in thought, emotion, and behavior, and demonstrate, primarily through nonhuman animal research, that social and physical pain share common physiological mechanisms. Finally, the authors explore the implications of social pain theory for rejection-elicited aggression and physical pain disorders.
Understanding and Quantifying Creativity in Lexical Composition

Why do certain combinations of words such as “disadvantageous peace” or “metal to the petal” appeal to our minds as interesting expressions with a sense of creativity, while other phrases such as “quiet teenager”, or “geometrical base” not as much? We present statistical explorations to understand the characteristics of lexical compositions that give rise to the perception of being original, interesting, and at times even artistic. We first examine various correlates of perceived creativity based on information theoretic measures and the connotation of words, then present experiments based on supervised learning that give us further insights on how different aspects of lexical composition collectively contribute to the perceived creativity.
Automatic error elimination by horizontal code transfer across multiple applications

We present Code Phage (CP), a system for automatically transferring correct code from donor applications into recipient applications that process the same inputs to successfully eliminate errors in the recipient. Experimental results using seven donor applications to eliminate ten errors in seven recipient applications highlight the ability of CP to transfer code across applications to eliminate out of bounds access, integer overflow, and divide by zero errors. Because CP works with binary donors with no need for source code or symbolic information, it supports a wide range of use cases. To the best of our knowledge, CP is the first system to automatically transfer code across multiple applications.
Binocular eye tracking in VR for visual inspection training

This paper presents novel software techniques for binocular eye tracking within Virtual Reality and discusses their application to aircraft inspection training. The aesthetic appearance of the environment is driven by standard graphical techniques augmented by realistic texture maps of the physical environment. The user's gaze direction, as well as head position and orientation, are tracked to allow recording of the user's fixations within the environment. Methods are given for (1) integration of the eye tracker into a Virtual Reality framework, (2) stereo calculation of the user's 3D gaze vector, (3) a new 3D calibration technique developed to estimate the user's inter-pupillary distance post-facto, and (4) a new technique for eye movement analysis in 3-space. The 3D eye movement analysis technique is an improvement over traditional 2D approaches since it takes into account the 6 degrees of freedom of head movements and is resolution independent. Results indicate that although the current signal analysis approach is somewhat noisy and tends to underestimate the identified number of fixations, recorded eye movements provide valuable human factors process measures complementing performance statistics used to gauge training effectiveness.
Emergency landing for a quadrotor in case of a propeller failure: A backstepping approach

A backstepping approach is proposed in this paper to cope with the failure of a quadrotor propeller. The presented methodology supposes to turn off also the motor which is opposite to the broken one. In this way, a birotor configuration with fixed propellers is achieved. The birotor is controlled to follow a planned emergency landing trajectory. Theory shows that the birotor can reach any point in the Cartesian space losing the possibility to control the yaw angle. Simulation tests are employed to validate the proposed controller design.
Exploring Millions of Footprints in Location Sharing Services

Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of userdriven footprints (i.e., “checkins”). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Lèvy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services.
3-D Facial Pose and Gaze Point Estimation Using a Robust Real-Time Tracking Paradigm

Facial pose and gaze point are fundamental to any visually directed human-machine interface. In this paper, we propose a system capable of tracking a face and estimating the 3-D pose and the gaze point all in a real-time video stream of the head. This is done by using a 3-D model together with multiple triplet triangulation of feature po sitions assuming an affine projection. Using feature-based tracking the calculation of a 3-D eye gaze direction vector is possible even with head rotation and using a monocular camera. The system is also able to automatically initialise the feature tracking and to recover from total tracking fail ures which can occur when a person becomes occluded or temporarily leaves the image.
Knock it off: profiling the online storefronts of counterfeit merchandise

We describe an automated system for the large-scale monitoring of Web sites that serve as online storefronts for spam-advertised goods. Our system is developed from an extensive crawl of black-market Web sites that deal in illegal pharmaceuticals, replica luxury goods, and counterfeit software. The operational goal of the system is to identify the affiliate programs of online merchants behind these Web sites; the system itself is part of a larger effort to improve the tracking and targeting of these affiliate programs. There are two main challenges in this domain. The first is that appearances can be deceiving: Web pages that render very differently are often linked to the same affiliate program of merchants. The second is the difficulty of acquiring training data: the manual labeling of Web pages, though necessary to some degree, is a laborious and time-consuming process. Our approach in this paper is to extract features that reveal when Web pages linked to the same affiliate program share a similar underlying structure. Using these features, which are mined from a small initial seed of labeled data, we are able to profile the Web sites of forty-four distinct affiliate programs that account, collectively, for hundreds of millions of dollars in illicit e-commerce. Our work also highlights several broad challenges that arise in the large-scale, empirical study of malicious activity on the Web.
Classifying and Characterizing Query Intent

Understanding the intent underlying user queries may help personalize search results and improve user satisfaction. In this paper, we develop a methodology for using ad clickthrough logs, query specific information, and the content of search engine result pages to study characteristics of query intents, specially commercial intent. The findings of our study suggest that ad clickthrough features, query features, and the content of search engine result pages are together effective in detecting query intent. We also study the effect of query type and the number of displayed ads on the average clickthrough rate. As a practical application of our work, we show that modeling query intent can improve the accuracy of predicting ad clickthrough for previously unseen queries.
Analysis of Safety-Critical Computer Failures in Medical Devices

Malfunctioning medical devices are one of the leading causes of serious injury and death in the US. Between 2006 and 2011, 5,294 recalls and approximately 1.2 million adverse events were reported to the US Food and Drug Administration (FDA). Almost 23 percent of these recalls were due to computer-related failures, of which approximately 94 percent presented medium to high risk of severe health consequences (such as serious injury or death) to patients. This article investigates the causes of failures in computer-based medical devices and their impact on patients by analyzing human-written descriptions of recalls and adverse event reports obtained from public FDA databases. The authors characterize computer-related failures by deriving fault classes, failure modes, recovery actions, and number of devices affected by the recalls. This analysis is used as a basis for identifying safety issues in life-critical medical devices and providing insights on the future challenges in the design of safety-critical medical devices.
Reflection Space Image Based Rendering

High quality, physically accurate rendering at interactiv e rates has widespread application, but is a daunting task. We attempt t o bridge the gap between high-quality offline and interactive render ing by using existing environment mapping hardware in combinatio with a novel Image Based Rendering (IBR) algorithm. The primary c ontribution lies in performing IBR in reflection space. This me thod can be applied to ordinary environment maps, but for more phy sically accurate rendering, we apply reflection space IBR to ra diance environment maps. A radiance environment map pre-integrat s Bidirectional Reflection Distribution Function (BRDF) wit h a lighting environment. Using the reflection-space IBR algorithm o n radiance environment maps allows interactive rendering of ar bitr ry objects with a large class of complex BRDFs in arbitrary ligh ting environments. The ultimate simplicity of the final algor ithm suggests that it will be widely and immediately valuable giv en the ready availability of hardware assisted environment mappi ng. CR categories and subject descriptors: I.3.3 [Computer Graphics]: Picture/Image generation; I.3.7 [Image Proces sing]: Enhancement.
FastRunner: A fast, efficient and robust bipedal robot. Concept and planar simulation

Bipedal robots are currently either slow, energetically inefficient and/or require a lot of control to maintain their stability. This paper introduces the FastRunner, a bipedal robot based on a new leg architecture. Simulation results of a Planar FastRunner demonstrate that legged robots can run fast, be energy efficient and inherently stable. The simulated FastRunner has a cost of transport of 1.4 and requires only a local feedback of the hip position to reach 35.4 kph from stop in simulation.
Automatically mining software-based, semantically-similar words from comment-code mappings

Many software development and maintenance tools involve matching between natural language words in different software artifacts (e.g., traceability) or between queries submitted by a user and software artifacts (e.g., code search). Because different people likely created the queries and various artifacts, the effectiveness of these tools is often improved by expanding queries and adding related words to textual artifact representations. Synonyms are particularly useful to overcome the mismatch in vocabularies, as well as other word relations that indicate semantic similarity. However, experience shows that many words are semantically similar in computer science situations, but not in typical natural language documents. In this paper, we present an automatic technique to mine semantically similar words, particularly in the software context. We leverage the role of leading comments for methods and programmer conventions in writing them. Our evaluation of our mined related comment-code word mappings that do not already occur in WordNet are indeed viewed as computer science, semantically-similar word pairs in high proportions.
An Efficient Learning Procedure for Deep Boltzmann Machines

We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.
Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification

In the literature of remote sensing, deep models with multiple layers have demonstrated their potentials in learning the abstract and invariant features for better representation and classification of hyperspectral images. The usual supervised deep models, such as convolutional neural networks, need a large number of labeled training samples to learn their model parameters. However, the real-world hyperspectral image classification task provides only a limited number of training samples. This paper adopts another popular deep model, i.e., deep belief networks (DBNs), to deal with this problem. The DBNs allow unsupervised pretraining over unlabeled samples at first and then a supervised fine-tuning over labeled samples. But the usual pretraining and fine-tuning method would make many hidden units in the learned DBNs tend to behave very similarly or perform as “dead” (never responding) or “potential over-tolerant” (always responding) latent factors. These results could negatively affect description ability and thus classification performance of DBNs. To further improve DBN’s performance, this paper develops a new diversified DBN through regularizing pretraining and fine-tuning procedures by a diversity promoting prior over latent factors. Moreover, the regularized pretraining and fine-tuning can be efficiently implemented through usual recursive greedy and back-propagation learning framework. The experiments over real-world hyperspectral images demonstrated that the diversity promoting prior in both pretraining and fine-tuning procedure lead to the learned DBNs with more diverse latent factors, which directly make the diversified DBNs obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods.
A Tutorial on Automated Text Categorisation

The automated categorisation (or classification) of texts into topical categories has a long history, dating back at least to 1960. Until the late ’80s, the dominant approach to the problem involved knowledge-engineering automatic categorisers, i.e. manually building a set of rules encoding expert knowledge on how to classify documents. In the ’90s, with the booming production and availability of on-line documents, automated text categorisation has witnessed an increased and renewed interest. A newer paradigm based on machine learning has superseded the previous approach. Within this paradigm, a general inductive process automatically builds a classifier by “learning”, from a set of previously classified documents, the characteristics of one or more categories; the advantages are a very good effectiveness, a considerable savings in terms of expert manpower, and domain independence. In this tutorial we look at the main approaches that have been taken towards automatic text categorisation within the general machine learning paradigm. Issues of document indexing, classifier construction, and classifier evaluation, will be touched upon. 1 A definition of the text categorisation task Document categorisation (or classification) may be seen as the task of determining an assignment of a value from {0,1} to each entry of the decision matrix d1 . . . . . . dj . . . . . . dn c1 a11 . . . . . . a1j . . . . . . a1n . . . . . . . . . . . . . . . . . . . . . . . . ci ai1 . . . . . . aij . . . . . . ain . . . . . . . . . . . . . . . . . . . . . . . . cm am1 . . . . . . amj . . . . . . amn where C = {c1, . . . , cm} is a set of pre-defined categories, and D = {d1, . . . , dn} is a set of documents to be categorised (sometimes called “requests”). A value of 1 for aij is interpreted as a decision to file dj under ci, while a value of 0 is interpreted as a decision not to file dj under ci. Fundamental to the understanding of this task are two observations: • the categories are just symbolic labels. No additional knowledge of their “meaning” is available to help in the process of building the categoriser; in particular, this means that the “text” constituting the label (e.g. Sports in a news categorisation task) cannot be used; • the attribution of documents to categories should, in general, be attributed on the basis of the content of the documents, and not on the basis of metadata (e.g. publication date,
Purely functional lazy non-deterministic programming

Functional logic programming and probabilistic programming have demonstrated the broad benefits of combining laziness (non-strict evaluation with sharing of the results) with non-determinism. Yet these benefits are seldom enjoyed in functional programming, because the existing features for non-strictness, sharing, and non-determinism in functional languages are tricky to combine.
 We present a practical way to write purely functional lazy non-deterministic programs that are efficient and perspicuous. We achieve this goal by embedding the programs into existing languages (such as Haskell, SML, and OCaml) with high-quality implementations, by making choices lazily and representing data with non-deterministic components, by working with custom monadic data types and search strategies, and by providing equational laws for the programmer to reason about their code.
AnnexML: Approximate Nearest Neighbor Search for Extreme Multi-label Classification

Extreme multi-label classification methods have been widely used in Web-scale classification tasks such as Web page tagging and product recommendation. In this paper, we present a novel graph embedding method called "AnnexML". At the training step, AnnexML constructs a k-nearest neighbor graph of label vectors and attempts to reproduce the graph structure in the embedding space. The prediction is efficiently performed by using an approximate nearest neighbor search method that efficiently explores the learned k-nearest neighbor graph in the embedding space. We conducted evaluations on several large-scale real-world data sets and compared our method with recent state-of-the-art methods. Experimental results show that our AnnexML can significantly improve prediction accuracy, especially on data sets that have larger a label space. In addition, AnnexML improves the trade-off between prediction time and accuracy. At the same level of accuracy, the prediction time of AnnexML was up to 58 times faster than that of SLEEC, which is a state-of-the-art embedding-based method.