nils-herrmann/clean_scirepeval huggingface dataset:
Duplicate bug report detection with a combination of information retrieval and topic modeling

Detecting duplicate bug reports helps reduce triaging efforts and save time for developers in fixing the same issues. Among several automated detection approaches, text-based information retrieval (IR) approaches have been shown to outperform others in term of both accuracy and time efficiency. However, those IR-based approaches do not detect well the duplicate reports on the same technical issues written in different descriptive terms. 
 This paper introduces DBTM, a duplicate bug report detection approach that takes advantage of both IR-based features and topic-based features. DBTM models a bug report as a textual document describing certain technical issue(s), and models duplicate bug reports as the ones about the same technical issue(s). Trained with historical data including identified duplicate reports, it is able to learn the sets of different terms describing the same technical issues and to detect other not-yet-identified duplicate ones. Our empirical evaluation on real-world systems shows that DBTM improves the state-of-the-art approaches by up to 20% in accuracy.
The EigenRumor Algorithm for Ranking Blogs

The advent of easy to use blogging tools is increasing the number of bloggers leading to more diversity in the quality blogspace. The blog search technologies that help users to find “good” blogs are thus more and more important. This paper proposes a new algorithm called “EigenRumor” that scores each blog entry by weighting the hub and authority scores of the bloggers based on eigenvector calculations. This algorithm enables a higher score to be assigned to the blog entries submitted by a good blogger but not yet linked to by any other blogs based on acceptance of the blogger's prior work. General Terms Algorithms, Management, Experimentation
Mind Games: Game Engines as an Architecture for Intuitive Physics

We explore the hypothesis that many intuitive physical inferences are based on a mental physics engine that is analogous in many ways to the machine physics engines used in building interactive video games. We describe the key features of game physics engines and their parallels in human mental representation, focusing especially on the intuitive physics of young infants where the hypothesis helps to unify many classic and otherwise puzzling phenomena, and may provide the basis for a computational account of how the physical knowledge of infants develops. This hypothesis also explains several 'physics illusions', and helps to inform the development of artificial intelligence (AI) systems with more human-like common sense.
MobiFish: A lightweight anti-phishing scheme for mobile phones

Recent years have witnessed the increasing threat of phishing attacks on mobile platforms. In fact, mobile phishing is more dangerous due to the limitations of mobile phones and mobile user habits. Existing schemes designed for phishing attacks on computers/laptops cannot effectively address phishing attacks on mobile devices. This paper presents MobiFish, a novel automated lightweight anti-phishing scheme for mobile platforms. MobiFish verifies the validity of web pages and applications (Apps) by comparing the actual identity to the identity claimed by the web pages and Apps. MobiFish has been implemented on the Nexus 4 smartphone running the Android 4.2 operating system. We experimentally evaluate the performance of MobiFish with 100 phishing URLs and corresponding legitimate URLs, as well as fake Facebook Apps. The result shows that MobiFish is very effective in detecting phishing attacks on mobile phones.
Methods And Metrics For Measuring The Success Of Enterprise Social Software - What We Can Learn From Practice And Vice Versa

With Enterprise Social Software (ESS) being increasingly used for internal communication and collaboration, an important question is how their success can be measured. In this paper we contribute to answering this question by investigating which methods and metrics organizations are currently applying for measuring ESS success. To do so, we have conducted 26 interviews with persons responsible for the deployment and use of ESS in their company. We found that measuring ESS success in the studied companies mainly focuses on the analysis of usage. We attribute this to the fact that ESS is primarily characterized by an abundance of user-generated content that can be easily analyzed. At the same time, companies still face difficulties in the evaluation of the business value of the platforms. We attribute this to some characteristics of ESS that we will discuss in this paper. Our study is supposed to provide a comprehensive overview of the methods and metrics applied in practice to measure ESS success.
Drawing on Air: Input Techniques for Controlled 3D Line Illustration

We present drawing on air, a haptic-aided input technique for drawing controlled 3D curves through space. Drawing on air addresses a control problem with current 3D modeling approaches based on sweeping movement of the hands through the air. Although artists praise the immediacy and intuitiveness of these systems, a lack of control makes it nearly impossible to create 3D forms beyond quick design sketches or gesture drawings. Drawing on air introduces two new strategies for more controlled 3D drawing: one-handed drag drawing and two-handed tape drawing. Both approaches have advantages for drawing certain types of curves. We describe a tangent preserving method for transitioning between the two techniques while drawing. Haptic-aided redrawing and line weight adjustment while drawing are also supported in both approaches. In a quantitative user study evaluation by illustrators, the one and two-handed techniques performed at roughly the same level and both significantly outperformed freehand drawing and freehand drawing augmented with a haptic friction effect. We present the design and results of this experiment, as well as user feedback from artists and 3D models created in a style of line illustration for challenging artistic and scientific subjects.
Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach

Cancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer. To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.
A Practical Approach to Calculating Luminance Contrast on a CRT

Luminance contrast is the basis of text legibility, and maintaining luminance contrast is essential for any color selection algorithm. In principle, it can be calculated precisely on a sufficiently well-calibrated display surface, but calibration is very expensive. Consequently, most current systems deal with contrast using heuristics. However, the usual CRT setup puts the display surface into a state that is relatively predictable. Luminance values can be estimated based on this state, and these luminance values have been used to calculate contrast using the Michelson definition. This paper proposes a method for determining the contrast of colored areas displayed on a CRT. It uses a contrast metric that is in wide use in visual psychophysics and shows that the metric can be approximated reasonably without display measurement, as long as it is possible to assume that the CRT has been adjusted according to usual CRT setup standards.
Platform-as-a-Service Architecture for Real-Time Quality of Service Management in Clouds

Cloud computing offers the potential to dramatically reduce the cost of software services through the commoditization of information technology assets and on-demand usage patterns. However, the complexity of determining resource provision policies for applications in such complex environments introduces significant inefficiencies and has driven the emergence of a new class of infrastructure called Platform-as-a-Service (PaaS). In this paper, we present a novel PaaS architecture being developed in the EU IST IRMOS project targeting real-time Quality of Service (QoS) guarantees for online interactive multimedia applications. The architecture considers the full service lifecycle including service engineering, service level agreement design, provisioning and monitoring. QoS parameters at both application and infrastructure levels are given specific attention as the basis for provisioning policies in the context of temporal constraints. The generic applicability of the architecture is being verified and validated through implemented scenarios from three important application sectors (film post-production, virtual augmented reality for engineering design, collaborative e-Learning in virtual worlds).
Highly adaptable MEMS-based display with wide projection angle

We demonstrate a MEMS-based display system with a very wide projection angle of up to 120deg. The system utilizes a gimbal-less two-axis micromirror scanner for high-speed laser beam-steering in both axes. The optical scan angle of the micromirrors is up to 16deg on each axis. A custom-designed fisheye lens is utilized to magnify scan angles. The system can display a variety of vector graphics as well as multiframe animations at arbitrary refresh rates, up to the overall bandwidth limit of the MEMS device. It is also possible to operate the scanners in point-to-point scanning, resonant and/or rastering modes. The system is highly adaptable for projection on a variety of surfaces including projection on specially coated transparent surfaces  The size of the displayed area, refresh rate, display mode (vector graphic or image raster,) and many other parameters are all adjustable by the user. The small size of the MEMS devices and lens as well as the ultra-low power consumption of the MEMS devices, in the milliwatt range, makes the overall system highly portable and miniaturizable.
Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization

Understanding large-scale document collections in an efficient manner is an important problem. Usually, document data are associated with other information (e.g., an author's gender, age, and location) and their links to other entities (e.g., co-authorship and citation networks). For the analysis of such data, we often have to reveal common as well as discriminative characteristics of documents with respect to their associated information, e.g., male- vs. female-authored documents, old vs. new documents, etc. To address such needs, this paper presents a novel topic modeling method based on joint nonnegative matrix factorization, which simultaneously discovers common as well as discriminative topics given multiple document sets. Our approach is based on a block-coordinate descent framework and is capable of utilizing only the most representative, thus meaningful, keywords in each topic through a novel pseudo-deflation approach. We perform both quantitative and qualitative evaluations using synthetic as well as real-world document data sets such as research paper collections and nonprofit micro-finance data. We show our method has a great potential for providing in-depth analyses by clearly identifying common and discriminative topics among multiple document sets.
Online Img2UML Repository: An Online Repository for UML Models

The Img2UML repository is a repository of UML mode ls. A huge amount of UML models is available on the Internet – mostly in the form of images. This repository aims to offer these UML class diagram as a searchable XMI Database. The information that is in the XMI fi les is stored in the repository database. This repository will be useful for res earch as the first corpus of UML models. This repository will provide a good pla ce for researchers and students to study and analyze UML models. This improve s UML studying in research, education, and industry. In this paper, we outline the Img2UML repository, illustrate some of the research made possible , and discuss future plans.
Deep learning in very high resolution remote sensing image information mining communication concept

This paper presents the image information mining based on a communication channel concept. The feature extraction algorithms encode the image, while an analysis of topic discovery will decode and send its content to the user in the shape of a semantic map. We consider this approach for a real meaning based semantic annotation of very high resolution remote sensing images. The scene content is described using a multi-level hierarchical information representation. Feature hierarchies are discovered considering that higher levels are formed by combining features from lower level. Such a level to level mapping defines our methodology as a deep learning process. The whole analysis can be divided in two major learning steps. The first one regards the Bayesian inference to extract objects and assign basic semantic to the image. The second step models the spatial interactions between the scene objects based on Latent Dirichlet Allocation, performing a high level semantic annotation. We used a WorldView2 image to exemplify the processing results.
Active Learning for Pipeline Models

For many machine learning solutions to complex applications, there are significant performance advantages to decomposing the overall task into several simpler sequential stages, commonly referred to as a pipeline model. Typically, such scenarios are also characterized by high sample complexity, motivating the study of active learning for these situations. While most active learning research examines single predictions, we extend such work to applications which utilize pipelined predictions. Specifically, we present an adaptive strategy for combining local active learning strategies into one that minimizes the annotation requirements for the overall task. Empirical results for a three-stage entity and relation extraction system demonstrate a significant reduction in supervised data requirements when using the proposed method.
Low Side-Lobe Substrate-Integrated-Waveguide Antenna Array Using Broadband Unequal Feeding Network for Millimeter-Wave Handset Device

A low side-lobe substrate-integrated-waveguide (SIW) antenna array is presented at the 28-GHz band using broadband unequal feeding network for millimeter-wave (mm-wave) handset devices. The ground-plane size of the proposed antenna is fixed to half of the size of the Samsung Galaxy Note 4. The antenna array has been implemented with a multilayer structure created by stacking three substrates and a copper plate. An 8-way SIW feeding network with broadband 4-stage T-junction dividers and a cavity-backed antenna are investigated to obtain broadband performance. The proposed unequal T-junction dividers with phase compensation are introduced and designed for various output ratios. Applying Taylor beam-pattern synthesis in the 8-way SIW feeding network, low side-lobe performance is achieved. The measured result of the fabricated antenna has 2.3 GHz bandwidth within S11 <; - 10 dB. The fabricated antenna can be performed with a gain up to 13.97 dBi with a low cross-polarization and symmetrical fan beam radiation patterns with low side-lobe levels. Most of the measured results are validated with the simulated results. The proposed antenna array provides low cost, broadband performance, and good radiation performances with low side-lobe levels for mm-wave handset devices.
Information Extraction: Beyond Document Retrieval

In this paper we give a synoptic view of the growth text processing technology of information extraction (IE) whose function is to extract information about a pre-specified set of entities, relations or events from natural language textsand to record this information in structured representations called templates. Here we describe the nature of the IE task, review the history of the area from its origins in AI work in the1960's and 70's till thepresent, discuss the techniques being used to carry out thetask, describeapplication areaswhereIE systemsareor areabout to beat work, and conclude with a discussion of the challenges facing the area. What emerges is a pictureof an exciting new text processing technology with ahost of new applications, both on its own and in conjunction with other technologies, such as information retrieval, machine translation and data mining.
Coplanar Waveguides Loaded with S-Shaped Split-Ring Resonators: Modeling and Application to Compact Microwave Filters

This letter studies the transmission characteristics of coplanar waveguides (CPWs) loaded with single-layer S-shaped split-ring resonators (S-SRRs) for the first time. Two structures are analyzed: 1) a CPW simply loaded with an S-SRR, and 2) a CPW loaded with an S-SRR and a series gap. The former exhibits a stopband functionality related to the resonance of the S-SRR excited by the contra-directional magnetic fluxes through the two connected resonator loops; the latter is useful for the implementation of compact bandpass filters. In both cases, a lumped-element equivalent circuit model is proposed with an unequivocal physical interpretation of the circuit elements. These circuits are then validated by comparing the circuit response with extracted parameters to full-wave electromagnetic simulations. The last part of the letter illustrates application of the S-SRR/gap-loaded CPW unit cell to the design of a bandpass filter. The resulting filter is very compact and exhibits competitive performance.
Not All Contexts Are Created Equal: Better Word Representations with Variable Attention

We introduce an extension to the bag-ofwords model for learning words representations that take into account both syntactic and semantic properties within language. This is done by employing an attention model that finds within the contextual words, the words that are relevant for each prediction. The general intuition of our model is that some words are only relevant for predicting local context (e.g. function words), while other words are more suited for determining global context, such as the topic of the document. Experiments performed on both semantically and syntactically oriented tasks show gains using our model over the existing bag of words model. Furthermore, compared to other more sophisticated models, our model scales better as we increase the size of the context of the model.
In vivo evidence of structural brain asymmetry in musicians.

Certain human talents, such as musical ability, have been associated with left-right differences in brain structure and function. In vivo magnetic resonance morphometry of the brain in musicians was used to measure the anatomical asymmetry of the planum temporale, a brain area containing auditory association cortex and previously shown to be a marker of structural and functional asymmetry. Musicians with perfect pitch revealed stronger leftward planum temporale asymmetry than nonmusicians or musicians without perfect pitch. The results indicate that outstanding musical ability is associated with increased leftward asymmetry of cortex subserving music-related functions.
SemRank: ranking complex relationship search results on the semantic web

While the idea that querying mechanisms for complex relationships (otherwise known as Semantic Associations) should be integral to Semantic Web search technologies has recently gained some ground, the issue of how search results will be ranked remains largely unaddressed. Since it is expected that the number of relationships between entities in a knowledge base will be much larger than the number of entities themselves, the likelihood that Semantic Association searches would result in an overwhelming number of results for users is increased, therefore elevating the need for appropriate ranking schemes. Furthermore, it is unlikely that ranking schemes for ranking entities (documents, resources, etc.) may be applied to complex structures such as Semantic Associations.In this paper, we present an approach that ranks results based on how predictable a result might be for users. It is based on a relevance model SemRank, which is a rich blend of semantic and information-theoretic techniques with heuristics that supports the novel idea of modulative searches, where users may vary their search modes to effect changes in the ordering of results depending on their need. We also present the infrastructure used in the SSARK system to support the computation of SemRank values for resulting Semantic Associations and their ordering.
The Extent and Consequences of P-Hacking in Science

A focus on novel, confirmatory, and statistically significant results leads to substantial bias in the scientific literature. One type of bias, known as "p-hacking," occurs when researchers collect or select data or statistical analyses until nonsignificant results become significant. Here, we use text-mining to demonstrate that p-hacking is widespread throughout science. We then illustrate how one can test for p-hacking when performing a meta-analysis and show that, while p-hacking is probably common, its effect seems to be weak relative to the real effect sizes being measured. This result suggests that p-hacking probably does not drastically alter scientific consensuses drawn from meta-analyses.
MIXED MNL MODELS FOR DISCRETE RESPONSE

This paper considers mixed, or random coeff icients, multinomial logit (MMNL) models for discrete response, and establishes the following results: Under mild regularity conditions, any discrete choice model derived from random utilit y maximization has choice probabiliti es that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairl y eff icient. The adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately defined artificial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a flexible and computationally practical approach to discrete response analysis.  Both authors are at the Department of Economics, University of Cali fornia, Berkeley CA 94720-3880. Correspondence should be directed to mcfadden@econ.berkeley.edu. We are indebted to the E. Morris Cox fund for research support, and to Moshe Ben-Akiva, David Brownstone, Denis Bolduc, Andre de Palma, and Paul Ruud for useful comments. This paper was first presented at the University of Paris X in June 1997.
Api hyperlinking via structural overlap

This paper presents a tool Altair that automatically generates API function cross-references, which emphasizes reliable structural measures and does not depend on specific client code. Altair ranks related API functions for a given query according to pair-wise overlap, i.e., how they share state, and clusters tightly related ones into meaningful modules.
 Experiments against several popular C software packages show that Altair recommends related API functions for a given query with remarkably more precise and complete results than previous tools, that it can extract modules from moderate-sized software (e.g., Apache with 1000+ functions) at high precision and recall rates (e.g., both exceeding 70% for two modules in Apache), and that the computation can finish within a few seconds.