nils-herrmann/clean_scirepeval huggingface dataset:
Latent social structure in open source projects

Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational "cathedrals" are to be contrasted with the "bazaar-like" nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. Still, in large, complex, successful, OSS projects, we do expect that subcommunities will form spontaneously within the developer teams. Studying these subcommunities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could well hold important lessons for how commercial software teams might be organized. Building on known well-established techniques for detecting community structure in complex networks, we extract and study latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed spontaneously arise within these projects as the projects evolve. These subcommunities manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour.
Super-resolution from a single image

Methods for super-resolution can be broadly classified into two families of methods: (i) The classical multi-image super-resolution (combining images obtained at subpixel misalignments), and (ii) Example-Based super-resolution (learning correspondence between low and high resolution image patches from a database). In this paper we propose a unified framework for combining these two families of methods. We further show how this combined approach can be applied to obtain super resolution from as little as a single image (with no database or prior examples). Our approach is based on the observation that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales. Recurrence of patches within the same image scale (at subpixel misalignments) gives rise to the classical super-resolution, whereas recurrence of patches across different scales of the same image gives rise to example-based super-resolution. Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales.
Process Mining: Discovering Direct Successors in Process Logs

Workflow management technology requires the existence of explicit process models, i.e. a completely specified workflow design needs to be developed in order to enact a given workflow process. Such a workflow design is time consuming and often subjective and incomplete. We propose a learning method that uses the workflow log, which contains information about the process as it is actually being executed. In our method we will use a logistic regression model to discover the direct connections between events of a realistic not complete workflow log with noise. Experimental results are used to show the usefulness and limitations of the presented method.
Effective Gaussian mixture learning for video background subtraction

Adaptive Gaussian mixtures have been used for modeling nonstationary temporal distributions of pixels in video surveillance applications. However, a common problem for this approach is balancing between model convergence speed and stability. This paper proposes an effective scheme to improve the convergence rate without compromising model stability. This is achieved by replacing the global, static retention factor with an adaptive learning rate calculated for each Gaussian at every frame. Significant improvements are shown on both synthetic and real video data. Incorporating this algorithm into a statistical framework for background subtraction leads to an improved segmentation performance compared to a standard method.
Inverse texture synthesis

The quality and speed of most texture synthesis algorithms depend on a 2D input sample that is small and contains enough texture variations. However, little research exists on how to acquire such sample. For homogeneous patterns this can be achieved via manual cropping, but no adequate solution exists for inhomogeneous or globally varying textures, i.e. patterns that are local but not stationary, such as rusting over an iron statue with appearance conditioned on varying moisture levels.
 We present inverse texture synthesis to address this issue. Our inverse synthesis runs in the opposite direction with respect to traditional forward synthesis: given a large globally varying texture, our algorithm automatically produces a small texture compaction that best summarizes the original. This small compaction can be used to reconstruct the original texture or to re-synthesize new textures under user-supplied controls. More important, our technique allows real-time synthesis of globally varying textures on a GPU, where the texture memory is usually too small for large textures. We propose an optimization framework for inverse texture synthesis, ensuring that each input region is properly encoded in the output compaction. Our optimization process also automatically computes orientation fields for anisotropic textures containing both low- and high-frequency regions, a situation difficult to handle via existing techniques.
Biologically-inspired locomotion of a 2g hexapod robot

Here we present the design, modeling, and fabrication of a 2g mobile robot. By applying principles from biology and existing meso-scale fabrication techniques, a 5.7cm hexapod robot with sprawled posture has been created, and is capable of locomotion up to 4 body-lengths per second using the alternating tripod gait at 20Hz actuation frequency. Furthermore, this work proves the viability of a new mechanical linkage design, fabricated using the smart composite microstructure process, to provide desirable leg trajectories for successful ambulation at the insect-scale.
Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we propose a vehicular sensor-rich platform and learning algorithms for maneuver anticipation. For this purpose we equip a car with cameras, Global Positioning System (GPS), and a computing device to capture the driving context from both inside and outside of the car. In order to anticipate maneuvers, we propose a sensory-fusion deep learning architecture which jointly learns to anticipate and fuse multiple sensory streams. Our architecture consists of Recurrent Neural Networks (RNNs) that use Long Short-Term Memory (LSTM) units to capture long temporal dependencies. We propose a novel training procedure which allows the network to predict the future given only a partial temporal context. We introduce a diverse data set with 1180 miles of natural freeway and city driving, and show that we can anticipate maneuvers 3.5 seconds before they occur in realtime with a precision and recall of 90.5% and 87.4% respectively.
Spectral subband centroid features for speech recognition

Cepstral coefficients derived either through linear prediction (LP) analysis or from filter bank are perhaps the most commonly used features in currently available speech rec ognition systems. In this paper, we propose spectral subband centroids as new features and use them as supplement to cepstral features for speech rec ognition. We show that these features have properties similar to formant frequencies and they are quite robust to noise. Recognition results are reported in the paper justifying the usefulness of these features as supplementary features.
CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling

CRISPR-Cas9 is a versatile genome editing technology for studying the functions of genetic elements. To broadly enable the application of Cas9 in vivo, we established a Cre-dependent Cas9 knockin mouse. We demonstrated in vivo as well as ex vivo genome editing using adeno-associated virus (AAV)-, lentivirus-, or particle-mediated delivery of guide RNA in neurons, immune cells, and endothelial cells. Using these mice, we simultaneously modeled the dynamics of KRAS, p53, and LKB1, the top three significantly mutated genes in lung adenocarcinoma. Delivery of a single AAV vector in the lung generated loss-of-function mutations in p53 and Lkb1, as well as homology-directed repair-mediated Kras(G12D) mutations, leading to macroscopic tumors of adenocarcinoma pathology. Together, these results suggest that Cas9 mice empower a wide range of biological and disease modeling applications.
Trains of thought: generating information maps

When information is abundant, it becomes increasingly difficult to fit nuggets of knowledge into a single coherent picture. Complex stories spaghetti into branches, side stories, and intertwining narratives. In order to explore these stories, one needs a map to navigate unfamiliar territory. We propose a methodology for creating structured summaries of information, which we call metro maps. Our proposed algorithm generates a concise structured set of documents maximizing coverage of salient pieces of information. Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development. We first formalize characteristics of good maps and formulate their construction as an optimization problem. Then we provide efficient methods with theoretical guarantees for generating maps. Finally, we integrate user interaction into our framework, allowing users to alter the maps to better reflect their interests. Pilot user studies with a real-world dataset demonstrate that the method is able to produce maps which help users acquire knowledge efficiently.
The leaking battery: A privacy analysis of the HTML5 Battery Status API

We highlight privacy risks associated with the HTML5 Battery Status API. We put special focus on its implementation in the Firefox browser. Our study shows that websites can discover the capacity of users’ batteries by exploiting the high precision readouts provided by Firefox on Linux. The capacity of the battery, as well as its level, expose a fingerprintable surface that can be used to track web users in short time intervals. Our analysis shows that the risk is much higher for old or used batteries with reduced capacities, as the battery capacity may potentially serve as a tracking identifier. The fingerprintable surface of the API could be drastically reduced without any loss in the API’s functionality by reducing the precision of the readings. We propose minor modifications to Battery Status API and its implementation in the Firefox browser to address the privacy issues presented in the study. Our bug report for Firefox was accepted and a fix is deployed.
Integration and evaluation of intrusion detection for CoAP in smart city applications

The Constrained Application Protocol (CoAP) is a promising candidate for future smart city applications that run on resource-constrained devices. However, additional security means are mandatory to cope with the high security requirements of smart city applications. We present a framework to evaluate lightweight intrusion detection techniques for CoAP applications. This framework combines an OMNeT++ simulation with C/C++ application code that also runs on real hardware. As the result of our work, we used our framework to evaluate intrusion detection techniques for a smart public transport application that uses CoAP. Our first evaluations indicate that a hybrid IDS approach is a favorable choice for smart city applications.
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity

This paper presents our submissions for semantic textual similarity task in SemEval 2016. Based on several traditional features (i.e., string-based, corpus-based, machine translation similarity and alignment metrics), we leverage word embedding from macro (i.e., first get representation of sentence, then measure the similarity of sentence pair) and micro views (i.e., measure the similarity of word pairs separately) to boost performance. Due to the various domains of training data and test data, we adopt three different strategies: 1) U-SEVEN: an unsupervised model, which utilizes seven straight-forward metrics; 2) S1-All: using all available datasets; 3) S2: selecting the most similar training sets for each test set. Results on test sets show that the unified supervised model (i.e., S1-All) achieves the best averaged performance with a mean correlation of 75.07%.
The horseshoe estimator for sparse signals

This paper proposes a new approach to sparsity, called the horseshoe estimator, which arises from a prior based on multivariate-normal scale mixtures. We describe the estimator’s advantages over existing approaches, including its robustness, adaptivity to different sparsity patterns and analytical tractability. We prove two theorems: one that characterizes the horseshoe estimator’s tail robustness and the other that demonstrates a super-efficient rate of convergence to the correct estimate of the sampling density in sparse situations. Finally, using both real and simulated data, we show that the horseshoe estimator corresponds quite closely to the answers obtained by Bayesian model averaging under a point-mass mixture prior.
Entities as Topic Labels: Combining Entity Linking and Labeled LDA to Improve Topic Interpretability and Evaluability

Digital humanities scholars strongly need a corpus exploration method that provides topics easier to interpret than standard LDA topic models. To move towards this goal, here we propose a combination of two techniques, called Entity Linking and Labeled LDA. Our method identifies in an ontology a series of descriptive labels for each document in a corpus. Then it generates a specific topic for each label. Having a direct relation between topics and labels makes interpretation easier; using an ontology as background knowledge limits label ambiguity. As our topics are described with a limited number of clear-cut labels, they promote interpretability and support the quantitative evaluation of the obtained results. We illustrate the potential of the approach by applying it to three datasets, namely the transcription of speeches from the European Parliament fifth mandate, the Enron Corpus and the Hillary Clinton Email Dataset. While some of these resources have already been adopted by the natural language processing community, they still hold a large potential for humanities scholars, part of which could be exploited in studies that will adopt the fine-grained exploration method presented in this paper.
Emotion drives attention: detecting the snake in the grass.

Participants searched for discrepant fear-relevant pictures (snakes or spiders) in grid-pattern arrays of fear-irrelevant pictures belonging to the same category (flowers or mushrooms) and vice versa. Fear-relevant pictures were found more quickly than fear-irrelevant ones. Fear-relevant, but not fear-irrelevant, search was unaffected by the location of the target in the display and by the number of distractors, which suggests parallel search for fear-relevant targets and serial search for fear-irrelevant targets. Participants specifically fearful of snakes but not spiders (or vice versa) showed facilitated search for the feared objects but did not differ from controls in search for nonfeared fear-relevant or fear-irrelevant, targets. Thus, evolutionary relevant threatening stimuli were effective in capturing attention, and this effect was further facilitated if the stimulus was emotionally provocative.
VideoQA: question answering on news video

When querying a news video archive, the users are interested in retrieving precise answers in the form of a summary that best answers the query. However, current video retrieval systems, including the search engines on the web, are designed to retrieve documents instead of precise answers. This research explores the use of question answering (QA) techniques to support personalized news video retrieval. Users interact with our system, VideoQA, using short natural language questions with implicit constraints on contents, context, duration, and genre of expected videos. VideoQA returns short precise news video summaries as answers. The main contributions of this research are: (a) the extension of QA technology to support QA in news video; and (b) the use of multi-modal features, including visual, audio, textual, and external resources, to help correct speech recognition errors and to perform precise question answering. The system has been tested on 7 days of news video and has been found to be effective.
Composite retrieval of heterogeneous web search

Traditional search systems generally present a ranked list of documents as answers to user queries. In aggregated search systems, results from different and increasingly diverse verticals (image, video, news, etc.) are returned to users. For instance, many such search engines return to users both images and web documents as answers to the query "flower". Aggregated search has become a very popular paradigm. In this paper, we go one step further and study a different search paradigm: composite retrieval. Rather than returning and merging results from different verticals, as is the case with aggregated search, we propose to return to users a set of "bundles", where a bundle is composed of "cohesive" results from several verticals. For example, for the query "London Olympic", one bundle per sport could be returned, each containing results extracted from news, videos, images, or Wikipedia. Composite retrieval can promote exploratory search in a way that helps users understand the diversity of results available for a specific query and decide what to explore in more detail. In this paper, we propose and evaluate a variety of approaches to construct bundles that are relevant, cohesive and diverse. Compared with three baselines (traditional "general web only" ranking, federated search ranking and aggregated search), our evaluation results demonstrate significant performance improvement for a highly heterogeneous web collection.
Approximation Algorithms for Reducing the Spectral Radius to Control Epidemic Spread

The largest eigenvalue of the adjacency matrix of a network (referred to as the spectral radius) is an important metric in its own right. Further, for several models of epidemic spread on networks (e.g., the ‘flu-like’ SIS model), it has been shown that an epidemic dies out quickly if the spectral radius of the graph is below a certain threshold that depends on the model parameters. This motivates a strategy to control epidemic spread by reducing the spectral radius of the underlying network. In this paper, we develop a suite of provable approximation algorithms for reducing the spectral radius by removing the minimum cost set of edges (modeling quarantining) or nodes (modeling vaccinations), with different time and quality tradeoffs. Our main algorithm, GreedyWalk, is based on the idea of hitting closed walks of a given length, and gives an O(log n)-approximation, where n denotes the number of nodes; it also performs much better in practice compared to all prior heuristics proposed for this problem. We further present a novel sparsification method to improve its running time. In addition, we give a new primal-dual based algorithm with an even better approximation guarantee (O(log n)), albeit with slower running time. We also give lower bounds on the worst-case performance of some of the popular heuristics. Finally we demonstrate the applicability of our algorithms and the properties of our solutions via extensive experiments on multiple synthetic and real networks.
An empirical study on principles and practices of continuous delivery and deployment

Despite substantial recent research activity related to continuous delivery and deployment (CD), there has not yet been a systematic, empirical study on how the practices often associated with continuous deployment have found their way into the broader software industry. This raises the question to what extent our knowledge of the area is dominated by the peculiarities of a small number of industrial leaders, such as Facebook. To address this issue, we conducted a mixed-method empirical study, consisting of a pre-study on literature, qualitative interviews with 20 software developers or release engineers with heterogeneous backgrounds, and a Web-based quantitative survey that attracted 187 complete responses. A major trend in the results of our study is that architectural issues are currently one of the main barriers for CD adoption. Further, feature toggles as an implementation technique for partial rollouts lead to unwanted complexity, and require research on better abstractions and modelling techniques for runtime variability. Finally, we conclude that practitioners are in need for more principled approaches to release decision making, e.g., which features to conduct A/B tests on, or which metrics to evaluate.
Wearable medical devices for tele-home healthcare

The world's ageing population and prevalence of chronic diseases have lead to high demand for tele-home healthcare, in which vital-signs monitoring is essential. An overview of state-of-art wearable technologies for remote patient-monitoring is presented, followed by case studies on a cuffless blood pressure meter, ring-type heart rate monitor, and Bluetooth/spl trade/-based ECG monitor. Aim of our project is to develop a tele-home healthcare system which utilizes wearable devices, wireless communication technologies, and multisensor data fusion methods. As an important part of this system, a cuffless BP meter has been developed and tested on 30 subjects in a total of 71 trials over a period of five months. Preliminary results show a mean error (ME) of 1.82 mmHg and standard deviation of error (SDE) of 7.62 mmHg in systolic pressure; while ME and SDE in diastolic pressure are 0.45 mmHg and 5.27 mmHg, respectively.
Analysis and visualisation of movement: an interdisciplinary review

The processes that cause and influence movement are one of the main points of enquiry in movement ecology. However, ecology is not the only discipline interested in movement: a number of information sciences are specialising in analysis and visualisation of movement data. The recent explosion in availability and complexity of movement data has resulted in a call in ecology for new appropriate methods that would be able to take full advantage of the increasingly complex and growing data volume. One way in which this could be done is to form interdisciplinary collaborations between ecologists and experts from information sciences that analyse movement. In this paper we present an overview of new movement analysis and visualisation methodologies resulting from such an interdisciplinary research network: the European COST Action "MOVE - Knowledge Discovery from Moving Objects" (http://www.move-cost.info). This international network evolved over four years and brought together some 140 researchers from different disciplines: those that collect movement data (out of which the movement ecology was the largest represented group) and those that specialise in developing methods for analysis and visualisation of such data (represented in MOVE by computational geometry, geographic information science, visualisation and visual analytics). We present MOVE achievements and at the same time put them in ecological context by exploring relevant ecological themes to which MOVE studies do or potentially could contribute.