nils-herrmann/clean_scirepeval huggingface dataset:
An information-theoretic approach to quantitative association rule mining

Quantitative association rule (QAR) mining has been recognized an influential research problem over the last decade due to the popularity of quantitative databases and the usefulness of association rules in real life. Unlike boolean association rules (BARs), which only consider boolean attributes, QARs consist of quantitative attributes which contain much richer information than the boolean attributes. However, the combination of these quantitative attributes and their value intervals always gives rise to the generation of an explosively large number of itemsets, thereby severely degrading the mining efficiency. In this paper, we propose an information-theoretic approach to avoid unrewarding combinations of both the attributes and their value intervals being generated in the mining process. We study the mutual information between the attributes in a quantitative database and devise a normalization on the mutual information to make it applicable in the context of QAR mining. To indicate the strong informative relationships among the attributes, we construct a mutual information graph (MI graph), whose edges are attribute pairs that have normalized mutual information no less than a predefined information threshold. We find that the cliques in the MI graph represent a majority of the frequent itemsets. We also show that frequent itemsets that do not form a clique in the MI graph are those whose attributes are not informatively correlated to each other. By utilizing the cliques in the MI graph, we devise an efficient algorithm that significantly reduces the number of value intervals of the attribute sets to be joined during the mining process. Extensive experiments show that our algorithm speeds up the mining process by up to two orders of magnitude. Most importantly, we are able to obtain most of the high-confidence QARs, whereas the QARs that are not returned by MIC are shown to be less interesting.
A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.
MEDIA CONSUMPTION AND PUBLIC ATTITUDES TOWARD CRIME AND JUSTICE : THE RELATIONSHIP BETWEEN FEAR OF CRIME , PUNITIVE ATTITUDES , AND PERCEIVED POLICE

Public knowledge of crime and justice is largely derived from the media. This paper examines the influence of media consumption on fear of crime, punitive attitudes and perceived police effectiveness. This research contributes to the literature by expanding knowledge on the relationship between fear of crime and media consumption. This study also contributes to limited research on the media’s influence on punitive attitudes, while providing a much-needed analysis of the relationship between media consumption and satisfaction with the police. Employing OLS regression, the results indicate that respondents who are regular viewers of crime drama are more likely to fear crime. However, the relationship is weak. Furthermore, the results indicate that gender, education, income, age, perceived neighborhood problems and police effectiveness are statistically related to fear of crime. In addition, fear of crime, income, marital status, race, and education are statistically related to punitive attitudes. Finally, age, fear of crime, race, and perceived neighborhood problems are statistically related to perceived police effectiveness.
The perceptual evaluation of information systems using the construct of user satisfaction: case study of a large french group

This research work concerns the perceptual evaluation of the performance of information systems (IS) and more particularly, the construct of user satisfaction. Faced with the difficulty of obtaining objective measures for the success of IS, user satisfaction appeared as a substitutive measure of IS performance (DeLone & McLean, 1992). Some researchers have indeed shown that the evaluation of an IS could not happen without an analysis of the feelings and perceptions of individuals who make use of it. Consequently, the concept of satisfaction has been considered as a guarantee of the performance of an IS. Also it has become necessary to ponder the drivers of user satisfaction. The analysis of models and measurement tools for satisfaction as well as the adoption of a contingency perspective has allowed the description of principal dimensions that have a direct or less direct impact on user perceptions
 The case study of a large French group, carried out through an interpretativist approach conducted by way of 41 semi-structured interviews, allowed the conceptualization of the problematique of perceptual evaluation of IS in a particular field study. This study led us to confirm the impact of certain factors (such as perceived usefulness, participation, the quality of relations with the IS Function and its resources and also the fit of IS with user needs). On the contrary, other dimensions regarded as fundamental do not receive any consideration or see their influence nuanced in the case studied (the properties of IS, the ease of use, the quality of information). Lastly, this study has allowed for the identification of the influence of certain contingency and contextual variables on user satisfaction and, above all, for the description of the importance of interactions between the IS Function and the users
A Study of MatchPyramid Models on Ad-hoc Retrieval

Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models. CCS Concepts •Information systems→ Retrieval models and ranking;
A Multi-tiered Recommender System Architecture for Supporting E-Commerce

Nowadays, many e-Commerce tools support customers with automatic recommendations. Many of them are centralized and lack in ef ciency and scalability, while other ones are distributed and require a computational overhead excessive for many devices. Moreover, all the past proposals are not open and do not allow new personalized terms to be introduced into the domain ontology. In this paper, we present a distributed recommender, based on a multi-tiered agent system, trying to face the issues outlined above. The proposed system is able to generate very effective suggestions without a too onerous computational task. We show that our system introduces signi cant advantages in terms of openess, privacy and security.
Learning Hypergraph-regularized Attribute Predictors

We present a novel attribute learning framework named Hypergraph-based Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the attribute relations in the data. Then the attribute prediction problem is casted as a regularized hypergraph cut problem, in which a collection of attribute projections is jointly learnt from the feature space to a hypergraph embedding space aligned with the attributes. The learned projections directly act as attribute classifiers (linear and kernelized). This formulation leads to a very efficient approach. By considering our model as a multi-graph cut task, our framework can flexibly incorporate other available information, in particular class label. We apply our approach to attribute prediction, Zero-shot and N-shot learning tasks. The results on AWA, USAA and CUB databases demonstrate the value of our methods in comparison with the state-of-the-art approaches.
Covariance-Based Descriptors for Efficient 3D Shape Matching, Retrieval, and Classification

State-of-the-art 3D shape classification and retrieval algorithms, hereinafter referred to as shape analysis, are often based on comparing signatures or descriptors that capture the main geometric and topological properties of 3D objects. None of the existing descriptors, however, achieve best performance on all shape classes. In this article, we explore, for the first time, the usage of covariance matrices of descriptors, instead of the descriptors themselves, in 3D shape analysis. Unlike histogram -based techniques, covariance-based 3D shape analysis enables the fusion and encoding of different types of features and modalities into a compact representation. Covariance matrices, however, are elements of the non-linear manifold of symmetric positive definite (SPD) matrices and thus \BBL2 metrics are not suitable for their comparison and clustering. In this article, we study geodesic distances on the Riemannian manifold of SPD matrices and use them as metrics for 3D shape matching and recognition. We then: (1) introduce the concepts of bag of covariance (BoC) matrices and spatially-sensitive BoC as a generalization to the Riemannian manifold of SPD matrices of the traditional bag of features framework, and (2) generalize the standard kernel methods for supervised classification of 3D shapes to the space of covariance matrices. We evaluate the performance of the proposed BoC matrices framework and covariance -based kernel methods and demonstrate their superiority compared to their descriptor-based counterparts in various 3D shape matching, retrieval, and classification setups.
HelpingHand: example-based stroke stylization

Digital painters commonly use a tablet and stylus to drive software like Adobe Photoshop. A high quality stylus with 6 degrees of freedom (DOFs: 2D position, pressure, 2D tilt, and 1D rotation) coupled to a virtual brush simulation engine allows skilled users to produce expressive strokes in their own style. However, such devices are difficult for novices to control, and many people draw with less expensive (lower DOF) input devices. This paper presents a data-driven approach for synthesizing the 6D hand gesture data for users of low-quality input devices. Offline, we collect a library of strokes with 6D data created by trained artists. Online, given a query stroke as a series of 2D positions, we synthesize the 4D hand pose data at each sample based on samples from the library that locally match the query. This framework optionally can also modify the stroke trajectory to match characteristic shapes in the style of the library. Our algorithm outputs a 6D trajectory that can be fed into any virtual brush stroke engine to make expressive strokes for novices or users of limited hardware.
Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques

1.  The biological immune system is a robust, complex, adaptive system that defends the body from foreign pathogens. It is able to categorize all cells (or molecules) within the body as self-cells or non-self cells. It does this with the help of a distributed task force that has the intelligence to take action from a local and also a global perspective using its network of chemical
Graph Cut Based Automatic Lung Boundary Detection in Chest Radiographs

The National Library of Medicine (NLM) is developing a digital chest x-ray (CXR) screening system for deployment in resource constrained communities. An important first step in the analysis of digital CXRs is the automatic detection of the lung regions. In this paper, we present a graph cut based robust lung segmentation method that detects the lungs with high accuracy. The method consists of two stages: (i) average lung shape model calculation, and (ii) lung boundary detection based on graph cut. Preliminary results on public chest x-rays demonstrate the robustness of the method.
Striatal activation during acquisition of a cognitive skill.

The striatum is thought to play an essential role in the acquisition of a wide range of motor, perceptual, and cognitive skills, but neuroimaging has not yet demonstrated striatal activation during nonmotor skill learning. Functional magnetic resonance imaging was performed while participants learned probabilistic classification, a cognitive task known to rely on procedural memory early in learning and declarative memory later in learning. Multiple brain regions were active during probabilistic classification compared with a perceptual-motor control task, including bilateral frontal cortices, occipital cortex, and the right caudate nucleus in the striatum. The left hippocampus was less active bilaterally during probabilistic classification than during the control task, and the time course of this hippocampal deactivation paralleled the expected involvement of medial temporal structures based on behavioral studies of amnesic patients. Findings provide initial evidence for the role of frontostriatal systems in normal cognitive skill learning.
What Drives a Successful MOOC? An Empirical Examination of Criteria to Assure Design Quality of MOOCs

Massive Open Online Courses (MOOCs) have gained a lot of attention in the last years as a new technology enhanced learning (TEL) approach in higher education. MOOCs provide more educational opportunities to a massive number of learners to attend free online courses around the globe. Discussions around MOOCs have been focusing on the potential, social, institutional, technological, relevance, and marketing issues and less on the quality design of MOOC environments. Several studies have reported a high drop-out rate in average of 95% of course participants and other pedagogical problems concerning assessment and feedback. Thus, the quality of MOOCs design is worth additional investigation. Although several studies identified a large set of criteria to the successful design of TEL systems in general, not all of them can be used in the MOOC context, due to some unique features of MOOCs. This study is a first step towards identifying specific criteria that need to be considered when designing and implementing MOOCs. The results of this empirical study are based on a large survey targeting learners as well as professors, both with MOOC experience. As a result, we identified and rated 74 indicators classified into our two main dimensions of pedagogical and technological criteria distributed over six categories. From these, the learning analytics and assessment categories were found to be the key features for effective MOOCs.
Liberal Event Extraction and Event Schema Induction

We propose a brand new “Liberal” Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g.,  and distributional semantics to detect and represent event structures and adopt a joint typing framework to simultaneously extract event types and argument roles and discover an event schema. Experiments on general and specific domains demonstrate that this framework can construct high-quality schemas with many event and argument role types, covering a high proportion of event types and argument roles in manually defined schemas. We show that extraction performance using discovered schemas is comparable to supervised models trained from a large amount of data labeled according to predefined event types. The extraction quality of new event types is also promising.
Silicon Photonic Circuits: On-CMOS Integration, Fiber Optical Coupling, and Packaging

Silicon photonics is a new technology that should at least enable electronics and optics to be integrated on the same optoelectronic circuit chip, leading to the production of low-cost devices on silicon wafers by using standard processes from the microelectronics industry. In order to achieve real-low-cost devices, some challenges need to be taken up concerning the integration technological process of optics with electronics and the packaging of the chip. In this paper, we review recent progress in the packaging of silicon photonic circuits from on-CMOS wafer-level integration to the single-chip package and input/output interconnects. We focus on optical fiber-coupling structures comparing edge and surface couplers. In the following, we detail optical alignment tolerances for both coupling architecture, discussing advantages and drawbacks from the packaging process point of view. Finally, we describe some achievements involving advanced-packaging techniques.
How to Make Causal Inferences Using Texts

New text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories of interest from large collections of text. We introduce a conceptual framework for making causal inferences with discovered measures as a treatment or outcome. Our framework enables researchers to discover high-dimensional textual interventions and estimate the ways that observed treatments affect text-based outcomes. We argue that nearly all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation. But estimating this latent representation, we show, creates new risks: we may introduce an identification problem or overfit. To address these risks we describe a split-sample framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic response. Our work provides a rigorous foundation for textbased causal inferences. ∗We thank Edo Airoldi, Peter Aronow, Matt Blackwell, Sarah Bouchat, Chris Felton, Mark Handcock, Erin Hartman, Rebecca Johnson, Gary King, Ian Lundberg, Rich Nielsen, Thomas Richardson, Matt Salganik, Melissa Sands, Fredrik Sävje, Arthur Spirling, Alex Tahk, Endre Tvinnereim, Hannah Waight, Hanna Wallach, Simone Zhang and numerous seminar participants for useful discussions about making causal inference with texts. We also thank Dustin Tingley for early conversations about potential SUTVA concerns with respect to STM and sequential experiments as a possible way to combat it. In addition, we thank a National Science Foundation grant under the Resource Implementations for Data Intensive Research program. †Ph.D. Candidate, Department of Politics, Princeton University, negami@princeton.edu ‡Ph.D. Candidate, Graduate School of Business, Stanford University, cjfong@stanford.edu §Associate Professor, Department of Political Science, University of Chicago, JustinGrimmer.org, grimmer@uchicago.edu. ¶Assistant Professor, Department of Political Science, University of California San Diego, meroberts@ucsd.edu ‖Assistant Professor, Department of Sociology, Princeton University, brandonstewart.org,
Short Text Classification Improved by Learning Multi-Granularity Topics

Understanding the rapidly growing short text is very important. Short text is different from traditional documents in its shortness and sparsity, which hinders the application of conventional machine learning and text mining algorithms. Two major approaches have been exploited to enrich the representation of short text. One is to fetch contextual information of a short text to directly add more text; the other is to derive latent topics from existing large corpus, which are used as features to enrich the representation of short text. The latter approach is elegant and efficient in most cases. The major trend along this direction is to derive latent topics of certain granularity through well-known topic models such as latent Dirichlet allocation (LDA). However, topics of certain granularity are usually not sufficient to set up effective feature spaces. In this paper, we move forward along this direction by proposing an method to leverage topics at multiple granularity, which can model the short text more precisely. Taking short text classification as an example, we compared our proposed method with the state-of-the-art baseline over one open data set. Our method reduced the classification error by 20.25 % and 16.68 % respectively on two classifiers.
PReMVOS : Proposal-generation , Refinement and Merging for the DAVIS Challenge on Video Object Segmentation 2018

We address semi-supervised video object segmentation, the task of automatically generating accurate and consistent pixel masks for objects in a video sequence, given the first-frame ground truth annotations. Towards this goal, we present the PReMVOS algorithm (Proposal-generation, Refinement and Merging for Video Object Segmentation). This method involves generating coarse object proposals using a Mask R-CNN like object detector, followed by a refinement network that produces accurate pixel masks for each proposal. We then select and link these proposals over time using a merging algorithm that takes into account an objectness score, the optical flow warping, and a Re-ID feature embedding vector for each proposal. We adapt our networks to the target video domain by fine-tuning on a large set of augmented images generated from the firstframe ground truth. Our approach surpasses all previous state-of-the-art results on the DAVIS 2017 video object segmentation benchmark and achieves first place in the DAVIS 2018 Video Object Segmentation Challenge with a mean of J & F score of 74.7.