nils-herrmann/clean_scirepeval huggingface dataset:
Recent trends in hierarchic document clustering: A critical review

This article reviews recent research into the use of hierarchic agglomerative clustering methods for document retrieval. After an introduction to the calculation of interdocument similarities and to clustering methods that are appropriate for document clustering, the article discusses algorithms that can be used to allow the implementation of these methods on databases of nontrivial size. The validation of document hierarchies is described using tests based on the theory of random graphs and on empirical characteristics of document collections that are to be clustered. A range of search strategies is available for retrieval from document hierarchies and the results are presented of a series of research projects that have used these strategies to search the clusters resulting from several different types of hierarchic agglomerative clustering method. It is suggested that the complete linkage method is probably the most effective method in terms of retrieval performance; however, it is also difficult to implement in an efficient manner. Other applications of document clustering techniques are discussed briefly; experimental evidence suggests that nearest neighbor clusters, possibly represented as a network model, provide a reasonably efficient and effective means of including interdocument similarity information in document retrieval systems.
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm

In many situations we have some measurement of confidence on “positiveness” for a binary label. The “positiveness” is a continuous value whose range is a bounded interval. It quantifies the affiliation of each training data to the positive class. We propose a novel learning algorithm called expectation loss SVM (eSVM) that is devoted to the problems where only the “positiveness” instead of a binary label of each training sample is available. Our e-SVM algorithm can also be readily extended to learn segment classifiers under weak supervision where the exact positiveness value of each training example is unobserved. In experiments, we show that the e-SVM algorithm can effectively address the segment proposal classification task under both strong supervision (e.g. the pixel-level annotations are available) and the weak supervision (e.g. only bounding-box annotations are available), and outperforms the alternative approaches. Besides, we further validate this method on two major tasks of computer vision: semantic segmentation and object detection. Our method achieves the state-of-the-art object detection performance on PASCAL VOC 2007 dataset.
A Study of Layout, Rendering, and Interaction Methods for Immersive Graph Visualization

Information visualization has traditionally limited itself to 2D representations, primarily due to the prevalence of 2D displays and report formats. However, there has been a recent surge in popularity of consumer grade 3D displays and immersive head-mounted displays (HMDs). The ubiquity of such displays enables the possibility of immersive, stereoscopic visualization environments. While techniques that utilize such immersive environments have been explored extensively for spatial and scientific visualizations, contrastingly very little has been explored for information visualization. In this paper, we present our considerations of layout, rendering, and interaction methods for visualizing graphs in an immersive environment. We conducted a user study to evaluate our techniques compared to traditional 2D graph visualization. The results show that participants answered significantly faster with a fewer number of interactions using our techniques, especially for more difficult tasks. While the overall correctness rates are not significantly different, we found that participants gave significantly more correct answers using our techniques for larger graphs.
Corpus callosum abnormalities in psychopathic antisocial individuals.

Psychopathic antisocial individuals have previously been characterized by abnormal interhemispheric processing and callosal functioning, but there have been no studies on the structural characteristics of the corpus callosum in this group.



To assess whether (1) psychopathic individuals with antisocial personality disorder show structural and functional impairments in the corpus callosum, (2) group differences are mirrored by correlations between dimensional measures of callosal structure and psychopathy, (3) callosal abnormalities are associated with affective deficits, and (4) callosal abnormalities are independent of psychosocial deficits.



Case-control study.



Community sample.



Fifteen men with antisocial personality disorder and high psychopathy scores and 25 matched controls, all from a larger sample of 83 community volunteers.



Structural magnetic resonance imaging measures of the corpus callosum (volume estimate of callosal white matter, thickness, length, and genu and splenium area), functional callosal measures (2 divided visual field tasks), electrodermal and cardiovascular activity during a social stressor, personality measures of affective and interpersonal deficits, and verbal and spatial ability.



Psychopathic antisocial individuals compared with controls showed a 22.6% increase in estimated callosal white matter volume (P<.001), a 6.9% increase in callosal length (P =.002), a 15.3% reduction in callosal thickness (P =.04), and increased functional interhemispheric connectivity (P =.02). Correlational analyses in the larger unselected sample confirmed the association between antisocial personality and callosal structural abnormalities. Larger callosal volumes were associated with affective and interpersonal deficits, low autonomic stress reactivity, and low spatial ability. Callosal abnormalities were independent of psychosocial deficits.



Corpus callosum abnormalities in psychopathic antisocial individuals may reflect atypical neurodevelopmental processes involving an arrest of early axonal pruning or increased white matter myelination. These findings may help explain affective deficits and previous findings of abnormal interhemispheric transfer in psychopathic individuals.
ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2015, presenting the results of the evaluation of eight methods - four submitted, two state-of-the-art systems (one commercial and one open-source) and their two immediately previous versions. Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one with emphasis on text and the other focusing only on text). The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-text content.
How do consumers react to cybercrime?

We conduct a secondary analysis of data collected to survey EU citizens' experiences and concerns with cybercrime. We devise a series of logistic regressions that measure how exposure to cybercrime can inhibit online banking, shopping and other activities. We consider three forms of exposure: directly falling victim, expressing concern about security, and reading news reports. We find that directly experiencing cybercrime decreases the likelihood of shopping and banking online by 4-5 percentage points. We find that expressing concern about cybercrime has nearly twice as much negative impact on online behavior than directly experiencing cybercrime. People who have not heard anything about cybercrime in news reports or from colleagues are more likely to bank online than those who have heard such reports. We conclude by reviewing limitations of existing survey approaches and make recommendations for improving questions in future cybercrime surveys.
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness

Machine learning is now being used to make crucial decisions about people’s lives. For nearly all of these decisions there is a risk that individuals of a certain race, gender, sexual orientation, or any other subpopulation are unfairly discriminated against. Our recent method has demonstrated how to use techniques from counterfactual inference to make predictions fair across different subpopulations. This method requires that one provides the causal model that generated the data at hand. In general, validating all causal implications of the model is not possible without further assumptions. Hence, it is desirable to integrate competing causal models to provide counterfactually fair decisions, regardless of which causal “world” is the correct one. In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification. We frame the goal of learning a fair classifier as an optimization problem with fairness constraints entailed by competing causal explanations. We show how this optimization problem can be efficiently solved using gradient-based methods. We demonstrate the flexibility of our model on two real-world fair classification problems. We show that our model can seamlessly balance fairness in multiple worlds with prediction accuracy.
Approximate clustering via core-sets

In this paper, we show that for several clustering problems one can extract a small set of points, so that using those core-sets enable us to perform approximate clustering efficiently. The surprising property of those core-sets is that their size is independent of the dimension.Using those, we present a (1+ ε)-approximation algorithms for the k-center clustering and k-median clustering problems in Euclidean space. The running time of the new algorithms has linear or near linear dependency on the number of points and the dimension, and exponential dependency on 1/ε and k. As such, our results are a substantial improvement over what was previously known.We also present some other clustering results including (1+ ε)-approximate 1-cylinder clustering, and k-center clustering with outliers.
Caching in information centric networking: A survey

Internet usage has drastically shifted from host-centric end-to-end communication to receiver-driven content retrieval. In order to adapt to this change, a handful of innovative information/content centric networking (ICN) architectures have recently been proposed. One common and important feature of these architectures is to leverage built-in network caches to improve the transmission efficiency of content dissemination. Compared with traditional Web Caching and CDN Caching, ICN Cache takes on several new characteristics: cache is transparent to applications, cache is ubiquitous, and content to be cached is more ine-grained. These distinguished features pose new challenges to ICN caching technologies. This paper presents a comprehensive survey of state-of-art techniques aiming to address these issues, with particular focus on reducing cache redundancy and improving the availability of cached content. As a new research area, this paper also points out several interesting yet challenging research directions in this subject.
Community Detection via Maximization of Modularity and Its Variants

In this paper, we first discuss the definition of modularity (Q) used as a metric for community quality and then we review the modularity maximization approaches which were used for community detection in the last decade. Then, we discuss two opposite yet coexisting problems of modularity optimization; in some cases, it tends to favor small communities over large ones while in others, large communities over small ones (so-called the resolution limit problem). Next, we overview several community quality metrics proposed to solve the resolution limit problem and discuss Modularity Density (Qds ), which simultaneously avoids the two problems of modularity. Finally, we introduce two novel fine-tuned community detection algorithms that iteratively attempt to improve the community quality measurements by splitting and merging the given network community structure. The first one, referred to as Fine-tuned , is based on modularity (Q), while the second is based on Modularity Density (Qds ) and denoted as Fine-tuned . Then, we compare the greedy algorithm of modularity maximization (denoted as Greedy ), Fine-tuned , and Fine-tuned on four real networks, and also on the classical clique network and the LFR benchmark networks, each of which is instantiated by a wide range of parameters. The results indicate that Fine-tuned Qds is the most effective among the three algorithms discussed. Moreover, we show that Fine-tuned Qds can be applied to the communities detected by other algorithms to significantly improve their results.
Deep learning for urban remote sensing

This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by detecting hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.
Converting A Trained Neural Network To a Decision Tree DecText - Decision Tree Extractor

Neural Networks are very successful in acquiring hidden knowledge in datasets. Their most important weakness is that the knowled ge they acquire is represented in a form not understandable to humans. Understandability problem of Neural Networks can be solved by extracti ng Decision Rules or Decision Trees from the trained network. There are s everal Decision Rule extraction methods and Mark Craven’s TREPAN which extracts MofN type Decision Trees from trained networks. We introduced new splitting techniques for extracting classical Decision Trees fr om trained Neural Networks. We showed that the new method (DecText) is effecti ve in extracting high fidelity trees from trained networks. We also introduced a new discretization technique to make DecText be able to hand le continuous features and a new pruning technique for finding simplest tree with the highest fidelity.
An Optimality Proof of the LRU-K Page Replacement Algorithm

This paper analyzes a recently published algorithm for page replacement in hierarchical paged memory systems [O'Neil et al. 1993]. The algorithm is called the LRU-<italic>K</italic> method, and reduces to the well-known LRU (Least Recently Used) method for <italic>K</italic> = 1. Previous work [O'Neil et al. 1993; Weikum et al. 1994; Johnson and Shasha 1994] has shown the effectiveness for <italic>K</italic> > 1 by simulation, especially in the most common case of <italic>K</italic> = 2. The basic idea in LRU-<italic>K</italic> is to keep track of the times of the last <italic>K</italic> references to memory pages, and to use this statistical information to rank-order the pages as to their expected future behavior. Based on this the page replacement policy decision is made: which  memory-resident page to replace when a newly accessed page must be read into memory. In the current paper, we prove, under the assumptions of the independent reference model, that LRU-<italic>K</italic> is optimal. Specifically we show: given the times of the (up to) <italic>K</italic> most recent references to each disk page, no other algorithm <italic>A</italic> making decisions to keep pages in a memory buffer holding <italic>n</italic> - 1 pages based on this infomation can improve on the expected number of I/Os to access pages over the LRU-<italic>K</italic> algorithm using a memory buffer holding <italic>n</italic> pages. The proof uses the Bayesian formula to relate the space of actual page probabilities of the model to the space of observable page numbers on which the replacement decision is  acutally made.
Communication-optimal Parallel and Sequential QR and LU Factorizations

We present parallel and sequential dense QR factorization algorithms that are both optimal (up to polylogarithmic factors) in the amount of communication they perform and just as stable as Householder QR. We prove optimality by deriving new lower bounds for the number of multiplications done by “non-Strassen-like” QR, and using these in known communication lower bounds that are proportional to the number of multiplications. We not only show that our QR algorithms attain these lower bounds (up to polylogarithmic factors), but that existing LAPACK and ScaLAPACK algorithms perform asymptotically more communication. We derive analogous communication lower bounds for LU factorization and point out recent LU algorithms in the literature that attain at least some of these lower bounds. The sequential and parallel QR algorithms for tall and skinny matrices lead to significant speedups in practice over some of the existing algorithms, including LAPACK and ScaLAPACK, for example, up to 6.7 times over ScaLAPACK. A performance model for the parallel algorithm for general rectangular matrices predicts significant speedups over ScaLAPACK.
Consumer Behavior Information Big Data Mining

With the Internet applications in the commercial area of rapid spread, consumer demand-driven experience of becoming a leading Internet development. At the same time, e-commerce as the representative of the social network of constantly new changes. In computer systems and social integration of e-commerce systems are highly interactive, the consumer bears an increasingly important role in consumer behavior characteristics and potential demand has become an important driving force for technological development. A large number of enterprises through the establishment of collaborative filtering recommendation systems predict consumer model, personalized recommendations and services to address the information overload problem. Based on the above requirements, in-depth study of the progress of theoretical research services and service quality management theory, according to a large data analysis and complex network, is recommended by consumer behavior.
Accurate recognition of words in scenes without character segmentation using recurrent neural network

Recognition of texts in scenes is one of the most important tasks in many computer vision applications. Though different scene text recognition techniques have been developed, scene text recognition under a generic condition is still a very open and challenging research problem. One major factor that defers the advance in this research area is character touching, where many characters in scene images are heavily touched with each other and cannot be segmented for recognition. In this paper, we proposed a novel scene text recognition technique that performs word level recognition without character segmentation. Our proposed technique has three advantages. First it converts each word image into a sequential signal for the scene text recognition. Second, it adapts the recurrent neural network (RNN) with Long Short TermMemory (LSTM), the technique that has been widely used for handwriting recognition in recent years. Third, by integrating multiple RNNs, an accurate recognition system is developed which is capable of recognizing scene texts including those heavily touched ones without character segmentation. Extensive experiments have been conducted over a number of datasets including several ICDAR Robust Reading datasets and Google Street View dataset. Experiments show that the proposed technique is capable of recognizing texts in scenes accurately.
Modeling roles of subjective norms and eTrust in customers ’ acceptance of airline B 2 C eCommerce websites

Airline companies have increasingly employed electronic commerce (eCommerce) for strategic purposes, most notably in order to achieve long-term competitive advantage and global competitiveness by enhancing customer satisfaction as well as marketing efficacy and managerial efficiency. eCommerce has now emerged as possibly the most representative distribution channel in the airline industry. In this study, we describe an extended technology acceptance model (TAM), which integrates subjective norms and electronic trust (eTrust) into the model, in order to determine their relevance to the acceptance of airline business-to-customer (B2C) eCommerce websites (AB2CEWS). The proposed research model was tested empirically using data collected from a survey of customers who had utilized B2C eCommerce websites of two representative airline companies in South Korea (i.e., KAL and ASIANA) for the purpose of purchasing air tickets. Path analysis was employed in order to assess the significance and strength of the hypothesized causal relationships between subjective norms, eTrust, perceived ease of use, perceived usefulness, attitude toward use, and intention to reuse. Our results provide general support for an extended TAM, and also confirmed its robustness in predicting customers’ intention to reuse AB2CEWS. Valuable information was found from our results regarding the management of AB2CEWS in the formulation of airlines’ Internet marketing strategies.
Efficient SVM Regression Training with SMO

The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver. In this work, we generalize SMO so that it can handle regression problems. However, one problem with SMO is that its rate of convergence slows down dramatically when data is non-sparse and when there are many support vectors in the solution—as is often the case in regression—because kernel function evaluations tend to dominate the runtime in this case. Moreover, caching kernel function outputs can easily degrade SMO's performance even more because SMO tends to access kernel function outputs in an unstructured manner. We address these problems with several modifications that enable caching to be effectively used with SMO. For regression problems, our modifications improve convergence time by over an order of magnitude.
Combinatorial Testing of ACTS: A Case Study

In this paper we present a case study of applying combinatorial testing to test a combinatorial test generation tool called ACTS. The purpose of this study is two-fold. First, we want to gain experience and insights about how to apply combinatorial testing in practice. Second, we want to evaluate the effectiveness of combinatorial testing applied to a real-life system. ACTS has 24637 lines of uncommented code, and provides a command line interface and a fairly sophisticated graphic user interface. The main challenge of this study was to model the input space in terms of a set of parameters and values. Once the model was designed, we generated test cases using ACTS, which were then later used to test ACTS. The results of this study show that input space modeling can be a significant undertaking, and needs to be carefully managed. The results also show that combinatorial testing is effective in terms of achieving high code coverage and fault detection.