nils-herrmann/clean_scirepeval huggingface dataset:
Relation extraction and the influence of automatic named-entity recognition

We present an approach for extracting relations between named entities from natural language documents. The approach is based solely on shallow linguistic processing, such as tokenization, sentence splitting, part-of-speech tagging, and lemmatization. It uses a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities. We present the results of experiments on extracting five different types of relations from a dataset of newswire documents and show that each information source provides a useful contribution to the recognition task. Usually the combined kernel significantly increases the precision with respect to the basic kernels, sometimes at the cost of a slightly lower recall. Moreover, we performed a set of experiments to assess the influence of the accuracy of named-entity recognition on the performance of the relation-extraction algorithm. Such experiments were performed using both the correct named entities (i.e., those manually annotated in the corpus) and the noisy named entities (i.e., those produced by a machine learning-based named-entity recognizer). The results show that our approach significantly improves the previous results obtained on the same dataset.
Examining the Affects of Student Multitasking with Laptops during the Lecture

This paper examines undergraduate student use of laptop computers during a lecture-style class that includes substantial problem-solving activities and graphic-based content. The study includes both a self-reported use component collected from student surveys as well as a monitored use component collected via activity monitoring “spyware” installed on student laptops. We categorize multitasking activities into productive (course-related) versus distractive (non course-related) tasks. Quantifiable measures of software multitasking behavior are introduced to measure the frequency of student multitasking, the duration of student multitasking, and the extent to which students engage in distractive versus productive tasks. We find that students engage in substantial multitasking behavior with their laptops and have non course-related software applications open and active about 42% of the time. There is a statistically significant inverse relationship between the ratio of distractive versus productive multitasking behavior during lectures and academic performance. We also observe that students under state the frequency of email and instant messaging (IM) use in the classroom when self-reporting on their laptop usage.
STDP-based spiking deep convolutional neural networks for object recognition

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in spiking neural networks (SNN) to extract visual features of low or intermediate complexity in an unsupervised manner. These studies, however, used relatively shallow architectures, and only one layer was trainable. Another line of research has demonstrated - using rate-based neural networks trained with back-propagation - that having many layers increases the recognition robustness, an approach known as deep learning. We thus designed a deep SNN, comprising several convolutional (trainable with STDP) and pooling layers. We used a temporal coding scheme where the most strongly activated neurons fire first, and less activated neurons fire later or not at all. The network was exposed to natural images. Thanks to STDP, neurons progressively learned features corresponding to prototypical patterns that were both salient and frequent. Only a few tens of examples per category were required and no label was needed. After learning, the complexity of the extracted features increased along the hierarchy, from edge detectors in the first layer to object prototypes in the last layer. Coding was very sparse, with only a few thousands spikes per image, and in some cases the object category could be reasonably well inferred from the activity of a single higher-order neuron. More generally, the activity of a few hundreds of such neurons contained robust category information, as demonstrated using a classifier on Caltech 101, ETH-80, and MNIST databases. We also demonstrate the superiority of STDP over other unsupervised techniques such as random crops (HMAX) or auto-encoders. Taken together, our results suggest that the combination of STDP with latency coding may be a key to understanding the way that the primate visual system learns, its remarkable processing speed and its low energy consumption. These mechanisms are also interesting for artificial vision systems, particularly for hardware solutions.
LANGUAGE MODELS FOR AUTOMATIC SPEECH RECOGNITION OF CZECH LECTURES

This paper describes improvements in Automatic Speech Recognition (ASR) of Czech lectures obtained by enhancing language models. Our baseline is a statistical trigram language model with Good-Turing smoothing, trained on half billion words from newspapers, books etc. The overall improvement from adding more training data is over 10% in accuracy absolute, while using advanced language modeling techniques mainly neural networks yields another 3%. Perplexity improvements and OOV reduction are discussed too.
A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling

Robust organ segmentation is a prerequisite for computer-aided diagnosis, quantitative imaging analysis, pathology detection, and surgical assistance. For organs with high anatomical variability (e.g., the pancreas), previous segmentation approaches report low accuracies, compared with well-studied organs, such as the liver or heart. We present an automated bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans. The method generates a hierarchical cascade of information propagation by classifying image patches at different resolutions and cascading (segments) superpixels. The system contains four steps: 1) decomposition of CT slice images into a set of disjoint boundary-preserving superpixels; 2) computation of pancreas class probability maps via dense patch labeling; 3) superpixel classification by pooling both intensity and probability features to form empirical statistics in cascaded random forest frameworks; and 4) simple connectivity based post-processing. Dense image patch labeling is conducted using two methods: efficient random forest classification on image histogram, location and texture features; and more expensive (but more accurate) deep convolutional neural network classification, on larger image windows (i.e., with more spatial contexts). Over-segmented 2-D CT slices by the simple linear iterative clustering approach are adopted through model/parameter calibration and labeled at the superpixel level for positive (pancreas) or negative (non-pancreas or background) classes. The proposed method is evaluated on a data set of 80 manually segmented CT volumes, using six-fold cross-validation. Its performance equals or surpasses other state-of-the-art methods (evaluated by “leave-one-patient-out”), with a dice coefficient of 70.7% and Jaccard index of 57.9%. In addition, the computational efficiency has improved significantly, requiring a mere 6 ~ 8 min per testing case, versus ≥ 10 h for other methods. The segmentation framework using deep patch labeling confidences is also more numerically stable, as reflected in the smaller performance metric standard deviations. Finally, we implement a multi-atlas label fusion (MALF) approach for pancreas segmentation using the same data set. Under six-fold cross-validation, our bottom-up segmentation method significantly outperforms its MALF counterpart: 70.7±13.0% versus 52.51±20.84% in dice coefficients.
SMART 6: recent updates and new developments

Simple modular architecture research tool (SMART) is an online tool (http://smart.embl.de/) for the identification and annotation of protein domains. It provides a user-friendly platform for the exploration and comparative study of domain architectures in both proteins and genes. The current release of SMART contains manually curated models for 784 protein domains. Recent developments were focused on further data integration and improving user friendliness. The underlying protein database based on completely sequenced genomes was greatly expanded and now includes 630 species, compared to 191 in the previous release. As an initial step towards integrating information on biological pathways into SMART, our domain annotations were extended with data on metabolic pathways and links to several pathways resources. The interaction network view was completely redesigned and is now available for more than 2 million proteins. In addition to the standard web access to the database, users can now query SMART using distributed annotation system (DAS) or through a simple object access protocol (SOAP) based web service.
Freebase: a collaboratively created graph database for structuring human knowledge

Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.
Human behavior prediction for smart homes using deep learning

There is a growing interest in smart homes and predicting behaviors of inhabitants is a key element for the success of smart home services. In this paper, we propose two algorithms, DBN-ANN and DBN-R, based on the deep learning framework for predicting various activities in a home. We also address drawbacks of contrastive divergence, a widely used learning method for restricted Boltzmann machines, and propose an efficient online learning algorithm based on bootstrapping. From experiments using home activity datasets, we show that our proposed prediction algorithms outperform existing methods, such as a nonlinear SVM and k-means, in terms of prediction accuracy of newly activated sensors. In particular, DBN-R shows an accuracy of 43.9% (51.8%) for predicting newly activated sensors based on MIT home dataset 1 (dataset 2), while previous work based on the n-gram algorithm has shown an accuracy of 39% (43%) on the same dataset.
Multiple-path testing for cross site scripting using genetic algorithms

Web applications suffer from different security vulnerabilities that could be exploited by hackers to cause harm in a variety of ways. A number of approaches have been proposed to test for such vulnerabilities. However, some gaps are still to be addressed. In this paper, we address one of such gaps: the problem of automatically generating test data (i.e., possible attacks) to test for cross site scripting (XSS) type of vulnerability. The objective is to generate a set of test data to exercise candidate security-vulnerable paths in a given script. The desirable set of test data must be effective in the sense that it uncovers whether any path can indeed be exploited to launch an attack. We designed a genetic algorithm-based test data generator that uses a database of XSS attack patterns to generate possible attacks and assess whether the attack is successful. We considered different types of XSS vulnerability: stored, reflected and DOM based. We empirically validated our test data generator using case studies of Web applications developed using PHP and MySQL. Empirical results show that our test data generator is effective in generating, in one run, multiple test data to cover multiple target
A survey on multistage/multiphase statistical modeling methods for batch processes

In industrial manufacturing, most batch processes are inherently multistage/multiphase in nature. To ensure both quality consistency of the manufactured products and safe operation of this kind of batch process, different multivariate statistical process control (MSPC) methods have been proposed in recent years. This paper gives an overview of multistage/multiphase statistical process control methods used for process analysis, monitoring, quality prediction and online quality improvement. Different types of phase divisions and modeling strategies are introduced and the method properties are discussed. For comparisons, a selection guide to these methods for different application purposes is provided. Finally, some promising research directions are suggested based on existing works.
SRILM - an extensible language modeling toolkit

SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation and evaluation of a variety of language model types based on N-gram statistics, as well as several related tasks, such as statistical tagging and manipulation of N-best lists and word lattices. This paper summarizes the functionality of the toolkit and discusses its design and implementation, highlighting ease of rapid prototyping, reusability, and combinability of tools.
Reconfigurable Multiband Antenna Designs for Wireless Communication Devices

New designs for compact reconfigurable antennas are introduced for mobile communication devices. The uniqueness of the antenna designs are that they allow various groups of their operating frequency bands to be selected electronically. In particular, each group of frequency bands, or mode, can be made to serve several different communication systems simultaneously. These systems may include various combinations of GSM, DCS, PCS, UMTS, Bluetooth, and wireless local-area network (LAN). Therefore, by electronically selecting different antenna modes, a variety of communication systems can be conveniently served by only one antenna. One advantage is that through the different operational modes, the total antenna volume can be reused, and therefore the overall antenna can be made compact. In these designs, the selection of the different modes is achieved by either i) switching different feeding locations of the antenna (switched feed) or ii) switching or breaking of the antenna's connection to the ground (switched ground). This paper demonstrates these two designs. For the first design of switched feed, it can support GSM, DCS, PCS, and UMTS. In the second design, the antenna makes use of a switched-ground technique, which can cover GSM, DCS, PCS, UMTS, Bluetooth, and 2.4 GHz wireless LAN. The designs are investigated when ideal switches and also various realistic active switches based on PIN diodes, GaAs field effect transistor, and MEMs configurations. The designs are verified through both numerical simulations and measurement of an experimental prototype. The results confirm good performance of the two multiband reconfigurable antenna designs.
Entanglement Entropy and Quantum Field Theory

We carry out a systematic study of entanglement entropy in relativistic quantum field theory. This is defined as the von Neumann entropy SA = −Tr ρA log ρA corresponding to the reduced density matrix ρA of a subsystem A. For the case of a 1+1-dimensional critical system, whose continuum limit is a conformal field theory with central charge c, we re-derive the result SA ∼ (c/3) log l of Holzhey et al. when A is a finite interval of length l in an infinite system, and extend it to many other cases: finite systems, finite temperatures, and when A consists of an arbitrary number of disjoint intervals. For such a system away from its critical point, when the correlation length ξ is large but finite, we show that SA ∼ A(c/6) log ξ, where A is the number of boundary points of A. These results are verified for a free massive field theory, which is also used to confirm a scaling ansatz for the case of finite-size off-critical systems, and for integrable lattice models, such as the Ising and XXZ models, which are solvable by corner transfer matrix methods. Finally the free-field results are extended to higher dimensions, and used to motivate a scaling form for the singular part of the entanglement entropy near a quantum phase transition.
Automated Intelligent Pilots for Combat Flight Simulation

TacAir-Soar is an intelligent, rule-based system that generates believable “human-like” behavior for large scale, distributed military simulations. The innovation of the application is primarily a matter of scale and integration. The system is capable of executing most of the airborne missions that the United States military flies in fixed-wing aircraft. It accomplishes this by integrating a wide variety of intelligent capabilities, including real-time hierarchical execution of complex goals and plans, communication and coordination with humans and simulated entities, maintenance of situational awareness, and the ability to accept and respond to new orders while in flight. The system is currently deployed at the Oceana Naval Air Station WISSARD Lab and the Air Force Research Laboratory in Mesa, AZ. Its most dramatic use was in the Synthetic Theater of War 1997, which was an operational training exercise that ran for 48 continuous hours during which TacAir-Soar flew all U.S. fixed-wing aircraft.
Integrating constraints and metric learning in semi-supervised clustering

Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.
Weighted Association Rule Mining using weighted support and significance framework

We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to handle weighted association rule mining problems where each item is allowed to have a weight. The goal is to steer the mining focus to those significant relationships involving items with significant weights rather than being flooded in the combinatornal explosion of insignificant relationships. We identify the challenge of using weights in the iterative process of generating large itemsets. The problem of invalidation of the "downward closure property" in the weighted setting is solved by using an improved model of weighted support measurements and exploiting a "weighted downward closure property". A new algorithm called WARM (Weighted Association Rule Mining) is developed based on the improved model. The algorithm is both scalable and efficient in discovering significant relationships in weighted settings as illustrated by experiments performed on simulated datasets.
Path integral guided policy search

3Sergey Levine is with Google Brain, Mountain View, CA 94043, USA. We present a policy search method for learning complex feedback control policies that map from high-dimensional sensory inputs to motor torques, for manipulation tasks with discontinuous contact dynamics. We build on a prior technique called guided policy search (GPS), which iteratively optimizes a set of local policies for specific instances of a task, and uses these to train a complex, high-dimensional global policy that generalizes across task instances. We extend GPS in the following ways: (1) we propose the use of a model-free local optimizer based on path integral stochastic optimal control (PI2), which enables us to learn local policies for tasks with highly discontinuous contact dynamics; and (2) we enable GPS to train on a new set of task instances in every iteration by using on-policy sampling: this increases the diversity of the instances that the policy is trained on, and is crucial for achieving good generalization. We show that these contributions enable us to learn deep neural network policies that can directly perform torque control from visual input. We validate the method on a challenging door opening task and a pick-and-place task, and we demonstrate that our approach substantially outperforms the prior LQR-based local policy optimizer on these tasks. Furthermore, we show that on-policy sampling significantly increases the generalization ability of these policies.
CHARADE: Remote Control of Objects Using Free-Hand Gestures

This paper presents an application that uses hand gesture input to control a computer while giving a presentation. In order to develop a prototype of this application, we have defined an interaction model, a notation for gestures, and a set of guidelines to design gestural command sets. This works aims to define interaction styles that work in computerized reality environments. In our application, gestures are used for interacting with the computer as well as for communicating with other people or operating other devices.
A Computational Model of Plan-Based Narrative Conflict at the Fabula Level

Conflict is an essential element of interesting stories. In this paper, we operationalize a narratological definition of conflict and extend established narrative planning techniques to incorporate this definition. The conflict partial order causal link planning algorithm (CPOCL) allows narrative conflict to arise in a plan while maintaining causal soundness and character believability. We also define seven dimensions of conflict in terms of this algorithm's knowledge representation. The first three-participants, reason, and duration-are discrete values which answer the “who?” “why?” and “when?” questions, respectively. The last four-balance, directness, stakes, and resolution-are continuous values which describe important narrative properties that can be used to select conflicts based on the author's purpose. We also present the results of two empirical studies which validate our operationalizations of these narrative phenomena. Finally, we demonstrate the different kinds of stories which CPOCL can produce based on constraints on the seven dimensions.
Black-white achievement gap and family wealth.

This article examines the extent to which family wealth affects the Black-White test score gap for young children based on data from the Panel Study of Income Dynamics (aged 3-12). This study found little evidence that wealth mediated the Black-White test scores gaps, which were eliminated when child and family demographic covariates were held constant. However, family wealth had a stronger association with cognitive achievement of school-aged children than that of preschoolers and a stronger association with school-aged children's math than on their reading scores. Liquid assets, particularly holdings in stocks or mutual funds, were positively associated with school-aged children's test scores. Family wealth was associated with a higher quality home environment, better parenting behavior, and children's private school attendance.