nils-herrmann/clean_scirepeval huggingface dataset:
On the treatment of speech attributions in discourse structure

In this paper, we present what we think is an elegant solution to some problems in the discourse-structural modelling of speech attribution. Using mostly examples from the Wall Street Journal Corpus, we show that the approach proposed by Carlson and Marcu (2001) leads to irresolvable dilemmas that can be avoided with a suitable treatment of attribution in an underspecified representation of discourse structure. Most approaches to discourse structure assume that textual coherence can be modelled as trees. In particular, it has been shown that coherent discourse follows the so-called rightfrontier constraint (RFC), which essentially ascertains a hierarchical structure without crossed dependencies. We will discuss putative counterexamples to these two assumptions, most of which involve reported speech as in (1) (cited in Wolf and Gibson 2005): (1) “Sure I’ll be polite,” promised one BMW driver who gave his name only as Rudolph. “As long as the trucks and the timid stay out of the left lane.” In (1) the second part of the quote should be linked to the first part (and not the whole first sentence) by a condition relation. If we were to analyse the parenthetical speech reporting clause (“promised one BMW driver ...”) as the nucleus of its host clause (i.e., the quote), the RFC would prevent linkage between the two parts of the quote. If the attribution is analysed as a satellite of the quote, as in Carlson and Marcu (2001), Wolf and Gibson argue, it should be a satellite to both parts of the quote, thus violating treeness. In this paper, we will explore the problems arising from this type of construction and propose a treatment of speech report attributions that we will argue allows us to preserve both, treeness and the RFC in building discourse structures. 1 The (non-)treatment of speech attribution in classic Rhetorical Structure Theory (RST) In ‘classic’ Rhetorical Structure Theory (RST; Mann and Thompson 1988), the problems in accommodating speech report attributions do not arise, because classic RST does not separate complements of verbs and parenthetical speech reporting clauses from their host clause. Leaving speech attribution implicit is in line with the general ‘philosophy’ of RST, which aims to represent not all possible links, but the most plausible structure licensed by 1 Markus Egg is now at Humboldt University, Berlin. This manuscript is a slightly updated (2009) version of our paper in the Proceedings of the Workshop on Constraints in Discourse, Maynooth, Ireland 2006 (http://www.constraints-in-discourse.org/cid06/).
Acceptor and Connector Design Patterns for Initializing Communication Services

This paper describes the Connector and Acceptor patterns. The intent of these patterns is to decouple the active and passive connection roles, respectively, from the tasks a communication service performs once connections are established. Common examples of communication services that utilize these patterns include WWW browsers, WWW servers, object request brokers, and “superservers” that provide services like remote login and file transfer to client applications. This paper illustrates how the Connector and Acceptor patterns can help decouple the connection-related processing from the service processing, thereby yielding more reusable, extensible, and efficient communication software. When used in conjunction with related patterns like the Reactor , Active Object , and Service Configurator , the Acceptor and Connector patterns enable the creation of highly extensible and efficient communication software frameworks [4] and applications . This paper is organized as follows: Section 2 outlines background information on networking and communication protocols necessary to appreciate the patterns in this paper; Section 3 motivates the need for the Acceptor and Connector patterns and illustrates how they have been applied to a production application-level Gateway; Section 4 describes the Acceptor and Connector patterns in detail; and Section 5 presents concluding remarks.
Comparison of Unigram, Bigram, HMM and Brill's POS tagging approaches for some South Asian languages

Part-of-Speech (POS) Tagging is a process that attaches each word in a sentence with a suitable tag from a given set of tags. POS Tagging is important in various areas of Natural Language Processing. Different methods of automating the process have been developed and employed for English and other Western languages. Some similar work, most of which utilize the stochastic approaches for POS Tagging has also been done in the same area for South Asian languages. We experimented with some of the widelyused approaches for POS Tagging on three South Asian languages, Bangla, Hindi and Telegu, using corpora of different sizes. We observed the performance of the approaches and found the Brill’s transformation based tagger’s performance to be superior to the other approaches in all of our experiments, though the use of this approach has been very limited until recently.
Any AND-OR Formula of Size N can be Evaluated in time N^{1/2 + o(1)} on a Quantum Computer

For any AND-OR formula of size N, there exists a bounded-error N1/2+o(1)-time quantum algorithm, based on a discrete-time quantum walk, that evaluates this formula on a black-box input. Balanced, or "approximately balanced," formulas can be evaluated in O(radicN) queries, which is optimal. It follows that the (2-o(1))th power of the quantum query complexity is a lower bound on the formula size, almost solving in the positive an open problem posed by Laplante, Lee and Szegedy.
Band-to-band tunneling in carbon nanotube field-effect transistors.

A detailed study on the mechanism of band-to-band tunneling in carbon nanotube field-effect transistors (CNFETs) is presented. Through a dual-gated CNFET structure tunneling currents from the valence into the conduction band and vice versa can be enabled or disabled by changing the gate potential. Different from a conventional device where the Fermi distribution ultimately limits the gate voltage range for switching the device on or off, current flow is controlled here by the valence and conduction band edges in a bandpass-filter-like arrangement. We discuss how the structure of the nanotube is the key enabler of this particular one-dimensional tunneling effect.
Multiscale conditional random fields for image labeling

We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set of labels. The features are incorporated into a probabilistic framework, which combines the outputs of several components. Components differ in the information they encode. Some focus on the image-label mapping, while others focus solely on patterns within the label field. Components also differ in their scale, as some focus on fine-resolution patterns while others on coarser, more global structure. A supervised version of the contrastive divergence algorithm is applied to learn these features from labeled image data. We demonstrate performance on two real-world image databases and compare it to a classifier and a Markov random field.
Kalman filtering for relative spacecraft attitude and position estimation

In this paper a novel approach is developed for relative state estimation of spacecraft flying in formation. The approach uses information from an optical sensor to provide multiple line-of-sight vectors from one spacecraft to another. The line-of-sight measurements are coupled with gyro measurements and dynamical models in an extended Kalman filter to determine relative attitude, position and gyro biases. The quaternion is used to describe the relative kinematics, while general relative orbital equations are used to describe the positional dynamics. Three different attitude formulations are Graduate Student, Department of Mechanical & Aerospace Engineering. Currently Researcher, Attitude and Orbit Control Team, Satellite Division, SaTReC, KAIST, 373-1 Kusong-dong, Yusong-gu, Taejon 305701, Republic Korea.  songoo@satrec.kaist.ac.kr. Member AIAA. Associate Professor, Department of Mechanical & Aerospace Engineering.  Associate Fellow AIAA. Postdoctoral Research Fellow, Department of Mechanical & Aerospace Engineering.  cheng3@eng.buffalo.edu. Member AIAA. Graduate Student, Department of Mechanical & Aerospace Engineering.  junkins@tamu.edu. Fellow AIAA.
Learning to Identify Sentence Parallelism in Student Essays

Parallelism is an important rhetorical device. We propose a machine learning approach for automated sentence parallelism identification in student essays. We b uild an essay dataset with sentence level parallelism annotated. We derive features by combining gen eralized word alignment strategies and the alignment measures between word sequences. The experiment al r sults show that sentence parallelism can be effectively identified with a F1 score of 82% at pair-wise level and 72% at parallelism chunk l evel. Based on this approach, we automatically identify sentence parallelism in more than 2000 student essays and study the correlation between the use of sentence parall elism and the types and quality of essays.
The Structure and Performance of Efficient Interpreters

Interpreters designed for high general-purpose performance typically perform a large number of indirect branches (3.2%–13% of all executed instructions in our benchmarks). These branches consume more than half of the run-time in a number of configurations we simulated. We evaluate how accurate various existing and proposed branch prediction schemes are on a number of interpreters, how the mispredictions affect the performance of the interpreters and how two different interpreter implementation techniques perform with various branch predictors. We also suggest various ways in which hardware designers, C compiler writers, and interpreter writers can improve the performance of interpreters.
Stranger: An Automata-Based String Analysis Tool for PHP

STRANGER is an automata-based string analysis tool for finding and eliminating string-related security vulnerabilities in P H applications. STRANGER uses symbolic forward and backward reachability analyses t o compute the possible values that the string expressions can take during progr am execution. STRANGER can automatically (1) prove that an application is free from specified attacks or (2) generate vulnerability signatures that c racterize all malicious inputs that can be used to generate attacks.
Anaphora and Discourse Structure

We argue in this article that many common adverbial phrases generally taken to signal a discourse relation between syntactically connected units within discourse structure instead work anaphorically to contribute relational meaning, with only indirect dependence on discourse structure. This allows a simpler discourse structure to provide scaffolding for compositional semantics and reveals multiple ways in which the relational meaning conveyed by adverbial connectives can interact with that associated with discourse structure. We conclude by sketching out a lexicalized grammar for discourse that facilitates discourse interpretation as a product of compositional rules, anaphor resolution, and inference.
Anomaly detection via 3D-HOF and fast double sparse representation

This paper presents a framework for anomaly detection in videos which considers both motion and appearance features. For motion cues, we propose a new feature called 3D-HOF, which effectively extracts both velocity and orientation from the optical flow map. At the same time, we introduce the concept of “depth of field” problem to make the detection more accurate when the velocity of an object may seem to be different according to its distance to the camera. For appearance cues, we use 3D gradients of spatio-temporal cuboids as features. Next in the detection part, we propose a fast double sparse representation method in order to make the process faster in some actual scenes. Finally, we integrate both the outcomes of using motion and appearance cues as the final outcomes. Results on discriminative datasets show efficiency and effectiveness compared to the state-of-the-art methods.
Constraining connectivity to encourage modularity in HyperNEAT

A challenging goal of generative and developmental systems (GDS) is to effectively evolve neural networks as complex and capable as those found in nature. Two key properties of neural structures in nature are regularity and modularity. While HyperNEAT has proven capable of generating neural network connectivity patterns with regularities, its ability to evolve modularity remains in question. This paper investigates how altering the traditional approach to determining whether connections are expressed in HyperNEAT influences modularity. In particular, an extension is introduced called a Link Expression Output (HyperNEAT-LEO) that allows HyperNEAT to evolve the pattern of weights independently from the pattern of connection expression. Because HyperNEAT evolves such patterns as functions of geometry, important general topographic principles for organizing connectivity can be seeded into the initial population. For example, a key topographic concept in nature that encourages modularity is locality, that is, components of a module are located near each other. As experiments in this paper show, by seeding HyperNEAT with a bias towards local connectivity implemented through the LEO, modular structures arise naturally. Thus this paper provides an important clue to how an indirect encoding of network structure can be encouraged to evolve modularity.
Aesthetic package design : A behavioral , neural , and psychological investigation

In four experiments, this research sheds light on aesthetic experiences by rigorously investigating behavioral, neural, and psychological properties of package design. We find that aesthetic packages significantly increase the reaction time of consumers' choice responses; that they are chosen over products with well-known brands in standardized packages, despite higher prices; and that they result in increased activation in the nucleus accumbens and the ventromedial prefrontal cortex, according to functional magnetic resonance imaging (fMRI). The results suggest that reward value plays an important role in aesthetic product experiences. Further, a closer look at psychometric and neuroimaging data finds that a paper-and-pencil measure of affective product involvement correlates with aesthetic product experiences in the brain. Implications for future aesthetics research, package designers, and product managers are discussed.
aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model

As an alternative to question answering methods based on feature engineering, deep learning approaches such as convolutional neural networks (CNNs) and Long Short-Term Memory Models (LSTMs) have recently been proposed for semantic matching of questions and answers. To achieve good results, however, these models have been combined with additional features such as word overlap or BM25 scores. Without this combination, these models perform significantly worse than methods based on linguistic feature engineering. In this paper, we propose an attention based neural matching model for ranking short answer text. We adopt value-shared weighting scheme instead of position-shared weighting scheme for combining different matching signals and incorporate question term importance learning using question attention network. Using the popular benchmark TREC QA data, we show that the relatively simple aNMM model can significantly outperform other neural network models that have been used for the question answering task, and is competitive with models that are combined with additional features. When aNMM is combined with additional features, it outperforms all baselines.
Fuzzy Darwinian Detection of Credit Card Fraud

Credit evaluation is one of the most important and difficult tasks for credit card companies, mortgage companies, banks and other financial institutes. Incorrect credit judgement causes huge financial losses. This work describes the use of an evolutionary-fuzzy system capable of classifying suspicious and non-suspicious credit card transactions. The paper starts with the details of the system used in this work. A series of experiments are described, showing that the complete system is capable of attaining good accuracy and intelligibility levels for real data.
Why is Stack Overflow Failing? Preserving Sustainability in Community Question Answering

Enormous amounts of knowledge sharing occur every day in community question answering (CQA) sites, some of which (for example, Stack Overflow or Ask Ubuntu) have become popular with software developers and users. In spite of these systems' overall success, problems are emerging in some of them - increased failure and churn rates. To investigate this trend, researchers conducted a case study of Stack Overflow. First, they evaluated the community perception that the emerging problems are heavily related to the growing amount of low-quality content created by undesired groups of users (help vampires, noobs, and reputation collectors). Reproducible data analyses of content and community evolution supported these findings. Suggestions to deal with the emerging problems include providing users with responder-oriented adaptive support that involves a whole community in QA. Such approaches represent an eminent attitude change regarding QA support, with the aim to preserve CQA ecosystems' long-term sustainability.
Toward a semantic granularity model for domain-specific information retrieval

Both similarity-based and popularity-based document ranking functions have been successfully applied to information retrieval (IR) in general. However, the dimension of semantic granularity also should be considered for effective retrieval. In this article, we propose a semantic granularity-based IR model that takes into account the three dimensions, namely similarity, popularity, and semantic granularity, to improve domain-specific search. In particular, a concept-based computational model is developed to estimate the semantic granularity of documents with reference to a domain ontology. Semantic granularity refers to the levels of semantic detail carried by an information item. The results of our benchmark experiments confirm that the proposed semantic granularity based IR model performs significantly better than the similarity-based baseline in both a bio-medical and an agricultural domain. In addition, a series of user-oriented studies reveal that the proposed document ranking functions resemble the implicit ranking functions exercised by humans. The perceived relevance of the documents delivered by the granularity-based IR system is significantly higher than that produced by a popular search engine for a number of domain-specific search tasks. To the best of our knowledge, this is the first study regarding the application of semantic granularity to enhance domain-specific IR.
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization

This paper tackles the reduction of redundant repeating generation that is often observed in RNN-based encoder-decoder models. Our basic idea is to jointly estimate the upper-bound frequency of each target vocabulary in the encoder and control the output words based on the estimation in the decoder. Our method shows significant improvement over a strong RNN-based encoder-decoder baseline and achieved its best results on an abstractive summarization benchmark.
A heads-up no-limit Texas Hold'em poker player: discretized betting models and automatically generated equilibrium-finding programs

We present Tartanian, a game theory-based player for headsup no-limit Texas Hold’em poker. Tartanian is built from three components. First, to deal with the virtually infinite strategy space of no-limit poker, we develop a discretized betting model designed to capture the most important strategic choices in the game. Second, we employ potential-aware automated abstraction algorithms for identifying strategically similar situations in order to decrease the size of the game tree. Third, we develop a new technique for automatically generating the source code of an equilibrium-finding algorithm from an XML-based description of a game. This automatically generated program is more efficient than what would be possible with a general-purpose equilibrium-finding program. Finally, we present results from the AAAI-07 Computer Poker Competition, in which Tartanian placed second out of ten entries.
Convolutional Kernel Networks

An important goal in visual recognition is to devise image representations that are invariant to particular transformations. In this paper, we address this goal with a new type of convolutional neural network (CNN) whose invariance is encoded by a reproducing kernel. Unlike traditional approaches where neural networks are learned either to represent data or for solving a classification task, our network learns to approximate the kernel feature map on training data. Such an approach enjoys several benefits over classical ones. First, by teaching CNNs to be invariant, we obtain simple network architectures that achieve a similar accuracy to more complex ones, while being easy to train and robust to overfitting. Second, we bridge a gap between the neural network literature and kernels, which are natural tools to model invariance. We evaluate our methodology on visual recognition tasks where CNNs have proven to perform well, e.g., digit recognition with the MNIST dataset, and the more challenging CIFAR-10 and STL-10 datasets, where our accuracy is competitive with the state of the art.
A Model and Framework for Visualization Exploration

Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as the data and depiction itself. Significant visualization research has focused on the generation of visualizations (the depiction); less effort has focused on the exploratory aspects of visualization (the process). However, without formal models of the process, visualization exploration sessions cannot be fully utilized to assist users and system designers. Toward this end, we introduce the P-Set model of visualization exploration for describing this process and a framework to encapsulate, share, and analyze visual explorations. In addition, systems utilizing the model and framework are more efficient as redundant exploration is avoided. Several examples drawn from visualization applications demonstrate these benefits. Taken together, the model and framework provide an effective means to exploit the information within the visual exploration process
A model for selecting an ERP system based on linguistic information processing

An enterprise resource planning (ERP) is an enterprise-wide application software package that integrates all necessary business functions into a single system with a common database. In order to implement an ERP project successfully in an organization, it is necessary to select a suitable ERP system. This paper presents a new model, which is based on linguistic information processing, for dealing with such a problem. In the study, a similarity degree based algorithm is proposed to aggregate the objective information about ERP systems from some external professional organizations, which may be expressed by different linguistic term sets. The consistency and inconsistency indices are defined by considering the subject information obtained from internal interviews with ERP vendors, and then a linear programming model is established for selecting the most suitable ERP system. Finally, a numerical example is given to demonstrate the application of the