nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge-Based Textual Inference via Parse-Tree Transformations

Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as “external” to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.
Our Princess Is in Another Castle : A Review of Trends in Serious Gaming for Education

http://rer.sagepub.com/content/82/1/61 The online version of this article can be found at: DOI: 10.3102/0034654312436980 February 2012 2012 82: 61 originally published online 1 REVIEW OF EDUCATIONAL RESEARCH Benedict Lai, Zeus Simeoni, Matthew Tran and Mariya Yukhymenko Michael F. Young, Stephen Slota, Andrew B. Cutter, Gerard Jalette, Greg Mullin, for Education Our Princess Is in Another Castle : A Review of Trends in Serious Gaming
Children ' s Participation Patterns in Online Communities : An Analysis of Israeli Learners in the Scratch Online Community

Online participation and content contribution are pillars of the Internet revolution and are core activities for younger generations online. This study investigated participation patterns, users' contributions and gratification mechanisms, as well as the gender differences of Israeli learners in the Scratch online community. The findings showed that: (1) Participation patterns reveal two distinct participation types "project creators" and "social participators", suggesting different users' needs. (2) Community members gratified "project creators" and "social participators" for their investment – using several forms of community feedback. Gratification at the user level was given both to "project creators" and "social participators" – community members added them as friends. The majority of the variance associated with community feedback was explained by seven predictors. However, gratification at the project level was different for the two participation types active "project creators" received less feedback on their projects, while active "social participators" received more. Project feedback positively correlated with social participation investment, but negatively correlated with project creation investment. A possible explanation is that community members primarily left feedback to their friends. (3) No gender differences were found in participation patterns or in project complexity, suggesting that Scratch provides similar opportunities to both genders in programming, learning, and participation.
Building Reconstruction Using DSM and Orthorectified Images

High resolution Digital Surface Models (DSMs) produced from airborne laser-scanning or stereo satellite images provide a very useful source of information for automated 3D building reconstruction. In this paper an investigation is reported about extraction of 3D building models from high resolution DSMs and orthorectified images produced from Worldview-2 stereo satellite imagery. The focus is on the generation of 3D models of parametric building roofs, which is the basis for creating Level Of Detail 2 (LOD2) according to the CityGML standard. In particular the building blocks containing several connected buildings with tilted roofs are investigated and the potentials and limitations of the modeling approach are discussed. The edge information extracted from orthorectified image has been employed as additional source of information in 3D reconstruction algorithm. A model driven approach based on the analysis of the 3D points of DSMs in a 2D projection plane is proposed. Accordingly, a building block is divided into smaller parts according to the direction and number of existing ridge lines for parametric building reconstruction. The 3D model is derived for each building part, and finally, a complete parametric model is formed by merging the 3D models of the individual building parts and adjusting the nodes after the merging step. For the remaining building parts that do not contain ridge lines, a prismatic model using polygon approximation of the corresponding boundary pixels is derived and merged to the parametric models to shape the final model of the building. A qualitative and quantitative assessment of the proposed method for the automatic reconstruction of buildings with parametric roofs is then provided by comparing the final model with the existing surface model as well as some field measurements. Remote Sens. 2013, 5 1682
Attention-Based End-to-End Speech Recognition on Voice Search

Recently, there has been a growing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. In this paper, we explore the use of attention-based encoder-decoder model for Mandarin speech recognition on a voice search task. Previous attempts have shown that applying attention-based encoder-decoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model. In this paper, we use character embedding to deal with the large vocabulary. Several tricks are used for effective model training, including L2 regularization, Gaussian weight noise and frame skipping. We compare two attention mechanisms and use attention smoothing to cover long context in the attention model. Taken together, these tricks allow us to finally achieve a character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on the MiTV voice search dataset. While together with a trigram language model, CER and SER reach 2.81% and 5.77%, respectively.
Motion-Appearance Co-memory Networks for Video Question Answering

Video Question Answering (QA) is an important task in understanding video temporal structure. We observe that there are three unique attributes of video QA compared with image QA: (1) it deals with long sequences of images containing richer information not only in quantity but also in variety; (2) motion and appearance information are usually correlated with each other and able to provide useful attention cues to the other; (3) different questions require different number of frames to infer the answer. Based on these observations, we propose a motion-appearance co-memory network for video QA. Our networks are built on concepts from Dynamic Memory Network (DMN) and introduces new mechanisms for video QA. Specifically, there are three salient aspects: (1) a co-memory attention mechanism that utilizes cues from both motion and appearance to generate attention; (2) a temporal conv-deconv network to generate multi-level contextual facts; (3) a dynamic fact ensemble method to construct temporal representation dynamically for different questions. We evaluate our method on TGIF-QA dataset, and the results outperform state-of-the-art significantly on all four tasks of TGIF-QA.
A review of stator fault monitoring techniques of induction motors

Condition monitoring of induction motors is a fast emerging technology for online detection of incipient faults. It avoids unexpected failure of a critical system. Approximately 30-40% of faults of induction motors are stator faults. This work presents a comprehensive review of various stator faults, their causes, detection parameters/techniques, and latest trends in the condition monitoring technology. It is aimed at providing a broad perspective on the status of stator fault monitoring to researchers and application engineers using induction motors. A list of 183 research publications on the subject is appended for quick reference.
Teleost fish interferons and their role in immunity.

Interferons (IFNs) are the hallmark of the vertebrate antiviral system. Two of the three IFN families identified in higher vertebrates are now known to be important for antiviral defence in teleost fish. Based on the cysteine patterns, the fish type I IFN family can be divided into two subfamilies, which possibly interact with distinct receptors for signalling. The fish type II IFN family consists of two members, IFN-γ with similar functions to mammalian IFN-γ and a teleost specific IFN-γ related (IFN-γrel) molecule whose functions are not fully elucidated. These two type II IFNs also appear to bind to distinct receptors to exert their functions. It has become clear that fish IFN responses are mediated by the host pattern recognition receptors and an array of transcription factors including the IFN regulatory factors, the Jak/Stat proteins and the suppressor of cytokine signalling (SOCS) molecules.
Recent advances on organic blue thermally activated delayed fluorescence (TADF) emitters for organic light-emitting diodes (OLEDs)

The design of highly emissive and stable blue emitters for organic light emitting diodes (OLEDs) is still a challenge, justifying the intense research activity of the scientific community in this field. Recently, a great deal of interest has been devoted to the elaboration of emitters exhibiting a thermally activated delayed fluorescence (TADF). By a specific molecular design consisting into a minimal overlap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) due to a spatial separation of the electron-donating and the electron-releasing parts, luminescent materials exhibiting small S1-T1 energy splitting could be obtained, enabling to thermally upconvert the electrons from the triplet to the singlet excited states by reverse intersystem crossing (RISC). By harvesting both singlet and triplet excitons for light emission, OLEDs competing and sometimes overcoming the performance of phosphorescence-based OLEDs could be fabricated, justifying the interest for this new family of materials massively popularized by Chihaya Adachi since 2012. In this review, we proposed to focus on the recent advances in the molecular design of blue TADF emitters for OLEDs during the last few years.
A comparative analysis of machine learning methods for classification type decision problems in healthcare

Advanced analytical techniques are gaining popularity in addressing complex classification type decision problems in many fields including healthcare and medicine. In this exemplary study, using digitized signal data, we developed predictive models employing three machine learning methods to diagnose an asthma patient based solely on the sounds acquired from the chest of the patient in a clinical laboratory. Although, the performances varied slightly, ensemble models (i.e., Random Forest and AdaBoost combined with Random Forest) achieved about 90% accuracy on predicting asthma patients, compared to artificial neural networks models that achieved about 80% predictive accuracy. Our results show that noninvasive, computerized lung sound analysis that rely on low-cost microphones and an embedded real-time microprocessor system would help physicians to make faster and better diagnostic decisions, especially in situations where x-ray and CT-scans are not reachable or not available. This study is a testament to the improving capabilities of analytic techniques in support of better decision making, especially in situations constraint by limited resources.
Boosting Scene Parsing Performance via Reliable Scale Prediction

Segmenting objects on suitable scales is a key factor to improve the scene parsing performance. Existing methods either simply average multi-scale results or predict scales by weakly-supervised models, due to the lack of scale labels. In this paper, we propose a novel fully-supervised Scale Prediction Model. On one hand, the proposed Scale Prediction Model learns parsing scales by the strong scale supervision, which is automatically generated from the scene parsing ground truth without any extra manually annotation. On the other hand, we explore the relationship between scale and object class, and propose to use the object class information to further improve the reliability of the scale prediction. The proposed Scale Prediction Model improves 23.1%, 20.1% and 29.3% scale prediction accuracies on the NYU Depth v2, PASCAL-Context and SIFT Flow datasets, respectively. Based on the Scale Prediction Model, we design a Scale Parsing Net (SPNet) for scene parsing, which segments each object on the scale predicted by the Scale Prediction Model. Moreover, SPNet leverages the intermediate result (i.e., the object class) to refine the parsing results. The experiment results show that SPNet outperforms many state-of-the-art methods on multiple scene parsing datasets.
BIM-based Sustainability Analysis : An Evaluation of Building Performance Analysis Software

With the rising cost of energy and growing environmental concerns, the demand for sustainable building facilities with minimal environmental impact is increasing. The most effective decisions regarding sustainability in a building facility are made in the early design and preconstruction stages. In this context, Building Information Modeling (BIM) can aid in performing complex building performance analyses to ensure an optimized sustainable building design. In this exploratory research, three building performance analysis software namely EcotectTM, Green Building StudioTM (GBS) and Virtual EnvironmentTM are evaluated to gage their suitability for BIM-based sustainability analysis. First presented in this paper are the main concepts of sustainability and BIM. Then an evaluation of the three abovementioned software is performed with their pros and cons. An analytical weight-based scoring system is used for this purpose. At the end, a conceptual framework is presented to illustrate how construction companies can use BIM for sustainability analysis and evaluate LEED (Leadership in Energy and Environmental Design) rating of a building facility.
Virtually Ostracized: Studying Ostracism in Immersive Virtual Environments

Electronic-based communication (such as Immersive Virtual Environments; IVEs) may offer new ways of satisfying the need for social connection, but they also provide ways this need can be thwarted. Ostracism, being ignored and excluded, is a common social experience that threatens fundamental human needs (i.e., belonging, control, self-esteem, and meaningful existence). Previous ostracism research has made use of a variety of paradigms, including minimal electronic-based interactions (e.g., Cyberball) and communication (e.g., chatrooms and Short Message Services). These paradigms, however, lack the mundane realism that many IVEs now offer. Further, IVE paradigms designed to measure ostracism may allow researchers to test more nuanced hypotheses about the effects of ostracism. We created an IVE in which ostracism could be manipulated experimentally, emulating a previously validated minimal ostracism paradigm. We found that participants who were ostracized in this IVE experienced the same negative effects demonstrated in other ostracism paradigms, providing, to our knowledge, the first evidence of the negative effects of ostracism in virtual environments. Though further research directly exploring these effects in online virtual environments is needed, this research suggests that individuals encountering ostracism in other virtual environments (such as massively multiplayer online role playing games; MMORPGs) may experience negative effects similar to those of being ostracized in real life. This possibility may have serious implications for individuals who are marginalized in their real life and turn to IVEs to satisfy their need for social connection.
Background Subtraction with Real-time Semantic Segmentation

Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter B and a real-time semantic segmenter S. The BGS segmenter B aims to construct background models and segments foreground objects. The realtime semantic segmenter S is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. B and S work in parallel on two threads. For each input frame It, the BGS segmenter B computes a preliminary foreground/background (FG/BG) mask Bt. At the same time, the real-time semantic segmenter S extracts the object-level semantics St. Then, some specific rules are applied on Bt and St to generate the final detection Dt. Finally, the refined FG/BG mask Dt is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves stateof-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.
ITHEA APPLICATION OF DATA MINING TECHNIQUES FOR DIRECT MARKETING Anatoli Nachev

This paper presents a case study of data mining modeling techniques for direct marketing. It focuses to three stages of the CRISP-DM process for data mining projects: data preparation, modeling, and evaluation. We address some gaps in previous studies, namely: selection of model hyper-parameters and controlling the problem of under-fitting and over-fitting; dealing with randomness and 'lucky' set composition; the role of variable selection and data saturation. In order to avoid overestimation of the model performance, we applied a double-testing procedure, which combines cross-validation, multiple runs over random selection of the folds and hyper-parameters, and multiple runs over random selection of partitions. The paper compares modeling techniques, such as neural nets, logistic regression, naive Bayes, linear and quadratic discriminant analysis, all tested at different levels of data saturation. To illustrate the issues discussed, we built predictive models, which outperform those proposed by other studies.
Story Comprehension for Predicting What Happens Next

Automatic story comprehension is a fundamental challenge in Natural Language Understanding, and can enable computers to learn about social norms, human behavior and commonsense. In this paper, we present a story comprehension model that explores three distinct semantic aspects: (i) the sequence of events described in the story, (ii) its emotional trajectory, and (iii) its plot consistency. We judge the model’s understanding of real-world stories by inquiring if, like humans, it can develop an expectation of what will happen next in a given story. Specifically, we use it to predict the correct ending of a given short story from possible alternatives. The model uses a hidden variable to weigh the semantic aspects in the context of the story. Our experiments demonstrate the potential of our approach to characterize these semantic aspects, and the strength of the hidden variable based approach. The model outperforms the stateof-the-art approaches and achieves best results on a publicly available dataset.
E-Participation , Transparency , and Trust in Local Government

Th is article examines the relationship between electronic participation (e-participation) and trust in local government by focusing on fi ve dimensions of the e-participation process: (1) satisfaction with e-participation applications, (2) satisfaction with government responsiveness to e-participants, (3) e-participants’ development through the participation, (4) perceived infl uence on decision making, and (5) assessment of government transparency. Using data from the 2009 E-Participation Survey in Seoul Metropolitan Government, this article fi nds that e-participants’ satisfaction with e-participation applications is directly associated with their development and their assessment of government transparency. Th e fi ndings reveal that e-participants’ satisfaction with government responsiveness is positively associated with their perceptions of infl uencing government decision making. Furthermore, there is a positive association between e-participants’ perception of infl uencing government decision making and their assessment of government transparency. Finally, the article fi nds that there is a positive association between e-participants’ assessment of government transparency and their trust in the local government providing the e-participation program.
A Trainable System for Object Detection

This paper presents a general, trainable system for object detection in unconstrained, cluttered scenes. The system derives much of its power from a representation that describes an object class in terms of an overcomplete dictionary of local, oriented, multiscale intensity differences between adjacent regions, efficiently computable as a Haar wavelet transform. This example-based learning approach implicitly derives a model of an object class by training a support vector machine classifier using a large set of positive and negative examples. We present results on face, people, and car detection tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver assistance system.
Multiobjective Reinforcement Learning: A Comprehensive Overview

Reinforcement learning (RL) is a powerful paradigm for sequential decision-making under uncertainties, and most RL algorithms aim to maximize some numerical value which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control systems, so recently there has been growing interest in solving multiobjective reinforcement learning (MORL) problems where there are multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. The basic architecture, research topics, and naïve solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are comprehensively reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical RL, and multiagent RL. Moreover, research challenges and open problems of MORL techniques are suggested.