nils-herrmann/clean_scirepeval huggingface dataset:
Entity Disambiguation with Web Links

Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known conll and tac data sets. Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.
Interaction for Visualization

Visualization has become a valuable means for data exploration and analysis. Interactive visualization combines expressive graphical representations and effective user interaction. Although interaction is an important component of visualization approaches, much of the visualization literature tends to pay more attention to the graphical representation than to interaction. e goal of this work is to strengthen the interaction side of visualization. Based on a brief review of general aspects of interaction, we develop an interaction-oriented view on visualization. is view comprises five key aspects: the data, the tasks, the technology, the human, as well as the implementation. Picking up these aspects individually, we elaborate several interaction methods for visualization. We introduce a multi-threading architecture for efficient interactive exploration. We present interaction techniques for different types of data (e.g., multivariate data, spatio-temporal data, graphs) and different visualization tasks (e.g., exploratory navigation, visual comparison, visual editing). With respect to technology, we illustrate approaches that utilize modern interaction modalities (e.g., touch, tangibles, proxemics) as well as classic ones. While the human is important throughout this work, we also consider automatic methods to assist the interactive part. In addition to solutions for individual problems, a major contribution of this work is the overarching view of interaction in visualization as a whole. is includes a critical discussion of interaction, the identification of links between the key aspects of interaction, and the formulation of research topics for future work with a focus on interaction.
The Creative Process: A Computer Model of Storytelling and Creativity, by Scott R. Turner

Within cognitive science and psychology, there has been a good deal of interest recently in the topic of creativity. In this book, Scott Turner of the University of California, Los Angeles, presents a theory of creativity applied to generating small stories. Turner can be thought of as a member of the third generation of the Schank family: first, of course, there was grandfather Roger Schank, who in the 1970s with Robert Abelson at Yale, embarked on the research project of understanding narrative text using the ideas of goals, plans, and scripts. The attempt was to propose computational models that would accomplish aspects of narrative understanding. The second generation was a talented group of people, including Wendy Lehnert and Robert Wilensky, who did their Ph.D.s at Yale on story understanding. Turner is a member of a third generation, advised by Michael Dyer who also obtained his Ph.D. at Yale and then moved to an academic position at UCLA. Dyer had turned his attention to story generation as well as story understanding. In the classic mode, Turner took on rather too much for his Ph.D. He wrote a large artificial intelligence program, 17,000 lines of Lisp code, that produces a reasonable output that could, with the suspension of a certain amount of disbelief, pass for the production of a human author. He calls his program "Minstrel." It generates stories of half a page or so about knights and ladies at the court of King Arthur. The program was the core of his Ph.D. thesis, and this is the book of the program. Here is a sample from one of Minstrel's stories (p. 72):
Factors Influencing Online Shopping Behavior of Urban Consumers in India

This study examines the factors influencing online shopping behavior of urban consumers in the State of Andhra Pradesh, India and provides a better understanding of the potential of electronic marketing for both researchers and online retailers. Data from a sample of 1500 Internet users (distributed evenly among six selected major cities) was collected by a structured questionnaire covering demographic profile and the factors influencing online shopping. Factor analysis and multiple regression analysis are used to establish relationship between the factors influencing online shopping and online shopping behavior. The study identified that perceived risk and price positively influenced online shopping behavior. Results also indicated that positive attitude, product risk and financial risk affect negatively the online shopping behavior. Factors Influencing Online Shopping Behavior of Urban Consumers in India
A Sentiment-aligned Topic Model for Product Aspect Rating Prediction

Aspect-based opinion mining has attracted lots of attention today. In this thesis, we address the problem of product aspect rating prediction, where we would like to extract the product aspects, and predict aspect ratings simultaneously. Topic models have been widely adapted to jointly model aspects and sentiments, but existing models may not do the prediction task well due to their weakness in sentiment extraction. The sentiment topics usually do not have clear correspondence to commonly used ratings, and the model may fail to extract certain kinds of sentiments due to skewed data. To tackle this problem, we propose a sentiment-aligned topic model(SATM), where we incorporate two types of external knowledge: product-level overall rating distribution and word-level sentiment lexicon. Experiments on real dataset demonstrate that SATM is effective on product aspect rating prediction, and it achieves better performance compared to the existing approaches.
Visualizing and Understanding Atari Agents

While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case study using Atari 2600 environments. In particular, we focus on using saliency maps to understand how an agent learns and executes a policy. We introduce a method for generating useful saliency maps and use it to show 1) what strong agents attend to, 2) whether agents are making decisions for the right or wrong reasons, and 3) how agents evolve during learning. We also test our method on non-expert human subjects and find that it improves their ability to reason about these agents. Overall, our results show that saliency information can provide significant insight into an RL agent’s decisions and learning behavior.
Learning Analytics: drivers, developments and challenges

Learning analytics is a significant area of technology-enhanced learning that has emerged during the last decade. This review of the field begins with an examination of the technological, educational and political factors that have driven the development of analytics in educational settings. It goes on to chart the emergence of learning analytics, including their origins in the 20th century, the development of data-driven analytics, the rise of learningfocused perspectives and the influence of national economic concerns. It next focuses on the relationships between learning analytics, educational data mining and academic analytics. Finally, it examines developing areas of learning analytics research, and identifies a series of future challenges.
Positive-Unlabeled Learning with Non-Negative Risk Estimator

From only positive (P) and unlabeled (U) data, a binary classifier could be trained with PU learning, in which the state of the art is unbiased PU learning. However, if its model is very flexible, empirical risks on training data will go negative, and we will suffer from serious overfitting. In this paper, we propose a non-negative risk estimator for PU learning: when getting minimized, it is more robust against overfitting, and thus we are able to use very flexible models (such as deep neural networks) given limited P data. Moreover, we analyze the bias, consistency, and mean-squared-error reduction of the proposed risk estimator, and bound the estimation error of the resulting empirical risk minimizer. Experiments demonstrate that our risk estimator fixes the overfitting problem of its unbiased counterparts.
MFCC and its applications in speaker recognition

Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step for speaker recognition. Many algorithms are suggested/developed by the researchers for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient (MFCC) feature has been used for designing a text dependent speaker identification system. Some modifications to the existing technique of MFCC for feature extraction are also suggested to improve the speaker recognition efficiency.
Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have gained popularity in many computer vision applications such as image classification, face detection, and video analysis, because of their ability to train and classify with high accuracy. Due to multiple convolution and fully-connected layers that are compute-/memory-intensive, it is difficult to perform real-time classification with low power consumption on today?s computing systems. FPGAs have been widely explored as hardware accelerators for CNNs because of their reconfigurability and energy efficiency, as well as fast turn-around-time, especially with high-level synthesis methodologies. Previous FPGA-based CNN accelerators, however, typically implemented generic accelerators agnostic to the CNN configuration, where the reconfigurable capabilities of FPGAs are not fully leveraged to maximize the overall system throughput. In this work, we present a systematic design space exploration methodology to maximize the throughput of an OpenCL-based FPGA accelerator for a given CNN model, considering the FPGA resource constraints such as on-chip memory, registers, computational resources and external memory bandwidth. The proposed methodology is demonstrated by optimizing two representative large-scale CNNs, AlexNet and VGG, on two Altera Stratix-V FPGA platforms, DE5-Net and P395-D8 boards, which have different hardware resources. We achieve a peak performance of 136.5 GOPS for convolution operation, and 117.8 GOPS for the entire VGG network that performs ImageNet classification on P395-D8 board.
Graph Clustering: Block-models and model free results

Clustering graphs under the Stochastic Block Model (SBM) and extensions are well studied. Guarantees of correctness exist under the assumption that the data is sampled from a model. In this paper, we propose a framework, in which we obtain “correctness” guarantees without assuming the data comes from a model. The guarantees we obtain depend instead on the statistics of the data that can be checked. We also show that this framework ties in with the existing model-based framework, and that we can exploit results in model-based recovery, as well as strengthen the results existing in that area of research.
Review of Silicon Carbide Power Devices and Their Applications

Silicon carbide (SiC) power devices have been investigated extensively in the past two decades, and there are many devices commercially available now. Owing to the intrinsic material advantages of SiC over silicon (Si), SiC power devices can operate at higher voltage, higher switching frequency, and higher temperature. This paper reviews the technology progress of SiC power devices and their emerging applications. The design challenges and future trends are summarized at the end of the paper.
Deep Recurrent and Convolutional Neural Networks for Automated Behavior Classification

In this thesis we investigate different methods of automating behavioral analysis in animal videos using shapeand motion-based models, with a focus on classifying large datasets of rodent footage. In order to leverage the recent advances in deep learning techniques a massive number of training samples is required, which has lead to the development of a data transfer pipeline to gather footage from multiple video sources and a custom-built web-based video annotation tool to create annotation datasets. Finally we develop and compare new deep convolutional and recurrent-convolutional neural network architectures that outperform existing systems.
Automatic Event Salience Identification

Identifying the salience (i.e. importance) of discourse units is an important task in language understanding. While events play important roles in text documents, little research exists on analyzing their saliency status. This paper empirically studies the Event Salience task and proposes two salience detection models based on content similarities and discourse relations. The first is a feature based salience model that incorporates similarities among discourse units. The second is a neural model that captures more complex relations between discourse units. Tested on our new largescale event salience corpus, both methods significantly outperform the strong frequency baseline, while our neural model further improves the feature based one by a large margin. Our analyses demonstrate that our neural model captures interesting connections between salience and discourse unit relations (e.g., scripts and frame structures).
Context and Syntax embeddings for Question Answering On Stack Overflow

Question answering (QA) systems rely on both knowledge bases and unstructured text corpora. Domain-specific QA presents a unique challenge, since relevant knowledge bases are often lacking and unstructured text is difficult to query and parse. This project focuses on the QUASAR-S dataset (Dhingra et al., 2017) constructed from the community QA site Stack Overflow. QUASAR-S consists of Cloze-style questions about software entities and a large background corpus of communitygenerated posts, each tagged with relevant software entities. We incorporate the tag entities as context for the QA task and find that modeling co-occurrence of tags and answers in posts leads to significant accuracy gains. To this end, we propose CASE, a hybrid of an RNN language model and a tag-answer co-occurrence model which achieves state-ofthe-art accuracy on the QUASAR-S dataset. We also find that this approach — modeling both question sentences and context-answer co-occurrence — is effective for other QA tasks. Using only language and co-occurrence modeling on the training set, CASE is competitive with the state-of-the-art method on the SPADES dataset (Bisk et al., 2016) which uses a knowledge base.
Object Hallucination in Image Captioning

Despite continuously improving performance, contemporary image captioning models are prone to “hallucinating” objects that are not actually in a scene. One problem is that standard metrics only measure similarity to ground truth captions and may not fully capture image relevance. In this work, we propose a new image relevance metric to evaluate current models with veridical visual labels and assess their rate of object hallucination. We analyze how captioning model architectures and learning objectives contribute to object hallucination, explore when hallucination is likely due to image misclassification or language priors, and assess how well current sentence metrics capture object hallucination. We investigate these questions on the standard image captioning benchmark, MSCOCO, using a diverse set of models. Our analysis yields several interesting findings, including that models which score best on standard sentence metrics do not always have lower hallucination and that models which hallucinate more tend to make errors driven by language priors.
Online Initialization and Automatic Camera-IMU Extrinsic Calibration for Monocular Visual-Inertial SLAM

Most of the existing monocular visual-inertial SLAM techniques assume that the camera-IMU extrinsic parameters are known, therefore these methods merely estimate the initial values of velocity, visual scale, gravity, biases of gyroscope and accelerometer in the initialization stage. However, it's usually a professional work to carefully calibrate the extrinsic parameters, and it is required to repeat this work once the mechanical configuration of the sensor suite changes slightly. To tackle this problem, we propose an online initialization method to automatically estimate the initial values and the extrinsic parameters without knowing the mechanical configuration. The biases of gyroscope and accelerometer are considered in our method, and a convergence criteria for both orientation and translation calibration is introduced to identify the convergence and to terminate the initialization procedure. In the three processes of our method, an iterative strategy is firstly introduced to iteratively estimate the gyroscope bias and the extrinsic orientation. Secondly, the scale factor, gravity, and extrinsic translation are approximately estimated without considering the accelerometer bias. Finally, these values are further optimized by a refinement algorithm in which the accelerometer bias and the gravitational magnitude are taken into account. Extensive experimental results show that our method achieves competitive accuracy compared with the state-of-the-art with less calculation.
If it goes up, must it come down? Chronic stress and the hypothalamic-pituitary-adrenocortical axis in humans.

The notion that chronic stress fosters disease by activating the hypothalamic-pituitary-adrenocortical (HPA) axis is featured prominently in many theories. The research linking chronic stress and HPA function is contradictory, however, with some studies reporting increased activation, and others reporting the opposite. This meta-analysis showed that much of the variability is attributable to stressor and person features. Timing is an especially critical element, as hormonal activity is elevated at stressor onset but reduces as time passes. Stressors that threaten physical integrity, involve trauma, and are uncontrollable elicit a high, flat diurnal profile of cortisol secretion. Finally, HPA activity is shaped by a person's response to the situation; it increases with subjective distress but is lower in persons with posttraumatic stress disorder.
Perseverance in self-perception and social perception: biased attributional processes in the debriefing paradigm.

Two experiments demonstrated that self-perceptions and social perceptions may persevere after the initial basis for such perceptions has been completely discredited. In both studies subjects first received false feedback, indicating that they had either succeeded or failed on a novel discrimination task and then were thoroughly debriefed concerning the predetermined and random nature of this outcome manipulation. In experiment 2, both the initial outcome manipulation and subsequent debriefing were watched and overheard by observers. Both actors and observers showed substantial perseverance of initial impressions concerning the actors' performance and abilities following a standard "outcome" debriefing. "Process" debriefing, in which explicit discussion of the perseverance process was provided, generally proved sufficient to eliminate erroneous self-perceptions. Biased attribution processes that might underlie perserverance phenomena and the implications of the present data for the ethical conduct of deception research are discussed.
License plate recognition system

The purpose of this article was to build a license plates recognition system with high accuracy at night. The system, based on regular PC, catches video frames which include a visible car license plate and processes them. Once a license plate is detected, its digits are recognized, and then checked against a database. The focus is on the modified algorithms to identify the individual characters. In this article, we use the template-matching method and neural net method together, and make some progress on the study before. The result showed that the accuracy is higher at night.
Health Information Systems.

This article provides surgical pathologists an overview of health information systems (HISs): what they are, what they do, and how such systems relate to the practice of surgical pathology. Much of this article is dedicated to the electronic medical record. Information, in how it is captured, transmitted, and conveyed, drives the effectiveness of such electronic medical record functionalities. So critical is information from pathology in integrated clinical care that surgical pathologists are becoming gatekeepers of not only tissue but also information. Better understanding of HISs can empower surgical pathologists to become stakeholders who have an impact on the future direction of quality integrated clinical care.
Transferring Grasp Configurations using Active Learning and Local Replanning

We present a new approach to transfer grasp configurations from prior example objects to novel objects. We assume the novel and example objects have the same topology and similar shapes. We perform 3D segmentation on these objects using geometric and semantic shape characteristics. We compute a grasp space for each part of the example object using active learning. We build bijective contact mapping between these model parts and compute the corresponding grasps for novel objects. Finally, we assemble the individual parts and use local replanning to adjust grasp configurations while maintaining its stability and physical constraints. Our approach is general, can handle all kind of objects represented using mesh or point cloud and a variety of robotic hands.
Density-based semi-supervised clustering

Semi-supervised clustering methods guide the data partitioning and grouping process by exploiting background knowledge, among else in the form of constraints. In this study, we propose a semi-supervised density-based clustering method. Density-based algorithms are traditionally used in applications, where the anticipated groups are expected to assume non-spherical shapes and/or differ in cardinality or density. Many such applications, among else those on GIS, lend themselves to constraint-based clustering, because there is a priori knowledge on the group membership of some records. In fact, constraints might be the only way to prevent the formation of clusters that do not conform to the applications’ semantics. For example, geographical objects, e.g. houses, separated by a borderline or a river may not be assigned to the same cluster, independently of their physical proximity. We first provide an overview of constraint-based clustering for different families of clustering algorithms. Then, we concentrate on the density-based algorithms’ family and select the algorithm DBSCAN, which we enhance with Must-Link and Cannot-Link constraints. Our enhancement is seamless: we allow DBSCAN to build temporary clusters, which we then split or merge according to the constraints. Our experiments on synthetic and real datasets show that our approach improves the performance of the algorithm.