nils-herrmann/clean_scirepeval huggingface dataset:
Compiling Pattern Matching

Pattern matching is a very powerful and useful device in programming. In functional languages it emerged in SASL [Turn76] and Hope [BursS0], and has also found its way into SML [Miln84]. The pattern mathing described here is that of LML which is a lazy ([Frie76] and [Henri76]) variant of ML. The pattern matching in LML evolved independently of that in SML so they are not (yet) the same, although very similar. The compilation of pattern matching in SML has been addressed in [Card84]. The LML compiler project began as an attempt to produce efficient code for a typed functional language with lazy evaluation. Since we regard pattern matching as an important language feature it should also yield efficient code. Only pattern matching in case expressions is described here, since we regard this as the basic pattern matching facility in the language. All other types of pattern mathing used in LML can be easily translated into case expressions, see [Augu84] for details. The compilation (of pattern matching) proceeds in several steps: • transform all pattern matching to case expressions. • transform complex case expressions into expressions that are easy to generate code for. • generate G-code for the case expressions, and from that machine code for the target machine.
Building, maintaining, and using knowledge bases: a report from the trenches

A knowledge base (KB) contains a set of concepts, instances, and relationships. Over the past decade, numerous KBs have been built, and used to power a growing array of applications. Despite this flurry of activities, however, surprisingly little has been published about the end-to-end process of building, maintaining, and using such KBs in industry. In this paper we describe such a process. In particular, we describe how we build, update, and curate a large KB at Kosmix, a Bay Area startup, and later at WalmartLabs, a development and research lab of Walmart. We discuss how we use this KB to power a range of applications, including query understanding, Deep Web search, in-context advertising, event monitoring in social media, product search, social gifting, and social mining. Finally, we discuss how the KB team is organized, and the lessons learned. Our goal with this paper is to provide a real-world case study, and to contribute to the emerging direction of building, maintaining, and using knowledge bases for data management applications.
Heart Rate Variability Biofeedback for Major Depression

Heart rate variability for the treatment of major depression is a novel, alternative approach that can offer symptom reduction with minimal-to-no noxious side effects. The following material will illustrate some of the work being conducted at our laboratory to demonstrate the efficacy of heart rate variability. Namely, results will be presented regarding our published work on an initial open-label study and subsequent results of a small, unfinished randomized controlled trial.
Bayesian Network Models in Cyber Security: A Systematic Review

Bayesian Networks (BNs) are an increasingly popular modelling technique in cyber security especially due to their capability to overcome data limitations. This is also exemplified by the growth of BN models development in cyber security. However, a comprehensive comparison and analysis of these models is missing. In this paper, we conduct a systematic review of the scientific literature and identify 17 standard BN models in cyber security. We analyse these models based on 8 different criteria and identify important patterns in the use of these models. A key outcome is that standard BNs are noticeably used for problems especially associated with malicious insiders. This study points out the core range of problems that were tackled using standard BN models in cyber security, and illuminates key research gaps.
Combined Tree Kernel-based classifiers for Assessing Quality of Scientific Text

This document describes Tree Kernel-SVM based methods for identifying sentences that could be improved in scientific text. This has the goal of contributing to the body of knowledge that attempt to build assistive tools to aid scientist improve the quality of their writings. Our methods consist of a combination of the output from multiple support vector machines which use Tree Kernel computations. Therefore, features for individual sentences are trees that reflect their grammatical structure. For the AESW 2016 Shared Task we built systems that provide probabilistic and binary outputs by using these models for trees comparisons.
SIMANN: FORTRAN module to perform Global Optimization of Statistical Functions with Simulated Annealing

Many statistical methods rely on numerical optimization to estimate a model’s parameters. Unfortunately, conventional algorithms sometimes fail. Even when they do converge, there is no assurance that they have found the global, rather than a local, optimum. We test a new optimization algorithm, simulated annealing, on four econometric problems and compare it to three common conventional algorithms. Not only can simulated annealing find the global optimum, it is also less likely to fail on difficult functions because it is a very robust algorithm. The promise of simulated annealing is demonstrated on the four econometric problems.
An attention model based on spatial transformers for scene recognition

Scene recognition is an important and challenging task in computer vision. We propose an end-to-end pipeline by combing convolutional neural networks (CNNs) with explicit attention model to determine several meaningful regions of original images for scene recognition. In the proposed pipeline, the spatial transformer network is leveraged as the attention module, which can automatically learn the scales and movements of centers of attention windows. As for feature extraction, the basic CNN architecture is utilized. Furthermore, the stronger descriptors of scenes are constructed by feature fusion. The highlight of our proposed network is that it is capable to localize discriminative regions from an image in a data-driven manner without any additional supervision. We conduct experiments on a subset of the Places205 database to evaluate the performance of the proposed basic network and the involved parameters. Our model achieves state-of-the-art top-1 accuracy 82.10% on the evaluation dataset comparing with fine-tuned PlacesCNN (80.98%). We find that our model is able to learn informative attention regions for discriminating scene categories.
Building Systematic Reviews Using Automatic Text Classification Techniques

The amount of information in medical publications continues to increase at a tremendous rate. Systematic reviews help to process this growing body of information. They are fundamental tools for evidence-based medicine. In this paper, we show that automatic text classification can be useful in building systematic reviews for medical topics to speed up the reviewing process. We propose a per-question classification method that uses an ensemble of classifiers that exploit the particular protocol of a systematic review. We also show that when integrating the classifier in the human workflow of building a review the per-question method is superior to the global method. We test several evaluation measures on a real dataset.
Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System

To establish an automatic conversation system between humans and computers is regarded as one of the most hardcore problems in computer science, which involves interdisciplinary techniques in information retrieval, natural language processing, artificial intelligence, etc. The challenges lie in how to respond so as to maintain a relevant and continuous conversation with humans. Along with the prosperity of Web 2.0, we are now able to collect extremely massive conversational data, which are publicly available. It casts a great opportunity to launch automatic conversation systems. Owing to the diversity of Web resources, a retrieval-based conversation system will be able to find at least some responses from the massive repository for any user inputs. Given a human issued message, i.e., query, our system would provide a reply after adequate training and learning of how to respond. In this paper, we propose a retrieval-based conversation system with the deep learning-to-respond schema through a deep neural network framework driven by web data. The proposed model is general and unified for different conversation scenarios in open domain. We incorporate the impact of multiple data inputs, and formulate various features and factors with optimization into the deep learning framework. In the experiments, we investigate the effectiveness of the proposed deep neural network structures with better combinations of all different evidence. We demonstrate significant performance improvement against a series of standard and state-of-art baselines in terms of p@1, MAP, nDCG, and MRR for conversational purposes.
Incorporating Knowledge Graph Embeddings into Topic Modeling

Probabilistic topic models could be used to extract lowdimension topics from document collections. However, such models without any human knowledge often produce topics that are not interpretable. In recent years, a number of knowledge-based topic models have been proposed, but they could not process fact-oriented triple knowledge in knowledge graphs. Knowledge graph embeddings, on the other hand, automatically capture relations between entities in knowledge graphs. In this paper, we propose a novel knowledge-based topic model by incorporating knowledge graph embeddings into topic modeling. By combining latent Dirichlet allocation, a widely used topic model with knowledge encoded by entity vectors, we improve the semantic coherence significantly and capture a better representation of a document in the topic space. Our evaluation results will demonstrate the effectiveness of our method.
EmoNets: Multimodal deep learning approaches for emotion recognition in video

The task of the Emotion Recognition in the Wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies. The videos depict acted-out emotions under realistic conditions with a large degree of variation in attributes such as pose and illumination, making it worthwhile to explore approaches which consider combinations of features from multiple modalities for label assignment. In this paper we present our approach to learning several specialist models using deep learning techniques, each focusing on one modality. Among these are a convolutional neural network, focusing on capturing visual information in detected faces, a deep belief net focusing on the representation of the audio stream, a K-Means based “bag-of-mouths” model, which extracts visual features around the mouth region and a relational autoencoder, which addresses spatio-temporal aspects of videos. We explore multiple methods for the combination of cues from these modalities into one common classifier. This achieves a considerably greater accuracy than predictions from our strongest single-modality classifier. Our method was the winning submission in the 2013 EmotiW challenge and achieved a test set accuracy of 47.67 % on the 2014 dataset.
Learning 3D Shape Completion Under Weak Supervision

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet (Chang et al. Shapenet: an information-rich 3d model repository, 2015. ) and ModelNet (Wu et al.,  2015) as well as on real robotics data from KITTI (Geiger et al.,  2012) and Kinect (Yang et al., 3d object dense reconstruction from a single depth view, 2018. ), we demonstrate that the proposed amortized maximum likelihood approach is able to compete with the fully supervised baseline of Dai et al. ( 2017) and outperforms the data-driven approach of Engelmann et al. ( 2016), while requiring less supervision and being significantly faster.
Real-time prediction of online shoppers’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks

In this paper, we propose a real-time online shopper behavior analysis system consisting of two modules which simultaneously predicts the visitor’s shopping intent and Web site abandonment likelihood. In the first module, we predict the purchasing intention of the visitor using aggregated pageview data kept track during the visit along with some session and user information. The extracted features are fed to random forest (RF), support vector machines (SVMs), and multilayer perceptron (MLP) classifiers as input. We use oversampling and feature selection preprocessing steps to improve the performance and scalability of the classifiers. The results show that MLP that is calculated using resilient backpropagation algorithm with weight backtracking produces significantly higher accuracy and F1 Score than RF and SVM. Another finding is that although clickstream data obtained from the navigation path followed during the online visit convey important information about the purchasing intention of the visitor, combining them with session information-based features that possess unique information about the purchasing interest improves the success rate of the system. In the second module, using only sequential clickstream data, we train a long short-term memory-based recurrent neural network that generates a sigmoid output showing the probability estimate of visitor’s intention to leave the site without finalizing the transaction in a prediction horizon. The modules are used together to determine the visitors which have purchasing intention but are likely to leave the site in the prediction horizon and take actions accordingly to improve the Web site abandonment and purchase conversion rates. Our findings support the feasibility of accurate and scalable purchasing intention prediction for virtual shopping environment using clickstream and session information data.
Corpora and collocations

3 Cooccurrence and frequency counts 11 12 3.1 Surface cooccurrence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 13 3.2 Textual cooccurrence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 14 3.3 Syntactic cooccurrence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 15 3.4 Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 16
Augmentation for Visual Question Answering

Data augmentation is widely used to train deep neural networks for image classification tasks. Simply flipping images can help learning by increasing the number of training images by a factor of two. However, data augmentation in natural language processing is much less studied. Here, we describe two methods for data augmentation for Visual Question Answering (VQA). The first uses existing semantic annotations to generate new questions. The second method is a generative approach using recurrent neural networks. Experiments show the proposed schemes improve performance of baseline and state-of-the-art VQA algorithms.
Synthesis and Electrochemical Properties of LiNi0.5Mn1.5O4 Cathode Materials with Cr3+ and F− Composite Doping for Lithium-Ion Batteries

A Cr3+ and F- composite-doped LiNi0.5Mn1.5O4 cathode material was synthesized by the solid-state method, and the influence of the doping amount on the material's physical and electrochemical properties was investigated. The structure and morphology of the cathode material were characterized by XRD, SEM, TEM, and HRTEM, and the results revealed that the sample exhibited clear spinel features. No Cr3+ and F- impurity phases were found, and the spinel structure became more stable. The results of the charge/discharge tests, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) test results suggested that LiCr0.05Ni0.475Mn1.475O3.95F0.05 in which the Cr3+ and F- doping amounts were both 0.05, had the optimal electrochemical properties, with discharge rates of 0.1, 0.5, 2, 5, and 10 C and specific capacities of 134.18, 128.70, 123.62, 119.63, and 97.68 mAh g-1 , respectively. After 50 cycles at a rate of 2 C, LiCr0.05Ni0.475Mn1.475O3.95F0.05 showed extremely good cycling performance, with a discharge specific capacity of 121.02 mAh g-1 and a capacity retention rate of 97.9%. EIS test revealed that the doping clearly decreased the charge-transfer resistance.
ICP and the Squid web cache

We describe the structure and functionality of the Internet Cache Protocol (ICP) and its implementation in the Squid Web Caching software. ICP is a lightweight message format used for communication among Web caches. Caches exchange ICP queries and replies to gather information to use in selecting the most appropriate location from which to retrieve an object. We present background on the history of ICP, and discuss issues in ICP deployment, e ciency, security, and interaction with other aspects of Web tra c behavior. We catalog successes, failures, and lessons learned from using ICP to deploy a global Web cache hierarchy.
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision. Here, for the first time, we demonstrate ResNet-18, ResNet-34, ResNet-50, ResNet152, Inception-v3, densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models. We also demonstrate for the first time ResNet-18, ResNet-34, and ResNet-50 4-bit models that match the accuracy of the full-precision baseline networks. Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary. The number of iterations required by stochastic gradient descent to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates. By drawing inspiration from this observation, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat noise introduced by quantizing weights and activations during training, by using larger batches along with matched learning rate annealing. Together, these two techniques offer a promising heuristic to discover low-precision networks, if they exist, close to fp32 precision baseline networks.
A 3 DTV Approach Using Depth-Image-Based Rendering ( DIBR )

This paper will present details of a system that allows for an evolutionary introduction of depth perception into the existing 2D digital TV framework. The work is part of the European Information Society Technologies (IST) project “Advanced Three-Dimensional Television System Technologies” (ATTEST), an activity, where industries, research centers and universities have joined forces to design a backwards-compatible, flexible and modular broadcast 3D-TV system . In contrast to former proposals, which often relied on the basic concept of “stereoscopic” video, this new idea is based on a more flexible joint transmission of monoscopic video and associated per-pixel depth information. From this data representation, one or more “virtual” views of the 3D scene can then be synthesized in real-time at the receiver side by means of so-called depthimage-based rendering (DIBR) techniques. This paper (a) highlights the advantages of this new approach on 3DTV and (b) develops an efficient algorithm for the generation of “virtual” 3D views that can be reproduced on any stereoscopicor autostereoscopic 3D-TV display.
Pattern-based Topics for Document Modelling in Information Filtering

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users' information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users' interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features; and (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user's information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models.
Wafer defect patterns recognition based on OPTICS and multi-label classification

In the industry of integrated circuits, defect patterns shown on a wafer map contain crucial information for quality engineers to find the cause of defect to increase yield. This paper proposes a method for wafer defect pattern recognition which could recognize more than one defect patterns based on Ordering Point to Identify the Cluster Structure(OPTICS) and Support Vector Machine(SVM). The effectiveness of the proposed method has been verified from following three aspects from a real-world data set of wafer maps(WM-811K): salient defect pattern recognition accuracy up to 94.3% and the accuracy of some types has an obvious improvement, multi-patterns recognition accuracy(82.0%), and computation time has a significantly reduction.