nils-herrmann/clean_scirepeval huggingface dataset:
Parser Adaptation and Projection with Quasi-Synchronous Grammar Features

We connect two scenarios in structured learning: adapting a parser trained on one corpus to another annotation style, and projecting syntactic annotations from one language to another. We propose quasisynchronous grammar (QG) features for these structured learning tasks. That is, we score a aligned pair of source and target trees based on local features of the trees and the alignment. Our quasi-synchronous model assigns positive probability to any alignment of any trees, in contrast to a synchronous grammar, which would insist on some form of structural parallelism. In monolingual dependency parser adaptation, we achieve high accuracy in translating among multiple annotation styles for the same sentence. On the more difficult problem of cross-lingual parser projection, we learn a dependency parser for a target language by using bilingual text, an English parser, and automatic word alignments. Our experiments show that unsupervised QG projection improves on parses trained using only highprecision projected annotations and far outperforms, by more than 35% absolute dependency accuracy, learning an unsupervised parser from raw target-language text alone. When a few target-language parse trees are available, projection gives a boost equivalent to doubling the number of target-language trees. ∗The first author would like to thank the Center for Intelligent Information Retrieval at UMass Amherst. We would also like to thank Noah Smith and Rebecca Hwa for helpful discussions and the anonymous reviewers for their suggestions for improving the paper.
Towards Reference Architecture for Cryptocurrencies: Bitcoin Architectural Analysis

Bit coin is a new protocol with a potential to revolutionize financial system. Bit coin has a complex structure, where several interacting components build a peer-to-peer currency and a payment system without relying on any trusted third party. Bit coin is continually improved by an open source community, and various Bit coin libraries, APIs, and alternative implementations are being developed. Nevertheless, there is no up-to-date protocol specification or architecture description since the official whitepaper was published back in 2008. In this paper, we provide an up-to-date protocol specification and architectural analysis of the Bit coin system. We perform this analysis as the first step towards specification of the crypto currency reference architecture. The future reference architecture will serve as a starting architectural point for the development of new systems that leverage Bit coin protocol in different contexts and for various purposes. We also discuss whether the current architecture satisfies the system's primary purpose, i.e., Providing a pure decentralized version of the crypto currency.
Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Recent past has seen a lot of developments in the field of image-based dietary assessment. Food image classification and recognition are crucial steps for dietary assessment. In the last couple of years, advancements in the deep learning and convolutional neural networks proved to be a boon for the image classification and recognition tasks, specifically for food recognition because of the wide variety of food items. In this paper, we report experiments on food/non-food classification and food recognition using a GoogLeNet model based on deep convolutional neural network. The experiments were conducted on two image datasets created by our own, where the images were collected from existing image datasets, social media, and imaging devices such as smart phone and wearable cameras. Experimental results show a high accuracy of 99.2% on the food/non-food classification and 83.6% on the food category recognition.
Stereovision-Based Multiple Object Tracking in Traffic Scenarios Using Free-Form Obstacle Delimiters and Particle Filters

In this paper we present a stereovision-based approach for tracking multiple objects in crowded environments where, typically, the road lane markings are not visible and the surrounding infrastructure is not known. The proposed technique relies on measurement data provided by an intermediate occupancy grid derived from processing a stereovision-based elevation map and on free-form object delimiters extracted from this grid. Unlike other existing methods that track rigid objects using also rigid representations, we present a particle filter-based solution for tracking visual appearance-based free-form obstacle representations. At each step, the particle state is described by two components, i.e., the object's dynamic parameters and its estimated geometry. In order to solve the high-dimensionality state-space problem, a Rao-Blackwellized particle filter is used. By accurately modeling the object geometry using the polygonal lines instead of a 3-D box and, at the same time, separating the position and speed tracking from the geometry tracking at the estimator level, the proposed solution combines the efficiency of the rigid model with the benefits of a flexible object model.
A new image filtering method: Nonlocal image guided averaging

Image guided filtering has been widely used in many image processing applications. However, it is a local filtering method and has limited propagation ability. In this paper, we propose a new image filtering method: nonlocal image guided averaging (NLGA). Derived from a nonlocal linear model, the proposed method can utilize the nonlocal similarity of the guidance image, so that it can propagate nonlocal information reliably. Consequently, NLGA can obtain a sharper filtering results in the edge regions and more smooth results in the smooth regions. It shows superiority over image guided filtering in different applications, such as image dehazing, depth map super-resolution and image denoising.
Prophet: what app you wish to use next

A variety of applications (app) installed on smart phones do greatly enrich our lives, but make it more difficult to organize our screens and folders. Predicting apps that will be in use next can benefit users a lot. In this poster, we propose some light-weighted Bayesian methods to predict the next app based on the app usage history. The evaluation on Mobile Data Challenge (MDC) dataset gives very encouraging results. In addition, we suggest a natural way to integrate the app prediction features to the user interface. Users would find it convenient to access the predicted apps with simple touches.
Convex and Semi-Nonnegative Matrix Factorizations

We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X = FGT, we focus on algorithms in which G is restricted to containing nonnegative entries, but allowing the data matrix X to have mixed signs, thus extending the applicable range of NMF methods. We also consider algorithms in which the basis vectors of F are constrained to be convex combinations of the data points. This is used for a kernel extension of NMF. We provide algorithms for computing these new factorizations and we provide supporting theoretical analysis. We also analyze the relationships between our algorithms and clustering algorithms, and consider the implications for sparseness of solutions. Finally, we present experimental results that explore the properties of these new methods.
An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks

Catastrophic forgetting is a problem faced by many machine learning models and algorithms. When trained on one task, then trained on a second task, many machine learning models “forget” how to perform the first task. This is widely believed to be a serious problem for neural networks. Here, we investigate the extent to which the catastrophic forgetting problem occurs for modern neural networks, comparing both established and recent gradient-based training algorithms and activation functions. We also examine the effect of the relationship between the first task and the second task on catastrophic forgetting. We find that it is always best to train using the dropout algorithm– the dropout algorithm is consistently best at adapting to the new task, remembering the old task, and has the best tradeoff curve between these two extremes. We find that different tasks and relationships between tasks result in very different rankings of activation function performance. This suggests that the choice of activation function should always be cross-validated.
Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me (ACE)

In this paper we describe the fundamentals of affective gaming from a physiological point of view, covering some of the origins of the genre, how affective videogames operate and current conceptual and technological capabilities. We ground this overview of the ongoing research by taking an in-depth look at one of our own early biofeedback-based affective games. Based on our analysis of existing videogames and our own experience with affective videogames, we propose a new approach to game design based on several high-level design heuristics: assist me, challenge me and emote me (ACE), a series of gameplay "tweaks" made possible through affective videogames.
THUMT: An Open Source Toolkit for Neural Machine Translation

This paper introduces THUMT, an opensource toolkit for neural machine translation (NMT) developed by the Natural Language Processing Group at Tsinghua University. THUMT implements the standard attention-based encoder-decoder framework on top of Theano and supports three training criteria: maximum likelihood estimation, minimum risk training, and semi-supervised training. It features a visualization tool for displaying the relevance between hidden states in neural networks and contextual words, which helps to analyze the internal workings of NMT. Experiments on ChineseEnglish datasets show that THUMT using minimum risk training significantly outperforms GroundHog, a state-of-the-art toolkit for NMT.
Globally Optimal Affine and Metric Upgrades in Stratified Autocalibration

We present a practical, stratified autocalibration algorithm with theoretical guarantees of global optimality. Given a projective reconstruction, the first stage of the algorithm upgrades it to affine by estimating the position of the plane at infinity. The plane at infinity is computed by globally minimizing a least squares formulation of the modulus constraints. In the second stage, the algorithm upgrades this affine reconstruction to a metric one by globally minimizing the infinite homography relation to compute the dual image of the absolute conic (DIAC). The positive semidefiniteness of the DIAC is explicitly enforced as part of the optimization process, rather than as a post-processing step. For each stage, we construct and minimize tight convex relaxations of the highly non-convex objective functions in a branch and bound optimization framework. We exploit the problem structure to restrict the search space for the DIAC and the plane at infinity to a small, fixed number of branching dimensions, independent of the number of views. Experimental evidence of the accuracy, speed and scalability of our algorithm is presented on synthetic and real data. MATLAB code for the implementation is made available to the community.
Spoken term detection with Connectionist Temporal Classification: A novel hybrid CTC-DBN decoder

This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional context information to reliably identify phonemes, whereas the DBN is able to discriminate between keywords and arbitrary speech while explicitly modeling substitutions, deletions, and insertions in the CTC phoneme output string. Our technique is vocabulary independent and does not require an explicit garbage model. Experiments show that our system architecture prevails over a standard Hidden Markov Model approach.
Hierarchical Embeddings for Hypernymy Detection and Directionality

EXPERIMENTAL SETTINGS • ENCOW14A corpus ≈14.5 billion tokens. • Baseline: default SGNS (word2vec). • 100 dimensions, window 5, negative samples:15, learning rate 0.025. • Learn HyperVec for nouns and verbs.  • SVM classifier based on four components: conc. + diff. + cos + magnitude(hyper) Models BLESS ENTAILMENT Yu et al. (2015) 0.90 0.87 Tuan et al. (2016) 0.93 0.91 HyperVec 0.94 0.91
Fast anomaly detection in traffic surveillance video based on robust sparse optical flow

Fast abnormal events detection in video is important for intelligent analysis of video. This paper proposes a fast anomaly detection algorithm based on sparse optical flow. We improve the efficiency of optical flow computation with foreground mask and spacial sampling and increase the robustness of optical flow with good feature (TK) points selecting and forward-backward filtering. A foreground channel is also added to the feature vector to help detect static or low speed objects. The algorithm is validated on real-life traffic surveillance to prove its effectiveness. It is also evaluated on a benchmark dataset and achieve detection results comparable to state-of-art methods and outperforms them at pixel-level when the false alarm rate is low. The strength of our algorithm is that it runs real-time on the benchmark dataset which is hundreds of times faster than comparative methods.
Learning Color Constancy

We decided to test a surprisingly simple hypothesis; namely, that the relationship between an image of a scene and the chromaticity of scene illumination could be learned by a neural network. The thought was that if this relationship could be extracted by a neural network, then the trained network would be able to determine a scene's illumination from its image, which would then allow correction of the image colors to those relative to a standard illuminant, thereby providing color constancy. Using a database of surface reflectances and illuminants, along with the spectral sensitivity functions of our camera, we generated thousands of images of randomly selected illuminants lighting `scenes' of 1 to 60 randomly selected reflectances. During the learning phase the network is provided the image data along with the chromaticity of its illuminant. After training, the network outputs (very quickly) the chromaticity of the illumination given only the image data. We obtained surprisingly good estimates of he ambient illumination lighting from the network even when applied to scenes in our lab that were completely unrelated to the training data.
Automatically repairing event sequence-based GUI test suites for regression testing

Although graphical user interfaces (GUIs) constitute a large part of the software being developed today and are typically created using rapid prototyping, there are no effective regression testing techniques for GUIs. The needs of GUI regression testing differ from those of traditional software. When the structure of a GUI is modified, test cases from the original GUI's suite are either reusable or unusable on the modified GUI. Because GUI test case generation is expensive, our goal is to make the unusable test cases usable, thereby helping to retain the suite's event coverage. The idea of reusing these unusable (obsolete) test cases has not been explored before. This article shows that a large number of test cases become unusable for GUIs. It presents a new GUI regression testing technique that first automatically determines the usable and unusable test cases from a test suite after a GUI modification, then determines the unusable test cases that can be repaired so that they can execute on the modified GUI, and finally uses repairing transformations to repair the test cases. This regression testing technique along with four repairing transformations has been implemented. An empirical study for four open-source applications demonstrates that (1) this approach is effective in that many of the test cases can be repaired, and is practical in terms of its time performance, (2) certain types of test cases are more prone to becoming unusable, and (3) certain types of “dominator” events, when modified, make a large number of test cases unusable.
Freebase QA: Information Extraction or Semantic Parsing?

We contrast two seemingly distinct approaches to the task of question answering (QA) using Freebase: one based on information extraction techniques, the other on semantic parsing. Results over the same test-set were collected from two state-ofthe-art, open-source systems, then analyzed in consultation with those systems’ creators. We conclude that the differences between these technologies, both in task performance, and in how they get there, is not significant. This suggests that the semantic parsing community should target answering more compositional open-domain questions that are beyond the reach of more direct information extraction methods.
Multi-task Recurrent Neural Network for Immediacy Prediction

In this paper, we propose to predict immediacy for interacting persons from still images. A complete immediacy set includes interactions, relative distance, body leaning direction and standing orientation. These measures are found to be related to the attitude, social relationship, social interaction, action, nationality, and religion of the communicators. A large-scale dataset with 10,000 images is constructed, in which all the immediacy measures and the human poses are annotated. We propose a rich set of immediacy representations that help to predict immediacy from imperfect 1-person and 2-person pose estimation results. A multi-task deep recurrent neural network is constructed to take the proposed rich immediacy representation as input and learn the complex relationship among immediacy predictions multiple steps of refinement. The effectiveness of the proposed approach is proved through extensive experiments on the large scale dataset.
Vehicles of the Future: A Survey of Research on Safety Issues

Information and communication technologies (ICTs) have a profound impact on the current state and envisioned future of automobiles. This paper presents an overview of research on ICT-based support and assistance services for the safety of future connected vehicles. A general classification and a brief description of the focus areas for research and development in this direction are given under the titles of vehicle detection, road detection, lane detection, pedestrian detection, drowsiness detection, and collision avoidance. Following an overview and taxonomy of the reviewed research articles, a categorized literature survey of safety critical applications is presented in detail. Future research directions are also highlighted.
OBLIVIATE: A Data Oblivious Filesystem for Intel SGX

Intel SGX provides confidentiality and integrity of a program running within the confines of an enclave, and is expected to enable valuable security applications such as private information retrieval. This paper is concerned with the security aspects of SGX in accessing a key system resource, files. Through concrete attack scenarios, we show that all existing SGX filesystems are vulnerable to either system call snooping, page fault, or cache based side-channel attacks. To address this security limitations in current SGX filesystems, we present OBLIVIATE, a data oblivious filesystem for Intel SGX. The key idea behind OBLIVIATE is in adapting the ORAM protocol to read and write data from a file within an SGX enclave. OBLIVIATE redesigns the conceptual components of ORAM for SGX environments, and it seamlessly supports an SGX program without requiring any changes in the application layer. OBLIVIATE also employs SGX-specific defenses and optimizations in order to ensure complete security with acceptable overhead. The evaluation of the prototype of OBLIVIATE demonstrated its practical effectiveness in running popular server applications such as SQLite and Lighttpd, while also achieving a throughput improvement of 2×8× over a baseline ORAM-based solution, and less than 2× overhead over an in-memory SGX filesystem.
Robust Object Recognition with Cortex-Like Mechanisms

We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex
Collision-streams: fast GPU-based collision detection for deformable models

We present a fast GPU-based streaming algorithm to perform collision queries between deformable models. Our approach is based on hierarchical culling and reduces the computation to generating different streams. We present a novel stream registration method to compact the streams and efficiently compute the potentially colliding pairs of primitives. We also use a deferred front tracking method to lower the memory overhead. The overall algorithm has been implemented on different GPUs and we have evaluated its performance on non-rigid and deformable simulations. We highlight our speedups over prior CPU-based and GPU-based algorithms. In practice, our algorithm can perform inter-object and intra-object computations on models composed of hundreds of thousands of triangles in tens of milliseconds.
Beyond Object Proposals: Random Crop Pooling for Multi-Label Image Recognition

Learning high-level image representations using object proposals has achieved remarkable success in multi-label image recognition. However, most object proposals provide merely coarse information about the objects, and only carefully selected proposals can be helpful for boosting the performance of multi-label image recognition. In this paper, we propose an object-proposal-free framework for multi-label image recognition: random crop pooling (RCP). Basically, RCP performs stochastic scaling and cropping over images before feeding them to a standard convolutional neural network, which works quite well with a max-pooling operation for recognizing the complex contents of multi-label images. To better fit the multi-label image recognition task, we further develop a new loss function-the dynamic weighted Euclidean loss-for the training of the deep network. Our RCP approach is amazingly simple yet effective. It can achieve significantly better image recognition performance than the approaches using object proposals. Moreover, our adapted network can be easily trained in an end-to-end manner. Extensive experiments are conducted on two representative multi-label image recognition data sets (i.e., PASCAL VOC 2007 and PASCAL VOC 2012), and the results clearly demonstrate the superiority of our approach.