nils-herrmann/clean_scirepeval huggingface dataset:
Sequence-based Structured Prediction for Semantic Parsing

We propose an approach for semantic parsing that uses a recurrent neural network to map a natural language question into a logical form representation of a KB query. Building on recent work by (Wang et al., 2015), the interpretable logical forms, which are structured objects obeying certain constraints, are enumerated by an underlying grammar and are paired with their canonical realizations. In order to use sequence prediction, we need to sequentialize these logical forms. We compare three sequentializations: a direct linearization of the logical form, a linearization of the associated canonical realization, and a sequence consisting of derivation steps relative to the underlying grammar. We also show how grammatical constraints on the derivation sequence can easily be integrated inside the RNNbased sequential predictor. Our experiments show important improvements over previous results for the same dataset, and also demonstrate the advantage of incorporating the grammatical constraints.
Image retargeting based on self-learning 3D saliency for content-aware data analysis

Image retargeting is a process to change the resolution of image while preserve interesting regions and avoid obvious visual distortion. In other words, it focuses on image content more than anything else that applies to filter the useful information for data analysis. Existing approaches may encounter difficulties on the various types of images since most of these approaches only consider 2D features, which are sensitive to the complexity of the contents in images. Researchers are now focusing on the RGB-D information, hoping depth information can help to promote the accuracy. However it is not easy to obtain the RGB-D image we need anywhere and how to utilize depth information is still at the exploration stage. In this paper, instead of using RGB-D data captured by 3D camera, we employ an iterative MRF learning model to predict depth information from a single still image. Then we propose our self-learning 3D saliency model based on the RGB-D data and apply it on the seam carving framework. In seam caving, the self-learning 3D saliency is combined with L1-norm of gradient for better seam searching. Experimental results demonstrate the advantages of our method using RGB-D data in the seam carving framework.
Traffic Incident Duration Prediction Based on Artificial Neural Network

The prediction of traffic incident duration is an important foundation of advanced incident management system and driver information system. In this paper, actual traffic incident data was used to study the prediction problem of traffic incident duration by the method of neural network. 660 sets of actual traffic incident data from a freeway management center were used to train a neural network model, and 170 sets of incident data in the same data collection, which are different from training data, were used to test the prediction effect of the model. The test result shows that the correlation of the prediction values and the actual values is 0.8535, which indicates that the prediction result of the neural network model can basically represent actual incident duration.
Method of Resolving the Range Ambiguity for High PRF Stepped-Frequency Radar

High PRF stepped-frequency radar can achieve simultaneously the advantages of pulse Doppler (PD) and high range resolution (HRR) radar, but it's difficult to resolve the range ambiguity brought about by high PRF. In this paper, a novel method based on the phase difference out of the FFT between two adjacent range cells is proposed to solve the problem. This method firstly solves the range/Doppler coupling in the FFT by iterative reckoning between the range and the Doppler of a target and the accuracy of both reach 5 high resolution bins respectively. Secondly, it eliminates the effects of the target motion to the phase difference, consequently the accuracy of resolving the range ambiguity is reasonably high. Simulation verifies its validity
My Computer Is an Honor Student - but How Intelligent Is It? Standardized Tests as a Measure of AI

tion can machines think? by replacing it with another, namely can a machine pass the imitation game (the Turing test). In the years since, this test has been criticized as being a poor replacement for the original enquiry (for example, Hayes and Ford [1995]), which raises the question: what would a better replacement be? In this article, we argue that standardized tests are an effective and practical assessment of many aspects of machine intelligence, and should be part of any comprehensive measure of AI progress. While a crisp definition of machine intelligence remains elusive, we can enumerate some general properties we might expect of an intelligent machine. The list is potentially long (for example, Legg and Hutter [2007]), but should at least include the ability to (1) answer a wide variety of questions, (2) answer complex questions, (3) demonstrate commonsense and world knowledge, and (4) acquire new knowledge scalably. In addition, a suitable test should be clearly measurable, graduated (have a variety of levels of difficulty), not gameable, ambitious but realistic, and motivating. There are many other requirements we might add (for example, capabilities in robotics, vision, dialog), and thus any comprehensive measure of AI is likely to require a battery of different tests. However, standardized tests meet a surprising number of requirements, including the four listed, and thus should be a key component of a future battery of tests. As we will show, the tests require answering a wide variety of questions, including those requiring commonsense and world knowledge. In addition, they meet all the practical requirements, a huge advantage for any component of a future test of AI.
Bidirectional Long Short-Term Memory Variational Autoencoder

Variational Autoencoder (VAE) has achieved promising success since its emergence. In recent years, its various variants have been developed, especially those works which extend VAE to handle sequential data [1, 2, 5, 7]. However, these works either do not generate sequential latent variables, or encode latent variables only based on inputs from earlier time-steps. We believe that in real-world situations, encoding latent variables at a specific time-step should be based on not only previous observations, but also succeeding samples. In this work, we emphasize such fact and theoretically derive the bidirectional Long Short-Term Memory Variational Autoencoder (bLSTM-VAE), a novel variant of VAE whose encoders and decoders are implemented by bidirectional Long Short-Term Memory (bLSTM) networks. The proposed bLSTM-VAE can encode sequential inputs as an equal-length sequence of latent variables. A latent variable at a specific time-step is encoded by simultaneously processing observations from the first time-step till current time-step in a forward order and observations from current time-step till the last timestep in a backward order. As a result, we consider that the proposed bLSTM-VAE could learn latent variables reliably by mining the contextual information from the whole input sequence. In order to validate the proposed method, we apply it for gesture recognition using 3D skeletal joint data. The evaluation is conducted on the ChaLearn Look at People gesture dataset and NTU RGB+D dataset. The experimental results show that combining with the proposed bLSTM-VAE, the classification network performs better than when combining with a standard VAE, and also outperforms several state-of-the-art methods.
Memory-based control with recurrent neural networks

Partially observed control problems are a challenging aspect of reinforcement learning. We extend two related, model-free algorithms for continuous control – deterministic policy gradient and stochastic value gradient – to solve partially observed domains using recurrent neural networks trained with backpropagation through time. We demonstrate that this approach, coupled with long-short term memory is able to solve a variety of physical control problems exhibiting an assortment of memory requirements. These include the short-term integration of information from noisy sensors and the identification of system parameters, as well as long-term memory problems that require preserving information over many time steps. We also demonstrate success on a combined exploration and memory problem in the form of a simplified version of the well-known Morris water maze task. Finally, we show that our approach can deal with high-dimensional observations by learning directly from pixels. We find that recurrent deterministic and stochastic policies are able to learn similarly good solutions to these tasks, including the water maze where the agent must learn effective search strategies.
What Is a Case Study and What Is It Good for ?

This paper aims to clarify the meaning, and explain the utility, of the case study method, a method often practiced but little understood. A “case study,” I argue, is best defined as an intensive study of a single unit with an aim to generalize across a larger set of units. Case studies rely on the same sort of covariational evidence utilized in non-case study research. Thus, the case study method is correctly understood as a particular way of defining cases, not a way of analyzing cases or a way of modeling causal relations. I show that this understanding of the subject illuminates some of the persistent ambiguities of case study work, ambiguities that are, to some extent, intrinsic to the enterprise. The travails of the case study within the discipline of political science are also rooted in an insufficient appreciation of the methodological tradeoffs that this method calls forth. This paper presents the familiar contrast between case study and non-case study work as a series of characteristic strengths and weaknesses—affinities— rather than as antagonistic approaches to the empirical world. In the end, the perceived hostility between case study and non-case study research is largely unjustified and, perhaps, deserves to be regarded as a misconception. Indeed, the strongest conclusion to arise from this methodological examination concerns the complementarity of single-unit and cross-unit research designs.
Augmenting Neural Sentence Summarization Through Extractive Summarization

Neural sequence-to-sequence model has achieved great success in abstractive summarization task. However, due to the limit of input length, most of previous works can only utilize lead sentences as the input to generate the abstractive summarization, which ignores crucial information of the document. To alleviate this problem, we propose a novel approach to improve neural sentence summarization by using extractive summarization, which aims at taking full advantage of the document information as much as possible. Furthermore, we present both of streamline strategy and system combination strategy to achieve the fusion of the contents in different views, which can be easily adapted to other domains. Experimental results on CNN/Daily Mail dataset demonstrate both our proposed strategies can significantly improve the performance of neural sentence summarization.
On the Capabilities and Limitations of Reasoning for Natural Language Understanding

Recent systems for natural language understanding are strong at overcoming linguistic variability for lookup style reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations, addressing the ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. Our formal model uses two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic symbol space that captures a noisy grounding of the meaning space in the symbols or words of a language. We apply this framework to study the connectivity problem in undirected graphs---a core reasoning problem that forms the basis for more complex multi-hop reasoning. We show that it is indeed possible to construct a high-quality algorithm for detecting connectivity in the (latent) meaning graph, based on an observed noisy symbol graph, as long as the noise is below our quantified noise level and only a few hops are needed. On the other hand, we also prove an impossibility result: if a query requires a large number (specifically, logarithmic in the size of the meaning graph) of hops, no reasoning system operating over the symbol graph is likely to recover any useful property of the meaning graph. This highlights a fundamental barrier for a class of reasoning problems and systems, and suggests the need to limit the distance between the two spaces, rather than investing in multi-hop reasoning with"many"hops.
A Sensor Simulation System for Mobile Robots

Ltd. develops Webots, a mobile robotics simulation software that provides you with a rapid prototyping environment for modelling, programming and simulating mobile robots. The provided robot libraries enable you to transfer your control programs to several commercially available real mobile robots. Webots lets you define and modify a complete mobile robotics setup, even several different robots sharing the same environment. For each object, you can define a number of properties, such as shape, color, texture, mass, friction, etc. You can equip each robot with a large number of available sensors and actuators. You can program these robots using your favorite development environment, simulate them and optionally transfer the resulting programs onto your real robots. Webots has been developed in collaboration with the Swiss Federal Institute of Technology in Lausanne, thoroughly tested, well documented and continuously maintained for over 7 years. It is now the main commercial product available from Cyberbotics Ltd.
A survey on measuring indirect discrimination in machine learning

Nowadays, many decisions are made using predictive models built on historical data. Predictive models may systematically discriminate groups of people even if the computing process is fair and well-intentioned. Discrimination-aware data mining studies how to make predictive models free from discrimination, when historical data, on which they are built, may be biased, incomplete, or even contain past discriminatory decisions. Discrimination refers to disadvantageous treatment of a person based on belonging to a category rather than on individual merit. In this survey we review and organize various discrimination measures that have been used for measuring discrimination in data, as well as in evaluating performance of discrimination-aware predictive models. We also discuss related measures from other disciplines, which have not been used for measuring discrimination, but potentially could be suitable for this purpose. We computationally analyze properties of selected measures. We also review and discuss measuring procedures, and present recommendations for practitioners. The primary target audience is data mining, machine learning, pattern recognition , statistical modeling researchers developing new methods for non-discriminatory predictive modeling. In addition, practitioners and policy makers would use the survey for diagnosing potential discrimination by predictive models.
Advancing acculturation theory and research: the acculturation process in its ecological context.

Continued intercultural contact leads to challenges and changes. As part of this process, the acculturating individual deals with acculturative stressors whose negative effects on well-being can be buffered or exacerbated by coping reactions. A second component of the acculturation process involves the acquisition, maintenance, and change of cultural behaviors, values and identities associated with heritage and settlement cultures. Both acculturative stress and acculturative change unfold in an ecological context. Within the family, acculturation discrepancies between parents and children affect acculturation trajectories and outcomes. At the institutional level, the school and workplace exert significant influences on the acculturation of young people and working adults, respectively. At the societal level attitudes, policies and prejudice affect the acculturation experiences of sojourners and immigrants and influence their psychological and socio-cultural adaptation.
M-Walk: Learning to Walk in Graph with Monte Carlo Tree Search

Learning to walk over a graph towards a target node for a given input query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be formulated as a reinforcement learning (RL) problem with a known state transition model. To overcome the challenge of sparse reward, we develop a graph-walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). The RNN encodes the state (i.e., history of the walked path) and maps it separately to a policy, a state value and state-action Q-values. In order to effectively train the agent from sparse reward, we combine MCTS with the neural policy to generate trajectories yielding more positive rewards. From these trajectories, the network is improved in an off-policy manner using Q-learning, which modifies the RNN policy via parameter sharing. Our proposed RL algorithm repeatedly applies this policy-improvement step to learn the entire model. At test time, MCTS is again combined with the neural policy to predict the target node. Experimental results on several graph-walking benchmarks show that M-Walk is able to learn better policies than other RL-based methods, which are mainly based on policy gradients. M-Walk also outperforms traditional KBC baselines.
The use of the virtual reality as intervention tool in the postoperative of cardiac surgery.

Cardiac surgery has been the intervention of choice in many cases of cardiovascular diseases. Susceptibility to postoperative complications, cardiac rehabilitation is indicated. Therapeutic resources, such as virtual reality has been helping the rehabilitational process. The aim of the study was to evaluate the use of virtual reality in the functional rehabilitation of patients in the postoperative period.



Patients were randomized into two groups, Virtual Reality (VRG, n = 30) and Control (CG, n = 30). The response to treatment was assessed through the functional independence measure (FIM), by the 6-minute walk test (6MWT) and the Nottingham Health Profile (NHP). Evaluations were performed preoperatively and postoperatively.



On the first day after surgery, patients in both groups showed decreased functional performance. However, the VRG showed lower reduction (45.712.3) when compared to CG (35.0612.09, P<0.05) in first postoperative day, and no significant difference in performance on discharge day (P>0.05). In evaluating the NHP field, we observed a significant decrease in pain score at third assessment (P<0.05). These patients also had a higher energy level in the first evaluation (P<0.05). There were no differences with statistical significance for emotional reactions, physical ability, and social interaction. The length of stay was significantly shorter in patients of VRG (9.410.5 days vs. 12.2 1 0.9 days, P<0.05), which also had a higher 6MWD (319.9119.3 meters vs. 263.5115.4 meters, P<0.02).



Adjunctive treatment with virtual reality demonstrated benefits, with better functional performance in patients undergoing cardiac surgery.
Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach

Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning signi cantly di cult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under di erent groupings. Based on this hypothesis we are proposing an approach that detects outliers in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not a ected by the problem of class imbalance. We show empirically that area under the precision-recall curve is a better than ROC as an evaluation metric. The proposed approach is tested on the modi ed version of the Landsat satellite dataset, the modi ed version of the ann-thyroid dataset and a large real world credit card fraud detection dataset available through Kaggle where we show signi cant improvement over the baseline methods.
The Impact of Small Business Enterprises on the Economy of Trinidad & Tobago

There appears to be growing consensus that Small Business Enterprises (SBEs) exert a major influence on the economy of Trinidad and Tobago. This study investigated how and to what extent small businesses influenced macroeconomic variables such as employment, growth and productivity in the important sectors of manufacturing and services. The paper used a methodology that traverses the reader though a combination of various literatures, and theories coupled with relevant statistics on small business. This process is aimed at accessing SBEs’ impact on the Trinidad & Tobago’s non-petroleum economy, especially as it relates to economic diversification. Although, there exists significant room for both improvement and expansion of these entities especially in light of the country’s dependence on hydro-carbons – this study provides collaborating evidence that small business enterprises perform an essential role in the future of Trinidad and Tobago’s economy. E c o n o m i c I m p a c t o f S B E s | 2