nils-herrmann/clean_scirepeval huggingface dataset:
Count-Based Exploration with Neural Density Models

Bellemare et al. (2016) introduced the notion of a pseudo-count, derived from a density model, to generalize count-based exploration to nontabular reinforcement learning. This pseudocount was used to generate an exploration bonus for a DQN agent and combined with a mixed Monte Carlo update was sufficient to achieve state of the art on the Atari 2600 game Montezuma’s Revenge. We consider two questions left open by their work: First, how important is the quality of the density model for exploration? Second, what role does the Monte Carlo update play in exploration? We answer the first question by demonstrating the use of PixelCNN, an advanced neural density model for images, to supply a pseudo-count. In particular, we examine the intrinsic difficulties in adapting Bellemare et al.’s approach when assumptions about the model are violated. The result is a more practical and general algorithm requiring no special apparatus. We combine PixelCNN pseudo-counts with different agent architectures to dramatically improve the state of the art on several hard Atari games. One surprising finding is that the mixed Monte Carlo update is a powerful facilitator of exploration in the sparsest of settings, including Montezuma’s Revenge.
An Anomaly Detection Method for Medicare Fraud Detection

With the improvement of medical insurance system, the coverage of medicare increases a lot. However, while the expenditure of this system is continuously rising, medicare fraud is causing huge losses for this system. Traditional medicare fraud detection greatly depends on the experience of domain experts, which is not accurate enough and costs much time and labor.In this study, we propose a medicare fraud detection framework based on the technology of anomaly detection. Our method consists of two parts. First part is a spatial density based algorithm, called improved local outlier factor (imLOF), which is more applicable than simple local outlier factor in medical insurance data. Second part is robust regression to depict the linear dependence between variables. Some experiments are conducted on real world data to measure the efficiency of our method.
From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction

In this work, we study the credit assignment problem in reward augmented maximum likelihood (RAML) learning, and establish a theoretical equivalence between the token-level counterpart of RAML and the entropy regularized reinforcement learning. Inspired by the connection, we propose two sequence prediction algorithms, one extending RAML with fine-grained credit assignment and the other improving Actor-Critic with a systematic entropy regularization. On two benchmark datasets, we show the proposed algorithms outperform RAML and Actor-Critic respectively, providing new alternatives to sequence prediction.
Attention Enhancement System using Virtual Reality and EEG Biofeedback

Attention Deficit Hyperactivity Disorder (ADHD) is a childhood syndrome characterized by short attention span, impulsiveness, and hyperactivity, which often lead ? to learning disabilities and various behavioral problems. For the treatment of ADHD, medication and cognitive-behavior therapy is applied in recent years. Although psychostimulant medication has been widely used for many years, current findings suggest that, as the sole treatment for ADHD, it is an inadequate form of intervention in that parents don’t want their child to use drug and the effects are limited to the period in which the drugs are physiologically active. On the other hand, EEG biofeedback treatment studies for ADHD have reported promising results not only in significant reductions in hyperactive, inattentive, and disruptive behaviors, but also improvements in academic performance and IQ scores. However it is too boring for children to finish the whole treatment. The recent increase in computer usage in medicine and rehabilitation has changed the way health care is delivered. Virtual Reality technology provides specific stimuli that can be used in removing distractions and providing environments that get the subjects’ attention and increasing their ability to concentrate. And Virtual Reality technology can hold a patient’s attention for a longer period of time than other methods can, because VR is immersive, interactive and imaginal. Based on these aspects, we developed Attention Enhancement System (AES) using Virtual Reality technology and EEG biofeedback for assessing and treating ADHD children as well as increasing the attention span of children who have attention difficulty.
Ergodic theory of chaos and strange attractors

Physical and numerical experiments show that deterministic noise, or chaos, is ubiquitous. While a good understanding of the onset of chaos has been achieved, using as a mathematical tool the geometric theory of differentiable’dynamical systems, moderately excited chaotic systems require new tools, which are provided by the ergodic theory o f dynamical systems. This theory has reached a stage where fruitful contact and exchange with physical experiments has become widespread. The present review is an account of the main mathematical ideas and, their concrete implementation in analyzing experiments. The main subjects are the theory of dimensions (number of excited degrees of freedom), entropy (production of information), and character is t ic exponents (describing sensi t ivi ty to ini t ia l condit ions) . The relat ions between these quant ities, as well as their experimental determination, are discussed. The systematic investigation of these quantities provides us for the first time with a reasonable understanding of dynamical systems, excited well beyond the quasiperiodic regimes. This is another s tep towards understanding highly turbulent f luids.
Personalized Research Paper Recommendation using Deep Learning

With the increasing number of scientific publications, research paper recommendation has become increasingly important for scientists. Most researchers rely on keyword-based search or following citations in other papers, in order to find relevant research articles. And usually they spend a lot of time without getting satisfactory results. This study aims to propose a personalized research paper recommendation system, that facilitate this task by recommending papers based on users' explicit and implicit feedback. The users will be allowed to explicitly specify the papers of interest. In addition, user activities (e.g., viewing abstracts or full-texts) will be analyzed in order to enhance users' profiles. Most of the current research paper recommendation and information retrieval systems use the classical bag-of-words methods, which don't consider the context of the words and the semantic similarity between the articles. This study will use Recurrent Neural Networks (RNNs) to discover continuous and latent semantic features of the papers, in order to improve the recommendation quality. The proposed approach utilizes PubMed so far, since it is frequently used by physicians and scientists, but it can easily incorporate other datasets in the future.
DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

Emotion detection in conversations is a necessary step for a number of applications, including opinion mining over chat history, social media threads, debates, argumentation mining, understanding consumer feedback in live conversations, etc. Currently systems do not treat the parties in the conversation individually by adapting to the speaker of each utterance. In this paper, we describe a new method based on recurrent neural networks that keeps track of the individual party states throughout the conversation and uses this information for emotion classification. Our model outperforms the state of the art by a significant margin on two different datasets.
A time delay neural network architecture for efficient modeling of long temporal contexts

Recurrent neural network architectures have been shown to efficiently model long term temporal dependencies between acoustic events. However the training time of recurrent networks is higher than feedforward networks due to the sequential nature of the learning algorithm. In this paper we propose a time delay neural network architecture which models long term temporal dependencies with training times comparable to standard feed-forward DNNs. The network uses sub-sampling to reduce computation during training. On the Switchboard task we show a relative improvement of 6% over the baseline DNN model. We present results on several LVCSR tasks with training data ranging from 3 to 1800 hours to show the effectiveness of the TDNN architecture in learning wider temporal dependencies in both small and large data scenarios.
A Method for Automated Prediction of Defect Severity Using Ontologies

Assessing severity of software defects is essential for prioritizing fixing activities as well as for assessing whether the quality level of a software system is good enough for release. In practice, filling out defect reports is done manually and developers routinely fill out default values for the severity levels. Moreover, external factors are a reason for assigning wrong severity levels to defects. The purpose of this research is to automate the prediction of defect severity. We have researched how this severity prediction can be achieved through incorporating knowledge of the software development process using ontologies. In addition, we also employ an IEEE standard to create a uniform framework for the attributes of the defects. The thesis presents MAPDESO – a Method for Automated Prediction of DEfect Severity using Ontologies. It was developed using industrial case studies during an internship at Logica Netherlands B. V. The method is based on classification rules that consider the software quality properties affected by a defect, together with the defect’s type, insertion activity and detection activity. The results from its validation and comparison with the Weka machine learning workbench indicate that MAPDESO is a good predictor for defect severity levels and it can be especially useful for medium-to-large projects with many defects.
Computational thinking for youth in practice

Computational thinking (CT) has been described as the use of abstraction, automation, and analysis in problem-solving . We examine how these ways of thinking take shape for middle and high school youth in a set of NSF-supported programs. We discuss opportunities and challenges in both in-school and after-school contexts. Based on these observations, we present a "use-modify-create" framework, representing three phases of students' cognitive and practical activity in computational thinking. We recommend continued investment in the development of CT-rich learning environments, in educators who can facilitate their use, and in research on the broader value of computational thinking.
An introduction to quantum computing for non-physicists

Richard Feynman's observation that certain quantum mechanical effects cannot be simulated efficiently on a computer led to speculation that computation in general could be done more efficiently if it used these quantum effects. This speculation proved justified when Peter Shor described a polynomial time quantum algorithm for factoring intergers.
In quantum systems, the computational space increases exponentially with the size of the system, which enables exponential parallelism. This parallelism could lead to exponentially faster quantum algorithms than possible classically. The catch is that accessing the results, which requires measurement, proves tricky and requires new nontraditional programming techniques.
The aim of this paper is to guide computer scientists through the barriers that separate quantum computing from conventional computing. We introduce basic principles of quantum mechanics to explain where the power of quantum computers comes from and why it is difficult to harness. We describe quantum cryptography, teleportation, and dense coding. Various approaches to exploiting the power of quantum parallelism are explained. We conclude with a discussion of quantum error correction.
End-to-End Learning of Driving Models from Large-Scale Video Datasets

Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm. We provide a novel large-scale dataset of crowd-sourced driving behavior suitable for training our model, and report results predicting the driver action on held out sequences across diverse conditions.
Effects of sleep deprivation on cognition.

Sleep deprivation is commonplace in modern society, but its far-reaching effects on cognitive performance are only beginning to be understood from a scientific perspective. While there is broad consensus that insufficient sleep leads to a general slowing of response speed and increased variability in performance, particularly for simple measures of alertness, attention and vigilance, there is much less agreement about the effects of sleep deprivation on many higher level cognitive capacities, including perception, memory and executive functions. Central to this debate has been the question of whether sleep deprivation affects nearly all cognitive capacities in a global manner through degraded alertness and attention, or whether sleep loss specifically impairs some aspects of cognition more than others. Neuroimaging evidence has implicated the prefrontal cortex as a brain region that may be particularly susceptible to the effects of sleep loss, but perplexingly, executive function tasks that putatively measure prefrontal functioning have yielded inconsistent findings within the context of sleep deprivation. Whereas many convergent and rule-based reasoning, decision making and planning tasks are relatively unaffected by sleep loss, more creative, divergent and innovative aspects of cognition do appear to be degraded by lack of sleep. Emerging evidence suggests that some aspects of higher level cognitive capacities remain degraded by sleep deprivation despite restoration of alertness and vigilance with stimulant countermeasures, suggesting that sleep loss may affect specific cognitive systems above and beyond the effects produced by global cognitive declines or impaired attentional processes. Finally, the role of emotion as a critical facet of cognition has received increasing attention in recent years and mounting evidence suggests that sleep deprivation may particularly affect cognitive systems that rely on emotional data. Thus, the extent to which sleep deprivation affects a particular cognitive process may depend on several factors, including the magnitude of global decline in general alertness and attention, the degree to which the specific cognitive function depends on emotion-processing networks, and the extent to which that cognitive process can draw upon associated cortical regions for compensatory support.
Knowledge Representation, Reasoning and Declarative Problem Solving

in the program. The Li’s are the literals (or atoms) of the Prolog language and may be supplied with a prefix ¬ sign, indicating the negation of a literal, while the prefix of not indicates negation as failure. Hence the semantics of the clause (C) may be read as follows: if all the literals L1, . . . , Lm are true and all the literals Lm+1, . . . , Ln can be safely assumed false then at least one of the literals L1, . . . , Lk is true. (The actual semantics of each AnsProlog ∗
Answer Extraction for Why Arabic Questions Answering Systems : EWAQ

with the increasing amount of web information, questions answering systems becomes very important to allow users to access to direct answers for their requests. This paper presents an Arabic Questions Answering Systems based on entailment metrics. The type of questions which this paper focuses on is why questions. There are many reasons lead us to develop this system: generally, the lack of Arabic Questions Answering Systems and scarcity Arabic Questions Answering Systems which focus on why questions. The goal of the proposed system in this research is to extract answers from re-ranked retrieved passages which are retrieved by search engines. This system extracts the answer only to why questions. This system is called by EWAQ: Entailment based Why Arabic Questions Answering. Each answer is scored with entailment metrics and ranked according to their scores in order to determine the most possible correct answer. EWAQ is compared with search engines: yahoo, google and ask.com, the wellestablished web-based Questions Answering systems, using manual test set. In EWAQ experiments, it is showed that the accuracy is increased by implementing the textual entailment in re-raking the retrieved relevant passages by search engines and deciding the correct answer. The obtained results show that using entailment based similarity can help significantly to tackle the why Answer Extraction module in Arabic language. KeywordsArabic Questions Answering System (AQAS); Entailment Similarity; Why Questions; Answer Extraction (AE); Information Retrieval (IR).
Chapter VI Rigor In Grounded Theory Research : An Interpretive Perspective on Generating Theory From Qualitative Field Studies

This chapter presents a set of principles for the use of Grounded Theory techniques in qualitative field studies. Some issues and controversies relating to rigor in Grounded Theory generation are discussed. These include: inductive theory generation and emergence, how theoretical saturation may be judged, the extent to which coding schemes should be formalized, the objectivistsubjectivist debate, and the assessment of quality and rigor in interpretive research. It is argued that Grounded Theory is often criticized for a lack of rigor because we apply positivist evaluations of rigor to research that derives from an interpretive worldview. Alternative assessments of rigor are suggested, that emphasize reflexivity in the inductive-deductive cycle of substantive theory
Smart cities and the Internet of Things

The smart city concept represents a compelling platform for IT-enabled service innovation. It offers a view of the city where service providers use information technologies to engage with citizens to create more effective urban organizations and systems that can improve the quality of life. The emerging Internet of Things (IoT) model is foundational to the development of smart cities. Integrated cloud-oriented architecture of networks, software, sensors, human interfaces, and data analytics are essential for value creation. IoT smart-connected products and the services they provision will become essential for the future development of smart cities. This paper will explore the smart city concept and propose a strategy development model for the implementation of IoT systems in a smart city context.
Automatic Legal Document Analysis: Improving the Results of Information Extraction Processes Using an Ontology

Information Extraction (IE) is a pervasive task in the industry that allows to obtain automatically structured data from documents in natural language. Current software systems focused on this activity are able to extract a large percentage of the required information, but they do not usually focus on the quality of the extracted data. In this paper we present an approach focused on validating and improving the quality of the results of an IE system. Our proposal is based on the use of ontologies which store domain knowledge, and which we leverage to detect and solve consistency errors in the extracted data. We have implemented our approach to run against the output of the AIS system, an IE system specialized in analyzing legal documents and we have tested it using a real dataset. Preliminary results confirm the interest of our approach.
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image. Limited by the nature of deep neural network, previous methods usually represent a 3D shape in volume or point cloud, and it is non-trivial to convert them to the more ready-to-use mesh model. Unlike the existing methods, our network represents 3D mesh in a graph-based convolutional neural network and produces correct geometry by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image. We adopt a coarse-to-fine strategy to make the whole deformation procedure stable, and define various of mesh related losses to capture properties of different levels to guarantee visually appealing and physically accurate 3D geometry. Extensive experiments show that our method not only qualitatively produces mesh model with better details, but also achieves higher 3D shape estimation accuracy compared to the state-of-the-art.
Embedding Based on Function Approximation for Large Scale Image Search

The objective of this paper is to design an embedding method that maps local features describing an image (e.g., SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the state-of-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.
Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization

For survival, a living agent (e.g., human ) must have the ability to assess risk (1) by temporally anticipating accidents before they occur ), and (2) by spatially localizing risky regions  in the environment to move away from threats. In this paper, we take an agent-centric approach to study the accident anticipation and risky region localization tasks. We propose a novel soft-attention Recurrent Neural Network (RNN) which explicitly models both spatial and appearance-wise non-linear interaction between the agent triggering the event and another agent or static-region involved. In order to test our proposed method, we introduce the Epic Fail (EF) dataset consisting of 3000 viral videos capturing various accidents. In the experiments, we evaluate the risk assessment accuracy both in the temporal domain (accident anticipation) and spatial domain (risky region localization) on our EF dataset and the Street Accident (SA) dataset. Our method consistently outperforms other baselines on both datasets.