nils-herrmann/clean_scirepeval huggingface dataset:
Intrinsic Motivation and Reinforcement Learning

Psychologists distinguish between extrinsically motivated behavior, which is behavior undertaken to achieve some externally supplied reward, such as a prize, a high grade, or a high-paying job, and intrinsically motivated behavior, which is behavior done for its own sake. Is an analogous distinction meaningful for machine learning systems? Can we say of a machine learning system that it is motivated to learn, and if so, is it possible to provide it with an analog of intrinsic motivation? Despite the fact that a formal distinction between extrinsic and intrinsic motivation is elusive, this chapter argues that the answer to both questions is assuredly “yes” and that the machine learning framework of reinforcement learning is particularly appropriate for bringing learning together with what in animals one would call motivation. Despite the common perception that a reinforcement learning agent’s reward has to be extrinsic because the agent has a distinct input channel for reward signals, reinforcement learning provides a natural framework for incorporating principles of intrinsic motivation.
An Overview of Heart Rate Variability Metrics and Norms

Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natural language is highly context-dependent, this further implies that in practice Softmax with distributed word embeddings does not have enough capacity to model natural language. We propose a simple and effective method to address this issue, and improve the state-of-the-art perplexities on Penn Treebank and WikiText-2 to 47.69 and 40.68 respectively. The proposed method also excels on the large-scale 1B Word dataset, outperforming the baseline by over 5.6 points in
Contour Detection and Hierarchical Image Segmentation

This paper investigates two fundamental problems in computer vision: contour detection and image segmentation. We present state-of-the-art algorithms for both of these tasks. Our contour detector combines multiple local cues into a globalization framework based on spectral clustering. Our segmentation algorithm consists of generic machinery for transforming the output of any contour detector into a hierarchical region tree. In this manner, we reduce the problem of image segmentation to that of contour detection. Extensive experimental evaluation demonstrates that both our contour detection and segmentation methods significantly outperform competing algorithms. The automatically generated hierarchical segmentations can be interactively refined by user-specified annotations. Computation at multiple image resolutions provides a means of coupling our system to recognition applications.
A Hand-Centric Classification of Human and Robot Dexterous Manipulation

This work contributes to the development of a common framework for the discussion and analysis of dexterous manipulation across the human and robotic domains. An overview of previous work is first provided along with an analysis of the tradeoffs between arm and hand dexterity. A hand-centric and motion-centric manipulation classification is then presented and applied in four different ways. It is first discussed how the taxonomy can be used to identify a manipulation strategy. Then, applications for robot hand analysis and engineering design are explained. Finally, the classification is applied to three activities of daily living (ADLs) to distinguish the patterns of dexterous manipulation involved in each task. The same analysis method could be used to predict problem ADLs for various impairments or to produce a representative benchmark set of ADL tasks. Overall, the classification scheme proposed creates a descriptive framework that can be used to effectively describe hand movements during manipulation in a variety of contexts and might be combined with existing object centric or other taxonomies to provide a complete description of a specific manipulation task.
Numerical Estimation of Shallow Water Resistance of a River-Sea Ship using CFD

In recent years a great deal of research efforts in ship hydromechanics have been devoted to practical navigation problems in moving larger ships safely into existing harbours and inland waterways and to ease congestion in existing shipping routes. The starting point of any navigational or design analysis lies in the accurate determination of the hydrodynamic forces generated on the ship hull moving in confined waters. The analysis of such ship motion should include the effects of shallow water. An area of particular interest is the determination of ship resistance in shallow or restricted waters at different speeds, forming the basis for the power calculation and design of the propulsion system. The present work describes the implementation of CFD techniques for determining the shallow water resistance of a river-sea ship at different speeds. The ship hull flow is analysed for different ship speeds in shallow water conditions. The results obtained from CFD analysis are compared with available standard results.
Fast Neural Cell Detection Using Light-Weight SSD Neural Network

Identifying the lineage path of neural cells is critical for understanding the development of brain. Accurate neural cell detection is a crucial step to obtain reliable delineation of cell lineage. To solve this task, in this paper we present an efficient neural cell detection method based on SSD (single shot multibox detector) neural network model. Our method adapts the original SSD architecture and removes the unnecessary blocks, leading to a light-weight model. Moreover, we formulate the cell detection as a binary regression problem, which makes our model much simpler. Experimental results demonstrate that, with only a small training set, our method is able to accurately capture the neural cells under severe shape deformation in a fast way.
Big data analytics in power distribution systems

Penetration of advanced sensor systems such as advanced metering infrastructure (AMI), high-frequency overhead and underground current and voltage sensors have been increasing significantly in power distribution systems over the past few years. According to U.S. energy information administration (EIA), the aggregated AMI installation experienced a 17 times increase from 2007 to 2012. The AMI usually collects electricity usage data every 15 minute, instead of once a month. This is a 3,000 fold increase in the amount of data utilities would have processed in the past. It is estimated that the electricity usage data collected through AMI in the U.S. amount to well above 100 terabytes in 2012. To unleash full value of the complex data sets, innovative big data algorithms need to be developed to transform the way we operate and plan for the distribution system. This paper not only proposes promising applications but also provides an in-depth discussion of technical and regulatory challenges and risks of big data analytics in power distribution systems. In addition, a flexible system architecture design is proposed to handle heterogeneous big data analysis workloads.
Ten Years of Knowledge Harvesting: Lessons and Challenges

This article is a retrospective on the theme of knowledge harvesting: automatically constructing large highquality knowledge bases from Internet sources. We draw on our experience in the Yago-Naga project over the last decade, but consider other projects as well. The article discusses lessons learned on the architecture of a knowledge harvesting system, and points out open challenges and research opportunities. 1 Large High-Quality Knowledge Bases Turning Internet content, with its wealth of latent-value but noisy text and data sources, into crisp “machine knowledge” that can power intelligent applications is a long-standing goal of computer science. Over the last ten years, knowledge harvesting has made tremendous progress, leveraging advances in scalable information extraction and the availability of curated knowledge-sharing sources such as Wikipedia. Unlike the seminal projects on manually crafted knowledge bases and ontologies, like Cyc  and WordNet , knowledge harvesting is automated and operates at Web scale. Automatically constructed knowledge bases – KB’s for short – have become a powerful asset for search, analytics, recommendations, and data integration, with intensive use at big industrial stakeholders. Prominent examples are the Google Knowledge Graph, Facebook’s Graph Search, Microsoft Satori as well as domainspecific knowledge bases in business, finance, life sciences, and more. These achievements are rooted in academic research and community projects starting ten years ago, most notably, DBpedia [2], Freebase , KnowItAll [13], WikiTaxonomy [34] and Yago . More recent major projects along these lines include BabelNet  ConceptNet , DeepDive , EntityCube (aka. Renlifang) , KnowledgeVault [9], Nell [6] Probase [50], , XLore [48]. The largest of the KB’s from these projects contain many millions of entities (i.e., people, places, products etc.) and billions of facts about them (i.e., attribute values and relationships with other entities). Moreover, entities are organized into a taxonomy of semantic classes, sometimes with hundred thousands of fine-grained types. All this is often represented in the form of subject-predicate-object (SPO) triples, following the RDF data model, and some of the KB’s – most notably DBpedia – are central to the Web of Linked Open Data [18]. For illustration, here are some examples of SPO triples about Steve  Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering
Meeting of minds: the medial frontal cortex and social cognition

Social interaction is a cornerstone of human life, yet the neural mechanisms underlying social cognition are poorly understood. Recently, research that integrates approaches from neuroscience and social psychology has begun to shed light on these processes, and converging evidence from neuroimaging studies suggests a unique role for the medial frontal cortex. We review the emerging literature that relates social cognition to the medial frontal cortex and, on the basis of anatomical and functional characteristics of this brain region, propose a theoretical model of medial frontal cortical function relevant to different aspects of social cognitive processing.
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. e result is a practical scalable algorithm that applies to real world data. e UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
The Role of Context Types and Dimensionality in Learning Word Embeddings

We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic tasks tend to exhibit a clear preference to particular types of contexts and higher dimensionality, more careful tuning is required for finding the optimal settings for most of the extrinsic tasks that we considered. Furthermore, for these extrinsic tasks, we find that once the benefit from increasing the embedding dimensionality is mostly exhausted, simple concatenation of word embeddings, learned with different context types, can yield further performance gains. As an additional contribution, we propose a new variant of the skip-gram model that learns word embeddings from weighted contexts of substitute words.
A review of process fault detection and diagnosis: Part I: Quantitative model-based methods

Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently. AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid abnormal event progression and reduce productivity loss. Since the petrochemical industries lose an estimated 20 billion dollars every year, they have rated AEM as their number one problem that needs to be solved. Hence, there is considerable interest in this field now from industrial practitioners as well as academic researchers, as opposed to a decade or so ago. There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches. From a modelling perspective, there are methods that require accurate process models, semi-quantitative models, or qualitative models. At the other end of the spectrum, there are methods that do not assume any form of model information and rely only on historic process data. In addition, given the process knowledge, there are different search techniques that can be applied to perform diagnosis. Such a collection of bewildering array of methodologies and alternatives often poses a difficult challenge to any aspirant who is not a specialist in these techniques. Some of these ideas seem so far apart from one another that a non-expert researcher or practitioner is often left wondering about the suitability of a method for his or her diagnostic situation. While there have been some excellent reviews in this field in the past, they often focused on a particular branch, such as analytical models, of this broad discipline. The basic aim of this three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives. We broadly classify fault diagnosis methods into three general categories and review them in three parts. They are quantitative model-based methods, qualitative model-based methods, and process history based methods. In the first part of the series, the problem of fault diagnosis is introduced and approaches based on quantitative models are reviewed. In the remaining two parts, methods based on qualitative models and process history data are reviewed. Furthermore, these disparate methods will be compared and evaluated based on a common set of criteria introduced in the first part of the series. We conclude the series with a discussion on the relationship of fault diagnosis to other process operations and on emerging trends such as hybrid blackboard-based frameworks for fault diagnosis.
Systems of Logic Based on Ordinals

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Deep ” Learning : Detecting Metaphoricity in Adjective-Noun Pairs ∗

Metaphor is one of the most studied and widespread figures of speech and an essential element of individual style. In this paper we look at metaphor identification in Adjective-Noun pairs. We show that using a single neural network combined with pre-trained vector embeddings can outperform the state of the art in terms of accuracy. In specific, the approach presented in this paper is based on two ideas: a) transfer learning via using pre-trained vectors representing adjective noun pairs, and b) a neural network as a model of composition that predicts a metaphoricity score as output. We present several different architectures for our system and evaluate their performances. Variations on dataset size and on the kinds of embeddings are also investigated. We show considerable improvement over the previous approaches both in terms of accuracy and w.r.t the size of annotated training data.
Avalanche Effect of AES Algorithm

Efficient implementation of block cipher is important on the way to achieving high efficiency with good understand ability. Numerous number of block cipher including Advance Encryption Standard have been implemented using different platform. However the understanding of the AES algorithm step by step is very complicated. This paper presents the implementation of AES algorithm and explains Avalanche effect with the help of Avalanche test result. For this purpose we use Xilinx ISE 9.1i platform in Algorithm development and ModelSim SE 6.3f platform for results confirmation and computation.
Transfer Learning for Deep Learning on Graph-Structured Data

Graphs provide a powerful means for representing complex interactions between entities. Recently, new deep learning approaches have emerged for representing and modeling graphstructured data while the conventional deep learning methods, such as convolutional neural networks and recurrent neural networks, have mainly focused on the grid-structured inputs of image and audio. Leveraged by representation learning capabilities, deep learning-based techniques can detect structural characteristics of graphs, giving promising results for graph applications. In this paper, we attempt to advance deep learning for graph-structured data by incorporating another component: transfer learning. By transferring the intrinsic geometric information learned in the source domain, our approach can construct a model for a new but related task in the target domain without collecting new data and without training a new model from scratch. We thoroughly tested our approach with large-scale real-world text data and confirmed the effectiveness of the proposed transfer learning framework for deep learning on graphs. According to our experiments, transfer learning is most effective when the source and target domains bear a high level of structural similarity in their graph representations.
Perceptual audio loss function for deep learning

PESQ, Perceptual Evaluation of Speech Quality , and POLQA, Perceptual Objective Listening Quality Assessment [1] , are standards comprising a test methodology for automated assessment of voice quality of speech as experienced by human beings. The predictions of those objective measures should come as close as possible to subjective quality scores as obtained in subjective listening tests, usually, a Mean Opinion Score (MOS) is predicted. Wavenet  is a deep neural network originally developed as a deep generative model of raw audio waveforms. Wavenet architecture is based on dilated causal convolutions, which exhibit very large receptive fields. In this short paper we suggest using the Wavenet architecture, in particular its large receptive filed in order to mimic PESQ algorithm. By doing so we can use it as a differentiable loss function for speech enhancement. 1 Problem formulation and related work In statistics, the Mean Squared Error (MSE) or Peak Signal to Noise Ratio (PSNR) of an estimator are widely used objective measures and are good distortion indicators (loss functions) between the estimators output and the size that we want to estimate. those loss functions are used for many reconstruction tasks. However, PSNR and MSE do not have good correlation with reliable subjective methods such as Mean Opinion Score (MOS) obtained from expert listeners. A more suitable speech quality assessment can by achieved by using tests that aim to achieve high correlation with MOS tests such as PEAQ or POLQA. However those algorithms are hard to represent as a differentiable function such as MSE moreover, as opposed to MSE that measures the average
Survey on web spam detection: principles and algorithms

Search engines became a de facto place to start information acquisition on the Web. Though due to web spam phenomenon, search results are not always as good as desired. Moreover, spam evolves that makes the problem of providing high quality search even more challenging. Over the last decade research on adversarial information retrieval has gained a lot of interest both from academia and industry. In this paper we present a systematic review of web spam detection techniques with the focus on algorithms and underlying principles. We categorize all existing algorithms into three categories based on the type of information they use: content-based methods, link-based methods, and methods based on non-traditional data such as user behaviour, clicks, HTTP sessions. In turn, we perform a subcategorization of link-based category into five groups based on ideas and principles used: labels propagation, link pruning and reweighting, labels refinement, graph regularization, and featurebased. We also define the concept of web spam numerically and provide a brief survey on various spam forms. Finally, we summarize the observations and underlying principles applied for web spam detection.
Procrastination and Impatience ∗

We use a combination of lab and field evidence to study whether preferences for immediacy and the tendency to procrastinate are connected as in O’Donoghue and Rabin (1999a). To measure immediacy, we have participants choose between smaller-sooner and larger-later rewards. Both rewards are paid by check to control for transaction costs. To measure procrastination, we record how fast participants cash their checks and complete other tasks. We find that individuals with a preference for immediacy are more likely to procrastinate. We also find evidence that individuals differ in the degree to which they anticipate their own procrastination.