nils-herrmann/clean_scirepeval huggingface dataset:
Robot as a Service in Cloud Computing

Service-oriented architecture and cloud computing are becoming a dominant computing paradigm, as all major computing companies are supporting this paradigm and more and more organizations are adopting this paradigm. Robotics and service-oriented robotics computing start to joint this new paradigm in the past five years and are now ready to participate in large scale. This paper reports our research on service-oriented robotics computing and our design, implementation, and evaluation of Robot as a Service (RaaS) unit. To fully qualify the RaaS as a cloud computing unit, we have kept our design to comply with the common service standards, development platforms, and execution infrastructure. We also keep the source code open and allow the community to configure the RaaS following the Web 2.0 principles of participation. Developers can add, remove, and modify the RaaS of their own. For this purpose, we have implemented our RaaS on Windows and Linux operating systems running on Atom and Core 2 Duo architectures. RaaS supports programming languages commonly used for service-oriented computing such as Java and C#. Special efforts have been made to support Microsoft Visual Programming Language (VPL) for graphic composition. We are working with high schools to use RaaS and VPL in robotics camps and robotics competitions.
Value Prediction Network

This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based RL methods into a single neural network. In contrast to typical model-based RL methods, VPN learns a dynamics model whose abstract states are trained to make option-conditional predictions of future values (discounted sum of rewards) rather than of future observations. Our experimental results show that VPN has several advantages over both model-free and model-based baselines in a stochastic environment where careful planning is required but building an accurate observation-prediction model is difficult. Furthermore, VPN outperforms Deep Q-Network (DQN) on several Atari games even with short-lookahead planning, demonstrating its potential as a new way of learning a good state representation.
Linear permanent magnet motor for reciprocating compressor applications

This paper investigates the performance of a three designs of tubular linear permanent magnet motors (TLPMMs) with quasi-Halbach magnetized moving-magnet armature and slotted stator with a single-slot. The motors were developed for reciprocating compressor applications such as household refrigerators. The three designs have different magnet shape structures, and the stator core of all designs have employed a soft magnetic composite (SMC) material, Somaloy 700. Based on finite element analysis (FEA) an extensively analysis on the opencircuit magnetic field distribution, back-EMF, thrust force, fluxlinkage, winding inductance and cogging force in the three designs have been established. The simulation results indicate the effectiveness of the designs to drive a linear reciprocating compressor in household refrigerators, and also the simulation results have shown the superiority of the two designs with T-shape and trapezoidal magnet configurations, over the TLPMM with conventional rectangular magnet. Moreover, the permanent magnets (PMs) array arrangement has significant effects on the motor performance.
Impact of singular excessive computer game and television exposure on sleep patterns and memory performance of school-aged children.

Television and computer game consumption are a powerful influence in the lives of most children. Previous evidence has supported the notion that media exposure could impair a variety of behavioral characteristics. Excessive television viewing and computer game playing have been associated with many psychiatric symptoms, especially emotional and behavioral symptoms, somatic complaints, attention problems such as hyperactivity, and family interaction problems. Nevertheless, there is insufficient knowledge about the relationship between singular excessive media consumption on sleep patterns and linked implications on children. The aim of this study was to investigate the effects of singular excessive television and computer game consumption on sleep patterns and memory performance of children.



Eleven school-aged children were recruited for this polysomnographic study. Children were exposed to voluntary excessive television and computer game consumption. In the subsequent night, polysomnographic measurements were conducted to measure sleep-architecture and sleep-continuity parameters. In addition, a visual and verbal memory test was conducted before media stimulation and after the subsequent sleeping period to determine visuospatial and verbal memory performance.



Only computer game playing resulted in significant reduced amounts of slow-wave sleep as well as significant declines in verbal memory performance. Prolonged sleep-onset latency and more stage 2 sleep were also detected after previous computer game consumption. No effects on rapid eye movement sleep were observed. Television viewing reduced sleep efficiency significantly but did not affect sleep patterns.



The results suggest that television and computer game exposure affect children's sleep and deteriorate verbal cognitive performance, which supports the hypothesis of the negative influence of media consumption on children's sleep, learning, and memory.
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon's navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures

ACL 2018 • Baseline: 6 layer Transformer, 512 hidden units •  IWSLT'16, WMT'17 •  BLEU (and METEOR in the paper) • 3 runs, reporting mean and standard deviation Encoder/Decoder Combinations • Architecture Definition Language • Allows easy experimentation of Neural Machine Translation (NMT) architecture variations • Detailed analysis of NMT architecture combinations • Multiple source attention layers and residual feedforward layer are key • Self-attention more important on the source side
Securing smart home: Technologies, security challenges, and security requirements

Smart homes are gaining vast popularity as the most promising application of the emerging Internet of Things (IoT) technology. Exploiting the high level of connectivity present in current electronic devices (such as smartphones, tablets, and multimedia systems), smart homes provide innovative, automated and interactive services for residential customers through distributed and collaborative operations. As these types of networks become enormously popular, it is fundamental to provide the adequate level of protection against cyber-attacks for the residential customers. However, the resource-constrained nature of many of the devices present in a smart home environment, does not permit to implement the standard security solutions and therefore smart homes currently present security vulnerabilities. In this paper the security challenges and threats to the existing solutions suited for smart homes are examined in detail with the objective of fostering the development of practical solutions to secure the smart homes.
Testing of Digital Systems

Device testing represents the single largest manufacturing expense in the semiconductor industry, costing over $40 million a year. The most comprehensive and wide ranging book of its kind, Testing of Digital Systems covers everything you need to know about this vitally important subject. Starting right from the basics, the authors take the reader through automatic test pattern generation, design for testability and built-in self-test of digital circuits before moving on to more advanced topics such as IDDQ testing, functional testing, delay fault testing, memory testing, and fault diagnosis. The book includes detailed treatment of the latest techniques including test generation for various fault modes, discussion of testing techniques at different levels of integrated circuit hierarchy and a chapter on system-on-a-chip test synthesis. Written for students and engineers, it is both an excellent senior/graduate level textbook and a valuable reference.
Visualizing Semantic Table Annotations with TableMiner+

This paper describes an extension of the TableMiner system, an open source Semantic Table Interpretation system that annotates Web tables using Linked Data in an effective and efficient approach. It adds a graphical user interface to TableMiner, to facilitate the visualization and correction of automatically generated annotations. This makes TableMiner an ideal tool for the semi-automatic creation of high-quality semantic annotations on tabular data, which facilitates the publication of Linked Data on the Web.
Statistical Nested Sensor Array Signal Processing

OF THE DISSERTATION Statistical Nested Sensor Array Signal Processing by Keyong Han Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, 2015 Professor Arye Nehorai, Chair Source number detection and direction-of-arrival (DOA) estimation are two major applications of sensor arrays. Both applications are often confined to the use of uniform linear arrays (ULAs), which is expensive and difficult to yield wide aperture. Besides, a ULA with N scalar sensors can resolve at most N − 1 sources. On the other hand, a systematic approach was recently proposed to achieve O(N) degrees of freedom (DOFs) using O(N) sensors based on a nested array, which is obtained by combining two or more ULAs with successively increased spacing. This dissertation will focus on a fundamental study of statistical signal processing of nested arrays. Five important topics are discussed, extending the existing nested-array strategies to more practical scenarios. Novel signal models and algorithms are proposed. First, based on the linear nested array, we consider the problem for wideband Gaussian sources. To employ the nested array to the wideband case, we propose effective strategies to apply nested-array processing to each frequency component, and combine all the spectral xi information of various frequencies to conduct the detection and estimation. We then consider the practical scenario with distributed sources, which considers the spreading phenomenon of sources. Next, we investigate the self-calibration problem for perturbed nested arrays, for which existing works require certain modeling assumptions, for example, an exactly known array geometry, including the sensor gain and phase. We propose corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares is used to deal with the phase error issue. We further propose a new class of nested vector-sensor arrays which is capable of significantly increasing the DOFs. This is not a simple extension of the nested scalar-sensor array. Both the signal model and the signal processing strategies are developed in the multidimensional sense. Based on the analytical results, we consider two main applications: electromagnetic (EM) vector sensors and acoustic vector sensors. Last but not least, in order to make full use of the available limited valuable data, we propose a novel strategy, which is inspired by the jackknifing resampling method. Exploiting numerous iterations of subsets of the whole data set, this strategy greatly improves the results of the existing source number detection and DOA estimation methods.
PC algorithm for nonparanormal graphical models

The PC algorithm uses conditional independence tests for mo del selection in graphical modeling with acyclic directed graphs. In Gaussian models, tests of c onditional independence are typically based on Pearson correlations, and high-dimensional consi ste cy results have been obtained for the PC algorithm in this setting. Analyzing the error propagati on from marginal to partial correlations, we prove that high-dimensional consistency carries over to a broader class of Gaussian copula or nonparanormalmodels when using rank-based measures of correlation. For g raph sequences with bounded degree, our consistency result is as strong as prior Gaussian results. In simulations, the ‘Rank PC’ algorithm works as well as the ‘Pearson PC’ algorit hm for normal data and considerably better for non-normal data, all the while incurring a neglig ible increase of computation time. While our interest is in the PC algorithm, the presented analysis o f err r propagation could be applied to other algorithms that test the vanishing of low-order parti al correlations.
Log Skeletons: A Classification Approach to Process Discovery

To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fullyautomated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.
A filter-based feature selection model for anomaly-based intrusion detection systems

Feature selection is an important factor in modeling anomaly-based intrusion detection systems. An irrelevant feature can result in overfitting and affect the modeling power of classification algorithms. The objective of feature selection is to remove irrelevant and redundant attributes from the dataset to improve the predictive power of a classification algorithm. In this paper, we introduce a filter-based feature selection model for anomaly-based intrusion detection systems. The proposed model evaluates the features based on information gain by considering consistency, dependency, information, and distance of each feature. The experimental results show that our proposed model has a key effect in reducing computational and time complexity. The accuracy of the proposed model was measured as 99.70 % and 99.90% for the ISCX and NSL-KDD datasets respectively.
Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach

Emergence of peer-to-peer lending has opened an appealing option for micro-financing and is growing rapidly as an option in the financial industry. However, peer-to-peer lending possesses a high risk of investment failure due to the lack of expertise on the borrowers' creditworthiness. In addition, information asymmetry, the unsecured nature of loans as well as lack of rigid rules and regulations increase the credit risk in peer-to-peer lending. This paper proposes a credit scoring model using artificial neural networks in classifying peer-to-peer loan applications into default and non-default groups. The results indicate that the neural network-based credit scoring model performs effectively in screening default applications.
Assessing the Quality of Model Differencing Engines

In recent years many tools and algorithms for model comparison and differencing were proposed. Typically, the main focus of the research laid on being able to compute the difference in the first place. Only very few papers addressed the quality of the delivered differences sufficiently. Hence, this is a general shortcoming in the state-of-the-art. Currently, there are no established community standards how to assess the quality of differences and it is neither possible to compare the quality of different algorithms, nor can developers decide whether or not an algorithm is able to produce adequate results in a given application scenario. We propose a parallel working session to be held to discuss this general problem and its implications. The goal of the working session is to achieve a common understanding of what the crucial factors in assessing the quality of differences are. Furthermore, it is planed to discuss possible solutions that help the research community as whole, e.g. by drafting the design of an initial benchmark corpus which later could be turned into a standardized, openly available benchmark set.
Self-Supervised Visual Planning with Temporal Skip Connections

In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction. If a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robot learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skipconnections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robot learning.
White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.
A Novel Planar Three-Way Power Divider

A novel planar three-way power divider is proposed. Based on the conventional planar microstrip coupled line technology, the proposed three-way power divider can modify a three-way Wilkinson power divider from a three-dimensional configuration into a two-dimensional one, meanwhile, to keep the length of the circuit to be lambda/4. The planar structure enables easy circuit design in printed circuit boards and monolithic microwave integrated circuits. The design concept and implementation are discussed. From the measured results, less than 4.8plusmn0.1dB of the three equivalent insertion losses, less than 19.5dB of the return loss, and better than 17.5dB of isolation at 2.4GHz can be achieved
Robust nonrigid ICP using outlier-sparsity regularization

We show how to incorporate a statistical shape model into the nonrigid ICP framework, and propose a robust nonrigid ICP algorithm. In the nonrigid ICP framework, a template surface is represented by a set of points, and the shape of the template is parametrized by a transformation matrix per one template point. In the proposed method, the statistics of the matrices are estimated based on a set of training surfaces, and the statistical shape model is incorporated into the nonrigid ICP framework by modifying the representation of the stiffness of the template. The statistical shape model and a noise model make it possible to discriminate outliers from inliers in given targets. Our proposed method detects the outliers, which are not represented by the models appropriately, based on their sparseness. The detected outliers are automatically excluded from the target to be registered, and the template is deformed to fit the inliers only. As the result, the accuracy of the registration is improved. The performance of the proposed method is evaluated qualitatively and quantitatively using synthetic data and clinical CT images.