nils-herrmann/clean_scirepeval huggingface dataset:
ON SEQUENTIAL SAMPLING FOR GLOBAL METAMODELING IN ENGINEERING DESIGN

Approximation models (also known as metamodels) have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the sampling strategies used. Our goal in this paper is to investigate the general applicability of sequential sampling for creating global metamodels. Various sequential sampling approaches are reviewed and new approaches are proposed. The performances of these approaches are investigated against that of the onestage approach using a set of test problems with a variety of features. The potential usages of sequential sampling strategies are also discussed. NOMENCLATURE d Distance between two sample points ds Scaled distance between two sample points k Number of input variables n Number of sample points l Number of sample points generated at all the previous sampling stages m Number of sample points generated at the new sampling stage XD A sample set with n sample points Dn D D x x x ,..., , 2 1 XP A sample set with all l previous sample points Pl P P x x x ,..., , 2 1 XC A sample set with m new sample points Cm C C x x x ,..., , 2 1 R Correlation matrix  Mathematical models have been widely used to simulate and analyze complex real world systems in the area of engineering design. These mathematical models, often implemented by computer codes (e.g., Computational Fluid Dynamics and Finite Element Analysis), could be computationally expensive. For example, one run of a finite element model for vehicle crashworthiness can take several hours. While the capacity of computer keeps increasing, to capture the real world systems more accurately, today’s simulation codes are even getting much more complex and unavoidably more expensive. The multidisciplinary nature of design and the need for incorporating uncertainty in design optimization have posed additional challenges. A widely used strategy is to utilize approximation models which are often referred to as metamodels as they provide a model of the model , replacing the expensive simulation model during the process. Recent studies on using metamodels in design applications include [2, 3, 4, 5, 6], etc. For dealing with multidisciplinary systems, Meckesheimer, et al.  presented a generic integration framework to integrate metamodels from multiple subsystems. An important research issue related to metamodeling is how to achieve a good accuracy of a metamodel with a reasonable number of sample points. While the accuracy of a metamodel is directly related to the metamodeling technique used and the properties of a problem itself, the types of sampling approaches also have direct influences on the performance of a metamodel. Koehler and Owen [8] provided a good review on various sampling approaches for computer experiments. Simpson, et al.  compared five sampling strategies and four metamodeling approaches in terms of their
Precise Indoor Positioning Using UWB: A Review of Methods, Algorithms and Implementations

The demand and growth of indoor positioning has increased rapidly in the past few years for a diverse range of applications. Various innovative techniques and technologies have been introduced but precise and reliable indoor positioning still remains a challenging task due to dependence on a large number of factors and limitations of the technologies. Positioning technologies based on radio frequency (RF) have many advantages over the technologies utilizing ultrasonic, optical and infrared devices. Both narrowband and wideband RF systems have been implemented for short range indoor positioning/real-time locating systems. Ultra wideband (UWB) technology has emerged as a viable candidate for precise indoor positioning due its unique characteristics. This article presents a comparison of UWB and narrowband RF technologies in terms of modulation, throughput, transmission time, energy efficiency, multipath resolving capability and interference. Secondly, methods for measurement of the positioning parameters are discussed based on a generalized measurement model and, in addition, widely used position estimation algorithms are surveyed. Finally, the article provides practical UWB positioning systems and state-of-the-art implementations. We believe that the review presented in this article provides a structured overview and comparison of the positioning methods, algorithms and implementations in the field of precise UWB indoor positioning, and will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the field.
On the estimation of radar polarization orientation shifts induced by terrain slopes

In recent studies, Schuler et al.  applied polarimetric imaging radar-derived orientation angles to measure topography, and Lee et ] used orientation angles for polarimetric SAR data compensation, to ensure accurate estimation of geophysical parameters in rugged terrain areas. To support these applications, it is important to accurately estimate shifts in orientation angles induced by the azimuth slope variations. However, in many cases, inconsistency in the estimation of orientation angle shifts was encountered in several areas, introducing noisy and erroneous results. In this paper, we develop a unified analysis of estimation algorithms based on the circular polarization covariance matrix. The concept of reflection symmetry is used to explain the soundness of the circular polarization method and to show problems associated with other algorithms. L-band polarimetric synthetic aperture radar (SAR) images of Camp Roberts, CA, are used to substantiate this theory.
A low-power, 26-GHz transformer-based regulated cascode transimpedance amplifier in 0.25µm SiGe BiCMOS

A 26 GHz transimpedance amplifier (TIA) with transformer-based regulated cascode (RGC) input stage is proposed and analyzed. The transformer enhances the effective transconductance of the TIA's input common-base transistor; reducing the input resistance and providing considerable bandwidth extension. The TIA is implemented in a 0.25µm BiCMOS technology. Measurement shows the single-ended transimpedance gain of 53dBΩ with −3dB bandwidth of 26 GHz. Total chip power, including an output buffer, is 28.2mW from a 2.5V supply; while core TIA power is 8.2mW. The measured average input-referred noise current spectral density is 21.3 pA/√Hz. Total chip area, including pads, is 960µm×780µm.
NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages

This paper describes the NILC USP system that participated in SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system adopts a hybrid classification process that uses three classification approaches: rulebased, lexicon-based and machine learning approaches. We suggest a pipeline architecture that extracts the best characteristics from each classifier. Our system achieved an Fscore of 56.31% in the Twitter message-level subtask.
An Exploration of Graphical Password Authentication for Children

In this paper, we explore graphical passwords as a child-friendly alternative for user authentication. We evaluate the usability of three variants of the PassTiles graphical password scheme for children, and explore the similarities and differences in performance and preferences between children and adults while using these schemes. Children were most successful at recalling passwords containing images of distinct objects. Both children and adults prefer graphical passwords to their existing schemes, but password memorization strategies differ considerably between the two groups. Based on our findings, we provide recommendations for designing more child-friendly authentication schemes.
A respecification and extension of DeLone and McLean model of IS success in the citizen-centric e-governance

Citizen-centric e-governance is an emerging approach in the case of designing and implementing the e-governance systems with the specific focus on the effectiveness. Based on this perspective, the citizens' needs and values on e-governance have to be put first to make these systems effective. This study is an attempt to explore and propose the needs and values of citizens on the e-governance systems based on the determinants of information system (IS) success in the DeLone and McLean 2003 (D&M 2003) model of IS success. After conducting a comprehensive literature review, this study proposed new determinants of e-governance success and the citizen-centric e-governance success model. This model can be considered as the respecified and extended model of D&M 2003 model of IS success in the specific context of the citizen-centric e-governance systems.
Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Phenomenally successful in practical inference problems, convolutional neural networks (CNN) are widely deployed in mobile devices, data centers, and even supercomputers. The number of parameters needed in CNNs, however, are often large and undesirable. Consequently, various methods have been developed to prune a CNN once it is trained. Nevertheless, the resulting CNNs offer limited benefits. While pruning the fully connected layers reduces a CNN’s size considerably, it does not improve inference speed noticeably as the compute heavy parts lie in convolutions. Pruning CNNs in a way that increase inference speed often imposes specific sparsity structures, thus limiting the achievable sparsity levels. We present a method to realize simultaneously size economy and speed improvement while pruning CNNs. Paramount to our success is an efficient general sparse-with-dense matrix multiplication implementation that is applicable to convolution of feature maps with kernels of arbitrary sparsity patterns. Complementing this, we developed a performance model that predicts sweet spots of sparsity levels for different layers and on different computer architectures. Together, these two allow us to demonstrate 3.1–7.3× convolution speedups over dense convolution in AlexNet, on Intel Atom, Xeon, and Xeon Phi processors, spanning the spectrum from mobile devices to supercomputers. We also open source our project at https://github.com/IntelLabs/SkimCaffe.
Sound source localization based on deep neural networks with directional activate function exploiting phase information

This paper describes sound source localization (SSL) based on deep neural networks (DNNs) using discriminative training. A naïve DNNs for SSL can be configured as follows. Input is the frequency-domain feature used in other SSL methods, and the structure of DNNs is a fully-connected network using real numbers. The training fails because its network structure loses two important properties, i.e., the orthogonality of sub-bands and the intensity- and time-information saved in complex numbers. We solved these two problems by 1) integrating directional information at each sub-band hierarchically, and 2) designing a directional activator that could treat the complex numbers at each sub-band. Our experiments indicated that our method outperformed the naive DNN-based SSL by 20 points in terms of the block-level accuracy.
A 22 nm All-Digital Dynamically Adaptive Clock Distribution for Supply Voltage Droop Tolerance

An all-digital dynamically adaptive clock distribution mitigates the impact of high-frequency supply voltage (VCC) droops on microprocessor performance and energy efficiency. The design integrates a tunable-length delay prior to the global clock distribution to prolong the clock-data delay compensation in critical paths during a VCC droop. The tunable-length delay prevents critical-path timing-margin degradation for multiple cycles after the VCC droop occurs, thus allowing a sufficient response time for dynamic adaptation. An on-die dynamic variation monitor detects the onset of the VCC droop to proactively gate the clock at the end of the tunable-length delay to eliminate the clock edges that would otherwise degrade critical-path timing margin. In comparison to a conventional clock distribution, silicon measurements from a 22 nm test chip demonstrate simultaneous throughput gains and energy reductions of 14% and 3% at 1.0 V, 18% and 5% at 0.8 V, and 31% and 15% at 0.6 V, respectively, for a 10% VCC droop.
The Institutionalization of Institutional Theory

Excerpt] Our primary aims in this effort are twofold: to clarify the independent theoretical contributions of institutional theory to analyses of organizations, and to develop this theoretical perspective further in order to enhance its use in empirical research. There is also a more general, more ambitious objective here, and that is to build a bridge between two distinct models of social actor that underlie most organizational analyses, which we refer to as a rational actor model and an institutional model. The former is premised on the assumption that individuals are constantly engaged in calculations of the costs and benefits of different action choices, and that behavior reflects such utility-maximizing calculations. In the latter model, by contrast, 'oversocialized' individuals are assumed to accept and follow social norms unquestioningly, without any real reflection or behavioral resistance based on their own particular, personal interests. We suggest that these two general models should be treated not as oppositional but rather as representing two ends of a continuum of decisionmaking processes and behaviors. Thus, a key problem for theory and research is to specify the conditions under which behavior is more likely to resemble one end of this continuum or the other. In short, what is needed are theories of when rationality is likely to be more or less bounded. A developed conception of institutionalization processes provides a useful point of departure for exploring this issue.
Pepper learns together with children: Development of an educational application

An educational use of Pepper, a personal robot that was developed by SoftBank Robotics Corp. and Aldebaran Robotics SAS, is described. Applying the two concepts of care-receiving robot (CRR) and total physical response (TPR) into the design of an educational application using Pepper, we offer a scenario in which children learn together with Pepper at their home environments from a human teacher who gives a lesson from a remote classroom. This paper is a case report that explains the developmental process of the application that contains three educational programs that children can select in interacting with Pepper. Feedbacks and knowledge obtained from test trials are also described.
Learning a Metric Embedding for Face Recognition using the Multibatch Method

This work is motivated by the engineering task of achieving a near state-of-the-art face recognition on a minimal computing budget running on an embedded system. Our main technical contribution centers around a novel training method, called Multibatch, for similarity learning, i.e., for the task of generating an invariant “face signature” through training pairs of “same” and “not-same” face images. The Multibatch method first generates signatures for a mini-batch of k face images and then constructs an unbiased estimate of the full gradient by relying on all k2 k pairs from the mini-batch. We prove that the variance of the Multibatch estimator is bounded by O(1/k2), under some mild conditions. In contrast, the standard gradient estimator that relies on random k/2 pairs has a variance of order 1/k. The smaller variance of the Multibatch estimator significantly speeds up the convergence rate of stochastic gradient descent. Using the Multibatch method we train a deep convolutional neural network that achieves an accuracy of 98.2% on the LFW benchmark, while its prediction runtime takes only 30msec on a single ARM Cortex A9 core. Furthermore, the entire training process took only 12 hours on a single Titan X GPU.
The interim test effect: testing prior material can facilitate the learning of new material.

A wealth of prior research has shown that testing can improve subsequent learning of the initially tested material. In contrast, only one recent study has shown that an interim test over prior material can improve learning of subsequent new material (i.e., an interim-test effect). Five experiments replicated and extended this initial work by exploring the extent to which interim test effects generalize to complex text material. Participants were prompted to recall each section of an expository text before moving on to study the next section, or were only prompted to recall after the final section. In all experiments, recall of the final, target section was greater when prior sections had received interim tests versus no interim tests. Experiment 3 established that the effect was due to interim testing in particular rather than to intervening activity in general. Experiment 4 established that the effect was not due to test expectancy differences. In contrast to prior research, Experiment 4 also provided evidence that the effect is not due to release from proactive interference. We discuss other possible mechanisms underlying interim-test effects with text, including shifting to more effective encoding strategies.
SDN-based 5G mobile networks: architecture, functions, procedures and backward compatibility

In this paper, we describe an SDN-based plastic architecture for 5G networks, designed to fulfill functional and performance requirements of new generation services and devices. The 5G logical architecture is presented in detail, and key procedures for dynamic control plane instantiation, device attachment, and service request and mobility management are specified. Key feature of the proposed architecture is flexibility, needed to support efficiently a heterogeneous set of services, including Machine Type Communication, Vehicle to X and Internet of Things traffic. These applications are imposing challenging targets, in terms of end-to-end latency, dependability, reliability and scalability. Additionally, backward compatibility with legacy systems is guaranteed by the proposed solution, and Control Plane and Data Plane are fully decoupled. The three levels of unified signaling unify Access, Non-access and Management strata, and a clean-slate forwarding layer, designed according to the software defined networking principle, replaces tunneling protocols for carrier grade mobility.
Longitudinal and Lateral Control for Automated Yielding Maneuvers

Automated driving is predicted to enhance traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually realize fully automated driving, the intelligent vehicle system must have the ability to plan different maneuvers while adapting to the surrounding traffic environment. This paper presents an algorithm for longitudinal and lateral trajectory planning for automated driving maneuvers where the vehicle does not have right of way, i.e., yielding maneuvers. Such maneuvers include, e.g., lane change, roundabout entry, and intersection crossing. In the proposed approach, the traffic environment which the vehicle must traverse is incorporated as constraints on its longitudinal and lateral positions. The trajectory planning problem can thereby be formulated as two loosely coupled low-complexity model predictive control problems for longitudinal and lateral motion. Simulation results demonstrate the ability of the proposed trajectory planning algorithm to generate smooth collision-free maneuvers which are appropriate for various traffic situations.
Text Classification Methodologies Applied to Micro-Text in Military Chat

We propose methods to classify lines of military chat, or posts, which contain items of interest. We evaluated several current text categorization and feature selection methodologies on chat posts. Our chat posts are examples of 'micro-text', or text that is generally very short in length, semi-structured, and characterized by unstructured or informal grammar and language. Although this study focused specifically on tactical updates via chat, we believe the findings are applicable to content of a similar linguistic structure. Completion of this milestone is a significant first step in allowing for more complex categorization and information extraction.
Fuzzy association rule mining approaches for enhancing prediction performance

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (FARs). The proposed model extracts knowledge from a database for a Fuzzy Inference System (FIS) that can be used in prediction of a future value. The knowledge extraction process and the performance of the model are demonstrated through two case studies of road traffic data sets with different sizes. The experimental results show the merits and capability of the proposed KD model in FARs based knowledge extraction. The second model (the FCM-MSapriori model) integrates FCM and a Multiple Support Apriori (MSapriori) approach to extract the FARs. These FARs provide the knowledge base to be utilized within the FIS for prediction evaluation. Experimental results have shown that the FCM-MSapriori model predicted the future values effectively and outperformed the FCM-Apriori model and other models reported in the literature.
Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.
Deep Learning for Image Denoising: A Survey

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising. abstract environment.