nils-herrmann/clean_scirepeval huggingface dataset:
A FINITE ELEMENT METHOD FOR NON-LINEAR HYPERELASTICITY APPLIED FOR THE SIMULATION OF OCTOPUS ARM MOTIONS

An implicit non-linear finite element (FE) numerical procedure for the simulation of biological muscular tissues is presented. The method has been developed for studying the motion of muscular hydrostats, such as squid and octopus arms and its general framework is applicable to other muscular tissues. The FE framework considered is suitable for the dynamic numerical simulations of three-dimensional non-linear nearly incompressible hyperelastic materials that undergo large displacements and deformations. Human and animal muscles, consisting of fibers and connective tissues, belong to this class of materials. The stress distribution inside the muscular FE model is considered as the superposition of stresses along the muscular fibers and the connective tissues. The stresses along the fibers are modeled as the sum of active and passive stresses, according to the muscular model of Van Leeuwen and Kier (1997) Philos.  Passive stress distribution is an experimentally-defined function of fibers’ deformation; while active stress distribution is the product of an activation level time function, a force-stretch function and a force-stretch ratio function. The mechanical behavior of the surrounding tissues is determined adopting a Mooney-Rivlin constitutive model. The incompressibility criterion is met by enforcing large bulk modulus and by introducing modified deformation measures. Due to the non-linear nature of the problem,
High frequency bus converter with low loss integrated matrix transformer

The trend in isolated DC/DC converters is increasing output power demands and higher operating frequencies. Improved topologies and semiconductors can allow for lower loss at higher frequencies. A major barrier to further improvement is the transformer design. With high current levels and high frequency effects the transformers can become the major loss component in the circuit. High values of transformer leakage inductance can also greatly degrade the performance of the converter. Matrix transformers offer the ability to reduce winding loss and leakage inductance. This paper will study the impact of increased switching frequencies on transformer size and explore the use of matrix transformers in high current high frequency isolated applications. This paper will also propose an improved integrated matrix transformer design that can decrease core loss and further improve the performance of matrix transformers.
The Institutional Origins of the Industrial Revolution

on an earlier version are acknowledged with gratitude. 1 North (1981, p. 166) comes close to linking the institutional changes of the late eighteenth century with the Industrial 1 Revolution when he maintains that it was explained by " a combination of better-specified and enforced property rights and increasingly efficient and expanding markets. " North and Weingast (1989, p. 831) are more prudent and wonder if arguing that without the Glorious Revolution the British economy would have followed a very different path and would not have experienced an Industrial Revolution would be " claiming too much. "  The new institutional economics has, so far, had little to say about the Industrial
A turnstile junction waveguide orthomode transducer

An orthomode transducer with a circular waveguide input and two rectangular waveguide outputs is described. The design utilizes a turnstile junction and two E-plane power combiners. A K-band version of this device has been constructed and tested. From 18 to 26 GHz, the input reflection coefficient was less than -19 dB, the cross-polarization was less than -48 dB, and the transmission loss was ~0.15 dB. We estimate the mechanical tolerances that would be required to scale this device to the 200-270-GHz band for use in a dual-polarization radio-astronomy receiver
The Use of the UTAUT Model in the Adoption of E-Government Services in Kuwait

E-government initiatives are in their infancy in many developing countries. The success of these initiatives is dependent on government support as well as citizens' adoption of e-government services. This study adopted the unified of acceptance and use of technology (UTAUT) model to explore factors that determine the adoption of e-government services in a developing country, namely Kuwait. 880 students were surveyed, using an amended version of the UTAUT model. The empirical data reveal that performance expectancy, effort expectancy and peer influence determine students' behavioural intention. Moreover, facilitating conditions and behavioural intentions determine students' use of e-government services. Implications for decision makers and suggestions for further research are also considered in this study.
Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs)

Human interaction proofs (HIPs) have become commonplace on the internet for protecting free online services from abuse by automated scripts/bots. They are challenges designed to be easily solved by humans, while remaining too hard for computers to solve. Reading based HIPs comprise a segmentation problem and one or more recognition problems. Recent studies have shown that computers are better at solving the recognition problem than the segmentation problem (Chellapilla and Simard, 2004; Chellapilla et al, 2005a). In this paper we compare human and computer single character recognition abilities through a sequence of human user studies and computer experiments using convolutional neural networks. In these experiments, we assume that segmentation has been solved and the approximate locations of individual HIP characters are known. Results show that computers are as good as or better than humans at single character recognition under all commonly used distortion and clutter scenarios used in todays HIPs.
Integrated Semantics Service Platform for the Internet of Things: A Case Study of a Smart Office

The Internet of Things (IoT) allows machines and devices in the world to connect with each other and generate a huge amount of data, which has a great potential to provide useful knowledge across service domains. Combining the context of IoT with semantic technologies, we can build integrated semantic systems to support semantic interoperability. In this paper, we propose an integrated semantic service platform (ISSP) to support ontological models in various IoT-based service domains of a smart city. In particular, we address three main problems for providing integrated semantic services together with IoT systems: semantic discovery, dynamic semantic representation, and semantic data repository for IoT resources. To show the feasibility of the ISSP, we develop a prototype service for a smart office using the ISSP, which can provide a preset, personalized office environment by interpreting user text input via a smartphone. We also discuss a scenario to show how the ISSP-based method would help build a smart city, where services in each service domain can discover and exploit IoT resources that are wanted across domains. We expect that our method could eventually contribute to providing people in a smart city with more integrated, comprehensive services based on semantic interoperability.
An improved k-means clustering algorithm

K-means algorithm used in the initial cluster centers are randomly generated, then clustering results are unstable and susceptible to the noise data-objects. In this paper presents a density-based algorithm to determine the initial cluster centers, eliminate the clustering results depend on the initial cluster centers. While, optimized the methods of cluster centers re-calculation and the distance from data-object to the cluster center, reduce noise impact on the clustering results, which meets the clustering of asymmetry density cluster. Experiments on UCI datasets show that the improved algorithm can eliminate the clustering results depend on the initial cluster centers, obtain more compact cluster. Therefore, the improved K-means clustering algorithm is effective.
A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks

Large-scale, self-organizing wireless sensor and mesh network deployments are being driven by recent technological developments such as The Internet of Things (IoT), Smart Grids and Smart Environment applications. Efficient use of the limited energy resources of wireless sensor network (WSN) nodes is critically important to support these advances, and application of topology control methods will have a profound impact on energy efficiency and hence battery lifetime. In this survey, we focus on the energy efficiency issue and present a comprehensive study of topology control techniques for extending the lifetime of battery powered WSNs. First, we review the significant topology control algorithms to provide insights into how energy efficiency is achieved by design. Further, these algorithms are classified according to the energy conservation approach they adopt, and evaluated by the trade-offs they offer to aid designers in selecting a technique that best suits their applications. Since the concept of "network lifetime" is widely used for assessing the algorithms' performance, we highlight various definitions of the term and discuss their merits and drawbacks. Recently, there has been growing interest in algorithms for non-planar topologies such as deployments in underwater environments or multi-level buildings. For this reason, we also include a detailed discussion of topology control algorithms that work efficiently in three dimensions. Based on the outcomes of our review, we identify a number of open research issues for achieving energy efficiency through topology control.
A New Paradigm for Intelligent Tutoring Systems: Example-Tracing Tutors

The Cognitive Tutor Authoring Tools (CTAT) support creation of a novel type of tutors called example-tracing tutors. Unlike other types of ITSs (e.g., model-tracing tutors, constraint-based tutors), exampletracing tutors evaluate student behavior by flexibly comparing it against generalized examples of problemsolving behavior. Example-tracing tutors are capable of sophisticated tutoring behaviors; they provide step-bystep guidance on complex problems while recognizing multiple student strategies and (where needed) maintaining multiple interpretations of student behavior. They therefore go well beyond VanLehn’s (2006) minimum criterion for ITS status, namely, that the system has an inner loop (i.e., provides within-problem guidance, not just end-of-problem feedback). Using CTAT, example-tracing tutors can be created without programming. An author creates a tutor interface through drag-and-drop techniques, and then demonstrates the problem-solving behaviors to be tutored. These behaviors are recorded in a “behavior graph,” which can be easily edited and generalized. Compared to other approaches to programming by demonstration for ITS development, CTAT implements a simpler method (no machine learning is used) that is currently more pragmatic and proven for widespread, real-world use by non-programmers. Development time estimates from a large number of real-world ITS projects that have used CTAT suggest that example-tracing tutors reduce development cost by a factor of 4 to 8, compared to “historical” estimates of ITS development time and cost. The main contributions of the work are a novel ITS technology, based on the use of generalized behavioral examples to guide students in problem-solving exercises, as well as a suite of mature and robust tools for efficiently building real-world ITSs without programming.
A Compile-Time Scheduling Heuristic for Interconnection-Constrained Heterogeneous Processor Architectures

This paper presents a compile-time scheduling heuristic called dynamic level scheduling, which accounts for interprocessor communication overhead when mapping precedence-constrained, communicating tasks onto heterogeneous processor architectures with limited or possibly irregular interconnection structures. This technique uses dynamicallychanging priorities to match tasks with processors at each step, and schedules over both spatial and temporal dimensions to eliminate shared resource contention. This method is fast, flexible, widely targetable, and displays promising performance.
Accurate Modeling and Robust Hovering Control for a Quad-rotor VTOL Aircraft

Quad-robot type (QRT) unmanned aerial vehicles (UAVs) have been developed for quick detection and observation of the circumstances under calamity environment such as indoor fire spots. The UAV is equipped with four propellers driven by each electric motor, an embedded controller, an Inertial Navigation System (INS) using three rate gyros and accelerometers, a CCD (Charge Coupled Device) camera with wireless communication transmitter for observation, and an ultrasonic range sensor for height control. Accurate modeling and robust flight control of QRT UAVs are mainly discussed in this work. Rigorous dynamic model of a QRT UAV is obtained both in the reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurately derived dynamics. The developed hovering robot shows stable flying performances under the adoption of DOB and the vision based localization method. Although a model is incorrect, DOB method can design a controller by regarding the inaccurate part of the model J. Kim Department of Mechanical Engineering, Seoul National University of Technology, Seoul, South Korea e-mail: jinhyun@snut.ac.kr M.-S. Kang Department of Mechatronics Engineering, Hanyang University, Ansan, South Korea e-mail: wowmecha@gmail.com S. Park (B) Division of Applied Robot Technology, Korea Institute of Industrial Technology, Ansan, South Korea e-mail: sdpark@kitech.re.kr 10 J Intell Robot Syst (2010) 57:9–26 and sensor noises as disturbances. The UAV can also avoid obstacles using eight IR (Infrared) and four ultrasonic range sensors. This kind of micro UAV can be widely used in various calamity observation fields without danger of human beings under harmful environment. The experimental results show the performance of the proposed control algorithm.
System on Chip Signal Conditioner for LVDT Sensors

Linear variable differential transformer (LVDT) sensors are widely used in hydraulic and pneumatic mechatronic systems for measuring physical quantities like displacement, force or pressure. The LVDT sensor consists of two magnetic coupled coils with a common core and this sensor converts the displacement of core into reluctance variation of magnetic circuit. LVDT sensors combines good accuracy (0.1 % error) with low cost, but they require relative complex electronics. Standard electronics for LVDT sensor conditioning is analog $the coupled coils constitute an inductive half-bridge supplied with 5 kHz sinus excitation from a quadrate oscillator. The output phase span is amplified and synchronous demodulated. This analog technology works well but has its drawbacks - hard to adjust, many components and packages, no connection to computer systems. To eliminate all these disadvantages, our team from "Politehnica" University of Bucharest has developed a LVDT signal conditioner using system on chip microcontroller MSP430F149 from Texas Instruments. This device integrates all peripherals required for LVDT signal conditioning (pulse width modulation modules, analog to digital converter, timers, enough memory resources and processing power) and offers also excellent low-power options. Resulting electronic module is a one-chip solution made entirely in SMD technology and its small dimensions allow its integration into sensor's body. Present paper focuses on specific issues of this digital solution for LVDT conditioning and compares it with classic analog solution from different points of view: error curve, power consumption, communication options, dimensions and production cost. Microcontroller software (firmware) and digital signal conditioning techniques for LVDT are also analyzed. Use of system on chip devices for signal conditioning allows realization of low cost compact transducers with same or better performances than their analog counterparts, but with extra options like serial communication channels, self-calibration, local storage of measured values and fault detection
A Generative Model for Simultaneous Estimation of Human Body Shape and Pixel-Level Segmentation

This paper addresses pixel-level segmentation of a human body from a single image. The problem is formulated as a multi-region segmentation where the human body is constrained to be a collection of geometrically linked regions and the background is split into a small number of distinct zones. We solve this problem in a Bayesian framework for jointly estimating articulated body pose and the pixel-level segmentation of each body part. Using an image likelihood function that simultaneously generates and evaluates the image segmentation corresponding to a given pose, we robustly explore the posterior body shape distribution using a data-driven, coarse-to-fine Metropolis Hastings sampling scheme that includes a strongly data-driven proposal term.
Sensible , sensable and desirable : a framework for designing physical interfaces

Movements of interfaces can be analysed in terms of whether they are sensible, sensable and desirable. Sensible movements are those that users naturally perform; sensable are those that can be measured by a computer; and desirable movements are those that are required by a given application. We show how a systematic comparison of sensible, sensable and desirable movements, especially with regard to how they do not precisely overlap, can reveal potential problems with an interface and also inspire new features. We describe how this approach has been applied to the design of three interfaces: the Augurscope II, a mobile augmented reality interface for outdoors; the Drift Table, an item of furniture that uses load sensing to control the display of aerial photographs; and pointing flashlights at walls and posters in order to play sounds.
Robust i-vector based adaptation of DNN acoustic model for speech recognition

In the past, conventional i-vectors based on a Universal Background Model (UBM) have been successfully used as input features to adapt a Deep Neural Network (DNN) Acoustic Model (AM) for Automatic Speech Recognition (ASR). In contrast, this paper introduces Hidden Markov Model (HMM) based ivectors that use HMM state alignment information from an ASR system for estimating i-vectors. Further, we propose passing these HMM based i-vectors though an explicit non-linear hidden layer of a DNN before combining them with standard acoustic features, such as log filter bank energies (LFBEs). To improve robustness to mismatched adaptation data, we also propose estimating i-vectors in a causal fashion for training the DNN, restricting the connectivity among hidden nodes in the DNN and applying a max-pool non-linearity at selected hidden nodes. In our experiments, these techniques yield about 5-7% relative word error rate (WER) improvement over the baseline speaker independent system in matched condition, and a substantial WER reduction for mismatched adaptation data.
A mathematical analysis of the DCT coefficient distributions for images

Over the past two decades, there have been various studies on the distributions of the DCT coefficients for images. However, they have concentrated only on fitting the empirical data from some standard pictures with a variety of well-known statistical distributions, and then comparing their goodness of fit. The Laplacian distribution is the dominant choice balancing simplicity of the model and fidelity to the empirical data. Yet, to the best of our knowledge, there has been no mathematical justification as to what gives rise to this distribution. We offer a rigorous mathematical analysis using a doubly stochastic model of the images, which not only provides the theoretical explanations necessary, but also leads to insights about various other observations from the literature. This model also allows us to investigate how certain changes in the image statistics could affect the DCT coefficient distributions.
The Building Blocks of Experience: An Early Framework for Interaction Designers

Design activity has recently attempted to embrace designing the user experience. Designers need to demystify how we design for user experience and how the products we design achieve specific user experience goals. This paper proposes an initial framework for understanding experience as it relates to user-product interactions. We propose a system for talking about experience, and look at what influences experience and qualities of experience. The framework is presented as a tool to understand what kinds of experiences products evoke.
Large Scale Online Kernel Learning

In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nyström Online Gradient Descent (NOGD) algorithm that applies the Nyström method to approximate large kernel matrices. We explore these two approaches to tackle three online learning tasks: binary classification, multi-class classification, and regression. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches.
THE EFFECT OF INFORMATION ON PRODUCT QUALITY : EVIDENCE FROM RESTAURANT HYGIENE GRADE CARDS

This study examines the effect of an increase in product quality information to consumers on rms’ choices of product quality. In 1998 Los Angeles County introduced hygiene quality grade cards to be displayed in restaurant windows. We show that the grade cards cause (i) restaurant health inspection scores to increase, (ii) consumer demand to become sensitive to changes in restaurants’ hygiene quality, and (iii) the number of foodborne illness hospitalizations to decrease. We also provide evidence that this improvement in health outcomes is not fully explained by consumers substituting from poor hygiene restaurants to good hygiene restaurants. These results imply that the grade cards cause restaurants to make hygiene quality improvements.
Sentiment Polarity Identification in Financial News: A Cohesion-based Approach

Text is not unadulterated fact. A text can make you laugh or cry but can it also make you short sell your stocks in company A and buy up options in company B? Research in the domain of finance strongly suggests that it can. Studies have shown that both the informational and affective aspects of news text affect the markets in profound ways, impacting on volumes of trades, stock prices, volatility and even future firm earnings. This paper aims to explore a computable metric of positive or negative polarity in financial news text which is consistent with human judgments and can be used in a quantitative analysis of news sentiment impact on financial markets. Results from a preliminary evaluation are presented and discussed.