nils-herrmann/clean_scirepeval huggingface dataset:
Design, implementation and test results of a robust control method for a powered ankle foot orthosis (AFO)

There are various methods to control a powered AFO. As different as they are in their approach each of them has certain advantages as well as difficulties. What is still needed is a robust control concept that meets the requirements for ankle gait assistance. A new, stiffness-control model has been developed that divides the stance phase of gait into five zones using either velocity or stiffness control for each zone. The design and implementation of this new control algorithm as well as some first test results are presented.
Strategy and architecture of a safety concept for fully automatic and autonomous driving assistance systems

As drivers back out of the driving task, when transported automatically by an intelligent car for a longer time, they are not always able to react properly, if a driver take over request occurs. This paper presents two ways, how to deal with this problem within the scope of a functional safety concept. Thereto, the difference between fully automatic and autonomous driving assistance systems is explained. Afterwards two different strategies to reach a safe state in consequence of a system boundary crossing are proposed. In the first case the fall back state is reached by a driver take over, in the second case by an automatic, active fail-safe mechanism. Subsequently the necessary components for monitoring and reaching a safe state and their embedment in a basic, functional architecture of a driving assistance system are described. In this context, special regard is paid to aspects of redundancy as well. In the end it is concluded, that the safety concept proposed here is crucial for guaranteeing enduring safety in an automatically driving car and in consequence for making automatic driving functions commercially ready for serial production.
A revised UTAUT model to investigate E -health acceptance of health professionals in Africa

The past two decades has seen research in the adoption of information and communication technology (ICT). E-health application is an emerging information technology in the health industry which has the potential to improve access to health care and providing quality and effective service delivery in Africa. However, the adoption and diffusion of E-health in Africa and other developing countries is slow and a complex issues. For a successful implementation of information technology applications, the attitude and a commitment of stakeholders should be considered. The behavioral intention of health care professionals is critical for successful E-health implementation. The paper highlights that though several studies about technology adoption exist, little attention has been given to E-health acceptance decision in Africa. The purpose of this paper is to revise the generic unified theory of acceptance and use of technology (UTAUT) model to include other constructs and moderators that can be tested for adoption of E-health in health care settings in Africa. The contribution of this paper is in terms of building theory in the combined areas of health care and ICT.
DEFO-NET: Learning Body Deformation Using Generative Adversarial Networks

Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (DEFO-NET), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-netinherits the generalisation properties of GANs. This means that the network is able to reconstruct the whole 3-D appearance of the object given a single depth view of the object and to generalise to unseen object configurations. Contrary to traditional finite element methods, our approach is fast enough to be used in real-time applications. We apply the network to the problem of safe and fast navigation of mobile robots carrying payloads over different obstacles and floor materials. Experimental results in real scenarios show how a robot equipped with an RGB-D camera can use the network to predict terrain deformations under different payload configurations and use this to avoid unsafe areas.
Odin: Team VictorTango's entry in the DARPA Urban Challenge

The DARPA Urban Challenge required robotic vehicles to travel more than 90 km through an urban environment without human intervention and included situations such as stop intersections, traffic merges, parking, and roadblocks. Team VictorTango separated the problem into three parts: base vehicle, perception, and planning. A Ford Escape outfitted with a custom drive-by-wire system and computers formed the basis for Odin. Perception used laser scanners, global positioning system, and a priori knowledge to identify obstacles, cars, and roads. Planning relied on a hybrid deliberative/reactive architecture to
Speech Emotion Recognition Based on Improved MFCC

Speech1 Emotion Recognition SER uses the Berlin EMO-DB database, seven emotions. Traditional emotional features and their statistics are used in SER. Two improved Mel Frequency Cepstrum Coefficients MFCC features are added to this experiment, which extract MFCC parameters from the energy curve and the fundamental frequency curve, that are energy MFCC EEMFCC and fundamental frequency MFCC F0MFCC, using Support Vector Machines SVM as the recognition machine, we obtained the highest average recognition rate of 85.37% for the seven categories and 100% for sad.
ECDH Key-Extraction via Low-Bandwidth Electromagnetic Attacks on PCs

We present the first physical side-channel attack on elliptic curve cryptography running on a PC. The attack targets the ECDH public-key encryption algorithm, as implemented in the latest version of GnuPG’s Libgcrypt. By measuring the target’s electromagnetic emanations, the attack extracts the secret decryption key within seconds, from a target located in an adjacent room across a wall. The attack utilizes a single carefully chosen ciphertext, and tailored timefrequency signal analysis techniques, to achieve full key extraction.
Birkbeck Sport Business Centre Case Study Series Stakeholder Management and Sport Facilities : A Case Study of the Emirates Stadium *

This case study presents an analysis of how Arsenal Football Club, a leading football club in England, manages different stakeholders in the context of the Emirates Stadium. The objectives of this case study are threefold. First, it identifies different stakeholders of the Emirates Stadium and, using the Mendelow power/interest matrix, considers the different levels of stakeholder power and interest. The second aim of this case study is to illustrate various stakeholder management strategies that Arsenal Football Club has implemented in relation to four stakeholder groups – the London Borough of Islington; local community residents; supporters; and disabled supporters. The third objective is to present a number of best practice recommendations in relation to stakeholder management and sport facility management. While best practice in sport facility management has tended to focus on internal-facing benchmarking this case highlights the importance of successful stakeholder management to achieving success in facility operation. However it notes that the tendency to focus on the qualitative aspects of stadium management through stakeholder consultation cannot be measured by traditional quantitative benchmarks.
Chaffing and Winnowing: Confidentiality without Encryption

Encryption: transforming the message to a ciphertext such that an adversary who overhears the ciphertext can not determine the message sent. The legitimate receiver possesses a secret decryption key that allows him to reverse the encryption transformation and retrieve the message. The sender may have used the same key to encrypt the message (with symmetric encryption schemes) or used a different, but related key (with public-key schemes). DES and RSA are familiar examples of encryption schemes.
A Tour of Reinforcement Learning: The View from Continuous Control

This manuscript surveys reinforcement learning from the perspective of optimization and control with a focus on continuous control applications. It surveys the general formulation, terminology, and typical experimental implementations of reinforcement learning and reviews competing solution paradigms. In order to compare the relative merits of various techniques, this survey presents a case study of the Linear Quadratic Regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. The manuscript describes how merging techniques from learning theory and control can provide non-asymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. This survey concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.
Dispersion characteristics of substrate integrated rectangular waveguide

Dispersion properties of the substrate integrated rectangular waveguide (SIRW) are rigorously obtained using the BI-RME method combined with the Floquet's theorem. Our analysis shows that the SIRW basically has the same guided-wave characteristics as the conventional rectangular waveguide. Empirical equations are derived from the calculated dispersion curves in order to estimate the cutoff frequency of the first two dominant modes of the SIRW To validate the analysis results, an SIRW guide was designed and measured. Very good agreements between the experimental and theoretical results were obtained.
A multivariable approach to supplier segmentation

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. The aim of this paper is to develop a new approach to supplier segmentation that considers the various variables used in existing literature to segment suppliers. A literature review reveals a serious problem from a management perspective. The problem is that many different supplier segmentation methods have been proposed in the last three decades, each of which uses different segmentation variables and hence results in different segments. An overarching supplier segmentation method, considering various segmentation variables, is lacking. Based on an extensive literature review, we have analysed the current methods and we conclude that the literature on supplier segmentation can be divided into three different schools of thoughts. By clearly identifying the deficiencies of the existing theory on supplier segmentation, we developed a new approach. As the basis for this new approach, we developed three requirements to make an overarching approach to supplier segmentation. Firstly, supplier segmentation should be based on their long-term potential, which we propose to assess in terms of supplier capabilities and willingness to cooperate. Secondly, other functional areas beyond purchasing have to be considered when segmenting suppliers. Thirdly, supplier segmentation should be viewed as a step in a longitudinal process that includes selecting suppliers, segmenting them, managing the relationship with them and actively developing their role over time. We illustrate the proposed approach by segmenting the suppliers of a company in the food industry. 1.  Faced with a competitive global market, firms have downsized, focused on core competencies and attempted to achieve competitive advantage by managing their relationships with suppliers more effectively (Tan et al. 1999). The relationships between buyers and suppliers in a supply chain management (SCM) context are investigated in various recent studies. In most cases, the main objective is to …
An indicator of research front activity: Measuring intellectual organization as uncertainty reduction in document sets

When using scientific literature to model scholarly discourse, a research specialty can be operationalized as an evolving set of related documents. Each publication can be expected to contribute to the further development of the specialty at the research front. The specific combinations of title words and cited references in a paper can then be considered as a signature of the knowledge claim in the paper: new words and combinations of words can be expected to represent variation, while each paper is at the same time selectively positioned into the intellectual organization of a field using context-relevant references. Can the mutual information among these three dimensions— title words, cited references, and sequence numbers—be used as an indicator of the extent to which intellectual organization structures the uncertainty prevailing at a research front? The effect of the discovery of nanotubes (1991) on the previously existing field of fullerenes is used as a test case. Thereafter, this method is applied to science studies with a focus on scientometrics using various sample delineations. An emerging research front about citation analysis can be indicated.
Is Predicting Software Security Bugs Using Deep Learning Better Than the Traditional Machine Learning Algorithms?

Software insecurity is being identified as one of the leading causes of security breaches. In this paper, we revisited one of the strategies in solving software insecurity, which is the use of software quality metrics. We utilized a multilayer deep feedforward network in examining whether there is a combination of metrics that can predict the appearance of security-related bugs. We also applied the traditional machine learning algorithms such as decision tree, random forest, naïve bayes, and support vector machines and compared the results with that of the Deep Learning technique. The results have successfully demonstrated that it was possible to develop an effective predictive model to forecast software insecurity based on the software metrics and using Deep Learning. All the models generated have shown an accuracy of more than sixty percent with Deep Learning leading the list. This finding proved that utilizing Deep Learning methods and a combination of software metrics can be tapped to create a better forecasting model thereby aiding software developers in predicting security bugs.
Integration of face recognition and sound localization for a smart door phone system

This paper proposes a smart system using both face recognition and sound localization techniques to identify the faces of visitors from a door phone in much efficient and accurate ways. This system is effectively used to recognize the faces when their locations are out of the boundaries of the camera scope of the door phone. The smart door phone system proposed in this paper uses a visitor's voice source to rotate the camera to his face and then to recognize the face accurately. The integrated system has been designed with one FPGA(Field Programmable Gate Array) chip and tested for actual use in door phone environments.
ReTiCaM: Real-time Human Performance Capture from Monocular Video

We present the first real-time human performance capture approach that reconstructs dense, space-time coherent deforming geometry of entire humans in general everyday clothing from just a single RGB video. We propose a novel two-stage analysis-by-synthesis optimization whose formulation and implementation are designed for high performance. In the first stage, a skinned templatemodel is jointly fitted to background subtracted input video, 2D and 3D skeleton joint positions found using a deep neural network, and a set of sparse facial landmark detections. In the second stage, dense non-rigid 3D deformations of skin and even loose apparel are captured based on a novel real-time capable algorithm for non-rigid tracking using dense photometric and silhouette constraints. Our novel energy formulation leverages automatically identified material regions on the template to model the differing non-rigid deformation behavior of skin and apparel. The two resulting nonlinear optimization problems per-frame are solved with specially-tailored data-parallel Gauss-Newton solvers. In order to achieve real-time performance of over 25Hz, we design a pipelined parallel architecture using the CPU and two commodity GPUs. Our method is the first real-time monocular approach for full-body performance capture. Our method yields comparable accuracy with off-line performance capture techniques, while being orders of magnitude faster.
A High-Dynamic Range Current Source Gate Driver for Switching-Loss Reduction of High-Side Switch in Buck Converter

In this letter, a high-dynamic range current source gate driver (HD-CSD) circuit is proposed to reduce the switching loss of the high-side switch in buck converter with wide variation of the gate resistance. Hard switching loss is the major loss in high-side switch and limits the high switching-frequency application of dc-dc converter. Comparing with conventional voltage source gate driver (VSD) and the reported four switches CSD (4S-CSD), the proposed HD-CSD behaves more like the ideal current source driver which can realize the fast switching of power switches to reduce the switching loss. In addition, with proposed HD-CSD, impact of gate resistance that limits the switching speed of the power switch can be greatly reduced. Experimental results are presented to show the power efficiency improvement of buck converter with HD-CSD high-side driver comparing with VSD and 4S-CSD high-side drivers at switching frequency of 1 MHz.
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective. In this paper, we propose a novel neural network architecture to perform weakly-supervised learning by suppressing irrelevant neuron activations. It localizes objects of interest by learning from image-level categorical labels in an end-to-end manner. We apply this algorithm to a practical challenge of transforming satellite images into a map of settlements and individual buildings. Experimental results show that the proposed algorithm achieves superior performance and efficiency when compared with various baseline models.
An Ontology-Based Approach to Text Summarization

Extractive text summarization aims to create a condensed version of one or more source documents by selecting the most informative sentences. Research in text summarization has therefore often focused on measures of the usefulness of sentences for a summary. We present an approach to sentence extraction that maps sentences to nodes of a hierarchical ontology. By considering ontology attributes we are able to improve the semantic representation of a sentence's information content. The classifier that maps sentences to the taxonomy is trained using search engines and is therefore very flexible and not bound to a specific domain. In our experiments, we train an SVM classifier to identify summary sentences using ontology-based sentence features. Our experimental results show that the ontology-based extraction of sentences outperforms baseline classifiers, leading to higher Rouge scores of summary extracts.
Crossbar RRAM Arrays: Selector Device Requirements During Write Operation

A comprehensive analysis of write operations (SET and RESET) in a resistance-change memory (resistive random access memory) crossbar array is carried out. Three types of resistive switching memory cells-nonlinear, rectifying-SET, and rectifying-RESET-are compared with each other in terms of voltage delivery, current delivery, and power consumption. Two different write schemes, V/2 and V/3, were considered, and the V/2 write scheme is preferred due to much lower power consumption. A simple numerical method was developed that simulates entire current flows and node voltages within a crossbar array and provides a quantitative tool for the accurate analysis of crossbar arrays and guidelines for developing reliable write operation.
Neural and behavioral responses to attractiveness in adult and infant faces

Facial attractiveness provides a very powerful motivation for sexual and parental behavior. We therefore review the importance of faces to the study of neurobiological control of human reproductive motivations. For heterosexual individuals there is a common brain circuit involving the nucleus accumbens, the medial prefrontal, dorsal anterior cingulate and the orbitofrontal cortices that is activated more by attractive than unattractive faces, particularly for faces of the opposite sex. Behavioral studies indicate parallel effects of attractiveness on incentive salience or willingness to work to see faces. There is some evidence that the reward value of opposite sex attractiveness is more pronounced in men than women, perhaps reflecting the greater importance assigned to physical attractiveness by men when evaluating a potential mate. Sex differences and similarities in response to facial attractiveness are reviewed. Studies comparing heterosexual and homosexual observers indicate the orbitofrontal cortex and mediodorsal thalamus are more activated by faces of the desired sex than faces of the less-preferred sex, independent of observer gender or sexual orientation. Infant faces activate brain regions that partially overlap with those responsive to adult faces. Infant faces provide a powerful stimulus, which also elicits sex differences in behavior and brain responses that appear dependent on sex hormones. There are many facial dimensions affecting perceptions of attractiveness that remain unexplored in neuroimaging, and we conclude by suggesting that future studies combining parametric manipulation of face images, brain imaging, hormone assays and genetic polymorphisms in receptor sensitivity are needed to understand the neural and hormonal mechanisms underlying reproductive drives.
Software Maintenance Evaluation of Agile Software Development Method Based on OpenStack

We evaluated software maintenance of an open source cloud platform system we developed using an agile software development method. We previously reported on a rapid service launch using the agile software development method in spite of large-scale development. For this study, we analyzed inquiries and the defect removal efficiency of our recently developed software throughout one-year operation. We found that the defect removal efficiency of our recently developed software was 98%. This indicates that we could achieve sufficient quality in spite of largescale agile development. In term of maintenance process, we could answer all enquiries within three business days and could conduct version-upgrade fast. Thus, we conclude that software maintenance of agile software development is not ineffective. key words: agile software development method, software maintenance, defect removal efficiency, cloud computing, OpenStack, open source software