nils-herrmann/clean_scirepeval huggingface dataset:
1 Design of Microstrip Patch Antenna Using Slotted Partial Ground And Addition Of Stairs And Stubs For UWB Application

This paper presents the design with optimum geometry of a novel UWB rectangular patch antenna. A simple narrowband patch antenna is designed before proceeding to the geometry of UWB antenna. A special configuration of patch antenna with slotted partial ground and addition of stairs and stubs was designed and optimized using CST Microwave Studio (CSTMWS). The designed antenna was fabricated, tested and compared with the simulation results. The proposed antenna’s characteristics were investigated with various options and found to operate satisfactorily. A remarkable improvement has been noticed in this design. Moreover, the antennas structure offers great advantages due to its simple designs and small dimensions.
Towards an Environmental Psychology of Workspace : How People are affected by Environments for Work

Inquiry into how people experience environmental conditions at work is a growing area of study. Until the 1980s, there was insufficient research on ‘workspaces’ –and on office environments in particular – to warrant review. Since that time, the range and number of studies of workspace have burgeoned. This paper will identify and review the main themes and findings of this area of research with the objective of defining basic parameters and prevailing theories of the environmental psychology of workspace. These will generate questions and directions for future research
Statistical Voice Conversion Based on Wavenet

This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acoustic features of a source speaker. In voice conversion based on statistical models, the relation between acoustic features, such as spectral parameters, extracted from source and target audio waveforms is generally modeled using statistical models, such as Gaussian mixture models and neural networks. Although modeling the relation between acoustic features is reasonable and efficient, these models are not optimized for predicting target audio waveforms because the vocoder parameters are used as intermediate representations. To overcome this problem, we developed a voice conversion method to model the relation between target audio waveforms and acoustic features extracted from source audio waveforms using WaveNet, which is a generative model for audio waveforms. The proposed model can directly generate converted audio waveforms without vocoders. Experimental results indicate that the proposed method can generate a more naturally sounding converted speech than that using a conventional DNN method.
HOW THE EYE MEASURES REALITY AND VIRTUAL REALITY

tures throughout our history. The paintings at Niaux, Altamira, and Lascaux (Clottes, 1995; Ruspoli, 1986), for example, are known to be about 14,000 years old, but with the recently discovered paintings in the Grotte Chauvet, the origin of representational art appears to have been pushed back even further (Chauvet, Brunel Deschamps, & Hillaire, 1995; Clottes, 1996), to 20,000 years ago if not longer.1 Thus, these paintings date from about the time at which homo sapiens sapiens first appeared in Europe (Nougier, 1969). We should remember these paintings in the context of virtual reality; our fascination with pictures is by no means recent. My intent is threefold: first, to discuss our perception of the cluttered layout, or space, that we normally find around us; second, to discuss the development of representational art up to our current appreciation of it; and third, to apply this knowledge to virtual reality systems. The first discussion focuses on the use of multiple sources of information specifying ordinal depth relations, within the theoretical framework that I have called directed perception (Cutting, 1986, 1991). The second discussion is embedded within the first, but it is steeped in neither history nor art history; instead, it is offered through the peculiar eyes of optics and psychology, particularly psychophysics. The third is addressed to one of the pressing problems of graphics and of virtual reality—how we perceive the layout of the environments that we simulate.
Advances in facial rejuvenation: botulinum toxin type a, hyaluronic acid dermal fillers, and combination therapies--consensus recommendations.

Facial aesthetics and rejuvenation are evolving rapidly due to changes in products, procedures, and patient demographics. Clinicians can benefit from ongoing guidance on products, tailoring treatments to individual patients, treating multiple facial areas, and using combinations of products and ways to optimize outcomes.



A multidisciplinary group of aesthetic treatment experts convened to review the properties and uses of botulinum toxin type A (BoNTA) and hyaluronic acid fillers and to update consensus recommendations for facial rejuvenation using these two types of products. The group considered paradigm shifts in facial aesthetics; optimal techniques for using BoNTA and hyaluronic acid fillers alone and in combination; the influence of patient sex, ethnicity, cultural ideals, and skin color on treatment; general techniques; patient education and counseling; and emerging trends and needs in facial rejuvenation.



The group provided specific recommendations by facial area, focusing on relaxing musculature, restoring volume, and recontouring using BoNTA and hyaluronic acid fillers alone and in combination. For the upper face, BoNTA remains the cornerstone of treatment, with hyaluronic acid fillers used to augment results. These fillers are central to the midface because of the need to restore volume. BoNTA and hyaluronic acid in combination can improve outcomes in the lower face.



Optimal outcomes in facial aesthetics require in-depth knowledge of facial aging and anatomy, an appreciation that rejuvenation is a three-dimensional process involving muscle control, volume restoration, and recontouring, and thorough knowledge of properties and techniques specific to each product in the armamentarium.
Organizational Learning Capabilities ( OLC ) toward Job Satisfaction : A Conceptual Framework

Organizational learning or organizational learning capability (OLC) is about the ability of one organization to apply the correct and appropriate management practices, its structures as well as the procedures which enhance, facilitate and encourage learning. Job satisfaction is defined as the extent to which people like (satisfaction) or dislike (dissatisfaction) their jobs. This paper reviews the literature that leads to developing a conceptual framework of a study on organizational learning capabilities and job satisfaction of employees in an organization. Based on previous models and scholarly literatures, four main independent variables are identified, namely, organizational culture, leadership, dialogue and participative decision-making; while the dependent variable is job satisfaction. The study is significant to employees of an organization for improving the skills of acquiring knowledge and learning capabilities. It will also give feedback to the management of the organization the extent of their awareness on Organizational Learning Capabilities (OLC) toward job satisfaction.
Personality Depends on The Medium: Differences in Self-Perception on Snapchat, Facebook and Offline

We investigate self-perception in social media through the lens of personality theory. Two mixed-methods studies involving 148 participants examine if people self-report different personality traits in social media compared with their offline traits. We first compare offline and Facebook traits, finding that on Facebook people are less Neurotic, Open and Agreeable. A second study compared offline, Facebook and Snapchat traits, replicating and extending our initial results. Again Facebook personality was less Neurotic and less Open than offline. In contrast, Snapchat personality was more Extravert than both offline and Facebook and more Open than Facebook. Interviews indicate how personality differences arise from social media affordances. Anxiety about audience judgments leads people to curate posts to appear less Neurotic on social media, but the transience of Snapchat promotes greater Extraversion than offline and Facebook. We discuss theory and design implications.
Domain Ontology for Programming Languages

Ontology have become a relevant representation formalism and many application domains are  considering adopting them. This attention claims for methods for reusing domain knowledge resources in the development of domain ontologies. Accordingly, in this paper we discuss a general methodology to create domain ontology for more than one object oriented language (OOP) like Java, PHP and C++. A lot of software development methods specially Web applications have presented most of these methods that are focusing on the structure of distributed systems and security, in which they are connected through networks and the internet; resulting in more valuable business and critical assets stored, searched and manipulated by World Wide Web. The aims of this study building domain ontology for OOP language classes for different OOP languages or different versions of the same language is an exciting opportunity for researchers to access the information required under the constant increase in the volume of information disseminated on the Internet. By creating Ontology domain for OOP, we can Improve methods of viewing and 1 E-mail: iabuhassan@newsoft.ps 2 E-mail: akram.othman@gmail.com  October 14, 2012.  November 19, 2012  2012 76 Domain Ontology for Programming Languages organizing information, improve the way of processing, in addition to increasing the vocabulary and their relationship to terminology as well as the rules used in natural language with OOP languages. The clear identification of the properties and relations of terms is the starting point to become Ontology domain. The importance of the domain Ontology among object oriented programming languages is that through the synthesis of these relationships or Ontology an OOP can be achieved through web by any junior programmers.
Subfigure and Multi-Label Classification using a Fine-Tuned Convolutional Neural Network

This paper describes the submission of the BMET group to the Subfigure Classification and Multi-Label Classification tasks of the ImageCLEF 2016 medical subtrack. Our method creates a new optimised feature extractor by using medical images to fine-tune a CNN that has been pre-trained on general image data. Our classification method shows promising result in both the the subfigure classification and multi-label classification subtasks.
Integrating Social Media Communications into the Rapid Assessment of Sudden Onset Disasters

Recent research on automatic analysis of social media data during disasters has given insight into how to provide valuable and timely information to formal response agencies—and members of the public—in these safety-critical situations. For the most part, this work has followed a bottom-up approach in which data are analyzed first, and the target audience’s needs are addressed later. Here, we adopt a top-down approach in which the starting point are information needs. We focus on the aid agency tasked with coordinating humanitarian response within the United Nations: OCHA, the Office for the Coordination of Humanitarian Affairs. When disasters occur, OCHA must quickly make decisions based on the most complete picture of the situation they can obtain. They are responsible for organizing search and rescue operations, emergency food assistance, and similar tasks. Given that complete knowledge of any disaster event is not possible, they gather information from myriad available sources, including social media. In this paper, we examine the rapid assessment procedures used by OCHA, and explain how they executed these procedures during the 2013 Typhoon Yolanda. In addition, we interview a small sample of OCHA employees, focusing on their uses and views of social media data. In addition, we show how state-of-the-art social media processing methods can be used to produce information in a format that takes into account what large international humanitarian organizations require to meet their constantly evolving needs.
CLIP: Continuous Location Integrity and Provenance for Mobile Phones

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.
A measurement system for wrist movements in biomedical applications

The design and proof of concept implementation of a biomedical measurement device specifically targeting human wrist movements is presented. The key aspects of development are the integrated measurement of wrist kinematics and lower arm muscle activities, wireless operation and the possibility of realtime data streaming. The designed system addresses these requirements using single chip 9 degrees-of-freedom inertial sensors for kinematic measurements, an active myoelectric electrode frontend design to record muscle activities and a Bluetooth communication interface for device control and data streaming. In addition to design considerations and proof of concept implementation, kinematic test measurement data is presented to validate system usability in a future wrist movement classification task.
Blockchain-based mobile fingerprint verification and automatic log-in platform for future computing

Door locks and user authentication are the major issues in the current banking industry. There are a number of user future computing techniques, and security is especially essential to these methods. Existing digital door locks have the problem of opening easily with a stolen PIN number or by electrical shock. Thus, this study proposed and implemented an integrated automatic log-in platform based on mobile fingerprint recognition by applying the blockchain theory. As a result of this research, a convenient, integrated automatic log-in platform with powerful security has been constructed using a smartphone-based fingerprint recognition function. There are three major functions of the platform. First, it is possible to authenticate the user in PC, mobile device, and IoT environments through fingerprint recognition. Second, the platform includes SDK to develop application software for user authentication and IoT services. Last is its strengthened security using the blockchain theory to prepare against tampering/forging/leaking of a user’s fingerprint information by hackers.
Partitioned residual echo power estimation for frequency-domain acoustic echo cancellation and postfiltering

Residual echo arises in hands–free telephony equipment due to insufficient echo canceler convergence, but can be suppressed using a postfilter. The residual echo po wer spectral density is the most crucial control parameter for both frequency–domain acoustic echo cancellation and c ombined residual echo and noise postfiltering. In this contribution we present and compare residual echo power spectr al estimation techniques. We introduce a new partitioned block-adaptive estimation technique delivering consider ably improved residual echo estimates in strongly reverber ant and noisy acoustic environments. We show that the adaptation lo p of the frequency–domain adaptive filter (FDAF) can be used simultaneously for residual echo power estimation and tracking of the echo path impulse response. In this way, the FDAF and the postfilter concept supplement each other in a tru e synergy with low complexity. The resulting echo and noise control system proves to be robust in double talk situa tions as well.
Wearable augmented reality system using gaze interaction

Undisturbed interaction is essential to provide immersive AR environments. There have been a lot of approaches to interact with VEs (virtual environments) so far, especially in hand metaphor. When the userpsilas hands are being used for hand-based work such as maintenance and repair, necessity of alternative interaction technique has arisen. In recent research, hands-free gaze information is adopted to AR to perform original actions in concurrence with interaction. . There has been little progress on that research, still at a pilot study in a laboratory setting. In this paper, we introduce such a simple WARS (wearable augmented reality system) equipped with an HMD, scene camera, eye tracker. We propose dasiaAgingpsila technique improving traditional dwell-time selection, demonstrate AR gallery - dynamic exhibition space with wearable system.
Improving the Quality of Point of Care Diagnostics with Real-Time Machine Learning in Low Literacy LMIC Settings

The scalability of medical technology in low resource settings requires a higher level of usability and clear decision support compared to conventional devices, since users often have very limited training. In particular, it is important to provide users with real time feedback on data quality during the patient information acquisition in a manner that enables the user to take immediate corrective action.
 In this work, we present an example of such a system, which provides real time feedback on the source of noise and interference on a low cost Doppler device connected to a smart-phone used by traditional birth attendants (TBAs) in rural Guatemala. A total of 195 fetal recordings made from 146 singleton pregnancies in the second and third trimester were recorded over 8 months by 19 TBAs. The resulting 33.7 hours of data were segmented into 0.75 s epochs and independently labeled by three trained researchers into one of five noise or quality categories that dominated the data.
 A two-step classifier, composed of a logistic regression and a multiclass support vector machine, was then trained to classify the data on epochs from 0.75 s to 3.75 s. After feature selection the highest micro-averaged test F1 score was 96.8% and macro-average F1 test score was 94.5% for 3.75 s segments using 23 features. A reduced real time model with 17 features produced comparable micro-and macro-averaged test F1 scores of 96.0% and 94.5% respectively.
 The code is portable back to a low-end smartphone to run on such a device in real time (under 400 ms) in order to provide an audiovisual cue for the TBAs via the smartphone. Future work will evaluate the classifier presented here as part of a decision support system for data quality improvement in an ongoing randomized control trial in Guatemala.
Neural computation and self-organizing maps - an introduction

In this age of modern era, the use of internet must be maximized. Yeah, internet will help us very much not only for important thing but also for daily activities. Many people now, from any level can use internet. The sources of internet connection can also be enjoyed in many places. As one of the benefits is to get the on-line neural computation and self organizing maps an introduction computation neural systems series book, as the world window, as many people suggest.
Lightweight Monitoring of Distributed Streams

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.
 Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.
 Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.
Automatic Segmentation of Hair in Images

Based on a multi-step process, an automatic hair segmentation method is created and tested on a database of 115 manually segmented hair images. By extracting various information components from an image, including background color, face position, hair color, skin color, and skin mask, a heuristic-based method is created for the detection and segmentation of hair that can detect hair with an accuracy of approximately 75% and with a false hair overestimation error of 34%. Furthermore, it is shown that downsampling the image down to a face width of 25px results in a 73% reduction in computation time with insignificant change in detection accuracy.
Dynamic analysis of toroidal winding switched reluctance motor driven by 6-switch converter

This paper presents a characteristic analysis of a wye-connected toroidal winding switched reluctance motor (TSRM) driven by 6-switch converter. The voltage equations considering both TSRM and the drive topology are introduced for steady state dynamic simulation. Inductances, which mainly affect motor characteristics, are calculated by finite element analysis and stored in a look-up table. During the dynamic analysis with the proposed voltage equations, the inductances are fitted using a spline interpolation. The dynamic analysis results are compared with those of conventional winding SRM which are already examined for verification of analysis method
SPANISH IMPERFECTO AND PRETÉRITO : TRUTH CONDITIONS AND AKTIONSART EFFECTS IN A SITUATION SEMANTICS*

offer an account of the semantics of these forms within a situation semantics, addressing a number of theoretically interesting questions about how to realize a semantics for tense and events in that type of framework. We argue that each of these forms is unambiguous, and that the apparent variety of readings attested for them derives from interaction with other factors in the course of interpretation. The meaning of the imperfecto is constrained to always reflect atelic aktionsart. In addition, it contains a modal element, and a contextually-given accessibility relation over situations constrains the interpretation of the modal in ways that give rise to all the attested readings. The pretérito is indeterminate with respect to aktionsart, neither telic nor atelic. One or the other aktionsart may be forced by other factors in the clause in which the pretérito occurs, as well as by pragmatic contrast with the possibility of using the imperfecto.