nils-herrmann/clean_scirepeval huggingface dataset:
Automated speech analysis to improve TMS based language mapping: Algorithm and proof of concept
Venue: Brain Stimulation
Year: 2020

Transcranial magnetic stimulation (TMS) during confrontational naming can induce anomia or speech arrest and paraphasias and those can be used to locate language areas. This method is qualitative and time consuming since a trained professional needs to analyze a long video file of the mapping session. For data analysis we adapted an existing speech analysis method  to cope with TMS noise, which exceeds 100 dB SPL with each discharge of the coil. Our algorithm deciphered what was said and the reaction time (RT) of each utterance with a resolution of up to 10ms, leading to a fast and quantitative analysis. To develop the noise reduction in combination with the speech analysis algorithms, semi synthetic data were created, since it is impossible for the human ear to discern speech onset in the loud TMS noises. During a confrontational naming task (without TMS) voice recordings were obtained and RTs were manually annotated. Separately, multiple realistic recordings of rTMS noise were recorded, using a figure8 coil. Semi synthetic data were then created by fusion of the rTMS noises (at the onset) and the voicerecordings with some random delay. Over 1000 recordings were created. Actual patient data were recorded from three male patients planned for neurosurgery near language areas. Setup was in accordancewith published guidelines [2] with two no TMS recordings as baseline and TMS mapping of language areas using a 5Hz 1s stimulation at an intensity of 120% of resting motor threshold. Stimulation targets were recorded (Brainsight, Rogue Research, Canada) for offline analysis. All three had direct cortical stimulation (DCS) during awake surgery which we used as gold standard. The study was approved by the local Ethics Committee of the University Hospitals Leuven. All patients gave written informed consent. The algorithm contrasted the data likelihood for the model of the expected response with the data likelihood for a generic model of words. An internal penalty parameter avoided the generic model from winning inappropriately. In this task, we adjusted the weight to recognize the correct response in order to maximize the true negatives correctly (but reducing true positives) Speech enhancement front end suppressed background noises and rTMS noise [3,4] recordings were decomposed in a weighted sum of thousands of spectro temporal exemplars of speech and noise and the noise components were suppressed. The speech recognizer yielded temporal alignments with differing offsets depending on the beginning phoneme of the response: these biases were compensated in the post processing stage. The algorithm could handle synonyms and new pictures, since the word models were built by joining phoneme models automatically. As output a
How does sensory brand experience influence brand equity? Considering the roles of customer satisfaction, customer affective commitment, and employee empathy
Venue: Journal of Business Research
Year: 2019

Building a favorable sensory brand experience is crucial in services settings to strengthen the competitive position of a brand and its equity. However, little empirical research exists in this area. Additionally, and surprisingly, most of the research on service brand experience neglects the importance of employees. This study investigates the effect of sensory brand experience on brand equity in the banking industry, through customer satisfaction and customer affective commitment. It also examines whether employee empathy moderates the impacts of sensory brand experience on customer satisfaction and customer affective commitment. Based on data collected through a panel of 1739 customers, the hypothesized structural model is tested using path analysis. Results show that sensory brand experience has a positive indirect impact on brand equity, through customer satisfaction and customer affective commitment. Customer satisfaction positively influences customer affective commitment, and employee empathy negatively moderates the relationship between sensory brand experience and customer satisfaction.
The impact of tourist destination on hotel efficiency: A data envelopment analysis approach
Venue: Eur. J. Oper. Res.
Year: 2019

This paper evaluates the impact of location on hotel efficiency using a sample of 400 Spanish hotels, the novel aspect being that location is considered at the tourist destination level. Moreover, for the first time, the location variables are based on the main theoretical models concerning location in the hotel sector, namely geographical positioning models, agglomeration and urbanization economic models and competitive environment models. The methodology consists of a four stage data envelopment analysis (DEA) model that decomposes super efficiency in the portion attributable to the tourist destination and the portion attributable to hotel management. Then, managerial efficiency is regressed against hotel characteristics, while tourist destination efficiency is explained by the characteristic of each location. The findings highlight the importance of tourist destinations, providing novel empirical support for the propositions of the main location models. Indeed, the tourist destination is the main cause of differences in the level of efficiency among hotels. The occupancy level, degree of seasonality and market concentration are the variables with the greater impact on efficiency.
Long term follow up and quality of life in patients with intracranial germinoma
Venue: Neurosurgical Review
Year: 2014

Intracranial germinomas are fairly rare tumors occurring mostly in children or young adults with a comparatively good prognosis. Radiation is the preferred treatment of choice for this diagnosis. It has been thoroughly studied to what extent radiation doses and fields can be limited in order to avoid side effects in these young patients. The role of chemotherapy remains unclear, whereas surgery is limited to biopsy for proof of histology. Regarding the good overall survival rate, quality of life is a significant aspect to consider in these patients. We present a single institution analysis of patients with intracranial germinoma and analyze the long term outcome with special regard to quality of life. Thirty three patients with intracranial germinomas were analyzed by chart review, telephone interview, and neurological assessment. Additionally, a survey on quality of life was performed. The 10 year overall survival rate was 82.1 at a mean follow up of 141 (22 306) months. Three quarters (76 of the patients reached a favorable neurological outcome on the Modified Rankin Scale (mRS 0 2) However, the self reported quality of life was significantly worse in germinoma patients compared with a healthy control group (p 0.001) Surgical resection of the tumor led to no improvement regarding overall survival, neurological outcome, and quality of life. In terms of cognitive functioning, patients with tumor resection were significantly more impaired than biopsied patients (p 0.04) Although germinomas are efficiently treatable tumors, the restrictions in quality of life in these often young patients are considerable, including financial difficulties. There seems no justification for tumor resection in newly diagnosed cases suspicious for germinoma as the cognitive outcome is worse than in biopsied patients, and there is no effect on overall survival.
Fano Resonances in Topological Photonic Systems
Year: 2018

Though Fano resonances were originally discovered in quantum physics, they can emerge in the variety of wave processes including photonic systems. In this chapter, we demonstrate how the concept of Fano resonances can be applied in topological photonics to describe photonic topological states supported by metasurfaces. As we prove, leaky nature of metasurface's modes allows one to probe them from the far field, while the observed Fano type features in transmittance spectrum can be unequivocally related to the topological properties of photonic bands. This fundamental link between Fano resonances in transmittance and photonic topological states opens an appealing perspective to control scattering of light through the topological characteristics of metasurface.
A Transaction Cost Theory of the TNC
Year: 2015

Business firms, along with nation states, are crucial agents in today's advanced economies. Transnational corporations (TNC) are business firms with activities in more than one nation state,1 and although they are but a small subset of all business firms, they play a dominant role in the world economy. According to UNCTAD (2009) there were some 82,000 TNCs in 2008 owning more than 800,000 foreign affiliates. Ten years earlier, their internal trade was estimated to already account for as much as three quarters of world exports (UNCTAD, 1999: 232)
On the asymptotic normality of persistent Betti numbers
Year: 2019

Persistent Betti numbers are a major tool in persistent homology, a subfield of topological data analysis. Many tools in persistent homology rely on the properties of persistent Betti numbers considered as a two dimensional stochastic process (r,s) \mapsto n^ 1/2} \beta^{r,s}_q \mathcal{K}(n^{1/d} S_n) \mathbb{E}\beta^{r,s}_q \mathcal{K} n^{1/d} S_n) So far, pointwise limit theorems have been established in different set ups. In particular, the pointwise asymptotic normality of (persistent) Betti numbers has been established for stationary Poisson processes and binomial processes with constant intensity function in the so called critical (or thermodynamic) regime, see Yogeshwaran et al. [2017] and Hiraoka et al. [2018] In this contribution, we derive a strong stabilizing property (in the spirit of Penrose and Yukich [2001] of persistent Betti numbers and generalize the existing results on the asymptotic normality to the multivariate case and to a broader class of underlying Poisson and binomial processes. Most importantly, we show that the multivariate asymptotic normality holds for all pairs (r,s) $0\le r\le s<\infty$ and that it is not affected by percolation effects in the underlying random geometric graph.
Sports Participation Among Females From Adolescence To Adulthood
Year: 2006

It is widely believed that active participation in sports during youth is an important prerequisite for adult involvement in sports. However, data from reliable longitudinal studies tracking patterns of sports participation from youth into adulthood are scarce. This study addresses the leisure time sports participation of adult women, 32 41 years of age, from a lifetime sports socialization perspective. Some 20 years after they participated in 1979 in the Leuven Growth Study on Flemish Girls, 257 female adults participated again in a comprehensive questionnaire and face to face interview. Inter age correlations for sports participation are calculated from adolescence into adulthood. Logistic regression modeling and structural equation modeling are used to explain individual differences in adult sports participation. Outcomes indicate that tracking of sports involvement between late adolescence and adulthood is moderately high (r .41; beta .42) The results from the multivariate analysis show that sport participation during adolescence is a better predictor of adults' involvement in sports than educational level or parental socioeconomic status. The variances accounted for are rather small, indicating that sport experiences and social background characteristics only partially explain the sport participation behavior of adults. In the sports socialization process, late adolescent sports experience, along with the school program in which an adolescent is involved, appear to play a crucial role in sport involvement in later life. We recommend that youth sports programs need to be examined critically with regard to their contribution to lifetime sports participation.
Understanding Holistic View And Complexities In Big Data Analytics And Business Intelligence (BI) Towards Establishing Strategic Performance Management:A Case Study
Year: 2018

We explore the contradictory pressures and features of holistic view in an organization that creates data value on organizational strategic performance using big data analytics and business intelligence (BI) engine within different conceptual frameworks and their impact on strategy development and implementation for strategic performance management.Big data analytics and BI are stressed due to the importance in enabling organizations to deriving better knowledge creation and decision making.Emerging knowledge from structuring big data and using BI together with observing MIT90s,McKinsey seven (7) S's framework and survey findings are used as the baseline information framework for the formulation and implementation of BI and big data analytics towards designing strategic performance management for an organization.This conceptual framework relates the current big data analytics and BI stages in strategies adoption and implementation via displaying the organizational performance indicator.The outcome will be a design of a strategic performance framework for a typical strategic application.
Analysis of multimodal Bayesian nonparametric autoregressive hidden Markov models for process monitoring in robotic contact tasks
Venue: International Journal of Advanced Robotic Systems
Year: 2019

Robot introspection aids robots to understand what they do and how they do it. Previous robot introspection techniques have often used parametric hidden Markov models or supervised learning techniques, implying that the number of hidden states or classes is defined a priori and fixed through the entire modeling process. Fixed parameterizations limit the modeling power of a process to properly encode the data. Furthermore, first order Markov models are limited in their ability to model complex data sequences that represent highly dynamic behaviors as they assume observations are conditionally independent given the state. In this work, we contribute a Bayesian nonparametric autoregressive Hidden Markov model for the monitoring of robot contact tasks, which are characterized by complex dynamical data that are hard to model. We used a nonparametric prior that endows our hidden Markov models with an unbounded number of hidden states for a given robot skill (or subtask) We use a hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model of wrench signatures and end effector pose for the manipulation contact tasks. The proposed scheme monitors both nominal skill execution and anomalous behaviors. Two contact tasks are used to measure the effectiveness of our approach: (i) a traditional pick and place task composed of four skills and (ii) a cantilever snap assembly task (also composed of four skills) The modeling performance or our approach was compared with other methods, and classification accuracy measures were computed for skill and anomaly identification. The hierarchical Dirichlet stochastic process prior to learn an hidden Markov model with a switching vector autoregressive observation model was shown to have excellent process monitoring performance with higher identification rates and monitoring ability.
Post installed shear connectors: Push out tests of coiled spring pins vs. headed studs
Venue: Journal of Constructional Steel Research
Year: 2019

Steadily increasing traffic volumes and traffic loads lead to a continuously growing demand for bridge rehabilitation, strengthening and replacement projects. For existing steel girder bridges with non composite concrete decks, the traffic load capacity can often be increased significantly if composite action can be created afterwards. Different kinds of shear connectors are more or less suitable for post installation. Coiled spring pins are one type of interference fit connector that can be installed from below the bridge deck during traffic, in order to minimize the impact on road users. This paper describes an experimental study on the static capacity and stiffness of coiled spring pins used as shear connectors at steel concrete interfaces. Six push out test series are presented, with a total of 28 tests, together with an alternative type of test set up. The results show that the failure of the coiled spring pins is very ductile and that the load capacity is predictable and sufficient for a cost effective application. The tests also indicate a significantly lower stiffness of the connectors in comparison to welded headed studs of similar dimensions, which might be of great importance if an existing shear connection is strengthened.
A decomposition based coevolutionary multiobjective local search for combinatorial multiobjective optimization
Venue: Swarm Evol. Comput.
Year: 2019

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) divides a multiobjective optimization problem into a number of single objective subproblems and solves them in a collaborative way. MOEA/D can be naturally extended by the common, intensification oriented method of local search for solving combinatorial multiobjective optimization problems (CMOPs) However, the performance of MOEA/D strongly depends on the distribution of direction vectors and the decomposition method it adopts. In this paper, an efficient coevolutionary multiobjective local search based on decomposition (CoMOLS/D) is proposed. In CoMOLS/D, two sets of direction vectors and two populations with different decomposition methods are adopted to coevolve with each other. Among them, one population aims to achieve fast convergence while the other one puts more effort for maintaining the complementarily diverse solutions based on the convergence population. In the experimental studies, CoMOLS/D is compared with four decomposition based local search heuristics, i.e. MOEA/D LS (WS, TCH, PBI and iPBI) a dominance based local search, i.e. e MOEA LS; an indicator based local search, i.e. IBEA LS; and a state of the art local search with dual populations, i.e. ND/DPP LS; on two well known CMOPs. The experimental results show that CoMOLS/D significantly outperforms the compared algorithms on most of the test instances.
A Collaboration Mechanism Between Wireless Sensor Network and a Cloud Through a Pub/Sub based Middleware Service
Year: 2013

Cloud computing has emerged as a new paradigm of computing platform. It covers almost every area of computing and provides platform to most of the data services. On the other hand, Wireless Sensor Networks (WSN) has gained attention for their potential supports and attractive solutions such as environment monitoring, bio median acknowledgment, healthcare monitoring, industrial automation, etc. Additionally, our virtual groups and social networks are in main role of information sharing. However, this sensor driven data is not available to community groups or cloud environment for general purpose research or utilization yet. If we reduce the gap between real and virtual world by adding this WSN driven data to cloud environment and virtual communities by providing censor driven contents to general researchers, and it can gain a remarkable attention from all over, by giving us the benefit in various sectors. Collaboration between WSN and the cloud environment can achieve this. We have proposed an integrated Publish/Subscribe (pub/sub) based middleware service for the cloud platform to collaborate with WSN. This collaboration will provide resource, service, and storage with sensor driven data to the community. Furthermore, we have proposed a content based event matching algorithm to analyze subscriptions and publish proper contents easily. We have evaluated our algorithm which sows better performance comparing with previously proposed algorithms.
Pengaruh Penggunaan Multimedia Interaktif Terhadap Peningkatan Prestasi Belajar Matematika Anak Tunarungu Kelas V di SLB B Pawestri Karanganyar Tahun Pelajaran 2018/2019
Year: 2019

ABSTRAK Mariastuti Wulandari. K5115043. PENGARUH PENGGUNAAN MULTIMEDIA INTERAKTIF TERHADAP PENINGKATAN PRESTASI BELAJAR MATEMATIKA ANAK TUNARUNGU KELAS V DI SLB B PAWESTRI KARANGANYAR TAHUN PELAJARAN 2018/2019. Skripsi, Surakarta Fakultas Keguruan dan Ilmu Pendidikan Universitas Sebelas Maret Surakarta, April 2019. Penelitian ini bertujuan untuk mengetahui pengaruh penggunaan multimedia interaktif terhadap peningkatan prestasi belajar matematika anak tunarungu kelas V di SLB B Pawestri Karanganyar tahun pelajaran 2018/2019. Penelitian ini menggunakan pendekatan kuantitatif eksperimen dengan desain one groups pretest posttest design. Subjek yang digunakan dalam penelitian berjumlah tujuh anak tunarungu kelas V di SLB B Pawestri Karanganyar. Pengumpulan data menggunakan tes objektif pilihan ganda. Analisis data menggunakan statistic non parametric uji Wilcoxson Sign Rank Test dengan bantuan software SPSS 22. Hasil analisis data deskriptif diperoleh nilai rata rata pretest sebesar 58,93 dan posttest 94,64. Hasil analisis statistic non parametric dengan uji Wilcoxson Sign Rank Test diperoleh nilai Zhitung= 2.392 dengan Asymp Sig. (2 tailed) 0,017 lebih kecil dari taraf signifikansi yang telah ditentukan atau 0,017 0,05 sehingga hipotesis alternatif diterima dan hipotesis nihil ditolak. Berdasarkan hasil analisis penelitian tersebut diperoleh kesimpulan bahwa penggunaan multimedia interaktif berpengaruh terhadap peningkatan prestasi belajar matematika anak tunarungu kelas V di SLB B Pawestri Karanganyar tahun pelajaran 2018/2019. Kata Kunci pengaruh, multimedia interaktif, prestasi belajar matematika, anak tunarungu
Child labour and academic achievement: Evidence from Gansu Province in China
Year: 2016

This paper considers the relationship between child labour and a child's academic achievement in rural China. Using a unique longitudinal, multi level survey, the Gansu Survey of Children and Families (GSCF) which was enrolled in the Gansu province, I use a quasi maximum likelihood estimation (QMLE) and find that more than 1h of child labour in the previous time period has a negative effect on a child's academic achievement in the subsequent period after controlling for child talent. I also show that previous academic achievement has no strong significant effect on current child labour by applying a logistic model. Based on the data, the fact that those effects are not very big or not significant suggests that child labour in China is not a big problem when compared with other developing countries (Bacolod Ranjan 2008)