nils-herrmann/clean_scirepeval huggingface dataset:
Milestones in Implementation of an Integrated Management System in the Health Sector. Case Study Radiologische Netzwerk Rheinland

Healthcare organizations in Germany exploit the benefits of the ISO 9000 family of international standards as it became compulsory to implement a quality management system in accordance with ISO 9001 requirements. Until the innovative ISO 9001:2015 proposal it was no direct connection to the other management systems like risk management, knowledge management or environmental management. So far, only few bodies ensured interconnections between different systems of management or associated the quality management system with the strategic planning process. However, healthcare encapsulates supplementary requirements which affect a number of different systems. Additionally, the financial crisis has encouraged the trend to operate integrated reporting beyond financial aspects. This paper aims at presenting the experience on the development of integrated management and reporting system integrated in an organization belonging to the health sector. The work clarifies the steps towards merging distinctly regulated management systems (quality, health or environmental management) with strategic planning and controlling, via a Balanced Dashboard (Balanced Scorecard BSC) as well as integrated reporting according to the model International Integrated Reporting Initiative (IIRI) in a German medical company Radiologische Netzwerk Rheinland RNR AG. Using the case study method, the paper's purpose is to highlight approaches and results of the company that could support practitioners from medical area and bezound. The literature review clarified theoretical concepts while the case study allowed converging comprehensive information and knowledge accumulated by RNR AG, thus helping to bridge the gap between literature on total integrated management reporting and reporting system in healthcare.
A Survey on Object Detection in Optical Remote Sensing Images
Venue: ArXiv

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban area. Covering about 270 publications we survey (1) template matching based object detection methods, (2) knowledge based object detection methods, (3) object based image analysis (OBIA) based object detection methods, (4) machine learning based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning based feature representation and weakly supervised learning based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.
Deep Contrast Learning for Salient Object Detection
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs) However, existing CNN based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end to end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel level fully convolutional stream and a segment wise spatial pooling stream. The first stream directly produces a saliency map with pixel level accuracy from an input image. The second stream extracts segment wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.
Flow in Sport

Sport offers rich opportunities to experience flow by posing both mental and physical challenges. Studies specifically investigating flow in sport were first published in 1992. Since then a body of empirical research has emerged in this area, which this chapter aims to review in terms of: (i) the methods commonly used to study flow in sport (i.e. interviews, questionnaires, and the Experience Sampling Method) and (ii) key research themes (i.e. the experience, occurrence, controllability and correlates of flow in sport) In turn, current issues within this field are examined, and recommendations are made for future research, including the need to build towards a causal explanation of flow, and potential refinement in understanding how athletes experience these optimal states.
Exploring athletes' perceptions of the relationship between mental toughness and dispositional flow in sport

Despite considerable scholarly attention over the last two decades, little is known about the influence of dispositional attributes on flow in sport. In achievement settings, mental toughness (MT) is a personal capacity supporting the process of high performance. Based on common overlaps with peak performance, confidence, control, concentration, and thriving in demanding situations, the present research aimed to explore the relationship between MT and dispositional flow and elucidate the psychological variables underlying dispositional flow.  A mixed method explanatory participant selection design was adopted, whereby a quantitative approach was used to identify individuals for a qualitative follow up phase to explore the relationship between MT and dispositional flow.  An intensity sampling strategy was used to identify individuals with higher/lower MT and dispositional flow. Semi structured interviews were conducted with 16 athletes (M age 25, SD 3.24; female n 10; male n 6) An inductive content analysis was undertaken to interpret data.  Seven general dimensions describing the psychological attributes related to dispositional flow in athletes with higher and lower MT emerged. Specifically, differences in confidence, perfectionism, goal orientation, coping mechanism selection, locus of control, optimism and concentration were apparent and could account for differences in dispositional flow.  While all athletes experienced flow, there were differences in dispositional flow and the processes involved in entering, maintaining and restoring flow between higher and lower MT subgroups. Findings are discussed in relation to the existing literature, and recommendations for future research into the MT flow relationship are outlined.
TASAWUF DALAM SOROTAN EPISTEMOLOGI DAN AKSIOLOGI

Aspek ajaran Islam seringkali dibagi secara dikotomis menjadi aspek syari'at dan hakikat. Aspek syari'at adalah ajaran yang bersumber dari al Qur'an dan sunnah berkenaan dengan aqidah, ibadah, akhlak, sosial, ekonomi dan aspek kehidupan lainnya yang bersifat lahiriyah dalam bentuk legal formal atau identik dengan fikih. Karena bersumber dari al Qur'an dan Sunnah maka ilmu syari'at digolongkan sebagai ilmu yang menggunakan epistemologi bayani. Sedangkan hakikat adalah aspek ajaran dalam Islam yang lebih menekankan pada penghayatan batin sehingga digolongkan sebagai ilmu yang menggunakan epistemologi irfani. Yang termasuk dalam hakikat ini adalah Tasawuf. Pembagian secara dikotomis seperti ini secara tidak langsung menimbulkan pemahaman bahwa tasawuf bukanlah bagian dari syari'at. Maka tidak jarang ada pihak pihak yang menganggap bahwa tasawuf adalah salah satu bentuk penyimpangan dalam Islam atau setidaknya tidak memperhatikan aspek syari'at. Selain dianggap mernyimpang, tasawuf juga sering dianggap sebagai ajaran yang tidak mampu menyelesaikan persoalan persoalan kemasyarakatan. Benarkah tasawuf itu tidak memperhatikan aspek syari'at dan tidak memiliki nilai guna untuk menyelesaikan persoalan persoalan kemasyarakatan? Dalam tulisan ini penulis akan mencoba menjawab pertanyaanpertanyaan tersebut dengan menggunakan metode analisis Critical Discouse Analysis (CDA) Secara aplikatif, pembahasannya dimulai dengan mendeskripsikan sejarah dan faktor penyebab munculnya aliran aliran tasawuf dalam Islam serta pokok pokok ajaran tasawuf. Adapun pendekatan yang digunakan adalah filsafat ilmu. Hasil dari penelitian ini adalah bahwa ajaran tasawuf juga menekankan aspek syari'ah sehingga tidak melulu hanya menggunakan epistemologi irfani. Selain itu nilai nilai tasawuf juga memiliki nilai guna untuk menyelesaikan persoalan persoalan kemasyarakatan.
An Enhanced Rectenna Using Differentially Fed Rectifier for Wireless Power Transmission
Venue: IEEE Antennas and Wireless Propagation Letters

This letter presents an enhanced rectenna with a differentially driven rectifier for wireless power transmission applications. By utilizing differential feeding, a patch antenna with increased gain is proposed. To be directly connected with the differentially fed patch antenna as a rectenna, a differentially driven rectifier is designed so that the use of balun can be omitted. The rectenna shows a maximum efficiency of 73.9% under the input power density of 207 mW/cm2. Furthermore, compared with a reference rectenna of the same aperture size, the proposed rectenna performs higher efficiency and yields larger output DC power, which is more than 1.5 times as high as that of the reference rectenna.
On line component level soft fault diagnostics for power converters
Venue: 2016 Prognostics and System Health Management Conference (PHM Chengdu)

The implementation of diagnostic methodologies to power devices and converters has become essential and critical in many fields. A lot of previous research demonstrated detection of hard faults, such as open or shorted switching elements in the power systems. However, little progress has been made on soft faults diagnosis. The soft faults related to components degradation degrade the system's performance but not completely affect the normal operation. A method of on line model based components soft faults diagnosis is proposed in this paper. The fault detection technique utilizes parameter identification method based on Particle Swarm Optimization algorithm to achieve the component level diagnostic purpose. Considering components degradation and variable operating conditions, several simulation experiments have been conducted to validate the presented approach. The results show that this technique characterizes high computation efficiency and good estimation accuracy. Also, it will be a basis of further study for the circuit level condition monitoring and remaining useful life prediction.
Fault diagnosis via PV panel integrated power electronics
Venue: 2016 IEEE 17th Workshop on Control and Modeling for Power Electronics (COMPEL)

This paper presents the design, analysis, and implementation of a fault diagnosis method for photovoltaic (PV) energy conversion systems. We present a model based state estimation approach for detecting and identifying three types of faults (1) converter input faults (e.g. faults in a PV panel) (2) converter component faults (e.g. switch faults or passive component degradation) and (3) sensor faults (e.g. voltage and current sensors) for PV panel integrated power electronics systems. The state estimator model includes a dynamic model of the PV source and a linear switched model of the switching power converter. The estimated state values are compared with measured values from the physical power stage, which generates an error residual vector. This residual is used to detect and identify faults in either the PV source or the switching power converter. The estimator, fault detection and identification logic, and the PV converter control system (including PWM generation and maximum power point tracking) are implemented entirely on a single all programmable system on chip (SoC) device, which includes an FPGA and ARM core. We present simulation and experimental results for a prototype 2 kW PV energy conversion system to demonstrate the efficacy of the proposed fault diagnosis and control platform. The experimental results demonstrate successful fault detection within 2 ms and precise fault identification within 31 ms for a collection of input, component, and sensor faults.
Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning
Venue: ACL

We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes extensions to structural correspondence learning needed to make it appropriate for the task. For example, we propose a median based classification instead of the standard binary classification used in previous work. Our results show that punctuation based character n grams form excellent pivot features. We also show how singular value decomposition plays a critical role in achieving domain adaptation, and that replacing (instead of concatenating) non pivot features with correspondence features yields better performance.
Stubborn Mining: Generalizing Selfish Mining and Combining with an Eclipse Attack
Venue: 2016 IEEE European Symposium on Security and Privacy (EuroS&P)

Selfish mining, originally discovered by Eyal et al.  is a well known attack where a selfish miner, under certain conditions, can gain a disproportionate share of reward by deviating from the honest behavior. In this paper, we expand the mining strategy space to include novel "stubborn" strategies that, for a large range of parameters, earn the miner more revenue. Consequently, we show that the selfish mining attack is not (in general) optimal. Further, we show how a miner can further amplify its gain by non trivially composing mining attacks with network level eclipse attacks. We show, surprisingly, that given the attacker's best strategy, in some cases victims of an eclipse attack can actually benefit from being eclipsed!
Discussion on Application of Multimedia Teaching in College English Vocabulary Teaching

The rapid development of science and technology has created new condition for the teaching methods of higher English education, which has greatly enriched the English teaching resources and has promoted the renovation of English teaching methods. As a second to none common language in the world, English has always been playing an important role in international communication, and has been valued highly in the educational circles. Compared with previous English teaching, modern college English teaching has the characteristics of convenient and efficient and is easily to be accepted by students. Considering the diversity and comprehensiveness of college English, it is necessary to divide it into such secondary subjects as vocabulary, grammar and reading, in order to help students to develop learning interest in English in daily life, and to achieve the purpose of reducing students' burden. This paper discusses the current application situation and problems of multimedia teaching in college English vocabulary teaching and the countermeasures with emphasis, hoping to provide beneficial views for the research and practice of innovative classroom teaching methods.
Study on Internet Information Technology in English Teaching in Higher Vocational College
Venue: 2016 8th International Conference on Information Technology in Medicine and Education (ITME)

The main teaching methods are still teacher blackboard textbook tape recorder. In this traditional class, English teaching seriously lagged behind the modern teaching technology. So it is high time that we must face the new opportunity and challenge and adopt various methods to improve our English teaching. In this paper, English teaching status and problems in vocational college was studied based on the policy of life long education, quality education and educational reformation. It aims to construct the ideal teaching model by comparing the modern and the traditional teaching models and the teaching models in other universities. Through applying the ideal teaching model, a new typed teaching system will be gradually constructed in vocational college.
Application of Flipped Classroom in College English Teaching

The presentation mode of college English class is not immutable, but it shall realize its unique development pattern under the background of modern science and technology innovation. Considering the complexity of college English itself and many secondary courses, so the college English syllabus particularly emphasizes students' autonomous learning interest and ability in the daily learning. As a result, the experts and scholars within education academic circle both at home and abroad create the teaching mode of "flipped classroom" after long term research and practice. Although shortcomings still exist in this new teaching model, it will be improved in the development in the future (Bergmann Sams, 2012) This paper will analyze briefly from the perspectives of current application situation, problems existed and reflections of "flipped classroom" in college English teaching, hoping to provide theoretical support and practical guidance for Chinese colleges to implement "flipped classroom" practice.
The Preliminary Exploration of the Application of MOOC in College Oral English Teaching
Venue: 2016 8th International Conference on Information Technology in Medicine and Education (ITME)

This paper gives an account of the appearance and the development of MOOC, then explores the advantages of using MOOC in oral English teaching by comparing it with the traditional, teacher centered teaching method, and finally puts forward some measures about the application of MOOC in college oral English teaching.
Research on integrated education mode in college English teaching

College English teaching compares with the traditional English teaching from visual audio oral aspects. The integrated English teaching is mainly manifested in the target, systematicness, normalization, efficiency and effectiveness of the integrated English teaching quality. With the criteria of practicability, quality, integration and language features of the integrated teaching, this paper carries out quantifiable weight analysis of the integrated college English teaching content based on AHP model, and obtains proportion in various aspects.
Uncovering Major Age Related Handwriting Changes by Unsupervised Learning

Understanding how handwriting (HW) style evolves as people get older may be key for assessing the health status of elder people. It can help, for instance, distinguishing HW change due to a normal aging process from change triggered by the early manifestation of a neurodegenerative pathology. We present, in this paper, an approach, based on a 2 layer clustering scheme that allows uncovering the main styles of online HW acquired on a digitized tablet, with a special emphasis on elder HW styles. The 1 level separates HW words into writer independent clusters according to raw spatialdynamic HW information, such as slant, curvature, speed, acceleration and jerk. The 2 level operates at the writer level by converting the set of words of each writer into a Bag of 1 Layer Clusters, that is augmented by a multidimensional description of his/her writing stability across words. This 2 layer representation is input to another clustering algorithm that generates categories of writer styles along with their age distributions. We have carried out extensive experiments on a large public online HW database, augmented by HW samples acquired at Broca hospital in Paris from people mostly between 60 and 85 years old. Unlike previous works claiming that there is only one pattern of HW change with age, our study reveals basically three major HW styles associated with elder people, among which one is specific to elders while the two others are shared by other age groups. KeywordsAge Characterization; HW Styles; Unsupervised Learning; Two Layer Clustering Scheme.
A life course approach to explore the biological embedding of socioeconomic position and social mobility through circulating inflammatory markers
Venue: Scientific reports

Lower socioeconomic position (SEP) has consistently been associated with poorer health. To explore potential biological embedding and the consequences of SEP experiences from early life to adulthood, we investigate how SEP indicators at different points across the life course may be related to a combination of 28 inflammation markers. Using blood derived inflammation profiles measured by a multiplex array in 268 participants from the Italian component of the European Prospective Investigation into Cancer and Nutrition cohort, we evaluate the association between early life, young adulthood and later adulthood SEP with each inflammatory markers separately, or by combining them into an inflammatory score. We identified an increased inflammatory burden in participants whose father had a manual occupation, through increased plasma levels of CSF3 (G CSF; b 0.29; P 0.002) and an increased inflammatory score (b 1.96; P 0.029) Social mobility was subsequently modelled by the interaction between father's occupation and the highest household occupation, revealing a significant difference between "stable Non manual" profiles over the life course versus "Manual to Non manual" profiles (b 2.38, P 0.023) Low SEP in childhood is associated with modest increase in adult inflammatory burden; however, the analysis of social mobility suggests a stronger effect of an upward social mobility over the life course.
Memristive Boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning
Venue: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA)

The Boltzmann machine is a massively parallel computational model capable of solving a broad class of combinatorial optimization problems. In recent years, it has been successfully applied to training deep machine learning models on massive datasets. High performance implementations of the Boltzmann machine using GPUs, MPI based HPC clusters, and FPGAs have been proposed in the literature. Regrettably, the required all to all communication among the processing units limits the performance of these efforts. This paper examines a new class of hardware accelerators for large scale combinatorial optimization and deep learning based on memristive Boltzmann machines. A massively parallel, memory centric hardware accelerator is proposed based on recently developed resistive RAM (RRAM) technology. The proposed accelerator exploits the electrical properties of RRAm to realize in situ, fine grained parallel computation within memory arrays, thereby eliminating the need for exchanging data between the memory cells and the computational units. Two classical optimization problems, graph partitioning and boolean satisfiability, and a deep belief network application are mapped onto the proposed hardware. As compared to a multicore system, the proposed accelerator achieves 57x higher performance and 25x lower energy with virtually no loss in the quality of the solution to the optimization problems. The memristive accelerator is also compared against an RRAM based processing in memory (PIM) system, with respective performance and energy improvements of 6.89x and 5.2x.