nils-herrmann/clean_scirepeval huggingface dataset:
Disablement and quality of life after stroke.

This study concerns the quality of life of patients after stroke and how this is influenced by disablement and emotional factors. Ninety-six consecutive patients of mean age 71 years were followed for two years. At the end of that time 23% had experienced a recurrence of stroke and 27% were deceased. Of the survivors 76% were independent as regards activities of daily life (ADL) and lived in their own homes. Age as well as initial function were prognostically important factors. Patients who could participate in interviews marked on a visual analogue scale their evaluation of quality of life before and after stroke. Most of them had experienced a decrease and no improvement was observed during the two years. The deterioration was more pronounced in ADL dependent patients than among the independent. However, depression and anxiety were found to be of similar importance for quality of life as was physical disablement. These findings call for a greater emphasis on psychological support in the care of post stroke patients. The visual analogue scale can be a useful tool for detecting special needs.
Efficiency comparison between doubler and Dickson charge pumps

The paper presents a comparison between two of the most popular charge pump structures, the Dickson charge pump and the cascade of voltage doublers. The comparison has been carried out considering power efficiency as the main parameter of interest. The discussion is supported by theoretical analysis and experimental results. To compare the two topologies, two voltage elevators were designed and integrated in a triple-well 0.18-/spl mu/m CMOS technology. The two charge pumps were designed with the same operating clock frequency, the same storage capacitance per stage, and the same number of stages (and, thus, approximately the same area). The comparison showed that the voltage doubler has the higher power efficiency by about 13%.
An incremental approach to software systems re-engineering

Software re-engineering can dramatically improve an organization’s ability to maintain and upgrade its legacy production systems. But the risks that accompany traditional re-engineering tend to offset the potential benefits. Incremental software re-engineering is the practice of re-engineering a system’s software components on a phased basis, and then re-incorporating those components into production also on a phased basis. Incremental software re-engineering allows for safer re-engineering, increased flexibility and more immediate return on investment. But commercial automation to support incremental software re-engineering is currently weak. In addition, project managers need a methodology to plan and implement software re-engineering projects based on the incremental approach. This paper covers the advantages of incremental software re-engineering and what is available concerning support technology. The paper describes a process methodology for planning and implementing incremental software re-engineering projects. Finally, gaps in the support technology are identified with suggestions for future tools from vendors. 1998 John Wiley & Sons, Ltd.
11-Level cascaded H-bridge grid-tied inverter interface with solar panels

This paper presents a single-phase 11-level (5 H-bridges) cascade multilevel DC-AC grid-tied inverter. Each inverter bridge is connected to a 200 W solar panel. OPAL-RT lab was used as the hardware in the loop (HIL) real-time control system platform where a Maximum Power Point Tracking (MPPT) algorithm was implemented based on the inverter output power to assure optimal operation of the inverter when connected to the power grid as well as a Phase Locked Loop (PLL) for phase and frequency match. A novel SPWM scheme is proposed in this paper to be used with the solar panels that can account for voltage profile fluctuations among the panels during the day. Simulation and experimental results are shown for voltage and current during synchronization mode and power transferring mode to validate the methodology for grid connection of renewable resources.
An Analysis of Video Lecture in MOOC

Video is a content delivery form used for delivering lecture content in Massive Open Online Course (MOOC). While institutions plan to launch MOOC on their own platform or adapt an existing one, there is a need to specify the features required for video lecture in MOOC. In this paper, we present a checklist of features for video lecture incorporated in MOOC from the learner‟s perspective. The use case based approach has been followed for identifying the features of video lecture in MOOC. The checklist helps during requirement specification of video in MOOC as the provider select the desired features from the checklist.
A Single Layer Wideband U-Slot Microstrip Patch Antenna Array

The design and measured results of a 2 times 2 microstrip line fed U-slot rectangular antenna array are presented. The U-slot patches and the feeding network are placed on the same layer, resulting in a very simple structure. The advantage of the microstrip line fed U-slot patch is that it is easy to form the array. An impedance bandwidth (VSWR < 2) of 18% ranging from 5.65 GHz to 6.78 GHz is achieved. The radiation performance including radiation pattern, cross polarization, and gain is also satisfactory within this bandwidth. The measured peak gain of the array is 11.5 dBi. The agreement between simulated results and the measurement ones is good. The 2 times 2 array may be used as a module to form larger array.
A Decision Framework for Cloud Computing

Cloud computing technology is garnering success and wisdom-like stories of savings, ease of use, and increased flexibility in controlling how resources are used at any given time to deliver computing capability. This paper develops a preliminary decision framework to assist managers who are determining which cloud solution matches their specific requirements and evaluating the numerous commercial claims (in many cases unsubstantiated) of a cloud's value. This decision framework and research helps managers allocate investments and assess cloud alternatives that now compete with in-house data centers that previously stored, accessed, and processed data or with another company's (outsourced) data center resources. The hypothetically newly captured corporate value (from cloud) is that resources are no longer idle most of the time, and are now much more fully utilized (with lower unit costs). This reduces high ownership and support costs, improves capital leverage, and delivers increased flexibility in the use of resources.
A Dependency Treebank of Classical Chinese Poems

As interest grows in the use of linguistically annotated corpora in research and teaching of foreign languages and literature, treebanks of various historical texts have been developed. We introduce the first large-scale dependency treebank for Classical Chinese literature. Derived from the Stanford dependency types, it consists of over 32K characters drawn from a collection of poems written in the 8 th century CE. We report on the design of new dependency relations, discuss aspects of the annotation process and evaluation, and illustrate its use in a study of parallelism in Classical Chinese poetry.
Lifting the veil: improving accountability and social transparency in Wikipedia with wikidashboard

Wikis are collaborative systems in which virtually anyone can edit anything. Although wikis have become highly popular in many domains, their mutable nature often leads them to be distrusted as a reliable source of information. Here we describe a social dynamic analysis tool called WikiDashboard which aims to improve social transparency and accountability on Wikipedia articles. Early reactions from users suggest that the increased transparency afforded by the tool can improve the interpretation, communication, and trustworthiness of Wikipedia articles.
60-GHz 5-bit Phase Shifter With Integrated VGA Phase-Error Compensation

A 57-64-GHz low phase-error 5-bit switch-type phase shifter integrated with a low phase-variation variable gain amplifier (VGA) is implemented through TSMC 90-nm CMOS low-power technology. Using the phase compensation technique, the proposed VGA can provide appropriate gain tuning with almost constant phase characteristics, thus greatly reducing the phase-tuning complexity in a phased-array system. The measured root mean square (rms) phase error of the 5-bit phase shifter is 2° at 62 GHz. The phase shifter has a low group-delay deviation (phase distortion) of +/- 8.5 ps and an excellent insertion loss flatness of ±0.8 dB for a specific phase-shifting state, across 57-64 GHz. For all 32 states, the insertion loss is 14.6 ± 3 dB, including pad loss at 60 GHz. For the integrated phase shifter and VGA, the VGA can provide 6.2-dB gain tuning range, which is wide enough to cover the loss variation of the phase shifter, with only 1.86° phase variation. The measured rms phase error of the 5-bit phase shifter and VGA is 3.8° at 63 GHz. The insertion loss of all 32 states is 5.4 dB, including pad loss at 60 GHz, and the loss flatness is ±0.8 dB over 57-64 GHz. To the best of our knowledge, the 5-bit phase shifter presents the best rms phase error at center frequency among the V-band switch-type phase shifter.
Locally Adaptive Translation for Knowledge Graph Embedding

Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.
ACOUSTIC SCENE CLASSIFICATION USING PARALLEL COMBINATION OF LSTM AND

Deep neural networks (DNNs) have recently achieved a great success in various learning task, and have also been used for classification of environmental sounds. While DNNs are showing their potential in the classification task, they cannot fully utilize the temporal information. In this paper, we propose a neural network architecture for the purpose of using sequential information. The proposed structure is composed of two separated lower networks and one upper network. We refer to these as LSTM layers, CNN layers and connected layers, respectively. The LSTM layers extract the sequential information from consecutive audio features. The CNN layers learn the spectro-temporal locality from spectrogram images. Finally, the connected layers summarize the outputs of two networks to take advantage of the complementary features of the LSTM and CNN by combining them. To compare the proposed method with other neural networks, we conducted a number of experiments on the TUT acoustic scenes 2016 dataset which consists of recordings from various acoustic scenes. By using the proposed combination structure, we achieved higher performance compared to the conventional DNN, CNN and LSTM architecture.
Design of output dv/dt filter for motor drives

Modern PWM inverter output voltage has high dv/dt, which causes problems such as voltage doubling that can lead to insulation failure, ground currents that results in electromagnetic interference concerns. The IGBT switching device used in such inverter are becoming faster, exacerbating these problems. This paper proposes a new procedure for designing the LC clamp filter. The filter increases the rise time of the output voltage of inverter, resulting in smaller dv/dt. In addition suitable selection of resonance frequency gives LCL filter configuration with improved attenuation. By adding this filter at output terminal of inverter which uses long cable, voltage doubling effect is reduced at the motor terminal. The design procedure is carried out in terms of the power converter based per unit scheme. This generalizes the design procedure to a wide range of power level and to study optimum designs. The effectiveness of the design is verified by computer simulation and experimental measurements.
Evolving Intelligent Mario Controller by Reinforcement Learning

Artificial Intelligence for computer games is an interesting topic which attracts intensive attention recently. In this context, Mario AI Competition modifies a Super Mario Bros game to be a benchmark software for people who program AI controller to direct Mario and make him overcome the different levels. This competition was handled in the IEEE Games Innovation Conference and the IEEE Symposium on Computational Intelligence and Games since 2009. In this paper, we study the application of Reinforcement Learning to construct a Mario AI controller that learns from the complex game environment. We train the controller to grow stronger for dealing with several difficulties and types of levels. In controller developing phase, we design the states and actions cautiously to reduce the search space, and make Reinforcement Learning suitable for the requirement of online learning.
Transforming a teacher education method course through technology: effects on preservice teachers' technology competency

In this study, an instructional design model was employed for restructuring a teacher education course with technology. The model was applied in a science education method course, which was offered in two different but consecutive semesters with a total enrollment of 111 students in the fall semester and 116 students in the spring semester. Using tools, such as multimedia authoring tools in the fall semester and modeling software in the spring semester, teacher educators designed high quality technology-infused lessons for science and, thereafter, modeled them in classroom for preservice teachers. An assessment instrument was constructed to assess preservice teachers technology competency, which was measured in terms of four aspects, namely, (a) selection of appropriate science topics to be taught with technology, (b) use of appropriate technology-supported representations and transformations for science content, (c) use of technology to support teaching strategies, and (d) integration of computer activities with appropriate inquiry-based pedagogy in the science classroom. The results of a MANOVA showed that preservice teachers in the Modeling group outperformed preservice teachers overall performance in the Multimedia group, F = 21.534, p = 0.000. More specifically, the Modeling group outperformed the Multimedia group on only two of the four aspects of technology competency, namely, use of technology to support teaching strategies and integration of computer activities with appropriate pedagogy in the classroom, F = 59.893, p = 0.000, and F = 10.943, p = 0.001 respectively. The results indicate that the task of preparing preservice teachers to become technology competent is difficult and requires many efforts for providing them with ample of 0360-1315/$ see front matter 2004  Tel.: +357 22 753772; fax: +357 22 377950. E-mail address: cangeli@ucy.ac.cy. 384 C. Angeli / Computers & Education 45 (2005) 383–398 opportunities during their education to develop the competencies needed to be able to teach with technology.
U-Healthcare System: State-of-the-Art Review and Challenges

With the increase of an ageing population and chronic diseases, society becomes more health conscious and patients become “health consumers” looking for better health management. People’s perception is shifting towards patient-centered, rather than the classical, hospital–centered health services which has been propelling the evolution of telemedicine research from the classic e-Health to m-Health and now is to ubiquitous healthcare (u-Health). It is expected that mobile & ubiquitous Telemedicine, integrated with Wireless Body Area Network (WBAN), have a great potential in fostering the provision of next-generation u-Health. Despite the recent efforts and achievements, current u-Health proposed solutions still suffer from shortcomings hampering their adoption today. This paper presents a comprehensive review of up-to-date requirements in hardware, communication, and computing for next-generation u-Health systems. It compares new technological and technical trends and discusses how they address expected u-Health requirements. A thorough survey on various worldwide recent system implementations is presented in an attempt to identify shortcomings in state-of-the art solutions. In particular, challenges in WBAN and ubiquitous computing were emphasized. The purpose of this survey is not only to help beginners with a holistic approach toward understanding u-Health systems but also present to researchers new technological trends and design challenges they have to cope with, while designing such systems.
Integrating structured biological data by Kernel Maximum Mean Discrepancy

Many problems in data integration in bioinformatics can be posed as one common question: Are two sets of observations generated by the same distribution? We propose a kernel-based statistical test for this problem, based on the fact that two distributions are different if and only if there exists at least one function having different expectation on the two distributions. Consequently we use the maximum discrepancy between function means as the basis of a test statistic. The Maximum Mean Discrepancy (MMD) can take advantage of the kernel trick, which allows us to apply it not only to vectors, but strings, sequences, graphs, and other common structured data types arising in molecular biology.



We study the practical feasibility of an MMD-based test on three central data integration tasks: Testing cross-platform comparability of microarray data, cancer diagnosis, and data-content based schema matching for two different protein function classification schemas. In all of these experiments, including high-dimensional ones, MMD is very accurate in finding samples that were generated from the same distribution, and outperforms its best competitors.



We have defined a novel statistical test of whether two samples are from the same distribution, compatible with both multivariate and structured data, that is fast, easy to implement, and works well, as confirmed by our experiments.



http://www.dbs.ifi.lmu.de/~borgward/MMD.
Distinguishing between irony and sarcasm in social media texts: Linguistic observations

Automatic detection of figurative constructions used in texts can be quite a tricky task. In order to make it possible for a program to detect such constructions in a text it is necessary to understand what principles underlie sarcastic and ironic utterances and thus can be used for their identification. The paper deals with the linguistic differentiation between irony and sarcasm based on the analysis of tweets. We discuss the linguistic peculiarities of eight corpora marked with #irony and #sarcasm hash tags.
The CubeSat Approach to Space Access

As advances in technology make payloads and instruments for space missions smaller, lighter, and more power efficient, a niche market is emerging from the university community to perform rapidly developed, low-cost missions on very small spacecraft - micro, nano, and picosatellites. Among this class of spacecraft, are CubeSats, with a basic form of 10 times 10 times 10 cm, weighing a maximum of 1kg. In order to serve as viable alternative to larger spacecraft, small satellite platforms must provide the end user with access to space and similar functionality to mainstream missions. However, despite recent advances, small satellites have not been able to reach their full potential. Without launch vehicles dedicated to launching small satellites as primary payloads, launch opportunities only exist in the form of co-manifest or secondary payload missions, with launches often subsidized by the government. In addition, power, size, and mass constraints create additional hurdles for small satellites. To date, the primary method of increasing a small satellite's capability has been focused on miniaturization of technology. The CubeSat Program embraces this approach, but has also focused on developing an infrastructure to offset unavoidable limitations caused by the constraints of small satellite missions. The main components of this infrastructure are: an extensive developer community, standards for spacecraft and launch vehicle interfaces, and a network of ground stations. This paper will focus on the CubeSat Program, its history, and the philosophy behind the various elements that make it a practical an enabling alternative for access to space.
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection

An integrated framework for density-based cluster analysis, outlier detection, and data visualization is introduced in this article. The main module consists of an algorithm to compute hierarchical estimates of the level sets of a density, following Hartigan’s classic model of density-contour clusters and trees. Such an algorithm generalizes and improves existing density-based clustering techniques with respect to different aspects. It provides as a result a complete clustering hierarchy composed of all possible density-based clusters following the nonparametric model adopted, for an infinite range of density thresholds. The resulting hierarchy can be easily processed so as to provide multiple ways for data visualization and exploration. It can also be further postprocessed so that: (i) a normalized score of “outlierness” can be assigned to each data object, which unifies both the global and local perspectives of outliers into a single definition; and (ii) a “flat” (i.e., nonhierarchical) clustering solution composed of clusters extracted from local cuts through the cluster tree (possibly corresponding to different density thresholds) can be obtained, either in an unsupervised or in a semisupervised way. In the unsupervised scenario, the algorithm corresponding to this postprocessing module provides a global, optimal solution to the formal problem of maximizing the overall stability of the extracted clusters. If partially labeled objects or instance-level constraints are provided by the user, the algorithm can solve the problem by considering both constraints violations/satisfactions and cluster stability criteria. An asymptotic complexity analysis, both in terms of running time and memory space, is described. Experiments are reported that involve a variety of synthetic and real datasets, including comparisons with state-of-the-art, density-based clustering and (global and local) outlier detection methods.