nils-herrmann/clean_scirepeval huggingface dataset:
The Digital Self-Tuning Control of Step a Down DC-DC Converter

A digital self-tuning control technique of DC-DC Buck converter is considered and thoroughly analyzed in this paper. The development of the small-signal model of the converter, which is the key of the control design presented in this work, is based on the state-space averaged (SSA) technique. Adaptive control has become a widely-used term in DC-DC conversion in recent years. A digital self-tuning Dahlin PID and a /Keviczky PID controller based on recursive leastsquares estimation are developed and designed to be applied to the voltage mode control (VMC) approach operating in a continuous conduction mode (CCM). A comparative study of these two digital self-tuning controllers for step change in input voltage magnitude or output load is also carried out. The simulation results obtained using a Matlab SimPowerSystems toolbox to validate the effectiveness of the proposed strategies are also given and discussed extensively.
An Overview of Graph Databases

Graph databases are used for social networking and website link structure as graphs are used for storing connections among users. For example, social networking sites like Facebook, LinkedIn, and Twitter etc. It has more complicated networks of relationships, so to represent these databases using relational model is not efficient because of large number of table joining. Several well known graph databases are available such as Neo4j, flockdb, allegrograph, sones, trinity, hypergraphdb, infinitegraph, infogrid, orientdb, DEX. In this paper, we have presented comparison of current graph databases and it has been observed that Neo4j is the best in all current current graph databases.
Using Failure Modes, Mechanisms, and Effects Analysis in Medical Device Adverse Event Investigations

In the United States, when medical devices are associated with adverse events that result in death or serious injury, or have malfunctions that could lead to death/serious injury, these events must be reported to the Food and Drug Administration’s Center for Devices and Radiologic Health by device manufacturers and user facilities. However, the defects in the medical device evaluation process (e.g., failing to identify the failure mechanisms), can result in assessment risks and reoccurrences of adverse events. This paper presents an approach for medical device evaluation by using failure modes, mechanisms, and effects analysis to identify the root causes and failure mechanisms, which can improve the designs and reliability of medical devices. This method can also help medical device manufacturers to generate an internal evaluation reports for medical device evaluation, which can improve the reporting process to Food and Drug Administration.
An Intra-Panel Interface With Clock-Embedded Differential Signaling for TFT-LCD Systems

In this paper, an intra-panel interface with a clock embedded differential signaling for TFT-LCD systems is proposed. The proposed interface reduces the number of signal lines between the timing controller and the column drivers in a TFT-LCD panel by adopting the embedded clock scheme. The protocol of the proposed interface provides a delay-locked loop (DLL)-based clock recovery scheme for the receiver. The timing controller and the column driver integrated with the proposed interface are fabricated in 0.13- μm CMOS process technology and 0.18-μm high voltage CMOS process technology, respectively. The proposed interface is verified on a 47-inch Full High-Definition (FHD) (1920RGB&times;1080) TFT-LCD panel with 8-bit RGB and 120-Hz driving technology. The maximum data rate per differential pair was measured to be as high as 2.0 Gb/s in a wafer test.
A Fractional-N Sub-Sampling PLL using a Pipelined Phase-Interpolator With an FoM of -250 dB

A fractional-N sub-sampling PLL architecture based on pipelined phase-interpolator and Digital-to-Time-Converter (DTC) is presented in this paper. The combination of pipelined phase-interpolator and DTC enables efficient design of the multi-phase generation mechanism required for the fractional operation. This technique can be used for designing a fractional-N PLL with low in-band phase noise and low spurious tones with low power consumption. The short-current-free pipelined phase-interpolator used in this work is capable of achieving high-linearity with low-power while minimizing the intrinsic jitter. A number of other circuit techniques and layout techniques are also employed in this design for ensuring high-performance operation with minimal chip area and power consumption. The proposed fractional-N PLL is implemented in standard 65 nm CMOS technology. The PLL has an operating range of 600 MHz from 4.34 GHz to 4.94 GHz. In fractional-N mode, the proposed PLL achieves -249.5 dB FoM and less than -59 dBc fractional spurs.
Germacrone and sesquiterpene-enriched extracts from Curcuma aeruginosa Roxb. increase skin penetration of minoxidil, a hair growth promoter

Minoxidil is approved for topical treatment of androgenic alopecia but hampered by poor cutaneous absorption. Recently, the randomized control trial showed that hair loss treatment of minoxidil was improved by co-application of the anti-androgen, Curcuma aeruginosa Roxb. extract. Here, we aimed to show that the apparent synergism arises from improved cutaneous penetration of minoxidil by bioactive compound, germacrone or C. aeruginosa (as an n-hexane extract, or essential oil). The partition coefficient of germacrone was determined by HPLC. Skin penetration was measured ex vivo on Franz diffusion cells using full thickness human foreskin as membranes. The receiver solution was sampled hourly for 8 h after which the skin was removed, the stratum corneum separated, and minoxidil assayed in this and in the remaining viable skin layer by HPLC. Skin penetration of minoxidil with 0.2 and 2% extract was increased ~ 4-fold (accumulated amount in receiver + skin viable layer after 8 h). Furthermore, germacrone enhanced minoxidil flux by ~ 10-fold and C. aeruginosa essential oil by ~ 20-fold. This work suggests three clinical consequences: (i) minoxidil efficacy is promoted, (ii) lower doses of minoxidil suffice, and (iii) C. aeruginosa extract/essential oil or germacrone can supplement treatment outcomes by acting as anti-androgen, thereby introducing a more effective topical treatment strategy for androgenic alopecia.
Selecting the Optimal Focus Measure for Autofocusing and Depth-From-Focus

A method is described for selecting the optimal focus measure with respect to grey-level noise from a given set of focus measures in passive autofocusing and depth-from-focus applications. The method is based on two new metrics that have been deened for estimating the noise-sensitivity of diierent focus measures. The rst metric { the Autofocusing Uncertainty Measure (AUM){ is useful in understanding the relation between grey-level noise and the resulting error in lens position for autofo-cusing. The second metric{Autofocusing Root-Mean-Square Error (ARMS error){ is an improved metric closely related to AUM. AUM and ARMS error metrics are based on a theoretical noise sensitivity analysis of focus measures, and they are related by a monotonic expression. The theoretical results are validated by actual and simulation experiments. For a given camera, the optimally accurate focus measure may change from one object to the other depending on their focused images. Therefore selecting the optimal focus measure from a given set involves computing all focus measures in the set.
Informing Pedagogical Action: Aligning Learning Analytics With Learning Design

This article considers the developing field of learning analytics and argues that to move from small-scale practice to broad scale applicability, there is a need to establish a contextual framework that helps teachers interpret the information that analytics provides. The article presents learning design as a form of documentation of pedagogical intent that can provide the context for making sense of diverse sets of analytic data. We investigate one example of learning design to explore how broad categories of analytics—which we call checkpoint and process analytics—can inform the interpretation of outcomes from a learning design and facilitate pedagogical action.
Self-Adhesive and Capacitive Carbon Nanotube-Based Electrode to Record Electroencephalograph Signals From the Hairy Scalp

We fabricated a carbon nanotube (CNT)/adhesive polydimethylsiloxane (aPDMS) composite-based dry electroencephalograph (EEG) electrode for capacitive measuring of EEG signals. As research related to brain-computer interface applications has advanced, the presence of hairs on a patient's scalp has continued to present an obstacle to recorder EEG signals using dry electrodes. The CNT/aPDMS electrode developed here is elastic, highly conductive, self-adhesive, and capable of making conformal contact with and attaching to a hairy scalp. Onto the conductive disk, hundreds of conductive pillars coated with Parylene C insulation layer were fabricated. A CNT/aPDMS layer was attached on the disk to transmit biosignals to the pillar. The top of disk was designed to be solderable, which enables the electrode to connect with a variety of commercial EEG acquisition systems. The mechanical and electrical characteristics of the electrode were tested, and the performances of the electrodes were evaluated by recording EEGs, including alpha rhythms, auditory-evoked potentials, and steady-state visually-evoked potentials. The results revealed that the electrode provided a high signal-to-noise ratio with good tolerance for motion. Almost no leakage current was observed. Although preamplifiers with ultrahigh input impedance have been essential for previous capacitive electrodes, the EEGs were recorded here by directly connecting a commercially available EEG acquisition system to the electrode to yield high-quality signals comparable to those obtained using conventional wet electrodes.
A fast stereo matching algorithm suitable for embedded real-time systems

In this paper, the challenge of fast stereo matching for embedded systems is tackled. Limited resources, e.g. memory and processing power, and most importantly real-time capability on embedded systems for robotic applications, do not permit the use of most sophisticated stereo matching approaches. The strengths and weaknesses of different matching approaches have been analyzed and a wellsuited solution has been found in a Census-based stereo matching algorithm. The novelty of the algorithm used is the explicit adaption and optimization of the wellknown Census transform in respect to embedded real-time systems in software. The most important change in comparison with the classic Census transform is the usage of a sparse Census mask which halves the processing time with nearly unchanged matching quality. This is due the fact that large sparse Census masks perform better than small dense masks with the same processing effort. The evidence of this assumption is given by the results of experiments with different mask sizes. Another contribution of this work is the presentation of a complete stereo matching system with its correlation-based core algorithm, the detailed analysis and evaluation of the results, and the optimized high speed realization on different embedded and PC platforms. The algorithm handles difficult areas for stereo matching, such as areas with low texture, very well in comparison to state-ofthe-art real-time methods. It can successfully eliminate false positives to provide Email addresses: martin.humenberger@ait.ac.at (Martin Humenberger), christian.zinner@ait.ac.at (Christian Zinner), michael.weber@ait.ac.at (Michael Weber), wilfried.kubinger@ait.ac.at (Wilfried Kubinger), vincze@acin.tuwien.ac.at (Markus Vincze)  2010 reliable 3D data. The system is robust, easy to parameterize and offers high flexibility. It also achieves high performance on several, including resource-limited, systems without losing the good quality of stereo matching. A detailed performance analysis of the algorithm is given for optimized reference implementations on various commercial of the shelf (COTS) platforms, e.g. a PC, a DSP and a GPU, reaching a frame rate of up to 75 fps for 640× 480 images and 50 disparities. The matching quality and processing time is compared to other algorithms on the Middlebury stereo evaluation website reaching a middle quality and top performance rank. Additional evaluation is done by comparing the results with a very fast and well-known sum of absolute differences algorithm using several Middlebury datasets and real-world scenarios.
Discovering Motion Primitives for Unsupervised Grouping and One-Shot Learning of Human Actions, Gestures, and Expressions

This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.
Connectionist Learning of Belief Networks

Neal, R.M., Connectionist learning of belief networks, Artificial Intelligence 56 (1992) 71-113. Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of probabilistic belief networks. These networks have previously been seen primarily as a means of representing knowledge derived from experts. Here it is shown that the "Gibbs sampling" simulation procedure for such networks can support maximum-likelihood learning from empirical data through local gradient ascent. This learning procedure resembles that used for "Boltzmann machines", and like it, allows the use of "hidden" variables to model correlations between visible variables. Due to the directed nature of the connections in a belief network, however, the "negative phase" of Boltzmann machine learning is unnecessary. Experimental results show that, as a result, learning in a sigmoid belief network can be faster than in a Boltzmann machine. These networks have other advantages over Boltzmann machines in pattern classification and decision making applications, are naturally applicable to unsupervised learning problems, and provide a link between work on connectionist learning and work on the representation of expert knowledge.
Transient effects in application of PWM inverters to induction motors

Standard squirrel cage induction motors are subjected to nonsinusoidal wave shapes, when supplied from adjustable frequency inverters. In addition to causing increased heating, these wave patterns can be destructive to the insulation. Pulse width modulated (PWM) inverter output amplitudes and risetimes are investigated, and motor insulation capabilities are discussed. Voltage reflections are simulated for various cable lengths and risetimes and are presented graphically. Simulations confirm potential problems with long cables and short risetimes. Application precautions are also discussed.<<ETX>>
Automatic mining of source code repositories to improve bug finding techniques

We describe a method to use the source code change history of a software project to drive and help to refine the search for bugs. Based on the data retrieved from the source code repository, we implement a static source code checker that searches for a commonly fixed bug and uses information automatically mined from the source code repository to refine its results. By applying our tool, we have identified a total of 178 warnings that are likely bugs in the Apache Web server source code and a total of 546 warnings that are likely bugs in Wine, an open-source implementation of the Windows API. We show that our technique is more effective than the same static analysis that does not use historical data from the source code repository.
OFDM With Interleaved Subcarrier-Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a recently developed technique which modulates part of the information bits using the indices of OFDM subcarriers. In this letter, a simple and efficient subcarrier-level interleaving scheme is introduced to improve the performance of conventional OFDM-IM through enlarging the Euclidean distances among the modulated symbols. Both theoretical analysis and simulation results are presented to show that the proposed OFDM with interleaved sucarrier-index modulation (OFDM-ISIM) can achieve better system performance than conventional OFDM-IM and OFDM with low-order modulation schemes such as binary phase shift keying, quadrature phase shift keying and 16 quadrature amplitude modulation.
Behavioral detection of malware: from a survey towards an established taxonomy

Behavioral detection differs from appearance detection in that it identifies the actions performed by the malware rather than syntactic markers. Identifying these malicious actions and interpreting their final purpose is a complex reasoning process. This paper draws up a survey of the different reasoning techniques deployed among the behavioral detectors. These detectors have been classified according to a new taxonomy introduced inside the paper. Strongly inspired from the domain of program testing, this taxonomy divides the behavioral detectors into two main families: simulation-based and formal detectors. Inside these families, ramifications are then derived according to the data collection mechanisms the data interpretation, the adopted model and its generation, and the decision support.
Financial Stock Market Forecast using Data Mining Techniques

The automated computer programs using data mining and predictive technologies do a fare amount of trades in the markets. Data mining is well founded on the theory that the historic data holds the essential memory for predicting the future direction. This technology is designed to help investors discover hidden patterns from the historic data that have probable predictive capability in their investment decisions. The prediction of stock markets is regarded as a challenging task of financial time series prediction. Data analysis is one way of predicting if future stocks prices will increase or decrease. Five methods of analyzing stocks were combined to predict if the day's closing price would increase or decrease. These methods were Typical Price (TP), Bollinger Bands, Relative Strength Index (RSI), CMI and Moving Average (MA). This paper discussed various techniques which are able to predict with future closing stock price will increase or decrease better than level of significance. Also, it investigated various global events and their issues predicting on stock markets. It supports numerically and graphically.
A New Distance-weighted k-nearest Neighbor Classifier

In this paper, we develop a novel Distance-weighted k -nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k -nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that our proposed classifier is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.
Data and text mining ALIBABA : PubMed as a graph

Thebiomedical literature containsawealthof information on associations betweenmany different types of objects, such as protein–protein interactions, gene–disease associations and subcellular locations of proteins. When searching such information using conventional search engines, e.g. PubMed, users see the data only one-abstract at a time and ‘hidden’ in natural language text. ALIBABA is an interactive tool for graphical summarizationofsearch results. Itparses thesetofabstracts that fit aPubMed query and presents extracted information on biomedical objects and their relationships as a graphical network. ALIBABA extracts associations between cells, diseases, drugs, proteins, species and tissues. Several filter options allow for a more focused search. Thus, researchers can grasp complex networks described in various articles at a glance.
Integrating Conflicting Data: The Role of Source Dependence

Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sourc es can often provide conflicting values. To present quality data to users, it is critical that data integration systems can resolve conflicts and discover true values. Typically, we expect a true value to be provided by more sources than any particular false one, so we can take the value provided by the majority of the sources as the truth. Unfortunately, a false value can be spread through copying and that makes truth discovery extremely tricky. In this paper, we consider how to find true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a novel approach that considers pendencebetween data sources in truth discovery. Intuitively, if two data sources provide a large number of common values and many of these values are rarely provided by other sources ( e.g., particular false values), it is very likely that one copies from the other. We apply Bayesian analysis to decide dependence between sources and design an algorithm that iteratively detects dependence and discovers truth from conflicting information. We also extend our model by considering accuracyof data sources and similarity between values. Our experiments on synthetic data as well as real-world data show that our algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.