nils-herrmann/clean_scirepeval huggingface dataset:
Self-Oscillating Flyback Converter for Mobile Batteries Charging Applications

The self-oscillating flyback converter is a popular circuit for cost-sensitive applications due to its simplicity and low component count. This paper presents self-oscillating fly-back converter for mobile batteries charging applications. The converter is designed to step-down the high voltage input to an acceptable level from 220 VAC to 7VDC. By adding indictor and capacitor the power factor and input currant increased, and became 500mA, where the old input currant was 250mA, Diode was connected between Emitter and collector, so the output voltage was improved, and has less ripple, in addition to the soft switching was obtained, simulation model is developed by using MULTISIM software. The operation principle of this converter is described using some operating waveforms based on simulation results and a detailed circuit operation analysis. Analytical results are verified by laboratory experiment. With an input of 220 VAC, 500mA and an output of 7V DC, the efficiency of circuit was75%.
Sentiment analysis and Twitter: a game proposal

Pervasive sensing of people’s opinions is becoming critical in strategic decision processes, as it may be helpful in identifying problems and strengthening strategies. A recent research trend is to understand users’ opinions through a sentiment analysis of contents published in the Twitter platform. This approach involves two challenges: the large volume of available data and the large variety of used languages combined with the brevity of texts. The former makes manual analysis unreasonable, whereas the latter complicates any type of automatic analysis. Since sentiment analysis is a difficult process for computers, but it is quite simple for humans, in this article, we transform the sentiment analysis process into a game. Indeed, we consider the game with a purpose approach and we propose a game that involves users in classifying the polarity (e.g., positive, negative, neutral) and the sentiment (e.g., joy, surprise, sadness) of tweets. To evaluate the proposal, we used a dataset of 52,877 tweets, we developed a Web-based game, we invited people to play the game, and we validated the results through two different methods: ground-truth and manual assessment. The obtained results showed that the game approach is effective in measuring people’ sentiments and also highlighted that participants liked to play the game.
Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex

Songbirds learn and produce complex sequences of vocal gestures. Adult birdsong requires premotor nucleus HVC, in which projection neurons (PNs) burst sparsely at stereotyped times in the song. It has been hypothesized that PN bursts, as a population, form a continuous sequence, while a different model of HVC function proposes that both HVC PN and interneuron activity is tightly organized around motor gestures. Using a large dataset of PNs and interneurons recorded in singing birds, we test several predictions of these models. We find that PN bursts in adult birds are continuously and nearly uniformly distributed throughout song. However, we also find that PN and interneuron firing rates exhibit significant 10-Hz rhythmicity locked to song syllables, peaking prior to syllable onsets and suppressed prior to offsets-a pattern that predominates PN and interneuron activity in HVC during early stages of vocal learning.
Data-Intensive Question Answering

Microsoft Research Redmond participated for the first time in TREC this year, focusing on the question answering track. There is a separate report in this volume on the Microsoft Research Cambridge submissions for the filtering and Web tracks (Robertson et al., 2002). We have been exploring data-driven techniques for Web question answering, and modified our system somewhat for participation in TREC QA. We submitted two runs for the main QA track (AskMSR and AskMSR2).
Adaptive hypermedia: from systems to framework

The navigational freedom in conventional hypermedia applications leads to comprehension and orientation problems [Nielsen 1990]. Adaptive hypermedia attempts to overcome these problems by adapting the presentation of information and the overall link structure, based on a user model. This paper introduces a framework for adaptive hypermedia systems (AHS). It briefly describes some popular methods and techniques for adaptation. Examples and evaluations of existing AHS are used to illustrate the potential benefits of using adaptation in hypermedia applications.
The emulation theory of representation: motor control, imagery, and perception.

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.
Automatic Radial Distortion Estimation from a Single Image

Many computer vision algorithms rely on the assumptions of the pinhole camera model, but lens distortion with off-the-shelf cameras is usually significant enough to violate this assumption. Many methods for radial distortion estimation have been proposed, but they all have limitations. Robust automatic radial distortion estimation from a single natural image would be extremely useful for many applications, particularly those in human-made environments containing abundant lines. For example, it could be used in place of an extensive calibration procedure to get a mobile robot or quadrotor experiment up and running quickly in an indoor environment. We propose a new method for automatic radial distortion estimation based on the plumb-line approach. The method works from a single image and does not require a special calibration pattern. It is based on Fitzgibbon’s division model, robust estimation of circular arcs, and robust estimation of distortion parameters. We perform an extensive empirical study of the method on synthetic images. We include a comparative statistical analysis of how different circle fitting methods contribute to accurate distortion parameter estimation. We finally provide qualitative results on a wide variety of challenging real images. The experiments demonstrate the method’s ability to accurately identify distortion parameters and remove distortion from images.
Improving bug localization using structured information retrieval

Locating bugs is important, difficult, and expensive, particularly for large-scale systems. To address this, natural language information retrieval techniques are increasingly being used to suggest potential faulty source files given bug reports. While these techniques are very scalable, in practice their effectiveness remains low in accurately localizing bugs to a small number of files. Our key insight is that structured information retrieval based on code constructs, such as class and method names, enables more accurate bug localization. We present BLUiR, which embodies this insight, requires only the source code and bug reports, and takes advantage of bug similarity data if available. We build BLUiR on a proven, open source IR toolkit that anyone can use. Our work provides a thorough grounding of IR-based bug localization research in fundamental IR theoretical and empirical knowledge and practice. We evaluate BLUiR on four open source projects with approximately 3,400 bugs. Results show that BLUiR matches or outperforms a current state-of-the-art tool across applications considered, even when BLUiR does not use bug similarity data used by the other tool.
Dual-microphone noise reduction based on semi-blind DUET

Noise reduction is a fundamental requirement of many speech applications. Sometimes the major interferences, such as music, song, cross-talking, etc., coming from loudspeakers nearby make it a more challenging problem. In this paper, by supposing the locations of interferences are fixed, a semi-blind Degenerate Unmixing Estimation Technique (DUET) approach using dual-microphone is proposed. Firstly, during system initialization, the spatial feature distribution of the interferences is estimated precisely. Then the feature distribution of unknown target speech is obtained using model adaptation. Finally, a time-frequency binary mask estimated based on likelihood comparison is used to separate the target speech. The experimental results show that the performances of our approach in adverse noise reduction are significant. 77.9% interferences are eliminated and the speech quality is preserved very well. The results are close to those of un-blind approach, where the locations of all sound sources are known.
Privacy Paradox Revised: Pre-Existing Attitudes, Psychological Ownership, and Actual Disclosure

Prior research has pointed to discrepancies between users' privacy concerns and disclosure behaviors, denoted as the privacy paradox, and repeatedly highlighted the importance to find explanations for this dichotomy. In this regard, three approaches have been proposed by prior literature: (1) use of actual disclosure behavior rather than behavioral intentions, (2) systematic distinction between pre-existing attitudes and situation-specific privacy considerations, and (3) limited and irrational cognitive processes during decision-making. The current research proposes an experiment capable to test these three assumptions simultaneously. More precisely, the authors aim to explore the contextual nature of privacy-related decisions by systematically manipulating (1) individuals’ psychological ownership with regard to own private information, and (2) individuals’ affective states, while measuring (3) pre-existing attitudes as well as situation-specific risk and benefit perceptions, and (4) intentions as well as actual disclosure. Thus, the proposed study strives to uniquely add to the understanding of the privacy paradox.
Memristor-Based Material Implication (IMPLY) Logic: Design Principles and Methodologies

Memristors are novel devices, useful as memory at all hierarchies. These devices can also behave as logic circuits. In this paper, the IMPLY logic gate, a memristor-based logic circuit, is described. In this memristive logic family, each memristor is used as an input, output, computational logic element, and latch in different stages of the computing process. The logical state is determined by the resistance of the memristor. This logic family can be integrated within a memristor-based crossbar, commonly used for memory. In this paper, a methodology for designing this logic family is proposed. The design methodology is based on a general design flow, suitable for all deterministic memristive logic families, and includes some additional design constraints to support the IMPLY logic family. An IMPLY 8-bit full adder based on this design methodology is presented as a case study.
Network robustness and fragility: percolation on random graphs.

Recent work on the Internet, social networks, and the power grid has addressed the resilience of these networks to either random or targeted deletion of network nodes or links. Such deletions include, for example, the failure of Internet routers or power transmission lines. Percolation models on random graphs provide a simple representation of this process but have typically been limited to graphs with Poisson degree distribution at their vertices. Such graphs are quite unlike real-world networks, which often possess power-law or other highly skewed degree distributions. In this paper we study percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolation, bond percolation, and models in which occupation probabilities depend on vertex degree. We discuss the application of our theory to the understanding of network resilience.
Deep Learning and Data Labeling for Medical Applications

This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can facilitate the diagnosis of many autoimmune diseases by finding antibodies in the patient serum. Recently, a lot of automatic HEp-2 cell classification strategies including both shallow and deep methods have been developed, wherein the deep Convolutional Neural Networks (CNNs) have been proven to achieve impressive performance. However, the deep CNNs in general requires a fixed size of image as the input. In order to conquer the limitation of the fixed size problem, a spatial pyramid pooling (SPP) strategy has been proposed in general object recognition and detection. The SPP-net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation, which achieved superior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the frequency: an important signature for identifying different types of images, of the activated patterns. Therefore, this study explores a generalized spatial pooling strategy, called K-support spatial pooling, in deep CNNs by integrating not only the maximum activated magnitude but also the response magnitude of the relatively activated patterns of a specific neuron together. This proposed K-support spatial pooling strategy in deep CNNs combines the popularly applied mean and max pooling methods, and then avoid awfully emphasizing of the maximum activation but preferring a group of activations in a supported region. The deep CNNs with the proposed K-support spatial pooling is applied for HEp-2 cell classification, and achieve promising performance compared with the state-of-the-art approaches.
A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition

In this paper, a novel deep neural network (DNN)-driven feature learning method is proposed and applied to multi-view facial expression recognition (FER). In this method, scale invariant feature transform (SIFT) features corresponding to a set of landmark points are first extracted from each facial image. Then, a feature matrix consisting of the extracted SIFT feature vectors is used as input data and sent to a well-designed DNN model for learning optimal discriminative features for expression classification. The proposed DNN model employs several layers to characterize the corresponding relationship between the SIFT feature vectors and their corresponding high-level semantic information. By training the DNN model, we are able to learn a set of optimal features that are well suitable for classifying the facial expressions across different facial views. To evaluate the effectiveness of the proposed method, two nonfrontal facial expression databases, namely BU-3DFE and Multi-PIE, are respectively used to testify our method and the experimental results show that our algorithm outperforms the state-of-the-art methods.
Identify the Benefits of the Different Steps in an i-Vector Based Speaker Verification System

This paper focuses on the analysis of the i-vector paradigm, a compact representation of spoken utterances that is used by most of the state of the art speaker verification systems. This work was mainly motivated by the need to quantify the impact of their steps on the final performance, especially their ability to model data according to a theoretical Gaussian framework. These investigations allow to highlight the key points of the approach, in particular a core conditioning procedure, that lead to the success of the i-vector paradigm.
Service oriented architectures: approaches, technologies and research issues

Service-oriented architectures (SOA) is an emerging approach that addresses the requirements of loosely coupled, standards-based, and protocol- independent distributed computing. Typically business operations running in an SOA comprise a number of invocations of these different components, often in an event-driven or asynchronous fashion that reflects the underlying business process needs. To build an SOA a highly distributable communications and integration backbone is required.  This paper reviews technologies and approaches that unify the principles and concepts of SOA with those of event-based programing. The paper also focuses on the ESB and describes a range of functions that are designed to offer a manageable, standards-based SOA backbone that extends middleware functionality throughout by connecting heterogeneous components and systems and offers integration services. Finally, the paper proposes an approach to extend the conventional SOA to cater for essential ESB requirements that include capabilities such as service orchestration, “intelligent” routing, provisioning, integrity and security of message as well as service management. The layers in this extended SOA, in short xSOA, are used to classify research issues and current research activities.
RAMP Gold : A High-Throughput FPGA-Based Manycore Simulator

Computer architects have long used simulators to explore microarchitectures and quantitatively analyze design tradeoffs. Though attractive because of their low cost and ease of modification, these software simulators suffer from poor performance. In this paper, we present RAMP Gold, an FPGA-based manycore simulator that outperforms software simulators by orders of magnitude. It models up to 64 in-order issue SPARC V8 cores and a shared memory hierarchy. We describe the implementation of RAMP Gold in detail and analyze its performance. We then conclude, proposing RAMP Gold’s use in the exploration of several important research problems that currently suffer greatly from the poor performance of software simulators.
A Review of Scar Scales and Scar Measuring Devices

Pathologic scarring affects millions of people worldwide. Quantitative and qualitative measurement modalities are needed to effectively evaluate and monitor treatments.



This article reviews the literature on available tools and existent assessment scales used to subjectively and objectively characterize scar.



We describe the attributes and deficiencies of each tool and scale and highlight areas where further development is critical.



An optimal, universal scar scoring system is needed in order to better characterize, understand and treat pathologic scarring.
The status, challenges, and future of additive manufacturing in engineering

Additive manufacturing (AM) is poised to bring about a revolution in the way products are designed, manufactured, and distributed to end users. This technology has gained significant academic as well as industry interest due to its ability to create complex geometries with customizable material properties. AM has also inspired the development of the maker movement by democratizing design and manufacturing. Due to the rapid proliferation of a wide variety of technologies associated with AM, there is a lack of a comprehensive set of design principles, manufacturing guidelines, and standardization of best practices. These challenges are compounded by the fact that advancements in multiple technologies (for example materials processing, topology optimization) generate a ‘‘positive feedback loop’’ effect in advancing AM. In order to advance research interest and investment in AM technologies, some fundamental questions and trends about the dependencies existing in these avenues need highlighting. The goal of our review paper is to organize this body of knowledge surrounding AM, and present current barriers, findings, and future trends significantly to the researchers. We also discuss fundamental attributes of AM processes, evolution of the AM industry, and the affordances enabled by the emergence of AM in a variety of areas such as geometry processing, material design, and education. We conclude our paper by pointing out future directions such as the ‘‘print-it-all’’ paradigm, that have the potential to re-imagine current research and spawn completely new avenues for exploration.  E-mail address: gao51@purdue.edu (W. Gao). http://dx.doi.org/10.1016/j.cad.2015.04.001 0010-4485/
Using ultra-low parasitic hybrid packaging method to reduce high frequency EMI noise for SiC power module

In order to reduce high frequency electromagnetic interference (EMI) noise for SiC devices, this paper presents a hybrid structure SiC power module with low parasitic inductance and low ground capacitance that can suppress EMI efficiently. To certify the improvement on EMI, two buck converters, using the same power loop and gate drive, are built for experiment. Two converters use the proposed power module and TO-247 packaged commercial devices respectively. The experimental results show that low parasitic inductance can increase switching speed considerably while have only slight impact on EMI. And low parasitic ground capacitance can decrease the ground current effectively. Considering soft switching can also reduce EMI, this paper further compares EMI noise of two converters under two conditions: continuous current modulation (CCM) and triangle current modulation (TCM). The result shows that the proposed power module can suppress the common mode (CM) noise effectively at both situations. Furthermore, this paper verifies the noise mode transformation from differential mode (DM) EMI to CM EMI.
Improving Acoustic Models in TORGO Dysarthric Speech Database

Assistive speech-based technologies can improve the quality of life for people affected with dysarthria, a motor speech disorder. In this paper, we explore multiple ways to improve Gaussian mixture model and deep neural network (DNN) based hidden Markov model (HMM) automatic speech recognition systems for TORGO dysarthric speech database. This work shows significant improvements over the previous attempts in building such systems in TORGO. We trained speaker-specific acoustic models by tuning various acoustic model parameters, using speaker normalized cepstral features and building complex DNN-HMM models with dropout and sequence-discrimination strategies. The DNN-HMM models for severe and severe-moderate dysarthric speakers were further improved by leveraging specific information from dysarthric speech to DNN models trained on audio files from both dysarthric and normal speech, using generalized distillation framework. To the best of our knowledge, this paper presents the best recognition accuracies for TORGO database till date.