nils-herrmann/clean_scirepeval huggingface dataset:
Decoding hand movement velocity from electroencephalogram signals during a drawing task

Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.



Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.



The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.



These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.
Resilient individuals use positive emotions to bounce back from negative emotional experiences.

Theory indicates that resilient individuals "bounce back" from stressful experiences quickly and effectively. Few studies, however, have provided empirical evidence for this theory. The broaden-and-build theory of positive emotions (B. L. Fredrickson, 1998, 2001) is used as a framework for understanding psychological resilience. The authors used a multimethod approach in 3 studies to predict that resilient people use positive emotions to rebound from, and find positive meaning in, stressful encounters. Mediational analyses revealed that the experience of positive emotions contributed, in part, to participants' abilities to achieve efficient emotion regulation, demonstrated by accelerated cardiovascular recovery from negative emotional arousal (Studies 1 and 2) and by finding positive meaning in negative circumstances (Study 3). Implications for research on resilience and positive emotions are discussed.
A Parallel-Strip Ring Power Divider With High Isolation and Arbitrary Power-Dividing Ratio

In this paper, a new power divider concept, which provides high flexibility of transmission line characteristic impedance and port impedance, is proposed. This power divider is implemented on a parallel-strip line, which is a balanced transmission line. By implementing the advantages and uniqueness of the parallel-strip line, the divider outperforms the conventional divider in terms of isolation bandwidths. A swap structure of the two lines of the parallel-strip line is employed in this design, which is critical for the isolation enhancements. A lumped-circuit model of the parallel-strip swap including all parasitic effects has been analyzed. An equal power divider with center frequency of 2 GHz was designed to demonstrate the idea. The experimental results show that the equal power divider has 96.5% -10-dB impedance bandwidth with more than 25-dB isolation and less than 0.7-dB insertion loss. In order to generalize the concept with an arbitrary power ratio, we also realize unequal power dividers with the same isolation characteristics. The impedance bandwidth of the proposed power divider will increase with the dividing ratio, which is opposite to the conventional Wilkinson power divider. Unequal dividers with dividing ratios of 1 : 2 and 1 : 12 are designed and measured. Additionally, a frequency independent 180 power divider has been realized with less than 2 phase errors.
A systematic review of software fault prediction studies

0957-4174/$ see front matter 2008  Corresponding author. E-mail addresses: cagatay.catal@bte.mam.gov.tr (C (B. Diri). This paper provides a systematic review of previous software fault prediction studies with a specific focus on metrics, methods, and datasets. The review uses 74 software fault prediction papers in 11 journals and several conference proceedings. According to the review results, the usage percentage of public datasets increased significantly and the usage percentage of machine learning algorithms increased slightly since 2005. In addition, method-level metrics are still the most dominant metrics in fault prediction research area and machine learning algorithms are still the most popular methods for fault prediction. Researchers working on software fault prediction area should continue to use public datasets and machine learning algorithms to build better fault predictors. The usage percentage of class-level is beyond acceptable levels and they should be used much more than they are now in order to predict the faults earlier in design phase of software life cycle.
Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

Synchronized stochastic gradient descent (SGD) optimizers with data parallelism are widely used in training large-scale deep neural networks. Although using larger mini-batch sizes can improve the system scalability by reducing the communication-to-computation ratio, it may hurt the generalization ability of the models. To this end, we build a highly scalable deep learning training system for dense GPU clusters with three main contributions: (1) We propose a mixed-precision training method that significantly improves the training throughput of a single GPU without losing accuracy. (2) We propose an optimization approach for extremely large minibatch size (up to 64k) that can train CNN models on the ImageNet dataset without losing accuracy. (3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs. On training ResNet-50 with 90 epochs, the state-of-the-art GPU-based system with 1024 Tesla P100 GPUs spent 15 minutes and achieved 74.9% top-1 test accuracy, and another KNL-based system with 2048 Intel KNLs spent 20 minutes and achieved 75.4% accuracy. Our training system can achieve 75.8% top-1 test accuracy in only 6.6 minutes using 2048 Tesla P40 GPUs. When training AlexNet with 95 epochs, our system can achieve 58.7% top-1 test accuracy within 4 minutes, which also outperforms all other existing systems.
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition

We present Listen, Attend and Spell (LAS), a neural speech recognizer that transcribes speech utterances directly to characters without pronunciation models, HMMs or other components of traditional speech recognizers. In LAS, the neural network architecture subsumes the acoustic, pronunciation and language models making it not only an end-to-end trained system but an end-to-end model. In contrast to DNN-HMM, CTC and most other models, LAS makes no independence assumptions about the probability distribution of the output character sequences given the acoustic sequence. Our system has two components: a listener and a speller. The listener is a pyramidal recurrent network encoder that accepts filter bank spectra as inputs. The speller is an attention-based recurrent network decoder that emits each character conditioned on all previous characters, and the entire acoustic sequence. On a Google voice search task, LAS achieves a WER of 14.1% without a dictionary or an external language model and 10.3% with language model rescoring over the top 32 beams. In comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0% on the same set.
Arabic Text Recognition

The issue of handwritten character recognition is still a big challenge to the scientific community. Several approaches to address this challenge have been attempted in the last years, mostly focusing on the English pre-printed or handwritten characters space. Thus, the need to attempt a research related to Arabic handwritten text recognition. Algorithms based on neural networks have proved to give better results than conventional methods when applied to problems where the decision rules of the classification problem are not clearly defined. Two neural networks were built to classify already segmented characters of handwritten Arabic text. The two neural networks correctly recognized 73% of the characters. However, one hurdle was encountered in the above scenario, which can be summarized as follows: there are a lot of handwritten characters that can be segmented and classified into two or more different classes depending on whether they are looked at separately, or in a word, or even in a sentence. In other words, character classification, especially handwritten Arabic characters, depends largely on contextual information, not only on topographic features extracted from these characters.
Computer-Suggested Facial Makeup

Finding the best makeup for a given human face is an art in its own right. Experienced makeup artists train for years to be skilled enough to propose a best-fit makeup for an individual. In this work we propose a system that automates this task. We acquired the appearance of 56 human faces, both without and with professional makeup. To this end, we use a controlled-light setup, which allows to capture detailed facial appearance information, such as diffuse reflectance, normals, subsurface-scattering, specularity, or glossiness. A 3D morphable face model is used to obtain 3D positional information and to register all faces into a common parameterization. We then define makeup to be the change of facial appearance and use the acquired database to find a mapping from the space of human facial appearance to makeup. Our main application is to use this mapping to suggest the best-fit makeup for novel faces that are not in the database. Further applications are makeup transfer, automatic rating of makeup, makeup-training, or makeup-exaggeration. As our makeup representation captures a change in reflectance and scattering, it allows us to synthesize faces with makeup in novel 3D views and novel lighting with high realism. The effectiveness of our approach is further validated in a user-study.
A robotic exoskeleton for rehabilitation and assessment of the upper limb following incomplete spinal cord injury

Robotic devices have been shown to be efficacious in the delivery of therapy to treat upper limb motor impairment following stroke. However, the application of this technology to other types of neurological injury has been limited to case studies. In this paper, we present a multi degree of freedom robotic exoskeleton, the MAHI Exo II, intended for rehabilitation of the upper limb following incomplete spinal cord injury (SCI). We present details about the MAHI Exo II and initial findings from a clinical evaluation of the device with eight subjects with incomplete SCI who completed a multi-session training protocol. Clinical assessments show significant gains when comparing pre- and post-training performance in functional tasks. This paper explores a range of robotic measures capturing movement quality and smoothness that may be useful in tracking performance, providing as feedback to the subject, or incorporating into an adaptive training protocol. Advantages and disadvantages of the various investigated measures are discussed with regard to the type of movement segmentation that can be applied to the data collected during unassisted movements where the robot is backdriven and encoder data is recorded for post-processing.
Service-oriented design and development methodology

SOA is rapidly emerging as the premier integration and architectural approach in contemporary complex, heterogeneous computing environments. SOA is not simply about deploying software: it also requires that organizations evaluate their business models, come up with service-oriented analysis and design techniques, deployment and support plans, and carefully evaluate partner/customer/supplier relationships. Since SOA is based on open standards and is frequently realized using Web services, developing meaningful Web service and business process specifications is an important requirement for SOA applications that leverage Web services. Designers and developers cannot be expected to oversee a complex service-oriented development project without relying on a sound design and development methodology. This paper provides an overview of the methods and techniques used in service-oriented design and development. Aim of this paper is to examine a service development methodology from the point of view of both service producers and requesters and review the range of elements in this methodology that are available to them.
Draco: A System for Uniform and Fine-grained Access Control for Web Code on Android

In-app embedded browsers are commonly used by app developers to display web content without having to redirect the user to heavy-weight web browsers. Just like the conventional web browsers, embedded browsers can allow the execution of web code. In addition, they provide mechanisms (viz., JavaScript bridges) to give web code access to internal app code that might implement critical functionalities and expose device resources. This is intrinsically dangerous since there is currently no means for app developers to perform origin-based access control on the JavaScript bridges, and any web code running in an embedded browser is free to use all the exposed app and device resources. Previous work that addresses this problem provided access control solutions that work only for apps that are built using hybrid frameworks. Additionally, these solutions focused on protecting only the parts of JavaScript bridges that expose permissions-protected resources. In this work, our goal is to provide a generic solution that works for all apps that utilize embedded web browsers and protects all channels that give access to internal app and device resources. Towards realizing this goal, we built Draco, a uniform and fine-grained access control framework for web code running on Android embedded browsers (viz., WebView). Draco provides a declarative policy language that allows developers to define policies to specify the desired access characteristics of web origins in a fine-grained fashion, and a runtime system that dynamically enforces the policies. In contrast with previous work, we do not assume any modifications to the Android operating system, and implement Draco in the Chromium Android System WebView app to enable seamless deployment. Our evaluation of the the Draco runtime system shows that Draco incurs negligible overhead, which is in the order of microseconds.
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013,  The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.
Usage of a mobile social learning platform with virtual badges in a primary school

This study presents and examines SamEx, a mobile learning system used by 305 students in formal and informal learning in a primary school in Singapore. Students use SamEx in situ to capture media such as pictures, video clips and audio recordings, comment on them, and share them with their peers. In this paper we report on the experiences of students in using the application throughout a one-year period with a focus on self-directedness, quality of contributions, and answers to contextual question prompts. We examine how the usage of tools such as SamEx predicts students' science examination results, discuss the role of badges as an extrinsic motivational tool, and explore how individual and collaborative learning emerge. Our research shows that the quantity and quality of contributions provided by the students in SamEx predict the end-year assessment score. With respect to specific system features, contextual answers given by the students and the overall likes received by students are also correlated with the end-year assessment score.
Comparison of two approaches to building a vertical search tool: a case study in the nanotechnology domain

As the Web has been growing exponentially, it has become increasingly difficult to search for desired information. In recent years, many domain-specific (vertical) search tools have been developed to serve the information needs of specific fields. This paper describes two approaches to building a domain-specific search tool. We report our experience in building two different tools in the nanotechnology domain -- (1) a server-side search engine, and (2) a client-side search agent. The designs of the two search systems are presented and discussed, and their strengths and weaknesses are compared. Some future research directions are also discussed.
Different types of wearable sensors and health monitoring systems: A survey

There is steep rise in diseases due to current lifestyle and issues related with aging. Healthcare systems need to keep pace with the changing requirements of the health problems. Some of the requirements of current health monitoring systems are that they should be portable, consume less power, user friendly and so on. With the rapid development in technologies such as wireless, embedded, nanotechnology and so on it has become possible to develop such systems and devices. In this paper we have done survey of different types of wireless standards, type of sensors used along with some of the recent methodologies used in the field of health monitoring.
A fingerprint retrieval system based on level-1 and level-2 features

0957-4174/$ see front matter 2012  ⇑ Corresponding author. E-mail addresses: raffaele.cappelli@unibo.it (R. C bo.it (M. Ferrara). This paper proposes a novel fingerprint retrieval system that combines level-1 (local orientation and frequencies) and level-2 (minutiae) features. Various scoreand rank-level fusion strategies and a novel hybrid fusion approach are evaluated. Extensive experiments are carried out on six public databases and a systematic comparison is made with eighteen retrieval methods and seventeen exclusive classification techniques published in the literature. The novel approach achieves impressive results: its retrieval accuracy is definitely higher than competing state-of-the-art methods, with error rates that in some cases are even one or two orders of magnitude smaller.
Stock trend prediction relying on text mining and sentiment analysis with tweets

Stock trend prediction based on text has gained much attention from researchers in recent years. According to investment theories, investors' behaviors will influence the stock market, and the way people invest their money is based on the history trend and information they hold. On account of this indirectly influential relationship between information of stock and stock trend, stock trend prediction based on text has been done by many researchers. However, due to the serious feature sparse problem in tweets and unreliability of using average sentiment score to indicate one day's sentiment, this work proposed a text-sentiment based stock trend prediction model with a hybrid feature selection method. Instead of applying sentiment analysis to add sentiment related features, this paper uses SentiWordNet to give an additional weight to the selected features. Besides, this work also compares the results with those of other learning algorithms. SVM linear algorithm based on leave-one-out cross validation yields the best performance of 90.34%.
Towards a Theory of Open Innovation : Three Core Process Archetypes

Open Innovation is a phenomenon that has become increasingly important for both practice and theory over the last few years. The reasons are to be found in shorter innovation cycles, industrial research and development’s escalating costs as well as in the dearth of resources. Subsequently, the open source phenomenon has attracted innovation researchers and practitioners. The recent era of open innovation started when practitioners realised that companies that wished to commercialise both their own ideas as well as other firms’ innovation should seek new ways to bring their in-house ideas to market. They need to deploy pathways outside their current businesses and should realise that the locus where knowledge is created does not necessarily always equal the locus of innovation they need not both be found within the company. Experience has furthermore shown that neither the locus of innovation nor exploitation need lie within companies’ own boundaries. However, emulation of the open innovation approach transforms a company’s solid boundaries into a semi-permeable membrane that enables innovation to move more easily between the external environment and the company’s internal innovation process.
A Bayesian approach to binocular steropsis

We develop a computational model for binocular stereopsis, attempting to explain the process by which the information detailing the 3-D geometry of object surfaces is encoded in a pair of stereo images. We design our model within a Bayesian framework, making explicit all of our assumptions about the nature of image coding and the structure of the world. We start by deriving our model for image formation, introducing a definition of half-occluded regions and deriving simple equations relating these regions to the disparity function. We show that the disparity function alone contains enough information to determine the half-occluded regions. We use these relations to derive a model for image formation in which the half-occluded regions are explicitly represented and computed. Next, we present our prior model in a series of three stages, or “worlds,” where each world considers an additional complication to the prior. We eventually argue that the prior model must be constructed from all of the local quantities in the scene geometry-i.e., depth, surface orientation, object boundaries, and surface creases. In addition, we present a new dynamic programming strategy for estimating these quantities. Throughout the article, we provide motivation for the development of our model by psychophysical examinations of the human visual system.