nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal Gesture Recognition Using 3-D Convolution and Convolutional LSTM

Gesture recognition aims to recognize meaningful movements of human bodies, and is of utmost importance in intelligent human–computer/robot interactions. In this paper, we present a multimodal gesture recognition method based on 3-D convolution and convolutional long-short-term-memory (LSTM) networks. The proposed method first learns short-term spatiotemporal features of gestures through the 3-D convolutional neural network, and then learns long-term spatiotemporal features by convolutional LSTM networks based on the extracted short-term spatiotemporal features. In addition, fine-tuning among multimodal data is evaluated, and we find that it can be considered as an optional skill to prevent overfitting when no pre-trained models exist. The proposed method is verified on the ChaLearn LAP large-scale isolated gesture data set (IsoGD) and the Sheffield Kinect gesture (SKIG) data set. The results show that our proposed method can obtain the state-of-the-art recognition accuracy (51.02% on the validation set of IsoGD and 98.89% on SKIG).
Fashioning with Networks: Neural Style Transfer to Design Clothes

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al  show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a user’s preference and by learning the user’s fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the user’s fashion style.
Passivity of a class of sampled-data systems: Application to haptic interfaces

Passivity of systems comprising a continuous time plant and discrete time controller is considered. This topic is motivated by stability considerations arising in the control of robots and force-reflecting human interfaces (“haptic interfaces”). A necessary and sufficient condition for the passivity of a class of sampled-data systems is derived. An example — implementation of a “virtual wall” via a one degree-of-freedom haptic interface — is presented.
Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance

T his paper describes a real-time prototype computer vision system for monitoring driver vigilance. The main components of the system consists of a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver’s level of vigilance. The visual behaviors include eyelid movement, face orientation, and gaze movement (pupil movement). The system was tested in a simulating environment with subjects of different ethnic backgrounds, different genders, ages, with/without glasses, and under different illumination conditions, and it was found very robust, reliable and accurate.
A Metric for Performance Evaluation of Multi-Target Tracking Algorithms

Performance evaluation of multi-target tracking algorithms is of great practical importance in the design, parameter optimization and comparison of tracking systems. The goal of performance evaluation is to measure the distance between two sets of tracks: the ground truth tracks and the set of estimated tracks. This paper proposes a mathematically rigorous metric for this purpose. The basis of the proposed distance measure is the recently formulated consistent metric for performance evaluation of multi-target filters, referred to as the OSPA metric. Multi-target filters sequentially estimate the number of targets and their position in the state space. The OSPA metric is therefore defined on the space of finite sets of vectors. The distinction between filtering and tracking is that tracking algorithms output tracks and a track represents a labeled temporal sequence of state estimates, associated with the same target. The metric proposed in this paper is therefore defined on the space of finite sets of tracks and incorporates the labeling error. Numerical examples demonstrate that the proposed metric behaves in a manner consistent with our expectations.
A Touchless Human-machine Interface for the Control of an Elevator

We present a touchless interface based on gesture recognition for the control of an elevator. Interacting with the interface, a user can select and confirm the desired floor without touching any physical device. The interface has to guarantee a usability comparable to the habitual button panels: the users are supposed to use the elevator without any specific training, and its functionality has to be the same of traditional elevators. In addition to describing three possible implementations of the touchless interface, the paper provides two contributions: a comparison of two different technologies used as the interface inputs, and the results from 10 preliminary user tests performed to measure the perceived usability. The results are promising: two implementations out of three got an average score around 84 on the “Usability Metrics for User Experience”.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks

Deep learning models are typically trained using stochastic gradient descent or one of its variants. These methods update the weights using their gradient, estimated from a small fraction of the training data. It has been observed that when using large batch sizes there is a persistent degradation in generalization performance known as the "generalization gap" phenomenon. Identifying the origin of this gap and closing it had remained an open problem.  We examine the initial high learning rate training phase. We find that the weight distance from its initialization grows logarithmically with the number of weight updates. We therefore propose a "random walk on a random landscape" statistical model which is known to exhibit similar "ultra-slow" diffusion behavior. Following this hypothesis we conducted experiments to show empirically that the "generalization gap" stems from the relatively small number of updates rather than the batch size, and can be completely eliminated by adapting the training regime used. We further investigate different techniques to train models in the large-batch regime and present a novel algorithm named "Ghost Batch Normalization" which enables significant decrease in the generalization gap without increasing the number of updates. To validate our findings we conduct several additional experiments on MNIST, CIFAR-10, CIFAR-100 and ImageNet. Finally, we reassess common practices and beliefs concerning training of deep models and suggest they may not be optimal to achieve good generalization.
Boosting slow oscillations during sleep potentiates memory

There is compelling evidence that sleep contributes to the long-term consolidation of new memories. This function of sleep has been linked to slow (<1 Hz) potential oscillations, which predominantly arise from the prefrontal neocortex and characterize slow wave sleep. However, oscillations in brain potentials are commonly considered to be mere epiphenomena that reflect synchronized activity arising from neuronal networks, which links the membrane and synaptic processes of these neurons in time. Whether brain potentials and their extracellular equivalent have any physiological meaning per se is unclear, but can easily be investigated by inducing the extracellular oscillating potential fields of interest. Here we show that inducing slow oscillation-like potential fields by transcranial application of oscillating potentials (0.75 Hz) during early nocturnal non-rapid-eye-movement sleep, that is, a period of emerging slow wave sleep, enhances the retention of hippocampus-dependent declarative memories in healthy humans. The slowly oscillating potential stimulation induced an immediate increase in slow wave sleep, endogenous cortical slow oscillations and slow spindle activity in the frontal cortex. Brain stimulation with oscillations at 5 Hz—another frequency band that normally predominates during rapid-eye-movement sleep—decreased slow oscillations and left declarative memory unchanged. Our findings indicate that endogenous slow potential oscillations have a causal role in the sleep-associated consolidation of memory, and that this role is enhanced by field effects in cortical extracellular space.
Topical Calendula officinalis L. successfully treated exfoliative cheilitis: a case report

Authors describe a case of recurrent exfoliative cheilitis that responded to treatment with a standardized topical preparation of Calendula officinalis L. An eighteen-year-old man was referred to UNESP - São Paulo State University, Department of Biosciences and Oral Diagnosis, São José dos Campos Dental School to investigate a chronic dry scaling lesion on his lips. The patient's main chief was aesthetic compromising. Corticoid therapy was suspended and Calendula officinalis ointment 10% for ad libitum use has been prescribed. The results presented allow the authors to consider Calendula officinalis L. as a potential therapy in cases of cheilitis exfoliative.
PREDICTION OF STUDENT ACADEMIC PERFORMANCE BY AN APPLICATION OF DATA MINING TECHNIQUES

-One of the significant facts in higher learning institution is the explosive growth educational data. These data are increasing rapidly without any benefit to the management. The main objective of any higher educational institution is to improve the quality of managerial decisions and to impart quality education. Good prediction of student’s success in higher learning institution is one way to reach the highest level of quality in higher education system. There are many prediction model available with difference approach in student performance was reported by researcher, but there is no certainty if there are any predictors that accurately determine whether a student will be an academic genius, a drop out, or an average performer. The aims of this study was to apply the kernel method as data mining techniques to analyze the relationships between students’s behavioral and their success and to develop the model of student performance predictors . This is done by using Smooth Support Vector Machine (SSVM) classification and kernel k-means clustering techniques. The results of this study reported a model of student academic performance predictors by employing psychometric factors as variables predictors.
Snap-together visualization: can users construct and operate coordinated visualizations?

Multiple coordinated visualizations enable users to rapidly explore complex information. However, users often need unforeseen combinations of coordinated visualizations. Snaptogether visualization (Snap) enables users to rapidly and dynamically construct coordinated}visualization interfaces, customized for their data, without programming. Users load data into desired visualizations, then construct coordinations between them for brushing and linking, overview and detail view, drill down, etc. Snap formalizes a conceptual model of visualization coordination based on the relational data model. Visualization developers can easily Snap-enable their independent visualizations using a simple API. Empirical evaluation reveals bene"ts, cognitive issues and usability concerns with coordination concepts and Snap. Two user studies explore coordination construction and operation. Data-savvy users successfully, enthusiastically and rapidly constructed powerful coordinated}visualization interfaces of their own. Operating an overview-anddetail coordination reliably improved user performance by 30}80% over detail-only and uncoordinated interfaces for most tasks.
Automatic Detection of Diabetic Retinopathy using Deep Convolutional Neural Network

The purpose of this project is to design an automated and efficient solution that could detect the symptoms of DR from a retinal image within seconds and simplify the process of reviewing and examination of images. Diabetic Retinopathy (DR) is a complication of diabetes that is caused by changes in the blood vessel of the retina and it is one of the leading causes of blindness in the developed world. Currently, detecting DR symptoms is a manual and time-consuming process. Recently, fullyconnected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. In our approach, we trained a deep Convolutional Neural Network model on a large dataset consisting around 35,000 images and used dropout layer techniques to achieve higher accuracy.
What is the function of the claustrum?

The claustrum is a thin, irregular, sheet-like neuronal structure hidden beneath the inner surface of the neocortex in the general region of the insula. Its function is enigmatic. Its anatomy is quite remarkable in that it receives input from almost all regions of cortex and projects back to almost all regions of cortex. We here briefly summarize what is known about the claustrum, speculate on its possible relationship to the processes that give rise to integrated conscious percepts, propose mechanisms that enable information to travel widely within the claustrum and discuss experiments to address these questions.
A highly linear 4GS/s uncalibrated voltage-to-time converter with wide input range

Analog-to-Digital Converts (ADC) are becoming essential to the function of ultra-high speed interconnects (IO) with complex modulation schemes, while at the same time reduction in supply voltage has negatively impacted the performance of such circuits. However the improvement in delay times and reduction in logic size has made time-based ADCs attractive. To accomplish this, a Voltage-to-Time Converter (VTC) is used as the first stage in a two-step conversion process. The performance of the VTC subsequently limits the overall performance of the ADC. This work presents a sampling-based VTC in 65nm CMOS with an input range of +/−0.5V achieving an ENOB of 7.6 bits at 4GS/s up to the Nyquist frequency with overall power of 8 mW only. Resulting output range of +/−63ps allows existing Time-to-Digital Converter (TDC) designs to realize close to this level of performance efficiently when combined to create a analog-time-digital converter (ATDC).
Attribution Modeling Increases Efficiency of Bidding in Display Advertising

Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clicks) after the fact. We argue that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising.
 Firstly we explain the inefficiency of the standard bidding policy with respect to attribution. Secondly we learn and utilize an attribution model in the bidder itself and show how it modifies the average bid after a click. Finally we produce evidence of the effectiveness of the proposed method on both offline and online experiments with data spanning several weeks of real traffic from Criteo, a leader in performance advertising.
Single-Channel and Multi-Channel Sinusoidal Audio Coding Using Compressed Sensing

Compressed sensing (CS) samples signals at a much lower rate than the Nyquist rate if they are sparse in some basis. In this paper, the CS methodology is applied to sinusoidally modeled audio signals. As this model is sparse by definition in the frequency domain (being equal to the sum of a small number of sinusoids), we investigate whether CS can be used to encode audio signals at low bitrates. In contrast to encoding the sinusoidal parameters (amplitude, frequency, phase) as current state-of-the-art methods do, we propose encoding few randomly selected samples of the time-domain description of the sinusoidal component (per signal segment). The potential of applying compressed sensing both to single-channel and multi-channel audio coding is examined. The listening test results are encouraging, indicating that the proposed approach can achieve comparable performance to that of state-of-the-art methods. Given that CS can lead to novel coding systems where the sampling and compression operations are combined into one low-complexity step, the proposed methodology can be considered as an important step towards applying the CS framework to audio coding applications.
Efficient Evaluation of the Terminal Response of a Twisted-Wire Pair Excited by a Plane-Wave Electromagnetic Field

Twisted-wire pair (TWP) loops are used as one of the primary communication channels in digital subscriber line (DSL) networks. As part of the development of channel noise models, a transmission-line-based model for predicting the terminal response of a single TWP to an illuminating plane-wave electromagnetic field is presented. Closed-form analytic approximations of the terminal response valid for an arbitrary direction of incidence and polarization of the incoming plane-wave field are provided. A worst-case model with the very same purpose has been previously proposed in the DSL literature; however, a worst-case approach only provides partial information about the field coupling mechanism. The goal of the presented transmission-line model is to introduce a more robust yet efficient approach for characterizing radio noise interference in DSL systems. Computed results for two TWP configurations of interest are presented together with measured results for one of them.
Pedestrian Parsing via Deep Decompositional Network

We propose a new Deep Decompositional Network (DDN) for parsing pedestrian images into semantic regions, such as hair, head, body, arms, and legs, where the pedestrians can be heavily occluded. Unlike existing methods based on template matching or Bayesian inference, our approach directly maps low-level visual features to the label maps of body parts with DDN, which is able to accurately estimate complex pose variations with good robustness to occlusions and background clutters. DDN jointly estimates occluded regions and segments body parts by stacking three types of hidden layers: occlusion estimation layers, completion layers, and decomposition layers. The occlusion estimation layers estimate a binary mask, indicating which part of a pedestrian is invisible. The completion layers synthesize low-level features of the invisible part from the original features and the occlusion mask. The decomposition layers directly transform the synthesized visual features to label maps. We devise a new strategy to pre-train these hidden layers, and then fine-tune the entire network using the stochastic gradient descent. Experimental results show that our approach achieves better segmentation accuracy than the state-of-the-art methods on pedestrian images with or without occlusions. Another important contribution of this paper is that it provides a large scale benchmark human parsing dataset that includes 3,673 annotated samples collected from 171 surveillance videos. It is 20 times larger than existing public datasets.
Applying particle swarm optimization-based decision tree classifier for cancer classification on gene expression data

The application of microarray data for cancer classification is important. Researchers have tried to analyze gene expression data using various computational intelligence methods.  We propose a novel method for gene selection utilizing particle swarm optimization combined with a decision tree as the classifier to select a small number of informative genes from the thousands of genes in the data that can contribute in identifying cancers. vailable online 27 August 2014
Retrieval of Song Lyrics from Sung Queries

Retrieving the lyrics of a sung recording from a database of text documents is a research topic that has not received much attention so far. Such a retrieval system has many practical applications, e.g. for karaoke applications or for indexing large song databases by their lyric content. We present a new method for lyrics retrieval. An acoustic model trained on singing is used to obtain phoneme probabilities from sung queries, which are then mapped to phoneme sequences. These are compared against lines of textual lyrics in a large corpus in order to retrieve the best-matching song. The approach is tested on three sung datasets. Lyrics are retrieved from a set of 300 possible songs (12,000 lines of lyrics). The results are highly encouraging and could be used further to perform automatic lyrics alignment and keyword spotting for large databases of songs, or for retrieving lyrics from the internet.
Deep convolutional neural networks based on semi-discrete frames

Deep convolutional neural networks have led to breakthrough results in practical feature extraction applications. The mathematical analysis of these networks was pioneered by Mallat . Specifically, Mallat considered so-called scattering networks based on identical semi-discrete wavelet frames in each network layer, and proved translation-invariance as well as deformation stability of the resulting feature extractor. The purpose of this paper is to develop Mallat's theory further by allowing for different and, most importantly, general semi-discrete frames (such as, e.g., Gabor frames, wavelets, curvelets, shearlets, ridgelets) in distinct network layers. This allows to extract wider classes of features than point singularities resolved by the wavelet transform. Our generalized feature extractor is proven to be translation-invariant, and we develop deformation stability results for a larger class of deformations than those considered by Mallat. For Mallat's wavelet-based feature extractor, we get rid of a number of technical conditions. The mathematical engine behind our results is continuous frame theory, which allows us to completely detach the invariance and deformation stability proofs from the particular algebraic structure of the underlying frames.
FACTORS INFLUENCING CITIZENS ’ ADOPTION OF AN E-GOVERNMENT SYSTEM : VALIDATION OF THE DECOMPOSED THEORY OF PLANNED BEHAVIOR ( DTPB )

The study explores the adoption of an electronic government (e-government) system called electronic district (e-District) system in context of India. The study validates the decomposed theory of planned behaviour (DTPB) to understand the impact of its factors on the potential adopter’s intention to adopt this system. The proposed research model is validated using the data collected from 304 respondents from a state called Bihar of India where this e-government was supposed to be implemented. The data is analysed through regression analysis technique. The empirical findings of the proposed research model indicated the significant relationships of all proposed hypotheses. The study also provides its limitations, future research directions, and implications for theory and practice toward the end.