Reviewerly/exHarmony huggingface dataset:
Attention rapidly reorganizes to naturally occurring structure in a novel activity sequence

Fluent event processing involves selectively attending to information-rich regions within dynamically unfolding sensory streams (e.g., Newtson, 1973). What counts as information-rich likely depends on numerous factors, however, including overall event novelty and local opportunity for repeated viewing. Using Hard, Recchia, and Tversky’s (2011) method, we investigated the extent to which these two variables affected viewers’ attentional patterns as events unfolded. Specifically, we recorded viewers’ “dwell times” as they advanced through two slideshows depicting distinct methods of shoelace tying varying in novelty but equated on other dimensions. Across two experiments, novelty sparked increased dwelling overall, and viewers’ dwelling patterns displayed rapid and systematic reorganization to structure within the activity stream after just one viewing of distinctively novel content. As well, increased dwelling positively predicted memory performance. These findings newly illuminate reorganization in attention as relevant information within novel activity sequences is quickly incorporated to guide event processing and support event memory.
Intelligent System for Identification of Wheelchair User’s Posture Using Machine Learning Techniques

This paper presents an intelligent system aimed at detecting a person's posture when sitting in a wheelchair. The main use of the proposed system is to warn an improper posture to prevent major health issues. A network of sensors is used to collect data that are analyzed through a scheme involving the following stages: selection of prototypes using condensed nearest neighborhood rule (CNN), data balancing with the Kennard-Stone algorithm, and reduction of dimensionality through principal component analysis. In doing so, acquired data can be both stored and processed into a micro controller. Finally, to carry out the posture classification over balanced, pre-processed data, and the K-nearest neighbors algorithm is used. It turns to be an intelligent system reaching a good tradeoff between the necessary amount of data and performance is accomplished. As a remarkable result, the amount of required data for training is significantly reduced while an admissible classification performance is achieved being a suitable trade given the device conditions.
Angle aided circle detection based on randomized Hough transform and its application in welding spots detection

The Hough transform has been widely used in image analysis and digital image processing due to its capability of transforming image space detection to parameter space accumulation. In this paper, we propose a novel Angle-Aided Circle Detection (AACD) algorithm based on the randomized Hough transform to reduce the computational complexity of the traditional Randomized Hough transform. The algorithm ameliorates the sampling method of random sampling points to reduce the invalid accumulation by using region proposals method, and thus significantly reduces the amount of computation. Compared with the traditional Hough transform, the proposed algorithm is robust and suitable for multiple circles detection under complex conditions with strong anti-interference capacity. Moreover, the algorithm has been successfully applied to the welding spot detection on automobile body, and the experimental results verifies the validity and accuracy of the algorithm.
LABIN: Balanced Min Cut for Large-Scale Data

Although many spectral clustering algorithms have been proposed during the past decades, they are not scalable to large-scale data due to their high computational complexities. In this paper, we propose a novel spectral clustering method for large-scale data, namely, large-scale balanced min cut (LABIN). A new model is proposed to extend the self-balanced min-cut (SBMC) model with the anchor-based strategy and a fast spectral rotation with linear time complexity is proposed to solve the new model. Extensive experimental results show the superior performance of our proposed method in comparison with the state-of-the-art methods including SBMC.
Learning What and Where to Transfer

As the application of deep learning has expanded to real-world problems with insufficient volume of training data, learning recently has gained much attention as means of improving the performance in such small-data regime. However, when existing methods are applied between heterogeneous architectures and tasks, it becomes more important to manage their detailed configurations and often requires exhaustive tuning on them for the desired performance. To address the issue, we propose a novel learning approach based on meta-learning that can automatically learn knowledge to from the source network to where in the target network. Given source and target networks, we propose an efficient training scheme to learn meta-networks that decide (a) which pairs of layers between the source and target networks should be matched for knowledge and (b) which features and how much knowledge from each feature should be transferred. We validate our meta-transfer approach against recent learning methods on various datasets and network architectures, on which our automated scheme significantly outperforms the prior baselines that find what and where to transfer in a hand-crafted manner.
Optical voice information hiding using enhanced iterative algorithm and computational ghost imaging

Recently, optical techniques have been studied extensively for securing grayscale and colour images. However, at the present time, a lot of sensitive information has also been transmitted in the form of audio/voice signals, thus, the development of secure optical transmission systems for voice signals is also very important. In this paper, we present a new optical cryptosystem for the secure transmission of voice/audio signals. The voice information is distributed among two phase masks and a security image using the enhanced iterative approach. In that, the 2D non-separable linear canonical transform (NS-LCT) is utilized. 2D NS-LCT is defined using ten independent parameters, all of which can be used as the security keys for the cryptosystem. Also, the security image containing the voice information can be further processed through optical encryption systems, which add an extra layer of security. The numerical simulation results are presented to demonstrate the validity and efficiency of the proposed method.
A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning

Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with large, artificial data generated through back-translations, a time-consuming process often no easier than training a MT system from scratch. in this paper, we propose an alternative where we fine-tune pre-trained BERT models on both the encoder and decoder of an APE system, exploring several parameter sharing strategies. By only training on a dataset of 23K sentences for 3 hours on a single GPU we obtain results that are competitive with systems that were trained on 5M artificial sentences. When we add this artificial data our method obtains state-of-the-art results.
Feasibility of Natural Language Processing–Assisted Auditing of Critical Findings in Chest Radiology

Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance initiatives require that institutions audit these communications, a time-intensive manual task. We propose using a rule-based natural language processing system to improve the process for auditing critical findings communications.We present a pilot assessment of the feasibility of using an automated critical finding identification system to assist quality assurance teams' evaluation of critical findings communication compliance. Our assessment is based on chest imaging reports. Critical findings are identified in radiology reports using pyConTextNLP, an open source Python implementation of the ConText algorithm.In our test set, there were 75 reports with critical findings and 591 reports without critical findings. pyConTextNLP correctly identified 69 of the positive cases with 8 false-positives for a sensitivity of 0.92 and a specificity of 0.99.Natural language processing can provide valuable assistance to auditing critical findings communications.
Resonant shortcuts for adiabatic rapid passage with only <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>z</mml:mi></mml:math> -field control

In this work we derive ultrafast shortcuts for adiabatic rapid passage for a qubit where the only control variable is the longitudinal $z$ field, while the transverse $x$ field remains constant. This restrictive framework is pertinent to some important tasks in quantum computing, for example, the design of a high fidelity controlled-phase gate can be mapped to the adiabatic quantum control of such a qubit. We study this problem in the adiabatic reference frame and with appropriately rescaled time, using as control input the derivative of the total field polar angle (with respect to rescaled time). We first show that a constant pulse can achieve perfect adiabatic rapid passage at only specific times, corresponding to resonant shortcuts to adiabaticity. We next show that, by using ``on-off-on-...-on-off-on'' pulse sequences with appropriate characteristics (amplitude, timing, and number of pulses), a perfect fidelity can be obtained for every duration larger than the limit $\ensuremath{\pi}/\mathrm{\ensuremath{\Omega}}$, where $\mathrm{\ensuremath{\Omega}}$ is the constant transverse $x$ field. We provide equations from which these characteristics can be calculated. The proposed methodology for generalized resonant shortcuts exploits the advantages of composite pulses in the rescaled time, while the corresponding control $z$ field varies continuously and monotonically in the original time. Of course, as the total duration approaches the lower limit, the changes in the control signal become more abrupt. These results are not restricted only to quantum information processing applications, but is also expected to impact other areas, where adiabatic rapid passage is used.
EMBA

On multi-core processors, contention on shared resources such as the last level cache (LLC) and memory bandwidth may cause serious performance degradation, which makes efficient resource allocation a critical issue in data centers. Intel recently introduces Memory Bandwidth Allocation (MBA) technology on its Xeon scalable processors, which makes it possible to allocate memory bandwidth in a real system. However, how to make the most of MBA to improve system performance remains an open question. In this work, (1) we formulate a quantitative relationship between a program's performance and its LLC occupancy and memory request rate on commodity processors. (2) Guided by the performance formula, we propose a heuristic bound-aware throttling algorithm to improve system performance and (3) we further develop a hierarchical clustering method to improve the algorithm's efficiency. (4) We implement these algorithms in EMBA, a low-overhead dynamic memory bandwidth scheduling system to improve performance on Intel commodity processors. The results show that, when multiple programs run simultaneously on a multi-core processor whose memory bandwidth is saturated, the programs with high memory bandwidth demand usually use bandwidth inefficiently compared with programs with medium memory bandwidth demand from the perspective of CPU performance. By slightly throttling the former's bandwidth, we can significantly improve the performance of the latter. On average, we improve system performance by 36.9% at the expense of 8.6% bandwidth utilization rate.
ReCoNet: Real-Time Coherent Video Style Transfer Network

Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively.
CaricatureShop: Personalized and Photorealistic Caricature Sketching

In this paper, we propose the first sketching system for interactively personalized and photorealistic face caricaturing. Input an image of a human face, the users can create caricature photos by manipulating its facial feature curves. Our system first performs exaggeration on the recovered 3D face model, which is conducted by assigning the laplacian of each vertex a scaling factor according to the edited sketches. The mapping between 2D sketches and the vertex-wise scaling field is constructed by a novel deep learning architecture. Our approach allows outputting different exaggerations when applying the same sketching on different input figures in term of their different geometric characteristics, which makes the generated results “personalized”. With the obtained 3D caricature model, two images are generated, one obtained by applying 2D warping guided by the underlying 3D mesh deformation and the other obtained by re-rendering the deformed 3D textured model. These two images are then seamlessly integrated to produce our final output. Due to the severe stretching of meshes, the rendered texture is of blurry appearances. A deep learning approach is exploited to infer the missing details for enhancing these blurry regions. Moreover, a relighting operation is invented to further improve the photorealism of the result. These further make our results “photorealistic”. The qualitative experiment results validated the efficiency of our sketching system.
Discovering Implied Serial Order Through Model-Free and Model-Based Learning

Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free (Q-learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.
Efficient Convolution Neural Networks for Object Tracking Using Separable Convolution and Filter Pruning

Object tracking based on deep learning is a hot topic in computer vision with many applications. Due to high computation and memory costs, it is difficult to deploy convolutional neural networks (CNNs) for object tracking on embedded systems with limited hardware resources. This paper uses the Siamese network to construct the backbone of our tracker. The convolution layers used to extract features often have the highest costs, so more improvements should be focused on them to make the tracking more efficient. In this paper, the standard convolution is optimized by the separable convolution, which mainly includes a depthwise convolution and a pointwise convolution. To further reduce the calculation, filters in the depthwise convolution layer are pruned with filters variance. As there are different weight distributions in convolution layers, the filter pruning is guided by a hyper-parameter designed. With the improvements, the number of parameters is decreased to 13% of the original network and the computation is reduced to 23%. On the NVIDIA Jetson TX2, the tracking speed increased to 3.65 times on the CPU and 2.08 times on the GPU, without significant degradation of tracking performance in VOT benchmark.
Probing Multilingual Sentence Representations With X-Probe

This paper extends the task of probing sentence representations for linguistic insight in a multilingual domain.In doing so, we make two contributions: first, we provide datasets for multilingual probing, derived from Wikipedia, in five languages, viz.English, French, German, Spanish and Russian.Second, we evaluate six sentence encoders for each language, each trained by mapping sentence representations to English sentence representations, using sentences in a parallel corpus.We discover that cross-lingually mapped representations are often better at retaining certain linguistic information than representations derived from English encoders trained on natural language inference (NLI) as a downstream task.
Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.
PRCash: Fast, Private and Regulated Transactions for Digital Currencies

Fiat currency implemented as a blockchain can enable multiple benefits such as reduced cost compared to expensive handling of cash and better transparency for increased public trust. However, such deployments have conflicting requirements including fast payments, strong user privacy and regulatory oversight. None of the existing blockchain transaction techniques supports all of these three requirements. In this paper we design a new blockchain currency, called PRCash, that addresses the above challenge. The primary technical contribution of our work is a novel regulation mechanism for transactions that use cryptographic commitments. We enable regulation of spending limits using zero-knowledge proofs. PRCash is the first blockchain currency that provides fast payments, good level of user privacy and regulatory control at the same time.
Electronic Module Design and Development: An Interactive Learning

The general objective of this research is to introduce a product in the form of interactive electronic modules that provide many features and flexibility in their use. The specific objectives are to design products and conduct feasibility tests involving media experts. This study uses the Borg & Gall development procedure. Following the model, several steps are adapted as the direction of development of the product to be generated, namely as follows: (1) needs analysis, (2) manufacturing of products, (3) validation, and (4) final products. The scope of this study is a lecturer who is an expert in aspects of learning media. Data obtained using a questionnaire and analyzed using quantitative descriptive analysis techniques. The results of this study generally produce a module design consisting of a cover, preface, table of contents, module position map on learning, glossary, learning introduction, learning, evaluation, and closing. The learning material has several features, such as animation, links, text, audio, images, videos, and quizzes. To complete and assist the learning process. As well as other supporting features to help facilitate navigation in the form of buttons namely: thumbnails, go to the page, zoom, bookmarks, sound controller, search, print, share by email, share by social media, back to home screen, auto flip, select text, about, and help. Other findings, according to media experts, indicate that this product is in the excellent category based on the results of the feasibility test, obtaining a percentage of 93.4%.
Generative adversarial networks with mixture of t-distributions noise for diverse image generation

Image generation is a long-standing problem in the machine learning and computer vision areas. In order to generate images with high diversity, we propose a novel model called generative adversarial networks with mixture of t-distributions noise (tGANs). In tGANs, the latent generative space is formulated using a mixture of t-distributions. Particularly, the parameters of the components in the mixture of t-distributions can be learned along with others in the model. To improve the diversity of the generated images in each class, each noise vector and a class codeword are concatenated as the input of the generator of tGANs. In addition, a classification loss is added to both the generator and the discriminator losses to strengthen their performances. We have conducted extensive experiments to compare tGANs with a state-of-the-art pixel by pixel image generation approach, pixelCNN, and related GAN-based models. The experimental results and statistical comparisons demonstrate that tGANs perform significantly better than pixleCNN and related GAN-based models for diverse image generation.
Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder

Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. In the discrete case, one can perform reparametrization using the Gumbel-Max trick, but the resulting objective relies on an $\arg \max$ operation and is non-differentiable. In contrast to previous works which resort to \emph{softmax}-based relaxations, we propose to optimize it directly by applying the \emph{direct loss minimization} approach. Our proposal extends naturally to structured discrete latent variable models when evaluating the $\arg \max$ operation is tractable. We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables.