Reviewerly/exHarmony huggingface dataset:
High-precision and flexible calibration method of non-overlapping array cameras

In this paper, a high-precision and flexible intrinsic and extrinsic parameters calibration method for non-overlapping array cameras is proposed. In the camera array system, each camera employs an individual planar calibration board to achieve intrinsic parameters calibration. Without assistance of any other coded or un-coded target, the extrinsic parameters of array cameras can be flexibly calibrated based on close-range photogrammetry. To overcome the disadvantages of low precision under a large field of view in close-range photogrammetry, a refinement coordinates mapping (RCM) method to achieve high-precision reconstruction of targets is developed. The principles of single-camera calibration, close-range photogrammetry, and the extrinsic calibration method are introduced, respectively. Experiments are performed to analyze the stability of single-camera calibration and the world coordinates reconstruction of targets. The results of the re-projection error in extrinsic parameters calibration show that the developed RCM method has high precision.
Lost in Conversation: A Conversational Agent Based on the Transformer and Transfer Learning

State of the art in dialogue and conversational models is currently being attained with generative approaches. In this work, we present our winning solution for the ConvAI2 Competition at NeurIPS 2018, created by the Lost in Conversation team. Our solution combines an encoder-decoder architecture based on a modified version of OpenAI GPT and a transfer learning approach to training, pretraining our model on a separate large dataset and then fine-tuning for the actual conversational datasets. Interestingly, our solution did not place first in automated evaluation with commonly used metrics, but then proceeded to win quite convincingly in human evaluation, which shows that automatic evaluation of conversational models still leaves a lot of room for further research.
Detection and Classification of Cervical Exfoliated Cells Based on Faster R-CNN

The detection and classification of cervical cells via Pap smear or Liquid-based cytology (LBC) have important clinical significance for pathological diagnosis. For the limitations of pathological images and the complexity of morphological changes of cells, the accurate classification of cervical cells remains a challenging task. DNA ploidy analysis can complete the classification and recognition of individual cells well, and misclassification is common because of the factors of inevitable imaging. Unlike traditional classification methods that rely on manual features, we proposed a region detection and classification method based on multi-semantic label combined with morphological information analysis. We followed the principle that a nucleus represents a cell and divided cells into five categories. The overlapping cell regions could be divided into multiple single cells. In this paper, the data pre-processing was made up for the imbalance in classification. The weight parameters obtained from ImageNet dataset pre-training were realized the transfer learning of cervical exfoliated cells based on Faster R-CNN on LBC dataset. The result showed that the mean average precision (mAP) was 66.98% and the accuracy was 91.61% by learning depth and superficial features of cervical exfoliated cells with ResNet-101 as the backbone. To some extent, it can assist pathologists in the early diagnosis of cervical cancer.
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators

Convolutional Neural Networks (CNNs) have emerged as highly successful tools for image generation, recovery, and restoration. This success is often attributed to large amounts of training data. On the contrary, a number of recent experimental results suggest that a major contributing factor to this success is that convolutional networks impose strong prior assumptions about natural images. A surprising experiment that highlights this structural bias towards simple, natural images is that one can remove various kinds of noise and corruptions from a corrupted natural image by simply fitting (via gradient descent) a randomly initialized, over-parameterized convolutional generator to this single image. While this over-parameterized model can eventually fit the corrupted image perfectly, surprisingly after a few iterations of gradient descent one obtains the uncorrupted image, without using any training data. This intriguing phenomena has enabled state-of-the-art CNN-based denoising as well as regularization in linear inverse problems such as compressive sensing. In this paper we take a step towards demystifying this experimental phenomena by attributing this effect to particular architectural choices of convolutional networks, namely fixed convolutional operations. We then formally characterize the dynamics of fitting a two layer convolutional generator to a noisy signal and prove that early-stopped gradient descent denoises/regularizes. This results relies on showing that convolutional generators fit the structured part of an image significantly faster than the corrupted portion.
Fusion identification and estimation of multisensor multichannel AR signals with missing measurements and sensor biases

This paper studies the problem of fusion identification and estimation for multisensor multichannel autoregressive (AR) signals with unknown missing measurement rates, model parameters (MPs), sensor biases and measurement noise variances. Optimal local filters, cross-covariance matrices (CCMs), and a distributed fusion filter for AR signal are presented when the system model is accurately known, respectively. A self-tuning (ST) fusion filter is presented when MPs of AR signal, sensor biases, missing measurement rates and measurement noise variances in the system model are unknown. First, MPs of AR signal and sensor biases are identified by using a multi-dimensional recursive extended least squares (MRELS) algorithm. Further, distributed fusion estimates of MPs of AR signal are obtained by using a matrix-weighted optimal fusion estimation algorithm in the sense of linear unbiased minimum variance (LUMV). Then, receiving measurement rates and measurement noise variances are identified by correlation functions. Finally, substituting the identified parameters into the proposed optimal filtering algorithms, a matrix-weighted self-tuning (ST) fusion filtering algorithm is obtained. An example verifies the effectiveness of algorithms.
Understanding the Limitations of Conditional Generative Models

Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate robust classification with likelihood-based generative models from a theoretical and practical perspective to investigate if they can deliver on their promises. Our analysis focuses on a spectrum of robustness properties: (1) Detection of worst-case outliers in the form of adversarial examples; (2) Detection of average-case outliers in the form of ambiguous inputs and (3) Detection of incorrectly labeled in-distribution inputs. Our theoretical result reveals that it is impossible to guarantee detectability of adversarially-perturbed inputs even for near-optimal generative classifiers. Experimentally, we find that while we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. We relate this failure to various undesirable model properties that can be traced to the maximum likelihood training objective. Despite being a common choice in the literature, our results indicate that likelihood-based conditional generative models may are surprisingly ineffective for robust classification.
A 3D Wide Residual Network with Perceptual Loss for Brain MRI Image Denoising

Image denoising is an essential pre-processing step in medical image analysis. It brings about an improvement in the accuracy of disease diagnosis (done by analysis of medical images), and helps to determine the correct course of treatment plan. Over the past few years, Convolutional Neural Networks (CNNs) have been proven to be extremely effective in image processing and computer vision applications. In this work we explore efficient wide residual CNNs for denoising human brain magnetic resonance images. Many state-of-the-art CNN based image denoising methods use squared Euclidean distance for training the neural network. This causes deep networks to produce over-smoothed image outputs, losing structural or anatomical details. Also, the details of structures (found in images) are often lost in the deep hierarchy of convolution layers. Unlike such existing methods, in this paper, we use perceptual loss alongside squared Euclidean distance for training our network. This helps restoring images that are visually desirable, in the sense that they restore considerably more anatomically refined features, and are not just similar to the ground-truth pixel intensity. Our experiments show that the proposed convolutional network surpasses the state-of-the-art methods of rician MRI denoising, and obtains better quality denoised brain MR images as compared to the state-of-the-art.
Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics

Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study.
Analyzing and clustering students' application preferences in higher education

We present a framework based on a higher education application preference list that allows a different type of flexible aggregation and, hence, the analysis and clustering of application data. Preference lists are converted into scores. The proposed approach is demonstrated in the context of higher education applications in Hungary over the period of 2006-2015. Our method reveals that efforts to leverage center-periphery differences do not fulfill expectations. Furthermore, the student's top preference is very hard to influence, and recruiters may build their strategy on information about the first and second choices.
Optical asymmetric cryptosystem for multi-image in cyan–magenta–yellow–black color space

In this paper, we propose an optical single-channel asymmetric cryptosystem for multi-image in cyan–magenta–yellow–black (CMYK) color space. To the best of our knowledge, this is the first time that multiple images in CMYK color space have been directly encrypted. The proposed optical asymmetric cryptosystem is based on the quick response (QR) encoding process and the designed Fresnel-linear canonical-fractional Fourier transform (FLFT) encryption process. Each FLFT encryption process consists of phase-truncated FLFT and random amplitude phase masks. The proposed cryptosystem without color space conversion can improve the quality of the decrypted images and avoid the loss of information. In addition, by utilizing the QR codes, the cross talk and quality-loss problems can be reduced efficiently. Numerical simulation results demonstrate that the proposed cryptosystem possesses high robustness against various types of attacks, high security for encrypting multiple color images, and fast encryption efficiency. Furthermore, the proposed cryptosystem outperforms the other relevant cryptosystems and can be extended to encrypt multiple color images in a straightforward way.
Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia

The text summarizer reduces unnecessary information by selecting the important sentences. In multi-document summarization, there is a possibility that two or more important sentences share similar information. Including those sentences to the summary result will cause redundant information. This research aims to reduce similar sentences from multi-document that share similar information to obtain a more concise text summary. In order to accomplish the objective, this research uses the combination of several online news articles, divided into six groups. The combined articles are pre-processed to produce a clean text. After obtaining the clean text, this research utilizes the TextRank algorithm to extract the important sentences by using the similarity measurement. This process yields the summarized text. However, the summarized text is still containing similar sentences. The next process is calculating Maximal Marginal Relevance (MMR) to reduce similar sentences. The result of this process is the final text summary. The evaluation uses ROUGE-1 and ROUGE-2 with the average F-score is 0.5103 and 0.4257, respectively.
Materials Data Platform - a FAIR System for Data-Driven Materials Science

We have identified five important approaches as requirements for a FAIR (findable, accessible, interoperable, reusable) materials data platform. To realize these, we are designing a data platform that consists of about ten interconnected subsystems. We aim to incorporate next generation repository (NGR) concepts into our platform; the subsystems share features such as persistent identifier (PID) based resource management, a common metadata model, and vocabulary control. Another vital requirement is that all datasets must be machine-readable and translatable. These features are highly important in developing a platform as a research pipeline capable of handling various materials data - from data creation and efficient collection, to advanced data usage, secure storage, and open publishing.
Fast and accurate two-frame phase demodulation algorithm with statistical averaging

This paper introduces a fast, compact phase demodulation method for two-frame random phase-shifting interferometry. A set of sine and cosine components is obtained with the addition and subtraction of two pre-filtered fringe patterns. Then, using the properties of the trigonometric function, the amount of phase shift is obtained by statistically averaging all the pixels. Finally, the phase distribution of the interferogram is obtained by the arctangent operation. The proposed algorithm has a very low computation burden and does not require iteration, optimization, and orthogonalization while ensuring measurement accuracy; as a result, the consumed time is very short. Simulation and experimental results are presented to verify the accuracy and efficiency of the proposed method.
Slope One Recommendation Algorithm Based on User Clustering and Scoring Preferences

The Slope One algorithm in the recommendation system has the characteristics of real time efficiency, convenient operation, but at the same time there are not considering between project and the similarity between user’s question. In order to improve such problems and improve the accuracy of the algorithm, this paper proposes a Slope One recommendation algorithm that integrates user clustering and scoring preference. First, the similarity among users is measured, and the improved K-means++ algorithm is used to divide users into several categories according to the similarity degree of project preferences. Then, in the category of target users, the Slope One algorithm that integrates users’ rating preferences is used to predict the score of projects. And finally top-n recommendation is made according to the predicted score. In this paper, using the Movielens dataset to experiment, the results show that the proposed algorithm can effectively reduce the mean absolute error and root mean square error of traditional algorithm, recommend have higher accuracy and better recommendation quality.
Defending Against Machine Learning Based Inference Attacks via Adversarial Examples: Opportunities and Challenges

As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various domains. In such an ML-equipped inference attack, an attacker has access to some data (called public data) of an individual, a software, or a system; and the attacker uses an ML classifier to automatically infer their private data. Inference attacks pose severe privacy and security threats to individuals and systems. Inference attacks are successful because private data are statistically correlated with public data, and ML classifiers can capture such statistical correlations. In this chapter, we discuss the opportunities and challenges of defending against ML-equipped inference attacks via adversarial examples. Our key observation is that attackers rely on ML classifiers in inference attacks. The adversarial machine learning community has demonstrated that ML classifiers have various vulnerabilities. Therefore, we can turn the vulnerabilities of ML into defenses against inference attacks. For example, ML classifiers are vulnerable to adversarial examples, which add carefully crafted noise to normal examples such that an ML classifier makes predictions for the examples as we desire. To defend against inference attacks, we can add carefully crafted noise into the public data to turn them into adversarial examples, such that attackers’ classifiers make incorrect predictions for the private data. However, existing methods to construct adversarial examples are insufficient because they did not consider the unique challenges and requirements for the crafted noise at defending against inference attacks. In this chapter, we take defending against inference attacks in online social networks as an example to illustrate the opportunities and challenges.
MSBA: Multiple Scales, Branches and Attention Network With Bag of Tricks for Person Re-Identification

Person re-identification (Re-ID) has become a hot topic in both
DUKMSVM: A Framework of Deep Uniform Kernel Mapping Support Vector Machine for Short Text Classification

This paper mainly deals with the problem of short text classification. There are two main contributions. Firstly, we introduce a framework of deep uniform kernel mapping support vector machine (DUKMSVM). The significant merit of this framework is that by expressing the kernel mapping function explicitly with a deep neural network, it is in essence an explicit kernel mapping instead of the traditional kernel function, and it allows better flexibility in dealing with various applications by applying different neural network structures. Secondly, to validate the effectiveness of this framework and to improve the performance of short text classification, we explicitly express the kernel mapping using bidirectional recurrent neural network (BRNN), and propose a deep bidirectional recurrent kernel mapping support vector machine (DRKMSVM) for short text classification. Experimental results on five public short text classification datasets indicate that in terms of classification accuracy, precision, recall rate and F1-score, the DRKMSVM achieves the best performance with the average values of accuracy, precision, recall rate, and F1-score of 87.23%, 86.99%, 86.13% and 86.51% respectively compared to traditional SVM, convolutional neural network (CNN), Naive Bayes (NB), and Deep Neural Mapping Support Vector Machine (DNMSVM) which applies multi-layer perceptron for kernel mapping.
A unified approach for inference on algorithm-agnostic variable importance

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response -- in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.
A Personalized English Teaching Design Based on Multimedia Computer Technology

In the Internet age, the proliferation of multimedia computer technology (MCT) brings unprecedented opportunities to English teaching in colleges. This paper firstly carries out a questionnaire survey on the current status of personalized teaching of college English, and summed up the main problems with the current teaching mode. On this basis, the authors developed an MCT-based personalized English teaching design, with the aid of information technology (IT) and online teaching platform. To verify its effect, the proposed design was tested through contrastive experiment. The results show that the MCT-based personalized English teaching design could improve the interests and performance of English learning, and cultivate a good learning habit. In summary, this paper explores the personalized teaching of college English from both theoretical and practical angles, laying a good basis for high-quality English teaching and personalized development of students.
Robust Attribute and Structure Preserving Graph Embedding

Graph embedding methods are useful for a wide range of graph analysis tasks including link prediction and node classification. Most graph embedding methods learn only the topological structure of graphs. Nevertheless, it has been shown that the incorporation of node attributes is beneficial in improving the expressive power of node embeddings. However, real-world graphs are often noisy in terms of structure and/or attributes (missing and/or erroneous edges/attributes). Most existing graph embedding methods are susceptible to this noise, as they do not consider uncertainty during the modelling process. In this paper, we introduce RASE, a Robust Attribute and Structure preserving graph Embedding model. RASE is a novel graph representation learning model which effectively preserves both graph structure and node attributes through a unified loss function. To be robust, RASE uses a denoising attribute auto-encoder to deal with node attribute noise, and models uncertainty in the embedding space as Gaussians to cope with graph structure noise. We evaluate the performance of RASE through an extensive experimental study on various real-world datasets. Results demonstrate that RASE outperforms state-of-the-art embedding methods on multiple graph analysis tasks and is robust to both structure and attribute noise.