Reviewerly/exHarmony huggingface dataset:
SafeMR: Privacy-aware Visual Information Protection for Mobile Mixed Reality

Mobile vision technologies have paved the way for augmented (AR) and mixed reality (MR) applications to be realizable on mobile devices. Mobile platforms such as Android and iOS have recently demonstrated the early opportunities for AR/MR applications using their devices. Now, while these technologies can still be considered in its infancy, it is opportune to start thinking about privacy and security while their functionalities are slowly being revealed to us. In this work, we present a visual access control mechanism in the form of object-level abstraction. Using readily-available object detection algorithms, we are able to demonstrate a proof-of-concept object-level abstraction for fine-grained access control in a mobile device. Furthermore, aside from the inherent confidentiality and content awareness guarantee of abstraction, reduction in execution times from visual processing resource sharing is another consequential benefit of abstraction without any energy consumption impact.
The Analysis of Application, Key Issues and the Future Development Trend of Blockchain Technology in the Insurance Industry

Blockchain, as an emerging technology, is essentially a shared database in which data or information is stored with features of "unforgeable", "whole-process traceable", "transparent" and "collective maintenance".Based on these characteristics, blockchain technology has a broad prospect of application in the insurance industry because it has laid a solid foundation of "trust", and created a reliable cooperation mechanism.At present, blockchain technology has been widely used in the fields of transportation, logistics, trade and charity.It has also attracted the attention of banking, insurance and other financial industries.The application of blockchain in other industries has been discussed in many other publications already, but it has not been systematically discussed in the insurance industry.This paper will make people have a deeper understanding of "blockchain + insurance" by introducing the basic concepts and technological advantages of blockchain technology, reviewing the researches and applications of blockchain by the insurance companies, and pointing out several key problems of applying blockchain technology to the insurance industry.Finally, the article will make predictions about the trend of development of applying blockchain technology in the insurance industry.
Evaluation of Five Classifiers for Depression Episodes Detection

Depression is a mental disorder manifested through a set of psychological and physical symptoms, such as the presence of sadness, apathy, hopelessness and irritability, among others.According to the World Health Organization (WHO), depression is affecting more than 300 million people worldwide, presenting a prevalence between 3 and 21%.One of the main problems of this high prevalence is the incorrect classification of patients, since many cases are false positive and false negative diagnoses.In this work it is proposed the study of the behavior of five different classification techniques, random forest (RF), conditional inference trees (cTree), K-nearest neighbor (K-NN), support vector machine (SVM) and Naïve Bayes, to identify depressive states through the motor activity of patients contained in the Depresjon dataset.The activity of this dataset is acquired through the smart watch "Actigraph", based on actigraphy.The evaluation of these classification techniques is finally performed in terms of sensitivity, specificity, the receiver operating characteristic (ROC) curve and area under the curve (AUC), to know their performance to automatically detect depressive patients.The results shown values of sensitivity, specificity and AUC, statistically significant, specially for the RF method, which presents sensitivity = 0.8148, specificity = 0.8158 and AUC = 0.8314.Therefore, it is concluded that these classifiers are able to distinguish patients with depression from controls, based on their motor activity, allowing the development of a non-invasive diagnosis tool to support specialists in the correct diagnosis of depression.
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning

Sample complexity bounds are a common performance metric in the Reinforcement Learning literature. In the discounted cost, infinite horizon setting, all of the known bounds have a factor that is a polynomial in $1/(1-γ)$, where $γ&lt; 1$ is the discount factor. For a large discount factor, these bounds seem to imply that a very large number of samples is required to achieve an $\varepsilon$-optimal policy. The objective of the present work is to introduce a new class of algorithms that have sample complexity uniformly bounded for all $γ&lt; 1$. One may argue that this is impossible, due to a recent min-max lower bound. The explanation is that this previous lower bound is for a specific problem, which we modify, without compromising the ultimate objective of obtaining an $\varepsilon$-optimal policy. Specifically, we show that the asymptotic covariance of the Q-learning algorithm with an optimized step-size sequence is a quadratic function of $1/(1-γ)$; an expected, and essentially known result. The new relative Q-learning algorithm proposed here is shown to have asymptotic covariance that is a quadratic in $1/(1- ρ^* γ)$, where $1 - ρ^* &gt; 0$ is an upper bound on the spectral gap of an optimal transition matrix.
A Kernel-Based Probabilistic Collaborative Representation for Face Recognition

A novel classifier for face recognition using an improved probabilistic collaborative representation named IPCR is proposed in this paper. The purpose of this paper is to improve the accuracy of face recognition. The testing sample is assumed to be linearly combined by a part of training samples in feature space. There is two-phase framework in IPCR. In the first phase, an adjusted parameter of the nearest neighbors of the samples is chosen for classification. In the second phase, a linear combination of the features and the sparse coefficients are used for new patterns. In the process of two-phase framework, the weight matrix is obtained according to the distance between all the training samples and each testing sample, and then it is applied to weight probabilistic collaborative representation coefficients. The kernel trick is implemented for the high-dimensional nonlinear information instead of linear information of data to improve the class separability. The second classifier named KPCR uses a kernel probabilistic collaborative representation for face recognition. Several renowned face databases, e.g., AR, GT, PIE, FERET, and LFW-crop are used for evaluating the performances of the proposed classifiers. The experimental results demonstrate that the proposed classifiers outperform the collaborative representation-based classification (CRC), the probabilistic collaborative representation-based classifier (ProCRC), and the other state-of-the-art classifiers in recognition accuracy.
SPOCC: Scalable POssibilistic Classifier Combination - toward robust aggregation of classifiers

We investigate a problem in which each member of a group of learners is trained separately to solve the same classification task. Each learner has access to a training dataset (possibly with overlap across learners) but each trained classifier can be evaluated on a validation dataset. We propose a new approach to aggregate the learner predictions in the possibility theory framework. For each classifier prediction, we build a possibility distribution assessing how likely the classifier prediction is correct using frequentist probabilities estimated on the validation set. The possibility distributions are aggregated using an adaptive t-norm that can accommodate dependency and poor accuracy of the classifier predictions. We prove that the proposed approach possesses a number of desirable classifier combination robustness properties. Moreover, the method is agnostic on the base learners, scales well in the number of aggregated classifiers and is incremental as a new classifier can be appended to the ensemble by building upon previously computed parameters and structures. A python implementation can be downloaded at this link https://github.com/john-klein/SPOCC.
Recognition of Baybayin Symbols (Ancient Pre-Colonial Philippine Writing System) using Image Processing

The goal of this paper is to accomplish an Optical Character Recognition (OCR) that gives an extremely contribution to the advancement of technology in terms of image recognition in Machine Learning.The researcher introduces the Feed-Forward Neural Network with Dropout Method (FFNNDM) and Convolutional Neural Network with Dropout Method (CNNDM) for the recognition of the Baybayin symbols.The phases of preprocessing of data also describe in this paper to be feed into the image recognition algorithms.The arrangement of FFNNDM is composed of one (1) dense input layer and then having a four dense (4) hidden layer and one (1) dense output layer, and the CNN structure is composed of three (3) convolutional layer, two (2) dense hidden layer and one (1) output layer.The result shows that FFNNDM is more accurate and gains an accuracy of 92.4%, loss of 0.25% and error rate of 7.55%, while the CNNDM had only an accuracy of 91.69%, loss of 0.31% and error rate of 8.31%.It also presents the confusion matrix of each algorithm to exhibit the true correct predictions of each Baybayin symbols.
Classical simulation of quantum circuits using fewer Gaussian eliminations

We show that the cost of strong simulation of quantum circuits using $t$ $T$ gate magic states exhibits non-trivial reductions on its upper bound for $t=1$, $t=2$, $t=3$, and $t=6$ with odd-prime-qudits. This agrees with previous numerical bounds found for qubits. We define simulation cost by the number of terms that require Gaussian elimination of a $t \times t$ matrix and so capture the cost of simulation methods that proceed by computing stabilizer inner products or evaluating quadratic Gauss sums. Prior numerical searchs for qubits were unable to converge beyond $t=7$. We effectively increase the space searched for these non-trivial reductions by $>10^{10^4}$ and extend the bounds to $t=14$ for qutrits. This is accomplished by using the Wigner-Weyl-Moyal formalism to algebraically find bounds instead of relying on numerics. We find a new reduction in the upper bound from the $12$-qutrit magic state of ${3^{\sim 0.469t}}$, which improves on the bound obtained from the $6$-qutrit magic state of ${3^{\sim 0.482t}}$.
EFFICIENT SECURITY AND PRIVACY MECHANISM FOR BLOCK CHAIN APPLICATION

The block chain has become one of the predominant term in the applications like financial, automobile, health care, risk management, and internet of things due to its reliability and more beneficial services. It executes every transaction by establishing trust in the open environment. The deployment of the block chain technology in the various applications remains as the recent research topic as the privacy and the security of the bock chain are undecided and unconvinced, so the paper tries to develop an efficient security and privacy mechanism for the block chain applications. The proposed method puts forth the biometric recognition in the block chain technology to improve the security and the privacy mechanism for the block chain application.
A Gated Few-shot Learning Model For Anomaly Detection

Anomaly detection, as one of the most important problems in the domain of network and service management, has been widely studied in statistics and machine learning. The supervised methods with plenty of labeled data have achieved great success in anomaly detection, but cannot integrate new anomaly types. In this paper, we propose a few-shot learning model for anomaly detection. Our model is trained with labeled data, and is then tested in terms of its ability to learn how to detect new types, given examples of the unseen classes. Due to a gap between the known anomaly data and unseen anomaly data, we designed a gated network structure to tackle the imbalanced data problem, to which we added a gate structure to aggregate known anomaly types and unknown types. We evaluated our proposed method based on the anomaly dataset NSL-KDD and our experimental results show that the proposed method achieved the state-of-the-art results in few-shot settings.
Stable Policy Optimization via Off-Policy Divergence Regularization

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are among the most successful policy gradient approaches in deep reinforcement learning (RL). While these methods achieve state-of-the-art performance across a wide range of challenging tasks, there is room for improvement in the stabilization of the policy learning and how the off-policy data are used. In this paper we revisit the theoretical foundations of these algorithms and propose a new algorithm which stabilizes the policy improvement through a proximity term that constrains the discounted state-action visitation distribution induced by consecutive policies to be close to one another. This proximity term, expressed in terms of the divergence between the visitation distributions, is learned in an off-policy and adversarial manner. We empirically show that our proposed method can have a beneficial effect on stability and improve final performance in benchmark high-dimensional control tasks.
Motion-Attentive Transition for Zero-Shot Video Object Segmentation

In this paper, we present a novel Motion-Attentive Transition Network (MATNet) for zero-shot video object segmentation, which provides a new way of leveraging motion information to reinforce spatio-temporal object representation. An asymmetric attention block, called Motion-Attentive Transition (MAT), is designed within a two-stream encoder, which transforms appearance features into motion-attentive representations at each convolutional stage. In this way, the encoder becomes deeply interleaved, allowing for closely hierarchical interactions between object motion and appearance. This is superior to the typical two-stream architecture, which treats motion and appearance separately in each stream and often suffers from overfitting to appearance information. Additionally, a bridge network is proposed to obtain a compact, discriminative and scale-sensitive representation for multi-level encoder features, which is further fed into a decoder to achieve segmentation results. Extensive experiments on three challenging public benchmarks (i.e. DAVIS-16, FBMS and Youtube-Objects) show that our model achieves compelling performance against the state-of-the-arts.
A low-cost, practical acquisition and rendering pipeline for real-time free-viewpoint video communication

Abstract We present a semiautomatic real-time pipeline for capturing and rendering free-viewpoint video using passive stereo matching. The pipeline is simple and achieves agreeable quality in real time on a system of commodity web cameras and a single desktop computer. We suggest an automatic algorithm to compute a constrained search space for an efficient and robust hierarchical stereo reconstruction algorithm. Due to our fast reconstruction times, we can eliminate the need for an expensive global surface reconstruction with a combination of high coverage and aggressive filtering. Finally, we employ a novel color weighting scheme that generates credible new viewpoints without noticeable seams, while keeping the computational complexity low. The simplicity and low cost of the system make it an accessible and more practical alternative for many applications compared to previous methods.
IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel

Steganography is conducive to communication security, but the abuse of steganography brings many potential dangers. And then, steganalysis plays an important role in preventing the abuse of steganography. Nowadays, steganalysis based on deep learning generally has a large number of parameters, and its pertinence to adaptive steganography algorithms is weak. In this article, we propose a lightweight convolutional neural network named IAS-CNN which targets to image adaptive steganalysis. To solve the limitation of manually designing residual extraction filters, we adopt the method of self-learning filter in the network. That is, a high-pass filter in spatial rich model is applied to initialize the weights of the first layer and then these weights are updated through the backpropagation of the network. In addition, the knowledge of selection channel is incorporated into IAS-CNN to enhance residuals in regions that have a high probability for steganography by inputting embedding probability maps into IAS-CNN. Also, IAS-CNN is designed as a lightweight network to reduce the consumption of resources and improve the speed of processing. Experimental results show that IAS-CNN performs well in steganalysis. IAS-CNN not only has similar performance with YedroudjNet in S-UNIWARD steganalysis but also has fewer parameters and convolutional computations.
The integration design of artificial intelligence and normal students’ Education

Abstract With the coming of the era of intelligence, the deep integration of artificial intelligence and normal education will become an important trend of education development. Normal students, as future teachers, are the key force to cultivate innovative talents in the era of intelligence. It is the current work and research focus to actively use artificial intelligence to improve the comprehensive quality of normal students. However, in the current rapid development of education informatization and intelligence, the development of normal education in China still faces four practical problems: the characteristics of normal students are not fully considered in teaching, and the quality of normal education needs to be improved; teachers’ level of education informatization and intelligence need to be strengthened; lack of comprehensive understanding of students’ personalized characteristics, and personalized teaching still needs efforts. Therefore, we try to build a deep integration framework of artificial intelligence and normal education, and explore the idea of using artificial intelligence technology to solve the four major problems of normal education development. Finally, combined with the design idea of the integration of artificial intelligence and normal education, this paper reflects on the problems that may be faced in the process of practice, and puts forward the corresponding solutions.
Generating Natural Language Descriptions From Tables

This paper proposes a neural generative architecture, namely NLDT, to generate a natural language short text describing a table which has formal structure and valuable information. Specifically, the architecture maps fields and values of a table to continuous vectors and then generates a natural language description by leveraging the semantics of a table. The NLDT architecture adopts a two-level neural model to make the most of the structure of a table to fully express the relationship between contents. To deal with the problem of out-of-vocabulary, this paper develops a simple and fast word-conversion method that replaces rare words appearing in texts with common field information in tables and directly replicates contents from table to the output sequence according to the field information. Besides, this paper adds the concept of theme to adapt the NLDT architecture to open domain and improves beam search algorithm to strengthen the results in the inference stage. On the WEATHERGOV dataset, the NLDT architecture improves the state-of-the-art BLEU-4 score from 61.01 to 62.89 and the current state-of-the-art F1 score from 73.21 to 78. On the WIKIBIO and WIKITABLE datasets, the NLDT architecture achieves a BLEU-4 score of 45.77 and 38.71 respectively which also outperform the state-of-the-art approaches. Furthermore, this paper introduces a Chinese dataset WIKIBIOCN including 33,244 biographies with corresponding tables. On the WIKIBIOCN dataset, the NLDT architecture achieves a BLEU-4 score of 38.87 and fairly good manual evaluation.
ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions

In this paper, we propose several ideas for enhancing a binary network to close its accuracy gap from real-valued networks without incurring any additional computational cost. We first construct a baseline network by modifying and binarizing a compact real-valued network with parameter-free shortcuts, bypassing all the intermediate convolutional layers including the downsampling layers. This baseline network strikes a good trade-off between accuracy and efficiency, achieving superior performance than most of existing binary networks at approximately half of the computational cost. Through extensive experiments and analysis, we observed that the performance of binary networks is sensitive to activation distribution variations. Based on this important observation, we propose to generalize the traditional Sign and PReLU functions, denoted as RSign and RPReLU for the respective generalized functions, to enable explicit learning of the distribution reshape and shift at near-zero extra cost. Lastly, we adopt a distributional loss to further enforce the binary network to learn similar output distributions as those of a real-valued network. We show that after incorporating all these ideas, the proposed ReActNet outperforms all the state-of-the-arts by a large margin. Specifically, it outperforms Real-to-Binary Net and MeliusNet29 by 4.0% and 3.6% respectively for the top-1 accuracy and also reduces the gap to its real-valued counterpart to within 3.0% top-1 accuracy on ImageNet dataset. Code and models are available at: https://github.com/liuzechun/ReActNet.
Independent Assessment of the Quality of Russian Higher Education: What is the Point and What Can Be Improved

An effective interaction between all stakeholders in the market of educational services is a necessary condition for improving the assessment of the quality of higher education, which, in turn, accentuates high relevance of studying the theory and practice of independent evaluation of higher education quality and quality assurance of educational programs. This is the main purpose of this publication, which is the result of the research in the field of independent evaluation and public accreditation of professional educational programs. During the examination of the projects on independent evaluation and professional and public accreditation of professional educational programs, the following methods were used: collection of empirical information, an analysis of official documents on quality assurance of education, generalization, interpretation. The article outlines a set of systemic problems hindering the process of formation of professional and public accreditation of professional educational programs launched in Russia and proposes the method of improving the independent evaluation and public accreditation of professional educational programs, which can be treated as a real leverage to improve the quality of domestic higher education.