Reviewerly/exHarmony huggingface dataset:
Fractionally delayed Kalman filter

The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: 1) the delay is an integer multiple of the sampling interval, and 2) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay. The proposed algorithm fixes the maximum delay ( problem specific ) , which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.
BLIM’s identifiability and parameter invariance under backward and forward transformations

The basic local independence model (BLIM) is one of the most widely applied probabilistic models in knowledge space theory. It is known that the BLIM is not identifiable in general and that its identifiability strictly depends on the properties of the knowledge structure to which it is applied. If the knowledge structure is either forward- or backward-graded in one or more items, then the BLIM is not identifiable. In such cases, there exist continuous transformations of the model’s parameters, named forward and backward transformations, that keep constant the value of the model’s prediction function. Under certain constraints on the model’s parameters, some of the transformations might lose this property. The type of constraints considered in this article consist of fixing the probability of a knowledge state to a constant value. The theoretical results contained in the article shed light on the role of the different state probabilities in reducing the collection of transformations and thus restoring the identifiability of the model’s parameters.
Bipolar morphological neural networks: convolution without multiplication

In the paper we introduce a novel bipolar morphological neuron and bipolar morphological layer models. The models use only such operations as addition, subtraction and maximum inside the neuron and exponent and logarithm as activation functions for the layer. The proposed models unlike previously introduced morphological neural networks approximate the classical computations and show better recognition results. We also propose layer-by-layer approach to train the bipolar morphological networks, which can be further developed to an incremental approach for separate neurons to get higher accuracy. Both these approaches do not require special training algorithms and can use a variety of gradient descent methods. To demonstrate efficiency of the proposed model we consider classical convolutional neural networks and convert the pre-trained convolutional layers to the bipolar morphological layers. Seeing that the experiments on recognition of MNIST and MRZ symbols show only moderate decrease of accuracy after conversion and training, bipolar neuron model can provide faster inference and be very useful in mobile and embedded systems.
A regularization interpretation of the proximal point method for weakly convex functions

<p style='text-indent:20px;'>Empirical evidence and theoretical results suggest that the proximal point method can be computed approximately and still converge faster than the corresponding gradient descent method, in both the stochastic and exact gradient case. In this article we provide a perspective on this result by interpreting the method as gradient descent on a regularized function. This perspective applies in the case of weakly convex functions where proofs of the faster rates are not available. Using this analysis we find the optimal value of the regularization parameter in terms of the weak convexity.
Traceable Inner Product Functional Encryption

Functional Encryption (FE) has been widely studied in the last decade, as it provides a very useful tool for restricted access to sensitive data: from a ciphertext, it allows specific users to learn a function of the underlying plaintext. In practice, many users may be interested in the same function on the data, say the mean value of the inputs, for example. The conventional definition of FE associates each function to a secret decryption functional key and therefore all the users get the same secret key for the same function. This induces an important problem: if one of these users (called a traitor) leaks or sells the decryption functional key to be included in a pirate decryption tool, then there is no way to trace back its identity. Our objective is to solve this issue by introducing a new primitive, called Traceable Functional Encryption: the functional decryption key will not only be specific to a function, but to a user too, in such a way that if some users collude to produce a pirate decoder that successfully evaluates a function on the plaintext, from the ciphertext only, one can trace back at least one of them. We propose a concrete solution for Inner Product Functional Encryption (IPFE). We first remark that the ElGamal-based IPFE from Abdalla et al. in PKC ’15 shares many similarities with the Boneh-Franklin traitor tracing from CRYPTO ’99. Then, we can combine these two schemes in a very efficient way, with the help of pairings, to obtain a Traceable IPFE with black-box confirmation.
Non‐linear calibration optimisation based on the Levenberg–Marquardt algorithm

An outstanding calibration algorithm is the most important factor that affects the precision of attitude measurement. This study proposes a non-linear optimisation algorithm to refine the solutions of the initial guess obtained using the Zhang's technique, the Bouget's technique, or the Hartley's algorithm. Large sets of point correspondences were adopted to test the validity of the proposed method. Extensive practical experiments demonstrated that the proposed method can significantly improve the accuracy of calibration and ultimately obtains higher measurement precision. The error of the reprojection in the proposed method was <0.13 px. At a range of 1 m, the error rate was 0.5% for the length test and about 3% for the angle test. This study proposes a new method to calibrate the relationship between laser radar and the camera. Binocular vision was used to reconstruct the point cloud of the non-cooperative target. At the same time, data was also obtained using laser radar. Finally, the two groups of systems were fused. Accurate and dense three-dimensional information of the target was obtained. It could not only obtain the dense pose information of the target surface but also the texture and colour feature information of the target surface.
Framelet pooling aided deep learning network: the method to process high dimensional medical data

Abstract Machine learning-based analysis of medical images often faces several hurdles, such as the lack of training data, the curse of the dimensionality problem, and generalization issues. One of the main difficulties is that there exists a computational cost problem in dealing with input data of large size matrices which represent medical images. The purpose of this paper is to introduce a framelet-pooling aided deep learning method for mitigating computational bundles caused by large dimensionality. By transforming high dimensional data into low dimensional components by filter banks and preserving detailed information, the proposed method aims to reduce the complexity of the neural network and computational costs significantly during the learning process. Various experiments show that our method is comparable to the standard unreduced learning method, while reducing computational burdens by decomposing large-sized learning tasks into several small-scale learning tasks.
Mixed spatial pyramid pooling for semantic segmentation

Semantic segmentation is a challenging task as each pixel should be labeled accurately in the image. To improve the performance of semantic segmentation, some Fully Convolutional Network (FCN) based semantic segmentation methods adopt a spatial pyramid pooling structure to enrich contextual information. Others employ an encoder–decoder architecture to recover object details gradually. In this paper, we propose a semantic segmentation framework which combines the benefits of these approaches. Specifically, we propose a Mixed Spatial Pyramid Pooling (MSPP) module based on region-based average pooling and dilated convolution to obtain dense multi-level contextual priors. To further refine the details of objects more effectively, we also propose a Global-Attention Fusion (GAF) module to provide global context as guidance for low-level features. Our proposed method achieves mIoU of 84.1% on PASCAL VOC 2012 dataset and 80.4% on Cityscapes dataset without using any post-processing or additional datasets for pretrained model.
Perceived information pollution: conceptualization, measurement, and nomological validity

Purpose Currently, employees are facing information explosion in the presence of disruptive information and communication technologies of industry 4.0. With the prevalent nature of information pollution, employees are suffering to process large volume of information in order to access quality information. The objective of present study is to develop a measurement scale of perceived information pollution in the context of workplace. Furthermore, this study aims to assess the nomological validity of the proposed construct. Design/methodology/approach This study has employed a sequential exploratory mixed-method design to develop and validate the measurement scale of perceived information pollution. The population of the present study comprised of the employees who work in the operations and credit department of banking sector. The present study has used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to analyze data in AMOS. Findings The present study has developed the second-order measurement scale of perceived information pollution. The perceived information pollution comprises of five dimensions – accessible, intrinsic, contextual, representational, and distractive information pollution. This study has also confirmed the nomological validity of the information pollution in relation to employee's job satisfaction, work effort, and learning effort. Research limitations/implications Management may employ the five dimensions as a benchmark in revealing polluted information as well as enhancing information quality through information processing. Originality/value This study has contributed to the literature of information management by providing a five-dimensional scale of perceived information pollution and confirming its nomological validity.
Toward virtual design and optimization of a structural test monitored by a multi-view system

In order to characterize the mechanical behavior of a reinforced concrete beam–column joint, an experiment is designed whereby different histories of increasing cyclic loads are applied, and the structure deformation (as well as the morphology of apparent surface cracks) is studied using multi-view correlation. The complex three-dimensional geometry and the slender nature of the beam and column call for numerous digital cameras were positioned around the specimen. Assessing the feasibility of image acquisition and estimating a priori uncertainties on multi-view correlation became a true challenge. It is shown that the recourse to photo-realistic rendering software provides the needed tools to perform this experiment design optimization.
Tight Time-Space Lower Bounds for Finding Multiple Collision Pairs and Their Applications

We consider a collision search problem (CSP), where given a parameter C, the goal is to find C collision pairs in a random function $$f:[N] \rightarrow [N]$$ (where $$[N] = \{0,1,\ldots ,N-1\})$$ using S bits of memory. Algorithms for CSP have numerous cryptanalytic applications such as space-efficient attacks on double and triple encryption. The best known algorithm for CSP is parallel collision search (PCS) published by van Oorschot and Wiener, which achieves the time-space tradeoff $$T^2 \cdot S = \tilde{O}(C^2 \cdot N)$$. In this paper, we prove that any algorithm for CSP satisfies $$T^2 \cdot S = \tilde{\varOmega }(C^2 \cdot N)$$, hence the best known time-space tradeoff is optimal (up to poly-logarithmic factors in N). On the other hand, we give strong evidence that proving similar unconditional time-space tradeoff lower bounds on CSP applications (such as breaking double and triple encryption) may be very difficult, and would imply a breakthrough in complexity theory. Hence, we propose a new restricted model of computation and prove that under this model, the best known time-space tradeoff attack on double encryption is optimal.
A Variable Batch Size Strategy for Large Scale Distributed DNN Training

Large batch distributed synchronous stochastic gradient descent (SGD) has been widely used to train deep neural networks on a distributed memory system with multi-nodes, which can leverage parallel resources to reduce the number of iterative steps and speed up the convergence of training process. However, the large-batch SGD leads to a poor test accuracy, which would counteract the benefits of large scale parallel SGD. Existing solutions for large-batch training either significantly degrade accuracy or require massive additional hyper-parameter tuning. To overcome the difficulty above, we propose a novel variable batchsize strategy. With an in-depth analysis of the different stages in the recent multi-step schedule, we find that the training process at the first stage is sensitive to the batchsize, while different batchsize do not significantly impact the latter stages. Based on this discovery, we first claim that different stages of training should use different batchsize. Hence, the variable batchsize strategy is proposed for the large scale distributed training. Furthermore, in order to turn existing hyper-parameters automatically, an auto-tuning engine is designed for the variable batchsize strategy to achieve higher testing accuracy in the extremely large batchsize cases. By using our strategy, we successfully scale the batchsize to 120K in latter stages on ImageNet-1K with ResNet50 without accuracy loss and 128K with slight accuracy loss. The experimental evaluation on 2048 GPUs shows that the variable batchsize strategy with our auto-tuning engine could complete the training of ResNet-50 in 25 minutes. Furthermore, the new strategy successfully decreases the number of parameter updates by about 1.7 times compared with Facebook's multi-step schedule.
Deep Learning-Based Denoising of Mammographic Images Using Physics-Driven Data Augmentation

Mammography is using low-energy X-rays to screen the human breast and is utilized by radiologists to detect breast cancer. Typically radiologists require a mammogram with impeccable image quality for an accurate diagnosis. In this study, we propose a deep learning method based on Convolutional Neural Networks (CNNs) for mammogram denoising to improve the image quality. We first enhance the noise level and employ Anscombe Transformation (AT) to transform Poisson noise to white Gaussian noise. With this data augmentation, a deep residual network is trained to learn the noise map of the noisy images. We show, that the proposed method can remove not only simulated but also real noise. Furthermore, we also compare our results with state-of-the-art denoising methods, such as BM3D and DNCNN. In an early investigation, we achieved qualitatively better mammogram denoising results.
GAN-TStega: Text Steganography Based on Generative Adversarial Networks

Steganography based on text auto-generation technology is a current topic with great promise and challenges. It has the advantages of large information hiding capacity compared with the modification-based text steganographic methods. The biggest challenge faced by previous methods is that they can hardly generate fluent steganographic texts, and only pay attention to the statistical distribution of individual sentences without considering the overall statistical distribution of all generated texts. This paper proposes a text steganography called GAN-TStega which based on generative adversarial networks (GANs). Firstly, we use strategy update algorithm to solve the problem that traditional GANs are difficult to generate discrete data. Through antagonistic training on different types of text datasets, GAN-TStega can generate high quality texts. Then, by encoding the conditional probability distribution of generator’s output at each iteration, GAN-TStega can achieve secret information hiding. Through this method, we achieve the statistical distribution fitting at the sentence level, thus enhancing the security of steganography system. Experiments show that our method has good performance.
Toward scalable blockchains with transaction aggregation

Blockchains (BCs) are back-linked chain of records termed as blocks. To establish decentralized trusted systems, BCs employ consensus mechanisms. During the past ten years, there have been various proposals of BC design and implementations. However, most of the developed sate of the art BCs suffer from scalability issues. In order to enhance the scalability of the BCs, this paper proposes a transaction aggregation mechanism on a Proof-of-Stake (PoS)-based BC. Having developed the transaction aggregation and double linked blocks, efficient prevention and control of the BC's size growth is observed in the evaluated scenarios.
Natural-Logarithm-Rectified Activation Function in Convolutional Neural Networks

Activation functions playa key role in providing remarkable performance in deep neural networks, and the rectified linear unit (ReLU) is one of the most widely used activation functions. Various new activation functions and improvements on ReLU have been proposed, but each carry performance drawbacks. In this paper, we propose an improved activation function, which we name the naturalIngarithnι-rectified linear unit (NLReLU). NLReLU uses the parametric natural logarithmic transform to improve ReLU, and is defined as f(x) = In(β max(0,x) + 1.0). NLReLU not only retains the sparse activation characteristic of ReLU, but it also alleviates the 'd'ing ReLU" and vanishing gradient problems to some extent. It also reduces the bias shift effect and heteroscedasticity of neuron data distributions among network layers in order to accelerate the learning process. The proposed method was verified across ten convolutional neural networks with different depths for two essential datasets. Experiments illustrate that convolutional neural networks with NLReLU exhibit higher accuracy than those with ReLU, and that NLReLU is comparable to other well-known activation functions. NLReLU provides 0.16% and 2.04% higher classification accuracy on average compared to ReLU when used in shallow convolutional neural networks with the MNIST and CIFAR-10 datasets, respectively. The average accuracy of deep convolutional neural networks with NLReLU is 1.35% higher oon average with the CIFAR-10 dataset.
An Emotion Expression Extraction Method for Chinese Microblog Sentences

With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method.
Neural Puppet: Generative Layered Cartoon Characters

We propose a learning based method for generating new animations of a cartoon character given a few example images. Our method is designed to learn from a traditionally animated sequence, where each frame is drawn by an artist, and thus the input images lack any common structure, correspondences, or labels. We express pose changes as a deformation of a layered 2.5D template mesh, and devise a novel architecture that learns to predict mesh deformations matching the template to a target image. This enables us to extract a common low-dimensional structure from a diverse set of character poses. We combine recent advances in differentiable rendering as well as mesh-aware models to successfully align common template even if only a few character images are available during training. In addition to coarse poses, character appearance also varies due to shading, out-of-plane motions, and artistic effects. We capture these subtle changes by applying an image translation network to refine the mesh rendering, providing an end-to-end model to generate new animations of a character with high visual quality. We demonstrate that our generative model can be used to synthesize in-between frames and to create data-driven deformation. Our template fitting procedure outperforms state-of-the-art generic techniques for detecting image correspondences.