Reviewerly/exHarmony huggingface dataset:
Collaborative Representation Using Non-Negative Samples for Image Classification

Collaborative representation based classification (CRC) is an efficient classifier in image classification. By using l 2 regularization, the collaborative representation based classifier holds competitive performances compared with the sparse representation based classifier using less computational time. However, each of the elements calculated from the training samples are utilized for representation without selection, which can lead to poor performances in some classification tasks. To resolve this issue, in this paper, we propose a novel collaborative representation by directly using non-negative representations to represent a test sample collaboratively, termed Non-negative Collaborative Representation-based Classifier (NCRC). To collect all non-negative collaborative representations, we introduce a Rectified Linear Unit (ReLU) function to perform filtering on the coefficients obtained by l 2 minimization according to CRC’s objective function. Next, we represent the test sample by using a linear combination of these representations. Lastly, the nearest subspace classifier is used to perform classification on the test samples. The experiments performed on four different databases including face and palmprint showed the promising results of the proposed method. Accuracy comparisons with other state-of-art sparse representation-based classifiers demonstrated the effectiveness of NCRC at image classification. In addition, the proposed NCRC consumes less computational time, further illustrating the efficiency of NCRC.
Perturbation Analysis of the 8-Point Algorithm: A Case Study for Wide FoV Cameras

This paper presents a perturbation analysis for the estimate of epipolar matrices using the 8-Point Algorithm (8-PA). Our approach explores existing bounds for singular subspaces and relates them to the 8-PA, without assuming any kind of error distribution for the matched features. In particular, if we use unit vectors as homogeneous image coordinates, we show that having a wide spatial distribution of matched features in both views tends to generate lower error bounds for the epipolar matrix error. Our experimental validation indicates that the bounds and the effective errors tend to decrease as the camera Field of View (FoV) increases, and that using the 8-PA for spherical images (that present 360°x180° FoV) leads to accurate essential matrices. As an additional contribution, we present bounds for the direction of the translation vector extracted from the essential matrix based on singular subspace analysis.
Database and Distributed Computing Foundations of Blockchains

The uprise of Bitcoin and other peer-to-peer cryptocurrencies has opened many interesting and challenging problems in cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains in both permissionless and permissioned settings. The focus of the tutorial is on the distributed systems and database aspects of the recent innovations in blockchains.
An adaptive boosting procedure for low-rank based image denoising

Low-rank methods have shown great potential in many low-level vision tasks, especially in image denoising. Nevertheless, state-of-the-art algorithms still have limitations in some aspects, such as a heavy reliance on empirical values when setting the denoising parameters and the lack of consideration of the noise effect on the selection of similar patches. To address these problems, an adaptive boosting technique for improving the low-rank based image denoising results is proposed. In particular, different from adding the residual back to the image, we consider strengthening the signal by leveraging the availability of the previous denoised image and combine this boosting technique with dynamic parameters applied to the weighted nuclear norm minimization (WNNM) method, whose feasibility is derived and proved theoretically. In each iteration of the boosting scheme, the dynamic boosting parameters are motivated by an optimal solution analysis. In addition, in the process of image denoising, an adaptive patch search scheme is put forward to gain an effective index of similar patches. Furthermore, to adaptively determine the optimal number of iterations, a stopping criterion with the correlation coefficient is applied. The experimental results demonstrate that the proposed approach can preserve more detail information while removing noise and can outperform various state-of-the-art denoising algorithms in terms of both PSNR and SSIM.
APT Attack Situation Assessment Model Based on optimized BP Neural Network

In this paper, it first analyzed the characteristics of Advanced Persistent Threat (APT). according to APT attack, this paper established an BP neural network optimized by improved adaptive genetic algorithm to predict the security risk of nodes in the network. and calculated the path of APT attacks with the maximum possible attack. Finally, experiments verify the effectiveness and correctness of the algorithm by simulating attacks. Experiments show that this model can effectively evaluate the security situation in the network, For the defenders to adopt effective measures defend against APT attacks, thus improving the security of the network.
An adaptive nonmonotone active set – weight constrained – neural network training algorithm

In this work, a new direction for improving the classification accuracy of artificial neural networks is proposed by bounding the weights of the network, during the training process. Furthermore, a new adaptive nonmonotone active set – weight constrained – neural network training algorithm is proposed in order to demonstrate the efficacy and efficiency of our approach. The proposed training algorithm consists of two phases: a gradient projection phase which utilizes an adaptive nonmonotone line search and an unconstrained optimization phase which exploits the box structure of the bounds. Also, a set of switching criteria is defined for efficiently switching between the two phases. Our preliminary numerical experiments illustrate that the classification efficiency of the proposed algorithm outperforms classical neural network training algorithms, providing empirical evidence that it provides more stable, efficient and reliable learning.
Non-Markovianity, information backflow, and system-environment correlation for open-quantum-system processes

A Markovian process of a system is defined classically as a process in which the future state of the system is fully determined by only its present state, not by its previous history. There have been several measures of non-Markovianity to quantify the degrees of non-Markovian effect in a process of an open quantum system based on information backflow from the environment to the system. However, the condition for the witness of the system information backflow does not coincide with the classical definition of a Markovian process. Recently, a new measure with a condition that coincides with the classical definition in the relevant limit has been proposed. Here, we focus on the new definition (measure) for quantum non-Markovian processes, and characterize the Markovian condition as a quantum process that has no information backflow through the reduced environment state (IBTRES) and no system-environment correlation effect (SECE). The action of IBTRES produces non-Markovian effects by flowing the information of quantum operations performed by an experimenter at earlier times back to the system through the environment, while the SECE can produce non-Markovian effect without carrying any earlier quantum operation information. We give the necessary and sufficient conditions for no IBTRES and no SECE, respectively, and show that a process is Markovian if and only if it has no IBTRES and no SECE. The quantitative measures and algorithms for calculating non-Markovianity, IBTRES, and solely SECE are explicitly presented.
Subversion-Resistant Simulation (Knowledge) Sound NIZKs

In ASIACRYPT 2016, Bellare, Fuchsbauer and Scafuro studied security of non-interactive zero-knowledge (NIZK) arguments in the face of parameter subversion. They showed that achieving subversion soundness (soundness without trusting to the third party) and standard zero-knowledge is impossible at the same time. On the positive side, in the best case, they showed that one can achieve subversion zero-knowledge (zero-knowledge without trusting to the third party) and soundness at the same time. In this paper, we show that one can amplify their best positive result and construct NIZK arguments that can achieve subversion zero-knowledge and simulation (knowledge) soundness at the same time. Simulation (knowledge) soundness is a stronger notion in comparison with (knowledge) soundness, as it also guarantees non-malleability of proofs. Such stronger security guarantee is a must in practical systems. To prove the result, we show that given a NIZK argument that achieves Sub-ZK and (knowledge) soundness, one can use an OR-based construction to define a new language and build a NIZK argument that will guarantee Sub-ZK and simulation (knowledge) soundness at the same time. We instantiate the construction with the state-of-the-art zk-SNARK proposed by Groth [Eurocrypt 2016] and obtain an efficient SNARK that guarantees Sub-ZK and simulation knowledge soundness.
Performance analysis of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math> -dimensional quantum cryptography under state-dependent diffraction

Standard protocols for quantum key distribution (QKD) require that the sender be able to transmit in two or more mutually unbiased bases. Here, we analyze the extent to which the performance of QKD is degraded by diffraction effects that become relevant for long propagation distances and limited sizes of apertures. In such a scenario, different states experience different amounts of diffraction, leading to state-dependent loss and phase acquisition, causing an increased error rate and security loophole at the receiver. To solve this problem, we propose a pre-compensation protocol based on pre-shaping the transverse structure of quantum states. We demonstrate, both theoretically and experimentally, that when performing QKD over a link with known, symbol-dependent loss and phase shift, the performance of QKD will be better if we intentionally increase the loss of certain symbols to make the loss and phase shift of all states same. Our results show that the pre-compensated protocol can significantly reduce the error rate induced by state-dependent diffraction and thereby improve the secure key rate of QKD systems without sacrificing the security.
A Proposed Framework for Recognition of Handwritten Cursive English Characters using DAG-CNN

Handwritten Character Recognition (HCR) plays an important role in Optical character Recognition (OCR) and Pattern Recognition (PR), as it has a good number of applications in various fields. HCR contributes extremely to the growth of automation and are applicable in the areas of bank cheque, medical prescriptions, tax returns etc. But handwritten characters are much more difficult to recognize than the printed characters due to difference in writing styles for different people. Both conventional approaches and deep learning techniques have been used for handwritten character recognition. Deep learning techniques such as Convolutional Neural Networks always shows better accuracy than the conventional techniques. In this paper a new deep learning techniques, namely Directed Acyclic Graph - Convolutional Neural Network (DAG-CNN) is used for handwritten character recognition.
Adaptive Resource Views for Containers

As OS-level virtualization advances, containers have become a viable alternative to virtual machines in deploying applications in the cloud. Unlike virtual machines, which allow guest OSes to run atop virtual hardware, containers have direct access to physical hardware and share one OS kernel. While the absence of virtual hardware abstractions eliminates most virtualization overhead, it presents unique challenges for containerized applications to efficiently utilize the underlying hardware. The lack of hardware abstraction exposes the total amount of resources that are shared among all containers to each individual container. Parallel runtimes (e.g., OpenMP) and managed programming languages (e.g., Java) that rely on OS-exported information for resource management could suffer from suboptimal performance. In this paper, we develop a per-container view of resources to export information on the actual resource allocation to containerized applications. The central design of the resource view is a per-container sys\_namespace that calculates the effective capacity of CPU and memory in the presence of resource sharing among containers. We further create a virtual sysfs to seamlessly interface user space applications with sys\_namespace. We use two case studies to demonstrate how to leverage the continuously updated resource view to enable elasticity in the HotSpot JVM and OpenMP. Experimental results show that an accurate view of resource allocation leads to more appropriate configurations and improved performance in a variety of containerized applications.
Neighbourhood Structure Preserving Cross-Modal Embedding for Video Hyperlinking

Video hyperlinking is a task aiming to enhance the accessibility of large archives, by establishing links between fragments of videos. The links model the aboutness between fragments for efficient traversal of video content. This paper addresses the problem of link construction from the perspective of cross-modal embedding. To this end, a generalized multi-modal auto-encoder is proposed. The encoder learns two embeddings from visual and speech modalities, respectively, whereas each of the embeddings performs self-modal and cross-modal translation of modalities. Furthermore, to preserve the neighbourhood structure of fragments, which is important for video hyperlinking, the auto-encoder is devised to model data distribution of fragments in a dataset. Experiments are conducted on Blip10000 dataset using the anchor fragments provided by TRECVid Video Hyperlinking (LNK) task over the years of 2016 and 2017. This paper shares the empirical insights on a number of issues in cross-modal learning, including the preservation of neighbourhood structure in embedding, model fine-tuning and issue of missing modality, for video hyperlinking.
A cyber network attack detection based on GM Median Nearest Neighbors LDA

The continuous development in network technologies causes a considerable hike in number of attacks and intrusions. Identification of these threats has become a critical part of security. To fulfill this task, the Intrusion Detection Systems (IDS) were created. Unfortunately, these tools have curse of dimensionality which tends to increase time complexity and decrease resource utilization. As a consequence, it is desirable that important features of network traffic must be analyzed. To obtain these features, previous work has employed a variant of Linear Discriminant Analysis (LDA) called Median Nearest Neighbors-LDA (Median NN-LDA). This approach finds the relevant features by working with network connections that are near to the median of every class. However, Median NN-LDA has an important drawback. It employs the class arithmetic mean vectors in the within and between scatter matrices formulation. As the arithmetic mean is sensitive to outliers, the approach will not produce optimal results. To deal with that, this paper introduces a new robust Median NN-LDA based on the generalized mean. Many experiments on KDDcup99 and NSL-KDD indicate the superiority of the approach over many LDA variants.
Fusion global and local deep representations with neural attention for aesthetic quality assessment

In recent years, deep-learning based aesthetics assessment methods have shown promising results. However, existing methods can only achieve limited success because 1) most of the methods take one fixed-size patch as the training example, which loses the fine grained details and the holistic layout information, and 2) most of the methods ignore ordinal issues in image aesthetic assessment, i.e. image scored 5.3 is more likely to be in the high quality class than image scored 4.5. To address these challenges, we presents a novel convolutional networks with two branches to encode global and local features. The first branch not only captures the spatial layout information but also feedbacks the top-down neural attention. The second branch selects the important attended region to extract the fine details features. A sobel-based attention layer is integrated with the second branch to enhance fine details encoding. Regarding the second problem, we combine the strength of classification approach and regression approach by a multi-task learning framework. Extensive experiments on challenging Aesthetic and Visual Analysis (AVA) dataset and Photo.net dataset indicate the effectiveness of the proposed method.
Detecting the Flow Pattern Transition in the Gas-Liquid Two-Phase Flow Using Multivariate Multi-Scale Entropy Analysis

Abstract Due to the complex flow structure and non-uniform phase distribution in the vertical upward gas-liquid two-phase flow, an eight-electrode rotating electric field conductance sensor is used to obtain multi-channel conductance signals. The flow patterns of the vertical upward gas-liquid two-phase flow are classified according to the images obtained from a high-speed camera. Then, we employ the multivariate weighted multi-scale permutation entropy (MWMPE) to detect the instability of flow pattern transition in the gas-liquid two-phase flow. Afterwards, we compare the results of the MWMPE with those of the single-channel weighted multi-scale permutation entropy (SCWMPE) and multivariate multi-scale sample entropy (MMSE). The comparison results indicate that, compared with the SCWMPE and MMSE, the MWMPE has superior performance in terms of the high-resolution presentation of flow instability in the gas-liquid two-phase flow. Finally, we extract the mean value of the MWMPE in whole scales and the entropy rate of the MWMPE in the small scales. The results indicate that the normalized mean value and normalized entropy rate of MWMPE are very sensitive to the transitions of flow patterns, thus allowing the detection of the instability of flow pattern transition.
Design, Large-Scale Usage Testing, and Important Metrics for Augmented Reality Gaming Applications

Augmented Reality (AR) offers the possibility to enrich the real world with digital mediated content, increasing in this way the quality of many everyday experiences. While in some research areas such as cultural heritage, tourism, or medicine there is a strong technological investment, AR for game purposes struggles to become a widespread commercial application. In this article, a novel framework for AR kid games is proposed, already developed by the authors for other AR applications such as Cultural Heritage and Arts. In particular, the framework includes different layers such as the development of a series of AR kid puzzle games in an intermediate structure which can be used as a standard for different applications development, the development of a smart configuration tool, together with general guidelines and long-life usage tests and metrics. The proposed application is designed for augmenting the puzzle experience, but can be easily extended to other AR gaming applications. Once the user has assembled the real puzzle, AR functionality within the mobile application can be unlocked, bringing to life puzzle characters, creating a seamless game that merges AR interactions with the puzzle reality. The main goals and benefits of this framework can be seen in the development of a novel set of AR tests and metrics in the pre-release phase (in order to help the commercial launch and developers), and in the release phase by introducing the measures for long-life app optimization, usage tests and hint on final users together with a measure to design policy, providing a method for automatic testing of quality and popularity improvements. Moreover, smart configuration tools, as part of the general framework, enabling multi-app and eventually also multi-user development, have been proposed, facilitating the serialization of the applications. Results were obtained from a large-scale user test with about 4 million users on a set of eight gaming applications, providing the scientific community a workflow for implicit quantitative analysis in AR gaming. Different data analytics developed on the data collected by the framework prove that the proposed approach is affordable and reliable for long-life testing and optimization.
Automation in Social Networking Comments With the Help of Robust fastText and CNN

Social networking and online conversation platforms provide us with the power to share our views and ideas. However, nowadays on social media platforms, many people are taking these platforms for granted, they see it as an opportunity to harass and target others leading to cyber-attack and cyber-bullying which lead to traumatic experiences and suicidal attempts in extreme cases. Manually identifying and classifying such comments is a very long, tiresome and unreliable process. To solve this challenge, we have developed a deep learning system which will identify such negative content on online discussion platforms and successfully classify them into proper labels. Our proposed model aims to apply the text-based Convolution Neural Network (CNN) with word embedding, using fastText word embedding technique. fastText has shown efficient and more accurate results compared to Word2Vec and GLOVE model. Our model aims to improve detecting different types of toxicity to improve the social media experience. Our model classifies such comments in six classes which are Toxic, Severe Toxic, Obscene, Threat, Insult and Identity-hate. Multi-Label Classification helps us to provide an automated solution for dealing with the toxic comments problem we are facing.
Towards Decentralized Deep Learning with Differential Privacy

In distributed machine learning, while a great deal of attention has been paid on centralized systems that include a central parameter server, decentralized systems have not been fully explored. Decentralized systems have great potentials in the future practical use as they have multiple useful attributes such as less vulnerable to privacy and security issues, better scalability, and less prone to single point of bottleneck and failure. In this paper, we focus on decentralized learning systems and aim to achieve differential privacy with good convergence rate and low communication cost. To achieve this goal, we propose a new algorithm, Leader-Follower Elastic Averaging Stochastic Gradient Descent (LEASGD), driven by a novel Leader-Follower topology and differential privacy model. We also provide a theoretical analysis of the convergence rate of LEASGD and the trade-off between the performance and privacy in the private setting. We evaluate LEASGD in real distributed testbed with poplar deep neural network models MNIST-CNN, MNIST-RNN, and CIFAR-10. Extensive experimental results show that LEASGD outperforms state-of-the-art decentralized learning algorithm DPSGD by achieving nearly $$40\%$$ lower loss function within same iterations and by $$30\%$$ reduction of communication cost. Moreover, it spends less differential privacy budget and has final higher accuracy result than DPSGD under private setting.