Reviewerly/exHarmony huggingface dataset:
Random two-frame phase-shifting interferometry via minimization of coefficient of variation

Random two-frame phase-shifting interferometry (PSI) is an advanced technique to retrieve the phase information from as few as two interferograms with unknown phase steps. Because of the advantages of no requirement for accurate phase shifters and much less time for data acquisition and processing, random two-frame PSI is attracting more and more interest in fast and high-precision optical metrology. However, reconstructing the phase from only two interferograms is challenging because it is an ill-posed problem in essence, especially when the phase step is unknown. Although some solutions have been proposed for this problem to date, most of them require complicated preprocessing or special usage preconditions for interferograms to be demodulated. In this letter, we developed an elegant phase reconstruction method for random two-frame PSI, which is much different from frameworks of existing methods. In the proposed approach, the phase of random two-frame PSI can be accurately reconstructed using the phase step value which minimizes the coefficient of variation (CV) of the modulation term of interferograms. Sufficient numerical simulations and experimental data demonstrate the high accuracy and high efficiency of this CV minimization (CVM) method. Moreover, its performance is not limited by the number of fringes in interferograms, in contrast to existing state-of-the-art approaches. We anticipate extensive applications of the CVM method in random two-frame PSI in the future.
A robust geometric mean-based subspace discriminant analysis feature extraction approach for image set classification

Discriminant analysis technique is an important research topic in image set classification because it can extract discriminative features. However, most existing discriminant analysis methods almost fail to work for feature extraction of data because there is only a small amount of valid discriminant information. The main weakness of most existing discriminant analysis models is that the class mean vector is constructed by using class sample average. That is not sufficient to provide an accurate estimation of the class mean. In this paper, we proposed a robust geometric mean-based subspace discriminant analysis feature extraction method for image set classification. This method is a combination of geometric mean vector, subspace and covariance matrix to jointly represent an image set because they are contain different discriminative information. The above three representation ways lie on different spaces. To reduce the dissimilarity between the heterogeneous spaces, a robust geometric mean-based subspace discriminant analysis learning (GMSDA) framework is developed, which includes three steps operation. At first, uses a new geometric mean vector to construct geometric between-class scatter matrix (Sgb) and geometric within-class scatter matrix (Sgw) instead of traditional mean vector of many methods. Secondly, maximizes the geometric between-class scatter matrix to increase the difference between extracted features. Finally, maximizes the subspace between-class scatter (SgbS) and minimizes the subspace within-class scatter (SgwS) simultaneously in the subspace of original space. Experiments on five datasets illustrate that our proposed GMSDA method works the best in small sample size situation by using maximum likelihood classification (MLC).
Fault-Tolerant Gates on Hypergraph Product Codes

One class of quantum error-correcting codes---hypergraph product codes---may offer fault tolerance with low hardware overhead. A new proposal shows how to perform universal quantum gates with these codes.
Training Binarized Neural Networks Using MIP and CP

Binarized Neural Networks (BNNs) are an important class of neural network characterized by weights and activations restricted to the set $$\{-1,+1\}$$ . BNNs provide simple compact descriptions and as such have a wide range of applications in low-power devices. In this paper, we investigate a model-based approach to training BNNs using constraint programming (CP), mixed-integer programming (MIP), and CP/MIP hybrids. We formulate the training problem as finding a set of weights that correctly classify the training set instances while optimizing objective functions that have been proposed in the literature as proxies for generalizability. Our experimental results on the MNIST digit recognition dataset suggest that—when training data is limited—the BNNs found by our hybrid approach generalize better than those obtained from a state-of-the-art gradient descent method. More broadly, this work enables the analysis of neural network performance based on the availability of optimal solutions and optimality bounds.
Cloud Resource Demand Prediction using Differential Evolution based Learning

Today's digital world generates ample amount of data through interconnected heterogeneous devices that must be stored and processed efficiently for uninterrupted services. The distributed infrastructures have shown the capability of addressing the storage and computing issues of big data. The cloud paradigm is enabled with characteristics including multi-tenancy, on-demand, virtualization, scalability and many more. However, the cloud resources must be used efficiently to reduce power consumption and carbon footprints. This paper presents a workload prediction scheme based on differential evolution that can be used for effective virtual machine allocation. The forecast accuracy of the proposed scheme is evaluated over Google's real world trace and compared with existing state-of-art prediction approaches. We observed a significant reduction in forecast error upto 71% and 88% over back propagation and linear regression based forecasting approaches respectively.
A Confidentiality Preserving Data Leaker Detection Model for Secure Sharing of Cloud Data using Integrated Techniques

Leakage of critical information through intentional or unintentional disclosure by the malicious entities to the aggrieved third party poses one of the most serious security hazards to various organizations. In the real world scenario, a data distributor has to share confidential data among various stakeholders. It comprises a number of threats in transferring the critical data, as it can be made public by any disgruntled employee or the indignant entity. Data Leakage Problem (DLP) has become a critical challenge and it keeps on increasing day by day. It is crucial to protect the critical data as it can be misused by any vicious entity. To resolve DLP, we present a generic Data Leaker Detection Model (DLDM), which identifies the malicious entity responsible for data leak. The proposed model ensures the data confidentiality and preserves the security of the sensitive information via applying an integration of cryptography, watermarking and hashing techniques. The results show that the computation times in the complete process are 2285.31 ms and 5628.84 ms when 200 documents of size 20MB are provided to the single user and distinct users respectively.
What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.
Lightweight Privacy-Preserving Training and Evaluation for Discretized Neural Networks

Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this article, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized NNs (DiNNs) is proposed. First, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain, including multivariate polynomial, nonlinear activation function, gradient function, and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for DiNNs is proposed, which can also be extended to convolutional NN. Finally, we give the formal security proofs for dataset privacy, model training privacy, and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in DiNN to demonstrate the high efficiency and accuracy of our proposed LPTE.
Binocular Light-Field: Imaging Theory and Occlusion-Robust Depth Perception Application

Binocular stereo vision (SV) has been widely used to reconstruct the depth information, but it is quite vulnerable to scenes with strong occlusions. As an emerging computational photography technology, light-field (LF) imaging brings about a novel solution to passive depth perception by recording multiple angular views in a single exposure. In this paper, we explore binocular SV and LF imaging to form the binocular-LF imaging system. An imaging theory is derived by modeling the imaging process and analyzing disparity properties based on the geometrical optics theory. Then an accurate occlusion-robust depth estimation algorithm is proposed by exploiting multibaseline stereo matching cues and defocus cues. The occlusions caused by binocular SV and LF imaging are detected and handled to eliminate the matching ambiguities and outliers. Finally, we develop a binocular-LF database and capture realworld scenes by our binocular-LF system to test the accuracy and robustness. The experimental results demonstrate that the proposed algorithm definitely recovers high quality depth maps with smooth surfaces and precise geometric shapes, which tackles the drawbacks of binocular SV and LF imaging simultaneously.
IoT Data Management and Lineage Traceability: A Blockchain-based Solution

The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.
Global types with internal delegation

This paper investigates a new form of delegation for multiparty session calculi. Usually, delegation allows a session participant to appoint a participant in another session to act on her behalf. This means that delegation is inherently an inter-session mechanism, which requires session interleaving. Hence delegation falls outside the descriptive power of global types, which specify single sessions. As a consequence, properties such as deadlock-freedom or lock-freedom are difficult to ensure in the presence of delegation. Here we adopt a different view of delegation, by allowing participants to delegate tasks to each other within the same multiparty session. This way, delegation occurs within a single session (internal delegation) and may be captured by its global type. To increase flexibility in the use of delegation, our calculus uses connecting communications, which allow optional participants in the branches of choices. By these means, we are able to express conditional delegation. We present a session type system based on global types with internal delegation, and show that it ensures the usual safety properties of multiparty sessions, together with a progress property.
Genetic Operators Applied to Symmetric Cryptography

The proposed algorithm is based on operators used in Genetic Algorithms (GA), adapted to encrypt and decrypt text.The principles of GA were exposed by Holland in 1975 [8], and described more broadly by Goldberg in 1989 [9], Poli, Langdon, McPhee, Michelle and Davis [23].The main limitation at the time of the creation of a cryptographic algorithm is the idea of innovation, since, currently, there are large amounts of algorithms of this type.With this motivation and in order to generate an original proposal, we decided to take advantage of GA techniques in cryptographic development, making some modifications without neglecting its philosophy.These alterations arise from the interest of creating a different work, which through a clear exemplification demonstrates that it is possible to create developments in the area of computer security using basic concepts.Our work takes the randomness and the order of operation of the genetic phases to arrive at a deterministic development, proposing in addition the use of two keys (auxiliary and main) without ceasing to be a symmetric algorithm. II. Theoretical Background A. Genetic AlgorithmsThe Genetic Algorithms are an adaptive method used to solve search and optimization problems, inspired by biological evolution
Exploring the Performance of Singularity for High Performance Computing Scenarios

Traditional hypervisor-based virtualization solutions have not been commonly used in High Performance Computing (HPC) due to the performance overhead. Container-based virtualization technologies represented by Linux Container and Docker can provide better resource sharing, customized environments and low overhead. However, they still can't satisfy the functional and security needs in HPC environments. Singularity is an attractive container-based approach to meet the requirements of scientific applications. Its characteristics such as mobility of compute, reproducibility, user freedom and support on existing traditional HPC, can effectively solve some flaws compared to other virtualization technologies. In this paper, we conducted a detailed performance evaluation of CPU, memory and network bandwidth between Singularity and bare metal by using HPL, STREAM and OSU Micro benchmarks. Four typical HPC applications such as NAMD, VASP, AMBER, and WRF are deployed to further validate the performance overhead of Singularity. Furthermore, migration and compatibility of Singularity are also investigated. Results from the experiments show that Singularity can achieve near-native performance in MPI and GPU parallel applications, which also demonstrate that it has a greater application prospect in HPC.
WSI-Net: Branch-Based and Hierarchy-Aware Network for Segmentation and Classification of Breast Histopathological Whole-Slide Images

This paper proposes a novel network WSI-Net for segmentation and classification of gigapixel breast whole-slide images (WSIs). WSI-Net can segment patches from the WSI into three types, including non-malignant, ductal carcinoma in situ, and invasive ductal carcinoma. It adds a parallel classification branch on the top of the low layer of a semantic segmentation model DeepLab. This branch can fast identify and discard those non-malignant patches in advance and thus the high layer of DeepLab can only focus on the remaining possible cancerous inputs. This strategy can accelerate inference and robustly improve segmentation performance. For training WSI-Net, a hierarchy-aware loss function is proposed to combine pixel-level and patch-level loss, which can capture the pathology hierarchical relationships between pixels in each patch. By aggregating patch segmentation results from WSI-Net, we generate a segmentation map for the WSI and extract its morphological features for WSI-level classification. Experimental results show that our WSI-Net can be fast, robust and effective on our benchmark dataset.
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning

Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years.While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfaction estimation.We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly.Furthermore, we apply this novel user satisfaction estimation model live in simulated experiments where the satisfaction estimation model is trained on one domain and applied in many other domains which cover a similar task.We show that applying this model results in higher estimated satisfaction, similar task success rates and a higher robustness to noise.…
Identification of Image Edge Using Quantum Canny Edge Detection Algorithm

Identification of image edges using edge detection is done to obtain images that are sharp and clear. The selection of the edge detection algorithm will affect the result. Canny operators have an advantage compared to other edge detection operators because of their ability to detect not only strong edges but also weak edges. Until now, Canny edge detection has been done using classical computing where data are expressed in bits, 0 or 1. This paper proposes the identification of image edges using a quantum Canny edge detection algorithm, where data are expressed in the form of quantum bits (qubits). Besides 0 or 1, a value can also be 0 and 1 simultaneously so there will be many more possible values that can be obtained. There are three stages in the proposed method, namely the input image stage, the preprocessing stage, and the quantum edge detection stage. Visually, the results show that quantum Canny edge detection can detect more edges compared to classic Canny edge detection, with an average increase of 4.05%.
Degenerate squeezing in waveguides: a unified theoretical approach

Abstract We consider pulsed-pump spontaneous parametric downconversion (SPDC) as well as pulsed single- and dual-pump spontaneous four-wave mixing processes in waveguides within a unified Hamiltonian theoretical framework. Working with linear operator equations in k -space, our approach allows inclusion of linear losses, self- and cross-phase modulation, and dispersion to any order. We describe state evolution in terms of second-order moments, for which we develop explicit expressions. We use our approach to calculate the joint spectral amplitude of degenerate squeezing using SPDC analytically in the perturbative limit, benchmark our theory against well-known results in the limit of negligible group velocity dispersion, and study the suitability of recently proposed sources for quantum sampling experiments.
Social media and bitcoin metrics: which words matter

We develop a new Data-Driven Phasic Word Identification (DDPWI) methodology to determine which words matter as the bitcoin pricing dynamic changes from one phase to another. With Google search volumes as a baseline, we find that Reddit submissions are both correlated with Google and have a comparable relationship with a variety of bitcoin metrics, using Spearman’s rho. Reddit provides complete access to the text of submissions. Rather than associating sentiment with market activity, we describe the DDPWI method for finding specific ‘price dynamic’ words associated with changes in the bitcoin pricing pattern through 2017 and 2018. We assess the significance of these changes using Wilcoxon Rank-Sum Tests with Bonferroni corrections. These price dynamic words are used to pull out associated words in the submissions thereby providing the context to their use. For example, the price dynamic word ‘ban’, which became significantly higher in frequency as prices fell, occurred in the context of both government regulation and internet companies banning cryptocurrency adverts. This approach could be used more generally to look at social media and discussion forums at a granular level identifying specific words that impact the metric under investigation rather than overall sentiment.
Robust and Accurate Hand–Eye Calibration Method Based on Schur Matric Decomposition

To improve the accuracy and robustness of hand–eye calibration, a hand–eye calibration method based on Schur matric decomposition is proposed in this paper. The accuracy of these methods strongly depends on the quality of observation data. Therefore, preprocessing observation data is essential. As with traditional two-step hand–eye calibration methods, we first solve the rotation parameters and then the translation vector can be immediately determined. A general solution was obtained from one observation through Schur matric decomposition and then the degrees of freedom were decreased from three to two. Observation data preprocessing is one of the basic unresolved problems with hand–eye calibration methods. A discriminant equation to delete outliers was deduced based on Schur matric decomposition. Finally, the basic problem of observation data preprocessing was solved using outlier detection, which significantly improved robustness. The proposed method was validated by both simulations and experiments. The results show that the prediction error of rotation and translation was 0.06 arcmin and 1.01 mm respectively, and the proposed method performed much better in outlier detection. A minimal configuration for the unique solution was proven from a new perspective.