Reviewerly/exHarmony huggingface dataset:
Generalizing RNN-Transducer to Out-Domain Audio via Sparse Self-Attention Layers

Recurrent neural network transducer (RNN-T) is an end-to-end speech recognition framework converting input acoustic frames into a character sequence. The state-of-the-art encoder network for RNN-T is the Conformer, which can effectively model the local-global context information via its convolution and self-attention layers. Although Conformer RNN-T has shown outstanding performance, most studies have been verified in the setting where the train and test data are drawn from the same domain. The domain mismatch problem for Conformer RNN-T has not been intensively investigated yet, which is an important issue for the product-level speech recognition system. In this study, we identified that fully connected self-attention layers in the Conformer caused high deletion errors, specifically in the long-form out-domain utterances. To address this problem, we introduce sparse self-attention layers for Conformer-based encoder networks, which can exploit local and generalized global information by pruning most of the in-domain fitted global connections. Also, we propose a state reset method for the generalization of the prediction network to cope with long-form utterances. Applying proposed methods to an out-domain test, we obtained 27.6% relative character error rate (CER) reduction compared to the fully connected self-attention layer-based Conformers.
&amp;lt;italic&amp;gt;L&amp;lt;/italic&amp;gt;&amp;lt;sub&amp;gt;0&amp;lt;/sub&amp;gt; Optimization Using Laplacian Operator for Image Smoothing

<p indent=0mm>Image smoothing often leads to the loss of image details and distortion because of over smoothing. An image smoothing method is presented which combines<inline-formula id=inline3><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>optimization and the second-order Laplacian operator. Laplacian operator is used to constrain the color change of the image, and <inline-formula id=inline4><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>optimization is used to minimize the change of the color gradient, so as to achieve the purpose of smooth color transition of the image. In order to keep the edge features of the image better in the process of smoothing, Sobel operator is introduced as the regular term of energy function, and the alternating solution strategy is adopted to solve the energy function. In the experiment, using the classical image in the field of image smoothing and the image obtained through network engine, the proposed method is compared qualitatively and quantitatively with 6 smoothing methods and 7 denoising methods. The experimental results show that the proposed method can reduce the loss of image details while smoothing the image, effectively deal with the phenomenon of stepped edges and color block distribution in the image smoothing, and effectively remove various noises in the image. And the peak signal-to-noise ratio and running time of the proposed method are improved compared with other methods.
The Art-of-Hyper-Parameter Optimization with Desirable Feature Selection

The development of cyber-attacks carried out with ransomware has become increasingly refined in practically all systems. Attacks with pioneering ransomware have the best complexities, which makes them considerably harder to identify. The radical ransomware can obfuscate much of these traces through mechanisms, such as metamorphic engines. Therefore, predictions and detection of malware have become a substantial test for ransomware analysis. Numerous Machine Learning (ML) algorithm exists; considering each algorithm’s Hyper-parameter (HP) just as feature selection strategies, there exist a huge number of potential options. This way, we deliberate more about the issue of simultaneously choosing a learning algorithm and setting its HPs, going past work that tends to address the issues in isolation. We show this issue determined by a completely automated approach, utilizing ongoing developments in ML optimizations. We also show that modifying the information preprocessing brings about more significant progress towards better classification recalls.
Efficient Asynchronous Semi-Stochastic Block Coordinate Descent Methods for Large-Scale SVD

Eigenvector computation such as Singular Value Decomposition (SVD) is one of the most fundamental problems in machine learning, optimization and numerical linear algebra. In recent years, many stochastic variance reduction algorithms and randomized coordinate descent algorithms have been developed to efficiently solve the leading eigenvalue problem. By taking full advantage of both variance reduction and randomized coordinate descent techniques, this paper proposes a novel Semi-stochastic Block Coordinate Descent algorithm (SBCD-SVD), which is more suitable than existing algorithms for large-scale leading eigenvalue problems of SVD, and can obtain linear convergence. Unlike existing stochastic variance reduction and randomized coordinate descent methods, our algorithm inherits their advantages. Moreover, we propose a new Asynchronous parallel Semi-stochastic Block Coordinate Descent algorithm (ASBCD-SVD) and one new Asynchronous parallel Sparse approximated Variance Reduction algorithm (ASVR-SVD) for large-scale dense and sparse datasets, respectively. Finally, we prove that both dense and sparse asynchronous parallel variants can converge linearly. Extensive experimental results show that our algorithms attain high parallel speedup and achieve almost the same performance with significantly shorter time, and thus they can be widely used in various practice applications.
A Survey of Query Expansion Methods to Improve Relevant Search Engine Results

Due to large volumes of documents available for retrieval in a search database, an intelligent method is required to retrieve relevant search results. Query expansion is one of such methods widely used in retrieving pertinent results of various search domains. The increased amount of information stored in a search engine database requires the use of query expansion. A query expansion deals with expanding the query by adding additional information to the query for effective retrieving relevant results. Recently, many query expansion techniques have been proposed to addresses the vocabulary mismatch problem that may arise in the information retrieval system. However, these techniques still have low precision results. This paper presents a systematic review of query expansion research from 1999 to 2018. The paper reviewed and discussed 573 research papers on query expansion methods and their application areas. It focuses only on the query expansion in text retrieval of search engines. This review's primary goal is to provide a broad overview of query expansion research and view how research approaches changed. The research paper analyzed and presented the contributions of each query expansion study. It also identifies major application areas of query expansions and their future opportunities. The finding of this study indicates a trend towards using semantic-ontology and pseudo-relevant feedbacks methods. This work will be beneficial to query expansion researchers in extending future work on query expansion research.
Unknown presentation attack detection against rational attackers

Despite the impressive progress in the field of presentation attack detection and multimedia forensics over the last decade, these systems are still vulnerable to attacks in real-life settings. Some of the challenges for the existing solutions are the detection of unknown attacks, the ability to perform in adversarial settings, few-shot learning, and explainability. In this study, these limitations are approached by reliance on a game-theoretic view for modelling the interactions between the attacker and the detector. Consequently, a new optimisation criterion is proposed and a set of requirements are defined for improving the performance of these systems in real-life settings. Furthermore, a novel detection technique is proposed using generator-based feature sets that are not biased towards any specific attack species. To further optimise the performance on known attacks, a new loss function coined categorical margin maximisation loss (C-marmax) is proposed, which gradually improves the performance against the most powerful attack. The proposed approach provides a more balanced performance across known and unknown attacks and achieves state-of-the-art performance in known and unknown attack detection cases against rational attackers. Lastly, the few-shot learning potential of the proposed approach as well as its ability to provide pixel-level explainability is studied.
Assessing the Precision of Quantum Simulation of Many-Body Effects in Atomic Systems Using the Variational Quantum Eigensolver Algorithm

The emerging field of quantum simulation of many-body systems is widely recognized as a very important application of quantum computing. A crucial step towards its realization in the context of many-electron systems requires a rigorous quantum mechanical treatment of the different interactions. In this pilot study, we investigate the physical effects beyond the mean-field approximation, known as electron correlation, in the ground state energies of atomic systems using the classical-quantum hybrid variational quantum eigensolver algorithm. To this end, we consider three isoelectronic species, namely Be, Li−, and B+. This unique choice spans three classes—a neutral atom, an anion, and a cation. We have employed the unitary coupled-cluster ansätz to perform a rigorous analysis of two very important factors that could affect the precision of the simulations of electron correlation effects within a basis, namely mapping and backend simulator. We carry out our all-electron calculations with four such basis sets. The results obtained are compared with those calculated by using the full configuration interaction, traditional coupled-cluster and the unitary coupled-cluster methods, on a classical computer, to assess the precision of our results. A salient feature of the study involves a detailed analysis to find the number of shots (the number of times a variational quantum eigensolver algorithm is repeated to build statistics) required for calculations with IBM Qiskit’s QASM simulator backend, which mimics an ideal quantum computer. When more qubits become available, our study will serve as among the first steps taken towards computing other properties of interest to various applications such as new physics beyond the Standard Model of elementary particles and atomic clocks using the variational quantum eigensolver algorithm.
Discriminative Codebook Hashing for Supervised Video Retrieval

In recent years, hashing learning has received increasing attention in supervised video retrieval. However, most existing supervised video hashing approaches design hash functions based on pairwise similarity or triple relationships and focus on local information, which results in low retrieval accuracy. In this work, we propose a novel supervised framework called discriminative codebook hashing (DCH) for large-scale video retrieval. The proposed DCH encourages samples within the same category to converge to the same code word and maximizes the mutual distances among different categories. Specifically, we first propose the discriminative codebook via a predefined distance among intercode words and Bernoulli distributions to handle each hash bit. Then, we use the composite Kullback–Leibler (KL) divergence to align the neighborhood structures between the high-dimensional space and the Hamming space. The proposed DCH is optimized via the gradient descent algorithm. Experimental results on three widely used video datasets verify that our proposed DCH performs better than several state-of-the-art methods.
The Structure of Conservative Gradient Fields

The classical Clarke subdifferential alone is inadequate for understanding automatic differentiation in nonsmooth contexts. Instead, we can sometimes rely on enlarged generalized gradients called “conservative fields,” defined through the natural pathwise chain rule: one application is the convergence analysis of gradient-based deep learning algorithms. In the semialgebraic case, we show that all conservative fields are in fact just Clarke subdifferentials plus normals of manifolds in underlying Whitney stratifications.
End‐to‐end global to local convolutional neural network learning for hand pose recovery in depth data

Despite recent advances in 3-D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups. This is mainly due to the highly non-linear dynamics of fingers and self-occlusions, which make hand model training a challenging task. In this study, a novel hierarchical tree-like structured CNN is exploited, in which branches are trained to become specialised in predefined subsets of hand joints called local poses. Further, local pose features, extracted from hierarchical CNN branches, are fused to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motions and deformations. Finally, a non-rigid data augmentation approach is introduced to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialised in local poses, into a fusion network for modelling joints' correlations and dependencies, helps to increase the precision of final estimations, showing competitive results on NYU, MSRA, Hands17 and SyntheticHand datasets.
Scope-Bounded Reachability in Valence Systems

Multi-pushdown systems are a standard model for concurrent recursive programs, but they have an undecidable reachability problem. Therefore, there have been several proposals to underapproximate their sets of runs so that reachability in this underapproximation becomes decidable. One such underapproximation that covers a relatively high portion of runs is scope boundedness. In such a run, after each push to stack i, the corresponding pop operation must come within a bounded number of visits to stack i.In this work, we generalize this approach to a large class of infinite-state systems. For this, we consider the model of valence systems, which consist of a finite-state control and an infinite-state storage mechanism that is specified by a finite undirected graph. This framework captures pushdowns, vector addition systems, integer vector addition systems, and combinations thereof. For this framework, we propose a notion of scope boundedness that coincides with the classical notion when the storage mechanism happens to be a multi-pushdown.We show that with this notion, reachability can be decided in PSPACE for every storage mechanism in the framework. Moreover, we describe the full complexity landscape of this problem across all storage mechanisms, both in the case of (i) the scope bound being given as input and (ii) for fixed scope bounds. Finally, we provide an almost complete description of the complexity landscape if even a description of the storage mechanism is part of the input.
Superpixel Segmentation via Contour Optimized Non-Iterative Clustering

Superpixels intuitively over-segment an image into small partitions with homogeneity. Owing to the superiority of region-level description, it has been widely used in various computer vision applications as a substitute tool for pixels. However, there is still a disharmony between color homogeneity and shape regularity among existing superpixel algorithms, which hinders the performance of the task at hand. This paper introduces a novel Contour Optimized Non-Iterative Clustering (CONIC) superpixel segmentation method. It incorporates contour prior into the non-iterative clustering framework, thus providing a balanced trade-off between segmentation accuracy and visual uniformity. During the joint online assignment and updating step in the conventional Simple Non-Iterative Clustering (SNIC), a subtle feature distance is well-designed to measure the color similarity that considers contour constraint and prevents the boundary pixels from being assigned prematurely. Consequently, superpixels could acquire better visual quality and their boundaries are more consistent with the outlines of objects. Experiments on the Berkeley Segmentation Data Set 500 (BSDS500) verify that CONIC outperforms several state-of-the-art superpixel segmentation algorithms, in terms of both time efficiency and segmentation effects.
Pervasive underwater passive target tracking for the computation of standard deviation solution in a 3D environment

Purpose Nowadays advancement in acoustic technology can be explored with marine assets. The purpose of the paper is pervasive computing underwater target tracking has aroused military and civilian interest as a key component of ocean exploration. While many pervasive techniques are currently found in the literature, there is little published research on the effectiveness of these paradigms in the target tracking context. Design/methodology/approach The unscented Kalman filter (UKF) provides good results for bearing and elevation angles only tracking. Detailed methodology and mathematical modeling are carried out and used to analyze the performance of the filter based on the Monte Carlo simulation. Findings Due to the intricacy of maritime surroundings, tracking underwater targets using acoustic signals, without knowing the range parameter is difficult. The intention is to find out the solution in terms of standard deviation in a three-dimensional (3D) space. Originality/value A new method is found for the acceptance criteria for range, course, speed and pitch based on the standard deviation for bearing and elevation 3D target tracking using the unscented Kalman filter covariance matrix. In the Monte Carlo simulation, several scenarios are used and the results are shown.
Model of professional creativity development of a foreign language teacher by means of art therapy in the conditions of additional education

The paper presents the results of the development and analysis of the model's effectiveness for a foreign language teacher's professional creative development utilizing art therapy in the framework of additional education. The paper aims to provide a theoretical and empirical study of the effectiveness of the model for the professional creativity development of a foreign language teacher through art therapy in the framework of additional education. Attention is drawn to the role and importance of creativity in the foreign language teacher’s activity, to the importance of creative learning in the context of linguistic education. The research methodology and its algorithm are selected, including conducting a complex psychological and pedagogical experiment, conducting diagnostics of the declared parameters, and statistical analysis of the results based on the Spearman rs criterion before and after implementing the developed model. The study of the effectiveness of the model for the professional creative development of a foreign language teacher using art therapy in the framework of additional education was carried out based on the Institute of Foreign Languages of the Peoples’ Friendship University of Russia (RUDN) in the Center for Additional Professional Education. The experimental and control group was formed. Each group consisted of 35 people. The sample is homogeneous by age and gender. The study results allow drawing a conclusion about the effectiveness of the developed development model and outlining the vector of further research in line with the stated problems.
Secure Remote Cloud File Sharing With Attribute-Based Access Control and Performance Optimization

The increasing popularity of remote Cloud File Sharing (CFS) has become a major concern for privacy breach of sensitive data. Aiming at this concern, we present a new resource sharing framework by integrating enterprise-side Attribute-Based Access Control/eXtensible Access Control Markup Language (ABAC/XACML) model, client-side Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme, and cloud-side CFS service. Moreover, the framework workflow is provided to support the encrypted-file writing and reading algorithms in accordance with ABAC/XACML-based access policy and attribute credentials. However, an actual problem of realizing this framework is that policy matrix, derived from access policy, seriously affects the performance of existing CP-ABE from Lattice (CP-ABE-L) schemes. To end it, we present an optimal generation algorithm of Small Policy Matrix (SPM), which only consists of small elements, and generates an all-one reconstruction vector. Based on such a matrix, the improved CP-ABE-L scheme is proposed to reduce the cumulative errors to the minimum and prevent the enlargement of error bounds. Furthermore, we give the optimal estimation of system parameters to implement a valid Error Proportion Allocation (EPA). Our experimental results indicate that our scheme has short size of parameters and enjoys efficient computation and storage overloads. Thus, our new framework with optimization methods is conducive to enhancing the security and efficiency of remote work on CFS.
Constructions of optimal rank-metric codes from automorphisms of rational function fields

We define a class of automorphisms of rational function fields of finite characteristic and employ these to construct different types of optimal linear rank-metric codes. The first construction is of generalized Gabidulin codes over rational function fields. Reducing these codes over finite fields, we obtain maximum rank distance (MRD) codes which are not equivalent to generalized twisted Gabidulin codes. We also construct optimal Ferrers diagram rank-metric codes which settles further a conjecture by Etzion and Silberstein.