Reviewerly/exHarmony huggingface dataset:
A CU-level adaptive decision method for CNN-based in-loop filtering

Convolutional Neural Network (CNN) has been introduced to in-loop filtering in video coding for further performance improvement. For intra frame coding, a CNN model can be directly trained through learning the correlation between the reconstructed and the original frames, and the obtained model can then be applied to every single reconstructed frame to help improve the video quality. In contrast, for inter frame coding, intertwined reference dependency exists across frames. If a similar procedure of model training and deployment is adopted for inter as that for intra coding, over-smoothed reconstructed frames may be generated, which may further seriously deteriorate the overall coding performance. To address such an issue, state-of-the-art work resorts to the Rate Distortion Optimization (RDO) to determine whether to adopt the conventional or the CNN-based scheme for in-loop filtering, however leading to high computational complexity. In this paper, we propose a Coding-unit (CU) level Adaptive Decision approach (CAD) which employs an early decision for each CU, based on their coding parameters. Experimental results show that the proposed scheme achieves comparable performance with that of the RDO scheme while effectively reduces the encoding time complexity.
Local Shearlet Energy Gammodian Pattern (LSEGP): A Scale Space Binary Shape Descriptor for Texture Classification

A novel texture feature is proposed in this paper to classify texture images. To represent the local texture feature in different scales and spaces, a novel local gammodian binary pattern (LGBP) is applied on the shearlet transform domain. The main advantage of the proposed gammodian structure is the size of the feature vector. Again, the LGBP is also very effective in capturing local edge information. Finally, the local shearlet energy gammodian pattern (LSEGP) is proposed. The output result of the proposed LSEGP on Outex database shows the effectiveness of the proposed descriptor in texture classification.
A new method for performance analysis in nonlinear dimensionality reduction

Abstract In this paper, we develop a local rank correlation (LRC) measure which quantifies the performance of dimension reduction methods. The LRC is easily interpretable, and robust against the extreme skewness of nearest neighbor distributions in high dimensions. Some benchmark datasets are studied. We find that the LRC closely corresponds to our visual interpretation of the quality of the output. In addition, we demonstrate that the LRC is useful in estimating the intrinsic dimensionality of the original data, and in selecting a suitable value of tuning parameters used in some algorithms.
Limited Authority Adaptive Control Architectures with Dynamic Inversion or Explicit Model Following

Aerospace adaptive control has exploited a variety of control techniques since its initial debut in the pursuit of robustness and fault tolerance. This variety of both taxonomy and approaches can create a system of systems which may make generalizations difficult. This paper contributes to the understanding of adaptive systems by synthesizing connected literature to explore the interactions of a baseline adaptive control architecture and trace its development. Comparable performance is shown with explicit model following (EMF) and dynamic inversion (DI) in series feedback with adaptive and linear controller. Articles were included in the review which led to the development of an architecture with dynamic inverse, control allocation, saturation, explicit model following, and adaptive element or demonstrated their interaction effects. A Lyapunov based stability proof was conducted to show stability of the EMF architecture with limited authority pseudocontrol hedge. Robust and limited authority cases were demonstrated by simulation. DI in series and EMF adaptive control architectures were found to have comparable performance under both conditions. Both techniques serve a similar purpose. While dynamic inversion has a clearer implementation and stability proof, many existing rotorcraft use explicit model following for its advantages; integrating EMF with adaptive control produces a viable alternative.
Research on Image Registration Algorithm and Its Application in Photovoltaic Images

Due to high similarity interference of photovoltaic images, repetitive EL image detection and photovoltaic image stitching pose a great challenge. This article proposes two application algorithms and a registration method, which are repetitive EL image detection and photovoltaic image stitching based on the proposed AKAZE-LATCH+GMS registration method. Considering the existence of duplicate EL images acquired indoors, a repetitive EL image detection based on global feature consistency and local cell's texture similarity is proposed. The AKAZE-LATCH+GMS is used to calculate global consistent feature ratio and dHash is used to quantify local cell's similarity to determine whether it is repetitive or not. To solve the problem of high similarity of outdoor photovoltaic images, which leads to dislocation of stitching, an image stitching algorithm based on geographic information is proposed in this article. Compared with AKAZE+GMS and ORB+GMS, the registration experimental results show that the AKAZE-LATCH+GMS method can obtain more initial matching features and higher correct matching rate. Indoor EL images with geometric and illumination differences are used to verify the proposed detection method, which can effectively eliminate repetitive EL images. Aerial photovoltaic images, acquired from the same or different view, are used to verify the validity of the stitching method, and the photovoltaic images can be stitched correctly compared with the stitching method without geographic information.
Live Emoji: A Live Storytelling VR System with Programmable Cartoon-Style Emotion Embodiment

We introduce a novel cartoon-style hybrid emotion embodiment model for live storytelling in virtual reality (VR). It contains an avatar with six basic emotions and an auxiliary multimodal display to enhance the expression of emotions. We further design and implement a system to teleoperate the embodiment model in VR for live storytelling. Specifically, 1) we design a novel visual programming tool that allows users to customize emotional effects based on the emotion embodiment model; 2) we design a novel face tracking module to map presenters' emotional states to the avatar in VR. Our web-based implementation makes the application easy to use. This is an accompanying paper extracted from [1] for the demo session in IEEE AIVR 2019.
A Sensitive Word Detection Method Based on Variants Recognition

In view of the complex characteristics of sensitive words, this paper proposes a sensitive word detection method based on variants recognition. The paper proposes methods of word recognition and similarity calculation, include synonyms, substitutes, abbreviations and inflections, and then the method of sensitive word detection is carried out. Experiments show that the proposed method is more efficient in detecting sensitive words than other methods.
Combining Perfect Shuffle and Bitonic Networks for Efficient Quantum Sorting

The emergence of quantum computers in the last decade has generated research interest in applications such as quantum sorting. Quantum sorting plays a critical role in creating ordered sets of data that can be better utilized, e.g., quantum ordered search or quantum network switching. In this paper, we propose a quantum sorting algorithm that combines highly parallelizable bitonic merge networks with perfect shuffle permutations (PSP), for sorting data represented in the quantum domain. The combination of bitonic networks with PSP improves the temporal complexity of bitonic merge sorting which is critical for reducing decoherence effects for quantum processing. We present space-efficient quantum circuits that can be used for quantum bit comparison and permutation. We also present a reconfigurable hardware quantum emulator for prototyping the proposed quantum algorithm. The emulator has a fully-pipelined architecture and supports double-precision floating-point computations, resulting in high throughput and accuracy. The proposed hardware architectures are implemented on a high-performance reconfigurable computer (HPRC). In our experiments, we emulated quantum sorting circuits of up to 31 fully-entangled quantum bits on a single FPGA node of the HPRC platform. To the best of our knowledge, our effort is the first to investigate a reconfigurable hardware emulation of quantum sorting using bitonic networks and perfect shuffle.
Genetic Algorithm-Based Privacy Preserving Collaborative Filtering

Collaborative filtering has become one of the most effective recommender system techniques for generating predictions. However, these systems typically overlook the privacy of individuals as they collect plain preferences of users. Privacy-preserving collaborative filtering systems propose ways of offering predictions without violating the confidentiality of users. Disguising user preferences reduces the accuracy as the data disguising process causes a level of distortion in the preferences. In this study, a genetic algorithm-based approach is proposed to maintain more accurate predictions based on disguised user data. Similarity calculation is improved compared to traditional privacy- preserving collaborative filtering by implementation of genetic algorithm onto a model-based scheme. Experimental outcomes obtained on a real-world data set confirm that the improved genetic algorithm-based scheme significantly outperforms traditional methods in terms of accuracy.
Feedback Structures for a Transfer Function Model of a Circular Vibrating Membrane

The attachment of feedback loops to physical or musical systems enables a large variety of possibilities for the modification of the system behavior. Feedback loops may enrich the echo density of feedback delay networks (FDN), or enable the realization of complex boundary conditions in physical simulation models for sound synthesis. Inspired by control theory, a general feedback loop is attached to a model of a vibrating membrane. The membrane model is based on the modal expansion of an initial-boundary value problem formulated in a state-space description. The possibilities of the attached feedback loop are shown by three examples, namely by the introduction of additional mode wise damping; modulation and damping inspired by FDN feedback loops; time-varying modification of the system behavior.
Advanced Security in 2LQR Code Generation and Document Authentication

This paper presents 2LQR Code generation and document authentication. Encryption technique and digital signature are used to give advanced security. In 2LQR Code generation, public level is generated by using standard procedure and private level is created by restoring black modules by particular textured modules. This application will be used in many applications where private information is shared.
A Decentralized Service-Platform towards Cross-Domain Entitlement Handling

As our world is becoming increasingly connected, secure and seamless cross-domain access to heterogeneous services is becoming more and more important. However, cooperation between client-server based systems in the sales domain is often difficult to achieve due to a lack of standardization as well as limited trust between service providers. Furthermore, the demand for anonymization techniques is becoming more important due to tighter regulations and increased privacy awareness of users. This is especially relevant when data needs to be shared by several parties. In this paper we describe a software service platform designed to increase collaboration between different cross-domain services. The general idea is to provide a common trusted layer for existing client-server systems that can be used for anonymizing and sharing services in the form of digital entitlements across independent service providers, their systems, and users, while ensuring secure authentication and authorization. Furthermore, we leverage the tamper-resistant properties of the blockchain and smart contracts in order to protect sensitive transactions against fraudulent manipulation and to introduce service-based agreements to the overall system.
On the Complexity of Hybrid $n$ -Term Karatsuba Multiplier for Trinomials

The n-term Karatsuba algorithm (KA) is an extension of 2-term KA, which can obtain even fewer multiplications than the original one. The existing n-term Karatsuba hybrid GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) multipliers rely on factorization of m or m - 1, so that put a confinement to these schemes. In this contribution, we use a new decomposition m = nℓ + r, such that 0 ≤ r <; n and 0 ≤ r <; I, and propose a novel n-term Karatsuba hybrid GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) multiplier for an arbitrary irreducible trinomial x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> + x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> + ℓ, m ≥ 2k. Combined with shifted polynomial basis, a new approach (other than Mastrovito approach) is introduced to exploit the spatial correlations among different subexpressions. We investigate the explicit space and time complexities, and discuss related upper and lower bounds. As a main contribution, the flexibilities of n, ℓ and r make our proposal approaching m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /2 + O(√11 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3/2</sup> /2), which roughly matches the best result optimal for any given m. The space complexity can achieve to of current hybrid multipliers for special trinomials. Meanwhile, its time complexity is slightly higher than the counterparts, but can be improved for a new class of trinomials. In particular, we demonstrate that the hybrid multiplier for x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> +x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> +1, where k is approaching m/2, can achieve a better space-time trade-off than any other trinomials.
An Introduction of Deep Learning Based Word Representation Applied to Natural Language Processing

In the area of Natural Language Processing, high-quality word representations play key roles in neural language processing tasks. Recently, various model designs and methods have blossomed in the domain of word representation. In this paper, we will explain the theories of two major language models such as autoencoding (AE) and autoregressive (AR), and illustrate the architectures of several notable examples of AE and AR including ELMo, GPT, BERT and XLnet. By comparing the pros and cons of these models theoretically and the performances of these models in experiments, we will deepen the understanding of various learning methods and realize the trend in the development of language models.
Video-based person re-identification with parallel spatial–temporal attention module

We propose a parallel network with spatial–temporal attention for video-based person re-identification. Many previous video-based person re-identification methods use two-dimensional convolutional neural networks to extract spatial features, then, temporal features are extracted by temporal pooling or recurrent neural networks. Unfortunately, these series networks will cause the loss of spatial information when extracting temporal information. Different from previous methods, our parallel network can extract temporal and spatial features simultaneously, which can effectively reduce the loss of space information. In addition, we design a global temporal attention module, which obtains the attention weight through the correlation between the current frame and all the frames in the sequence. At the same time, the temporal module can act on the information extraction of spatial module. In this way, we can increase the temporal and spatial constraints. Experiments show that our method can effectively improve the re-id accuracy, better than the state-of-the-art methods.
Modeling Multi-label Recurrence in Data Streams

Most of the existing data stream algorithms assume a single label as the target variable. However, in many applications, each observation is assigned to several labels with latent dependencies among them, which their target function may change over time. Classification of such non-stationary multi-label streaming data with the consideration of dependencies among labels and potential drifts is a challenging task. The few existing studies mostly cope with drifts implicitly, and all learn models on the original label space, which requires a lot of time and memory. None of them consider recurrent drifts in multi-label streams and particularly drifts and recurrences visible in a latent label space. In this paper, we propose a graph-based framework that maintains a pool of multi-label concepts with transitions among them and the corresponding multi-label classifiers. As a base classifier, a fast linear label space dimension reduction method is developed that transforms the labels into a random encoded space and trains models in the reduced space. An analytical method updates the decoding matrix which is used during the test phase to map the labels back into the original space. Experimental results show the effectiveness of the proposed framework in terms of prediction performance and pool management.
Two-Stage Column Block Parallel LU Factorization Algorithm

Parallel computing is increasingly important in computer architectures, parallel architecture has become ubiquitous in our everyday lives. Novel architectures and programming models pose new challenges to algorithm design and system software development. This paper presents a two-stage column block parallel LU factorization algorithm for multiple-processor architectures. Any given matrix is first partitioned into large blocks, and then, every large block is partitioned into a number of small blocks according to the number of processors. Finally, the small column blocks are allocated to processors in an orderly “serpentine arrangement.” Each iteration of the column block parallel LU factorization is separated into two stages of operation. In the first stage, the first-step factorization operation is processed in advance and nonblocking communication is used to reduce the processor idle and waiting time and improve parallelism. In the second stage, the large blocks are used to satisfy more powerful processors, such as GPUs, which require more data to exploit their computing capabilities. Experiments are conducted on a multicore system and multi-GPU system with different configurations to test the algorithm's performance. Compared with other related column block parallel LU factorizations, the two-stage algorithm exhibits better load balancing and parallel execution time performance.
Optimizing Risk-Free Model of Development of Educational Organization Based on Modified Risk Thermometer

We propose new original mathematical approach to the problem of a risk management concerning ensuring quality of educational activity of higher education institution. As a basic method of risk management the risk thermometer identifying a risk background of educational process is used. Estimation of risks is carried out by means of fuzzy composition of the obtained temperature data with the fuzzy relation of compliance of risks to their key indicators. The method of fuzzy clustering is applied to construction of risk temperature's function with optimum parameters. As examination results the complex of actions for developing educational organization formed, which ranging is carried out also on the basis of temperature data.
Open-Domain Document-Based Automatic QA Models Based on CNN and Attention Mechanism

The open domain automatic question answering models have been widely studied in recent years. When dealing with automatic question answering systems, the RNN-based models are the most commonly used models. However we choose the CNN-based model to construct our question answering models, and use the attention mechanism to enhance the performance. We test our models on Microsoft open domain automatic question answering dataset. Experiments show that compared with the models without attention mechanism, our models get the best results. Experiments also show that adding the RNN network in our model can further improve the performance.
A Cross-Genre Morphological Tagging and Lemmatization of the Russian Poetry: Distinctive Test Sets and Evaluation

The poetic texts pose a challenge to full morphological tagging and lemmatization since the authors seek to extend the vocabulary, employ morphologically and semantically deficient forms, go beyond standard syntactic templates, use non-projective constructions and non-standard word order, among other techniques of the creative language game. In this paper we evaluate a number of probabilistic taggers based on decision trees, CRF and neural network algorithms as well as a state-of-the-art dictionary-based tagger. The taggers were trained on prosaic texts and tested on three poetic samples of different complexity. Firstly, we suggest a method to compile the gold standard datasets for the Russian poetry. Secondly, we focus on the taggers’ performance in the identification of the part of speech tags and lemmas. We reveal what kind of POS classes, paradigm classes and syntactic patterns mostly affect the quality of processing.
Cooperative Multi-agent Deep Reinforcement Learning in a 2 Versus 2 Free-Kick Task

In multi-robot reinforcement learning the goal is to enable a team of robots to learn a coordinated behavior from direct interaction with the environment. Here, we provide a comparison of the two main approaches to tackle this challenge, namely independent learners (IL) and joint-action learners (JAL). IL is suitable for highly scalable domains, but it faces non-stationarity issues. Whereas, JAL overcomes non-stationarity and can generate highly coordinated behaviors, but it presents scalability issues due to the increased size of the search space. We implement and evaluate these methods in a new multi-robot cooperative and adversarial soccer scenario, called 2 versus 2 free-kick task, where scalability issues affecting JAL are less relevant given the small number of learners. In this work, we implement and deploy these methodologies on a team of simulated NAO humanoid robots. We describe the implementation details of our scenario and show that both approaches are able to achieve satisfying solutions. Notably, we observe joint-action learners to have a better performance than independent learners in terms of success rate and quality of the learned policies. Finally, we discuss the results and provide conclusions based on our findings.