Reviewerly/exHarmony huggingface dataset:
LITNet: A Light-weight Image Transform Net for Image Style Transfer

Recently, style transfer networks have received widespread attention in computer vision field, which combine stylistic features from a style image and content information from a content image to generate an output. However, the high-resolution synthesized outputs come at the cost of intensive computation, making it difficult to employ style transfer networks on embedded devices with limited computational resources. To address this issue, we propose a compression algorithm for one of the influential CNN-based style transfer networks, which is named Image Transform Net (ITNet), and gain a Light-weight Image Transform Net (LITNet) accordingly. To improve the performance of ITNet, normalization layers and the structure of upsampling blocks are modified, and depthwise separable convolutions combined with width multiplier are employed to obtain a brand-new light-weight network. However, since the representation ability of light-weight networks is too weak for unsupervised learning tasks such as style transfer, directly using the above techniques to compress the model leads to unstable training processes and yields poor outputs. To solve this problem, a novel distillation loss is proposed to convert unsupervised learning into supervised learning. Besides, the weights between the original losses and the distillation loss are balanced for better visual results. Experimental results demonstrate the effectiveness of our LITNet. With minimal visual quality degradation, the light-weight network can achieve more than 67 × compression in model size and 63× reduction in FLOPs. Codes and pre-trained models are available at https://github.com/shihuihong214/LITNet.
The properties of eigenvalue and its application in postgraduate entrance examination mathematics

The eigenvalue and eigenvector of matrix is one of the key contents of linear algebra,and it is also the hot spot of postgraduate entrance examination. Candidates should be careful and careful when reviewing this content. The first step is to understand the concept of eigenvalues and eigenvectors, and master the method of solving eigenvalue and eigenvector of matrix.Secondly, we should understand the concept of matrix similarity, master the related properties, understand the conditions of matrix similarity diagonalization, and master the method of matrix similarity diagonalization.Finally, we must be familiar with the special properties of eigenvalues and eigenvectors of real symmetric matrices, and master the method of transforming real symmetric matrices into diagonal matrices by using orthogonal matrices. (Abstract)
An Improved particle swarm optimization algorithm based on chaotic BAS strategy

To improve the simplified particle swarm optimization (SPSO) algorithm that is easy to fall into local optimum, the Beetle Antennae Search Chaos Simplified Particle Swarm Optimization (BCSPSO) is proposed based on the Beetle Antennae Search Algorithm and chaos strategy. The Logistic chaos is used to generate chaotic sequences for initialization. Beetle Antennae Search (BAS) has its own judgment of the environment space in each iteration, and not only depends on the best solution and the particles in SPSO individuals of the current global optimal solution, after the particle swarm into local optimum effectively jump out, improved the search speed and precision of the particle swarm, the simulation results show that the algorithm can jump out of local optimal, optimization effect is good, has the good dynamics, stability and efficiency.
Controle por Lógica Difusa de um Ventilador Industrial de uma Planta de Pelotização

This article presents the implementation of a specialist system with diffuse logic for controlling a fan in a mobile grid oven in an iron ore pelletizing plant. These fans are important to maintain the temperature of the pellets at predetermined values, this process is multivariable and has multiple restrictions, thus, diffuse logics were chosen for their ability to adapt to different operating conditions. The constraints and experiences of the employees were used to create rules with the objective of improving the efficiency of the process, in the article results are presented showing the improvement of the performance.
Research on Multi-level Container Security Mechanism Based on Lightweight Virtualization Environment

As an increasingly popular virtualization technology in recent years, Docker technology has attracted more and more attention due to its light weight, low resource consumption, and rapid deployment. However, due to the limitations of Docker's own mechanism, the container lacks the boundaries owned by traditional virtual machines, and there are security risks such as horizontal expansion of permissions and data leakage during operation. This paper has designed a multi-level based on lightweight virtualization environment The container security mechanism applies multi-level security access control technology to the security protection of the container, thereby effectively controlling the flow of information in the container, Establish a container-based security access control boundary.
Generating full-view face images from a single-view image

In the fields of face identification and virtual reality, it is important to generate a face image at arbitrary views from a single-view face image. In this work, a generative adversarial network is used to design an end-to-end network that can generate full-view face images with only an arbitrary single-view image input. We aim to tackle the problem of the existing databases containing the ground-truth only in specific views, with no data available for network training in arbitrary views. We propose to share the generator weights for specific views and arbitrary views during training and use optical flow interpolation between two adjacent specific views to constrain the consistency of facial structure in continuous views. Finally, it is proved using a public database that the proposed method is significantly better than the existing network for the generation of the full-view face images, particularly under the continuous rotation view.
Partial Label Optimal Margin Distribution Machine

In typical partial label learning, each training sample corresponds to a candidate set with only one true label. Eliminating the ambiguous candidate set is the core task of partial label learning. The maximum margin method is a popular paradigm to solve partial label learning problem. It is an effective attempt to directly optimize the margin between the real label and the rest of the labels. However, recent studies have found that the optimal margin distribution is the key to better generalization performance rather than maximizing the minimum margin. In this paper, we propose a novel approach, named PL-ODM (Partial Label Optimal Margin Distribution Machine), which tries to disambiguate the candidate label sets and achieve optimal margin distribution simultaneously. First of all, the mean and variance are used to describe the margin distribution. After that, an alternating optimization process is used to derive the true label information and optimize the margin distribution. By observing the experimental results on the real data set and the controlled UCI data set, the effectiveness of our proposed method is fully demonstrated.
A Method for Identifying the Key Information of Electronic Invoicing in Complex Scenes

Under the trend of electronic invoicing recording, replacing labour with automatic key information recognition will help regulators improve their work efficiency. Due to the low image quality of some electronic invoices, the existing text recognition methods still have some drawbacks. On the one hand, some factors such as lighting, tilt, scale, etc., may affect the definition of the photograph in the process of electronic invoicing, resulted in blurring the key information and increasing the difficulty of text recognition. On the other hand, the templates of electronic invoices are often fixed, and the existing methods lack the use of prior knowledge, which increases the computational cost. Given the above facts, this paper proposes a novel knowledge-based key information detection and recognition method for electronic invoices in complex scenes. In the training stage, we simulate complex scenes by using data augmentation techniques such as adding random noise, colour jitter, horizontal lines, and random rotation to improve the accuracy of text recognition. In the modelling stage, the proposed method makes full use of prior knowledge to obtain key information slices to improve the efficiency of recognition processing. Finally, in practice, the effectiveness of the method proposed in this paper is proved.
A Hybrid Attention Ensemble Framework for Zonal Prostate Segmentation

Accurate and automatic segmentation of the prostate sub-regions is of great importance for the diagnosis of prostate cancer and quantitative analysis of prostate. By analyzing the characteristics of prostate images, we propose a hybrid attention ensemble framework (HAEF) to automatically segment the central gland (CG) and peripheral zone (PZ) of the prostate from a 3D MR image. The proposed attention bridge module (ABM) in the HAEF helps the Unet to be more robust for cases with large differences in foreground size. In order to deal with low segmentation accuracy of the PZ caused by small proportion of PZ to CG, we gradually increase the proportion of voxels in the region of interest (ROI) in the image through a multi-stage cropping and then introduce self-attention mechanisms in the channel and spatial domain to enhance the multi-level semantic features of the target. Finally, post-processing methods such as ensemble and classification are used to refine the segmentation results. Extensive experiments on the dataset from NCI-ISBI 2013 Challenge demonstrate that the proposed framework can automatically and accurately segment the prostate sub-regions, with a mean DSC of 0.881 for CG and 0.821 for PZ, the 95% HDE of 3.57 mm for CG and 3.72 mm for PZ, and the ASSD of 1.08 mm for CG and 0.96 mm for PZ, and outperforms the state-of-the-art methods in terms of DSC for PZ and average DSC of CG and PZ.
Skeleton-Based Action Recognition with Improved Graph Convolution Network

Most previous skeleton-based action recognition methods ignore weight information of joints and data features beyond labels, which is harmful to action recognition. In this paper, we propose a skeleton-based action recognition with improved Graph Convolution Network, which is based on Spatial Temporal Graph Convolutional Network (STGCN). And we add a predictive cluster network, weight generation networks on it. The model uses K-means algorithm to cluster and get the data information beyond the labels. Besides, each cluster traines weight generation networks independently. To find the best clusters, we propose a evaluation criterion with less computational effort. We perform extensive experiments on the Kinetics dataset and the NTU RGB+D dataset to verify the effectiveness of each network of our model. The comparison results show that our approach achieves satisfactory results.
Small-scale pedestrian detection based on feature fusion refinement and improved Faster R-CNN

In view of the existing pedestrian detection methods based on deep learning with low resolution and small pedestrian size, such as false detection, missed detection and low detection accuracy, a small-scale pedestrian detection method based on feature fusion refinement and improved Faster R-CNN was proposed. A fusion and refinement strategy that enhances the ability of multi-layer feature expression is used to improve FPN, and a variable-shape anchor is constructed in RPN to extract pedestrian candidate regions, in order to reduce the influence of background on detection results. In addition, a feature adaptive module is added to the detection network, and the shape information is incorporated into the feature map to adapt to the anchor with variable shape. The experimental results show that compared with Faster R-CNN, the proposed method has an improved effect on the data sets INRIA and CityPersons under the condition of equivalent detection efficiency.
Application of experiential teaching based on Linux information processing technology platform : Take "accounting basis" course as an example

With the rapid development of social economy, the traditional mode of talent demand has been changed, and the comprehensive ability of accounting personnel has higher requirements. As an introductory course of accounting major, accounting basis adopts experiential teaching method, which significantly improves the teaching effect and is welcomed by students. The open teaching platform based on Embedded Linux information processing technology, with imx6 as the core processor, runs independently in process mode according to the functional requirements of different information processing, which is convenient for uploading and playing video resources, and effectively improves the quality of teaching.
Brain Tumor Volumetric Segmentation in Multimodal MRI Using 3D Convolutional Neural Networks

Complications of the central nervous system in removal brain tumors lie in the differentiation between the neoplasm and healthy tissues. Tumor segmentation is a fundamental task in the detection and clinical treatment planning. In this work, two neural networks architectures based on the U-net network have been designed and trained to automatically segment different tumor substructures using the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training dataset. The first network was obtained by transferring all the operations and 2D layers of the U-net network to their 3D versions and replacing all its skip connections with concatenation blocks, the second architecture is similar to the first by adding a max pooling layer, a transposed convolution layer and a concatenation block. In both architectures the group normalization layer was used instead of batch normalization as a suggestion of the state-of-the-art. Our best model is capable of segmenting gliomas on the validation dataset with a Dice similarity coefficient of 0.85, 0.84 and 0.87 for the whole tumor, tumor core and enhancing tumor core respectively, therefore, the inference process takes 0.21 seconds per patient with a precision of 0.89. Different values of hyperparameters were tested, deeper architectures and different kernel numbers were implemented in the convolution and concatenation blocks, in no case was a significant improvement in our metrics obtained. Our models outperform most state-of-the-art methods in terms of Dice coefficient, sensitivity, specificity and precision.
Solid State Qubits

In this chapter we will study qubits encoded in superconducting circuits and semiconductor quantum dots. In contrast to the qubits discussed so far these approaches do not involve optics or lasers and are therefore closer to the technologies that are widely used today for information processing. On the other hand maintaining and controlling quantum coherence in a solid state material is difficult at room temperatures so the approaches to be discussed require cooling of the circuit elements to mK temperatures.
ボルツマンマシンを基盤とした変化検知システムの検証

Detection of the state changes in the system, which may result in the failure of the system, would be very important in various fields of real-world engineering. We propose a novel unsupervised method for detection of changes in the framework of Energy Based Model (EBM). The proposed method incorporates the Restricted Boltzmann Machine (RBM) and Conditional Restricted Boltzmann Machine (CRBM) as EBM. The state change can be evaluated by observing the free energy in the RBM/CRBM. The proposed method is evaluated by using two sets of time series; one is a sin wave with Gaussian noises and another is the state in a CartPole system. Experimental results show that the free energy is an effective indicator for detecting the state change and that CRBM have better ability for detecting the state change than RBM.
A Pseudo-Random Generator Whose Output is a Normal Sequence

Pseudo-random number generators (PRNGs) are widely used in computer simulation, cryptography, and many other fields. In this paper, we describe a PRNG class, which, firstly, has been successfully tested using the most powerful modern test batteries, and secondly, is proved to consist of generators that generate normal sequences. The latter property means that, for any generated sequence [Formula: see text] and any binary word [Formula: see text], we have [Formula: see text] where [Formula: see text] is the number of occurrences of [Formula: see text] in the sequence [Formula: see text], [Formula: see text].
Approximating Closest Vector Problem in <i>ℓ</i>∞-Norm Revisited

Abstract The security of most lattice-based cryptography schemes are based on two computational hard problems which are the short integer solution (SIS) and learning with errors (LWE) problems. The computational complexity of SIS and LWE problems are related to approximating shortest vector problem and bounded distance decoding (BDD) problem. Approximating BDD is a special case of approximating closest vector problem (CVP). In this paper, we revisit the study for approximating CVP. We give a proof that approximating the CVP over $\ell _\infty $-norm (CVP$_\infty $) within any constant factor is NP-hard. The result is obtained by the gap-preserving reduction from Min Total Label Cover problem in $\ell _1$-norm to to CVP$_\infty $. This proof is simpler than known proofs [ 10].
Let’s Chat Internal: User Acceptance of an In-Company Service Desk Chatbot

Due to remote working models, which were strengthened by the COVID-19 pandemic, it became highly relevant to digitalize on-site service desk consultations. Virtual enterprise assistants which simplify the interaction with in-company services provide similar advantages to their popular peers in customer service: high availability and customer satisfaction, and low response time and costs. To find out how their usage affects user acceptance we developed an early high-fidelity prototype for an in-company UX service desk chatbot and evaluated it in an online survey (N=53) and user study (N=14) with employees from the industry using the Technology Acceptance Model. The results show that the prototype’s acceptance does not differ from the classical service and prior experiences with textual chatbots in a private usage context but was rated higher than a private sample bot.
Autoencoder and Modified YOLOv3 Based Firearms Object Detection in X-ray Baggage Images to Enhance Aviation Safety

At airports and especially the baggage inspection task, the vital question that the human operator must answer is how to strike a balance between security screening, facilitation in a confined space, the good impression of passengers through their passage, and speed of inspection. In order to help them reinvent their approach to control in such an environment, the help of automatic intelligent tools is necessary. This paper proposes firearms object detection based on modified YOLOv3 and autoencoder for security defense in dual X-ray images. The object detection is performed by a modified version of YOLOv3, to detect all the objects presented in the baggage. The object features are carried out by an autoencoder. The classification is performed by a Multi-Layer Perceptron (MLP) to classify a new object as a weapon or not. The proposed system has shown high efficiency in detecting firearms with a precision of 96.50%.
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods

When designing algorithms for finite-time-horizon episodic reinforcement learning problems, a common approach is to introduce a fictitious discount factor and use stationary policies for approximations. Empirically, it has been shown that the fictitious discount factor helps reduce variance, and stationary policies serve to save the per-iteration computational cost. Theoretically, however, there is no existing work on convergence analysis for algorithms with this fictitious discount recipe. This paper takes the first step towards analyzing these algorithms. It focuses on two vanilla policy gradient (VPG) variants: the first being a widely used variant with discounted advantage estimations (DAE), the second with an additional fictitious discount factor in the score functions of the policy gradient estimators. Non-asymptotic convergence guarantees are established for both algorithms, and the additional discount factor is shown to reduce the bias introduced in DAE and thus improve the algorithm convergence asymptotically. A key ingredient of our analysis is to connect three settings of Markov decision processes (MDPs): the finite-time-horizon, the average reward and the discounted settings. To our best knowledge, this is the first theoretical guarantee on fictitious discount algorithms for the episodic reinforcement learning of finite-time-horizon MDPs, which also leads to the (first) global convergence of policy gradient methods for finite-time-horizon episodic reinforcement learning.
Modern Methods of Extracting Key Information From Regulatory Documents

This article is an attempt to comprehend the difficulties and propose approaches to eliminate them when analyzing legal documents in the framework of economic and interdisciplinary research. The utmost goal is to seek incorporating advances in computational linguistics and natural language analysis into the discourse of the digital economy in order to develop methods involved in decision-making and strategy development based on the analysis of textual information. In conditions when the amount of information is too large, is constantly updated and / or the area of study is new, the most expedient at the first stage is to obtain the general structure of the entire collection of documents, some kind of semantic compression of information. The practical part contains the development of an approach for the analysis of regulations governing food and nutrition issues, in particular, related to the prevention of the development of iron deficiency anemia (IDA). The approach includes the extraction of key information of voluminous texts (keywords and key sentences) based on the TextRank graph algorithm. An important link contributing to cognition is also the visualization of semantic relationships between words within documents. In our opinion, it is the combination of semantic compression and visualization of information as a “close-up” of text documents, as well as the possibility of further detailing by linear reading and analysis, which are the most relevant approach in conditions of information overload and attention deficit. The active introduction of text analytics methods for systems that are not involved in attention markets, which lag significantly behind in the convenience of extracting meaningful information, is especially important. Approaches to improve the understanding of large volumes of regulations will be of significant value to researchers in economic, legal or multidisciplinary research.
Study on Virtual Assembly for Satellite Based on Augmented Reality

Assembly activities are a very important part of the production process of large and complex equipment. In order to solve the huge resource problem of large and complex equipment assembly process, this paper proposes a virtual assembly for satellite. Microsoft's latest holographic glasses HoloLens2 is used as a display facility, and Unity3D is applied to develop a satellite part assembly scene based on augmented reality (AR) technology. Voice input and gaze in the Mixed Reality Toolkit (MRTK), provided by Microsoft, are adopted in the development scenario to enable users to interact with virtual models in a more natural way. Finally, the development scenario has been tested in a small space. The results show its validity and good performance.