Reviewerly/exHarmony huggingface dataset:
On Black-Hole Numbers of Iteration of Digital Biquadratic Power Sum in Senary, Novenary or Duodecimal System

There are different periodic and fixed points under the iteration of biquadratic sum of digits of positive integer in different carry systems. There are one fixed point, one 3-circle, one 9-circle and one 11-circle in senary; three fixed point, one 9-circle and one 22-circle in novenary; one fixed points, one 3-circle, one 8-circle and one 14-circle in duodecimal system.
Have You Made a Decision? Where? A Pilot Study on Interpretability of Polarity Analysis Based on Advising Problem

The general approaches for polarity analysis in dialogue, e.g. Multiple Instance Learning (MIL), have achieved significant progress. However, one significant drawback of current approaches is that the contribution of an utterance towards the polarity being a black-box. For existing methods, the polarity contained in each utterance, which we call meta-polarity, is not explicitly utilized. In this paper, we study the problem of adding interpretability to the overall polarity by predicting the meta-polarity at the same time. First, we reformulate a large advising dataset [1], where the meta-polarity of each utterance is given. Second, we propose an utterance classification model (UCM) and a two-stage progressive training method that strengthens the connection between the meta-polarity and the overall polarity. Experimental results show that our overall approach outperforms all competitive base-lines by substantial margins, achieving a new state-of-the-art performance on this dataset.
Blockchain Bookkeeping Optimization Scheme Based on Threshold Ring Signature

According to the characteristics of blockchain data concurrent processing, the threshold ring signature algorithm is improved by combining ring signature and threshold signature scheme. Threshold is set to dynamically adjust the number of threshold ring signature nodes, and threshold ring signature process is designed. By modifying the block data structure of Dag, the concept of redundant transaction is proposed and its formal definition is given. According to the type of redundant transaction, the transaction optimization processing method is given, the group verification is realized by Threshold Ring Signature, and the DAG bookkeeping optimization scheme based on threshold ring signature is given, signature security, transaction deletion security and chain security are analyzed. Finally, a simulation experiment was conducted through the concurrent execution of the block data construction program and the optimization program, and the results show that the optimization scheme basically does not affect the execution time of the program and the storage efficiency is significantly improved.
A Novel Method for Offline Handwritten Chinese Character Recognition Under the Guidance of Print

In this paper, we present a new method that views offline handwritten chinese character recognition (HCCR) as a Re-identification (ReID) task. We introduce a print dataset as the target that needs to be retrieved, and make the test set of offline HCCR as the object of interest. According to ReID’s scene, the goal is to find the most similar print sample as the prediction result for each object of interest. We also employ triplet loss for metric learning, and train model together with cross-entropy loss, which has a good effect on improving performance. Compared with the classification model, the experimental results show that our method achieves much better results in few-shot learning, whose dataset is randomly selected from overall datasets. When the training set used is 5% of HWDB1.1, the gap between them even reached 9.8%. At the same time, it also obtains an accuracy of 97.69% on ICDAR-2013 offline HCCR competition dataset.
Progressive Splitting and Upscaling Structure for Super-Resolution

Recently, very deep convolutional neural networks (CNNs) have shown great success in single image super-resolution (SISR). Most of these methods focus on the design of network architecture and adopt a sub-pixel convolution layer at the end of network, but few have paid attention to exploring potential representation ability of upscaling layer. Sub-pixel convolution layer aggregates several low resolution (LR) feature maps and builds super-resolution (SR) images in a single step. However, those LR feature maps share similar patterns as they are extracted from a single trunk network. Inspired by this, we propose a novel progressive splitting and upscaling structure, termed PSUS, which generates decoupled feature maps for upscaling layer to get better SR image. Experiments show that our method can not only speed up the convergence, but also achieve considerable improvement on image quality with fewer parameters and lower computational cost.
Knowledge-Based Chat Detection with False Mention Discrimination

Chat detection is critical for recently emerged personal intelligent assistants (PIA), which can be seen as a hybrid of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. Recent advances have attempted to utilize external domain knowledge to enhance utterance semantics understanding and can contribute to chat detection. However, it also inevitably introduces false mention (i.e., token spans being misidentified as entity mentions) in Chat utterances, causing performance to degrade. To deal with this issue, this paper proposes a new model for knowledge-based chat detection with false mention discrimination (FMD-KChat). A two-stage pipeline is adopted, which contains an additional neural network-based classifier in the first stage for distinguishing the false mentions and a feature fusion gate in the chat detection stage for combining the contextual representation with the external knowledge feature based on the false mention discrimination probability. Experiments on the SMP-ECDT benchmark corpus show the well performance of the proposed model.
Developing a Reliable System for Real-Life Emails Classification Using Machine Learning Approach

Cyber World has become accessible, public and commonly used to distribute and exchange messages between malicious actors, terrorists, and illegally motivated persons. Electronic mail is one of the most frequently used transfers of information on internet media. E-mails are the most important digital proof that courts in various countries and communities use to condemn and that enables researchers to work continually to improve e-mail analysis using state-of-the-art technology to find digital evidence from e-mails. This work introduces a distinctive technology to analyze emails. It is based on consecutive phases, starting with data processing, extraction, compilation, then implementing the SWARM algorithm to adjust the output and to transfer these electronic mails for realistic and precise results by adjusting the support algorithm of vector machines. For email forensic analysis this system includes all the sentiment terms plus positives and negatives cases. It can deal with the machine learning algorithm (Sent WordNet 3.0). Enron Data set is used to test the proposed framework. In the best case, a high accuracy rate is 92%.
Combinatorial primality test

In 1879, Laisant-Beaujeux gave the following result without proof: If n is a prime, then [EQUATION] This paper provides proofs of the result of Laisant-Beaujeux in two cases explicitly: (1) If an integer of the form n = 4k + 1, k &gt; 0 is prime, then ([EQUATION]) and (2) If an integer of the form n = 4k + 3, k ≥ 0 is prime, then [EQUATION]. In addition, the author proposes important conjectures based on the converse of the above theorems which aim to establish primality of n. These conjectures are scrutinized by the given combinatorial primality test algorithm which can also distinguish patterns of prime n whether it is of the form 4k + 1 or 4k + 3.
No Reference Image Quality Assessment for JPEG Images using Machine Learning Approach

Popular and widely used JPEG images suffer from blocking artifact. Blocking artifact degrades the quality of an image. No Reference Image Quality Assessment (NRIQA) metric using block based features are designed in this paper. Artificial Neural Network (ANN) and Neuro Fuzzy Classifier (NFC) are employed to evaluate the performance of the proposed metric. It is tested for LIVE, TID2008 and TID2013 datasets. Results show improvement in accuracy independent of image databases. The proposed metric is compared with state-of-the-art metrics for LIVE dataset.
AC0 Unpredictability

We prove that for every distribution D on n bits with Shannon entropy ≥ n − a , at most O (2 d a log d +1 g )/γ 5 of the bits D i can be predicted with advantage γ by an AC 0 circuit of size g and depth D that is a function of all of the bits of D except D i . This answers a question by Meir and Wigderson, who proved a corresponding result for decision trees. We also show that there are distributions D with entropy ≥ n − O (1) such that any subset of O ( n / log n ) bits of D on can be distinguished from uniform by a circuit of depth 2 and size poly( n ). This separates the notions of predictability and distinguishability in this context.
The Monitoring and Early Warning System of Geological Disaster Mass Survey and Prevention in Linzhi

Based on Beidou satellite navigation and communication technology, this paper develops a monitoring and early warning system for geological disaster mass survey and prevention, aiming at the problems of great harminess and difficulty in monitoring geological disasters in Linzhi. The whole system is constructed using B/S architecture. The server is developed by Asp.Net language, the client is developed by HTML5, CSS, JavaScript and other technologies. The data transmission between the client and server is realized by Ajax technology. The client includes two parts: web-end and app-end, which are respectively developed by layui and uniapp framework. The app-end determines the disaster location and reports the scene pictures based on the Beidou satellite navigation technology (BDS), and then transmits the collected disaster data to the platform with the help of the 4G or 5G network. The administrator pushes the reviewed disaster information to the public through the audit and intelligent analysis of the disaster data. This system has been tested in Linzhi, and the results show the system can effectively assist the mass survey and prevention work.
Semi-Automatic 2D to 3D Video Conversion

In this chapter, the process of converting conventional video sequences to stereo format is considered. Key frame detection, depth assignment, depth propagation, motion vector estimation, background inpainting, and stereo synthesis comprise the stereo conversion pipeline. We also consider several aspects of content production, such as stereo shooting and stereo conversion. The depth propagation algorithm is based on patch matching using patch hash codes, and the results are compared with motion vector-based depth propagation results. Motion vector estimation is an essential algorithm that is used to reveal video clip attributes, and it plays an important role during conversion. We propose an optical flow algorithm based on a primal-dual optimization algorithm. This work is based on the authors’ experience during a large-scale project at the Samsung Research centre in support of newly appearing TVs on the market, which were equipped with a “3D-ready” feature.
Chip and System Design of Intrinsic Image Decomposition and Enhancement Based on Conditional Generative Adversarial Network

A new Conditional Generative Adversarial Network architecture the paper proposed, which has two layers of neural networks. The first layer neural network uses the concept of intrinsic image decomposition to train a model that can transfer styles and generate images of reflectance layers. The intrinsic image decomposition algorithm generates the shadow layer image. The second layer of the neural network is used to train luminance correction, and the image of the shadow layer is enhanced to achieve the effect of bright and dark correction. Finally, results of two layers are combined to finish the enhancement, we implement the convolutional layer into the digital circuit through the arrangement of the array, and reducing the computation time.
Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?

Substantial efforts have been made on improving the generalization abilities of deep neural networks (DNNs) in order to obtain better performances without introducing more parameters. On the other hand, meta-learning approaches exhibit powerful generalization on new tasks in few-shot learning. Intuitively, few- shot learning is more challenging than the standard supervised learning as each target class only has a very few or no training samples. The natural question that arises is whether the meta-learning idea can be used for improving the generalization of DNNs on the standard supervised learning. In this paper, we propose a novel meta-learning based training procedure (MLTP) for DNNs and demonstrate that the meta-learning idea can indeed improve the generalization abilities of DNNs. MLTP simulates the meta-training process by considering a batch of training samples as a task. The key idea is that the gradient descent step for improving the current task performance should also improve a new task performance, which is ignored by the current standard procedure for training neural networks. MLTP also benefits from all the existing training techniques such as dropout, weight decay, and batch normalization. We evaluate MLTP by training a variety of small and large neural networks on three benchmark datasets, i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet. The experimental results show a consistently improved generalization performance on all the DNNs with different sizes, which verifies the promise of MLTP and demonstrates that the meta-learning idea is indeed able to improve the generalization of DNNs on the standard supervised learning.
Image decomposition based on nonlinear reaction–diffusion systems

The separation of image content into semantic parts plays a vital role in applications such as compression, enhancement, restoration, and more. In recent years, several pioneering works suggested such a separation be based on variational formulation and others using independent component analysis and sparsity. This paper presents a novel method for separating images into texture and piecewise smooth (cartoon) parts. The new model is based on a class of degenerate and singular reaction–diffusion systems coupled by the minimizing total variation flow and the p-Laplace flow. The existence and uniqueness of entropy solutions to the system with BV initial data and Neumann boundary conditions are established by the regularization method. Experimental results illustrate that the new model can preserve textures better than other methods.
Position, Velocity, and Time Solutions

In this chapter, a detailed explain will be given of how to use the pseudo-range, carrier phase, and Doppler observations discussed in Chap. 5 to calculate the position, velocity, and time deviation of the receiver. This chapter will be divided into two parts.
JT-MGCN: Joint-temporal Motion Graph Convolutional Network for Skeleton-Based Action Recognition

Recently, action recognition methods using graph convolutional networks (GCN) have shown remarkable performance thanks to its concise but effective representation of human body motion. Prior methods construct human body motion graph building edges between neighbor or distant body joints. On the other hand, human action contains lots of temporal variations showing strong temporal correlations between joint motions. Thus the characterization of an action requires a comprehensive analysis of joint motion correlations on spatial and temporal domains. In this paper, we propose Joint-temporal Motion Graph Convolutional Networks (JT-MGCN) in which joint-temporal edges learn the correlations between different joints at different time. Experimental evaluation on large public data sets such as NTU rgb+d data set and kinetics-skeleton data set show outstanding action recognition performance.
A fast algorithm for underwater image restoration based on CUDA

Due to the severe underwater environment condition and the effects of water scattering, the images acquired by underwater optical vision system often have many problems such as low resolution, contrast and serious color shifts. Many scholars have proposed many image restoration algorithms and proved to be effective, but there is still a problem of excessive time consumption. In this regard, this article comprehensively considers the principle of dark channel underwater image imaging, combined with parallel computing related theories, and modified the serial model of the traditional underwater image restoration algorithm to be suitable for CUDA (Compute Unified Device Architecture) Parallel model. Through experimental tests, while ensuring a restoration effect similar to similar algorithms, a speed increase of about 14 times has been achieved, and the real-time restoration of underwater images is realized.
Using digital technologies in inclusive education for preschool children

The article reveals the features and conditions of digital technologies in the inclusive education of preschool children with different nosologies. The very concepts used by modern teachers of preschool educational organizations are disclosed. Data on the role of parents in the digital education space and the peculiarities of working with them on the use of these technologies in a distance form are presented. Joint actions of teachers and parents in the preschool educational organization and separate parents’ actions only on the organization of work on this or that digital platform and their application in the education of preschool children are shown. The tools and technologies used in preschool educational organizations by different professionals are reflected. The requirements for the organization of the child’s workplace during the use of digital technology in preschool educational organizations and at home are disclosed in detail.
RLST: A Reinforcement Learning Approach to Scene Text Detection Refinement

Within the research of scene text detection, some previous work has already achieved significant accuracy and efficiency. However, most of the work was generally done without considering about the implicit relationship between detection and eye movements. In this paper, we propose a new method for scene text detection especially for its refinement based on reinforcement learning. The idea of this method is inspired by Saccadic Eye Movements and Peripheral Vision. A saccade makes it possible for humans to orient the gaze to the location where a visual object has appeared. Peripheral vision gathers visual information of surroundings which provides supplement to foveal vision during gazing. We propose a simple pipeline, imitating the way human eyes do a saccade and collect peripheral information, to locate scene text roughly and to refine multi-scale vision field iteratively using reinforcement learning. For both training and evaluation, we use ICDAR2015 Challenge 4 dataset as a base and design several criteria to measure the feasibility of our work.
Hand Gesture Recognition System Using IoT and Machine Learning

Internet of things and machine learning being niche domains with fast-paced development, it paves the way for improved usage of technology. Disability is one of the major issues that humanity faces, and people with disabilities may not be able to use modern-day computers with ease. Therefore, the concept of hand gesture recognition was implemented. Built using the latest IoT devices, hand gesture recognition is implemented. Most of the hand gesture recognition algorithms utilize image recognition and processing and therefore increase in development complexity. To overcome this, we came up with an application of hand gesture recognition using Arduino and machine learning implemented using SVM for hand gesture classification. Hand movements are captured using the MPU-6050, an IoT device that provides six-axis readings based on the movement performed and sent wirelessly to the computer via Bluetooth. Once data is received by the device, ML is implemented, achieving an accuracy of 98%.
Recommendation algorithm based on community structure and user trust

Abstract While contemporary community‐based recommendation algorithms based on a single community structure are more capable of processing large datasets than ever, they lack recommendation precision. This article proposes a collaborative filtering recommendation algorithm that integrates community structure and user implicit trust. The algorithm first applies a method based on the Gaussian function to fill the matrix of item ratings of users to alleviate data sparsity. It then uses the trust matrix to obtain the asymmetric trust relationship of the trustor and trustee, based on which the degree of users' implicit trust is calculated. The users are divided into communities based on the implicit trust degree to determine the influence among users more accurately. The algorithm then predicts the target user's rating using the ratings of users in the community to generate recommendations. To verify the performance of the proposed algorithm, we compared the proposed algorithm with three contemporary algorithms under the same conditions using FilmTrust datasets. The recommendation accuracy as well as the mean absolute error and root mean square error values of the proposed algorithm were better than those of the other four algorithms by approximately 14% and 4%, respectively. The experimental results demonstrate that the proposed algorithm can achieve better recommendation efficiency than existing algorithms.
Towards Modal Software Engineering

In this paper we introduce the notion of Modal Software Engineering: automatically turning sequential, deterministic programs into semantically equivalent programs efficiently operating on inputs coming from multiple overlapping worlds. We are drawing an analogy between modal logics, and software application domains where multiple sets of inputs (multiple worlds) need to be processed efficiently. Typically those sets highly overlap, so processing them independently would involve a lot of redundancy, resulting in lower performance, and in many cases intractability. Three application domains are presented: reasoning about feature-based variability of Software Product Lines (SPLs), probabilistic programming, and approximate programming.
Word Relationship Extraction from Short Text Message

Thesaurus construction is based on well-organized sources such as news articles. Word usage changes with time, and therefore, it is desirable to use a daily communication tool such as Twitter for thesaurus construction. Unfortunately, sources comprising limited text content like Twitter are rarely used as a source in the construction of a thesaurus owing to the data sparseness problem. In this paper, we propose a thesaurus construction method that efficiently extracts relationships between words from Twitter using tensor decomposition.
Construction of Heterogeneous Dynamic Grouping Pattern Based on Neural Network

Regardless of online or offline learning, there are operational difficulties in “facing all students”, and it is very difficult to pay attention to the “individual differences” between students. As we all know, students are developing people. During the teaching process, students' mentality, knowledge, and abilities will change, which may shouldn't be taken into account by static grouping. In this study, neural network model was used to construct the mapping relationship between students' characteristics and heterogeneous grouping, and the trained model was used to predict the grouping position at the next moment. This dynamic grouping algorithm can ensure that every student is in a heterogeneous group for a long time. Finally, we propose a heterogeneous dynamic grouping teaching pattern.