Reviewerly/exHarmony huggingface dataset:
A Reward Shaping Method based on Meta-LSTM for Continuous Control of Robot

In the continuous control of robots based on reinforcement learning, Proximal Policy Optimization (PPO) is a highly popular policy-based reinforcement learning algorithm. However, the PPO algorithm reduces the policy update area prematurely due to the influence of the advantage function on the policy search, which causes the policy search to progress slowly. Drawing inspiration from meta-learning, a reward shaping algorithm based on the Meta-LSTM is proposed. To ensure the stability of the learning reward shaping process, we innovatively added constraints on the importance of actions in Meta-LSTM and through the motivate coefficient to ensure the adaptability of reward to different environments. The experimental results show that the reward based on the Meta-LSTM method is better than the benchmark algorithm in guiding the policy search, and the promotion effect is more evident in the task with higher dimensions.
Efficient Random Grid Visual Cryptographic Schemes having Essential Members

This paper deals with both “OR”- and “XOR”-based black and white random grid visual cryptographic schemes (RGVCS) for (t, k, n) essential access structure. The (t, k, n) essential access structure is considered to be a generalization of the threshold (k, n) access structure in the sense that the t essential participants or members must always be present in all the successful attempts to recover the secret image. To the best of our knowledge, the current proposed work is the first attempt in the field of RGVCS, which provides efficient constructions of the (t, k, n)-RGVCS for “OR” as well as “XOR”-based models. Finding the closed forms of light contrasts is challenging and mathematically hard problems. However, this paper mathematically brings the closed forms of the light contrasts for the “OR” as well as “XOR”-based models. Our schematic mathematical analyses and experimental results exhibit the practicality of our scheme.
Unpaired Multimodal Neural Machine Translation via Reinforcement Learning

End-to-end neural machine translation (NMT) heavily relies on parallel corpora for training. However, high-quality parallel corpora are usually costly to collect. To tackle this problem, multimodal content, especially image, has been introduced to help build an NMT system without parallel corpora. In this paper, we propose a reinforcement learning (RL) method to build an NMT system by introducing a sequence-level supervision signal as a reward. Based on the fact that visual information can be a universal representation to ground different languages, we design two different rewards to guide the learning process, i.e., (1) the likelihood of generated sentence given source image and (2) the distance of attention weights given by image caption models. Experimental results on the Multi30K, IAPR-TC12, and IKEA datasets show that the proposed learning mechanism achieves better performance than existing methods.
Size and Frequency

Part IV thus far has examined how statistical universals might contribute to the formation of linguistic units such as words and their values. This chapter will continue to examine these units, especially in terms of the length distribution of words and compounds.
How Do Simple Transformations of Text and Image Features Impact Cosine-Based Semantic Match?

Practitioners often resort to off-the-shelf feature extractors such as language models (e.g., BERT or Glove) for text or pre-trained CNNs for images. These features are often used without further supervision in tasks such as text or image retrieval and semantic similarity with cosine-based semantic match. Although cosine similarity is sensitive to centering and other feature transforms, their impact on task performance has not been systematically studied. Prior studies are limited to a single domain (e.g., bilingual embeddings) and one data modality (text). Here, we systematically study the effect of simple feature transforms (e.g., standardizing) in 25 datasets with 6 tasks covering semantic similarity and text and image retrieval. We further back up our claims in ad-hoc laboratory experiments. We include 15 (8 image + 7 text) embeddings, covering the state-of-the-art models. Our second goal is to determine whether the common practice of defaulting to the cosine similarity is empirically supported. Our findings reveal that: (i) some feature transforms provide solid improvements, suggesting their default adoption; (ii) cosine similarity fares better than Euclidean similarity, thus backing up standard practices. Ultimately, our takeaways provide actionable advice for practitioners.
Design of online teaching quality evaluation system for Private University: —Research based on deep learning algorithm

The paper takes private university as research object, analyzes the existing problems of online teaching quality evaluation. According to actual situation, this paper constructs online teaching quality evaluation index system combines deep learning algorithm, designs teaching quality evaluation system from four kinds of user roles and five evaluation units. The system designed by this paper is highly intelligent and easy to use, which also makes up the gap and deficiency of online evaluation and teaching, and plays a great role in improving the online teaching quality of private colleges and universities and promoting the educational reform of private colleges and universities.
Issues in Grammatical Description

This chapter outlines a number of issues which are important in grammatical description (though they are often taken for granted) and which can affect readers’ understanding: the depth and refinement of the description, how to distinguish between different uses, and how to recognise and verbalise the concepts of formality and acceptability. Following this there is a consideration of three other important issues based on a research project (METALANG) using a corpus of grammatical description:
Dynamic User Interface Composition

With continuous innovation and a persistent attempt to achieve natural human machine interactions, user interfaces have evolved into the current phase of Voice User Interfaces. Voice User Interfaces augmented with visual information are becoming prominent in a variety of devices with different form-factors. Developing user interfaces for such a wide range of display configurations is a challenging task. This paper puts forward an approach to dynamically compositing such user interfaces without compromising on User Experience. This work examines details of devising a neural network model to automate user interface layout composition based on aesthetics and saliency. A 40000 dataset was created for this work with eight trained annotators. Ground truth is estimated from the above annotations using Expectation Maximization algorithm. Experiments show that users are highly satisfied with the model results. This model is deployed in Lock Screen auto layout available in latest Samsung Android phones.
COMPARATIVE ANALYSIS OF PERSONAL FEATURES IN NAVAL SPECIALISTS WITH SIGNS OF PROFESSIONAL BURN OUT

Introduction. The formation of professional burnout syndrome is largely determined by stable personality characteristics, which determine the plasticity of responding to a situation and its adaptation, which predetermines the relevance of their study. Purpose: to assess personal characteristics based on the application of the SMIL methodology in naval specialists who have (or do not have) signs of professional burnout. Materials and methods. The study involved 250 naval specialists aged 25 to 45, divided into 2 groups — with the absence (group 1 (n=91 people)) and the presence (group 2 (n=159 people)) signs of professional burnout, in which personality traits. Results and its discussion. It was found that naval specialists with signs of professional burnout, in comparison with those who do not have it, have significantly higher indicators on the scales of correction, activity, masculinity, as well as significantly lower indicators on the scales of rigidity and anxiety; however, these indicators were within the average normative values and were of a relative nature. Conclusion. The obtained data can be used in applied terms as one of the criterio Introduction. The formation of professional burnout syndrome is largely determined by stable personality characteristics, which determine the plasticity of responding to a situation and its adaptation, which predetermines the relevance of their study. Purpose: to assess personal characteristics based on the application of the SMIL methodology in naval specialists who have (or do not have) signs of professional burnout. Materials and methods. The study involved 250 naval specialists aged 25 to 45, divided into 2 groups — with the absence (group 1 (n=91 people)) and the presence (group 2 (n=159 people)) signs of professional burnout, in which personality traits. Results and its discussion. It was found that naval specialists with signs of professional burnout, in comparison with those who do not have it, have significantly higher indicators on the scales of correction, activity, masculinity, as well as significantly lower indicators on the scales of rigidity and anxiety; however, these indicators were within the average normative values and were of a relative nature. Conclusion. The obtained data can be used in applied terms as one of the criterion guidelines at the stage of selection of naval specialists, since personality traits, being stable psychological formations, can be a kind of damper preventing the formation of unfavorable functional states in the examined persons. n guidelines at the stage of selection of naval specialists, since personality traits, being stable psychological formations, can be a kind of damper preventing the formation of unfavorable functional states in the examined persons.
Double Ensemble Soft Transfer Network for Unsupervised Domain Adaptation

Domain adaptation aims to transfer the enriched label knowledge from large amounts of source data to unlabeled target data. Recent methods start to solve the class-wise domain adaptation problem by incorporating the soft labels to each target data. Although the soft label strategy could alleviate the negative influence caused by the hard label strategy to some extent, the improper propagation sequence ignoring the labeling difficulties of different target examples will lead to confusing probabilities problem. Moreover, the instability of a single propagation model in dealing with various data may also hinder the performance of target label inference. To address these limitations, we propose a Double Ensemble Soft Transfer Network (DESTN) to jointly optimize the class-wise adaptation and learn the discriminative domain-invariant features with clear soft target labels. Our motivation is to construct a Label Propagation Ensemble (LPE) model by various feature subspaces so as to get robust and clear soft target labels for class-wise domain adaptation. Meanwhile, the other Classifiers Ensemble Framework (CEF) is trained on the labeled source samples and the reliable pseudo-labeled target samples for learning the discriminative features during the iteration. Extensive experiments show that DESTN significantly outperforms several state-of-the-art methods.
2D Phase Unwrapping Based on U-Net and SVD for Fringe Projection Profilometry

Phase unwrapping, a vital step with challenges in the Fringe Projection Profilometry (FPP), is directed to retrieve the origin phases from the wrapped phases to finish 3D reconstruction of objects. But Noise and spectrum aliasing from image capturing will lead to error diffusion in traditional phase unwrapping algorithms. This paper proposes a new, fast and robust 2D phase unwrapping method (called SVD-UNet) based on Singular Value Decomposition (SVD) and semantic segmentation model U-Net. We equivalently convert the question of unwrapping phase into a classification issue of the order number of phase unwrapping. According to the features of the wrapped phase, it is easier for our method to obtain global information and improve the effect of semantic segmentation when taking the results of linear decomposition through SVD as the input of U-Net. The experimental results show that the new method has more stable performance, faster convergence speed and is more robust than the state-of-the-art.
DOES COVID-19 INFLUENCE THE IMPACT OF CORPORATE PARTNERSHIPS ON ORGANIZATIONAL PERFORMANCE? TAKE THE ELECTRONIC INDUSTRY INVESTIGATION METHOD OF ZHEJIANG PROVINCE AS AN EXAMPLE

Due to the influence of COVID-19, the global electronic industry has encountered problems in cooperation. Including a number of chip supply problems, so want to know whether the electronics industry executives have an impact on the company's performance?This study through the investigation to the region in zhejiang province, 2020 local top one thousand electronic industry enterprise executives as questionnaire survey, the questionnaire (N = 331, excluding the reserved cross validity with data and error data, the number of available data and 195 pen, comply with the minimum sample demand, the main research object is for zhejiang area electronic industry as investigation object, and to establish its enterprise system agree with will affect the overall performance and profit of enterprise.Questionnaire design of this study was made through Likert seven-point scale, and reliability and validity verification analysis was conducted for this topic
New Thoughts on the Course Teaching of Computer Specialty Under the Background of Big Data

This article analyzes the current problems in the teaching of computer majors in the era of big data and proposes that teachers should change their teaching methods. Teachers should implement personalized teaching reforms with the help of student learning resource data and combine teaching with big data massive teaching resources. To improve teaching methods, guide students to independently formulate learning programs based on their own knowledge of knowledge, promote students’ autonomous learning ability, and achieve the goal of improving teaching quality. Colleagues suggested that teaching in the era of big data needs to strengthen the construction of resource platforms as a strong support.
Transitive Analysis of News Reports on Covid-19 of New York Times From the Perspective of Ideological Tendency

As an important meta-function in Systemic Functional Grammar (SFG) proposed by Halliday, the ideational function sets an effective model to do critical discourse analysis. This paper attempts to use transitivity theory to analyze two New York Times reports on the double standard of the lockdown against Coronavirus in China and Italy. The study explores the ideological tendency in American news reports, which shows great disparity between the West Media and China.
Copy-Move Forgery Detection Using Scale Invariant Feature Transform

In the era of digital technology, digital images play an important role in the day to day applications starting from medical diagnosis to court of laws as evidence for the crime investigation. Nowadays it becomes easy to tamper the image with low-cost software tools and hence, only by visual perception one cannot guarantee the authenticity and the integrity of its originality. Copy-move image forgery (CMIF) is one such tampering technique where a region of the image itself is copied and pasted into another region in the same image. This is done either to hide important information or to pass irrelevant data to others. This image forgery (IF) seems to be realistic and hard to detect as the forged region encompasses similar structural characteristics as the original one. This report represents a key-point based method to detect CMIF using Scale Invariant Feature Transform (SIFT). The experimental results and analysis of this method are discussed in detail on online available datasets. This method can detect the CMIF, even the image is distorted by intermediate attacks such as scale, rotation, and some post-processing attacks like image blurring, noise adding, contrast adjustment, color reduction, brightness changes, JPEG compression, etc. This method is also able to detect multiple CMIF.
Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.
A Prototype Implementation of NNEF Execution Framework with CUDA Acceleration

Recently, we have many research works on the neural networks and their related issues. For exchangeability of neural network frameworks, the Neural Network Exchange Format (NNEF) specification is now widely used. Due to very large size of these neural networks, their accelerations are actively explored, and can be achieved through parallel processing techniques. In this paper, we present a prototype implementation of NNEF execution system with parallel-processing accelerations based on CUDA (compute unified device architecture). We will tune the prototype acceleration to achieve more remark-able speed ups.
Fast scalar multiplication of degenerate divisors for hyperelliptic curve cryptosystems

Scalar multiplication is the main operation in the implementation of hyperelliptic curve cryptosystems, where the basic arithmetic of reduced divisor classes is required. In this work, some explicit formulas are proposed for the arithmetic of reduced divisor classes by exploiting Jacobian coordinates when the degenerate divisor is involved. Moreover, an efficiency analysis shows that the degenerate divisor as a base element can be a valid alternative in hyperelliptic curve cryptosystems as well.
Effectiveness of Classifiers to Identify Hand Gestures with Motion Capture Coordinate Markers

In a world where technology is increasingly moving towards less wires, less devices, and more portability, the ability of a system to accurately interpret hand gestures is extremely valuable. The innovation of small, inexpensive cameras only makes this even more practical. The practicality of such a system rests on the assumption that hand gestures can be recognized from participants based solely on the shape of their hand. The dataset used for this research, while captured with node markers on a special glove, is formatted independent of a marker indexing system and could be applied in conjunction with a marker identification system for real world application of hand gesture classification. This paper presents an analysis and comparison of the effectiveness classifiers have when trying to determine hand gestures using motion capture coordinate markers provided by a Viacon camera. Stochastic Gradient Descent, Decision Trees, Logistic Regression, Random Forests, and Bagging are applied in this study. Overall, the Random Forests classifier performed the best with respect to performance measures such as recall, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score, and precision.
Random Number Generators in Training of Contextual Neural Networks

Much care should be given to the cases when there is a need to compare results of machine learning (ML) experiments performed with the usage of different Pseudo Random Number Generators (PRNGs). This is because the selection of PRNG can be regarded as a source of measurement error, e.g. in repeated N-fold Cross Validation (CV). It can be also important to verify if the observed properties of a model or algorithm are not due to the effects of the use of a particular PRNG. In this paper we conduct experiments so that we can observe the possible level of differences in obtained values of various measures of classification quality of simple Contextual Neural Networks and Multilayer Perceptron (MLP) models for various PRNGs. It is presented that the results for some pairs of PRNGs can be significantly different even for large number of repeats of 5-fold CV. Observations suggest that when different ML models and algorithms are compared with the usage of 5-fold CV when different PRNGs were used, the confidence interval should be doubled or confidence level higher than 95% should be used. Additionally, it is shown that even under such conditions classification properties of Contextual Neural Networks are found statistically better than of not-contextual MLP models.
Reforming Privacy Law in America: A Comparative Analysis on Digital Contact Tracing Data and Its Implications in the Age of COVID-19

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Green’s Functions for Localized Excitations

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Metaheuristic Optimization Algorithms

In this introductory chapter, specific state-of-the-art metaheuristic optimization algorithms are outlined. The presented algorithms belong to the broad trajectory-based and population-based categories, and they are particularly selected as they constitute the building blocks of algorithm portfolios presented in the forthcoming chapters.
The Chebotarev density theorem for function fields — Incomplete intervals

We prove a Pólya-Vinogradov type variation of the Chebotarev density theorem for function fields over finite fields valid for “incomplete intervals” I ⊂ F p , provided ( p 1 / 2 log ⁡ p ) / | I | = o ( 1 ) . Applications include density results for irreducible trinomials in F p [ x ] , i.e. the number of irreducible polynomials in the set { f ( x ) = x d + a 1 x + a 0 ∈ F p [ x ] } a 0 ∈ I 0 , a 1 ∈ I 1 is ∼ | I 0 | ⋅ | I 1 | / d provided | I 0 | > p 1 / 2 + ϵ , | I 1 | > p ϵ , or | I 1 | > p 1 / 2 + ϵ , | I 0 | > p ϵ , and similarly when x d is replaced by any monic degree d polynomial in F p [ x ] . Under the above assumptions we can also determine the distribution of factorization types, and find it to be consistent with the distribution of cycle types of permutations in the symmetric group S d .
Modeling measurement and its quality

This chapter aims to propose a general model of a measurement process, consistent with the ontological and epistemological commitments developed in the previous chapters. Starting from the premise that any measurement is grounded on an empirical process, we propose a characterization of measurement methods related to the complementary roles of empirical and informational components, by broadly distinguishing between direct and indirect methods of measurement, where indirect measurements necessarily include at least one direct measurement. The stages of direct measurement are then analyzed and exemplified in reference to both physical and psychosocial properties. This is the basis for discussing once again the quality of measurement, now described in terms of the complementary requirements of object relatedness (“objectivity”) and subject independence (“intersubjectivity”).