Reviewerly/exHarmony huggingface dataset:
Kernelized discriminative–collaborative representation-based approach for pattern classification

In representation-based classification methods, Gaussian function is adopted for realizing the nonlinear representation of samples. However, there are some problems. The definition of Gaussian kernel includes exponential operation, which causes the parameters to be sensitive and consumes exponential computational time. In addition, the performance of pattern classification may be dropped with noise (outliers). To solve these problems, a new Euclidean Kernel is designed to replace Gaussian Kernel. Moreover, a new weight and an uncorrelated sparsity constraint are introduced to enhance within-class representation consistency and between-class representation discrepancy. Then, we propose a kernelized discriminative–collaborative representation-based approach for pattern classification by incorporating the cooperative and competitive representation. The proposed model fully excavates the intrinsic factors of the samples and presents promising performance and better interpretability . Finally, extensive experiments are conducted in comparison with other popular methods on different types of databases, and these experiments verify that the designed approach performs better than its competitors. • A new Euclidean Kernel and a new weight are constructed to capture the intrinsic structural features and nonlinear attributes among samples. • An uncorrelated sparsity constraint is designed to reduce the correlation among different-label samples and generate a sparse solution. • We propose a kernelized discriminative–collaborative representation-based approach to transform the samples from low-dimensional space into the discriminative nonlinear one.
Role of Artificial Intelligence in Gamification for the Emerging Markets

The last few years have witnessed massive artificial intelligence (AI) and gaming adoption that has navigated the emerging markets. Moreover, according to the WOG summit (world government summit report, by Nielsen) 2020 reports, AI with gaming mechanisms are expected to enrich marketing services in the coming future in the emerging markets. Countries such as India, China and South Korea contribute significantly to this area, and recent forecasting allows the need to increase in emerging markets. Similarly, these countries have a maximum number of youth gamers and AI-driven technology adopters. The adoption of AI-driven technologies and amplification of gamification in marketing services are new phenomena. Moreover, gaming and AI dynamics are relatively new in emerging countries and need greater attention. Thus, this book chapter proposes a dyad model that would explain users' and companies' perspectives to understand the role of AI and gamification for the emerging markets. The chapter will explain how AI-driven gamification helps the users of emerging markets. The chapter will also illustrate how companies in emerging markets use AI for gamification. Therefore, the dyad model would also comprehend the gap, opportunities and challenges in this area and the subsequent strategies to help all the stakeholders.
THE PROCESS OF DEVELOPING A SOFTWARE FOR DEAD RECKONING PROBLEMS USING PYTHON

This paper aims to give an insight into the process of building a software from scratch, using Python. In this respect, every stage of building a software will be presented thoroughly, namely: requirements analysis, the software design projection, the computational implementation, the assembling and conversion to a standalone application. Furthermore, there will be made a short presentation of the final product, NavyCalc software, along with its functions, in order to complete both the paper and the process of development.
Modeling the optical properties of transparent and absorbing dielectrics by means of symbolic regression

In this contribution we explore the possibilities and limitations of symbolic regression as an alternative to the approaches currently used to characterize the dispersive behavior of a given material. To this end, we make use of genetic programming to retrieve, from either ellipsometric or spectral data, closed-form expressions that model the optical properties of the materials studied. In a first stage we consider transparent dielectrics for our numerical experiments. Next we increase the complexity of the problem and consider absorbing dielectrics, which not only require the use of complex functions to model their dielectric function, but also imply a supplementary constraint imposed by the verification of the causality principle.
Construction of College English Teaching Environment Assessment Model Based on BP Neural Network and Multiple Intelligence Theory

College English has almost always been a required course, and a college student's level of English proficiency is one of the factors used to assess their learning capacity. The quality of students' English learning is largely influenced by the level of English instruction provided in colleges. However, there are still a lot of issues with college English instruction today, the most glaring of which is that English instruction is being overly simplified, and that the methods, modes, and purposes of instruction are also very narrow. Due to this, it is challenging for most colleges and universities' English teaching levels to satisfy the requirements of high-level education. The PSO-BP neural network model, which optimizes the BP neural network (BPNN), is used in this study to build a high-precision and diversified English teaching evaluation model in order to address the aforementioned issues. According to the experimental findings, the PSO-BPNN algorithm has a relative error of just 0.29 percent and an average accuracy rate of 97.02 percent. Overall performance is superior to that of the conventional BPNN algorithm, and it is the most adaptable in terms of creating various evaluation modes.
Phase-change material assisted on-chip wavefront shaping for optical switching and beam splitting

On-chip wavefront shaping is indispensable for implementing optical routing and signal processing. By referring to the concept of metasurface used in free space, we can realize the on-chip wavefront shaping to manipulate the guided wave. In this paper, we combine the phase-change material Sb2S3 nanorods with 1D on-chip meta-lens to manipulate the optical routing direction. We use the 1550 nm TE0 mode to realize the functions of 1 × 2 and 1 × 3 optical switching, and then the 1 × 2 and 1 × 3 beam splitting. By combining the function of beam deflection and beam splitting, we can realize tunable beam steering and complex optical flow control.
Construction of a Random Matrix Model of Curriculum System Based on Digital Technology in the Context of Smart Education

This paper adopts the method of digital technology fused with a random matrix to conduct in-depth research and analysis on the curriculum system under the context of smart education and designs a corresponding model for practical use. Firstly, we analyze the status of digital learning research, take the digital teaching model based on independent inquiry as to the basis of teaching design, and carry out digital-based teaching experiments under the premise of fully examining the actual teaching conditions. The digital learning platform is designed to meet the teaching model, platform design, platform application, and implementation of the school, and the digital learning platform is designed and developed through PHP technology. Then, the matrix filling algorithm is used to fill in some of the missing data in the observation matrix to obtain the complete data matrix, and the Hankel matrix inverse transformation is used to finally obtain the reconstructed original signal, thereby forming the ability of innovative works. It is an important aspect of cultivating students’ innovative spirit and practical ability in the development of educational informatization. Ultimately, students use the platform to try out and be able to learn the knowledge. The experimental class and the control class experimented with a quasi-experimental research method, the experimental class used a digital teaching mode, and the control class used a traditional IT teaching mode. The digital learning ability of students was measured by a digital learning ability measurement scale before and after the experiment, and then the data before and after the experiment were analyzed by horizontal and vertical comparison to verify whether the digital teaching model can effectively cultivate junior high school students compared with the traditional teaching model. The digital learning ability of junior high school students was verified.
Design and Implementation of Financial Service and Management Platform considering Support Vector Machine Algorithm

With the rapid economic development, the financial industry has quietly become the leader of industries, the core and lifeblood of promoting economic development. At the same time, various financial services and management platforms emerge one after another. However, the emergence of financial services and management platforms cannot effectively alleviate the current financial crisis. In the face of increasingly complex financial risks, traditional financial service and management platforms cannot achieve effective information sharing, which leads to continued low service and management efficiency and frequent financial risk problems. Support vector machine is a data classification algorithm based on supervision, which can realize data sharing and improve the efficiency of data processing. The article firstly readjusted the underlying architecture of the financial service and management platform to break through the barriers of data interaction. Then on this basis, the article further combines the support vector machine algorithm and extends it from binary data classification to multivariate classification. Finally, the paper redesigns the financial service and management platform considering support vector machines. After a series of experiments, it can be found that the financial service and management platform based on the support vector machine algorithm can reduce the financial risk by 17.2%, improve the financial service level by 30.2%, and improve the financial comprehensive service level by 45.2%. At the same time, thanks to information sharing and interaction, the financial service and management platform can effectively predict financial risks, and the accuracy of the prediction basically reaches 78.9%. This shows that a financial service and management platform that takes into account the support vector machine algorithm can effectively prevent financial risks and improve the efficiency of financial services and management.
Construction and Application of a New Metal Random Matrix-Based Theory in a Numerical Phantom of the Metaverse NFT

As the metaverse is hot, nonhomogenized tokens (NFT) as digital artwork identifiers present different characteristics and application values from other homogenized tokens, and their use for copyright verification will suffer from the problems of storage space limitation and data verification reliance on database, this study designs an NFT digital copyright authentication model for textual works. To cater for the uncontrollability of conventional Hash algorithms in stream matching due to the high conflict rate, a new random matrix theory is applied to propose a new Hash algorithm, which is used on the block structure of NFT credential authentication, while extending the block structure so that the data within the work is completely stored in the blockchain with NFT as the credential, allowing the database to store the work data in a relatively safe manner. The verification of the work data has the immutability and unique cryptographic solution of NFT. The NFT-based digital model collects TR information and conducts 30 tests, and the average test time for generating blocks is 0.53 s. Through the block query for detection, 248,655 words have exceeded the number of words of an article, and the consumption time is only 0.23s, to meet the customer's real-time query requirements for the system. According to the overhead ratio, the record storage expenditure is about 2-3 times of the text storage expenditure, the work storage expenditure, and the storage expenditure for storing 240,000 words for authentication is about 3720 KB.
Non-uniform illumination underwater image enhancement via events and frame fusion

Absorption and scattering by aqueous media can attenuate light and cause underwater optical imagery difficulty. Artificial light sources are usually used to aid deep-sea imaging. Due to the limited dynamic range of standard cameras, artificial light sources often cause underwater images to be underexposed or overexposed. By contrast, event cameras have a high dynamic range and high temporal resolution but cannot provide frames with rich color characteristics. In this paper, we exploit the complementarity of the two types of cameras to propose an efficient yet simple method for image enhancement of uneven underwater illumination, which can generate enhanced images containing better scene details and colors similar to standard frames. Additionally, we create a dataset recorded by the Dynamic and Active-pixel Vision Sensor that includes both event streams and frames, enabling testing of the proposed method and frame-based image enhancement methods. The experimental results conducted on our dataset with qualitative and quantitative measures demonstrate that the proposed method outperforms the compared enhancement algorithms.
Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems

Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of medical-order sequence variants , where a “frequent pattern” can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system’s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.
A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation

With the resurgent interest in building open-domain dialogue systems, the dialogue generation task has attracted increasing attention over the past few years. This task is usually formulated as a conditional generation problem, which aims to generate a natural and meaningful response given dialogue contexts and specific constraints, such as persona. And maintaining a consistent persona is essential for the dialogue systems to gain trust from the users. Although tremendous advancements have been brought, traditional persona-based dialogue models are typically trained by leveraging a large number of persona-dense dialogue examples. Yet, such persona-dense training data are expensive to obtain, leading to a limited scale. This work presents a novel approach to learning from limited training examples by regarding consistency understanding as a regularization of response generation. To this end, we propose a novel stack-propagation framework for learning a generation and understanding pipeline. Specifically, the framework stacks a Transformer encoder and two Transformer decoders, where the first decoder models response generation and the second serves as a regularizer and jointly models response generation and consistency understanding. The proposed framework can benefit from the stacked encoder and decoders to learn from much smaller personalized dialogue data while maintaining competitive performance. Under different low-resource settings, subjective and objective evaluations prove that the stack-propagation framework outperforms strong baselines in response quality and persona consistency and largely overcomes the shortcomings of traditional models that rely heavily on the persona-dense dialogue data.
A Complete Affordable Control System for Remote Astronomical Observing Accompanied by an Intelligent Controller

This paper comprises two studies; the first one provides an advanced and low-cost implementation for a remote astronomical platform applicable for small and medium-sized telescopes. It has been carried out for the 14-inch observatory, which includes a Celestron German Equatorial (CGE) telescope at the Kottamia astronomical observatory (KAO) in Egypt. This integrated control system is based on embedded systems, internet of things (IoT) technology, row packets communication procedure, and the Transmission Control Protocol (TCP) based on the Internet Protocol (IP). Using this platform, remote astronomers could control the whole system, observe, receive images and view them efficiently and safely without any human physical intervention. The proposed design has been achieved without dependence on commercial control kits or software. Indeed, many previous studies have focused on this field; however, their area of interest was limited or non-affordable. The excellence in this practical research is revealed and compared with others in terms of cost, inclusiveness, and communication speed. The other contribution of this research is to enhance the performance of the telescope pointing and tracking to be adapted with remote action. It has been achieved based on the mathematical model of the telescope where two fractional controllers have been applied, tilt integral-derivative (TID) and integral derivative-tilted (ID-T) controllers. After that, they have been optimized using a recent optimization algorithm called peafowl optimization algorithm (POA) and compared with one of the well-known algorithms; particle swarm optimization (PSO). Simulation results under the MATLAB/SIMULINK environment reveal that modified ID-T-based POA has minimized the pointing error sharply. Moreover, compared with previous studies, it has significantly improved the telescope system characteristics represented in the times of overshoot, settling, and rising.
Scalable K-FAC Training for Deep Neural Networks With Distributed Preconditioning

The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural network (DNN) training on GPU clusters. However, existing D-KFAC algorithms require to compute and communicate a large volume of second-order information, i.e., Kronecker factors (KFs), before preconditioning gradients, resulting in large computation and communication overheads as well as a high memory footprint. In this paper, we propose DP-KFAC, a novel distributed preconditioning scheme that distributes the KF constructing tasks at different DNN layers to different workers. DP-KFAC not only retains the convergence property of the existing D-KFAC algorithms but also enables three benefits: reduced computation overhead in constructing KFs, no communication of KFs, and low memory footprint. Extensive experiments on a 64-GPU cluster show that DP-KFAC reduces the computation overhead by 1.55×-1.65×, the communication cost by 2.79×-3.15×, and the memory footprint by 1.14×-1.47× in each second-order update compared to the state-of-the-art D-KFAC methods. Our codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lzhangbv/kfac\_pytorch</uri> .
Optimized Implementation of PIPO Block Cipher on 32-Bit ARM and RISC-V Processors

A lightweight block cipher PIPO-64/128 was presented in ICISC'2020.PIPO of the 8-bit unit using an unbalanced-bridge S-box showed better performance than other lightweight block cipher algorithms on an 8-bit AVR environment.So far, optimization methods for implementing PIPO have been proposed in various environments; however, no optimization research has been conducted for two popular 32-bit based processors: ARM Cortex-M4 and RISC-V.Since RISC-V and ARM Cortex-M series platforms do not support bit-based Single Instruction Multiple Data (SIMD) instructions, several aspects should be considered to apply a forced parallelization strategy.In this article, we discuss the implementation methodology of PIPO for 32-bit RISC-V and ARM Cortex-M4 environments.We optimize the performance of S-Layer via proposed register-scheduling and masking technique while we maintain parallelism to the R-Layer implementation.Moreover, we propose an on-the-fly key scheduling technique for further performance improvement.Finally, compared to the existing reference implementations in RISC-V and ARM Cortex-M4 platforms, when 4 plaintext encrypted simultaneously, our software achieved performance of 229% and 370%, respectively.
Eye Tracking, Saliency Modeling and Human Feedback Descriptor Driven Robust Region-of-Interest Determination Technique

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Region of interest</i> (ROI) analysis is widely used in image analytics, video coding, computer graphics, computer vision, medical imaging, nuclear medicine, computer tomography and many more areas in medical applications. This ROI determination process using subjective method (e.g. using human vision) often differ from the objective ones (e.g. using mathematical modelling). However, there is no existing method in the literature that could provide a single decision when both method’s ROI data are available. To address this limitation, in this paper, a robust algorithm is developed through a combining process of human eye tracking (subjective) and graph-based saliency modelling (objective) information to determine a more realistic ROI for a scene. To carry out this process, in one hand, a number of different independent human visual saliency factors such as pupil size, pupil dilation, central tendency, fixation pattern, and gaze plot for a group of twenty-two participants are collected by applying on a set of publicly available eighteen video sequences. On the other hand, the features of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph based visual saliency</i> (GBVS) highlights conspicuity in the scene. Gleaned from this two information, the proposed algorithm determines the final ROI based on some heuristics. Experimental results show that for a wide range of video sequences and compared to the existing deep learning based (MxSalNet) and depth pixel (DP) based ROI, the proposed ROI is consistent to the benchmark ROI, which was previously decided by a group video coding expert. As the subjective and objective options frequently create an ambiguity to reach a single decision on ROI, the proposed algorithm could determine an ultimate decision, which is eventually validated by experts’ opinion.