Reviewerly/exHarmony huggingface dataset:
An Algorithm for Correcting Video-Audio Asynchronization Based on Syncnet

The research on video-audio synchronization has attracted much attention in recent years. With the popularity of online education, the asynchrony between video and audio affects the quality of teaching and learning. This paper introduces a correction algorithm for video-audio asynchronization in online education. First, the video data were preprocessed using the S3FD and Librosa package to extract the lip images and MFCC as visual and auditory features; then, the Syncnet, consisting of a two-stream neural network, was retrained on the preprocessed dataset to obtain the semantic similarity of video-audio. Next, it calculated the synchronization error by using a sliding window method based on the similarity. Finally, a correction program adopting the FFmpeg framework fixed the asynchronous problem. In the experiment, 300 hours of Chinese video-audio synchronization dataset and 20 hours of Chinese live classroom record dataset were collected to test the algorithm. The experimental results show that the proposed algorithm can achieve 94% correction accuracy.
Flexible, Robust, Scalable Semi-supervised Learning via Reliability Propagation

Semi-supervised learning aims to generate a model with a better performance using plenty of unlabeled data. However, most existing methods treat unlabeled data equally without considering whether it is safe or not, which may lead to the degradation of prediction performance. In this paper, towards reliable semi-supervised learning, we propose a data-driven algorithm, called Reliability Propagation (RP), to learn the reliability of each unlabeled instance. The basic idea is to take local label regularity as a prior, and then perform reliability propagation on an adaptive graph. As a result, the most reliable unlabeled instances could be selected to construct a safer classifier. Beyond, the distributed RP algorithm is introduced to scale up to large volumes of data. In contrast to existing approaches, RP exploits the structural information and shed light on the soft instance selection for unlabeled data in a classifier-independent way. Experiments on both synthetic and real-world data have demonstrated that RP allows extracting most reliable unlabeled instances and supports a gained prediction performance compared to other algorithms.
Tree Transformation and Neural Network Based Hand-written Formula Recognizer

Automatic recognition of mathematical expressions is one of the key techniques to improve the access to large amount of document collections and retrieval on the design of fifth generation cellular systems (5G) and future networks. A formula recognizer mainly contains by three parts, such as components separation, components recognition and the connection construction between components, etc. It is common to employ a neural network as the core of the whole system, and the performance can be further improved by combining with tree transformation. In this paper, we considers a novel tree transformation based neural network to analyze the structure of formulas. Then, we propose an error checking method after symbol recognition so that our system is able to distinguish the upper-case and lower-case of a letter. For imbalanced database, we propose to use sampling and thus to obtain an uniform distribution database. Numerical results show that our proposed method performs well in delay reduction and accuracy improvement compared with other existing schemes.
Weather Forecasting Rain Probability in Cebu Using ANFIS and Bayesian Network

The necessity of assessing the probability of rain and weather conditions of Cebu City is useful for providing its residents with valuable information for their daily activities. This paper describes the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model to provide an estimation for the possibility of rainfall utilizing the following parameters: month (expressed in numerical form), date of the month, minimum temperature reading, maximum temperature reading, mean temperature reading, mean humidity percentage, average wind speed, and mean cloud percentage. The model has an 87.67% accuracy in weather forecasting, where the forecasted weather conditions are used to assess the probability of rain. The study recommends using the weather data set of the recent year, and that a survey on whether rain has occurred within a certain day be performed, as it would prove useful as output for teaching the ANFIS model, rather than relying on the Bayesian network.
On the Two Synthetic Programs of the Fundamental Physics in the XXth Century: to the Centenary of the Geometric Field Program and 60th Anniversary of the Discovery of Standard Model Symmetry in the Elementary Particles

The article is dedicated to the 100th anniversary of the geometric field program (GFP) and the 60th anniversary of the discovery of standard model symmetry in elementary particle physics. These events are related to two large-scale programs of building a unified field theory of fundamental interactions. The GFP originated on the basis of the general theory of relativity and the first unified geometric theories of the gravitational and electromagnetic fields of H.Weyl and T. Kaluza (1921). Soon its leader was A. Einstein, whose intense 30-year efforts never led to success, despite the mathematical depth of this program. Particles and their quantum properties within the GFP were to be obtained as solutions to non-linear equations of the unified field. This has also led to a critical Einstein’s relationship to the Copenhagenprobabilistic interpretation of quantum mechanics. Despite its defeat, the GFP and the associatedEinsteinian critique of the foundations of quantum mechanics had an important heuristic significance for theoretical physics. The discovery of symmetries of strong, weak and electromagnetic interactions, made in 1961 by M. Gell-Mann, Y. Ne’eman, S. Glashaw and A.Salam, together with the concept of gauge fields proposed earlier by C. Yang and R. Mills (1954), formed the basis of the symmetry gauge quantum field program of building a unified theory of the three fundamental interactions in the elementary particles physics. It was within this program that all theoretical and experimental difficulties were overcome and triumphantly completed by the mid-1970s the construction of electroweak theory and quantum chromodynamics, in other words, the standard model. A firm belief in the beauty and power of the symmetry gauge program was the key to its success. The impact of the GFP on symmetry gauge program has been noted, in particular in relation of the «symmetry ergo dynamics» principle.
Multivariate hidden semi-Markov models for longitudinal data: a dynamic regression modeling

The collection of multiple responses is common in longitudinal studies. It is of interest to assess the effects of covariates on multiple responses simultaneously using regression models. In some longitudinal studies, response variables are related to hidden states during the time-course study. The trend of responses for each subject can be segmented by hidden states. In the present study, we proposed a multivariate hidden semi-Markov regression model for longitudinal data having a multivariate normal distribution for responses in each hidden state. Based on the model, the effects of covariates on multiple responses have been calculated in the presence of hidden states. Simulation studies were conducted to evaluate the properties of the suggested model under different scenarios. The application of the suggested method was shown in hemodialysis patients’ data.
A Survey of The Relationship Between Human Faces And Body Mass Index (BMI)

Nowadays, under the influence of the COVID 19 epidemic, risks of obesity are increasing. Body mass index (BMI) is a good measure of obesity and studies have proved that face is affected by BMI. If we could find a model to predict BMI by human face, it will reduce the time costs and the infection risk of the hospital. However, the number of relevant studies is limited. There are predictive models for BMI but not relevant models for Asian faces. In this paper, collected relevant studies were analyzed and classified based on research objectives and research methods, according to the classification, a face prediction BMI model which uses residual network and Asian face datasets was recommended. In the end, we summarize all the researches and list the directions not researched and innovative places.
Machine learning integrated blockchain model for Industry 4.0 smart applications

In the last few years, machine learning (ML) and blockchain are the most prominent innovations. Blockchain’s potential has been widely explored in literature and media, especially in finance and payment industries. Data confidentiality and privacy are prioritized in blockchain’s decentralized database. However, this procedure is time consuming and inconvenient, which is one of the explanations why blockchain technology has yet to gain widespread acceptance. To solve the invalid dataset, we used integrated blockchain and ML approaches to secure system transactions and manage a dataset. Mostly, blockchain can greatly facilitate the exchange of training data and ML models, as well as decentralized information, stability, anonymity, and trustworthy ML decision making. We study the literature on integrating blockchain and ML systems in this paper and show how they can work together efficiently and effectively. We will go through the problems that each industry faces when it comes to implementing blockchain. We present a systematic report on ML and blockchain-based smart Industry 4.0 applications more robust to attacks in this article. Finally, we suggest some potential research avenues and anticipate further studies into the deeper convergence of the two promising technologies. We hope that our results will help decision-makers embrace blockchain technology and invest in Industry 4.0 by empowering and promoting research in this field.
Particle filter as the localization of the robot on the football pitch

RoboCup competition is a football tournament played by robots. In one of the categories, matches are played between teams of humanoid robots. Accurate position tracking for each team member is essential for building more advanced strategies. However, rules of the game forbid using any sensors that allow global positioning. This article describes an implementation of a positioning system based on a particle system, which is a standard approach for localization problems in known environments. Proposed algorithm uses a monocular camera to obtain feedback from the environment and calculates the similarity between real observation and each particle. Developed system searches for the region with highest local density of particles for best state approximation.
Application of VR Technology in Physical Education

This paper studies the application of VR technology in physical education based on the analysis of the VR technical concepts and the presentation process. The combination scheme of VR technology and physical education can be divided into the following five aspects: the combination of teaching material and VR technology, the combination of theoretical knowledge and VR technology, the combination of motor skill’s movement symbols learning level and VR technology, the combination of motor skill’ s movement logic learning level and VR technology, and the combination of motor skills’ application significance with VR technology. The application process of VR technology in physical education and its effects are analyzed through experiments to provide theoretical guidance for the application of VR technology in physical education.
Multi-Dimensional Contract Design for Blockchain Deployment in WSN under Information Asymmetry

The integration of blockchain technology and Internet of Things (IoT) enables a wide range of business or industrial decentralized applications. However, incentivizing the self-interested IoT devices to participate in the blockchain net-work faces the challenges of the information asymmetry, energy constraints, and the wireless communication environment. In this paper, we focus on the blockchain system deployment in the classical wireless sensor networks (WSN). We design a multi-dimensional contract as the incentive mechanism which aims to maximize the WSN based blockchain operation time and data utilities. In particular, we derive the energy consumption model in maintaining the wireless blockchain network by analyzing the block mining process and the wireless block broadcast features. Moreover, we give the feasible conditions of the contract. Numerical results demonstrate that our proposed contract effectively incentivizes sensors to participate in the consensus process, and maintain the WSN based blockchain effectively from the economics perspective.
Custom TTA Operations for Accelerating Kyber Algorithm

In recent years, quantum computer development projects have progressed considerably in terms of quality and quantity. These studies also pose a serious risk for widely used security systems based on classical public-key cryptography algorithms. Therefore, development of hardware and software applications that use post-quantum methods are considered a necessity both for today and for the future. Kyber is one of the most promising key encapsulation mechanism in the final round of NIST post-quantum cryptography standardization process. In this study, we present a 64-bit application specific transport triggered architecture processor which has custom operations for accelerating the Kyber algorithm. Moreover, we compare the processor and its various versions with a 64-bit RISC-V core in terms of performance, total energy consumption and resource utilization. According to the test results, our custom processor provides at least 2x better performance and 1.5x lower energy consumption while increasing the required chip area approximately 1.4x.
Study of the experience of cooperation among undergraduate students from the perspective of the cultivation of key competencies －A focus group interview with high-performers in general education courses based on the POD class model

With regard to the innovation of the general education at the level of higher education in Korea based on the approach of competency-based education, researchers have predominantly focused on the individual development of key competencies and skills. However, little attention has been paid to one of the main axes of the key competency, the experience of cooperation among students. Drawing on the interview data with undergraduate students who participated in online general education courses which were based on the ‘Peer-tutoring Online Discussion (POD)’ class model, this study explores modalities and significance of their cooperation experiences in the courses. Findings suggest that the experience of cooperation based on the POD class model has several differences from traditional classes and that these features contribute to unique characteristics of the cooperation experience in the POD class model. Through their experience of cooperation in the general education courses based on the POD class model, students were able to feel a sense of achievement that they had never felt before in other classes, and also able to realize the growth of their team members as well as themselves. Moreover, respondents also explained that their experience of cooperation through the POD class model actually enabled them to foster positivity, broad perspective, leadership and collaborative skills.
Dynamic urban solar harvesting: anisotropic shadows in energy estimation

As one of the most sustainable alternatives regarding environmental impact, cost-effectiveness, and social integration, solar energy is expected to become an ever more ubiquitous part of our intricate human world. Dropping prices in photovoltaics that can harvest clean energy in a decentralized, safe, and modular manner are making it more viable for solar devices to be implemented in complex environments, such as urban settings. These scenes involve more constrained and dynamic conditions, encouraging the use of solar devices that can adopt arbitrary positions and personalized tracking behaviors to make the most of available resources. In modeling the incoming solar radiation for such conditions, some common simplifying assumptions may be too limiting, in particular, not considering the anisotropic nature of diffuse shadows. We develop a variety of shadow modeling approaches for all anisotropic components of the radiation; four approaches for Beam radiation and three for Diffuse components. Through thousands of simulations in urban scenes of varying complexity, these approaches are tested, characterized, and compared in terms of accuracy, precision, run-time efficiency, and practicality. Critical trade-offs are revealed between accuracy and run-time as a function of the type of approach and resolution. Our characterizations support the development and selection of modeling frameworks that are better suited to the application. This may be useful in the design, optimization, control, and forecasting of more widely adopted solar harvesting in the challenging human environment.
The Effects of Sentiment Magnitude on Sales among Social Media Micro-Influencers

With the development of new social media platforms, micro-influencers are gradually becoming an integral part of the advertising process. Their number of followers is relatively small, but their fan bond is strong. Through data-based sentiment analysis, the researcher found that the use of strongly worded messages (either positive or negative in sentiment) has a positive impact on micro-influencers’ sales. However, for relatively popular micro-influencers who can sell 1000+ products in a month with a single message, the number of products they sell is positively correlated only with positive sentiments.
On the Shutdown Price of Blockchain Mining Machines

The security of PoW-based blockchains relies on the total amount of mining power and the ratio of mining power possessed by the honest miners. Loosely speaking, a system with higher mining power makes an attack more difficult. To incentivise miners joining the network and contributing their mining power, reward mechanisms are designed to provide economic profit to miners in exchange for their mining power. We identify shutdown price of mining machines as an overlooked factor that has an impact on the total mining power in the network, so the level of system security of PoW-based blockchains. We formalise the concept of shutdown price, which represents the break-even point of operating a mining machine. Once the shutdown price of a type of machines is reached, mining coins with them can be more expensive than buying coins directly in the cryptocurrency market. Therefore a rational operator would switch off these machines. This reduces the mining power in the network. However, due to the high market volatility and the coin price may recover from the break-even point quickly, the miners may delay shut down or may choose a partial shutdown strategy to hedge risk. We define and analyse such shutdown tolerance by applying real option theory. We also provide a discussion on the key factors determining shutdown price and their impact on the blockchain security.
Inverted Classroom in Self-regulated Learning during the Covid-19 Pandemic in University Students

The year 2020, the Peruvian authorities decreed the development of virtual learning in universities, due to the appearance of the pandemic that originated the COVID.19, in order to avoid the increase of contagions, it is this context that the higher houses of study should, implemented strategies to improve the performance academice of the students, being one of them the use of the inverted classroom, the objective of this study was to demonstrate that the use of the Inverted Classroom significantly influences the self-regulated learning during the pandemic of the COVID-19. The method used was quantitative, basic, with a quasi-experimental design, applying a program to second cycle students of a public university. From the statistical processing it was possible to conclude that the inverted classroom significantly influences self-regulated learning.
The Post-quantum Probabilistic Signature Scheme

In this paper, we present a variant of the standard PSS-R (Probabilistic Signature Scheme with message Recovery) signature scheme, called pqPSS. Our scheme is an RSA-based signature scheme but with a random element generated for each signature process. It is proved secure against chosen message attacks in the random oracle model. Its security level is close to that of RSA. For a random of 5 bits, we have $$\varepsilon _{\mathcal {R}}=0.96875 \ \varepsilon _{\mathcal {A}}$$ , where $$\varepsilon _{\mathcal {R}}$$ is the success probability of a reduction algorithm $$\mathcal {R}$$ that can invert RSA by using an attacker $$\mathcal {A}$$ that breaks pqPSS with probability $$\varepsilon _{\mathcal {A}}$$ . We have also the success probability of the simulation independent of the number of signing and hashing oracle queries and it is possible to sign and recover a message with a large size while keeping the size of the random salt also large. This new signature scheme is more secure than PSS-R relatively to all known reductions, but it is less efficient. It is also intended to be used to obtain the integrity and authenticity of GOOSE (Generic Object Oriented Substation Event) messages in the same way as other RSA-based signature schemes such as PSS.
Literature Review

In this section, we describe the literature review that was used in this book in order to have basic concepts and information about computational intelligence, bioinspired algorithms and different techniques that the researchers use in optimizationOptimization problems.
PPBT: A High Performance Parallel Search Tree

Search trees are one of the most important and widely used data structures, and parallelization is an effective method to improve their performance. However, many existing parallel search trees incur high synchronization costs and low memory I/O efficiency, which limits their performance. We propose PPBT, a batched parallel search tree which minimizes synchronization by partitioning the tree using novel algorithms and minimizing I/O cost using buffering. We give a new sequential algorithm for batch processing on search trees with optimal I/O efficiency for insert and delete operations, and also present a fast parallel algorithm for joining disjoint search trees. We show experimentally that PPBT is over 6×faster than the state-of-the-art parallel tree in [1] and over 40× faster than the concurrent search tree in [7], and achieves 21×speedup using 32 threads. PPBT's throughput on searches is lower due to reduced opportunities for buffering, but is still 1.3 × that of [1]. In addition, PPBT has good response times for searches, for example completing 100K searches in under 1 ms in a tree with 10M elements.