Reviewerly/exHarmony huggingface dataset:
STUDENTS’ ABILITY IN COMPREHENDING MOVIE ENTITLED “MY BIG FAT GREEK WEDDING” OF ENGLISH EDUCATION STUDENTS OF BATANGHARI UNIVERSITY: A CROSS-CULTURAL UNDERSTANDING

The purpose of this research is to identify how far students of English Education program of Batanghari University can understand the differences of cultures that can be seen from the movie entitled “My Big Fat Greek Wedding”. Their comprehension about the cross cultural understanding will be notified from their analysis towards the movie. The subject of this research is semester VI students of class A1 which consists of 23 students. Meanwhile the object of the research is students’ analysis of cross cultural matters in a form of interview after they watch the movie. The method used in this research is mix method. Qualitative approach is used in analyzing the cross cultural matters in their analysis towards the movie and quantitative approach is used to count the forms of cross cultural matters that occured from the movie. The hypothesis of this research is that the students are able to  understand the difference of other cultures especially American, British, or Australian culture which is related to their major.
Online Bayesian learning for mixtures of spatial spline regressions with mixed effects

Classification and clustering methods based on univariate functions have been well developed. Recent work has extended the techniques to the domain of bivariate functions by incorporating the techniques based on mixtures of spatial spline regression with mixed-effects models. An Expectation Maximization (EM) algorithm is implemented to facilitate model inference. In this paper, we further extend the mixtures of spatial spline regression with mixed-effects model under the Bayesian framework to accommodate streaming image data. First, we derive a Markov chain Monte Carlo (MCMC) algorithm as an alternative approach to the EM algorithm to make inference on the model. However, MCMC is not scalable to streaming image data since it requires all observed information to update the posterior distribution of the parameters. To tackle this issue, we propose a sequential Monte Carlo (SMC) algorithm to analyse online fashion image data. The existence of model sufficient statistics improves the efficiency of the proposed online SMC algorithm. Instead of saving all batch data for inference, we only require storage of the model sufficient statistics and every data point is only used once, which is well suited for large-scale stream type data. In addition, the proposed algorithm provides an unbiased estimator of the marginal likelihood as a by-product of the approach, which can be used for model selection. Numerical experiments are used to demonstrate the effectiveness of our method. Our implementation is available at https://github.com/ShufeiGe/Online-Bayesian-learning-for-MMSRm.
Research and Improvement of Huffman Compression Method Based on Linear Linked Forest

By analyzing the implementation of traditional Huffman algorithm, an improved Huffman compression algorithm is proposed. Firstly, the linear linked list is used to establish Huffman tree, which can reduce the time complexity of the algorithm without frequent sorting. The second is coding with the transfer of function parameters in recursion, which reduces the time complexity of the algorithm from O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of the traditional algorithm to O(n), and does not need to rely on other data structures. The improved algorithm also optimizes other details to ensure the process of coding and compression is simple and efficient.
A Deep Metric Learning Method with Combined Loss of Triplet Network and Autoencoder

In this paper, a deep metric learning method with combined loss of the triplet network and autoencoder is presented. Autoencoder is regarded as the regulation network to enable the embedding vector to have some latent features of the input image, and improve its performance. Compared with the pure triplet network, although it increases some complexity during training due to the addition of the decoder, but during testing, their complexities are exactly the same, because the decoder can be completely removed after training. The experiments of the proposed method, triplet network, and one-hot encoded network are performed on various character datasets to show that the proposed method not only achieve better classification performance, but also inherit the benefits of deep metric learning.
Study on Reconstruction of Living Environment and Ecological Symbiosis in Reservoir Area Based on Geographic Information System

With the continuous advancement of the reconstruction of living environment and ecological symbiosis in the reservoir area, these ecological environmental problems will be more and more significantly exposed to different scales of living environment in the reservoir area. The establishment of hydropower station is a severe test for the ecological environment of river basin. In order to effectively prevent and solve the negative impact of the ecological environment brought by the completion of hydropower stations, the study of regional ecological sensitivity is carried out to understand its spatial distribution pattern, which provides a practical basis for the protection and remediation of the problem environment. Based on geographic information system (GIS), the conceptual framework of regional eco-environmental sensitivity evaluation and the conceptual framework of evaluation index system selection are established by combining eco-environmental system with analytic hierarchy process. This achievement can provide theoretical guidance for the reconstruction of living environment and ecological symbiosis in the reservoir area.
Research on the Application of WeChat Public Platform in College Student Education under the Background of Big Data

In the current era of rapid development of big data network intelligence, smart phones have become popular, especially among college students. WeChat has become a must-have social software. College students seriously reject traditional concepts and educational methods, so the learning rate using WeChat is increasing every day. The WeChat public platform is a comprehensive network application platform with powerful advantages. At present, the development of the WeChat public platform in universities, especially in the field of mobile learning, has brought great changes to the way of college students’ online education. The WeChat public platform is convenient Unique advantages such as use, diverse functions, and accurate information are widely used in the field of university education, and a systematic online learning platform has gradually formed to promote the reform of education under the new education method. Explains the close relationship between the WeChat platform and the university's philosophy and social education. Through the application of the WeChat public platform, education is standardized, new status is broadened, and new topics are proposed. The application of the WeChat public platform provides new ideas and education for college students.
The Application of Artificial Intelligence Technology in Oral English Assessment

In the process of oral English learning, oral assessment can make students understand their oral problems and correct them. The traditional manual assessment is low in efficiency, high in cost, and has many limitations. However, the automatic assessment of spoken English can be realized with the help of artificial intelligence technology, which is an important reform direction for the future oral English teaching and evaluation. This article introduces the principle of automatic oral English assessment based on artificial intelligence technology, and discusses the feasibility of using artificial intelligence technology to partially replace manual scoring in oral English assessment, exploring the way to improve the stability and objectivity of assessment by using artificial intelligence technology, discussing the application of artificial intelligence technology in oral English autonomous learning, and also putting forward some suggestions on how to further improve the accuracy of artificial intelligence scoring in oral English assessment.
Suppression of an Outbreak of COVID-19 without Lockdown: The Challenge of Inefficient Contact Tracing and Testing

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Analysis of 8-bit prefix parallel binary comparator based on 3 to 1 merging circuit

Binary Comparators that are also called as digital comparators are used for testing a binary word or number that is greater than less than or equal with another binary word or number. Generally, a binary comparator gives three outputs after comparison they are (G>F) G greater than F, (G<F) G less than F, (G=F) G equals F. But in not many cases based on single inequality relation we require only one output. If we take an example CLCG based PRBG gives only one output decision (F>G) based on single inequality relation (F>G). However, the power, area and delay of the prefix - parallel structure of comparator will be more. Therefore, in this paper an 8-bit parallel - prefix binary comparator using a merging circuit is proposed for the reduction of power, area and delay. The advantage of using merging circuit is that it reduces the power and delay of proposed binary comparator is correlated to other comparators. The proposed comparator is modelled in Verilog HDL with the help of Xilinx to study its performance. It is reported that the 8-bit parallel prefix binary comparator consumes 19.12%, 0.3% and 0.6% less power, area and delay compared to 8-bit comparator respectively.
Design of Hybrid Recommendation Algorithm Based on User Dynamic Behavior and Static Attributes

Abstract Collaborative filtering recommendation algorithm is one of the most successful recommendation algorithms, but the traditional collaborative filtering recommendation algorithm is proved to have a series of problems in data sparsity, cold boot and scalability. Based on the problems mentioned above, this paper analyses user dynamic behavior and a hybrid recommendation algorithm based on user dynamic behavior and static attributes (UDBSA) was proposed in this paper. By determining the optimal value of BP critical point for the number of ratings, the recommendation strategy was dynamically selected according to the number of ratings. This method can comprehensively alleviate the influence of problems of new users and concept drift related problem on recommendation results.
Track Conventions, Not Attack Signatures: Fortifying X86 ABI and System Call Interfaces to Mitigate Code Reuse Attacks

Code Reuse Attacks (CRAs) are dangerous exploitation strategies that allow attackers to compose malicious programs out of existing application and library code gadgets, without requiring code injection. Previously, researchers explored hardware-assisted protection schemes that track attack signatures to identify malicious behavior. This paper makes two main contributions. First, we show that previously proposed signature-based schemes are impractical because they do not always distinguish attack patterns from the behavior of benign programs. Second, we demonstrate that instead of tracking attack signatures, a more robust defense mechanism is to track legitimate usage of system calls and ABI compliance in hardware, and detect deviations from established conventions as possible attacks. We propose two specific tracking mechanisms: the setting of arguments for system calls and register usage across function calls. We demonstrate that our solution severely hinders practical CRAs and completely stops code-reuse execution of sensitive system calls like mprotect. Our solution imposes very low performance overhead and modest design complexity.
Pedagogical digital tool for the area of Mathematics in Quechua Language

The native languages of many countries have had a considerable reduction in their number of speakers due to factors like globalization, the predominance of ruling languages, and precarious State policies for their revaluation and survival, among others. Despite this, the population conserves them as the spoken language in their daily lives and many contexts, as the sole language. Such is the case of Peru, which has three official languages: Spanish, Aymara, and Quechua; the latter is spoken at the national level by 13.6% of the population and in the Department of Apurímac the total is 69.69% of its inhabitants. In these contexts, policies have been implemented to achieve bilingual education; however, these efforts are not reflected in the construction of digital teaching/educational material in the Quechua language. For this reason, this research presents the design, development, and deployment of an educational digital tool for the Mathematics area of 2nd grade of primary school in the Quechua language in a web version, which was built using the Census Assessment of Students as a guide. The results obtained show that the students present a positive attitude to the use of a digital application in their mother tongue, however, there was a significant finding referred to the difficulty in reading the sentences proposed in the evaluation, since the language is above all verbal, an approach to a writing system has been null or little.
DeepGlobal: A Global Robustness Verifiable FNN Framework

Feed forward neural networks (FNNs) have been deployed in a variety of domains, though achieving great success, also pose severe safety and reliability concerns. Existing adversarial attack generation and automatic verification techniques cannot formally verify a network globally, i.e., finding all adversarial dangerous regions (ADRs) of a network is out of their reach. To address this problem, we develop a global robustness verifiable FNN framework DeepGlobal with three components: 1) a rule-generator finding all potential boundaries of a network by logical reasoning; 2) a new network architecture Sliding Door Network (SDN) enabling rule generation in a feasible way; 3) a selection approach which selects real boundaries from the generated potential boundaries. The ADRs can be further represented by the identified real boundaries. We demonstrate the effectiveness of our approach on both synthetic and real datasets.
Research on electronic inclinometer calibration method and uncertainty budget based on GUM method

An electronic inclinometer can measure the angle between faces or between a face and a horizontal plane. In recent years, electronic inclinometers have played an important role in bridge monitoring, medical devices calibration, and aerospace, with a wider measuring range and higher accuracy. However, there are few studies on the calibration methods and uncertainty budget of the electronic inclinometer. In this paper, the traceability chain, calibration environment, measurement standard, and calibration method are investigated to provide a direct measurement method to calibrate the indication error of the electronic inclinometer by using the dividing head as a standard angle generator and a special fixture. A mathematical model is established, and the uncertainty budget is performed for the calibration result of the indication error. The result shows that the ratio of the expanded uncertainty of the electronic inclinometer to the indication error is less than 1»3. Thus, a feasible calibration method for the indication error of the electronic inclinometer is obtained.
Analysis of loss correction with the Gottesman-Kitaev-Preskill code

The Gottesman-Kitaev-Preskill (GKP) code is a promising bosonic quantum error-correcting code, encoding logical qubits into a bosonic mode in such a way that many physically relevant noise types can be corrected effectively. A particularly relevant noise channel is the pure loss channel, which the GKP code is known to protect against. In particular, it is commonly pointed out that losses can be corrected by the GKP code by transforming the losses into random Gaussian displacements through a quantum-limited amplification channel. However, implementing such amplification in practice is not ideal and could easily introduce an additional overhead of noise from associated experimental imperfections. Here, we analyze the performance of teleportation-based GKP error correction against loss in the absence of an amplification channel. We show that amplification is not required to perform GKP error correction and that performing amplification actually worsens the performance for practically relevant parameter regimes.
An empirically-based spatial segmentation and coreference annotation scheme for comics

There is recent work in applying concepts from discourse analysis to comics in order to formalise visual content across comics sequences, however much of this work lacks empirical verification. We address this gap by assessing inter-annotator agreement on a preliminary annotation scheme for segmenting areas of comic pages, and assigning referents or classifications to these segmentations. A browser-based annotation tool and inter-annotator agreement measures are introduced. We find high agreement for segmentation tasks, while reference tasks show adequate agreement as well as instructive disagreements showing constraints on reader interpretation.
In-Situ Artificial Intelligence for Self-* Devices: The Elastic AI Ecosystem (Tutorial)

Artificial Intelligence (AI) is an important topic for today's self-* systems. It can e.g. be used to analyze sensor data, to derive a model of a system's runtime situation, and to make dynamic adaptation decisions. To this end, in-situ AI is a powerful tool that enables individual devices to use AI autonomously, leading to truly decentralized self-* behavior. However, developing, deploying and executing in-situ AI is not trivial. Researchers therefore often fall back to classical Cloud-based AI solutions, which restricts what kinds of research studies are possible. In this tutorial we present our solution for this, the so-called elastic AI ecosystem.
Path Tracking of U nderwater Vehicles Based on Adaptive Model Predictive Control

Aiming at the three-dimensional trajectory tracking problem of unmanned autonomous underwater vehicle (AUV) in maritime work, an adaptive predictive path tracking control method is designed in this paper. According to the motion control model of underwater vehicle space-time system, after the path error in three-dimensional space is linearized, the trajectory tracking problem is transformed into a standard quadratic programming problem, and the actual constraints of system input and state variables are included in the optimization calculation to ensure the accuracy, dynamics and anti-interference of path tracking. Aiming at the shortcomings of fixed model parameters in traditional predictive control. This paper proposes to design adaptive predictive controller by using Kalman filter algorithm to update the parameters in each control cycle. This method effectively improves the dynamic and stability of the system and further improves the control accuracy. The simulation results of Matlab/Simulink show that the adaptive predictive control has better control performance.
Research on Technical and Tactical Characteristics of College Students' Men's Three-on-Three Basketball Match Based on Image Video Sequence Analysis

With the rapid development of three-person basketball, the International Olympic Committee has brought three-person basketball into the Olympic family. It provides a theoretical basis for the development of three-player basketball and the cultivation of reserve talents, and is of great significance to teaching and training. In this paper, the optical flow equation is analyzed, and the change of moving block pixels is judged by the time domain change and correlation of pixel intensity data in basketball image sequence. Acquire the affine transformation of arm action pixel information features, track the dynamic trajectory of the basketball player's shooting action arm in the image sequence, and complete the contour tracking and extraction of the basketball shooting action arm video image. The real results show that the proposed algorithm can accurately establish a three-dimensional model of basketball players' jump shots and improve the accuracy of basketball players' jump shots.
EMBRACE: Electronic Medical Record Safety, Blockchain to the Rescue

The cutting edge technologies have changed the perspective to an intelligent virtual world where machines will interact with humans and data is the fuel to it. Data carries an infinite amount of potential to change the way of decision making. Healthcare industry has also been focusing on collecting large amounts of data through different types of smart devices, care unit systems, and medical equipment. This big data is stored in cloud for its processing and can be used to create smart and intelligent diagnosis and health management systems with the back support of AI and ML making it easier for the healthcare providers and executives. The major issue faced is the security and the privacy of healthcare data collected from patients and storing it. Blockchain can serve the right purpose to secure the data collection and sharing in the cloud based applications in future. Though, it has some limitations too. This paper focuses on the development and implementation of a plugin for health management system which ensures the storage of all the data entered by the user into the Blockchain. The hashing technique used to store the information is Secure Hash Algorithm (SHA-256) to securely save the data. The application developed can successfully save the data and allows the sharing of it with other providers too. For testing the application, records from liver and chronic kidney disease datasets collected from Kaggle, are stored in the blocks to create the Blockchain network. This can prove to be a major step towards the practical implementation of such applications at an enterprise level.
Analysis of the Characteristics of China's Undergraduate Teaching Review and Evaluation Standards based on Artificial Intelligence

In the context of artificial intelligence, the Nvivo12 software was used to analyze the word frequency of the national undergraduate teaching audit and evaluation standards, and perform primary coding and mark reference points for the elements in the national audit and evaluation standards. In addition, text analysis of the audit evaluation standards of 8 provinces that are different from the documents issued by the Ministry of Education among the audit evaluation standards of 29 local governments was carried out, and the audit evaluation standards and self-evaluation reports of 33 universities were randomly sampled. From the perspective of organic philosophy, this paper explains the characteristics of China's undergraduate teaching audit and evaluation standards, which provides a basis for the improvement of China's undergraduate teaching audit and evaluation standards.