Reviewerly/exHarmony huggingface dataset:
Research on the Application of Intelligent Computer Equipment in Higher Vocational Physics Experiment Teaching

Abstract With the rapid development of computer, intelligent computer equipment has been gradually applied to the field of teaching, which not only strengthens the authenticity of teaching, but also improves the students’ interest in learning. At present, intelligent computer equipment management applied to higher vocational physics experiment teaching, which has begun to replace the traditional physical experimental instruments, such as simple pointer type, electromagnetic instrument. With the intelligent experimental teaching instruments, the teaching methods, teaching means and teaching quality of physical experiment in higher vocational colleges have been greatly improved. Through the introduction of intelligent experimental platform, higher vocational colleges have realized real-time measurement technology, which is based on computer, sensor and traditional instrument. Through the measurement, processing, storage and output of physical experiments, students can observe and learn physical phenomena more clearly, which also provides a practical and effective teaching environment for experimental teaching. First of all, this paper analyzes the importance of introducing intelligent computer equipment. Then, this paper analyzes the implementation principle of the intelligent experimental platform. Finally, some examples are given.
Feature Variance Ratio-Guided Channel Pruning for Deep Convolutional Network Acceleration

Most existing channel pruning approaches utilize the magnitude of network parameters to guide the pruning process. However, these methods suffer from some limitations in modern networks, where the magnitude of parameters can vary independently of the importance of corresponding channels. To recognize redundancies more accurately and therefore, accelerate networks better, we propose a novel channel pruning criterion based on the Pearson correlation coefficient. The criterion preserves the features that are essentially informative to the given task and avoids the influence of useless parameter scales. Based on this criterion, we further establish our channel pruning framework named Feature Variance Ratio-guided Channel Pruning (FVRCP). FVRCP prunes channels globally with little human intervention. Moreover, it can automatically find important layers in the network. Extensive numerical experiments on CIFAR-10 and ImageNet with widely varying architectures present state-of-the-art performance of our method.
Construction and Application Technology Architecture of Domain-specific Knowledge Graphin Military Field

Abstract Domain-specific Knowledge Graph in Military Field is a bridge to connect all kinds of battle factors, such as combat forces, command system, weapon platforms. It’s also an important means to break through the information gap between different areas of different services. Most of the existing knowledge graph is general knowledge graph for general fields, but there is no mature method of knowledge graph construction and expression for the industry data of military specific fields. So based on the special needs for military knowledge acquisition, storage, representation, query and other technologies in the future information and intelligent operations, and application scenarios, this paper discusses the current opportunities and challenges of the knowledge graph in military domain. Then the construction of knowledge graph in military domain and application technology framework are proposed.
Bayesian MISE convergence rates of Polya urn based density estimators: asymptotic comparisons and choice of prior parameters

Bhattacharya [Gibbs sampling based Bayesian analysis of mixtures with unknown number of components. Sankhya B. 2008;70:133–155] introduced a mixture model based on the Dirichlet process, where an upper bound on the unknown number of components is to be specified. Defining a Bayesian analogue of the mean integrated squared error (Bayesian MISE), here we consider a Bayesian asymptotic density estimation framework for objectively specifying the upper bound, as well as the precision parameter of the Dirichlet process, such that the Bayesian MISE converges at a desired rate. As a byproduct of our approach, we also investigate Bayesian MISE convergence rate of the traditional Dirichlet process mixture model, which leads to asymptotic specification of the precision parameter. Various asymptotic issues related to the two aforementioned mixtures, including comparative performances, are also investigated. The theoretical studies, supplemented with simulation experiments, bring out the superiority of the approach of Bhattacharya (2008).
High capacity reversible data hiding algorithm based on central prediction

This paper proposes a reversible data hiding algorithm based on central prediction and histogram shift, aiming to increase the embedding capacity at a lower distortion rate. The cover image is scanned and divided into blocks firstly and the value of central pixel of each blocks is took out to predict other values in the same block to obtain the prediction error histogram. Data embedding can be implemented by modifying the prediction error histogram according to the secret data and the prediction error values. Since the value of central pixel is close to other values in the same block, the prediction error histogram in our scheme is steeper and the embedding capacity becomes larger. Experiments show that this scheme has a lower distortion rate and better performance under the same embedding capacity.
WITHDRAWN: Design and development of artificial intelligence (AI) based board game (Gobang) using android

In modern society, more and more cell phones come into the work and life of ordinary people. With the popularization and application of the smart-phone technology, the operating system of mobile devices in the use of mobile phones is very broad, like Android, Symbian and iPhone. Among them, Android is a very popular open source mobile operating system for mobile phone and tablet personal computer. As the rapid development of computer and smart-phone, intelligent games have become an integral part of the universal life, not only does it make people entertainment, but also to develop people's intelligence. Intelligent game is one of the important branches of Artificial Intelligence. The artificial intelligence techniques are applied to the Gobang in this article. An intelligent Gobang game is designed and realized in the game between human and computer in Android operating system.
Resonate and Fire Neuromorphic Node based on two - section Quantum Dot Laser with multi-waveband dynamics

We present the experimental results regarding an excitable, two - section quantum dot laser exhibiting resonate and fire dynamics. Moreover, we explore the beneficial role of quantum dot assisted multi - waveband emission in enhancing the firing rate and the temporal resolution of photonic neuromorphic processors.
Improvement of non-negative matrix-factorization-based and Trust-based approach to collaborative filtering for recommender systems

Recommender systems are a subset of intelligent systems for information filtering systems that identify users' interests on the Internet. These systems provide appropriate and relevant suggestions related to the personal taste of the user by filtering the available information. Providing suggestions tailored to the needs and tastes of individuals and improving the efficiency of these systems increases users' trust in them. One of the most important challenges of recommender systems is the problem of data dispersion and cold start, that affects the performance of these systems. To confront these challenges, this paper uses a new method using trust information and combines it with negative matrix decomposition. In the proposed method, the use of collaborative filtering based on the negative analysis of the rank matrix enables us to compute a low rank approximation in order to manage large matrices. To solve the problem of cold start and data dispersion, the data of non-negative matrix decomposition algorithm and trust information has been used to improve the system of recommending to users. The results of experiments have been compared with state-of-the-art methods, which show the superiority of the proposed method in terms of accuracy and a computational complexity.
Cloud Services in Improving the Quality of E-Learning

In this article, we concentrate on using the E-learning cloud framework. In Iraqi universities Because of COVID19, it became essential to use cloud services to facilitate the task of education and the absence of students in universities. Cloud computing has many features that facilitate the task of e-learning, data storage and electronic testing through Google models. We use the Google Classroom framework  to collecting student data, send them electronic lectures, take examinations and directly, In this essay, we propose a new cloud application system for the practical application of materials that are so demanding.Keywords-Cloud Computing , E-learning , Classroom , COVID19.
Implementation of Distributed Smart House Using Multi-Layer AI

In recent years, research and development on smart houses that intelligently control various indoor devices (lighting, air-conditioning, etc.) have been active due to advances in communication and machine learning technology. In this paper, we propose and implement a system to realize novel smart house. The proposed system has two major features. One is various devices required for the system (sensing, processing, and actuation). It is an architecture called “distributed smart house” that disperses and arranges the distributed inference devices. In the other, the reasoners necessary for intelligent control are distributed within the device, local network, and cloud network. It is a “multilayer AI” that is implemented by dividing it into multiple layers. Due to these features, it is possible to construct a smart home system flexibly and efficiently. As for the system to be implemented, we used voice recognition using Google Cloud Speech and human posture recognition using Jetson TX2 placed in the local network. In addition, we used ROS as the software platform. This makes it possible to easily expand future functions and improve performance.
A Two-Stream Network For Driving Hand Gesture Recognition

The number of traffic accident deaths caused by driving is increasing every year, in which the improper driving behaviors account for a large proportion of traffic accidents. To alert the driver's behaviors, we design a light and fast neural network (LFNN). On this basis, we construct a convolutional two-stream interactive network framework. One stream is used to acquire the spatial information of hand appearance; the other stream is used to obtain hand movement's temporal information. The features generated by the two streams are fused and classified through a short, interactive connection network. Our network structure has been tested on the CVRR-HANDS 3D data set. The accuracy reaches up to 96.5%, which obtains an obvious improvement compared with state of the art.
An ad hoc topology-based graph signal sampling

Graph signal processing (GSP) is an attractive solution to process signals produced on the irregular structures. Sampling of graph signals is one of the fundamental tenets in GSP. One key issue of the graph signal sampling is to reduce the computational complexity of the sampling process. In this paper, we propose a topology-based sampling set selection. Additionally, the reconstruction is performed through conjugate gradient (CG) approach to tackle matrix inversion. Experimental results show the efficacy of the proposed method in comparison with the existing solutions.
Information system for waste bank savings

Abstract The purpose of this study is to design an application in savings waste bank management information systems. The management of waste bank includes inputting data on waste savings, calculating waste prices, viewing transaction data, and preparing reports. This research used the observation method by visiting Hijau Lestari waste bank Pasteur unit to obtain the required data related waste savings data. The results of this study indicated that savings waste bank management information system can provide ease in managing and processing data for officers and customers. This research conducting by analyzing the system that runs on the waste bank then develops and designs a proposed system. This research is expected to make it easier for officers and customers to improve waste bank management by utilizing technology.
Detecting Online Game Malicious Chargeback by using k-NN

It has been estimated that the global gaming market is worth nearly US$150 billion. Its consumer chargeback services often end up being used by some online gamers as a tool to commit fraud, causing a huge adverse impact on the industry. A gaming company in Taiwan found itself falling victim of malicious chargeback fraud. Nearly NT$10 million of fraudulent chargebacks were made during the period from January to April 2019 alone, making a huge dent in the revenue of the company. To counter chargeback fraud, some gaming companies resorted to manually checking for and blocking malicious accounts of their users, incurring huge labor cost in the process. Manual checking might have alleviated the problems to some extent; however, when new games came online, gaming companies would see a surge of malicious chargebacks, causing subsequent exponential increases in losses. To help reduce labor cost incurred by manual account checking, potential human errors and potential losses that may be caused by malicious chargebacks, this study proposed a k-NN model to detect malicious chargebacks by analysing online gamers' transactional records and gameplay data. The numbers of times and the amounts of prepayment, the numbers of times of chargebacks, and the times of the transactions that the gamers of our study gaming company made were used as characteristics for our k-NN model. The use of these characteristics enabled us to score a minimum of 0.81 in F1-Measure. In addition, three SMOTE (Synthetic Minority Over-sampling Technique) sampling methods were used to deal with the imbalance data provided by our study company and improve the F1-Measure of our proposed k-NN model (scoring up to 0.89 in our experiments). It is hoped that the use of our k-NN model can help reduce potential losses of online gaming companies that may be caused by malicious chargeback fraud, deter to malicious gamers against illegal gains, and prevent the online gaming ecosystem from being sabotaged by malicious chargebacks.
Proposal of Automatic Offloading Method in Mixed Offloading Destination Environment

When using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL are high. Based on that, I have proposed environment-adaptive software that enables automatic conversion, configuration. However, including existing technologies, there has been no research to properly and automatically offload the mixed offloading destination environment such as GPU, FPGA and many core CPU. In this paper, as a new element of environment-adaptive software, I study a method for offloading applications properly and automatically in the environment where the offloading destination is mixed with GPU, FPGA and many core CPU.
A New Password- and Position-Based Authenticated Key Exchange

Password-based authenticated key exchange is a popular method for secure authentication and key exchange. With the wide application of unmanned aerial vehicles, position information has also become an important factor in authentication. In this paper, we present a new key exchange protocol, which firstly realizes dual authentication for both password and position, and we propose two applicable scenarios for the PPAKE mechanism: one is unmanned aerial vehicle authentication, and the other one is authentication in the military base. By adding position authentication, the reliability of authentication has improved, and the difficulty of adversarial attacks also increases. Any arbitrary adversary who can listen, tamper, and send messages can only perform an online attack for password guessing at a specified position. Finally, we provide security proofs under the defined model.
A Study on Comparative Evaluation of Credit Card Fraud Detection Using Tree-Based Machine Learning Models

Credit card fraud is a severe problem that distresses financial companies and cardholders around the world and is becoming more and more serious along with the development of technology. The loss every year due to these fraudulent acts is billions of dollars.
A study on the cultivation model of information-based teaching ability of English majors state-funded normal students

In-depth development of information technology education background for the teaching ability of teachers in information technology has become one of the skills teachers must adapt to the current needs of the community. For English major’s state-funded normal students, as the main reserve force of English teachers, the cultivation of information-based teaching ability of English majors is not satisfactory. Therefore, this paper analyzes the training goal, the course structure, the teaching idea, the teaching method, the teaching practice link, and so on, and puts forward the method of training. It is expected to be helpful in the cultivation of information-based teaching ability of English majors.
Model of Decision Support Method with SMART According to Choosing Venue

Abstract Many factors are considered when choosing venue for event, including budget, facility, location convenience, and capacity. This has become one of the factor for the success of an event. Choosing wrong event location results in inconvenience in organizing the event. Therefore, we need a model so that the event can be held comfortably. the model is created by selecting a location that matches the criteria of the event that matches the desired criteria. One of the appropriate methods to be used in decision making is the SMART (Simple Multi Attribute Rating Technique) method. To support research, a simulation module is created to select the location of the event. Based on the test scenario, it was found that the solution given by the system was accurate in choosing events and approaching human ability to make decisions.
Generation of malicious webpage samples based on GAN

Machine learning needs a large amount of labeled data to train classifiers. However, it's hard to collect malicious web samples because of the short survival times and changeable attack means. In this paper, we propose Web Feature Samples Generative Adversarial Network (WFS-GAN) to generate malicious webpage feature samples. In the proposed scheme, the 48 features are extracted from relatively small number of real malicious webpages, and then convert them into webpage feature vectors. The WFS-GAN is trained by these feature vectors to get a generator which can generate webpage feature samples. Then, the classifier is trained to identify malicious webpages by webpage feature samples generated by the WFS-GAN. The WFS-GAN is based on the CGAN, and the conditional information is webpage's class label. Especially, there are four discriminators in the WFS-GAN, one is global discriminator and the other three are feature discriminators. The global discriminator determines the authenticity of the whole samples to control the quality of the whole generated samples, while each feature discriminator determines the authenticity of the specific feature data of the samples to make the generated samples detailed. The experimental results show that the feature samples generated by the WFS-GAN can be used to train malicious webpage classifier, and the quality of the feature samples generated by the WFS-GAN is better than CGAN and CVAE.
Objective Evaluation of MPEG-7 Visual Descriptors in CBIR Systems

In this work, we present an objective evaluation using metrics, for the evaluation we implemented three MPEG-7 descriptors present in LIRe and we proposed an adjustment for the metric ANMRR. Since this metric punishes the evaluation when the total recovered images are less than the class of the query image. To improve the ANMRR metric evaluation, we proposed an adjustment considering the number of recovered images. The results showed which descriptor presented a better evaluation in each one metrics, as well as the difference between the most popular evaluation metrics used in image retrieval systems and the proposed ANMRR metric. This simple adjustment of ANMRR appears to be a good evaluation alternative for cases where the recovered images are less than the class of the query.
P‐5.5: An Adaptive Video Scaling System Implementation Based on FPGA

We propose an adaptive video scaling system based on bilinear interpolation implemented on FPGA. Different from the previous system for converting from a specific input resolution to a specified output resolution, we have realized adaptive output for multiple resolution video stream inputs. At the same time, the system's resource occupancy rate is small, which facilitates integration with other modules. Experimental results show that the system can achieve real—time low‐latency and high‐quality scaling for low‐resolution video streams, and theoretically can support 4K video super‐resolution output.
Style Transfer for Videos with Audio

In the art of painting, from early era of beginning of the human civilization, human beings have been creating artistic images with content from the real world but style from their imagination. Consider one such art called The Starry Night by painter van Gogh. Here the mountains, moon and houses are content taken from the real world but the style of painting is totally from the painter’s imagination and is unique. Style Transfer is the problem of taking content of an image and style of other image to create third image having content of the former but style of the latter. Clearly, such work cannot be obtained by simple overlapping the two images. Until recently, due to not so optimized GPUs and slower hardware, image processing was a time consuming computation problem. But now we can use technological optimizations to use Convolutional Neural Networks (CNNs) to do the Style Transfer. In this paper, we discuss how style transfer can be done in videos having audio in them. We shall also compare one of the existing methods with our implementation. Our proposed work has potential applications in the domains of social media communication, entertainment industry and mobile applications.
Complementary resources and application transformation

This paper analyzes the historical characteristics of the development of Fujian Province and the great opportunity of SAR + Experimental Zone + Free Trade Zone + New District, analyzes the characteristics of educational resources and teachers in Taiwan from the other side of the Taiwan Straits, and combines the status quo of Taiwan’s high-quality teachers. This paper expounds the cooperation of cross-strait teachers in teaching and focuses on the information technology courses in university education under the new situation of Internet +, taking computer application foundation, literature retrieval, and computer network technology, and the characteristics and positioning of their respective courses. Guided by the project-driven teaching method, using a series of related auxiliary means, we pay attention to the combination of theory and practice of teaching characteristics of the achievements of our college characteristics.