Reviewerly/exHarmony huggingface dataset:
Study on innovation capability data of central cities in Pearl River Delta based on factor analysis

In recent years, the innovation capability of the Pearl River Delta region has undergone significant changes. The paper, based on cluster analysis and factor analysis, seeks to analyze the development level, differences, and influencing factors of the innovation capability of the central cities in the PRD in 2020. The results indicate that those factors driving innovation include spending on research, science and technology profit, science and technology output, research potential, education, and consumption level. Based on the scores of the three factors, Shenzhen ranks first with an overall score of 1.233, while Zhuhai ranks last with an overall score of -0.468. The development of innovation capability in the PRD city clusters forms an innovative city network with Guangzhou and Shenzhen as the core, Foshan, and Dongguan as the subcenters, and other cities such as Jiangmen, Zhaoqing, Huizhou, Zhuhai, and Zhongshan as the third echelon. To enhance the overall innovation capacity in the PRD, an innovation community must be built based on the integration strategy of the PRD region. Greater efforts must be made to enhance the innovation capacity in three aspects: spending on research as well as science and technology profit, science and technology output together with research potential, and education and consumption level.
A grating type dual-image-element demodulation method based on liquid crystal spatial light modulator for spatial phase-shift-interferometry

A demodulation method based on liquid crystal spatial light modulator for space domain phase-shift-interferometry is proposed in this paper. In the method, the grating type with dual-image-element mode is used as the phase modulation mode, and the ρ-δ algorithm is proposed with which the interference image is recovered from the modulated image. Finally, the demodulation method is introduced by using the gradient projection algorithm and the arctangent operation. Simulation and experimental results show the effectiveness of the proposed method, in which the image recovery error can be reduced by 1.3 grayscales, and the improvement rate of recovery accuracy is 31.38%. In the 90pixels truncation interface, the effective demodulation pixels are 84 pixels, accounting for 93.33%.
Speech denoising algorithm and artificial intelligence application in network security

Speech denoising refers to people finding useful information in noise-affected speech data and processing it. This technology is an important part of speech signal processing technology. With the continuous progress of science and technology, its application in various fields is gradually improving, among which voice noise reduction technology has also been well developed. Especially in recent years, with the rapid development of artificial intelligence science and technology, anti-network speech denoising technology based on deep learning has been derived. Compared with traditional speech denoising techniques, it has better performance in resolving speech data distortion and denoising results. However, in practical applications, this method still has some shortcomings, such as: some unique noises are not removed, and the effective information extraction is distorted under the background of high-intensity noise. In response to these outstanding problems, this research is based on the anti-network speech denoising technology, combined with the Wiener filter speech denoising algorithm, and applies artificial intelligence technology to network security technology management. In this paper, two algorithms, COVL and SSNR, are used to compare the original input speech data signal with noise and the speech data signal preprocessed by Wiener filtering. The additional information is input into the generation network and the discriminant network, and the additional information is used to guide the data generation. The least squares loss function of the binary coding algorithm is used to replace the cross entropy loss function, so that the original discriminant network loss function is not changed, which improves the accuracy of discrimination. This improved technology is superior in terms of speed, productivity and improved accuracy. While building the system, mature network technologies and equipment applications are fully applied, and speech de-noising technology is used for network security situation assessment and prediction simulation tests. The simulation test results show that the network security situation assessment is reasonable and effective, and the prediction is accurate in real time.
Convergent autoencoder approximation of low bending and low distortion manifold embeddings

Autoencoders, which consist of an encoder and a decoder, are widely used in machine learning for dimension reduction of high-dimensional data. The encoder embeds the input data manifold into a lower-dimensional latent space, while the decoder represents the inverse map, providing a parametrization of the data manifold by the manifold in latent space. A good regularity and structure of the embedded manifold may substantially simplify further data processing tasks such as cluster analysis or data interpolation. We propose and analyze a novel regularization for learning the encoder component of an autoencoder: a loss functional that prefers isometric, extrinsically flat embeddings and allows to train the encoder on its own. To perform the training it is assumed that for pairs of nearby points on the input manifold their local Riemannian distance and their local Riemannian average can be evaluated. The loss functional is computed via Monte Carlo integration with different sampling strategies for pairs of points on the input manifold. Our main theorem identifies a geometric loss functional of the embedding map as the $\Gamma$-limit of the sampling-dependent loss functionals. Numerical tests, using image data that encodes different explicitly given data manifolds, show that smooth manifold embeddings into latent space are obtained. Due to the promotion of extrinsic flatness, these embeddings are regular enough such that interpolation between not too distant points on the manifold is well approximated by linear interpolation in latent space as one possible postprocessing.
General-to-Specific Transfer Labeling for Domain Adaptable Keyphrase Generation

Training keyphrase generation (KPG) models require a large amount of annotated data, which can be prohibitively expensive and often limited to specific domains. In this study, we first demonstrate that large distribution shifts among different domains severely hinder the transferability of KPG models. We then propose a three-stage pipeline, which gradually guides KPG models' learning focus from general syntactical features to domain-related semantics, in a data-efficient manner. With Domain-general Phrase pre-training, we pre-train Sequence-to-Sequence models with generic phrase annotations that are widely available on the web, which enables the models to generate phrases in a wide range of domains. The resulting model is then applied in the Transfer Labeling stage to produce domain-specific pseudo keyphrases, which help adapt models to a new domain. Finally, we fine-tune the model with limited data with true labels to fully adapt it to the target domain. Our experiment results show that the proposed process can produce good-quality keyphrases in new domains and achieve consistent improvements after adaptation with limited in-domain annotated data. All code and datasets are available at https://github.com/memray/OpenNMT-kpg-release.
ESP COURSE DESIGN: THE NEED ANALYSIS ON ENGLISH FOR TOURISM BOOK FOR TRAVEL BUSINESS DEPARTMENT OF ELEVENTH GRADE STUDENTS AT SMKN 1 TARAKAN

English subject content is compared to the high school level based on the 2013 Curriculum. While English is differentiated for high school and vocational high school students for specific objectives, both high school and vocational high school students receive the same English curriculum. Finding an English textbook that matches the needs of the students in a vocational high school might be challenging. Teachers frequently lack the time necessary to create English materials for high school vocational programs. This study aims to investigate the information that should be covered in SMKN 1 Tarakan while creating an English for Tourism Book for the Travel Business Department and to ascertain the instructors' reactions to the creation of English materials in that department. This study used a mixed-method, quantitative and qualitative analysis. The study's findings suggest that English language material should be added. Speaking and pronunciation for language skills and components, media, pictures, and asking questions regarding pedagogical approaches are among the topics covered in the SMKN 1 Tarakan Travel Business Department's course materials. A variety of speaking practice presentations, a variety of audio for listening, speaking, and reading practice, reading sections and texts, and some information that the learner of travel business has to be aware of make up the creation of English language products.
Sign bit is enough

Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive high-performance computing systems such as public clouds. According to our theoretical findings, due to the cascading compression, the training process has considerable deterioration on the convergence performance. To overcome this limitation, we implement a sign-bit compression-based learning synchronization framework, Marsit. It prevents cascading compression via an elaborate bit-wise operation for unbiased sign aggregation and its specific global compensation mechanism for mitigating compression deviation. The proposed framework retains the same theoretical convergence rate as non-compression mechanisms. Experimental results demonstrate that Marsit reduces up to 35% training time while preserving the same accuracy as training without compression.
Hardware-efficient stochastic rounding unit design for DNN training

Stochastic rounding is crucial in the training of low-bit deep neural networks (DNNs) to achieve high accuracy. Unfortunately, prior studies require a large number of high-precision stochastic rounding units (SRUs) to guarantee the low-bit DNN accuracy, which involves considerable hardware overhead. In this paper, we propose an automated framework to explore hardware-efficient low-bit SRUs (ESRUs) that can still generate high-quality random numbers to guarantee the accuracy of low-bit DNN training. Experimental results using state-of-the-art DNN models demonstrate that, compared to the prior 24-bit SRU with 24-bit pseudo random number generator (PRNG), our 8-bit with 3-bit PRNG reduces the SRU resource usage by 9.75× while achieving a higher accuracy.
CNN-BERT for measuring agreement between argument in online discussion

Purpose With the rise of online discussion and argument mining, methods that are able to analyze arguments become increasingly important. A recent study proposed the usage of agreement between arguments to represent both stance polarity and intensity, two important aspects in analyzing arguments. However, this study primarily focused on finetuning bidirectional encoder representations from transformer (BERT) model. The purpose of this paper is to propose convolutional neural network (CNN)-BERT architecture to improve the previous method. Design/methodology/approach The used CNN-BERT architecture in this paper directly uses the generated hidden representation from BERT. This allows for better use of the pretrained BERT model and makes finetuning the pretrained BERT model optional. The authors then compared the CNN-BERT architecture with the method proposed in the previous study (BERT and Siamese-BERT). Findings Experiment results demonstrate that the proposed CNN-BERT is able to achieve a 71.87% accuracy in measuring agreement between arguments. Compared to the previous study that achieve an accuracy of 68.58%, the CNN-BERT architecture was able to increase the accuracy by 3.29%. The CNN-BERT architecture is also able to achieve a similar result even without further pretraining the BERT model. Originality/value The principal originality of this paper is the proposition of using CNN-BERT to better use the pretrained BERT model for measuring agreement between arguments. The proposed method is able to improve performance and also able to achieve a similar result without further training the BERT model. This allows separation of the BERT model from the CNN classifier, which significantly reduces the model size and allows the usage of the same pretrained BERT model for other problems that also did not need to finetune their BERT model.
Research on Security Mechanism of Hadoop Big Data Platform

As a virtualized resource realization mode, Hadoop cloud platform has become an open-source cloud computing architecture and big data analysis platform. The platform plays a pivotal role in the information field, but the security mechanism of the Hadoop cloud computing platform is relatively fragile. Users are concerned about privacy leakage using which severely restricts the application rate of Hadoop cloud computing platform. This paper conducts research and analysis on big data security and privacy protection technology under cloud computing, and proposes a strategy based on optimizing Hadoop security mechanism, from Hadoop platform identity authentication, Hadoop platform access authorization, Hadoop platform big data encryption security integrated mechanism to architecture, and implement the Hadoop platform, A big data security strategy of Hadoop provides technical support for enterprise applications with security requirements on the Hadoop platform.
Multilabel Image classification using optimized ensemble deep learning

Deep learning is presently giving very good performance for many applications like Image identification, Speech recognition, Natural language processing, Recommendation systems etc. Ensemble learning is giving very good performance when applied in deep learning. In this paper, we use deep learning as well as ensemble learning for the classification of multi-label dataset. The novelty of the model is that we combine three different models. In the first model we use AutoEncoder+CNN by using only the encoder part to reduce the dimension. The second model is created by adding the noise to the input data and then using AutoEncoder to denoise the noisy data so that the model can work even for noisy dataset in real-time applications. The third model uses the vision Transformer. The proposed ensemble deep learning model combines these different models to predict the final result. The ensemble model outperforms each individual model and the state-of-the-art models.
Security Access Control System Enhanced with Face Mask Detection and Temperature Monitoring for Pandemic Trauma

COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization.
An enhanced searching strategy for multi-agent mobile applications

Multi-agent mobile applications play an essential role in mobile applications and have attracted more and more researchers' attention. Previous work has always focused on multi-agent applications with perfect information. Researchers are usually based on human-designed rules to provide decision-making searching services. However, existing methods for solving perfect-information mobile applications cannot be directly applied to imperfect-information mobile applications. Here, we take the Contact Bridge, a multi-agent application with imperfect information, for the case study. We propose an enhanced searching strategy to deal with multi-agent applications with imperfect information. We design a self-training bidding system model and apply a Recurrent Neural Network (RNN) to model the bidding process. The bridge system model consists of two parts, a bidding prediction system based on imitation learning to get a contract quickly and a visualization system for hands understanding to realize regular communication between players. Then, to dynamically analyze the impact of other players' unknown hands on our final reward, we design a Monte Carlo sampling algorithm based on the bidding system model (BSM) to deal with imperfect information. At the same time, a double-dummy analysis model is designed to efficiently evaluate the results of sampling. Experimental results indicate that our searching strategy outperforms the top rule-based mobile applications.
Theater Music Data Acquisition and Genre Recognition Using Edge Computing and Deep Brief Network

Artificial intelligence (AI) and the Internet of Things (IoT) make it urgent to push the frontier of AI to the network edge and release the potential of edge big data. The model’s accuracy in data acquisition and music genre classification (MGC) is further improved based on theater music data acquisition. First, machine learning and AI algorithms are used to collect data on various devices and automatically identify music genres. The data collected by edge devices are safe and private, which shortens the time delay of data processing and response. In addition, the deep belief network (DBN)-based MGC algorithm has better overall recognition and classification effect on music genres. The MGC accuracy of the proposed improved DBN algorithm is nearly 80%, compared to 30%–40% of the traditional algorithms. The DBN algorithm is more accurate than the traditional classical algorithm in MGC. The research has an important reference value for developing Internet technology and establishing a music recognition model.
ADT-SQLi : An Automated Detection of SQL Injection Vulnerability in Web Applications

Web applications are constantly being developed to make life easier and more convenient for businesses and customers; it makes intruders involved in conducting malicious activities. Intruders use vulnerabilities to perform malicious attacks, and injection is the top-ranked vulnerability of web applications. SQL injection is a technique of code injection that places malicious code through web page input in SQL statements. Several numbers of case studies are found in previous research on vulnerability in the web application layer. Various models are introduced, built, and compared with many current SQL injection models and other vulnerabilities in the web application layer. However, there are few automation detections works on SQL injection that provide high precision and no finite state model-based works. This research aimed to propose a model and develop an automated SQL injection detection tool called ADT-SQLi based on the proposed model. In addition, this work was intended to simulate the proposed model with automata called a finite state machine. ADT-SQLi provides better efficacy on the identification of SQL injection and found ADT-SQLi as a finite model as it has exactly one of a finite number of states at any given time.
Convolutional Neural Networks-Based VQA Model

In visual question answering (VQA) task, we generally use a convolutional neural network (CNN) to extract image features, a recurrent neural network (RNN) for question representation. Instead of using a RNN for language representation, we use a CNN for question representation. The advantage of using a CNN for question representation is that CNN is more effective in capturing image and question words interactions which are not expressed by a RNN. Thus, in this paper, we have used three CNNs for VQA task. One CNN extracts visual features, second CNN for extracting question features, and third CNN for combining both extracted feature vectors. Further, we have employed a softmax layer to generate answer for a given question. The proposed VQA model is evaluated on DAQUAR, COCO-QA, and VQA2.0 datasets.
Classification of Breast Tumor from Ultrasound Images Using No-Reference Image Quality Assessment

A computer-aided diagnosis (CAD) system can be helpful for the detection of malignant tumors in the breast. Ultrasound imaging is a type modality with low cost and lower health risk. In this paper, we have classified benign and malignant breast tumors from ultrasound images. We have used the image quality assessment approach for this purpose. No-reference image quality metrics have been used as features for the classification task. We have used a public database of ultrasound images of breast tumors containing 780 images. The classification of breast ultrasound images using image quality assessment is a very novel approach, producing significant results.
Memory Acquisition Process for the Linux and Macintosh Based Operating System Using Open-Source Tool

Memory forensics indicates the examination and analysis of data available in a computer's memory dump. Cyber forensics experts perform memory forensics operation to analyze and find out the attacks or suspicious behaviors that cannot find in hard drive data. Currently, criminals or attacker hardly leave their footprints in computer system. Therefore consequently, the memory (RAM) must be analyzed for forensic information. This chapter focus on results of two important memory-acquisition tools: LiMe for Linux and OSXPmem for Macintosh operating system respectively. Our research assesses the memory acquisition process which is most challenging today in Macintosh and Linux operating system. Later on, the same can be analyzed using any open-source memory analysis tools like Autopsy which provides accurate and deep understanding of the important forensic artefacts present in memory. We found that, tools discussed in this chapter, LiMe and OSXPmem performs well in terms of reliability and support for all memory configurations and versions of the operating system.
THE ROLE OF EMOTIONAL-VOLITIONAL SELF-REGULATION IN MUSIC TEACHER TRAINING

In the article the author investigates essence of musical aesthetic activity of graduates, music teachers and also the factors increasing it. The pedagogical technology of formation the students’ readiness of to the activisation of the elementary shoolchildren’s art activity is cleared up and experimentally approved. The necessary and sufficient pedagogical conditions of its functioning are defined in the Work of a higher pedagogical school.
PSYCHOLOGIST-PEDAGOGICAL PREREQUISITES AND REGULARITIES OF EFFECTIVE MANAGEMENT STUDENT PERSONALITY DEVELOPMENT in EDUCATIONAL PROCESS

The article deals with psychological and pedagogical preconditions and original laws of governing the effective development of pupil’s personality in the process of education.
METHODICAL PREPARATION OF STUDENTS FACULTIES OF ARTS PEDAGOGICAL UNIVERSITIES

The article investigates the ways o f a methodic preparation o f students at faculties teaching arts and Music. The author analyses the system o f methodical preparation at the music teaching faculty.