Reviewerly/exHarmony huggingface dataset:
Research on Regression Model of Multi-parameter English Pronunciation Quality Evaluation Based on Computer Technology

The quality of English pronunciation determines the level of spoken English.Students learning English often have trouble judging whether their pronunciation is correct.The multi-parameter-based English pronunciation quality evaluation model proposed in this paper can provide students with an easy way to use computers to independently evaluate their English pronunciation quality.Based on the previous speech recognition algorithm, this paper extracts the Melfrequency cepstrum feature parameters in the speech signal, and uses the feature parameters as the central structure of the model to construct a model based on deep learning neural network.Within the model, the system refers to the speech rate, intonation, rhythm and intonation in the speech as evaluation treatments, and constructs an evaluation model for the quality of spoken English pronunciation based on how far regression.The model proposed in this paper can help students carry out autonomous learning and improve their learning efficiency, which is of great significance to English teaching.
Course Syllabus Design for Comprehensive English and Cultivation of Students’ Ability

Comprehensive English Course is very important for English majors in colleges and universities.To help contemporary college English students understand the course better, master the skills in this course faster and develop their abilities more, an adaptable and effective course syllabus is necessary.To make it
PNRE: Proactive Learning for Neural Relation Extraction with Multiple Annotators

Relation extraction is one of the essential tasks of information extraction, and it is also a fundamental part of knowledge graph construction.Many works have been proposed for supervised relation extraction, which commonly requires a massive amount of human-annotated data with both time and cost.To reduce the labeling time and cost, active learning has been proposed with the assumption of a single perfect annotator that always furnishes the correct label.However, more generally, the annotator will provide incorrect labels according to their labeling capabilities, and different labeling capabilities correspond to distinct costs.To unleash the power of annotators with diverse expertise level and unlabeled data for better model performance with the lowest cost, we develop PNRE, a novel proactive learning based framework for neural relation extraction that actively select the most suitable sample-annotator pairs to construct high-quality relation extraction corpus.Specifically, PNRE utilizes (1) Expert Performance Estimation module to precompute each annotator's performance considering class prediction probability; (2) Sample Selection module to select the most informative and representative sample based on a hybrid query strategy; (3) Sample Allocation module to allocate appropriate sample to each annotator under the condition of annotation utility maximization.The framework is evaluated on three corpora and is shown to achieve promising results with a significant reduction in labeling costs.
Design of English Practice System Based on Cloud Computing Technology

With the development of information technology, the field of education has gradually used information technology as an important tool to improve teaching efficiency and teaching quality.Nowadays, schools have relatively high requirements for students' self-learning ability.In order to meet the students' selflearning needs, this paper constructs an English practice system based on cloud computing technology.The system is self-adaptive, can screen and sort the topics, and provide users with an efficient and practical English practice system.
A Study of Facilitating the Enabling of the Lexical Chunk Output in the POA

The method of chunks in college English teaching has been continuously concerned.Chunk output involves a complicated cognition process and language learners' output quality is decided by effectiveness and enabling strategies of the output.Three criteria-alignment, gradualness and variety to check the enabling effectiveness during the teaching process "Motivating-Enabling-Assessing", a significant part in the the POA, proposed and led by the professor Wen Qiufang, are applied to explore the enabling design of the chunk output in college English teaching by elaborating the enabling of the topic-related chunk output via a case study.This paper focuses on how to fix the general goal and the sub-goals in chunk output enabling and how to take advantage of the cognitive gradualness based on the hierarchy of cognitive difficulties (Bloom 1956) for the teacher and students to build up together the scaffolding in order to integrate the content, language and discourse structure to facilitate the enabling of chunk output.
On Negative Transfer and Structure of Latent Functions in Multioutput Gaussian Processes

The multi-output Gaussian process ($\mathcal{MGP}$) is based on the assumption that outputs share commonalities, however, if this assumption does not hold negative transfer will lead to decreased performance relative to learning outputs independently or in subsets. In this article, we first define negative transfer in the context of an $\mathcal{MGP}$ and then derive necessary conditions for an $\mathcal{MGP}$ model to avoid negative transfer. Specifically, under the convolution construction, we show that avoiding negative transfer is mainly dependent on having a sufficient number of latent functions $Q$ regardless of the flexibility of the kernel or inference procedure used. However, a slight increase in $Q$ leads to a large increase in the number of parameters to be estimated. To this end, we propose two latent structures that scale to arbitrarily large datasets, can avoid negative transfer and allow any kernel or sparse approximations to be used within. These structures also allow regularization which can provide consistent and automatic selection of related outputs.
Implementation of the Fuzzy Inference System Tsukamoto Method in the Decision Support System

Education is one of the basic and initial formations for the growth and development of a child in the process of obtaining a better future. Once the importance of education, the state really supports every citizen in achieving a decent education. The assessment system in each school has a different system, but the assessment system refers to the national education regulations that have been set by the government. Grade promotion aims to measure the achievement of learning outcomes in accordance with the standard of graduate ability and measure the value of quality. An important decision-making, especially in the field of education, is the decision-making to increase grades in school for students. In making the decision to increase this class, it should refer to the results of the competence or academic ability of the student, not because of the intervention of several parties who do not have the capacity to determine whether or not a student is promoted. Therefore, it is necessary to make a decision support system that will assist in obtaining a decision on the feasibility of a grade increase in students using the Fuzzy Inference System of the Tsukamoto Method. The results of this study are a decision support system that can determine the decision to increase grades in students.
Unsupervised Summarization Approaches for Slide Generation

Presentation slides are essential for introducing new topics in an educational context, but they take time to create. Automatic slides generation aims at minimising the time required for preparing slide decks. This is often accomplished in two main steps: summarizing a document and organizing the result into slides. This paper focuses on the former, particularly in unsupervised approaches, in order to determine whether these approaches, which do not require training data, are viable when compared with supervised ones. For that, six methods are assessed, based on the ROUGE metric, when applied to two datasets of scientific papers and the slides used for presenting them. Despite being unsupervised, the performance of the tested methods is in line with the state-of-the-art, suggesting that they can be regarded as a good alternative, especially when training data is not available.
optimizing Security based on Face Recognition in Small Spaces

In the current pandemic, which started on last 2020, there are many limitations we should do for reducing the spread of the virus named covid19. Keeping the distance from others is the way to prevent infected covid19. Contactless technology should be developed further, for example, in this paper. Access Control System by Face recognition and Biometrics is one reason to be developed. OpenCV is a tool that will use to build face recognition. This system is only intended for small office areas or smart homes. Currently, only five registered users. The data registration process is carried out outside the system or only loads an algorithm model. This system will also provide information on social media, Whatsapp, if a known person enters the room or an unknown person.
Design of a Tourism Recommendation System Based on Sentiment Analysis with Lexicon LSTM

The growing tourism sector encourages the development of a system that can assist tourists in finding the best tourist attractions. The system that is being popularly developed in tourism is the recommendation system. It provides recommendations for tourist attractions based on several parameters. One of the parameters that can help in developing a recommender system is tourist satisfaction. Tourist satisfaction can be known by sentiment analysis on reviews on social media or tourism sites. One way of sentiment analysis is using machine learning. However, machine learning requires quite a lot of data labeled for learning. This process takes a lot of time, especially for a large amount of data, so the idea arose to use a lexicon dictionary. In this study, we tested two Indonesian datasets with four classifiers SVM, Random Forest, KNN, and LSTM. Its classifiers will combine with lexicon corpus, BOW (Bag of Words), and TFIDF (Term Frequency Inverse Document Frequency) to determine the best combination method. The experiments found that the scenario lexicon corpus, The LSTM achieves the best performance for all datasets with each accuracy is 90.93% and 76.98%. Meanwhile, in the scenario of lexicon corpus-BOW and lexicon corpus-TFIDF, the SVM method is the best on all datasets. In the Online Lecture dataset, it gets S7.40% and S7.36% of accuracy. On IDSA Dataset, lexicon corpus-BOW, and lexicon corpus-TFIDF, it gets 73.71% and 72.97% of accuracy.
Accuracy Improvement of CNN MobileNet-V1 and Residual Network 50 Layers Models using Adam setting for Car Type Classification

The industry 4.0 era is coming. This causes companies and agencies are competing in building a system that integrates cyber technology and automation technology. One example is the system for recognizing, detecting, and sorting cars according to their type as the number of cars dramatically increases. For the case when calculating certain types of cars passing the road, instant and accurate sorting of car types is required. This certainly influences the development process of related agencies. This paper presents an accuracy improvement of convolutional neural network in MobileNet-V1 and ResNet-50 models using Adam setting for car type classification. Maximum results are obtained by varying different parameters, the use of the optimizer Adam and RMSprop, employing ImageNet as transfer learning, as well as implementing dropout layers and 2 kinds of learning rate. This system is trained using 10,708 pictures of City Car, Pick Up, Sedan, Sport ultra-vehicle, and Van. Accuracy of car type classification reaches 92,1% for MobileNet-V1 and 88,2% for ResNet-50 models.
Weight-Based Convolutional Neural Network with Sparse Optimization

The Convolutional Neural Network (CNN) algorithm is widely used in modern AI systems and has been applied to various technologies. CNN is widely used for image processing, such as detecting and classifying objects from an image. However, the large size of the network presents challenges to the throughput and energy efficiency of the hardware it uses. The YoloV3-Tiny model is one of the architectures for real-time object detection, and it will create significant data movements. In the inference process, most of the processes used are convolution. While the results using pruning process applied on YoloV3-Tiny have a sparsity level of up to 75% in several layers, it means that the convolution process for the inference process will do many multiplication processes with zero values. In this accelerator, all weight data with a value of zero will be removed after the training process by pruning, then the input weight that enters the accelerator and carries out the convolution process without delay using “weight stream” dataflow. The results of the design test show that the results of the accelerator give the output of the convolution process faster when applied using trimmed sparse data. The calculation results of all YoloV3-Tiny layers when carrying out the convolution process using an accelerator designed have been estimated reduced 56% of the convolution process. The amount of data weight required for this accelerator’s convolution process is 4.2 MB. This data size is smaller than the weight data from the original weight YoloV3-Tiny model, which is 33 MB.
Multi-step stereo matching algorithm based on image fragment and confidence propagation

This paper presents a multi-step stereo matching algorithm that can be applied to multiple scenes. To adapt to different application scenarios, the algorithm divides the stereo matching process into three steps: point, fragment, and plane. First, the texture points of an image are extracted and the stereo matching (point disparity) of these points is performed using the improved self-aware matching measure (SAMM) algorithm. Then, according to the edge information of the image, a smooth region is divided into different fragments in the horizontal direction. The disparity estimation of the smooth region (segment disparity) is obtained through the confidence propagation of disparity values of texture points in the fragments. Finally, based on the similarity of plane disparity, a disparity map is generated using the disparity refining algorithm (plane disparity), and a final high-precision disparity value is obtained. The experimental results show that the proposed algorithm has high operational efficiency and accurate disparity estimation. Moreover, the algorithm may be adapted for more application scenarios.
Convolutional Attention-enabled Underwater Object Detection with Electro-optical Image

Aiming at the problems of low detection accuracy and insufficient feature fusion in the underwater environment, an efficient object detection aprroach is proposed based on YOLOv5 with the convolutional attention module. Firstly, the YOLOv5s network model is optimized and improved by integrating convolutional attention, and feature extraction is performed for the input image. Secondly, the weighted bidirectional feature pyramid network is used to enhance the original structure to make multi-scale feature fusion more convenient. Finally, the post-processing algorithm of non-maximum suppression is improved. The experimental results show that the mAP of this method is 85.8%, which is 3.8% higher than that of YOLOv5s, and the accuracy is 4.7% higher than that of YOLOv5s. The proposed model can meet the real-time and accuracy requirements of seabed biological detection in the underwater environment.
Evaluating 3D Printed VR Controller Prototypes to Increase VR Accessibility for Older Adults

Virtual reality (VR) is transforming how technology benefits society, including the areas of training, socialization, and entertainment. While VR’s growth has been substantial, VR hardware and applications are inaccessible in many ways, excluding a large population from experiencing its benefits. Current VR controllers potentially create an accessibility barrier for older adults who may experience confusion with the complexity of buttons, triggers, and joysticks. In the presented work, we recruited ten older adult participants to develop and evaluate 3D printed VR controller prototypes for usability and learnability. A series of participatory design focus groups discussed participants’ needs, opinions, and design preferences, while usability scales evaluated the prototypes. Iterations were implemented between sessions based on this feedback to further refine the designs. Findings suggest that a simplified VR controller layout can decrease confusion and increase learnability, potentially improving older adults’ ability to interact with virtual environments.
AFMTD: Anchor-free Frame for Multi-scale Target Detection

Target detection task plays the most fundamental and important role in computer vision. The appearance of deep learning method has produced a positive effect on target detection, but multi-scale target detection is poor. The reasons could be attributed to two aspects; the first one is that the small target tends to contain less semantic information, which leads algorithm be hard to detect it; the other is that the sample distribution in the practical application scenarios is random, and the different-scaled target features will interfere with each other, which poses negative effect on multi-scale target detection. Based on existing technical issues, we propose an anchor-free frame for the multi-scale target detection (AFMTD) algorithm as solution. First, from the direction of feature fusion, we propose a spatial attention fusion module (SAFM), which designs same scale transformation (SST) based on Bi-FPN, strengthens the valuable information between adjacent feature layers, and suppresses interference features, improving the detection accuracy and resolution ability of targets of different scales. Then, from the direction of anchor-free frame detection, the heatmap-based multi-scale detection module (HMDM) is proposed; by introducing a scale distribution mechanism (SDM) and Heatmap-IOU (HIOU) loss function, the module allocates different targets to different corresponding feature maps, which makes the model converge faster and more accurately. Through experiments on the MS COCO dataset, our approach achieved 40.5% average precision (AP), and the AP of large, medium, and small-scale targets is 24.5%, 44.1%, and 53.9%, respectively.
A 10-Gbps, 0.121-pJ/bit, All-Digital True Random-Number Generator using Middle Square Method

The rapid development of mobile and cloud markets has resulted in an exponential increase in sensitive data such as biometric or financial information. Therefore, the need for random-number generators with high entropy for encryption is increasing. For mass-production at a low cost and easy integration into a general system-on-chip (SoC), true random-number generators (TRNGs) are often fabricated in commercial CMOS processes. Most CMOS-based TRNGs use either a jitter- or metastability-based entropy source (ES) and combine it with a post-processor to improve the random-number quality. Although a TRNG using a jitter-based ES [1] has a small area and low power consumption, it is weak against power supply attack (PSA). Therefore, additional circuits are required to prevent such an attack. Moreover, it is slow in terms of speed. Meanwhile, the use of a metastability-based ES increases the power consumption of a TRNG; however, it can generate random numbers at a faster rate and is robust against PSA. In this work, we propose a high-throughput TRNG using the middle square method (MSM). To lower the post-processing power, a robust digital comparator with offset calibration is used as an ES, which allows simplification of the MSM post-processing.
A keyframe extraction method based on transition detection and image entropy

With the massive growth of video data on the internet, users need a strategy to quickly browse video content. Extracting the key information from the redundant information of video data is a hot topic in recent research. There are two kinds of video summarization: static video summarization and dynamic video summarization. A set of keyframes is a form of a static video summary. Through a set of keyframes, users can understand the main content of the video. In addition, keyframes can also be used in video retrieval services. In this paper, shot segmentation based on transition frame detection is realized, and then, keyframes based on video shots are extracted. There are different processing strategies for long and short shots. The frame with the most information is selected from each segmented shot as the keyframe, and finally, the key frame is made nonredundant by using visual features. In this paper, the proposed strategy is tested on the OpenVideoProject dataset and compared with VSUMM and other keyframe extraction methods.
Construction Technology of Software Defined Modular Swarm Network Information Architecture

The swarm information network requires outstanding features such as flexible networking, fast networking, security, and controllability. According to the information interaction requirements of the swarm information network, a Software Defined Network (SDN) modularized swarm network information architecture is proposed, the business requirements and characteristics of the swarm network are analyzed, and the swarm network information architecture mode is designed to improve the openness and reliability of the swarm network information system, which provides support for swarms to carry out tasks autonomously in complex scenes.
Designing Scenarios for In-Organization Training Using the CyberCIEGE Game

Abstract Cyber security awareness training needs to be personalized to address the policies and requirements of a particular organization, although many of the concepts in this training practice are universal. The main reason for the failure of training programs is that the users do not think about applying security concepts. Therefore, the study aims to design scenarios for in-organization training using CyberCEIGE. In this context, CyberCIEGE is considered a flexible and highly interactive video game that is a security awareness tool. It is responsible for supporting organizational training objectives in an engaging security adventure in the presence of typical users. This game has shown success in utilizing information assurance education. The present study indicates that CyberCIEGE is effective in basic information awareness training programs for security purposes, such as the balance between corporate convenience and individual privacy.
Improved related‐tweakey rectangle attacks on round‐reduced Deoxys‐BC

Abstract Deoxys‐BC is the internal tweakable block cipher of the authenticated encryption (AE) Deoxys family, in which Deoxys‐II is the primary choice for the use case of ‘Defence in depth’ among the portfolio of CAESAR competition. Improvements of the related‐tweakey rectangle attacks on round‐reduced Deoxys‐BC using the known distinguishers is focussed in this study. Under the new related‐key rectangle attack framework proposed by Dong et al. in EUROCRYPT 2022, we present three kinds of precomputed tables to further reduce the time complexity in the key‐recovery phase. In the related‐tweakey rectangle attack, the invalid quartets are filtered or the subtweakey candidates are obtained by lookup the precomputed tables without more computation. Based on the precomputed table technique, we improved the related‐tweakey rectangle attacks on 11‐round Deoxys‐BC‐256, 13‐round and 14‐round Deoxys‐BC‐384. Furthermore, we reduce the time complexity of the 13‐round related‐tweakey rectangle attack on Deoxys AE scheme Deoxys‐I‐256‐128 by a factor of 2 24 compared with the best previous attack.