Reviewerly/exHarmony huggingface dataset:
Paragraph shrinking as a strategy to improve reading comprehension

This research was carried out to discover the effectiveness of using the paragraph shrinking strategy in improving students' reading comprehension, specifically in understanding general information, detailed information, reference, and vocabulary. The method of this was quasi-experimental design research. The population of this research was 224 students in the first grade of a junior high school. The results showed that students' average scores on general information increased from 410 in the pretest to 565 in the posttest. Likewise, their score of detailed information increased from 430 in the pretest became 605 in the posttest. Then, the students’ reference average score increased from 590 in the pretest become 610 in the posttest. Last, their vocabulary average score increased from 600 in the pretest to 630 in the posttest. Based on the results of the t-test, the df value was 29, and the deT value was 16.370. Based on the SPSS output of the table, the Sig. (2-tailed) had a p-value = 0.00, meaning that the p-value was &lt;0.05. The researchers found that the null hypothesis (H0) was rejected. In conclusion, there is a significant improvement in students' reading comprehension after learning using the paragraph shrinking strategy.
A SIFT registration and modified CNN model-based human eye detection and location algorithm

Considering that traditional statistical learning methods are vulnerable to illumination interference and difficult to obtain accurate human eye centers when locating human eyes, this paper proposed a human eye detection and location algorithm based on SIFT registration and a modified CNN model. Firstly, face images were registered using SIFT. Secondly, the processed images were transferred into the modified CNN model to extract left and right eye anchor on the human faces and determine whether there were human eyes in the anchor. Finally, the size of the left and right eye anchor was calculated to obtain the accurate human eye center in the anchor. Tests based on JAFFE face database and CMU Multi-PIE face database showed that the proposed algorithm had a strong anti-interference ability to complex illumination, irregular face images and facial expression transformation, and could obtain accurate human eye center points.
Intrusion detection model based on genetic algorithm optimization extreme learning machine of K-fold stratified cross-validation

Aiming at the problem of random generation of Extreme Learning Machine (ELM) parameters, an intrusion detection model based on GA-ELM of K-fold stratified cross-validation is proposed. Genetic Algorithm (GA) is used to optimize the parameters of ELM. The simulation experiment is carried out on NSL-KDD data set, and the GA-ELM model is trained by K-fold stratified cross-validation method. The experimental results show that the proposed model has a higher detection rate than ELM, GA-ELM and GA-ELM of nonstratified cross-validation.
Speech aerodynamic model analysis of mono stop initials in Xiahe dialect

In this paper, the principle of speech aerodynamics is used, and the acoustic spectrogram and pronunciation airflow waveform are one-to-one corresponded by means of signal processing. The results show that from the airflow waveform, it can be clearly seen that the cooperative pronunciation influence mode between the plosives and vowels /a/ is cis assimilation. The plosives will not only affect the size of vowel airflow and airflow velocity, but also affect the trend of vowel formant. When the aspirated stop is pronounced, the greater the pressure under the glottis results in a longer duration and a higher average airflow velocity, which is about twice as long as the unaspirated stop and more than four times as fast as the unaspirated stop; The stability of the duration of the air flow of the aspirated stop is worse as the voice position is more and more backward, and the stability of the average air flow velocity of the aspirated stop is worse than that of the unaspirated stop.
Abdominal CT pancreas segmentation using multi-scale convolution with aggregated transformations

Convolutional neural networks (CNNs) are a popular choice for medical image segmentation. However, they may be challenged by the large inter-subject variation in organ shapes and sizes due to CNNs typically employing convolutions with fixed-sized local receptive fields. To address this limitation, we proposed multi-scale aggregated residual convolution (MARC) and iterative multi-scale aggregated residual convolution (iMARC) to capture finer and richer features at various scales. Our goal is to improve single convolutions’ representation capabilities. This is achieved by employing convolutions with varying-sized receptive fields, combining multiple convolutions into a deeper one, and dividing single convolutions into a set of channel-independent sub-convolutions. These implementations result in an increase in their depth, width, and cardinality. The proposed MARC and iMARC can be easily integrated into general CNN architectures and trained end-to-end. To evaluate the improvements of MARC and iMARC on CNNs’ segmentation capabilities, we integrated MARC and iMARC into a standard 2D U-Net architecture for pancreas segmentation on abdominal computed tomography (CT) images. The results showed that our proposed MARC and iMARC enhanced the representation capabilities of single convolutions, resulting in improved segmentation performance with lower computational complexity.
Blueprint for a digital-analog variational quantum eigensolver using Rydberg atom arrays

We address the task of estimating the ground-state energy of Hamiltonians coming from chemistry. We study numerically the behavior of a digital-analog variational quantum eigensolver for the ${\mathrm{H}}_{2}$, LiH, and ${\mathrm{BeH}}_{2}$ molecules, and we observe that one can estimate the energy to a few percent points of error leveraging on learning the atom register positions with respect to selected features of the molecular Hamiltonian and then an iterative pulse-shaping optimization, where each step performs a derandomization energy estimation.
Application and Development of QKD-Based Quantum Secure Communication

Quantum key distribution (QKD) protocols have unique advantages of enabling symmetric key sharing with information-theoretic security (ITS) between remote locations, which ensure the long-term security even in the era of quantum computation. QKD-based quantum secure communication (QSC) enhancing the security of key generation and update rate of keys, which could be integrated with a variety of cryptographic applications and communication protocols, has become one of the important solutions to improve information security. In recent years, the research on QKD has been active and productive, the performance of novel protocol systems has been improved significantly, and the feasibility of satellite-based QKD has been experimentally verified. QKD network construction, application exploration, and standardization have been carried out in China as well as other countries and regions around the world. Although QKD-based QSC applications and industrialization are still in the initial stage, the research and exploration momentum is positive and more achievements could be expected in the future.
Cognitive Sensors, Volume 2

In the modern world, sensors and sensor networks are critical features in developing applications for control and automation in engineering and technology. Cognitive Science is a multidisciplinary area of research that covers artificial intelligence, neuroscience, mathematics, philosophy, psychology, linguistics, automated diagnosis etc. Cognitive sensors and associated AI and algorithms are most important components of cognitive science research and studies. This book will provide a review of the emerging theme of cognitive sensors, how it functions and its applications in a range of disciplines. Volume 2 features cognitive sensors in smart healthcare, including appropriate signal processing, filtering, machine learning and AI for medical cognitive science analysis and applications; case studies, brain disorder and stress analysis. Volume 1 is available <a href="https://iopscience.iop.org/book/edit/978-0-7503-5326-7">here</a>. Part of <a href="https://iopscience.iop.org/bookListInfo/iop-series-in-sensors-and-sensor-systems#series">IOP Series in Sensors and Sensor Systems</a>. Key features • First book highlighting cognitive aspect of sensing and applications. • Reviews Intelligent Sensing, IoTs and Embedded Machine Learning for sensor data. • Discussion on challenges related to wireless sensor network and machine learning. • Incudes smart healthcare and other smart automated systems exploiting cognitive sensing techniques. • Psychological studies on sensor data for educational and other research work.
A tale of two interfaces: vitrivr at the lifelog search challenge

The past decades have seen an exponential growth in the amount of data which is produced by individuals. Smartphones which capture images, videos and sensor data have become commonplace, and wearables for fitness and health are growing in popularity. Lifelog retrieval systems aim to aid users in finding and exploring their personal history. We present two systems for lifelog retrieval: vitrivr and vitrivr-VR, which share a common retrieval model and backend for multi-modal multimedia retrieval. They differ in the user interface component, where vitrivr relies on a traditional desktop-based user interface and vitrivr-VR has a Virtual Reality user interface. Their effectiveness is evaluated at the Lifelog Search Challenge 2021, which offers an opportunity for interactive retrieval systems to compete with a focus on textual descriptions of past events. Our results show that the conventional user interface outperformed the VR user interface. However, the format of the evaluation campaign does not provide enough data for a thorough assessment and thus to make robust statements about the difference between the systems. Thus, we conclude by making suggestions for future interactive evaluation campaigns which would enable further insights.
UNMMIT: A Unified Framework on Unsupervised Multimodal Multi-domain Image-to-Image Translation

We address the open problem of unsupervised multimodal multi-domain image-to-image (I2I) translation using a generative adversarial network.Previous works, such as MUNIT and DRIT, are able to translate images among multiple domains, but they generate images of inferior quality and less diverse.Moreover, they require training generators and discriminators for learning to translate images among domains, which is computationally expensive.In this paper, we propose a simpler yet more effective framework for unsupervised multimodal multi-domain I2I translation.Our approach only consists of a mapping network, a encode-decoder pair (generator), and a discriminator.Our method assumes that the latent space can be decomposed into content and style subspaces by the encoder, where content space is deemed domaininvariant and style space is domain-dependent.Unlike MUNIT and DRIT that simply sample style codes from a standard normal distribution when translating, we employ a mapping network to learn the style of different domains, which yields better translation results.Translation is done through the decoder by keeping content codes and exchanging the style codes.To encourage diversity in translated images, we employ style regularizations and inject Gaussian noise into the decoder.Extensive experiments show that our framework is superior or comparable to state-of-the-art baselines.Index Terms-Unsupervised multimodal multi-domain image-to-image translation, style codes, content codes, mapping network I. INTRODUCTION Image-to-image (I2I) translation refers to translating images from one domain to another featuring different styles, which are visually distinctive among different domains.An example is the task of turning images of cartoon sketches into real-life graphs.Many tasks in computer vision can be viewed as I2I translation, such as image translation (MUNIT [1], AMMUNIT [2]), image inpainting [3], style transfer (StyleGANs [4], DRIT [5]), and super-resolution [6].Supervised I2I translation tasks need paired data sets that are costly to obtain, and such tasks are relatively easier to solve than their unsupervised counterpart.Under paired data supervision, I2I translation can be done by taking a regression approach [7] or using conditional generative models [8].Our work addresses the more challenging unsupervised I2I translation task without access to paired data sets.Most of works on unsupervised I2I translation draw inspiration from CycleGANs [9] using the cycle consistency constraint and have achieved impressive results.More recent studies have improved upon on CycleGANs and are able to translate images among multiple domains.They, such as MUNIT often
Discover two Neural Machine Translation model variables' effects on Chatbot's performance

To offer automatic online advice and help, chatbots are sophisticated conversational computer systems that resemble a human conversation. As chatbots' advantages grew, a variety of sectors began to use them extensively to give customers virtual support. Chatbots take advantage of methods and algorithms from two Artificial Intelligence areas: Machine Learning and Natural Language Processing. There are still several obstacles and restrictions to their use. In order to discover two NMT model variables' effects on chatbot performance, this paper does several experiments on a deep neural network chatbot model. Two straightforward and useful kinds of attentional mechanisms are used in this chatbot model: a local technique that only considers a small subset of source words at a time, as opposed to a global approach that always pays attention to all source words. This paper conducts experiments to examine how different model variables affect chatbot performance. This paper created a question template with eight general questions to test chatbot performance. Through the whole experiment results, increasing the number of iterations and increasing the dataset scale can improve the vocabulary and logic of the chatbot dialog to achieve better performance.
Research on the Progress of VR in Game

Virtual reality technology has evolved from the last century to the present, with huge improvements in performance, graphics quality and portability of headset devices. Major game manufacturers and studios have launched virtual reality games that use virtual reality technology. Compared with traditional games, virtual reality games have a strong sense of immersion, presence and interactivity, and are very popular among players. This paper summarizes the development history of VR devices and VR games, outlines the importance of VR in game design and development - the strong immersion and interactivity of VR games compared with traditional games, and explains the applications of VR in various types of games, among which are scenario-experiencing games with exploration games and social games, action-simulation games with sports and role-playing games, and educational games for teaching purposes, and points out that there are still shortcomings in the development and design of current virtual reality games, and developers of virtual reality devices and games need to work together to make virtual reality games have more application scenarios and meanings. The hotspots and development prospects of VR games at the current stage are prospected.
Game Designs based on Machine Learning Systems

Contemporarily, there are many games as follow a storyline in the game industry. On this basis, NPCs are acting under a setup. In this paper, several machine learning systems are creating games and art pieces by themselves. Readers can see how different machine learning system works to form different games. Based on the procedure and evaluations, it will help a lot of game developers to create a full AI system for creating different levels, NPC, monsters, and missions. Therefore, it will create a world that is even more freedom for players to play. ANGELINA system and those two art systems based on machine learning they are creating some random production by themselves, and all the works are high quality. The purpose of this paper is to demonstrate the functions machine learning works in the field of game creation. These results shed light on guiding further exploration of game developing in terms of machine learning scenarios.
A Comparative Study of Zebra English and You Mei English Courses

Under the background of Internet + education, online education is booming. In order to abandon the traditional English education mode and let children accept more pure English, many parents begin to try to choose online English courses. However, there are many kinds of children's English courses on the market, so how to choose a high-quality course brand among the dazzling brands has become the top priority. After tracking and observing a variety of online English brands in the past two years, this paper finally selected two relatively representative online English courses, Zebra English and You Mei English, for comparison and analysis, and suggested that You Mei English should introduce AI technology to design interactive scene animation of real people. It is not necessary to set the reading link in the first-level courses of Zebra English. Children's games and operation components should be added to Zebra English, and daily unlocking task points should be added to Zebra English. Zebra English is more suitable for zero-basis enlightenment, and You Mei English can be added as a supplement in the later stage.
Facial Expression Recognition Based on Pruning Optimization Technology

Facial expression recognition is based on deep learning, which has become one of the main topics in artificial intelligence. Nevertheless, the complex network structure causes numerous parameter redundancy, which leads to low recognition efficiency. Therefore, researching how to reduce parameter redundancy makes sense. In addition, today's machine expression recognition has some limitations and constraints, while the generalization ability is poor. Aiming at addressing the problems that excessive parameter redundancy brings, for example, low efficiency and massive limitations. This paper will put forward a facial expression recognition technology that is based on pruning optimization to improve existing models. Based on VGGNet, ResNet and SENet, these three convolutional neural networks modify the convolution layer of the network, then add the attention module and pruning optimization. Through face detection, face area feature representation, expression recognition, and other steps. Realize the recognition of multiple facial expressions. The CK+ dataset and JAFFE dataset are independently divided into 4/5 for training and 1/5 for testing. Experimental results show that after inserting CBAM, except ResNet18, the accuracy of the other two networks all improved, SENet18 improved the most significantly. After pruning, the accuracy and speed of VGG16 and ResNet18 improved, but the accuracy of SENet18 decreased. In summary, the optimized model has good applicability and generalization ability.
Mitigate Long-lasting Ethical Issues in the Healthcare Industry with Blockchain-based Solutions

As one of the most promising innovations in the recent computing paradigm, blockchain technology has been introduced into many fields such as finance. With the outbreak of the epidemic and the intensification of the global public health crisis, medical care has also undergone rapid revolutionary changes in replacing traditional systems with new technologies. Among them, the ethical issues exposed in the process of transformation are under intense debate. Traditional research and surveys focus on the privacy of patient data management and only include some roles in the medical ecosystem, which cannot meet the existing needs. To this end, based on system thinking, this paper studies blockchain technology and emphasizes its application potential in the field of health care to alleviate some long-standing ethical problems, mainly including manipulation of organ transplantation, counterfeit drugs, and medical insurance fraud. It also discussed the technical challenges and limitations of the implementation of the blockchain-based system and ended with the possibility of reimagining the future.
The Benefit of a Regression in VR Game

Nowadays, the Virtual reality advances in a startling speed. More and more game company start to make VR games. There are five types of VR game: Non-immersive Virtual Reality, Fully Immersive Virtual Reality, Semi-Immersive Virtual Reality, Augmented Reality, Collaborative VR. However, this article only talks about semi-immersive virtual reality. Semi-immersive virtual reality which provides a three-dimensional space by headsets is the combination of non-immersive and fully-immersive virtual reality. This article will explore the relationship between Degree of Freedom and semi-Virtually Reality Game. Even though most of the people believe higher the Degree of Freedom is greater the game itself is, this work believes even in Virtual Reality Games, high or low Degree of Freedom server certain purposes but never defines a game’s quality. This paper made two versions of a VR game called In the Castle: one version is low Degree of Freedom, the other is high Degree of Freedom, and organized several experiments to explore the benefits of the low degree of freedom. The result of the experiments shows that the low degree of freedom in VR game can help players concentrate on the game and simplify the preparation.
Analysis on Chatbot Performance based on Attention Mechanism

The chatbot is a way to imitate the dialogue between people through natural language, enabling human beings to communicate with machines more naturally. The chatbot is a prevalent natural language processing task (NLP) because it has broad application prospects in real life. This is also a complex task involving many natural language processing tasks that must be studied. The chatbot is an intelligent dialogue system that can simulate human dialogue to achieve online guidance and support. The main work of this paper is to summarize the chatbot's academic background and research status and introduce the Cornell Movie-Dialogs Corpus dataset. The methods of artificial intelligence and natural language processing are outlined. Two attention mechanisms used to improve neural machine translation (NMT) are discussed. Finally, this paper tests the performance of chatbots under the influence of N_ITERATION and data scale summarizes the relevant optimization strategies and makes a prospect for the future of chatbots. The main work of this paper is to test the performance of the proposed method under different experimental Settings, including dialog templates, adjusting the amount of training data, and to adjust the number of iterations. The results show that the chatbot's vocabulary changes with N_ITERATION and that increasing the data in the training dataset improves the chatbot's understanding.