Reviewerly/exHarmony huggingface dataset:
Few-shot Text Classification Method Based on Feature Optimization

For the poor effect of few-shot text classification caused by insufficient data for feature representation, this paper combines wide and deep attention bidirectional long short time memory (WDAB-LSTM) and a prototypical network to optimize text features for better classification performance. With this proposed algorithm, text enhancement and preprocessing are firstly adopted to solve the problem of insufficient samples and WDAB-LSTM is used to increase word attention to get output vectors containing important context-related information. Then the prototypical network is added to optimize the distance measurement module in the model for a better effect on feature extraction and sample representation. To test the performance of this algorithm, Amazon Review Sentiment Classification (ARSC), Text Retrieval Conference (TREC), and Kaggle are selected. Compared with the Siamese network and the prototypical network, the proposed algorithm with feature optimization has a relatively higher accuracy rate, precision rate, recall rate, and F1 value.
Analysis of application of digital image analysis in histopathology quality control

A correct histopathological diagnosis is dependent on an array of technical variables. The quality and completeness of a histological section on a slide is extremely prudent for correct interpretation. However, this is mostly done manually and depends largely on the expertise of histotechnician. In this study, we analysed the application of digital image analysis for quality control of histological section as a proof-of-concept. Images of 1000 histological sections and their corresponding blocks were captured. Area of the section was measured from these digital images of tissue block (Digiblock) and slide (Digislide). The data was analysed to calculate DigislideQC score, dividing the area of tissue on the slide by the tissue area on the block and it was compared with the number of recuts done for incomplete section. Digislide QC score ranged from 0.1 to 0.99. It showed an area under curve (AUC) of 98.8%. A cut-off value of 0.65 had a sensitivity of 99.6% and a specificity of 96.7%. Digiblock and Digislide images can provide information about quality of sections. DigislideQC score can correctly identify the slides which require recuts before it is sent for reporting and potentially reduce histopathologists’ slide screening effort and ultimately turnaround time. These can be incorporated in routine histopathology workflows and lab information systems. This simple technology can also improve future digital pathology and telepathology workflows.
Sensor-based cloud computing data system and long distance running fatigue assessment

Wireless sensor networks are widely used in sports training, medical and health care, smart home, environmental monitoring, cloud data and other fields because of their large scale, self-organization, reliability, dynamic, integration and data centralization. Based on this point, this article conducts a comprehensive analysis and research on cloud computing data systems, and designs and implements a dynamic replication strategy. Since different users have different demands for different data at different times, it is necessary to record and analyze recent users' data access, so as to actively adjust the number and location of data blocks. Subsequently, a multi-source blockchain transmission method was proposed and implemented, which can significantly reduce the time cost of data migration and improve the overall performance of cloud storage data systems. Finally, the article provides an in-depth analysis of long-distance running fatigue. This study will design a simulated specialized exercise load experiment to reproduce the load characteristics of excellent athletes during mid to long distance running, in order to induce exercise fatigue in the main muscles of different parts of their bodies. At the same time, the amplitude frequency joint analysis of the surface changes of EMG signal in this process is carried out. This article conducts research on sensor based cloud computing data systems and long-distance running fatigue assessment, promoting the development of cloud computing data systems and improving long-distance running fatigue assessment methods.
Utilization of School Library as Determinant of Reading Habit of Senior Secondary School Students in Abuja Metropolis

This study was conducted to determine the utilization of school library as determinant of reading habit of senior secondary school students in Abuja Metropolis. Two research questions and one hypothesis were formulated to guide the study. Descriptive survey research design was used for the study. The population of the study was all 16362 students in public secondary schools in Abuja Metropolis. A sample of 579 students was selected to take part in the study using simple random sampling technique. An instrument entitled “Utilization of Public School Library as determinants of reading habits questionnaire (UPSLDRQ)” was used in collecting data for the study. The instrument was validated by three experts and test re-test reliability was conducted for the instrument. A reliability coefficient of .83 was obtained for the instrument using Pearson Product Moment Correlation. Data collected for the study were analysed using mean and standard deviation to answer the research questions and independent t-test to test the hypothesis. The findings of the study indicated that there is a significant influence of library utilization on the reading habit of students. It was recommended among others that government should provide all secondary schools in Abuja metropolis with modern and functional libraries.
Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound

Abstract Purpose This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. Methods The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. Result The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe ( p &lt; 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). Conclusion The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.
Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data

With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors’ data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data.
Satellite Image Classification Using Extended Local Binary Patterns, SVM AND CNN

In many disciplines and applications, image processing has shown to be an effective research tool. Numerous uses, such as disaster response, law enforcement, and environmental monitoring, depend on satellite imaging. Applications demand manual object and device identification from photos. ELBP-SVM technique is used to categorize the satellite images into a set of distinct classes. Although this work is able to classify distinct classes in addition to the class of satellite images, identifying the characteristics of these other classes, such as forest, desert, oceans, etc., is also straightforward because these other classes have some unique characteristics that can be easy to distinguish and therefore easy to classify. This work uses the suggested ELBP approach to first find local binary patterns. The SVM classifies the test picture class after acquiring the extended features. The ELBP-SVM approach is utilized in this study, and the percentage of correctly identified satellite images is 97%. The results discovered and experimentally acquired on MATLAB 2020a are superior to other research currently accessible for the classification of satellite photos.
An Empirical Study on the Influencing Factors of Professional Degree Graduate Students’ Vocational Competence

The vocational ability of graduate students is related to the future development of the industry, and is one of the key objectives of graduate training.The article researches the professional degree graduate students and empirically studies the influencing factors of graduate students' vocational ability by means of questionnaires.The results show that course teaching, tutor guidance, professional practice and in-school success have significant influence on professional degree graduate students' vocational ability, among which tutor guidance and inschool achievement have extremely significant influence and are the key influencing factors to enhance graduate students' vocational ability, and thus put forward constructive suggestions and recommendations for professional degree graduate students' cultivation.
Design and Construction of Web-based Remote Interactive Training System for Agriculture-related Talents

In order to promote the further development of the process of "integration of urban and rural construction", the state has launched the strategy of "rural revitalization".In this regard, the society has also put forward higher requirements for technical talents related to agriculture.In order to ensure the training quality of agriculture-related talents, this paper takes computer as the development hardware, and combines web technology with multimedia technology to construct a web-based remote interactive training system for agriculture-related talents.The development of the system adopts B/S architecture, follows MVC design pattern, and uses Java Web application development framework to develop the system server.The system evaluates the actual application effect of the platform with AHP algorithm model, and improves the training evaluation system.The FFmpeg software of the system is redeveloped, and the function of decoding and storing data of FFmpeg is implanted into this teaching system, so as to realize the audio and video playing function of interactive training classroom, arouse students' enthusiasm and improve teaching effectiveness.
Visualizing Invisible Environmental Data in VR: Development and Implementation of Design Concepts for Communicating Urban Air Quality in a Virtual City Model

As cities continue to grow, the desire to combine densification with sustainability and greenery may present a challenge to air quality, resulting from reduced ventilation caused by dense buildings and vegetation. To support the careful urban planning required, effective and interactive tools that can visualize and communicate information about air quality to stakeholders are essential. In a transdisciplinary research project aiming to explore such visualizations a prototype pedagogical virtual reality tool was developed, allowing users to explore the impact of aspects of the built environment upon urban air quality. The tool was evaluated with adolescents in upper secondary school through interviews and observations, as well as with the general public through a questionnaire study. This paper provides insights, potential solutions, and initial assessments relevant to data visualization in 3D and immersive analytics in urban planning and stakeholder communication. Identified challenges include difficulties with color association and data distinguishability, and as well as tool complexity relating to the many features requested by experts involved in a transdisciplinary project.
Evaluation of Stacked Embeddings for Arabic Word Sense Disambiguation

Word Sense Disambiguation (WSD) aims to determine the correct meaning of words that can have multiple interpretations. Recently, contextualized word embeddings, whose goal is to give different representations of the same word in diverse contexts, have been shown to have a tremendous impact on several natural language processing tasks including question answering, semantic analysis and even word sense disambiguation. This paper reports on experiments with different stacks of word embeddings and evaluation of their usefulness for Arabic word sense disambiguation. Word embeddings stay in the core of the development of NLP, with multiple key language models being created over the last two years like FastText, ElMo, BERT and Flair.\newlineIt's worth pointing out that the Arabic language can be divided into three major historical periods: old Arabic, middle-age Arabic, and Modern Arabic. Actually, contemporary Arabic has proved to be the greatest concern of many researchers. The main gist of our work is to disambiguate Arabic words according to the historical period in which they appeared. To perform the WSD task, we suggest a method that deploys stacked embeddings models. The experimental evaluation demonstrates that stacked embeddings outperforms the previously proposed methods for Arabic WSD.
Quantum entanglement &amp; purity testing: A graph zeta function perspective

We assign an arbitrary density matrix to a weighted graph and associate to it a graph zeta function that is both a generalization of the Ihara zeta function and a special case of the edge zeta function. We show that a recently developed bipartite pure state separability algorithm based on the symmetric group is equivalent to the condition that the coefficients in the exponential expansion of this zeta function are unity. Moreover, there is a one-to-one correspondence between the nonzero eigenvalues of a density matrix and the singularities of its zeta function. Several examples are given to illustrate these findings.
A study on the Security Enhancement of Enterprise Business System based on Zero Trust Network

본 연구에서는 기업이 클라우드로 업무 시스템을 전환 시 가장 문제가 되는 기업 데이터 탈취 부분에 대한 예방을 위해 제로 트러스트 네트워크 기반의 설계를 통해 보안성을 강화하는데 그 목적을 두고 있다. ISMS(Information Security Management System, 이하 ISMS)의 기본인 ISO/IEC 27017, NIST SP 800-207에서 정의한 원칙과 Framework을 활용하여 사용자의 엔드포인트를 지속적으로 모니터링하고 통합 관제를 통해 로그인 정책과 네트워크 정책을 중심으로 실제 설계 방법을 제시하여 기업이 클라우드 내에서 기업 시스템 운영을 안전하게 할 수 있는 설계 가이드를 제공하고자 한다. 설계 가이드는 인증 부분과 네트워크 보안성 2가지 관점에서 Jason Garbis와 Jerry Champman이 설계한 ZTS(Zero Trust Security, 이하 ZTS) 방법론 중 인증 부분은 Enclave-Gateway-based Model을, 네트워크 보안성 부분은 cloud-routed model을 기반으로 실제 기업 환경에 맞추어 디자인하였다. The purpose of this study was to enhance security by implementing a zero-trust network-based design to prevent the problematic issue of corporate data breaches during transitions of corporate business systems to the cloud. This study utilized the principles and frameworks defined in ISO/IEC 27017 and NIST SP 800-207, which are basic standards for information security management systems (ISMS). Based on this, users" endpoints were continuously monitored, and design methods centered around login policies and network policies through integrated control were developed. The goal was to provide a design guide for enterprises to safely operate their business systems in the cloud. A design guide was developed by Jason Garbis and Jerry Champman for the zero-trust security (ZTS) methodology, which focuses on two aspects: authentication and network security. For the authentication part, they based their approach on an enclave-gateway-based model, while for the network security part, they used a cloud-routed model. These design choices were made to align with real-world enterprise environments.
A Study on Music Education Utilizing Metaverse

The purpose of this paper is to suggest methods of activating the general public’s music education by utilizing metaverse. To achieve the purpose and complete the research, this paper referred to the previous papers and related data to examine the theoretical backgrounds, problems, and suggest improvement measures. Music education plays an important role for increasing the students’ academic performance as well as their creativity and social skills. It also improves human emotions. However, music education involves noise and time-space limitations. So, music education needs a new method. To solve this problem, this paper suggests on utilizing the virtual world, augmented reality, mirror world, and lifelogging that compose the metaverse. The metaverse-based music education overcomes time-space limitations, provides high-quality education at lower cost, and holds potential for effectiveness.
Understanding and Modelling Brittle Deformation in the Earth: Insight from a Novel Experimental Approach

Current experimental approaches on faults provide a significant contribution to our understanding of brittle deformation in the crust. However, they rarely present a multiscale view of fault initiation, propagation and growth or the complex geometry of natural faults combined with other geological features such as folds and flow structures. We suggest new types of experiments that are inspired by the observations of natural phenomena on the water surface of a lake. In order to create similar structure to the nature, we have investigated different possibilities, hence designed and run the first series of novel experiments in a large water tank setup at the hydrodynamics laboratory of the University of Oslo. The water tank is equipped with cameras to dynamically image the experimental process. Using capillary waves and added impurity (pollen) to the water surface in a controlled laboratory set-up, our experiments created structures similar to faults, folds, and flow structures observed in nature. We suggest that these experiments can be utilised to not only study the fault growth process but also to answer some fundamental questions with regard to material properties such as friction and the stress configuration needed to create brittle and ductile deformation in the Earth.
DQN-based Vehicle Dispatch Automation Model

우리나라의 인구 부족은 대표적인 사회적 문제이며, 그에 따른 대응 수단으로 자동화가 제시되고 있다. 자동화는 인공지능을 적용하여 더욱 확대되고 있으며 군에서도 감시정찰, 지휘통제 분야뿐 아니라 행정업무에 대해서도 자동화가 추진되고 있다. 본 연구에서는 군의 행정업무 분야 중 일일 단위로 반복되며 많은 시간이 소모되는 차량배차 업무에 대하여 다루었다. 군의 차량배차 업무는 배차신청이 접수되면 가용자원(차량, 운전병)을 편성해주는 것이다. 본 연구는 이러한 차량배차 업무를 대체 할 수 있는 차량배차 자동화 모델을 만들기 위하여 진행하였다. 연구에서 활용한 모델은 스케줄링 문제에 강점이 있는 강화학습을 선정하였으며, 그중에서도 기본 모델인 Q-learning을 개선하여 더욱 복잡한 환경에도 뛰어난 성능을 보이는 DQN(Deep Q-Network)을 활용하였다. DQN기반의 차량배차 자동화 모델은 가용차량, 가용 운전병, 배차신청 내역을 바탕으로 차량배차 편성 수를 최대화하도록 모델링하고 학습을 진행하였다. 이후 모델의 성능이 사람을 대체할 수 있는 수준인지 판단하기 위해 사람과 모델을 대상으로 차량배차 편성실험을 진행하였으며, 그 결과 모델의 차량배차 편성량은 사람이 편성하는 것 대비 95.63%의 성능을 보였으며, 배차편성 시간과 편성오류측면에서는 모델이 더 높은 성능을 보였다. 본 연구를 시작으로 향후 군 차량배차 업무의 자동화 및 효율성 증대가 이루어지길 기대한다. The population shortage in Korea is a representative social problem, and automation is being proposed as a response measure. Automation is expanding further by applying artificial intelligence. Moreover, automation is being promoted in the military in surveillance, command and control, and administrative work. This study focused on the vehicle dispatch task, which is repeated daily and is time-consuming. The vehicle dispatch task of the military is to arrange available resources (vehicles, drivers) when a dispatch request is received. This study aimed to develop a vehicle dispatch automation model to replace this task. Reinforcement learning, which has strengths in scheduling problems, was selected as the model for this study. DQN, which showed excellent performance in a more complex environment by improving the basic model of Q-learning, was used. The DQN-based vehicle dispatch automation model was modeled and learned to maximize the number of vehicle dispatches based on available vehicles, drivers, and dispatch request records. Subsequently, to determine if the model performance could replace humans, a vehicle dispatch experiment was conducted with both human and model subjects. As a result, the vehicle dispatch volume of the model showed 95.63% performance of that of a person. The model showed higher performance in terms of dispatch time and dispatch error. Through this study, the automation and increased efficiency of vehicle dispatch tasks are anticipated.
Forecasting Calling Activity Based on ARIMA Model

Call frequency time series are extracted from Call Data Record and widely used to explore human activity patterns as well as predict calling activity. Accurate call frequency forecasting is a fascinating and challenging work. In this paper, we propose a recursive approach based on ARIMA model to forecast short term call frequency. This approach contain three steps. First two steps are modeling process and the third one is forecasting. The main work of step1 is data preprocessing and determining d (differential order) in ARIMA model. Further, a series of potential p (auto-regressive order) auto-regressive parameters values and q (moving average order) parameters values can be determined by examining the ACF (Auto Correlation Function) diagram and PACF (Partial Auto correlation Function) diagram. Then, the minimum BIC (Bayesian Information Criterion) rule is used to determine a p parameter value and a p parameter value. In the third step, the next day call frequency is predicted and all parameters are updated in a automatically recursive manner. We apply this approach on 10 call frequency time series and analyze forecasting error. The average MAPE (Mean Absolute Percentage Error) is 4.792%, which is much lower than other methods based on ARIMA model. The results show that our approach is efficient and can be used to in practical work.