Reviewerly/exHarmony huggingface dataset:
DRL-based Multi-Hop Relational Reasoning for Personnel Management Knowledge Graph

In the personnel management knowledge graph, the absence of relations will lead to an incomplete knowledge graph, which affects downstream applications. While existing approaches can handle relational reasoning tasks, many neglect reasoning efficiency and implicit semantic information in multi-hop relational paths. Therefore, we proposed a multi-hop relational reasoning model based on deep reinforcement learning to reason out paths with implicit semantic information. We optimize the proximal policy optimization algorithm to implement reasoning. The irrelevant actions masking scheme makes the reasoning more efficient. And the reward shaping make the reasoned multi-hop relational paths semantically interpretable. We construct a formal dataset containing a large number of triples to represent the personnel management knowledge graph. By comparing the experimental results on metrics about MAP, MRR, Hits@k, and frequency of paths with other baselines, our proposed model is more advantageous in the personnel management knowledge graph. In addition, experimental results on the dataset NELL-995 show that our proposed model's performance can compare to the mainstream methods.
What Do Undergraduate Students Learn? An Analysis of Elective Course Syllabi related to Marriage and Parenthood

본 연구는 대학교 교양 수업 강의계획서 분석을 통하여 결혼과 부모됨의 준비에 대해 살펴보고자 수행되었다. 서울 및 수도권, 강원도에 위치한 4년제 대학교 7곳을 대상으로 2022학년도 가을 학기와 2023학년도 봄 학기에 걸쳐 결혼, 가정, 가족, 자녀, 부모 등의 키워드를 통해 수집된 강의계획서를 이용하여 관련 강좌의 특성을 살펴보았다. 분석 결과 첫째, 학교별로 교양강좌 개설의 편차가 심하여 관련된 교양과목 수강의 기회가 제공 또는 제한됨을 알 수 있었다. 둘째, 학교별로 제공되는 교양강좌의 내용 범위가 결혼과 가족, 부모 역할 등의 일반적 내용뿐 아니라, 인간관계, 법, 간호 등의 전문분야와 접목하는 등 매우 다양함을 알 수 있었다. 셋째, 결혼과 가족, 부모교육 관련 교과목의 평가가 학교별로 절대평가와 상대평가로 다르게 진행됨을 알 수 있었다. 본 연구의 결과를 토대로 결혼과 가족, 부모교육과 관련한 예비교육으로서의 교양강좌에 대한 논의점과 향후 연구 방향이 제시되었다.The purpose of this study is to understand the trends and features of syllabi related to marriage and parenthood. Undergraduate elective courses were selected by using keywords: marriage, family, child(ren), and parent from 7 4-year universities across the country during one academic year. The findings of this study are: 1. There was a great variation of the offering courses related to marriage and parenthood, which means that students can have an opportunity to learn these courses depending on their school curricula. 2. The course contents related to marriage and parenthood varied considerably, such as marriage and family, romantic relationships, family laws, and family health care. 3. The assessment strategy differed depending on school decisions, from absolute grading to grade on a curve. Based on these findings, implications and future directions are discussed reflecting the era of low birth rates.
Impact of PSO on Retrieval Performance in QBIC System

The aim of this chapter is to propose an efficient retrieval method that can retrieve appropriate information from the gigantic collections of images utilized in heterogeneous applications which has a pronounced impact on retrieval performance when compared to standard and customary Gabor filter methods. In recent times, there is a magnificent technical progression in the research area related to image retrieval, in specific the Query By Image Content (QBIC) system. Retrieval of information has become a very challenging area of research in various applications like databases related to multimedia, Google retrieval and digital libraries. The study developed a hybrid QBIC retrieval system that depicts the impact of PSO on retrieval performance in QBIC system by retrieving color features, texture features and shape features of the images in three consecutive stages. In the suggested technique, color features are initially extracted using a color histogram. In the following step, Log Gabor filters are tuned using Particle Swarm Optimization (PSO), retrieving the texture characteristics. The extraction of shape features is completed by using a polygonal fitting technique. When compared to the current standard systems, the suggested method shows a superior retrieval rate in terms of mean recall and mean precision. The novelty is that it can exploit global minima features that result in high accuracy without affecting the computation.
Meteorological forecast based on image recognition technology

In real life, whether it is in the fields of weather forecast at airports, traffic control, weather services at ports, or industries such as agriculture, aquaculture, and transportation, all need to know the real situation of the weather. If it cannot be accurately identified and judged in time, will lead to threats to human life and property safety. In order to reduce the impact of weather on people's production and life in various industries and improve the accuracy of weather recognition, this paper establishes an improved convolutional neural network model based on the AdaBoost algorithm to identify three types of weather: rainy, snowy, and foggy. First, the convolutional neural network is trained and iterated many times, and then each convolutional neural network is used as a weak classifier, and multiple weak classifiers are weighted and combined based on the AdaBoost algorithm to form the final strong classifier model. Experiments show that the strong classifier optimized by the AdaBoost algorithm has a stronger ability to identify misclassified data and can realize dynamic reinforcement learning of data.
A text classification method based on capsule network

As an important research topic, text classification is often deeply studied by scholars. Convolutional neural network is a commonly used deep learning algorithm for text classification. However, CNN will lose some information features in the pooling process due to the limitation of fixed-size convolution kernel when extracting features from complex semantic information. In order to avoid CNN's problems in text classification tasks, Capsule network is used to extract text features. CapsNet model has the problem of redundant information transmission. In order to reduce redundant information, a capsule screening network is proposed in this paper to remove the weak connection between the screened child capsule and the parent capsule, optimize the dynamic routing algorithm, and reduce the redundant information transmission of the capsule layer. In addition, in the classification of text, the text may exist, such as less useful information, serious colloquial language, unable to reflect the importance of words and so on. In order to improve this problem, this paper introduces the attention model. Combined with improved CapsNet to handle text sorting tasks. Three experimental results show that CapsNet with improved mechanism of attention is the best in text classification task.
Research on Recommendation Method Based on Fusion of Knowledge Graph and Behavioral Time Interval

Recommendation system is an significant study goal in the realm of information filtering system. The recommender system predicts the items that users are interested in depended on the user’s past operational data. Dynamic behavior sequence can be extracted by self-attention mechanism, and most models assume that the interaction history is regarded as an ordered sequence without considering the time interval between each interaction. Our input entities and items are both interconnected and highly correlated in the knowledge graph and recommendation modules respectively. This paper fuses them to recommend to users, and proposes a multi-task feature learning recommendation model that fuses time interval and knowledge graph, explicitly modeling interaction timestamps within a sequential modeling framework, and fused with knowledge graph embedding (KGE) to assist with the recommended task. The experimental results show that on the real dataset MovieLens1M, the AUC, ACC, Precision and Recall indicators are used for evaluation, and the proposed model is better than the mainstream benchmark models.
An Efficient Approach to Monocular Depth Estimation for Autonomous Vehicle Perception Systems

Depth estimation is critical for autonomous vehicles (AVs) to perceive their surrounding environment. However, the majority of current approaches rely on costly sensors, making wide-scale deployment or integration with present-day transportation difficult. This issue highlights the camera as the most affordable and readily available sensor for AVs. To overcome this limitation, this paper uses monocular depth estimation as a low-cost, data-driven strategy for approximating depth from an RGB image. To achieve low complexity, we approximate the distance of vehicles within the frontal view in two stages: firstly, the YOLOv7 algorithm is utilized to detect vehicles and their front and rear lights; secondly, a nonlinear model maps this detection to the corresponding radial depth information. It is also demonstrated how the attention mechanism can be used to enhance detection precision. Our simulation results show an excellent blend of accuracy and speed, with the mean squared error converging to 0.1. The results of defined distance metrics on the KITTI dataset show that our approach is highly competitive with existing models and outperforms current state-of-the-art approaches that only use the detected vehicle’s height to determine depth.
A Novel Warning Identification Framework for Risk-Informed Anomaly Detection

Abstract Cyber-physical systems are taking on a permanent role in the industry, such as in oil and gas or mining. These systems are expected to perform increasingly autonomous tasks in complex settings removing human operators from remote and potentially hazardous environments. High autonomy necessitates a more extensive use of artificial intelligence methods, such as anomaly detection, to identify unusual occurrences in the monitored environment. The absence of data characterizing potentially hazardous events leads to disruptive noise displayed as false alarms, a common anomaly detection issue for hazard identification applications. Contrastingly, disregarding the false alarms can result in the opposite effect, causing loss of early indications of hazardous occurrences. Existing research introduces simulating and extrapolating less represented data to expand the information on hazards and semi-supervise the methods or by introducing thresholds and rule-based methods to balance noise and meaningful information, necessitating intensive computing resources. This research proposes a novel Warning Identification Framework that evaluates risk analysis objectives and applies them to discern between true and false warnings identified by anomaly detection. We demonstrate the results by analyzing three seismic hazard assessment methods for identifying seismic tremors and comparing the outcomes to anomalies found using the unsupervised anomaly detection method. The demonstrated approach shows great potential in enhancing the reliability and transparency of anomaly detection outcomes and, thus, supporting the operational decision-making process of a cyber-physical system.
Analysis of the Use of Android-Based Edusan Learning Media on Students' ICT Literacy Skills

This study aims to describe students’ digital literacy after learning science using android-based Edusan learning media. The research was conducted at SMP Negeri 2 Mlati using descriptive analysis research method. Edusan application based on android is used as a medium for delivering teaching materials and evaluation tools in the learning process. The data collection technique in this study was a questionnaire. The dimensions of digital literacy analysed in this study are creativity, collaboration, communication, the ability to find and select information, critical thinking and evaluation, e-safety, functional skills and beyond. Results The research shows that students have a very good category in the digital literacy dimension. Therefore, it can be concluded that the use of Edusan android-based learning media can foster and train students' ICT literacy
Shallow Depth Factoring Based on Quantum Feasibility Labeling and Variational Quantum Search

Large integer factorization is a prominent research challenge, particularly in the context of quantum computing. This holds significant importance, especially in information security that relies on public key cryptosystems. The classical computation of prime factors for an integer has exponential time complexity. Quantum computing offers the potential for significantly faster computational processes compared to classical processors. In this paper, we propose a new quantum algorithm, Shallow Depth Factoring (SDF), to factor a biprime integer. SDF consists of three steps. First, it converts a factoring problem to an optimization problem without an objective function. Then, it uses a Quantum Feasibility Labeling (QFL) method to label every possible solution according to whether it is feasible or infeasible for the optimization problem. Finally, it employs the Variational Quantum Search (VQS) to find all feasible solutions. The SDF utilizes shallow-depth quantum circuits for efficient factorization, with the circuit depth scaling linearly as the integer to be factorized increases. Through minimizing the number of gates in the circuit, the algorithm enhances feasibility and reduces vulnerability to errors.
Cloud computing English teaching application platform based on machine learning algorithm

Due to the progress of computer technology at this stage, English teaching has gradually achieved reforms with the help of the Internet. This paper builds an English teaching application platform based on machine learning algorithms, and realizes personalized online teaching with the help of cloud computing technology. In view of the complex characteristics of learners’ online learning behavior data, this paper uses Pearson’s correlation coefficient to quantify the correlation between various learning behavior variables. In order to further improve the accuracy of system data analysis, regression analysis is used to detect abnormal data points, and data with a data prediction deviation greater than 1 is determined as abnormal data. In order to reduce the loss of a large amount of data loss caused by the regression analysis to judge some anomalies, this paper uses the local outlier detection method again to detect local outliers, and compares the data repeatedly detected in the two identification algorithms, and judges it as abnormal data special handling. In order to further verify the practicability of the system, network simulation is used to increase the number of concurrent operators in the system. The experiment proves that the click delay time of the English teaching video of the system is maintained below 1.8 s under 5000 concurrent operations, and the running speed of the system is significantly improved when the dynamic scheduling function is enabled, which proves that the system can meet the needs of multiple users for concurrent operations. Finally, a survey is conducted on the personalized education of the English teaching platform, and the relevant experimental and survey data are analyzed in detail, and a series of constructive strategies for the development of personalized English teaching in the cloud education environment are proposed.
Multispectral Feature Fusion for Deep Object Detection on Embedded NVIDIA Platforms

Multispectral images can improve object detection systems' performance due to their complementary information, especially in adverse environmental conditions. To use multispec-tral image data in deep-learning-based object detectors, a fusion of the information from the individual spectra, e.g., inside the neural network, is necessary. This paper compares the impact of general fusion schemes in the backbone of the YOLOv4 object detector. We focus on optimizing these fusion approaches for an NVIDIA Jetson AGX Xavier and elaborating on their impact on the device in physical metrics. We optimize six different fusion architectures in the network's backbone for the TensorRT framework and compare their inference time, power consumption, and object detection performance. Our results show that multispectral fusion approaches with little design effort can benefit resource usage and object detection metrics compared to individual networks.
Application analysis of Internet of Things technology in civil engineering construction

In the development of urban construction, civil engineering construction as an important content to ensure the quality of urban construction, in the social economy and technological innovation and development, to combine the Internet of things technology innovation research, so as to improve the comprehensive level of civil engineering construction, but also to provide quality services for social residents. Due to the large investment scale of civil engineering construction projects, the overall design structure is relatively complex, which involves a lot of science and technology and discipline knowledge, so the application of Internet of Things technology for innovation and optimization, the requirements of relevant technical theories are extremely high. Therefore, based on the accumulated experience of civil engineering construction projects in recent years, this paper determines the basic principles and positive effects of Internet of things technology, and deeply studies how to apply Internet of Things technology in civil engineering construction, so as to promote the steady development of the construction industry.
Research on information security protection technology of university office system

Under the background of the development of information technology, increasingly, information network not only has brought people convenience, also produced more safe hidden trouble, the hidden danger also affects the running quality of office system in colleges and universities, therefore in the college office network and information technology development, how to ensure the office of information security is to explore the main topic of administrative personnel. In this paper, based on the understanding of the changes of the office information environment in colleges and universities under the information environment, according to the current problems faced by the office information security in colleges and universities, deep discussion on how to build a standard information security protection system, clear practice and development can be used to protect technology. The final empirical results show that the information security protection system of university office system with computer as the core can better protect its own information security.
The search for storybook endings

In PracticeVolume 45, Issue 5 p. 304-304 Opinion The search for storybook endings First published: 02 June 2023 https://doi.org/10.1002/inpr.337Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume45, Issue5June 2023Pages 304-304 RelatedInformation
Cybersecurity Awareness Amongst Youth – A Survey in Delhi/NCR

Cyberspace has become an integral part of modern society, especially in the last two decades. As the internet has rapidly progressed globally, the community of cyber users in cyberspace has widely expanded. One group of cyber users that is swiftly becoming active in cyberspace is the youth, particularly, school and college students. However, young cyber users are increasingly becoming the soft targets of cybercriminals. Taking cognizance of the rising cybercrime cases against young cyber users, this research study surveyed several schools and colleges in the Delhi-NCR region to assess the cyber awareness among students and academic professionals. It was found that most of the participants were not aware of ethical and safe digital practices. These observations are attributed to the poor application of initiatives to ensure cyber safety among school children, especially in developing countries like India. Based on the findings from the survey, this study provides suggestions and recommendations to various role-players involved in cyberspace to assure safe cyber behaviour.
A Review of Metaverse

The Metaverse is an evolution of the internet. It is a virtual reality simulation that can be used for various purposes, from entertainment and social networking to education and business. The present paper discusses how Metaverse can be accessed, its uses in various industries, and the technologies required for it. The paper also presents the challenges for Metaverse.
Language Model Evolution : GPT and Impact Beyond

Language models are considered a way of machine level of understanding and predicting human languages as a part of human communication relevant to the context. The present research paper tries to understand the growth of such language models popularly known as GPT or Generative Pre-trained Transformer. It tries to understand the meaning, growth, working of model, and some of the applications where GPT is being used. Several applications on business, website development, and conversational applications are now being powered by GPT with tremendous potential for future convergence. Artificial Intelligence and sub-domains. This research paper can be very useful for academicians, researchers, and professionals who handle business and information technology applications.
AI Fundamentals

One of history’s most important conferences was in 1956 at Dartmouth University. Professor John McCarthy organized the ten-week event, which he called “Cerebral Mechanisms in Behavior.” He also did something else that was historic. He coined the phrase “artificial intelligence.”
Core Generative AI Technology

From 2004 to 2009, Ian Goodfellow attended Stanford University, where he studied computer science, with an emphasis on AI. Then he would get his PhD in machine learning at the Université de Montréal. Through his education, he learned from some of the best researchers in AI like Yoshua Bengio and Andrew Ng.
An automatic correction system of singing intonation based on deep learning

In order to solve the problems of low accuracy and slow correction speed in traditional singing intonation correction system, an automatic singing intonation correction system based on deep learning is proposed. In the hardware, floating-point DSP and TDSP-TF984 chip are selected as the core chips of automatic correction processor of singing intonation. The data input module and parameter calculation module of singing intonation are designed to improve the singing intonation data collector. In the software, the group delay estimation method is used to collect the singing intonation signal, and the deep learning algorithm is used to decompose the false component of the singing intonation signal. The autocorrelation function and characteristic distribution operator of the singing intonation signal are obtained to realise the singing intonation signal correction. The experimental results show that the highest accuracy of the proposed system is about 97.8%, and the shortest correction time is about 1 s.