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Solar Observation with the Fourier Transform *** Results of Solar Spectrum near the CO 4.66μm and Mg I 12.32μm

Research in Astronomy and Astrophysics 2023年第12期23卷 128-137页

作者： Xianyong Bai Zhiwei Feng Zhiyong Zhang Yuanyong Deng Xiaoming Zhu Kaifan Ji Song Feng Xiao Yang Yongliang Song National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 101408China Yunnan Observatories Chinese Academy of SciencesKunming 650011China Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and TechnologyKunming 650500China

An infrared solar spectrum observed by ground-based telescopes is seriously affected by the background radiation both from the telescope and sky,relative to the visible *** accuracy is also influenced by the spectral resolution of the Fourier transform *** the paper,we developed a CO_(2)gas cell and installed it in the sample compartment to calibrate the spectral resolution of the Bruker IFS-125HR at infrared *** measured spectral resolution is 0.00342±0.00086 cm^(-1)and 0.0059±0.00024 cm^(-1)at the wavenumbers of798 cm^(-1)and 2136 cm^(-1),*** also updated a fully reflective sunlight feeding system to observe the solar spectrum near CO 4.66μm and Mg I 12.32μ*** quickly pointing the sunlight feeding system about1 degree away from the solar disk center,we are able to measure the background radiation from the telescope and the sky at Huairou Solar Observing *** removing the background radiation,our observed solar spectrum at CO 4.66μm is consistent with that from the National Solar *** Mg I 12.32μm working line selected by the Accurate Infrared Magnetic Field Measurements of the Sun(AIMS)project is also *** method is helpful not only for the spectral resolution calibration and background radiation correction of AIMS but also for other infrared astronomical telescopes.

关键词： Sun infrared-methods observational-instrumentation interferometers

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Supervised and Unsupervised Coupled Generative Adversarial Networks in Multivariate Time Series 4

Supervised and Unsupervised Coupled Generative Adversarial N...

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Wang, Qinfen Li, Yifei Xia, Yong School of Computer Science and Engineering Northwestern Polytechnical University Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an710072 China

ISBN: (纸本)9798350337181

Generative adversarial networks (GAN) have been successful in demonstrating the efficiency of generative time series. In this paper, we proposed a novel end-to-end supervised and unsupervised coupled generative adversarial networks (SUC-GAN) framework that consists of a gated recurrent unit (GRU) for generating multivariate time series. We test SUC-GAN on three datasets (i.e. Sines, Energy, Stocks) to investigate the efficacy and properties. The preliminary results show the potential power of supervised, unsupervised, and coupled learning for data generation. © 2023 IEEE.

关键词： Generative adversarial networks

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Long-Term and Short-Term Perception in Knowledge Tracing 1

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11th CCF Big Data Conference, BigData 2023

作者： Chen, Zihang Zhu, Mengxiao Wang, Fei Shen, Shuanghong Huang, Zhenya Liu, Qi Anhui Province Key Laboratory of Science Education and Communication Institute of Advanced Technology and School of Humanities and Social Sciences University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science and School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9789819989799

ISBN: (纸本)9789819989782

Knowledge Tracing (KT) is a fundamental task in contemporary intelligent educational systems, which tracks the knowledge states of the learners based on their response sequences. KT is crucial for the effectiveness of computer-assisted intelligent educational systems, such as intelligent tutoring systems and educational resource recommendation systems. In recent years, KT models benefited from the deep learning approaches and improved dramatically compared with the traditional probabilistic approaches. However, deep learning based KT models also have significant limitations. For example, Recurrent Neural Network (RNN)-based KT models can not capture dependencies in long-term sequences effectively, and attention mechanism-based KT models rely on the positional encoding to perceive sequential information, which may disrupt the semantics of the original embeddings. Moreover, the data sparsity question remains a big challenge in existing KT models. This study is based on the observation that KT shows a stronger sequential dependence in the long term than in the short term. In this paper, we propose a novel KT model called "Long-term and Short-term perception in knowledge tracing (LSKT)", which uses multilayer perceptrons and attention mechanism to capture the long-term and short-term dependencies, and employs 2PL-IRT based embedding to alleviate the data sparsity question. Extensive experiments on multiple datasets demonstrate the effectiveness of our proposed LSKT model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Deep Learning Educational Data Mining Intelligent Education Knowledge Tracing

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Rankings vs Realities: Exploring Competency Differences in Graduate Data Science Programs 53

Rankings vs Realities: Exploring Competency Differences in G...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Li, Duo Milonas, Elizabeth Zhang, Qiping Shenyang Institute of Technology Department of Big Data Management and Application Fushun China Computer Systems Technology Brooklyn United States Palmer School of Library and Information Science Long Island University Brookville United States

ISBN: (纸本)9798350336429

Numerous educational institutions, including those specializing in computer science, engineering, business, and science, now offer graduate programs in data science. However, it remains uncertain whether the data science programs provided by universities with different rankings deliver similar or distinct data science competencies. This study aims to compare the competencies of graduate data science programs offered in the United States. It addressed the research question of 'Are there any differences in graduate data science program competencies based on their university rankings?' The program competencies of 228 data science graduate programs as well as their U.S. News university rankings were collected and compared. The findings indicate that regardless of their national rankings, all universities offer most of the competencies required for data science programs, except for Machine Learning and Sensor Networks. Forty-six percent of data science programs are offered in universities that hold national rankings within the top 75, focusing on majors such as data science and business analytics. Furthermore, the top-ranked universities provide data science programs across all departments or schools, with the most prominent ones being Business, computer Science, Information Science & technology, and Math/Statistics. The primary research contribution of this study revolves around conducting a comprehensive analysis of data science program competencies within the United States. The implications of the findings have significant relevance to computing education. The study provides valuable guidelines for educational practices in the field of data science: 1) naming the new majors as either 'Data Science' or 'Business Analytics';2) housing the new data science programs within existing departments or schools specializing in Business, computer Science, Information Science & technology, and Math/Statistics;3) incorporating all ACM Data Science competencies into the curriculum o

关键词： ACM Competencies computer Science Computing Education Data Science Competence Data Science Education Graduate Education

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Database Availability Security: Handling Denial of Service (DoS) Attacks

Database Availability Security: Handling Denial of Service (...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Jeffrey Surianto Irma Kartika Wairooy Sandy Wirananda Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia Computer Science Department Mobile Application & Technology Program Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

In the data based digital age, there are risks of attacks that cause denial of service (DoS) or distributed denial of service (DDoS) which pose threats to the security and availability of databases especially with those who are affected by this which is very disturbing. This research aims at analysing the methods of detecting and preventing DoS and DDoS attacks, with more focus on the security of databases. This research identifies and evaluates various strategies employed to counter such threats to cloud and Internet of Things (IoT) systems from the literature review conducted using the PRISMA approach. The analyses show that machine learning algorithms are capable of enhancing the detection of DoS/DDoS attack's efficiency and accuracy, and the cloud and IoT security solutions, including complex load balancing and powerful firewalls, are crucial for preventing and mitigating such attacks. This study also shows that, AI should be included in the security plans and security measures should be adaptable and proactive. This work is useful for scholars, developers, and cybersecurity experts who are interested in enhancing measures to counter and prevent advanced cybercrimes and ensuring continued and reliable functionality in data centric settings.

关键词： Firewalls (computing) Prevention and mitigation Distributed databases Telecommunication traffic Denial-of-service attack Load management Security Internet of Things computer crime Protection

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HyperCalm Sketch: One-Pass Mining Periodic Batches in Data Streams 39

HyperCalm Sketch: One-Pass Mining Periodic Batches in Data S...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Liu, Zirui Kong, Chaozhe Yang, Kaicheng Yang, Tong Miao, Ruijie Chen, Qizhi Zhao, Yikai Tu, Yaofeng Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Zte Corporation China

ISBN: (纸本)9798350322279

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. We propose a one-pass sketching algorithm, namely the HyperCalm sketch, which takes two phases to detect periodic batches in real time. In phase 1, we propose a time-aware Bloom filter, namely HyperBloomFilter (HyperBF), to detect the start of batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. We theoretically derive the error bounds for HyperBF and CalmSS. Extensive experiments show HyperCalm outperforms the strawman solutions 4× in term of average relative error and 13.2× in term of speed. We also apply HyperCalm to a cache system and integrate HyperCalm into Apache Flink. All related codes are open-sourced. © 2023 IEEE.

关键词： Error analysis

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GCFuzz: An Intelligent Method for Generating IoT Protocols Test Cases Using GAN with CVAE 7th

GCFuzz: An Intelligent Method for Generating IoT Protocols ...

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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Zhong, Ming Zeng, Zisheng Guo, Yijia Zhao, Dandan Zhang, Bo Li, Shenghong Peng, Hao Ding, Zhiguo School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Key Laboratory of Intelligent Educational Technology and Application in Zhejiang Normal University Jinhua321004 China School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031855924

The importance of Internet of Things (IoT) systems security cannot be ignored, particularly in the realm of communication. IoT protocols serve as standards for communication and interaction among devices in IoT environments. This paper enhances IoT security by identifying and exposing protocol vulnerabilities through fuzzing, a crucial method for discovering security flaws. Traditional generation-based fuzzers require reverse engineering to understand protocol grammar and generate test cases. In this paper, we propose an intelligent method called GCFuzz, which generates high-quality test cases without prior knowledge of the protocol. To understand different classes of protocol grammar, GCFuzz applies Generative Adversarial Networks (GAN) with Conditional Variational Autoencoders (CVAE) to train a generative model on various classes of protocol messages. After training, the model generates fake but credible messages. Additionally, to accurately cluster real protocol messages, we devise a novel clustering method based on a keyword. Results show that, compared to GANFuzz, Peach, BooFuzz, and Sulley, the test cases generated by GCFuzz have stronger targeting and less redundancy, while GCFuzz has higher accurate testing efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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WorkEv: Development and Evaluation of a Web Based Electronic Human Resource Management using Delphi Method 4

WorkEv: Development and Evaluation of a Web Based Electronic...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Niccosan Tommy, Ray Kurniawan, Christian Makalew, Brilly Andro Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mobile Application & Technology Program School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

This research is aimed to develop and evaluate an Electronic Human Resource Management (E-HRM) system called WorkEv to assist in monitoring and improving employee's performance. The application was developed based on Website using Django and Bootstrap framework. The evaluation process was conducted using Delphi method. Based on the results of the User Acceptance Test (UAT) with the Delphi method, the WorkEv application can be accepted by the target user because the design and development results meet the expectations of the target user's needs, namely easy-to-use navigation and control, simple and clear User Interface (UI) design, and the existing functions and features are effective in helping small to medium scale businesses/businesses in performing human resource management (HR). © 2022 IEEE.

关键词： Acceptance tests

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Cooperative Carrier Aircraft Support Operation Scheduling via Multi-Agent Reinforcement Learning 24

Cooperative Carrier Aircraft Support Operation Scheduling vi...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Hao, Hongjie Zhang, Xueqin Chi, Yuan Gao, Rongxin Xie, Anke Xu, Mingliang Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China Beihang University Yunnan Innovation Institute Kunming China Yunnan Key Laboratory of Blockchain Application Technology Kunming China

ISBN: (纸本)9798350341010

Carrier Aircraft support operation is an important component of the carrier aviation support system, and its efficiency is closely related to the aircraft sortie rate. However, the carrier deck faces significant challenges such as limited space, scarce resources and constantly changing environmental conditions. These factors have a profound impact on the aircraft support operations, particularly in complex scenarios, requiring careful and rational scheduling. To solve above problems, we propose a collaborative fast scheduling framework for Multi-Agent support operations, which is based on a distributed partially observable Markov decision process (Dec-POMDP) model. Within this framework, we develop a dynamic scheduling algorithm based on centralized training with decentralized execution framework (CTDE). To evaluate the performance of our approach, we establish a simulation environment for aircraft scheduling and compare our algorithm's performance with VDN and QMIX in various carrier aircraft scenarios. Experimental results demonstrate that our algorithm outperforms others in terms of both training efficiency and scheduling quality. © 2023 IEEE.

关键词： Reinforcement learning

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Advancing Intrusion Detection Systems Innovative Approaches for Prevention and Response

Advancing Intrusion Detection Systems Innovative Approaches ...

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2024 International Conference on Communication, computer Sciences and Engineering, IC3SE 2024

作者： Reddy, M. Siva Swetha Krishna, B.S.S. Murali Asha, V. Nijhawan, Ginni Rana, Ajay Jabbar, Ahmed Sabah Abed Al-Zahra Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Computer Application New Horizon College Of Engineering Bangalore India Lovely Professional University Phagwara India Amity School Of Engineering And Technology Amity University Greater Noida India The Islamic University Medical Laboratory Technology Department Najaf Iraq

ISBN: (纸本)9798350366846

A group intruder detection system built on K-Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines, and Random Forests is shown in this study. A thorough study on ablation shows how the algorithms work together to make breach detection better. We compare how well the suggested method works in terms of memory, F1 score, false negative rate, accuracy, and detection time. Bar charts, line graphs, pie charts, stacked bar graphs, area plots, and scatter plots all show that the new way is better than the old ones. Tables are also used to organize data. An ensemble takes the best features of Random Forests, Support Vector Machines, Neural Networks, Decision Trees, and K-Nearest Neighbors and puts them all together in a single package. Broad powers make the suggested method better at solving cybersecurity problems across a range of review factors. The ablation study shows why each method is important and how they work together to make an adaptive intruder detection system. In a world where safety is always changing, the proposed answer is scalable, flexible, easy to set up, and cheap. © 2024 IEEE.

关键词： Nearest neighbor search

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