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19th Annual ACM conference on international computing Education Research V.1 (ICER)

作者： Koutcheme, Charles Sarsa, Sami Leinonen, Juho Haaranen, Lassi Hellas, Arto Aalto Univ Espoo Finland Univ Auckland Auckland New Zealand

ISBN: (纸本)9781450399760

Background and Context: Struggling with programming assignments while learning to program is a common phenomenon in programming courses around the world. Supporting struggling students is a common theme in computing Education Research (CER), where a wide variety of support methods have been created and evaluated. An important stream of research here focuses on program repair, where methods for automatically fixing erroneous code are used for supporting students as they debug their code. Work in this area has so far assessed the performance of the methods by evaluating the closeness of the proposed fixes to the original erroneous code. The evaluations have mainly relied on the use of edit distance measures such as the sequence edit distance and there is a lack of research on which distance measure is the most appropriate. Objectives: Provide insight into measures for quantifying the distance between erroneous code written by a student and a proposed change. We conduct the evaluation in an introductory programming context, where insight into the distance measures can provide help in choosing a suitable metric that can inform which fixes should be suggested to novices. Method: A team of five experts annotated a subset of the Dublin dataset, creating solutions for over a thousand erroneous programs written by students. We evaluated how the prominent edit distance measures from the CER literature compare against measures used in Natural Language processing (NLP) tasks for retrieving the experts' solutions from a pool of proposed solutions. We also evaluated how the expert-generated solutions compare against the solutions proposed by common program repair algorithms. The annotated dataset and the evaluation code are published as part of the work. Findings: Our results highlight that the ROUGE score, classically used for evaluating the performance of machine summarization tasks, performs well as an evaluation and selection metric for program repair. We also highlight the p

关键词： program repair automated program repair automatic program repair distance measures distance metrics ROUGE BLEU dataset bug fixing feedback natural language processing educational data mining computing education

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Construction of an Artificial Intelligence Student Management Information System Under Big data Analysis algorithms 2

Construction of an Artificial Intelligence Student Managemen...

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2nd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2023

作者： Wang, Ying Sun, Yunfan Shandong Institute of Commerce and Technology Shandong Jinan China

ISBN: (纸本)9798350371451

The management of student information on campus is gradually shifting from paper-based to electronic, and from offline decentralization to online centralization. This means of making campus management efficient is to establish a student management information system. With the gradual complexity and quantization of the information and data to be processed, this paper argued that there is a need to improve the basic student management information system through artificial intelligence (AI) technology and big data analysis algorithms. The paper concluded with an experimental analysis of the information processing efficiency of an intelligent student management information system based on the random forest algorithm, a representative algorithm for big data analysis. The processing time of a system trained 0 times for a certain segment of information is 163.75 seconds, while the processing time of a system trained 200 times for the same segment of information is only 78.64 seconds. Just 200 training sessions have doubled the processing speed of the student management information system for the same piece of information. Therefore, this study found that big data analysis algorithms can increase the efficiency of intelligent student management information systems in information processing. © 2023 IEEE.

关键词： Big data

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AMPP: An Adaptive Multilayer Perceptron Prefetcher for Irregular data Prefetching 25

AMPP: An Adaptive Multilayer Perceptron Prefetcher for Irreg...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Fang, Juan Li, Jingjing Yang, Huijing Wang, Yuening Song, Shuying Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350330014

With the continuous increase in modern data scale, there is a higher demand for processor computing power for time-consuming and computationally intensive data mining problems. High performance computing (HPC) can enable faster processing of large datasets and complex algorithms. data prefetching is an effective technique used in HPC. It can hide memory delay and boost execution performance by obtaining data before the program needs it. Traditional data prefetchers show poor performance on programs with irregular access patterns due to the complex access patterns in its workload is difficult to predict, and may lead to serious cache miss. Applying machine learning algorithm into data prefetching policy for access prediction can effectively improve system performance. In this paper, we propose a machine learning-based prefetcher called Adaptive Multilayer Perceptron Prefetcher (AMPP), combined with a cosine annealing learning rate scheduler. AMPP regards prefetching as a classification problem, it analyzes the given instruction pointer using a multilayer perceptron model and can use the learning rate scheduler to optimize the model during the training process. We apply AMPP on last level cache and use ChampSim simulator to conduct simulation experiments based on GAP and SPEC CPU benchmark suits. Our experiments demonstrate that AMPP provides average IPC improvement of 35.58%, MPKI improvement of 46.08% and coverage of 51.32% compared to no-prefetching baseline. And it performs especially well on benchmarks with irregular data access patterns. © 2023 IEEE.

关键词： Large datasets

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Security Techniques in Cloud computing for Cloud data Storage 14

Security Techniques in Cloud Computing for Cloud Data Storag...

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Bedse, Akshay Padiya, Puja Vidhate, Amarsinh Ramrao Adik Institute of Technology Nerul Navi Mumbai India

Cloud and Internet of things application are growing day by day. Most of the organization are using the cloud storage services for own application. IOT application are building under the 5G wireless communication technologies. Security is the major concern for data security and safety. The basic techniques start from using the watermarking with advancement of the cryptography algorithm. Most of the online and cloud application are using the cryptography-based data security. The most common and high secure cryptography algorithms are AES, RSA, Hash etc. This paper presents the cryptography techniques and challenges for the security in cloud data storage for IOT applications. © Grenze Scientific Society, 2023.

关键词： Cryptography

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Analysis of Security Methods for Medical data in Cloud Systems 11th

Analysis of Security Methods for Medical Data in Cloud Syste...

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11th international conference on E-Health and Bioengineering (EHB)

作者： Pirnau, Mironela Priescu, Iustin Joita, Daniela Priescu, Catalina Mihaela Titu Maiorescu Univ Fac Informat Bucharest Romania Natl Univ Sci & Technol Politehn Bucharest Fac Automat Control & Comp Sci Bucharest Romania

ISBN: (纸本)9783031625015;9783031625022

Healthcare providers need to manage large amounts of data both for patients and medical care suppliers. Part of the processed data is sensitive and keeping it secured is important. Regardless of the medical data exploitation methods or of the algorithms used for their processing, it is necessary to ensure the maintenance of confidentiality and integrity of data. At the same time, it is also recommended to maintain a high level of security for the systems in which the medical data is stored and processed. The present study aims at combining information security methods with cloud computing technologies, blockchain and quantum cryptography to identify efficient methods for the protection of medical digital data.

关键词： Blockchain Cloud Medical data Security

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An Investigation of Machine Learning algorithms for High-bandwidth SQL Injection Detection Utilising BlueField-3 DPU Technology 36

An Investigation of Machine Learning Algorithms for High-ban...

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36th IEEE international System-on-Chip conference (SOCC)

作者： Tasdemir, Kasim Khan, Rafiullah Siddiqui, Fahad Sezer, Sakir Kurugollu, Fatih Bolat, Alperen Queens Univ Belfast Ctr Secure Informat Technol Belfast Antrim North Ireland QUB GDR LAB Grp Belfast Antrim North Ireland NVIDIA Corp Belfast Antrim North Ireland Univ Sharjah Dept Comp Sci Coll Comp & Informat Sharjah U Arab Emirates

ISBN: (纸本)9798350300116

SQL injection attacks present a significant risk to data center security. Traditional rule-based pattern matching techniques exhibit limitations, such as inability to adapt to new attack types, to give decision confidence and lower detection accuracy. Machine learning (ML) based approaches offer promising alternatives;however, their computational requirements and the increasing volume of network traffic pose challenges for their application in conventional hardware. data processing Units (DPUs) have emerged as the tailored computing platform for infrastructure related workloads within data centers including security. This paper evaluates the performance and efficiency of classical ML methods for SQL injection detection utilising computing resources on DPUs. In this study, 20 prominent ML models are tested against a dataset comprising 30,000 SQL payloads, and their performance is compared in a series of experiments. The results indicate that the Passive Aggressive Classifier is the most suitable model for near-real-time detection, achieving a detection latency of approximately 0.3 mu s/sample with an accuracy of 99.78%. This paper demonstrates that ML methods can be efficiently and effectively deployed on DPUs for SQL injection detection, providing valuable insights into threat intelligence for enhancing data center security. The codes of this study can be found at: https://***/gdrlab/dpu- sqli- detection.

关键词： SQL injection Attack Detection Machine Learning Passive Aggressive Analysis data processing Unit Network

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RF Challenge: The data-Driven Radio Frequency Signal Separation Challenge

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 4083-4100页

作者： Lancho, Alejandro Weiss, Amir Lee, Gary C. F. Jayashankar, Tejas Kurien, Binoy G. Polyanskiy, Yury Wornell, Gregory W. Univ Carlos III Madrid Signal Theory & Commun Leganes 28911 Spain Gregorio Maranon Hlth Res Inst Madrid 28007 Spain Bar Ilan Univ Ramat Gan Israel Inst Infocomm Res Commun & Networks Singapore Singapore MIT Elect Engn & Comp Sci Dept Cambridge MA USA MIT Lincoln Lab Lexington MA USA

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP'24).

关键词： Interference Source separation Radio frequency RF signals Time-frequency analysis Prevention and mitigation Filtering Vectors Standards Speech processing Interference rejection deep learning source separation wireless communication

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A PROCESS FLOW COMPOSABLE OPENdata MASHUP TOOL AND ITS EFFECTS EVALUATION 20

A PROCESS FLOW COMPOSABLE OPENDATA MASHUP TOOL AND ITS EFFEC...

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20th international conference Applied computing, AC 2023 and the 22nd international conference WWW/Internet, ICWI 2023

作者： Yashiro, Satoshi Komoda, Norihisa Ohkawa, Takenao Graduate School of System Informatics Kobe University 1-1 Rokkodai-cho Nada Kobe657-8501 Japan Osaka University 1-1 Yamadaoka Suita Osaka565-0871 Japan

Open data is a data that is provided by public sectors or social infrastructure companies like transportation service providers. Recently, many people are trying to utilize open data in various applications. Even if a user is not a specialist who have enough data science skills, the user may use common data processing software such as a spread sheet software or a text editor to handle open data. Such common software requires many hours to handle the data, whereas does not require higher skills. In this paper, an open data mashup tool which can reduce hours to handle open data in utilizing the data is proposed. This tool supports a user to design data process flow easily by providing a user interface with composable processing unit components. Also, its effect in some specific cases is clarified. © ICWI *** rights reserved.

关键词： Open data

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international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

International Conference on Integrated Intelligence and Comm...

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2023 international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

ISBN: (纸本)9798350315455

The proceedings contain 286 papers. The topics discussed include: utilizing machine learning to analyze and implement task scheduling algorithms in cloud;real time fire detection and alert triggering system;a dynamic model for blended learning of university English based on K-Means clustering algorithm;real time eye-tracking mouse control using recurrent neural network;collaborative virtual storage technology based on mobile cloud computing;enhanced intrusion detection system using Pearson correlation based long short-term memory;construction of group algorithm model under green innovation capability technology;numerical simulation and analysis of integrated mobile medical building based on finite element method;the implementation and optimization of high-performance image processing algorithms based on artificial intelligence;and research on photographic image classification based on multi-model fusion and data augmentation.

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Trusted Auditing of data Operation Behaviors in Cloud based on Blockchain and TEE 21

Trusted Auditing of Data Operation Behaviors in Cloud based ...

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21st IEEE international Symposium on Parallel and Distributed processing with Applications, 13th IEEE international conference on Big data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Shujiang Wang, Fansheng Wang, Lianhai Mihaljevic, Miodrag J. Zhang, Shuhui Shao, Wei Huang, Qihao Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Mathematical Institute The Serbian Academy of Sciences and Arts Belgrade Serbia Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350329223

With the rapid development of the new generation of information technologies, including cloud computing, 5G, and the Internet of Things, data outsourcing storage in the cloud has brought great convenience to data storage or access anytime and anywhere. Due to the separation of ownership and management of outsourced stored data, it faces data security risks, including data loss and personal privacy disclosure. The third-party audit of data operational behavior by logs is often employed to strengthen the security of outsourced data. However, some privacy breach incidents in recent years show that third parties who may leak information about data and users' identities are not always trustworthy. To achieve data security and privacy protection, an operation behavior auditing scheme for outsourced data is proposed with the blockchain rather than third parties. The data operation behavior audit is carried out from three dimensions: integrity of the operation behavior log, the credibility of the operation behavior information, and compliance of operation behavior. The security analysis and experiment results indicate that this scheme is secure and feasible. © 2023 IEEE.

关键词： Blockchain

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