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Research On High-quality Patent Evaluation Methods Based on machine learning 3

Research On High-quality Patent Evaluation Methods Based on ...

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Sun, Jiyu Zhang, Shengnan School of Software Shenyang University of Technology Shenyang China

ISBN: (纸本)9781510664814

The development of today's society is inseparable from the continuous renewal of science and technology. As an important indicator of scientific and technological innovation, patents reflect the core competitiveness of a country. As patent output continues to increase, the quality and value of patents become increasingly important. In this paper, the machine learning method is used to evaluate the quality of electronic information patents and identify high-quality patents from many patents, which can promote patent transfer and transformation, acquisition and custody, transaction flow, pledge financing, and other economic activities. machine learning is more reliable and scientific than other methods. It is the task of learning corresponding rules and patterns from data and applying them to new data to make predictions. © 2023 SPIE.

关键词： Support vector machines

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Proceedings - 2023 IEEE/ACM 20th international conference on Mining software Repositories, MSR 2023

Proceedings - 2023 IEEE/ACM 20th International Conference on...

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20th IEEE/ACM international conference on Mining software Repositories, MSR 2023

ISBN: (纸本)9798350311846

The proceedings contain 77 papers. The topics discussed include: understanding the time to first response in GitHub pull requests;dealing with popularity bias in recommender systems for third-party libraries: how far are we?;improving agile planning for reliable software delivery;characterizing and understanding software security vulnerabilities in machine learning libraries;evaluating software documentation quality;Picaso: enhancing API recommendations with relevant stack overflow posts;enabling analysis and reasoning on software systems through knowledge graph representation;feature toggle usage patterns: a case study on google chromium;cross-domain evaluation of a deep learning-based type inference system;enriching source code with contextual data for code completion models: an empirical study;an empirical study on the performance of individual issue label prediction;and investigating the resolution of vulnerable dependencies with dependabot security updates.

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Incentive Mechanism for Federated learning Participants Based on Statistical Analysis Features 4

Incentive Mechanism for Federated Learning Participants Base...

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4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Li, Hao Shi, Kai Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798350329490

Compared to traditional machine learning approaches, Google's innovative framework for federated learning has emerged as a prominent focus within the realm of collaborative efforts involving multi-source data and multiple users, all aimed at enhancing model performance. Distinguished by its high learning efficiency and robust privacy-preserving capabilities, this framework has garnered significant attention. In the context of cross-device federated learning, potential challenges can stem from participants who display a lack of enthusiasm for engaging in model training or who meticulously manipulate their submitted model parameters. These malicious behaviors carry the potential to undermine the integrity of the global model, thereby exerting a notable influence on the trajectory of the global model training *** tackle these challenges, this paper introduces an incentive mechanism tailored for federated learning participants, grounded in the analysis of statistical features. By harnessing insights from statistical features, this mechanism dynamically adjusts participants' weights, thereby curbing the impact of malicious users. Under the assumption of rational participant behavior, the mechanism not only mitigates potential harm but also encourages proactive involvement. Empirical findings underscore the effectiveness of this adaptive mechanism in constraining the behavior of malicious participants in federated learning, thereby fortifying protective measures for the global model. The practical utility of this mechanism is demonstrated convincingly. © 2023 IEEE.

关键词： learning systems

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machine learning Integrated software Defined Networking Architecture for Congestion Control 2

Machine Learning Integrated Software Defined Networking Arch...

引用

2nd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sha, Akhbar Madhan, S. Neemkar, Sandeep Varma, Vanapala Bharath Chandra Nair, Lekha S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350347456

Data center networks rely upon the excellent throughput of the network to ensure data transfer happens at incredible speeds. Many factors can determine the network's throughput, such as the design, routing mechanism, and congestion control model. The introduction of the cloud made it more challenging as high traffic can happen and disrupt the data transfer for the cloud applications;this is a challenge in hosting cloud services and applications. The introduction of software Defined Networking (SDN) made it possible to program the networks and thus provide scope for enhancing the congestion control mechanisms. Separating the control plane from the data plane helps in independence and extracting and processing the data from different perspectives. This paper proposes a machine learning approach implementable in an SDN context that helps reduce congestion control by learning data transfer patterns. We propose a modified version of SDN in which an additional layer is implemented above the control plane called ML plane. ML plane takes care of the trend learning in the network that guides the control plane for better decisions. In this paper, we exhibit the architecture of ML plane for the purpose of congestion control in busy networks. © 2023 IEEE.

关键词： software defined networking

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Can we Knapsack software Defect Prediction? Nokia 5G Case 23

Can we Knapsack Software Defect Prediction? Nokia 5G Case

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Stradowski, Szymon Madeyski, Lech Nokia Mobile Networks Radio Frequency Wroclaw Poland Wroclaw Univ Sci & Technol Dept Appl Informat Wroclaw Poland

ISBN: (纸本)9798350322637

As software products become larger and more complex, the test infrastructure needed for quality assurance grows similarly, causing a constant increase in operational and maintenance costs. Although rising in popularity, most Artificial Intelligence (AI) and machine learning (ML) software Defect Prediction (SDP) solutions address singular test phases. In contrast, the need to address the whole software Development Life Cycle (SDLC) is rarely explored. Therefore in this paper, we define the problem of extending the SDP concept to the entire SDLC, as this may be one of the significant next steps for the field. Furthermore, we explore the similarity between the defined challenge and the widely known Multidimensional Knapsack Problem (MKP). We use Nokia's 5G wireless technology test process to illustrate the proposed concept. Resulting comparison validates the applicability of MKP to optimize the overall test cycle, which can be similarly relevant to any large-scale industrial software development process.

关键词： artificial intelligence software defect prediction software testing continuous integration software development life cycle Nokia 5G

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REAL-TIME TEMPERATURE PREDICTION OF A MOVING HEAT SOURCE PROBLEM USING machine learning 18

REAL-TIME TEMPERATURE PREDICTION OF A MOVING HEAT SOURCE PRO...

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ASME 18th Int Mfg Sci and Engn conf held jointly with the 51st N Amer Res conf / Japan-Soc-of-Mech-Engineers Int conf on Leading Edge Mfg/Mat and Proc

作者： Heydari, Mahtab Kung, Pei-Ching Tai, Bruce L. Tsou, Nien-Ti Texas A&M Univ College Stn TX 77843 USA Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9780791887240

Moving heat source problem is commonly seen in many manufacturing applications, such as machining, laser cutting, welding, and additive manufacturing processes, while numerical modeling often takes time to analyze. This paper presents a neural network (NN) and linear-time invariant (LTI) system-based framework, aiming at real-time temperature prediction both spatially and temporally. Training data are generated from finite element analysis (FEA) and processed with convolution neural network (CNN) to form a surrogate model for location-dependent thermal response. LTI is used to superimpose thermal responses based on the heat source's path and magnitude. The suitability of this framework is evaluated for materials of both low and high thermal diffusivities as well as adiabatic and nonadiabatic cases. In the training of the model, the low thermal diffusivity and high thermal diffusivity cases both showed training and testing correlations of over 99%. Overall, all validation studies show good agreement between the predicted temperature and the ground truth. More errors are seen when the material has a high thermal diffusivity (< 21.7 %), and the heat is applied adjacent to the boundaries (< 23.6 %).

关键词： convolutional neural network (CNN) linear time invariant (LTI) system finite element analysis (FEA) realtime temperature prediction moving heat source heat map machine learning

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Digital Twins of Smart Campus: Performance Evaluation Using machine learning Analysis 21

Digital Twins of Smart Campus: Performance Evaluation Using ...

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21st IEEE/ACIS international conference on software engineering Research, Management and Applications, SERA 2023

作者： Hussaini, Adamu Qian, Cheng Guo, Yifan Lu, Chao Yu, Wei Towson University Dept. of Computer & Information Sciences United States

ISBN: (纸本)9798350345889

The Internet of Things (IoT) paradigm is gradually becoming more prevalent through numerous devices and technologies, including sensors, actuators, microcontrollers, cloud-enabled services, and analytics. IoT objects gain intelligence by integrating with wireless sensor networks (WSNs), mobile computing and communication, and others. With sensors, smart things can be enabled by monitoring and identifying environmental changes related to motion, temperature, humidity, pressure, light, vibration, etc. To timely keep track of state changes, researchers are considering developing a cyber replicator, denoted as Digital Twin (DT), of real physical systems as a way to visualize, model, and work with complex cyber-physical systems (CPS). In this paper, we first refine the dataset to a format that can be easily used for deep learning (DL) experiments, IoT data pipeline development, data modeling and simulation, data aggregation, etc. We then demonstrate that DT data can be used to determine space occupancy based on the ambient light sensor, which tends to indicate occupancy in particular spaces because the building has smart lighting that will switch off when rooms are unoccupied after a certain time. Given the apparent developments in machine learning technology, it is clear that machine learning-based prediction has the ability to enhance resource utilization and further forecast future events. Particularly, we use a DT-based dataset and Long-Short-Term Memory (LSTM) neural network architecture to forecast the campus building's internal temperature. © 2023 IEEE.

关键词： Internet of things

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A Tertiary Study on AI for Requirements engineering 30th

A Tertiary Study on AI for Requirements Engineering

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30th international Working conference on Requirements engineering - Foundation for software Quality (REFSQ)

作者： Mehraj, Ali Zhang, Zheying Systa, Kari Tampere Univ Tampere Finland

ISBN: (纸本)9783031573262;9783031573279

Context and Motivation: Rapid advancements in Artificial Intelligence (AI) have significantly influenced requirements engineering (RE) practices. Problem: While many recent secondary studies have explored AI's role in RE, a thorough understanding of the use of AI for RE (AI4RE) and its inherent challenges remains in its early stages. Principal Ideas: To fill this knowledge gap, we conducted a tertiary review on understanding how AI assists RE practices. Contribution: We analyzed 28 secondary studies from 2017 to September 2023 about using AI in RE tasks such as elicitation, classification, analysis, specification, management, and tracing. Our study reveals a trend of combining natural language process techniques with machine learning models like Latent Dirichlet Allocation (LDA) and Naive Bayes, and a surge in using large language models (LLMs) for RE. The study also identified challenges of AI4RE related to ambiguity, language, data, algorithm, and evaluation. The study gives topics for future research, particularly for researchers who want to start new research in this field.

关键词： Tertiary study Requirements engineering Artificial intelligence machine learning Natural language processing

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Research methods on telecom user churn 2

Research methods on telecom user churn

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2nd international conference on Applied Statistics, Computational Mathematics, and software engineering, ASCMSE 2023

作者： Li, Zhujia Mechanical Engineering and Automation Institute Beihang University HaiDian District Beijing China

ISBN: (数字)9781510668195

ISBN: (纸本)9781510668188

The prediction of telecom customer churn is of great significance for the telecommunications industry, and effective and accurate predictions can make countermeasures for the status of customer churn. In order to make more effective predictions, we aim to investigate the most suitable machine learning-based algorithm for the task. In this article, a dataset of telecom customer churn was selected, which includes over 1000 sets of data and involves a comprehensive range of influencing factors. In order to find the algorithm with the best prediction performance, this paper implements various algorithms, such as XGBoost, random forest, decision tree and Adaboost, to analyze and process the data set. By comparing several statistical metrics, it is finally found that XGBoost is the most suitable algorithm for processing this data set, and random forest is not a good choice to process the data set. © 2023 SPIE.

关键词： Decision trees

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Blockage of Phishing Attacks Through machine learning Classification Techniques and Fine Tuning its Accuracy 3

Blockage of Phishing Attacks Through Machine Learning Classi...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mittal, Khushi Gill, Kanwarpartap Singh Chauhan, Rahul Joshi, Kireet Banerjee, Deepak Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun24800 India

ISBN: (纸本)9798350319125

Blocking phishing attacks using machine learning classification techniques is a promising approach to enhancing email security. Phishing attacks can involve the use of fictitious identities to trick victims into giving personal information or installing risky software. machine learning may help distinguish these fake emails by looking at various features and patterns. Phishing exploits inherent human tendencies in order to elicit the disclosure of sensitive information via online means. Phishing assaults often start via the use of electronic mail and include a range of potential aims. The cybercriminal will use social engineering tactics to deceive the victim into clicking on the link included inside the phishing email, so redirecting them to the compromised website. The complexity of these attacks increases as hackers include a human element into their fraudulent activities, therefore creating communications that possess a real appearance. Phishing faces billions of dollars in annual expenditures for Internet users. Identity thieves employ seductive methods to sift through a pool of trusting Internet users in search of personal information. Phishing software and phony emails are used by scammers to get personal information, including passwords and other bank account details. There are three types of tuning elements used: feature selection, hyperparameter optimization, and data balancing. The experiment utilizes two separate datasets acquired from online sources, namely the UCI repository and the Mendeley repository, with the inclusion of the eight most often used machine learning algorithms. The proposed research aims to block phishing attacks and improve accuracy using feature extraction and the CatBoost algorithm. The results will be shown, and the tuning parameters will be shown to increase the effectiveness of machine learning methods. © 2023 IEEE.

关键词： Accuracy Improvement Classification Techniques Cyber Threats Cybersecurity Detection Email Security Feature Extraction Fine-Tuning machine learning Phishing Attacks Website Security

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