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检索条件"任意字段=18th International Conference on Software Engineering and Knowledge Engineering"

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Exploring Stock Traders' Cognitive Biases: Research Design and Simulator Framework 27

Exploring Stock Traders' Cognitive Biases: Research Design a...

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27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Tkacz, Maciej Zurada, Jozef Weichbroth, Pawel Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gabriela Narutowicza 11/12 Gdansk Poland Department of Information Systems Analytics and Operations College of Business University of Louisville LouisvilleKY40292 United States Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Software Engineering Gabriela Narutowicza 11/12 Gdansk80-233 Poland

Cognitive bias is a phenomenon that has been extensively studied in stock trading and many other fields. this paper presents a framework for a Mobile Stock Trading Simulator (MSTS) that facilitates automatic investment in stocks with minimal human influence, by investigating the behavioral patterns and cognitive errors of stock market investors. the paper aims to determine whether investors' investment strategies can be improved by detecting investment threats and reducing investment errors based on investors' transaction histories. To accomplish this, we built a stock exchange simulator and implemented a decision tree to classify cognitive biases into one of six categories. By incorporating the behavioral patterns and cognitive biases of stock market investors into the MSTS's architecture, and by implementing a decision tree and stock exchange simulator, we can minimize the impact of human influence on automatic investments. © 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Behavioral Patterns Cognitive Bias Nasdaq Stock Market Stock Trading

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putt...

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11th Mining Humanistic Data Workshop, MHDW 2022, 7th 5G-Putting Intelligence to the Network Edge Workshop, 5G-PINE 2022, 1st workshop on AI in Energy, Building and Micro-Grids, AIBMG 2022, 1st Workshop/Special Session on Machine Learning and Big Data in Health Care, ML@HC 2022 and 2nd Workshop on Artificial Intelligence in Biomedical engineering and Informatics, AIBEI 2022 held as parallel events of the 18th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations, AIAI 2022

ISBN: (纸本)9783031083402

the proceedings contain 35 papers. the special focus in this conference is on Mining Humanistic Data. the topics include: Digitally Assisted Planning and Monitoring of Supportive Recommendations in Cancer Patients;CAIPI in Practice: Towards Explainable Interactive Medical Image Classification;a Deep Q Network-Based Multi-connectivity Algorithm for Heterogeneous 4G/5G Cellular Systems;simulating Blockchain Consensus Protocols in Julia: Proof of Work vs Proof of Stake;Maximum Likelihood Estimators on MCMC Sampling Algorithms for Decision Making;employing Natural Language Processing Techniques for Online Job Vacancies Classification;Probabilistic Quantile Multi-step Forecasting of Energy Market Prices: A UK Case Study;proactive Buildings: A Prescriptive Maintenance Approach;performance Meta-analysis for Big-Data Univariate Auto-Imputation in the Building Sector;non-intrusive Diagnostics for Legacy Heat-Pump Performance Degradation;a 5G-Based Architecture for Localization Accuracy;anomaly Detection in Small-Scale Industrial and Household Appliances;an Innovative software Platform for Efficient Energy, Environmental and Cost Planning in Buildings Retrofitting;deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images;An Intelligent Grammar-Based Platform for RNA H-type Pseudoknot Prediction;An Automated 2D U-Net Segmentation Method for the Identification of Cancer Brain Metastases Using MRI Images;the Use of Robotics in Critical Use Cases: the 5G-ERA Project Solution;fundamental Features of the Smart5Grid Platform Towards Realizing 5G Implementation;experimentation Scenarios for Machine Learning-Based Resource Management;efficient Data Management and Interoperability Middleware in Business-Oriented Smart Port Use Cases;5G for the Support of Smart Power Grids: Millisecond Level Precise Distributed Generation Monitoring and Real-Time Wide Area Monitoring;monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions An

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Automating software Product Line Adoption Based on Feature Models Using Large Language Models

Automating Software Product Line Adoption Based on Feature M...

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international conference on Emerging Technologies and Factory Automation (ETFA)

作者： Johannes Stümpfle Sebastian Baum Daniel Dittler Nasser Jazdi Michael Weyrich Institute of Industrial Automation and Software Engineering University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

software-intensive systems emerge in a multitude of variations to meet diverse customer requirements. To develop such variant-rich software systems, software product line (SPL) engineering has emerged as a key strategy for managing the variability. However, the adoption of SPLs is highly complex due to the diversity of feature model formats and specifications, and the complexity of implementing variability. Leveraging the capabilities of powerful large language models (LLMs) can facilitate the adoption of SPLs. Nonetheless, these LLMs often lack knowledge of the various specifications of potential feature models as well as efficient implementation of different variability mechanisms. To address these challenges, we propose a novel method based on retrieval-augmented generation. this method generates reusable artefacts and a corresponding feature mapping based on a given feature model, thereby aiding system engineers in adopting an SPL.

关键词： Large language models software systems software product lines Complexity theory Manufacturing automation

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CCE 2021 - 2021 18th international conference on Electrical engineering, Computing Science and Automatic Control

CCE 2021 - 2021 18th International Conference on Electrical ...

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18th international conference on Electrical engineering, Computing Science and Automatic Control, CCE 2021

ISBN: (纸本)9781665400299

the proceedings contain 87 papers. the topics discussed include: open-ended coaxial probe technique for the measurement of the ionic strength due to magnesium sulfate heptahydrate in water;simplified reactive power control of a multilevel inverter for grid-connected photovoltaic applications;an input error method for parameter identification of a class of Euler-Lagrange systems;a systematic method for backstepping via linear matrix inequalities;graphene for a green-environmentally methodology with organic surfactants;nonlinear least squares-based identification of a continuous friction model;adaptive tracking control of an uncertain Duffing-Holmes system;and obstacle avoidance in leader-follower formation using artificial potential field algorithm.

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MetaGater: Fast Learning of Conditional Channel Gated Networks via Federated Meta-Learning 18

MetaGater: Fast Learning of Conditional Channel Gated Networ...

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18th IEEE international conference on Mobile Ad hoc and Smart Systems (IEEE MASS)

作者： Lin, Sen Yang, Li He, Zhezhi Fan, Deliang Zhang, Junshan Arizona State Univ Sch ECEE Tempe AZ 85287 USA Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781665449359

there has recently been an increasing interest in computationally-efficient learning methods for resource-constrained applications, e.g., pruning, quantization and channel gating. In this work, we advocate a holistic approach to jointly train the backbone network and the channel gating which can speed up subnet selection for a new task at the resource-limited node. In particular, we develop a federated meta-learning algorithm to jointly train good meta-initializations for both the backbone networks and gating modules, by leveraging the model similarity across learning tasks on different nodes. In this way, the learnt meta-gating module effectively captures the important filters of a good meta-backbone network, and a task-specific conditional channel gated network can be quickly adapted from the meta-initializations using data samples of the new task. the convergence of the proposed federated meta-learning algorithm is established under mild conditions. Experimental results corroborate the effectiveness of our method in comparison to related work.

关键词： Learning systems knowledge engineering Adaptation models Quantization (signal) conferences Logic gates Filtering algorithms

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A Fault Analysis and Reasoning Method for Vehicle Information Systems Based on knowledge Graphs

A Fault Analysis and Reasoning Method for Vehicle Informatio...

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IEEE international conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Tu Sun Fuping Zeng Xiaoze Liu School of Reliability and System Engineering Beihang University Beijing China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

In the information age, vehicle information systems are evolving with increased complexity. Traditional fault diagnosis methods struggle to keep pace with new software faults. Leveraging knowledge graphs, which can represent intricate connections and rules within language knowledge, offers a fresh approach. this paper introduces a comprehensive fault analysis and reasoning framework for vehicle information systems using knowledge graphs. By gathering internet data, conducting entity and relationship extraction, and constructing a knowledge graph, the model enables intelligent question-answering capabilities based on logical rule reasoning. Deep learning techniques, specifically the TransE model for knowledge representation learning, facilitate entity prediction, relationship prediction, and triple correctness verification. this framework aims to enhance fault diagnosis and maintenance guidance in vehicle information systems.

关键词： Fault diagnosis Deep learning Databases knowledge graphs Predictive models Cognition Question answering (information retrieval) Maintenance Data mining Information systems

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Employer Perspectives on Bridging Skills Gaps in Romania's Labor Market 18th

Employer Perspectives on Bridging Skills Gaps in Romania's L...

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18th international conference on Interdisciplinarity in engineering, INTER-ENG 2024

作者： Veres, Cristina Kardos, Mihaela Stefanescu, Daniela Anitei, Mihai Daniel Faculty of Engineering and Information Technology George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Nicolae Iorga Str. 1 Targu Mureș540088 Romania Doctoral School of I.O.S.U.D. George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Gheorghe Marinescu Street 38 Targu Mures540142 Romania Faculty of Economics and Law George Emil Palade University of Medicine Pharmacy Science and Technology of Targu Mures Livezeni Street 69 Targu Mures540472 Romania Faculty of Industrial Engineering Robotics and Production Management Technical University of Cluj-Napoca Memorandumului 28 Cluj-Napoca400114 Romania

ISBN: (纸本)9783031813771

the paper presents the results of a survey-based study on the opinions of employers regarding professional education and training in Romania, with additional analysis conducted using chi-square tests to evaluate six hypotheses related to employer satisfaction with graduate skills. the research uses a structured questionnaire as the primary data collection method, targeting employers from various economic sectors, specifically those hiring graduates from technical schools and universities. the study's aim is to gather insights into how the Romanian education system aligns with labor market demands, with particular attention given to the role of practical experience and digital skills in vocational training. Key findings include a strong consensus among employers on the need for more practical training within education and the growing importance of digital skills in the modern workforce. Additionally, most of the respondents expressed interest in collaborating with educational institutions to create customized training programs. the study identifies several challenges facing the current education and training system. the conclusions drawn from the survey and the results of the chi-square tests aim to inform strategic actions that could improve the alignment between education systems and the labor market, fostering better-prepared graduates to meet the evolving demands of industries. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Personnel training

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Physical Layer Investigation on the Coexistence Strategies of GPON, NGPON1 and NGPON2

Physical Layer Investigation on the Coexistence Strategies o...

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international conference on software, knowledge Information, Industrial Management and Applications (SKIMA)

作者： Sanzida Rahman Nadiatulhuda Zulkifli Akmal Hakim Sham Faculty of Electrical Engineering Universiti Teknologi Malaysia (UTM) Johor Malaysia Communication Engineering Department Faculty of Electrical Engineering University Teknologi Malaysia (UTM)

Passive Optical Network (PON) technology is a cost-effective solution for delivering high-speed internet services to residential areas using optical fiber in access networks. the introduction of Next Generation Passive Optical Network 2 (NGPON2) with point-to-point wavelength division multiplexing (WDM) technology has addressed the increasing demand for higher bandwidth. this paper presents a comprehensive analysis of related work in the field, showcasing previous studies on PON coexistence and the challenges they addressed. In addition, a design using Time Division Multiplexing (TDM) and Time Wavelength Division Multiplexing (TWDM) systems is proposed in this paper to demonstrate an effective coexistence of multiple PON technologies. Simulation findings show the system performances that were evaluated at various fiber distances, downstream bit rates. It also identifies areas for future research, such as investigating advanced coexistence techniques, emerging PON technologies, and collaboration with industry partners for validation.

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Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach 18th

Decoding Android Malware with a Fraction of Features: An ...

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18th international conference on Network and System Security, NSS 2024

作者： Hakim, Safayat Bin Adil, Muhammad Acharya, Kamal Song, Houbing Herbert Department of Information Systems University of Maryland Baltimore County BaltimoreMD21250 United States Department of Computer Science and Engineering University at Buffalo BuffaloNY14260 United States

ISBN: (纸本)9789819635306

the escalating sophistication of Android malware poses significant challenges to traditional detection methods, necessitating innovative approaches that can efficiently identify and classify threats with high precision. this paper introduces a novel framework that synergistically integrates an attention-enhanced Multi-Layer Perceptron (MLP) with a Support Vector Machine (SVM) to make Android malware detection and classification more effective. By carefully analyzing a mere 47 features out of over 9,760 available in the comprehensive CCCS-CIC-AndMal-2020 dataset, our MLP-SVM model achieves an impressive accuracy over 99% in identifying malicious applications. the MLP, enhanced with an attention mechanism, focuses on the most discriminative features and further reduces the 47 features to only 14 components using Linear Discriminant Analysis (LDA). Despite this significant reduction in dimensionality, the SVM component, equipped with an RBF kernel, excels in mapping these components to a high-dimensional space, facilitating precise classification of malware into their respective families. Rigorous evaluations, encompassing accuracy, precision, recall, and F1-score metrics, confirm the superiority of our approach compared to existing state-of-the-art techniques. the proposed framework not only significantly reduces the computational complexity by leveraging a compact feature set but also exhibits resilience against the evolving Android malware landscape. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android malware

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Source Code Recommender Systems: the Practitioners' Perspective

Source Code Recommender Systems: The Practitioners' Perspect...

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international conference on software engineering (ICSE)

作者： Matteo Ciniselli Luca Pascarella Emad Aghajani Simone Scalabrino Rocco Oliveto Gabriele Bavota SEART @ Software Institute Universitá della Svizzera italiana (USI) Switzerland STAKE Lab @ University of Molise Italy

the automatic generation of source code is one of the long-lasting dreams in software engineering research. Several techniques have been proposed to speed up the writing of new code. For example, code completion techniques can recommend to developers the next few tokens they are likely to type, while retrieval-based approaches can suggest code snippets relevant for the task at hand. Also, deep learning has been used to automatically generate code statements starting from a natural language description. While research in this field is very active, there is no study investigating what the users of code recommender systems (i.e., software practitioners) actually need from these tools. We present a study involving 80 software developers to investigate the characteristics of code recommender systems they consider important. the output of our study is a taxonomy of 70 “requirements” that should be considered when designing code recommender systems. For example, developers would like the recommended code to use the same coding style of the code under development. Also, code recommenders being “aware” of the developers' knowledge (e.g., what are the framework/libraries they already used in the past) and able to customize the recommendations based on this knowledge would be appreciated by practitioners. the taxonomy output of our study points to a wide set of future research directions for code recommenders.

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