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NEC Technical Journal 2024年第2期17卷 42-45页

作者： Kazuo, Yanoo Software and System Engineering Department United States

This paper provides an overview of how we can utilize generative AI in software and system development and introduces the initiatives of the NEC Group. Generative AI has great potential, but it currently has several technical challenges. Therefore, software and system development remains, for the present, a process that must be primarily performed by people. However, depending on the nature of the development project, generative AI can be used to streamline human work in a variety of tasks and to improve quality. The NEC Group plans to improve productivity and quality in software and system development by systematically using generative AI. © 2024 NEC Mediaproducts. All rights reserved.

关键词： software design

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Knowledge-enhanced Prompt Learning for Open-domain Commonsense Reasoning

NEC Technical Journal

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NEC Technical Journal 2024年第2期17卷 91-95页

作者： Zhao, Xujiang Liu, Yanchi Cheng, Wei Oishi, Mika Osaki, Takao Matsuda, Katsushi Chen, Haifeng NEC Laboratories America United States Software and System Engineering Department

Neural language models for commonsense reasoning often formulate the problem as a QA task and make predictions based on learned representations of language after fine-tuning. However, without providing any fine-tuning data and pre-defined answer candidates, can neural language models still answer commonsense reasoning questions only relying on external knowledge? In this work, we investigate a unique yet challenging problem-open-domain commonsense reasoning that aims to answer questions without providing any answer candidates and fine-tuning examples. A team comprising NECLA (NEC Laboratories America) and NEC Digital Business Platform Unit proposed method leverages neural language models to iteratively retrieve reasoning chains on the external knowledge base, which does not require task-specific supervision. The reasoning chains can help to identify the most precise answer to the commonsense question and its corresponding knowledge statements to justify the answer choice. This technology has proven its effectiveness in a diverse array of business domains. © 2024 NEC Mediaproducts. All rights reserved.

关键词： Iterative methods

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Application of Artificial Intelligence Methods for Generating Mathematical Problems in Primary Education: Analysis of Effectiveness and Prospects for Automated Assessment 7

Application of Artificial Intelligence Methods for Generatin...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Salavatovich, Nazipov Rustam Yurievna, Avksentieva Elena ITMO University Faculty of Software Engineering and Computer System Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer System Saint-Petersburg Russia

ISBN: (纸本)9798350354973

The present study aims to evaluate the effectiveness of using modern neural network models (ChatGPT 4o, Claude 3.5 Sonnet, GigaChat 3.0, YandexGPT 3) for generating mathematical problems for elementary school students. The research conducts a comparative analysis of the quality of generated problems, assesses the possibility of their direct use without expert review, and develops recommendations for automating the quality control process. The methodology includes the generation of various types of mathematical problems, their expert evaluation according to developed criteria, and statistical analysis of the results. The study identifies the strengths and weaknesses of each model, determines the types of problems in which the models demonstrate the best results, and suggests directions for further improvement of AI systems in the educational context. © 2024 IEEE.

关键词： Neural network models

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Pre-training Two BERT-Like Models for Moroccan Dialect: MorRoBERTa and MorrBERT

Mendel

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Mendel 2023年第1期29卷 55-61页

作者： Moussaoui, Otman El Younoussi, Yacine Information System and Software Engineering National School of Applied Sciences Abdelmalek Essaadi University Morocco

This research article presents a comprehensive study on the pre-training of two language models, MorRoBERTa and MorrBERT, for the Moroccan Dialect, using the Masked Language Modeling (MLM) pre-training approach. The study details the various data collection and pre-processing steps involved in building a large corpus of over six million sentences and 71 billion tokens, sourced from social media platforms such as Facebook, Twitter, and YouTube. The pre-training process was carried out using the HuggingFace Transformers API, and the paper elaborates on the configurations and training methodologies of the models. The study concludes by demonstrating the high accuracy rates achieved by both MorRoBERTa and MorrBERT in multiple downstream tasks, indicating their potential effectiveness in natural language processing applications specific to the Moroccan Dialect. © 2023, Brno University of Technology. All rights reserved.

关键词： Machine learning

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Towards Selection and Transition Between Behavior-Based Neural Networks for Automated Driving 24

Towards Selection and Transition Between Behavior-Based Neur...

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8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Aslam, Iqra Anpilogov, Igor Rausch, Andreas Institute for Software and System Engineering Clausthal University of Technology Clausthal Zellerfeld Germany

ISBN: (纸本)9798400718182

Autonomous driving technology is progressing rapidly, largely due to complex End-To-End systems based on deep neural networks. While these systems are effective, their complexity can make it difficult to understand their behavior, which raises safety concerns. This paper presents a new solution: a Behavior Selector that uses multiple smaller artificial neural networks (ANNs) to manage different driving tasks, such as lane following and turning. Rather than relying on a single large network, which can be burdensome, require extensive training data and is hard to understand. The developed approach allows the system to dynamically select the appropriate neural network for each specific behaviour (i.e. turns) in real time. We focus on ensuring smooth transitions between behaviors and taking into account the vehicle’s current speed and orientation to improve stability and safety. The proposed system has been tested using the AirSim simulation environment, demonstrating its effectiveness. © 2024 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Ant Colony Optimization with Genetic Algorithm for Solving the Job Shop Scheduling 1st

Ant Colony Optimization with Genetic Algorithm for Solving t...

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1st International Conference on Electrical systems and Smart Technologies, ICESST 2024

作者： Kriouich, Mohamed Sarir, Hicham Information System and Software Engineering ENSA Tetouan Abdelmalek Esaadi University Tetouan Morocco

ISBN: (纸本)9783031867040

In dynamic manufacturing contexts, the Job-Shop Scheduling Problem (JSP) presents considerable hurdles, particularly in light of uncertainties such as machine breakdowns and job reworks. In this article, multiple scheduling algorithms are evaluated in various scenarios, with a special emphasis on lowering makespan, or overall completion time. Advanced algorithms like Genetic Algorithm (GA) and Deep Reinforcement Learning (DRL) are contrasted with heuristic techniques like Shortest Processing Time (SPT), Longest Processing Time (LPT), and First-In-First-Out (FIFO). The research emphasizes the advantages of the hybrid Ant Colony Optimization with Genetic Algorithm (ACO_GA) solution that has been suggested. Although GA and DRL produce outcomes that are competitive, ACO_GA regularly performs better than them and, in most cases, comes quite close to the optimal solution. Even in the face of difficult, real-world problems, our hybrid approach yields more reliable and nearly optimal scheduling solutions by fusing the exploration potential of ACO with the refinement power of GA. The results show how adaptable and successful ACO_GA is, which makes it the best option for solving JSP in dynamic contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant colony optimization

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PiVisionSort: Integrating Image Processing and Machine Learning for Material Recognition on Conveyor Belts 2

PiVisionSort: Integrating Image Processing and Machine Learn...

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2nd International Conference on Data, Information and Computing Science, CDICS 2024

作者： Kia, Shohreh Leiding, Benjamin Technische Universität Clausthal Institute for Software and System Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9781643685762

This research aims to develop an advanced material detection system for conveyor belts, utilizing state-of-the-art image processing and machine learning techniques to automate the identification of various materials, thereby enhancing operational efficiency and accuracy in industrial settings. Current methods in material detection, such as traditional manual sorting and basic automated systems, often need more precision and adaptability in dynamic industrial environments. This paper identifies a gap in the practical and reliable detection of diverse materials under varying environmental conditions. The primary research questions addressed include: How can modern image processing and machine learning techniques improve material detection accuracy? What optimizations can be applied to ensure real-time processing efficiency? The proposed solution integrates a strategic camera setup with controlled lighting, robust image preprocessing algorithms (noise reduction, normalization, and resizing), and custom-designed detection algorithms using machine learning models. This system outperforms existing solutions by offering higher detection accuracy and adaptability to diverse industrial conditions. An interface is developed to display detection results and provide intuitive controls for system adjustments, ensuring practical use by industrial professionals. Rigorous testing and validation processes are implemented to enhance detection accuracy and processing speeds, with specific performance metrics established to measure efficacy. This paper provides a transformative impact on the manufacturing and processing industries by addressing environmental variability and material diversity. © 2025 The Authors.

关键词： Noise abatement

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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Frontiers of Computer Science 2023年第5期17卷 103-118页

作者： Jinwei LUO Mingkai HE Weike PAN Zhong MING College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China

Session-based recommendation(SBR)and multibehavior recommendation(MBR)are both important problems and have attracted the attention of many researchers and *** from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics,heterogeneous SBR(HSBR)that exploits different types of behavioral information(e.g.,examinations like clicks or browses,purchases,adds-to-carts and adds-to-favorites)in sequences is more consistent with real-world recommendation scenarios,but it is rarely *** efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of ***,all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of ***,all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of *** limitation hinders the development of HSBR and results in unsatisfactory *** a response,we propose a novel behavior-aware graph neural network(BGNN)for *** BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a ***,our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified *** then conduct extensive empirical studies on three real-world datasets,and find that our BGNN outperforms the best baseline by 21.87%,18.49%,and 37.16%on average correspondingly.A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our *** exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can e

关键词： session-based recommendation graph neural network heterogeneous behaviors

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Development of a Methodology for Assessing the Performance of Information Exchanges with Sources of Code Messages

Development of a Methodology for Assessing the Performance o...

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2024 International Russian Automation Conference, RusAutoCon 2024

作者： Bain, A.M. Portnov, E.M. Sin, Si Thu Thant Institute of System and Software Engineering and Information Technologies National Research University Miet Moscow Zelenograd Russia

In this paper, we developed a method to calculate the time parameters for interfacing with code message sources (CMS). This method enables the evaluation of how the collection and delivery time of information from CMS to the automated control system of the energy supply control system dispatcher varies based on the communication channel speed. Additionally, it accounts for the different numbers of CMS connected to a single control point. by analyzing these factors, the method provides insights into optimizing information transmission efficiency and ensures reliable data flow from CMS to the dispatcher's automated control system, thereby enhancing the overall performance and responsiveness of the energy supply control system. This development is crucial for improving the management and coordination within energy supply systems, ensuring timely and accurate information delivery, and ultimately contributing to more effective energy resource management. © 2024 IEEE.

关键词： Resource allocation

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Processing of Nonlinear Heterogeneous Data in Information and Control systems

Processing of Nonlinear Heterogeneous Data in Information an...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Shevnina, Yu.S. Tsarapkin, S.F. Sin, Si Thu Thant Institute of System and Software Engineering and Information Technologies National Research University Miet Moscow Zelenograd Russia

ISBN: (纸本)9798350395044

The article presents a method for cluster analysis of heterogeneous data using the provisions of fuzzy logic. Heterogeneous are data of various formats and collected from various sources. In addition, such data is usually incomplete and inaccurate, which makes it difficult to process and cluster them. The article presents mathematical models for representing a candidate for a vacant position, which is characterized by heterogeneous data. The apparatus of algebraic systems was used to develop mathematical models. A method for determining the membership function of fuzzy sets using a probabilistic approach, as the most effective when working with heterogeneous data, is described in detail. An example of the formation of a base of logical rules for the formation and selection of classification features in a set of heterogeneous data of the personnel reserve of a manufacturing enterprise is given. The selected classification features allow for further accurate and efficient verification and evaluation of information about candidates for a vacant position. In addition, the proposed method of cluster analysis of heterogeneous data can be applied in various subject areas that involve the use of incomplete and inaccurate data, for example, socio-economic, technical, biological systems. © 2024 IEEE.

关键词： Membership functions

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