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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Fanchang TangYaLing Yadav, Amit Sah, Abhishek Pratap Khan, Asif Department of Software Engineering Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Application Integral University Lucknow India

ISBN: (纸本)9798331522667

Existing research on speaker and emotional voice conversion often focuses on separate tasks, neglecting their joint exploration. Furthermore, the limited availability of emotional corpora for target speakers poses a significant challenge for training robustness and generalized models. This paper proposes an improved scheme for speaker-emotion voice conversion with limited target speaker's emotional corpus, integrating a large language model and a pre-trained emotional speech synthesis model. It introduces several enhancements to enhance the quality of converted speech in terms of speaker similarity and emotional expressiveness. First, emotionally tagged text is generated using a large language model and emotional speech is synthesized from this text using a fine-tuned pre-trained emotional speech synthesis model. Then, a speaker-emotion voice conversion model is co-trained with both synthesized and real target emotional speech. Finally, the model is fine-tuned with the real target emotional speech to further boost the speaker and emotional similarity. © 2025 IEEE.

关键词： Speech enhancement

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Ontological Approach to the Document Synthesis Based on Data Mining 7

Ontological Approach to the Document Synthesis Based on Data...

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7th International Conference on Information Technologies in engineering Education, Inforino 2024

作者： Borisov, V.V. Misnik, A.E. Sharaburka, A.M. Zakharchenkov, K.V. National Research University 'MPEI' Branch in Smolensk Department of Computer Engineering Smolensk Russia Belarusian-Russian university Information Technology Software Department Mogilev Belarus

ISBN: (纸本)9798350371369

This article presents a comprehensive study on the utilization of data mining technologies and ontological approaches in the field of government procurement. It highlights the complexities involved in defining procurement object requirements and developing technical specifications, which vary significantly from case to case. The study underscores the need for procurement professionals to possess extensive knowledge across various sectors due to the diversity of goods, works, and services procured. The adoption of Intelligent Data Analysis is proposed as a solution to enhance the accuracy and efficiency of procurement processes. The research investigates the potential of automating ontology construction to facilitate decision-making in creating technical specifications. It examines the legal framework governing government procurement and explores the capabilities of the Unified Information System for public procurement in Russia. The paper also describes the development of a typed ontology for public procurement, emphasizing the importance of an ontological approach in managing the large volume of multistructured information typical in procurement documentation. This approach is vital for the automated synthesis of necessary documents, offering significant support in decision-making processes. The article concludes with a detailed exploration of the ontological approach to document synthesis in government procurements. It outlines the process of selecting procurement subjects, conducting intellectual analysis of texts, and using intelligent data analysis techniques to ensure compliance with legal requirements. © 2024 IEEE.

关键词： Data mining

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A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

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computers, Materials & Continua 2022年第6期71卷 4523-4554页

作者： Palli Padmini C.Paramasivam G.Jyothish Lal Sadeen Alharbi Kaustav Bhowmick Department of Electronics&Communication Engineering Amrita School of EngineeringBengaluruAmrita Vishwa VidyapeethamIndia Center for Computational Engineering and Networking(CEN) Amrita School of EngineeringCoimbatoreAmrita Vishwa VidyapeethamIndia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Electronics and Communication Engineering PES UniversityBengaluruIndia

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages,captured by four oral sensors *** an experimental demonstration of the system used in the oral cavity,a prototype tooth model was *** on the principle developed in a previous publication by the author(s),the proposed system has been implemented using the oral cavity(tongue,teeth,and lips)features alone,without the glottis and the *** positions of the sensors in the proposed system were optimized based on articulatory(oral cavity)gestures estimated by simulating the mechanism of human *** system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm,with limit switches,potentiometer,and flex sensors emulating the tongue in an artificial oral *** system produces the sounds of vowels,consonants,and words in English,along with the pronunciation of meanings of their translations in four major Indian languages,all from oral cavity *** experimental setup also caters to gender mapping of *** sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners,with∼98%and∼95%accuracy,*** a model may be useful to interpret speech for those who are speech-disabled because of accidents,neuron disorder,spinal cord injury,or larynx disorder.

关键词： English vowels and consonants oral cavity proposed system sensors speech-disabled speech production vocal tract model

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A New Method of Search Engine Optimization Based on Semantic Kernel Idea

A New Method of Search Engine Optimization Based on Semantic...

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作者： Orekhov, Sergey Godlevsky, Michail Malyhon, Hennadiy Goncharenko, Tetiana Computer Science and Software Engineering Department National Technical University Kharkiv Polytechnical Institute Kharkiv61002 Ukraine

The results of more than 30 projects in the field of search engine optimization on the Internet show that the effectiveness of existing approaches is catastrophically reduced. Paid promotion methods no longer guarantee a banal increase in the traffic of a given web resource, and even more so the number of leads. The reason is that competition has moved to the Internet as much as possible. So, we need to look for new alternative methods. Therefore, the paper proposes to consider a new alternative way to promote goods on the Internet. This approach is a new information technology – virtual promotion. It is based on the creation of a virtual image of a product on the Internet. We called this virtual image the semantic kernel. Such a kernel is placed on the Internet in order to attract a potential buyer. It is placed in the Internet nodes according to a special online sales scheme, which is based on a map of the user's travels on the Internet. This scheme is called the virtual promotion map. The map must guarantee maximum traffic to a given web resource. However, it will not be possible to immediately build a map. Therefore, its creation is iterative. We are, as it were, setting ourselves up to find effective ways between the semantic core and the user who is ready to buy our product online. The article offers a description of the virtual promotion technology. The main idea of virtual promotion is that promotion is a logistical channel between a company and a client. We put a special message (semantic core) into the channel. He moves through the logistics channel and tries to attract as many new buyers of the goods as possible. This article presents the results of the work of special software that implements the synthesis, quality assessment and management of the semantic kernel of web content. The program generates and manages two channels: the first is intended for the distribution of the semantic kernel, the second is for assessing the quality of such distribution. T

关键词： Semantics

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Emotion-Aware Music Recommendations: A Transfer Learning Approach Using Facial Expressions 1

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5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Annam, Sai Teja Bodapati, Jyostna Devi Konda, RajaSekhar Department of Advanced Computer Science and Engineering VFSTR Deemed to Be University Vadlamudi522213 India Software Engineering Manager & IEEE Senior Member IEEE San FranciscoCA94536 United States

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

The influence of music on mood and emotions has been widely studied, highlighting its potential for self-expression and personal delight. As technology continues to advance exponentially, manually selecting and analyzing music from the vast array of artists, songs, and listeners becomes impractical. In this study, we propose a system called the "emotion-aware music recommendations" that leverages real-time facial expressions to determine a person’s emotional state. Deep learning models are employed to accurately detect facial emotions, leveraging the principles of transfer learning. By combining the model’s output with the mapped songs from the dataset, a personalized playlist is created. The main objective of the study is to effectively classify user emotions into six distinct categories using pre-trained models. Experimental studies conducted on the proposed approach employ the RAF-ML benchmark facial expression dataset. The findings indicate that the model outperforms existing approaches, demonstrating its effectiveness in generating tailored music recommendations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Emotion Recognition

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Sentiment Analysis Scheme for Image-Text Comments based on Multimodal Deep Learning and Spatiotemporal Attention 6

Sentiment Analysis Scheme for Image-Text Comments based on M...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

ISBN: (纸本)9798331522667

A sentiment analysis scheme for image and text comments based on multimodal deep learning and spatiotemporal attention is proposed to address the issues of incomplete spatiotemporal considerations, incomplete implementation details, and cutting-edge theoretical algorithms in graphic and textual sentiment analysis schemes. The proposed model has clear layering including data preprocessing layer, modal encoding layer, modal fusion layer, sentiment classification layer, loss function and optimizer, evaluation and feedback. The implementation details of each layer are introduced. The entire scheme model incorporates Multimodal Fusion Neural Network (MFNN) deep learning and spatiotemporal attention mechanism, which makes the scheme perform well in terms of security, robustness and performance, making up for the shortcomings of existing research schemes. © 2025 IEEE.

关键词： Contrastive Learning

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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PSCS4CPP: A Generative PSCS Implementation for C++ 1

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Model-Driven engineering and software Development - 9th International Conference, MODELSWARD 2021, and 10th International Conference, MODELSWARD 2022, Revised Selected Papers

作者： Hammer, Maximilian Maschotta, Ralph Wichmann, Alexander Jungebloud, Tino Bedini, Francesco Zimmermann, Armin Systems and Software Engineering Group Computer Science and Automation Department Technische Universität Ilmenau Ilmenau Germany

ISBN: (数字)9783031388217

ISBN: (纸本)9783031388200

Since the early 2000s, Model-driven engineering has aimed to accomplish executable UML models. With fUML and PSCS, the Object Management Group (OMG) published standardized specifications of precise semantics for certain parts of UML in the form of metamodels, which form an execution environment. A certain characteristic of these environments is that static information about a model is analyzed and evaluated during runtime. With composite structures being a concept for describing structural properties of a model, the majority of execution semantics specified by PSCS concern analysis and processing of such static information about the model’s fine-grained structure. Thus, the PSCS specification appears suitable for a generative realization approach. Using Model-To-Text-Transformation to generate source code, which serves as an input for the actual execution environment, the runtime level of model executions can be relieved by outsourcing analysis and processing of static information to the level of code generation. By inserting this preprocessing step, the performance of the actual model execution at runtime can be improved. This paper introduces an implementation of the PSCS specification for C++ based on code generation using Model-to-Text-Transformation. This also includes a generative approach to realize an extension mechanism of the fUML and PSCS execution environments by introducing user-defined semantic execution strategies. The proposed PSCS implementation was developed as a part of the MDE4CPP project. Moreover, this paper presents a set of test models validating the correct functionality of the implementation as well as a performance benchmark. Finally, analysis results of an application example for the presented realization are evaluated and discussed. © 2023, Springer Nature Switzerland AG.

关键词： Specifications

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Synthetic Aircraft Trajectory Generation Using Time-Based VQ-VAE

Synthetic Aircraft Trajectory Generation Using Time-Based VQ...

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2025 Integrated Communications, Navigation and Surveillance Conference, ICNS 2025

作者： Murad, Abdulmajid Ruocco, Massimiliano SINTEF Digital Department of Software Engineering Safety and Security Trondheim Norway Norwegian University of Science and Technology Department of Computer Science Trondheim Norway

ISBN: (纸本)9798331534738

In modern air traffic management, generating synthetic flight trajectories has emerged as a promising solution for addressing data scarcity, protecting sensitive information, and supporting large-scale analyses. In this paper, we propose a novel method for trajectory synthesis by adapting the Time-Based Vector Quantized Variational Autoencoder (TimeVQVAE). Our approach leverages time-frequency domain processing, vector quantization, and transformer-based priors to capture both global and local dynamics in flight data. By discretizing the latent space and integrating transformer priors, the model learns long-range spatiotemporal dependencies and preserves coherence across entire flight paths. We evaluate the adapted TimeVQVAE using an extensive suite of quality, statistical, and distributional metrics, as well as a flyability assessment conducted in an open-source air traffic simulator. Results indicate that TimeVQVAE outperforms a temporal convolutional VAE baseline, generating synthetic trajectories that mirror real flight data in terms of spatial accuracy, temporal consistency, and statistical properties. Furthermore, the simulator-based assessment shows that most generated trajectories maintain operational feasibility, although occasional outliers underscore the potential need for additional domain-specific constraints. Overall, our findings underscore the importance of multi-scale representation learning for capturing complex flight behaviors and demonstrate the promise of TimeVQVAE in producing representative synthetic trajectories for downstream tasks such as model training, airspace design, and air traffic forecasting. © 2025 IEEE.

关键词： Aerobraking

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Automated computer Vision-based Detection of Solar Panel Defects Using a Thermal Camera Mobile Application 10

Automated Computer Vision-based Detection of Solar Panel Def...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Hajjdiab, Hassan Mohamed, Gasm ELbary Alzaabi, Salim Alhalabi, Marah Gad, Abdalla Yaghi, Maha Alkhedher, Mohammad Ghazal, Mohammed Concordia University Department of Computer Science and Software Engineering Montreal Canada Abu Dhabi University College of Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350316353

Solar energy production has risen significantly globally in recent years. Solar Panels and Photovoltaic PV modules are the key components of solar energy production. Therefore, ensuring their health and efficiency and finding technologies to facilitate this became more important. However, the PV inspection costs money, time, and human resources. Thus, many technologies and techniques have been introduced to facilitate PV cell inspection, such as using a drone with a thermal camera. Moreover, as technology evolves every day, so do mobile devices. Nowadays, smartphones have brought many conveniences to our lives. Smartphones have become smarter with applications, and now they can be used as health monitoring devices, projectors, and more. One of smartphones' most revolutionary features and technology is that they can come with a thermal camera or be compatible with thermal cameras. Thermal camera application varies and is diverse, from detecting heat on an object to inspecting photovoltaic PV cells. This paper proposes a novel system consisting of a thermal camera mobile app to detect the defects in PV modules and estimate the defect percentage. The result of this work has shown a potential research and further development area in the field of renewable energy and PV cell inspection. © 2023 IEEE.

关键词： Solar panels

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