检索结果-内蒙古大学图书馆

Methods for Pitch Analysis in Contemporary Popular Music: Deviations From 12-Tone Equal Temperament in Vitalic’s Work

引用

AES: Journal of the Audio Engineering Society 2025年第5期73卷 256-281页

作者： Deruty, Emmanuel Arbez-Nicolas, Pascal Meredith, David Sony Computer Science Laboratories Paris France Citizen Records Dijon France Department of Architecture Design and Media Technology Aalborg University Denmark

Aims: Twelve-tone equal temperament (12-TET) is recognized as the modern standard tuning for music. However, contemporary popular music may exhibit significant deviations from this framework. This study investigates such deviations in the music of the electronic musician Vitalic and others. Methods: The study examines relations between signal features and perceived pitches. The artist’s involvement ensures that the analysis focuses on aspects relevant to the music. Results: Deviations from 12-TET can be observed as a result of 1) tuning of quasiharmonic tones outside of 12-TET, 2) audible mistuned partials within an otherwise quasiharmonic context, and 3) subsets of partials perceived as distinct pitches outside 12-TET. Examples from other artists suggest that the use of pitches outside 12-TET is not limited to Vitalic’s music. Conclusions: The deviations from 12-TET are deliberate and pervasive. They are often linked to the acoustic properties of the tones, suggesting 1) a continuum between timbre and pitch and 2) the concept of a "resulting pitch" analogous to the idea of "resulting harmony" in Renaissance polyphonic music. Such conclusions challenge the traditional view of musical pitch. © 2025 Audio Engineering Society. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing Elderly Care with Socially Assistive Robots: A Holistic Framework for Mobility, Interaction, and Well-Being

引用

IEEE Access 2025年 13卷 82698-82717页

作者： Naseer, Fawad Nasir Khan, Muhammad Tahir, Muhammad Addas, Abdullah Kashif, Hasnain Beaconhouse International College Computer Science and Software Engineering Department Faisalabad38000 Pakistan Government College University Lahore Electrical Engineering Department Lahore54000 Pakistan Sir Syed University of Engineering and Technology Department of Software Engineering Karachi75300 Pakistan Prince Sattam Bin Abdulaziz University College of Engineering Department of Civil Engineering Al-Kharj11942 Saudi Arabia King Abdulaziz University Faculty of Architecture and Planning Landscape Architecture Department Jeddah21589 Saudi Arabia University of South Asia Department of Computer Science Lahore54000 Pakistan

The global elderly population is projected to double by 2050, creating challenges in mobility, social isolation, and cognitive decline. Socially Assistive Robots (SARs) offer a promising solution, yet gaps remain in integrating mobility assistance with cognitive and emotional support. The research developed a comprehensive framework for SARs, emphasizing user-centered design, technological innovation, and cultural adaptability. A mixed-methods approach was employed with 200 participants aged 60+, from diverse settings, including rehab centers, retired groups, old age communities and nursing homes. Results demonstrated a 33.3% improvement in mobility scores and a 40% increase in social engagement. Health metrics revealed a 22% reduction in falls, a 15% rise in physical activity, and a 90% user satisfaction rate. Gender and age differences were evident, with males aged 71+ showing higher mobility improvements (35.6%) and females aged 71+ reporting the highest satisfaction (93%). Long-term data indicated sustained benefits, with participants retaining 80% of mobility gains and social engagement levels 38% above baseline after six months. These findings highlight the importance of culturally sensitive and user-centered SAR designs in promoting elderly well-being. The research offers a robust foundation for integrating SARs into elderly care systems, emphasizing adaptability, sustainability, and ethical considerations for future implementation. © 2013 IEEE.

关键词： Nursing

来源：评论

学校读者我要写书评

暂无评论

Machine learning model for predicting long-term energy consumption in buildings

引用

Discover Internet of Things 2025年第1期5卷 1-24页

作者： Hussien, Aseel Maksoud, Aref Al-Dahhan, Aisha Abdeen, Ahmed Baker, Thar Department of Architectural Engineering University of Sharjah Sharjah United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Civil Mining Environmental and Architectural Engineering University of Wollongong Wollongong Australia School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom

The rapid growth in the construction sector has led to increased energy consumption and carbon emissions. Calculating energy usage and emissions is essential to energy security and promoting sustainable sector development. Therefore, the study objective is to investigate the utilazation of machine learning algorithm to predict long-term energy consumption in buildings sector, aiming to improve sustainable design and energy optimization, via the implementation of three machine learning models, XGBoost, Support Vector Regression, and Long-Short-Term Memory networks, to predict energy consumption. These models are adept at capturing complex interactions between building characteristics, environmental factors, and energy patterns. Although previous studies have explored various machine learning techniques for energy efficiency, limited research links these models to practical applications in building performance simulation. Furthermore, there is a lack of comparative evaluation of advanced machine learning models such as XGBoost, Support Vector Regression, and Long-Short-Term Memory to predict the energy consumption of building envelopes, particularly in hot climates such as the UAE. This research aims to fill this gap by providing a detailed comparison of these models against alternative approaches mentioned in the literature. The findings position Long-Short-Term Memory as a transformative force in predictive modeling, demonstrating exceptional precision with an R-squared value of 0.993 and a Mean Squared Error of 0.004. In contrast, Support Vector Regression and XGBoost showed limited predictive capabilities, with R-squared values of 0.462 and 0.94, respectively. This study establishes a solid data-driven foundation for architects and engineers to inform decisions on energy-efficient building designs, advocating Long-Short-Term Memory as the superior model for predicting energy performance. © The Author(s) 2025.

关键词： Green buildings

来源：评论

学校读者我要写书评

暂无评论

RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

引用

Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

引用

International Journal of Crowd science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

来源：评论

学校读者我要写书评

暂无评论

A Binary PSO Based Ensemble Under-Sampling Model for Rebalancing Imbalanced Training Data

arXiv

引用

arXiv 2025年

作者： Li, Jinyan Wu, Yaoyang Fong, Simon Tallón-Ballesteros, Antonio J. Yang, Xin-She Mohammed, Sabah Department of Computer and Information Science University of Macau Taipa China Department of Languages and Computer Systems University of Seville Seville Spain Design Engineering and Math School of Science & Technology Middlesex University London United Kingdom Department of Computer Science Lakehead University Thunder Bay Canada

Ensemble technique and Under-sampling technique are both effective tools for resolvig imbalanced dataset classification problems, which commonly denotes the quantitative imbalance of a binary class dataset where minority class is the target class. In this paper, a novel ensemble method absorbing the advantages of both ensemble learning for biasing classifiers and a new evolutional under-sampling method is proposed. The under-sampling method is named Binary PSO instance selection, it gathers with ensemble classifiers to find the most suitable length and combination of majority class samples to build a new dataset with minority class samples. The proposed method adopts multi-objective strategy, objectives of this method is to improve the performances of imbalanced classification and to guarantee the maximum integrity of the original dataset. We examined our proposed method, the Binary PSO instance selection by comparing its performance of processing imbalanced dataset with several other conventional basic ensemble methods. Experiments are also conducted with Binary PSO instance selection wrapping with ensemble classifiers for further improvement of imbalanced classification. Based on comaprison experiments, our proposed methods outperform single ensemble methods, state-of-the-art under-sampling methods as well as their combinations with traditional PSO instance selection algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Data assimilation

来源：评论

学校读者我要写书评

暂无评论

Exploring 2D representation and transfer learning techniques for indoor localization

引用

Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (case study: protein-peptide region residue-level interaction) using structural information 29

Integrating Structural Information: Comparing Classification...

引用

29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland Brisbane QLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Binding classification protein-peptide interaction segmentation structure-based

来源：评论

学校读者我要写书评

暂无评论

Assessment of changes in a technical condition of railway bridges using a novel probabilistic algorithm

引用

Structure and Infrastructure Engineering 2025年

作者： Fabianowski, Dariusz Jakiel, Przemyslaw Rydel, Marek Bialic, Grzegorz Stanislawski, Rafal Faculty of Civil Engineering and Architecture Opole University of Technology Opole Poland Department of Computer Science Opole University of Technology Opole Poland

Planning of rehabilitation work is important for the safety of transport infrastructure. This problem needs to be properly solved, therefore a new algorithm for planning railway bridge maintenance is proposed. The input data come from periodic inspections that assess the technical condition of a bridge. These assessments are processed using hybrid Extended Fuzzy Analytic Hierarchy Process and Dominant Analytic Hierarchy Process methods. The main part of the algorithm is the Statistic Bridge Analyser, which allows a multi-variant analysis of the impact of inaccuracy and uncertainties in sub-assessments. It also takes into account a relationship between individual bridge components and estimates missing assessments using archive data. The algorithm performance has been confirmed by analysing nine different scenarios involving ranking of 10 selected railway bridges on Maintenance, Repair & Rehabilitation (MR&R) list. It has been demonstrated that even a small error in the sub-assessment of the technical condition of one element can result in a significant change in assessment of the bridge technical condition. Considering the relationship between criteria also generates changes in the final assessment of the bridge technical condition and may result in changes in position on MR&R list. This allows to avoid sudden failures, especially with limited financial resources. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Railroad bridges

来源：评论

学校读者我要写书评

暂无评论

Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning 28th

Enhancing LLM Code Generation Using Natural Language Process...

引用

28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Nelson, Jordan Pavlidis, Michalis Fish, Andrew Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031834714

The rapid rise in popularity of Generative AI and Large Language Models (LLMs) has brought both innovation and controversy, particularly regarding plagiarism and IP law infringements. However, one underexplored concern is the generation of code by these models, which, despite their potential, often includes errors and promotes poor programming practices. This paper explores new methods to address these issues by integrating LLMs with Automated Machine Learning (AutoML). By leveraging AutoML’s capabilities in hyperparameter tuning and model selection, we propose a novel approach for generating robust machine learning algorithms. This integration aims to enhance the accuracy and reliability of code generation while mitigating legal risks. Our findings include the application of Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to interpret chatbot prompts, thereby improving the generation and customization of machine learning models. The proposed methodology demonstrates practical implementation and high prediction accuracy, offering a promising solution to the current challenges faced by LLM-based code generation. In summary the findings of the paper are as follows: A new implementation of natural language processing for natural language understanding in the context of chatbot prompts aims to serve as an initial step for feature extraction, which will be utilised by an AutoML system to generate machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：