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Polymorphic signal responses of phase-change quantum dot string upon stochastic resonance

Nano Research 2025年第2期18卷 717-724页

作者： Qin Wan Fei Zeng Ziao Lu Junwei Yu Feng Pan Key Laboratory of Advanced Materials(MOE) School of Materials Science and EngineeringTsinghua UniversityBeijing 100084China Center for Brain Inspired Computing Research(CBICR) Tsinghua UniversityBeijing 100084China

We successfully constructed phase-change quantum dots string(PCQDS)systems and studied their signal *** PCQDs actually is a cascaded structure consisted of several stochastic resonance(SR)two-state systems,in which inherent non-linearity,i.e.,phase-change of quantum dots(QDs),plays elementary and important roles to modulate signal *** established an SR model to simulate signal responses depending on stimulation *** know that some QDs will oscillate with input forcing frequency,while certain QDs will oscillate in their own frequency triggered by phase *** two effectscooperate togeneratepolymorphic response patterns,including action potential patterns exhibited by envelope of spike peak *** interesting and important simulation is that we replicate the memory effect in Nb-doped AINO,i.e.,a QDs dispersed *** result indicates that memory can occur in a system only constructed by volatile elementary units,implying memory existing in ***-term plasticity and spike-rate dependent plasticity can also be realized by using frequency and phase *** study provides a new scope to study signal handling and memory effect in quantum system.

关键词： quantum-dot string phase-change inherent nonlinearity signal modulation niobium oxide stochastic resonance

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SYNAuG: Exploiting synthetic data for data imbalance problems

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Pattern Recognition Letters 2025年 193卷 115-121页

作者： Ye-Bin, Moon Hyeon-Woo, Nam Choi, Wonseok Kim, Nayeong Kwak, Suha Oh, Tae-Hyun Department of Electrical Engineering POSTECH Pohang Korea Republic of Grad. School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering POSTECH Pohang Korea Republic of School of Computing KAIST Daejeon Korea Republic of

Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the enormous scale of modern neural networks, this is prohibitively labor-intensive and thus impractical. Inspired by recent developments in generative models, this paper explores the potential of synthetic data to address the data imbalance problem. To be specific, our method, dubbed SYNAuG, leverages synthetic data to equalize the unbalanced distribution of training data. Our experiments demonstrate that although a domain gap between real and synthetic data exists, training with SYNAuG followed by fine-tuning with a few real samples allows us to achieve impressive performance on diverse tasks with different data imbalance issues, surpassing existing task-specific methods for the same purpose. © 2025 Elsevier B.V.

关键词： Data augmentation Fairness Model robustness Synthetic data long-tailed recognition

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Enhancing sarcasm detection through grasshopper optimization with deep learning based sentiment analysis on social media

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1785-1791页

作者： Srinu, Nidamanuri Sivaraman, K. Sriram, M. School of Computing Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai 600073 India

Detecting sarcasm in social media presents challenges in natural language processing (NLP) due to the informal language, contextual complexities, and nuanced expression of sentiment. Integrating sentiment analysis (SA) with sarcasm detection enhances the understanding of text meaning. Deep learning (DL), utilizing neural networks to grasp lexical and contextual features, offers a method for sarcasm detection. However, current DL-based sarcasm detection methods often overlook sentiment semantics, a crucial aspect for improving detection outcomes. Therefore, this study develops a new sarcasm detection using grasshopper optimization algorithm with DL (SD-GOADL) technique. The SD-GOADL technique aims to explore the patterns that exist in social media data and detect sarcasm. To obtain this, the SD-GOADL technique undergoes data pre-processing and Glove based word embedding technique. Next, the classification of sarcasm takes place using deep belief network (DBN) system. For enhancing the detection results of the DBN approach, the SD-GOADL technique uses GOA for hyperparameter selection process. The stimulation outcome of the SD-GOADL technique is tested on a sarcasm dataset and the results highlight the significant performance of the SD-GOADL technique compared to recent models. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Deep learning Grasshopper optimization algorithm Natural language processing Sarcasm detection Sentiment analysis

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On Neutrosophy logic for Multiple Correlation Analysis in Fuzzy Uncertain Database

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Neutrosophic Sets and Systems 2025年 76卷 1-17页

作者： Wazir, Samar Beg, M.M. Sufyan Ahmad, Tanvir Sohail, Shahab Saquib Department of Computer Engineering Jamia Millia Islamia New Delhi India Department of Computer Engineering Aligarh Muslim University Aligarh India School of Computing Science and Engineering VIT Bhopal University MP Sehore466114 India

The primary goal of this paper is to introduce a novel method for mining frequent and interesting items by incorporating correlation analysis between two items in an uncertain transactional database using the OWA operator. The work is further expanded by proposing two additional methods for mining frequent and interesting items, utilizing fuzzy means and the OWA operator along with multiple correlation analysis for more than two items in uncertain transactional databases. The effectiveness of the proposed methods is evaluated by running the algorithms on both standard and example datasets, with results compared to the traditional probabilistic approach for identifying frequent and interesting items through multiple correlation. While multiple correlation analysis highlights the relationships of interestingness and uninterestingness between items, the OWA operator enhances the results when combined with fuzzy means and probabilistic methods. Additionally, the paper suggests a future research direction using Neutrosophy logic, which is anticipated to open new avenues for further exploration in this field. © 2025, University of New Mexico. All rights reserved.

关键词： Database systems

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Bridging Fashion and Technology: Synthetic Human Models for an Enhanced E-Commerce Experience 23rd

Bridging Fashion and Technology: Synthetic Human Models for...

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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Duarte, Ana Rita Conceição, Luís Department of Informatics Engineering School of Engineering Polytechnic of Porto Porto Portugal Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development School of Engineering Polytechnic of Porto Porto Portugal

ISBN: (纸本)9783031734960

The fashion industry is undergoing a significant transformation, driven by advancements in digitalization and artificial intelligence (AI). This paper explores the integration of Stable Diffusion Models (SDMs) and AI to create high-quality images of synthetic models wearing a target cloth in multiple poses, enhancing the users’ experience on e-commerce websites and addressing the fast-paced demands of fashion trends. The proposed pipeline includes multiple steps: face generation, pose estimation, cloth warping, human synthesis, and refinement networks, each designed to enhance the realism and quality of the final images for e-commerce platforms. Experimental results on a small VITON-HD dataset demonstrate this approach’s overall success. BRISQUE, NIQE, and entropy were used for objective evaluation, scoring 14.1449, 4.1354, and 7.1238 respectively, indicating a high level of detail, naturalness, and complexity on the generated images. Future work should focus on enhancing the representation of clothing with complex patterns and lower and full garments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Clothes

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Forecasting Urban Growth: Applying Prophet Model to Landsat NDBI Data for Sustainable City Development 12th

Forecasting Urban Growth: Applying Prophet Model to Landsat ...

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12th International Conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Mohanty, Vidya Panda, Amiya Ranjan Behera, Dayal Kumar Swetanisha, Subhra School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computing Trident Academy of Technology Bhubaneswar India

ISBN: (纸本)9789819601387

Urban growth forecasting is a critical component of sustainable city development, enabling policymakers to make informed decisions about land use and infrastructure planning. This study presents a comparative analysis of two prominent forecasting models, Auto ARIMA and Prophet, applied to time series data derived from the Landsat Normalized Difference Built-up Index (NDBI) for the Bhubaneswar-Cuttack twin city region. The dataset spans from January 1, 2000, to January 1, 2024, providing a comprehensive view of urban expansion over nearly two and a half decades. The Auto ARIMA model, known for its robustness in time series forecasting, was first employed to predict future urban growth patterns. Subsequently, the Prophet model, a decomposable time series model that accommodates seasonal fluctuations was applied to the same dataset. The performance of both models was evaluated based on their prediction accuracy and computational efficiency. Results indicate that the Prophet model outperformed Auto ARIMA in forecasting urban growth in the Bhubaneswar-Cuttack region. The findings serve as a foundation for further exploration into the integration of remote sensing data and predictive modeling for urban planning and management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sustainable city

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A survey on textual emotion cause extraction in social networks

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Digital Communications and Networks 2025年第2期11卷 524-536页

作者： Sancheng Peng Lihong Cao Guojun Wang Zhouhao Ouyang Yongmei Zhou Shui Yu Laboratory of Language Engineering and Computing Center for Linguistics and Applied Linguistics Guangdong University of Foreign Studies School of English Education Guangdong University of Foreign Studies School of Computer Science and Cyber Engineering Guangzhou University South China University of Technology School of Information Science and Technology Guangdong University of Foreign Studies School of Computer Science University of Technology Sydney

With the rapid development of web technology, Social Networks(SNs) have become one of the most popular platforms for users to exchange views and to express their emotions. More and more people are used to commenting on a certain hot spot in SNs, resulting in a large amount of texts containing emotions. Textual Emotion Cause Extraction(TECE) aims to automatically extract causes for a certain emotion in texts, which is an important research issue in natural language processing. It is different from the previous tasks of emotion recognition and emotion classification. In addition, it is not limited to the shallow-level emotion classification of text, but to trace the emotion source. In this paper, we provide a survey for TECE. First, we introduce the development process and classification of TECE. Then, we discuss the existing methods and key factors for TECE. Finally, we enumerate the challenges and developing trend for TECE.

关键词： Text Emotion Emotion cause Machine learning Deep learning

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Comprehensive Web Analysis of Automobile Websites: Performance, SEO, and Accessibility Insights

Comprehensive Web Analysis of Automobile Websites: Performan...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Busetty, Abhishek Balam, Ruchith Chillakuru, Hari Radha, R. Saranya Devi, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331512965

The current research work proposes a difficultydriven comparison of three most visited automobile websites namely the Automobile Site1, Automobile Site2, Automobile Site3 on parameters including performance, Search engine optimization (SEO), accessibility and traffic. The research involves tool such as Yellow Labs, Google Page Speed Insight and Semrush with regard to the website's weight, loading time, user's interactions and search engine optimization. The findings reveal distinct strengths across the sites: From the three Automobile Site1 is the most optimized and accessibly friendly site, the factors of search engine friendliness is as follows. Looking at the traffic and users of Automobile Site2, it comfortably outclasses the other sites in terms of page views as well as the audience loyalty. At the same time, Automobile Site3 has relatively low first load time which indicates fast website browsing while it is still relatively weak in terms of its SEO and the usability of the site has to be improved in terms of theoretical potential. In conclusion, the realized comparison exposes the strengths and weaknesses of each site and is informative to future developments of the sites with an aim at better user interaction levels and competitive advantage. © 2025 IEEE.

关键词： Automobiles

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Radius-Based Authentication: UFrames’ Novel Approach to Prevent Account Sharing 5th

Radius-Based Authentication: UFrames’ Novel Approach to Pre...

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5th International Conference on computing and Network Communications, CoCoNet 2023

作者： Vardhan, D. Sai Tanmayi, Doddapaneni Rakesh, Kakaraparthi Sreevidya, B. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819745395

OTT platforms such as Netflix and Amazon Prime have been in the market for more than a decade. They have come to full-fledged usage especially during the lockdown. The main reason for their magnanimous success is ease of use and feasibility to access unlimited content anywhere and anytime with basic Internet connection. The features in OTT have made accessing the content and viewing the content easy and delightful for the users. However, the "sharing account" option enabled for a single user to access the content from multiple devices is instead being used to share one account among multiple users, negatively affecting the business of OTT creators. In this paper, the focus is to build an OTT platform that proves advantageous to both users and providers. Besides the prevailing features that are found across platforms, including sign-in and sign-up functionalities, categorized content selection, and payment options, the proposed system enables automatic sign-out when the non-primary device strays beyond range from the primary device, content uploading, and more. Furthermore, there is a proposal to implement a GPS technology-based mechanism aimed at preventing users from sharing their accounts with others. The proposed approach conceptually and experimentally proves to be beneficial to the OTT platforms from a business perspective. The scheme is able to prevent users from sharing the account beyond a geographical limit, thus improving the profit. At the same time, these stringent requirements are developed in such a way that a balance is made between user experience, ease of use experience along with improved security measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Global positioning system

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