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A Review on Rainfall Prediction Using Neural Networks 1

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Mandal, Sudipta Biswas, Saroj Kumar Purayastha, Biswajit Chakraborty, Manomita Kundu, Saranagata Computer Science and Engineering National Institute of Technology Assam Silchar India Computer Science and Engineering VIT-AP Campus Andhra Pradesh Amaravati India Computer Science and Engineering The University of Burdwan West Bengal Bardhaman India

ISBN: (数字)9789819900473

ISBN: (纸本)9789819900466

Rainfall prediction is a significant and demanding task in today's world. Due to the nonlinearity and complication, rainfall and climate require advanced computer modeling and simulation to predict accurately. This paper comes up with a comprehensive survey and a fair comparison of several types of neural network architectures for rainfall prediction and mentions the challenges of using traditional and advanced methods in doing this task. Finally, the paper discusses the utility of deep learning methods for rainfall forecasting. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Rain

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BootsTAP: Bootstrapped Training for Tracking-Any-Point 17th

BootsTAP: Bootstrapped Training for Tracking-Any-Point

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17th Asian Conference on computer Vision, ACCV 2024

作者： Doersch, Carl Luc, Pauline Yang, Yi Gokay, Dilara Koppula, Skanda Gupta, Ankush Heyward, Joseph Rocco, Ignacio Goroshin, Ross Carreira, João Zisserman, Andrew Google DeepMind London United Kingdom VGG Department of Engineering Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9789819609000

To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes. This can be formalized as Tracking-Any-Point (TAP), which requires the algorithm to track any point on solid surfaces in a video, potentially densely in space and time. Large-scale ground-truth training data for TAP is only available in simulation, which currently has a limited variety of objects and motion. In this work, we demonstrate how large-scale, unlabeled, uncurated real-world data can improve a TAP model with minimal architectural changes, using a self-supervised student-teacher setup. We demonstrate state-of-the-art performance on the TAP-Vid benchmark surpassing previous results by a wide margin: for example, TAP-Vid-DAVIS performance improves from 61.3% to 67.4%, and TAP-Vid-Kinetics from 57.2% to 62.5%. For visualizations, see our project webpage at https://***/ © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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A Graph Structure-Feature Learning Network for Diagnosing Alzheimer’s Disease Based on Multi-modal Brain Biometric Feature 18th

A Graph Structure-Feature Learning Network for Diagnosing A...

引用

18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Shang, Dongxu Wang, Huabin Zhang, Mengxin Peng, Yuhang Ye, Xingjian Wang, Zilin Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui Hefei China Stony Brook Institute at Anhui University Anhui Hefei China

ISBN: (纸本)9789819610709

Alzheimer’s Disease (AD) is a neurodegenerative disease, typically identified and diagnosed through brain biometric feature. However, certain common brain biometric feature exist among patients in different stages of AD, which result in incorrect edges within the graph structure. To address above issues, this paper proposes a Graph Structure-Feature Learning Network (GSFLN) model. The model simultaneously learns a graph structure that appropriately represents the relationships among patients and selects the significant pathogenic brain biometric feature of patients. Firstly, a Graph Convolutional Clustering (GCC) module is designed to capture the relationships between non-image data of patients. Secondly, a Pathogenic Feature Selection (PFS) module is designed to capture the relationships between image data of patients and select the pathogenic brain biometric feature. Thirdly, an Unsupervised Predictive Clustering (UPC) module is designed to control the above two modules. Our proposed model has surpassed the current state-of-the-art level in diagnostic performance on both the NACC and Tadpole datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s Disease brain biometric feature Feature Selection Graph Convolutional Network Graph Structure Learning

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From Caesar Shifts to Kid-Enigma. The CS Unplugged-Like Path in the MuMa science Centre 7th

From Caesar Shifts to Kid-Enigma. The CS Unplugged-Like Pat...

引用

7th International Conference on Creative Mathematical sciences Communication, CMSC 2024

作者： Ren, Michal Perekietka, Pawel Nitschke, Lukasz Adam Mickiewicz University Wieniawskiego 1 Poznań61-712 Poland Fundacja Zaklady Kórnickie Flensa 2b Kórnik62–035 Poland Kórnik Poland

ISBN: (纸本)9783031732560

We present a proposal for a learning path for children to teach them cryptography during visits to the MuMa science Centre. As the goal of the Math Museum (MuMa) for which the track is being developed is to make youngsters enthusiastic about mathematics and computer science, we have decided to focus on cryptography. In our past experience this proved to be very effective – children love secrets and spy games. We will prepare a set of CS-unplugged-like activities which will cover the broad range from the simplest, historical ciphers up to the Enigma cipher. In contrast to previously described CS-unplugged activities, we will focus on methods of breaking those ciphers, not on merely using them. Breaking of Enigma deserves attention thanks both to clever use of pure mathematics, but also due to its historical significance in ending WWII. In this paper we will present ideas for activities to teach cryptanalysis of the simplest ciphers, starting from Caesar and Vigenère ciphers, as well as the design for a simple, paper teaching aid that simulates the simplified Enigma to show its properties. We share pertinent feedback we have received after several presentations we had already made to small groups of children and adults. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： cipher cryptanalysis cryptography CS Unplugged Enigma science centre

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CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in Healthcare 16th

CollectiveSFT: Scaling Large Language Models for Chinese Me...

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16th International Conference on Social Robotics, ICSR + InnoBiz 2024

作者： Zhu, Jingwei Tan, Minghuan Yang, Min Li, Ruixue Alinejad-Rokny, Hamid University of Science and Technology of China Hefei China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Xiangshui County Party School Yancheng China School of Computer Science and Engineering The University of New South Wales Sydney Australia

ISBN: (纸本)9789819611508

The rapid progress in Large Language Models (LLMs) has prompted the creation of numerous benchmarks to evaluate their capabilities. This study focuses on the Comprehensive Medical Benchmark in Chinese (CMB) [25], showcasing how dataset diversity and distribution in supervised fine-tuning (SFT) may enhance LLM performance. Remarkably, We successfully trained a smaller base model to achieve scores comparable to larger models, indicating that a diverse and well-distributed dataset can optimize performance regardless of model size. This study suggests that even smaller models may reach high performance levels with carefully curated and varied datasets. By integrating a wide range of instructional content, our approach addresses potential issues such as data quality inconsistencies. Our results imply that a broader spectrum of training data may enhance a model’s ability to generalize and perform effectively across different medical scenarios, highlighting the importance of dataset quality and diversity in fine-tuning processes (https://***/CAS-SIAT-XinHai/CollectiveSFT). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Benchmarking Performance Dataset Diversity Fine-Tuning Methodologies Large Language Models Medical Applications

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Offensive Tweets Detection in Hinglish Using HingBERT 1

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Jain, Lakshit Sharma, Moolchand Abdulsada, Zainab R. Department of Computer Science and Engineering MAIT GGSIPU Delhi India National University of Science and Technology Thi-Qar Iraq

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

Social media is the perfect platform for people to interact, share news, and express their own thoughts. Unfortunately, there is a lot of negativity in it, including hate and abusive speech. People in multilingual nations like India frequently blend two or more languages to create hybrids. Hinglish is an example of a hybrid language that combines Hindi and English. This research concentrates on identifying offensive Hinglish-language tweets. Fine-tuning on HingBERT, HingMBERT, and HingRoBERTa has been done to improve upon the existing results. With a 0.95 F1-score, HingRoBERTa achieved benchmark results, while HingMBERT and HingBERT scored 0.93 and 0.93, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： HingBERT Hinglish tweets HingMBERT HingRoBERTa HOT Offensive speech

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Data-Driven Genetic Algorithm for the Optimization of Water Distribution Networks: A New Surrogate Model for Estimating Investment and Operational Costs in Pumping Stations 27th

Data-Driven Genetic Algorithm for the Optimization of Water ...

引用

27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Gajardo-Sepúlveda, Nicolás Faúndez-Lizama, Thalía Gutiérrez-Bahamondes, Jimmy H. Mora-Melia, Daniel Astudillo, César A. Master’s in Operations Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó3340000 Chile Department of Engineering and Construction Management Faculty of Engineering Universidad de Talca Curicó3340000 Chile

ISBN: (纸本)9783031766039

The increase in water consumption and demand has highlighted the need to improve the efficiency of water distribution networks (WDNs). Pumping stations (PS) represent a significant challenge due to their high energy consumption and associated operational costs. This study presents a data-driven evolutionary optimization methodology focused on predicting cost and penalties in the design and operation of PS. The methodology integrates the functioning of genetic algorithms with machine learning techniques, developing a surrogate model capable of predicting associated costs and increasing the computational efficiency of optimization models. A case study is presented to validate the methodology, showing a significant reduction of 31.8% in evaluation times and a decrease in optimization time compared to traditional models. The results indicated that despite the reduction in computational effort, the increase in the final cost of the network was minimal, with only a 2.42% increase compared to the baseline model. These findings underscore the effectiveness of combining machine learning with genetic algorithms for the optimization of PS in WDNs, improving computational efficiency while maintaining high standards in solution quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Educational Effect of Molecular Dynamics Simulation in a Smartphone Virtual Reality System 1

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10th International Conference on Learning and Collaboration Technologies (LCT) Held as Part of the 25th International Conference on Human-computer Interaction (HCII)

作者： Matsuda, Kenroh Kikkawa, Nobuaki Kajita, Seiji Sato, Sota Tanikawa, Tomohiro Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi Japan Univ Tokyo Dept Appl Chem 6 Chome 6-2 Kashiwanoha Kashiwa Chiba Japan Univ Tokyo Next Generat Artificial Intelligence Res Ctr 7-3-1 HongoBunkyo Ku Tokyo Japan

ISBN: (数字)9783031345500

ISBN: (纸本)9783031345494;9783031345500

Students have difficulty understanding complex molecular structures and chemical bonds in a two-dimensional media such as textbooks and writing on the blackboard. Teachers also use molecular models or viewers to teach, but the cost of using them is not negligible. To easily share the molecular images of experts and enhance the understanding and motivation of novices, we have developed a prototype of a standalone smartphone virtual reality (VR) interface with a molecular dynamics (MD) simulator for chemical education, called VR-MD. In this application, users can touch, and move molecules whose coordinates are updated in real time by the MD engine. For teachability and safety, we adopted handheld VR glasses and a VR/augmented reality (AR) mode changer responsive in the state of the user's hand. As a demonstration experiment, we conducted on-site lectures at a high school with one of these applications was provided for each student. A 7-point scale and a free-response questionnaire were administered following the lecture. The results confirmed the effectiveness of the program for improving comprehension and the motivation to learn.

关键词： Chemistry lecture Smartphone VR Molecular Dynamics Calculations

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Artificial Intelligence-Guided Fully-Automatic Renal Segmentation 21st

Artificial Intelligence-Guided Fully-Automatic Renal Segment...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Huang, Haijun Tian, Teng Zhao, Jing Gu, Yidong Jiao, Ruwang Peng, Tao School of Computer Engineering Jiangsu University of Technology Changzhou China School of Future Science and Engineering Soochow University Suzhou China Department of Ultrasound Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital Beijing China Department of Medical Ultrasound The Affiliated Suzhou Hospital of Nanjing Medical University Suzhou Municipal Hospital Jiangsu Suzhou China

ISBN: (纸本)9789819601219

Developing accurate boundary extraction methods for ultrasound images of the kidney is a significant challenge on account of the lack of boundaries or blurred boundaries. This study puts forward a method from rough to fine from four aspects of innovation. First, we use the characteristics of a principal curve (PC) to adjust the generated boundary outline automatically as well as utilize a neural net to cut down the errors generated by the model. Second, an initial boundary extraction phase is performed using a deep parallel fusion training network, with a parallel structure to boost the segmentation achievement of deep learning. Third, we designed an automatic system for extracting the polygonal boundary, which used a mean-shift cluster-based method to substitute for the traditional PC-based methodologies consisting of the projection and vertex optimization procedure. Fourth, an explicable mathematic mapping function of the renal outline was developed, which is expressed via parameters of the neural network. Finally, we carried out several experiments to demonstrate the performance of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ultrasonic imaging

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