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arXiv 2024年

作者： Neagu, Andrei Godin, Frédéric Simard, Clarence Kosseim, Leila Concordia University Department of Computer Science and Software Engineering Montreal Canada Concordia University Department of Mathematics & Statistics Montreal Canada Université du Québec à Montréal Département de Mathématiques Montreal Canada

Dynamic hedging is the practice of periodically transacting financial instruments to offset the risk caused by an investment or a liability. Dynamic hedging optimization can be framed as a sequential decision problem;thus, Reinforcement Learning (RL) models were recently proposed to tackle this task. However, existing RL works for hedging do not consider market impact caused by the finite liquidity of traded instruments. Integrating such feature can be crucial to achieve optimal performance when hedging options on stocks with limited liquidity. In this paper, we propose a novel general market impact dynamic hedging model based on Deep Reinforcement Learning (DRL) that considers several realistic features such as convex market impacts, and impact persistence through time. The optimal policy obtained from the DRL model is analysed using several option hedging simulations and compared to commonly used procedures such as delta hedging. Results show our DRL model behaves better in contexts of low liquidity by, among others: 1) learning the extent to which portfolio rebalancing actions should be dampened or delayed to avoid high costs, 2) factoring in the impact of features not considered by conventional approaches, such as previous hedging errors through the portfolio value, and the underlying asset's drift (i.e. the magnitude of its expected return). © 2024, CC BY.

关键词： Reinforcement learning

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PATR: Periodicity-Aware Trajectory Recovery for Express System via Seq2Seq Model

PATR: Periodicity-Aware Trajectory Recovery for Express Syst...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Xinrui Zhang Xingyuan Liang Hai Wang Shuai Wang Tian He College of Software Engineering Southeast University School of Computer Science and Engineering Southeast University Department of Technology and Data Intelligence JD Logistics

ISBN: (纸本)9781665435413

The analysis of express trajectory is essential for delivery service optimization and courier management. Express trajectory consists of a large amount of community paths which is different from the trajectory of transportation tools such as taxis which run in the main road of a city. Due to the complexity of the community road networks and the energy constraint of devices, trajectories are collected under a low sampling rate and accuracy. These trajectories need to be recovered and mapped to the road networks for further analysis. Current recovery approaches utilize two-stage or end-to-end models to generate the upsampled trajectories. These methods fail to fully exploit the periodicity relation in historical data, which leads to accuracy losses for complex community road networks. In this paper, we design PATR, a periodicity-aware Seq2Seq model, to recover the couriers' trajectories by leveraging the periodical patterns of the historical data. We conduct experimental evaluations based on real-world GPS data of JD logistics consisting of 836,959 points and 25,726 road segments, and our model outperforms the state-of-the-art baselines by reducing recovery error by 2.4%-21.9%.

关键词： Roads Urban areas Data models Trajectory Complexity theory Global communication Public transportation

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Intelligent larval zebrafish phenotype recognition via attention mechanism for high-throughput screening

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computers in Biology and Medicine 2025年 188卷 109892-109892页

作者： Wang, Baihua Sun, Qi Liu, Yujia Zhang, Jiheng Li, Gaozheng Wu, Sifang Zheng, Houbing Ye, Jialin Zhou, Meihua Zheng, Haisu Yu, Yongqiang Zhong, Yi Wu, Yuanzi Huang, Da Wang, Biao Weng, Zuquan College of Biological Science and Engineering Fuzhou University Fujian Fuzhou China Department of Plastic and Cosmetic Surgery The First Affiliated Hospital of Fujian Medical University Fujian Fuzhou China The Center for Big Data Research in Burns and Trauma College of Computer and Data Science/College of Software Fuzhou University Fujian China

Background: Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects is time-consuming and requires specialized knowledge. Method: We proposed a deep network model called RECNet, which combines attention mechanisms and residual structures. In terms of data processing, we applied the mixup data augmentation technique and accumulated a collection of 6805 larval zebrafish phenotype images, mostly generated from our laboratory. Our proposed model was deployed to execute two distinct tasks, including a four-classification of zebrafish phenotypes and a seven-classification involving mixed labels for abnormalities. Results: In the four-class classification task, the RECNet model achieved an accuracy of 0.949, with a mean area under the curve of 0.986 and an F1-score of 0.966. Through interpretable research, attention mechanisms enable the model to focus more accurately on regions of interest. In the mixed-label seven-classification task for anomalies, our model achieved an accuracy of 0.913 and a mean average precision value of 0.847 by employing the weighted loss function (DFBLoss). Furthermore, in a new test dataset, the RECNet model achieved accuracy rates of 0.924 and 0.876 for the two tasks, respectively. Our RECNet model was trained by orders of magnitude larger dataset than previous studies and also showed better accuracy rates. Conclusions: Our method holds promise for diverse applications within zebrafish laboratories and fields such as toxicology, providing indispensable support to scientific research. © 2025 Elsevier Ltd

关键词： Invertebrates

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Imbalanced Nodes Classification for Graph Neural Networks Based on Valuable Sample Mining 6

Imbalanced Nodes Classification for Graph Neural Networks Ba...

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6th International Conference on Electronic Information Technology and computer engineering, EITCE 2022

作者： Liu, Min Jin, Siwen Jin, Luo Wang, Shuohan Fang, Yu Shi, Yuliang School of Software Beijing University of Technology Beijing China School of Electronic Information Engineering Sias University Henan province Zhengzhou China Computer Department Beijing Technology and Business University Beijing China School of Mechanical and Marine Engineering Beibu Gulf University Guangxi Zhuang Autonomous Region Qinzhou City China Department of Computer Science Wenzhou-Kean University Zhejiang Province Wenzhou China

ISBN: (纸本)9781450397148

Node classification is an important task in graph neural networks, but most existing studies assume that samples from different classes are balanced. However, the class imbalance problem is widespread and can seriously affect the model's performance. Reducing the adverse effects of imbalanced datasets on model training is crucial to improve the model's performance. Therefore, a new loss function FD-Loss is reconstructed based on the traditional algorithm-level approach to the imbalance problem. Firstly, we propose sample mismeasurement distance to filter edge-hard samples and simple samples based on the distribution. Then, the weight coefficients are defined based on the mismeasurement distance and used in the loss function weighting term, so that the loss function focuses only on valuable samples. Experiments on several benchmarks demonstrate that our loss function can effectively solve the sample node imbalance problem and improve the classification accuracy by 4% compared to existing methods in the node classification task. © 2022 Association for Computing Machinery.

关键词： Classification (of information)

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Towards Autonomous Multi-UAVs Surveillance Mission: A Study of Nigerian Telecommunication Masts Surveillance 1

Towards Autonomous Multi-UAVs Surveillance Mission: A Study ...

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1st International Conference on Multidisciplinary engineering and Applied science, ICMEAS 2021

作者： Yusuf, Sagir M. Abdullahi, Ibrahim Bappi, Adamu Aliyu, Aliyu Modi, Bala Ibrahim, Usman Y. Gombe State University Department of Computer Science Gombe State Nigeria Software Engineering and It Nile University of Nigeria Abuja Nigeria Department of Computer Science Gombe State Nigeria Cyber Technology Institute De Montfort University Leicester United Kingdom

ISBN: (纸本)9781665434935

This paper proposes a multi-drone surveillance coordination algorithm that controls the random behaviour of naturally-inspired strategies. The main drawback of naturally inspired strategies, such as Levy flight, is the agents' poor coordination and redundant search (continuous exploration of single location within minimum time and space). The redundant search was removed by assigning probability values to cells and subjecting agents' waypoints acceptance to distance and time threshold. The proposed algorithm ensures broad coverage, minimised redundant search, scalability, and adaptable swarm coordination. The benefits of the algorithm were evaluated on the simulation of the Nigerian telecommunication masts surveillance mission using a proposed architecture. Future work focuses attention on the implementation of algorithms on real drones and sensor information organisations. © 2021 IEEE.

关键词： Drones

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AI-Based Child Care Parental Control System

AI-Based Child Care Parental Control System

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Advancements in Computing (ICAC), International Conference on

作者： Udara Jayasekara Hansindu Maniyangama Kalhan Vithana Tharana Weerasinghe Janaka Wijekoon Rrubaa Panchendrarajan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.

关键词： Pediatrics Social networking (online) Multimedia Web sites Process control Control systems Real-time systems Mobile applications

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Audio Based Machine Fault Diagnosis using Hybrid Feature Extraction and Ensemble Learning

Audio Based Machine Fault Diagnosis using Hybrid Feature Ext...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Shashvat Singhal Bhavya Goel Kshitij Agrawal Rithwick Sethi Shashi Sah Rachit Jain Dinesh K Vishwakarma Department of Computer Science Delhi Technological University Delhi India Department of Mechanical Engineering Delhi Technological University Delhi India Department of ECE Delhi Technological University Delhi India Department of Software Engineering Delhi Technological University Delhi India Department of Information Technology Delhi Technological University Delhi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Over the last decade, proliferation of mechanical machines has surged exponentially, amplifying the challenge of monitoring their operational health due to the inevitability of wear and tear. Consequently, the conventional human centric approach to tracking machine conditions has become impractical. This underscores the demand for an innovative and efficient strategy. This research involves machine generated audio signals to detect and analyze faults or irregularities. Analyzing the acoustic patterns generated during machine operation makes detecting abnormal sounds associated with specific faults possible. This paper examines various techniques and algorithms for achieving this goal and compares them to recommend the most effective method. Initially, Short Time Fourier transform was performed on the preprocessed audio data. Then, various techniques to perform feature extraction on the transformed audio, such as spectral kurtosis, spectral centroid, zero crossing rate, and Mel spectrogram. Subsequently, the aggregate feature vector was input into various algorithms, including XGBoost, Support Vector Classifier, Random Forest Classifier and Ensemble Learning. The MIMII dataset was trained on these models. The evaluation showed that XGBoost Classifier performed the best as compared to the other models, achieving a total accuracy rate of 98 percent upon incorporating features extraction Melspectrogram, Spectral Kurtosis and Spectral Centroid.

关键词： Fault diagnosis Accuracy Feature extraction Vectors Kurtosis Classification algorithms Ensemble learning Sustainable development Spectrogram Monitoring

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Evolution of Research on Adaptive Gamification: A Bibliometric Study

Evolution of Research on Adaptive Gamification: A Bibliometr...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Margret Vijay J. P. Jayan Department of Computer Science Noorul Islam Centre for Higher Education (NICHE) Tamil Nadu India Department of Software Engineering Noorul Islam Centre for Higher Education (NICHE) Tamil Nadu India

This study explores the evolving landscape of adaptive gamification-related research from 2014 to 2022. A comprehensive analysis of 1110 documents from journals and books was conducted to investigate publication trends, prominent countries, authors, and the development of knowledge in the field. The findings reveal a consistent growth in publications, with an annual growth rate of 26.89%. Gamification emerges as a dominant cluster, consistently exhibiting high centrality and density scores, indicating its strong interconnectedness and influence within the research network. The country-wise analysis demonstrates a diverse global research landscape, with the United States contributing the highest number of publications, followed by Spain and Germany. Prominent authors from universities and institutions worldwide have made significant contributions to the field. The thematic evolution demonstrates a shift from collaborative learning to gamification and online learning, followed by a focus on technology-driven themes and the emergence of machine learning as a prominent area of research. The increasing relevance of machine learning indicates its growing significance in the domain of adaptive gamification. These findings contribute to the knowledge base of adaptive gamification and offer insights for researchers in shaping the future of gamified learning environments.

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Mining software Repository for Cleaning Bugs Using Data Mining Technique

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computers, Materials & Continua 2021年第10期69卷 873-893页

作者： Nasir Mahmood Yaser Hafeez Khalid Iqbal Shariq Hussain Muhammad Aqib Muhammad Jamal Oh-Young Song University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Computer Science COMSATS University IslamabadAttock CampusAttock43600Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Mathematics and Statistics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindi46000Pakistan Department of Software Sejong UniversitySeoul05006Korea

Despite advances in technological complexity and efforts,software repository maintenance requires reusing the data to reduce the effort and ***,increasing ambiguity,irrelevance,and bugs while extracting similar data during software development generate a large amount of data from those data that reside in ***,there is a need for a repository mining technique for relevant and bug-free data *** paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality *** predict errors in mining data,the Apriori algorithm was used to discover association rules by fixing confidence at more than 40%and support at least 30%.The pruning strategy was adopted based on evaluation ***,the rules were extracted from three projects of different domains;the extracted rules were then combined to obtain the most popular rules based on the evaluation measure *** evaluate the proposed approach,we conducted an experimental study to compare the proposed rules with existing ones using four different industrial *** evaluation showed that the results of our proposal are *** and developers can utilize these rules for defect prediction during early software development.

关键词： Fault prediction association rule data mining frequent pattern mining

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SMS Spam Filtering Using Machine Learning Technique 3rd

SMS Spam Filtering Using Machine Learning Technique

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3rd International Conference on Communications and Cyber Physical engineering, ICCCE 2020

作者： Vishwakarma, Arvind Kumar Ansari, Mohd Dilshad Rai, Gaurav ZEBEC Software Technologies Private Limited DehradunUttrakhand India Department of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad India Gurukula Kangri Vishwavidyalaya Haridwar India

ISBN: (纸本)9789811579608

Sending and receiving SMS is very ordinary thing for any individual’s daily life. But when at the moment, we receive undesirable SMS frequently that waste our time and money as well and consequently this moment gives us unpleasant feeling. If undesirable messages are to be sent to a huge volume of recipients erratically have resulted in displeasure by consumers but gives large profit to spammers. There are lots of reasons like high internet speed, very cheap smart phones and user friendly interface of mobile web and mobile applications that attracts a huge volume of mobile phone users. These are the key factors expected to shape the future of the market. This paper focuses on SMS Spam filtering techniques and compared their performance. We compared the machine learning model’s performance and finally result indicates that the Logistic Regression model performed well with accuracy reaching up to 96.59%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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