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Proceedings of the 2023 4th International Conference on Machine Learning and computer Application

作者： Yiming Luo Rui Zhang Fei Wang Tao Wei Business Schoo The University of Sydney Austialia Computer and information sciences program Harrisburg University of Science and Technology United States Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College China School of Chemical and Environmental Engineering Sichuan University of Science & Engineering China

ISBN: (纸本)9798400709449

Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple machine learning methods to classify Australian Retail company BIGW's customer segments and first to apply multiple model explanation methods to find insights related to customer segmentation identification. After rigorous comparison and hyperparameter fine-tuning, XGBoost is the most adept for this dataset. We derive three key insights through the model results and model interpretive methods. First, BIGW's primary clientele comprises young families in urban areas who prefer cost-effective products, establishing the foundation of their consumer base. Second, the model result indicates a notable gap in BIGW's understanding of its high-end customers, suggesting an area for immediate attention. Third, a specific customer segment emerges from the data: individuals favoring online shopping, demonstrating high total expenditure but low interest in promotions, representing a high-value segment.

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Privacy-preserving Biometric-driven Data for Student Identity Management: Challenges and Approaches 21

Privacy-preserving Biometric-driven Data for Student Identit...

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Fidas, Christos Belk, Marios Portugal, David Pitsillides, Andreas University of Patras Department of Electrical and Computer Engineering Greece Cognitive Ux GmbH Germany University of Coimbra Institute of Systems and Robotics Portugal University of Cyprus Department of Computer Science Cyprus

ISBN: (纸本)9781450383677

Biometric technologies are being considered lately for student identity management in Higher Education Institutions, as they provide several advantages over the traditional knowledge-based and token-based authentication methods, i.e., biometrics provide high security entropies, convenience and a sense of technological modernity to the end-users. While biometric technologies have many benefits from both a security and usability point of view, still there is a need for innovative user identity management solutions that continuously identify and authenticate students during academic and teaching activities. In addition, biometrics entail several threats and weaknesses with regards to the privacy of data stored about the user, which negatively affect the user acceptance and the wider adoption of biometrics due to regulatory and legal issues. In this paper, we refer to our ongoing research on intelligent and continuous online student identity management for improving security and trust in European Higher Education Institutions. We further highlight based on the literature, existing challenges, threats and state-of-the-art approaches with regards to preserving the privacy of biometric-driven data. © 2021 Owner/Author.

关键词： Blockchain

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Enhanced Keypoint-Based Approach for Identifying Copy-Move Forgery in Digital Images

Enhanced Keypoint-Based Approach for Identifying Copy-Move F...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Girish Singh Bisht Arpit Jain Vandna Bansla Khemraj Sharma Rudramani Bhutia Vikas Kumar Department of Computer Science & Engineering Tula’s Institute Dehradun India Department of Computer Science & Engineering Koneru Lakshamaiah Education Foundation Vadessahwaram A.P. India Shivalik Engineering College Dehradun India Department of Business Administration Doctoral Program in Management Sciences Nanhua University Dalin Chiayi Taiwan R.O.C. Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation A.P. India School Engineering and Computing Dev Bhoomi Uttrakhand University Dehradun Uttrakhand India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Copy-move forgeries often exploit homogeneous regions in images with large-scale attacks to either highlight or conceal target objects. These manipulations are simple to execute but challenging to notice. Forgery detection techniques like Copy-Move Forgery Detection (CMFD) cannot detect these forged documents because they are unable to identify a sufficient number of effective keypoints in homogeneous areas, leading to inaccurate and inefficient results. SURF stands for Speeded-Up Robust Features and is used in this paper along with A-KAZE and Scale-Invariant Feature Transforms. According to our experiment, A-KAZE offers superior detection performance under diverse attacks, especially when it comes to large-scale attacks targeting homogeneous regions. A-KAZE is found to be more accurate than SIFT, SURF, and A-KAZE when applied to the NB-CASIA dataset, achieving detection accuracies of $\mathbf{8 9. 2 \%}, \mathbf{9 3. 9 \%}$ and $\mathbf{9 8. 9 8 \%}$.)

关键词： Accuracy Digital images Forensics Transforms Forgery Computational efficiency Reliability Informatics Software development management

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Semantically Informed Slang Interpretation

arXiv

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arXiv 2022年

作者： Sun, Zhewei Zemel, Richard Xu, Yang Department of Computer Science University of Toronto Toronto Canada Department of Computer Science Columbia University New York United States Cognitive Science Program University of Toronto Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada

Slang is a predominant form of informal language making flexible and extended use of words that is notoriously hard for natural language processing systems to interpret. Existing approaches to slang interpretation tend to rely on context but ignore semantic extensions common in slang word usage. We propose a semantically informed slang interpretation (SSI) framework that considers jointly the contextual and semantic appropriateness of a candidate interpretation for a query slang. We perform rigorous evaluation on two large-scale online slang dictionaries and show that our approach not only achieves state-of-the-art accuracy for slang interpretation in English, but also does so in zero-shot and few-shot scenarios where training data is sparse. Furthermore, we show how the same framework can be applied to enhancing machine translation of slang from English to other languages. Our work creates opportunities for the automated interpretation and translation of informal language. Copyright © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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PM2: A New Prompting Multi-modal Model Paradigm for Few-shot Medical Image Classification

arXiv

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arXiv 2024年

作者： Wang, Zhenwei Sun, Qiule Zhang, Bingbing Wang, Pengfei Zhang, Jianxin Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education Dalian University of Technology Dalian116024 China School of Computer Science and Technology Dalian University of Technology Dalian116024 China School of Computer Science and Engineering Dalian Minzu University Dalian116600 China School of Information and Communication Engineering Dalian University of Technology Dalian116024 China

Few-shot learning has been successfully applied to medical image classification as only very few medical examples are available for training. Due to the challenging problem of limited number of annotated medical images, image representations should not be solely derived from a single image modality which is insufficient for characterizing concept classes. In this paper, we propose a new prompting multi-modal model paradigm on medical image classification based on multi-modal foundation models, called PM2. Besides image modality, PM2 introduces another supplementary text input, known as prompt, to further describe corresponding image or concept classes and facilitate few-shot learning across diverse modalities. To better explore the potential of prompt engineering, we empirically investigate five distinct prompt schemes under the new paradigm. Furthermore, linear probing in multi-modal models acts as a linear classification head taking as input only class token, which ignores completely merits of rich statistics inherent in high-level visual tokens. Thus, we alternatively perform a linear classification on feature distribution of visual tokens and class token simultaneously. To effectively mine such rich statistics, a global covariance pooling with efficient matrix power normalization is used to aggregate visual tokens. Then we study and combine two classification heads. One is shared for class token of image from vision encoder and prompt representation encoded by text encoder. The other is to classification on feature distribution of visual tokens from vision encoder. Extensive experiments on three medical datasets show that our PM2 significantly outperforms counterparts regardless of prompt schemes and achieves state-of-the-art performance. © 2024, CC BY.

关键词： Classification (of information)

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A Heuristic Approach using Block Chain to Fight Novel COVID-19 during an Election 6

A Heuristic Approach using Block Chain to Fight Novel COVID-...

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6th IEEE International Conference on Innovative Technologies in Intelligent System and Industrial Application, CITISIA 2021

作者： Nath, Ira Biswas, Pratnadeep Ganguly, Kankana Singh, Dharmpal Islam, Sardar M.N. Quang Cuong, Ton Pal, Souvik JIS College of Engineering Computer Science Engineering Kalyani India Victoria University ISILC and Decision Sciences and Modeling Program Australia Vietnam National University Hanoi Viet Nam Global Institute of Management and Technology Computer Science Engineering Krisnanagar India

ISBN: (纸本)9781665417846

India is a highly densely populated country. India is facing a very serious challenge due to the novel COVID-I9 outbreak. In this situation, the economic state of the people is very unstable. Blockchain technology proves helpful to subdue the situation. A voting system with ballot papers is quite tuff now. It is very costly for the government. The pandemic may also spread very quickly. The system of developing ledger serves the idea of developments of a new e-voting system that is economical, easy to use, and highly secure. This paper proposes ways and solutions to design an e-voting system using blockchain technology. The paper's primary goal is to develop a highly secure e-voting technique using which people can cast their valuable votes from their home, working place - anywhere from the world. This e-voting technique is least costly, highly secure, and able to prevent further spreading of any infectious disease in the near future. In this paper, an e-voting system using blockchain has been designed to overcome all these difficulties mentioned above during traditional voting using ballot. The significance of the proposed work is to design of a highly safe and least costly e- voting system using blockchain. Using this system the people can cast their vote easily, securely and without wasting any time. Casting vote will be now just from few clicks away and from any place with a stable internet connection. © 2021 IEEE.

关键词： COVID-19

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PLM-based World Models for Text-based Games

PLM-based World Models for Text-based Games

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Kim, Minsoo Jung, YeonJoon Lee, Dohyeon Hwang, Seung-Won Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Computer Science and Engineering Seoul National University Korea Republic of Department of Artificial Intelligence Yonsei University Korea Republic of

World models have improved the ability of reinforcement learning agents to operate in a sample efficient manner, by being trained to predict plausible changes in the underlying environment. As the core tasks of world models are future prediction and commonsense understanding, our claim is that pre-trained language models (PLMs) already provide a strong base upon which to build world models. Worldformer is a recently proposed world model for text-based game environments, based only partially on PLM and transformers. Our distinction is to fully leverage PLMs as actionable world models in text-based game environments, by reformulating generation as constrained decoding which decomposes actions into verb templates and objects. We show that our model improves future valid action prediction and graph change prediction. Additionally, we show that our model better reflects commonsense than standard PLM. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Improving Out-of-Distribution Generalization in Graphs via Hierarchical Semantic Environments

Improving Out-of-Distribution Generalization in Graphs via H...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yinhua Piao Sangseon Lee Yijingxiu Lu Sun Kim Department of Computer Science and Engineering Seoul National University Institute of Computer Technology Seoul National University AIGENDRUG Co. Ltd. Interdisciplinary Program in Artificial Intelligence Seoul National University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts. Recent methods attempt to enhance graph OOD generalization by generating flat environments. However, such flat environments come with inherent limitations to capture more complex data distributions. Considering the DrugOOD dataset, which contains diverse training environments (e.g., scaffold, size, etc.), flat contexts cannot sufficiently address its high heterogeneity. Thus, a new challenge is posed to generate more seman-tically enriched environments to enhance graph invariant learning for handling distribution shifts. In this paper, we propose a novel approach to generate hierarchical seman-tic environments for each graph. Firstly, given an input graph, we explicitly extract variant subgraphs from the in-put graph to generate proxy predictions on local environ-ments. Then, stochastic attention mechanisms are employed to re-extract the subgraphs for regenerating global environ-ments in a hierarchical manner. In addition, we introduce a new learning objective that guides our model to learn the diversity of environments within the same hierarchy while maintaining consistency across different hierarchies. This approach enables our model to consider the relationships between environments and facilitates robust graph invariant learning. Extensive experiments on real-world graph data have demonstrated the effectiveness of our framework. Par-ticularly, in the challenging dataset DrugOOD, our method achieves up to 1.29% and 2.83% improvement over the best baselines on IC50 and EC50 prediction tasks, respectively.

关键词： Training computer vision Attention mechanisms Computational modeling Semantics Stochastic processes Pattern recognition

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Locality-sensing Fast Neural Network (LFNN): An Efficient Neural Network Acceleration Framework via Locality Sensing for Real-time Videos Queries

Locality-sensing Fast Neural Network (LFNN): An Efficient Ne...

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IEEE International Symposium on Quality Electronic Design

作者： Xiaotian Ma Jiaqi Tang Yu Bai Department of Computer Science California State University Fullerton Fullerton CA USA Computer Engineering Program California State University Fullerton Fullerton CA USA

As deep neural networks have continuously advanced computer version tasks, researchers from academia and industry focus on developing a powerful deep neural network model to process volumes of data. With the increasing size of DNN models, their inference process is computationally expensive and limits the employment of DNNs in real-time *** response, we present the proposed Locality-sensing Fast Neural Network (LFNN), a generalized framework for accelerating the querying videos process via locality sensing to reduce the cost of DNN in video evaluation by three times saving in inference time. The LFNN framework can automatically sense the similarity between two input frames via a defined locality from a given input video. The LFNN framework enables us to process the input videos within the specialized processing method that is far less computationally expensive than conventional DNN inference that conducts detection for each frame. Within the highlighted temporal locality information across frames, the Yolov5 algorithm can be accelerated by two to three times. Experimental results show that the proposed LFNN is easily implemented on the FPGA board with neglectable extra hardware costs.

关键词： Deep learning Costs Computational modeling Neural networks Real-time systems Hardware Software

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