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8th Symposium on Working Formal Methods, FROM 2024

作者： Trufas, Dafina LOS Faculty of Mathematics and Computer Science University of Bucharest Institute for Logic and Data Science Bucharest Romania

In this paper we present a formalization of Intuitionistic Propositional Logic in the Lean proof assistant. Our approach focuses on verifying two completeness proofs for the studied logical system, as well as exploring the relation between the two analyzed semantical paradigms - Kripke and algebraic. In addition, we prove a large number of theorems and derived deduction rules. © Dafina Trufas.

关键词： computer circuits

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Customer Segmentation Based on Recency, Frequency, Monetary, Variety and Duration (RFMVD) 2

Customer Segmentation Based on Recency, Frequency, Monetary,...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Sundari, Agus Lydia, Maya Silvi Muchtar, Muhammad Anggia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Department of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

In banking, maintaining customer retention and customer satisfaction are important. Effective customer segmentation can be a strategic tool to improve customer loyalty and business performance. This research can assist banks in developing marketing strategies to retain customers and improve services based on bill payment transactions. The data that will be used in this study is bill payment transaction data at regional banks in 2023, as for the types of bill payments made by customers, namely electricity, water, telephone payments, and internet bills. The amount of data used is 702,174 rows. This research uses K-Means clustering to group customers based on recency (the last time the customer made a transaction), frequency (the number of customers making transactions), monetary (the nominal amount of money spent by the customer), variety (the number of types of bills paid) and duration (the average duration of time the customer makes the previous transaction until the next transaction) (RFMVD). The results of customer segmentation based on recency, frequency, monetary, variety and duration using K-Means clustering produce 3 customer groups, namely passive customers, loyal customers, and VIP customers. The silhouette score value on customer segmentation using K-Means clustering is 0.7303, which indicates that the grouping is quite good and the number of clusters is correct. © 2024 IEEE.

关键词： bill payments Customer segmentation K-Means clustering RFMVD Model silhouette score

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Efficient Image Captioning Based on Vision Transformer Models

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computers, Materials & Continua 2022年第10期73卷 1483-1500页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computer and Information ScienceMansouraEgypt

Image captioning is an emerging field in machine *** refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an *** captioning requires a complex machine learning process as it involves two sub models:a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful ***-based vision transformers models have a great impact in vision field *** this paper,we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO(self-distillation with no labels).The second is PVT(Pyramid Vision Transformer)which is a vision transformer that is not using convolutional *** third is XCIT(cross-Covariance Image Transformer)which changes the operation in self-attention by focusing on feature dimension instead of token *** last one is SWIN(Shifted windows),it is a vision transformer which,unlike the other transformers,uses shifted-window in splitting the *** a deeper evaluation,the four mentioned vision transformers have been tested with their different versions and different configuration,we evaluate the use of DINO model with five different backbones,PVT with two versions:PVT_v1and PVT_v2,one model of XCIT,SWIN *** results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.

关键词： Image captioning sequence-to-sequence self-distillation transformer convolutional layer

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Defense Against Graph Injection Attack in Graph Neural Networks 9

Defense Against Graph Injection Attack in Graph Neural Netwo...

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9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Tong, Hanjin Zhu, Tianqing Guan, Faqian Zhou, Wanlei China University of Geosciences School of Computer Science China University of Macau Faculty of Data Science City China

ISBN: (纸本)9798350391367

Graph neural networks (GNNs) have shown outstanding performance in graph node classification. However, as a deep learning model, GNNs can be influence by adversarial attacks, such as graph injection attacks or graph modification attacks, which modify edges or node features on the original graph. This paper focus on Graph Injection Attacks (GIA), which adds a small number of nodes and edges to the original graph to change the prediction results. GIA has stronger attack potential and it can cause more damage to the homogeneity of graphs. To tackle this problem, this paper proposes a novel defense strategy. We observe that real graphs are normally sparse, so that a link prediction model may be adopted to tell reliable edges from adversarial edges. By increasing the interaction information of edges, and reducing the sensitivity of vulnerable nodes to adversarial edges, the proposed method can increase the prediction acccuracy. Meanwhile, we designe a homogeneous filtering to help to identify adversarial edges, reducing the interference of the adversarial to the model. Experiments show our method has better defense performance than other baseline defense methods. © 2024 IEEE.

关键词： Adversarial machine learning

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Toward Improved Knowledge Retention: A Template for Describing data science Projects

Toward Improved Knowledge Retention: A Template for Describi...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Haertel, Christian Staegemann, Daniel Pohl, Matthias Daase, Christian Turowski, Klaus Otto-von-Guericke-University Faculty of Computer Science Magdeburg Germany Institute of Data Science Jena Germany

ISBN: (纸本)9798350362480

data science (DS) aims to extract knowledge from large amounts of data. Organizations can use the retrieved insights to achieve various performance improvements. However, DS projects often fail to fulfill their objectives due to the explorative nature of this discipline and technical as well as managerial challenges. Consequently, new approaches to support DS project execution are sought after. A viable contribution in this regard is improving knowledge retention in DS to predict socio-technical obstacles of an undertaking and derive best practices. Therefore, in this work, a template for describing the central characteristics of DS projects is proposed using a Design science Research approach. The artifact is structured based on the common DS project stages and features 32 fields, enabling comparability and transparency in DS. The applicability of the template is demonstrated based on three DS use cases from the literature. While further steps for evaluation are pending, the template can serve as a foundation for developing a categorization model for DS projects in the future. © 2024 IEEE.

关键词： data science

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Efficient Evaluation Methods for Neural Architecture Search: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5990-6011页

作者： Song, Xiaotian Xie, Xiangning Lv, Zeqiong Yen, Gary G. Ding, Weiping Lv, Jiancheng Sun, Yanan Sichuan University College of Computer Science Chengdu610065 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

Neural architecture search (NAS) has received increasing attention because of its exceptional merits in automating the design of deep neural network (DNN) architectures. However, the performance evaluation process, as a key part of NAS, often requires training a large number of DNNs. This inevitably makes NAS computationally expensive. In past years, many efficient evaluation methods (EEMs) have been proposed to address this critical issue. In this article, we comprehensively survey these EEMs published up to date, and provide a detailed analysis to motivate the further development of this research direction. Specifically, we divide the existing EEMs into four categories based on the number of DNNs trained for constructing these EEMs. The categorization can reflect the degree of efficiency in principle, which can in turn help quickly grasp the methodological features. In surveying each category, we further discuss the design principles and analyze the strengths and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs. Furthermore, we also discuss the current challenges and issues to identify future research directions in this emerging topic. In summary, this survey provides a convenient overview of EEM for interested users, and they can easily select the proper EEM method for the tasks at hand. In addition, the researchers in the NAS field could continue exploring the future directions suggested in the article. © 2020 IEEE.

关键词： Network architecture

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Semantic-Based Detection of Ponzi Smart Contracts: Enhancing Bytecode Analysis with Source Code 4

Semantic-Based Detection of Ponzi Smart Contracts: Enhancing...

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4th International Conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Weng, Bingjie Ma, Jianhui University of Science and Technology of China Faculty of Artificial Intelligence and Data Science Anhui Hefei China University of Science and Technology of China Faculty of Computer Science and Technology Anhui Hefei China

ISBN: (纸本)9798331528850

The rapid development of blockchain technology has led to many innovations, with the widespread use of smart contracts being particularly notable. However, this trend has spawned diverse blockchain scams, including the Ponzi scheme, which takes the form of Ponzi contracts. Due to blockchain users' misunderstanding of blockchain transparency, this scam can often result in significant financial losses. To complicate matters further, the vast majority of smart contracts deployed on blockchain networks are not open source, and users can only see the contract's bytecode. This situation makes Ponzi contracts even more challenging to identify, making it difficult for users to judge the security and legitimacy of a contract beforehand, thus increasing the risk of being defrauded. Most existing work uses only bytecode for detection and cannot fully utilize the corresponding source code information. In this paper, we propose a novel bimodal learning-fusion framework for enhancing Ponzi contract detection only in the presence of bytecode. Firstly, we employ the semantic-aware neural network to extract semantic feature from source code and bytecode. Then, we propose a modal transformation strategy to learn the relationship between source code and bytecode. Finally, a modal attention mechanism is introduced to construct a fusion representation to detect the contract even in the absence of source code modality. Experimental results show that the method performs better than existing methods that use machine learning for detection of Ponzi contracts. © 2024 IEEE.

关键词： Blockchain

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Enhance Transferability of Adversarial Examples with Model Architecture 48

Enhance Transferability of Adversarial Examples with Model A...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Fan, Mingyuan Guo, Wenzhong Ying, Zuobin Liu, Ximeng Fuzhou University College of Computer and Data Science China City University of Macau Faculty of Data Science China

ISBN: (纸本)9781728163277

Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical setting, the crafted adversarial examples are always prone to overfitting to the proxy model employed, presenting poor transferability. In this paper, we suggest alleviating the overfitting issue from a novel perspective, i.e., designing a fitted model architecture. Specifically, delving the bottom of the cause of poor transferability, we arguably decompose and reconstruct the existing model architecture into an effective model architecture, namely multi-track model architecture (MMA). The adversarial examples crafted on the MMA can maximumly relieve the effect of model-specified features to it and toward the vulnerable directions adopted by diverse architectures. Extensive experimental evaluation demonstrates that the transferability of adversarial examples based on the MMA significantly surpass other state-of-the-art model architectures by up to 40% with comparable overhead. © 2023 IEEE.

关键词： Architecture

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Effectiveness of K-means Clustering-Based Outlier Handling Techniques in Colonic Condition Endoscopy Image Classification Using CNN 13

Effectiveness of K-means Clustering-Based Outlier Handling T...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Athiyah, Ummi Rustad, Supriadi Soeleman, Moch Arief Muljono Akrom, Muhamad Dian Nuswantoro University Faculty of Computer Science Semarang Indonesia Telkom University Department of Data Science Purwokerto Indonesia

ISBN: (纸本)9798350368086

Colorectal cancer is one of the leading causes of cancer death worldwide, so accurate early detection is needed. Endoscopic imaging technology plays a vital role in diagnosis, but the presence of outliers in endoscopic images can significantly affect the accuracy of the classification model. This study addresses this challenge by applying Histogram of Oriented Gradients (HOG) for feature extraction and K-means Clustering for outlier detection and removal. A dataset of colon endoscopic images was obtained from the public database Gastrovision, which was divided into two classes: normal and abnormal conditions. This study applied a two-step modeling process, first with the original dataset and then with the cleaned dataset after outlier removal. The results showcased a significant improvement in model performance after outlier handling, with accuracy increasing from 61% to 85%. The Area Under Curve (AUC) of the ROC curve also increased from 0.84 to 0.92, indicating a better model ability to distinguish between normal and abnormal conditions. These findings emphasize the importance of data quality and effective outlier management in training a machine-learning model for colon cancer endoscopic image conditions. © 2024 IEEE.

关键词： Diseases

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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