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International Conference on Computing, engineering, and Design (ICCED)

作者： Arief Agus Sukmandhani Yulyani Arifin Muhammad Zarlis Widodo Budiharto Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Management Department BINUS Graduate Program - Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Text summarization is a natural language processing (NLP) technique in artificial intelligence that has been studied in recent years. Every document containing text is tested to get a good summary result. In producing a good summary, proper accuracy is required; therefore, various techniques and methods are used, and another challenge is the use of language in the documents being tested. Various types of documents have been used as research for text summarization, including scientific documents, so researchers are interested in studying them more intensely to get the state of the art in research. This research was conducted to review previous research using literature studies from various publications that published research on text summarization in scientific documents. The researcher collected data from several publishers who had published this type of research from 1988 to 2023 quarter 1 and obtained three hundred and eleven articles, then the researcher analyzed the data using the Kitchenham method to get the right literature to answer the research question. Thirty-seven articles were selected for further in-depth analysis. In addition, the researcher found some interesting findings beyond the research question, including other techniques used to solve the text summarization problem and other types of output that are not commonly produced so that it can increase knowledge in researching text summarization.

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Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation

arXiv

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arXiv 2023年

作者： Yandan, Zheng Anran, Hao Tuan, Luu Anh School of Computer Science and Engineering Singapore Interdisciplinary Graduate Program HealthTech Nanyang Technological University Singapore

Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition and Relation Extraction) separately and ignore the cross-correlation of entity and relation instances as well as the existence of similar instances across unlabeled data. To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data. Specifically, we construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We then employ a propagation learning scheme to leverage the affinities between labelled and unlabeled samples. Experiments on benchmark datasets show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks. We show that the joint semi-supervised learning of the two tasks benefits from their codependency and validates the importance of utilizing the shared information between unlabeled data. © 2023, CC BY.

关键词： Extraction

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Gas Cost Analysis of Fractional NFT on the Ethereum Blockchain

Gas Cost Analysis of Fractional NFT on the Ethereum Blockcha...

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Wonseok Choi Jongsoo Woo James Won-Ki Hong Computer Science and Engineering POSTECH Pohang Korea Graduate School of Information Technology POSTECH Pohang Korea

With the rise of NFTs, which serve as proof of ownership for assets, security tokens that enable transactions without intermediaries have become a popular topic. Tokenization eliminates the need for centralized markets and allows for fast trades. In addition to tokenization, there are attempts to increase asset liquidity by fractionalizing them, a concept known as fractional ownership. Despite the existence of some platforms that use tokenization and fractional ownership, there is still limited research on fractional NFTs, and institutional support is lacking. In this paper, we propose possible implementations of fractional NFTs and evaluate their gas costs, which are crucial for providing fractional NFT-related services. As most NFTs are minted based on the Ethereum blockchain, we implement fractional NFTs using ERC standards. Our evaluation shows that ERC-721 or ERC-1155 NFTs fractionalized into ERC-20 FTs have the lowest long-term gas costs.

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Blockchain and API Development to Improve Relational Database Integrity and System Interoperability

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Procedia computer science 2023年 216卷 151-160页

作者： Andhika Naafi Ramadhan Kharisma Naufal Pane Kun Rifki Wardhana Suharjito Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia

Technological developments have resulted in a trend of cryptocurrencies that use a technology called blockchain to create and record all transactions made into a digital ledger. Along with the emergence of the trend of cryptocurrencies, this development has also resulted in crimes that hit the digital world, such as data leakage and cyber espionage. This threat can be prevented by applying blockchain technology to the database that has been used. Therefore, we need a system that can facilitate the use of blockchain technology and can process data from an existing relational database into a blockchain-based database. The system developed in this study was built with the FastAPI framework that uses the Python programming language and the React framework that uses the Javascript programming language. This system was tested using Katalon and Wireshark software to perform throughput testing and man-in-the-middle attacks. Evaluation of this system is assessed based on the average throughput time and also the results of Wireshark packet capture. The system designed in this research is expected to help overcome interoperability problems when using blockchain and improve relational database integrity. The results of the test show that the system is safe from man-in-the-middle attacks while sending data through API and has a faster throughput time than BigchainDB system by 4.151 seconds.

关键词： Blockchai Cybercrim Database Interoperability

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Sample complexity of posted pricing for a single item 24

Sample complexity of posted pricing for a single item

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Proceedings of the 38th International Conference on Neural information Processing Systems

作者： Billy Jin Thomas Kesselheim Will Ma Sahil Singla Cornell University School of Operations Research and Information Engineering University of Bonn Institute of Computer Science and Lamarr Institute for Machine Learning and Artificial Intelligence Columbia University Graduate School of Business and Data Science Institute Georgia Tech School of Computer Science

ISBN: (纸本)9798331314385

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings.

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Image-Based Fashion Recommendation with Attention to Users' Interests 22

Image-Based Fashion Recommendation with Attention to Users' ...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Wenxin Yao Kaoru Uchida Jianpeng Zou Xiaolin Zhong School of Software Engineering University of Science and Technology of China China and Graduate School of Computer and Information Sciences Hosei University Japan Graduate School of Computer and Information Sciences Hosei university Japan Meituan China

ISBN: (纸本)9781450397810

Building an effective fashion recommendation system is challenging due to the high level of subjectivity and the semantic complexity of the features involved. Users’ decision depends largely on their interest and the appearance of the product. Such information is often hidden in implicit feedback from users' purchase histories and product images. Most interest-based recommendation systems like Deep Interest Network (DIN) and Deep Interest Evolution Network (DIEN) only take advantage of product attributes and context review, which are all text information. There are also some studies focusing on the use of image features for a fashion product recommendation. They try to extract features from images and recommend products based on their similarity. However, for DIEN. It works not well when there are few interactions between users and items, the model can not find users' interests effectively. On the other hand, these image-based recommendation systems tend to ignore an important factor: the user's interest. We propose a new system trying to find users' interests by introducing the visual information of the product and our image-based deep interest attention model based on DIEN. Product attributes and user preference can both be represented by introducing visual information. We can model similar attribute items in the same place and find user interests more effectively. We conducted a series of experiments on our own dataset to compare user click-through rate (CTR) AUC and Accuracy using our model with DIEN and other existing *** experimental results demonstrate that visual information effectively aids CTR prediction and achieved better recommendation results.

关键词： Fashion recommendation User interest Image feature Attention Mechanism

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Alleviating Crypto Gas War in NFT Launching

Alleviating Crypto Gas War in NFT Launching

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IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

作者： Kyungchan Ko Taeyeol Jeong Jongsoo Woo James Won-Ki Hong Department of Computer Science and Engineering POSTECH Korea Nodebridge Graduate School of Information Technology POSTECH Korea

Non-Fungible Tokens (NFT) is a unique digital token based on blockchain technology, which is used as a means to prove ownership of assets in various areas. With the huge popularity of NFT, the launch of new NFTs attracted many people to mint NFTs. However, because Ethereum has low processing speed and then cannot accommodate the explosive demands, it causes Crypto Gas War, which increases the overall transaction fee and wastes unnecessary gas. In this work, we propose a Raffle-based N FT launch to solve t he C rypto Gas War. We demonstrated that our proposed NFT launch solves this problem but other existing solutions do not solve the problem. Moreover, our NFT smart contract is gas-efficient. Our proposed method reduces the gas usage by 15.5% or more compared to general method, and the efficiency is improved a ccording to increasing the total number of NFTs.

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Data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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KCE-Unet: A novel music denoising method with KANConv ECA Unet*

KCE-Unet: A novel music denoising method with KANConv ECA Un...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shijie Zhang Yulun Wu Ganghui Ru Yi Yu Wei Li School of Computer Science and Technology Fudan University Shanghai China Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan School of Computer Science and Technology Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

During concerts, people often spontaneously record memorable moments with their phones. However, these recordings are frequently accompanied by noise, such as cheering and applause, which diminishes the playback experience. In this paper, we introduce a novel task specifically designed for denoising music in concert environments, a challenge that has been largely overlooked in previous research. To support this task, we created a new concert denoising dataset that includes songs performed in various major languages at concerts, with noise segments like cheering and applause. Building on this, we propose KANConv ECA Unet (KCE-Unet), a method that combines the U-Net network, efficient channel attention (ECA), and the recently proposed KAN network to flexibly remove noise in the mid-to-high frequency range of spectrograms. Extensive experiments demonstrate that our method outperforms previous models in denoising performance and effectively restore disrupted musical structures.

关键词： Noise reduction Noise Buildings Acoustics Recording Multiple signal classification Speech processing Spectrogram

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ECC Based Dynamic Multiparty Key Agreement

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Chinese Journal of Electronics 2023年第2期18卷 355-360页

作者： Chunxiang Xu Junhui Zhou School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China State Key Laboratory of Information Security Graduate School of the Chinese Academy of Sciences Beijing China

Multiparty key agreement (MKA) is a secure and robust approach to establish a group key for secure group oriented applications over non-private underlying networks, and it can be efficiently implemented with ECC (Elliptic curve cryptography). In this paper, we propose a generic two round model for ECC based MKA. All users involved are organized into a ring in this model and distributedly compute a group key by two round message exchanges. Upon this model, we construct a concrete ECC based MKA protocol with scalability and low computational complexity. The protocol is dynamic, that is, it supports key update when user group membership changes to ensure forward secrecy and backward secrecy. The protocol is proved to be secure under the Decision Diffie-Hellman (DDH) assumption on the elliptic curve. Its security is reduced to the intractable DDH problem on the elliptic curve.

关键词： Protocols Elliptic curves Computational modeling Scalability Elliptic curve cryptography Cryptography Computational complexity

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