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共 2238 条记录，以下是321-330 订阅

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A Practical Cloud API Complementary Recommendation Service for Mashup Creation

A Practical Cloud API Complementary Recommendation Service f...

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International Conference on computer Supported Cooperative Work in Design

作者： Xiaowei Liu Wenhui Chen Mengmeng Sun Yali Si Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China School of Computer Science and Engineering Changshu Institute of Technology Changshu China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the popularity of the service-oriented development paradigm in the cloud era, numerous cloud APIs have been developed. The continuous proliferation of cloud APIs has submerged developers in a sea of options, bringing the need for cloud API recommendation techniques. Most existing cloud API recommendation methods focus on theoretical studies and neglect practical applications. As a result, these methods face several challenges, such as covering recommendation scenarios, generating real-time responses, and handling concurrent requests. In this paper, we propose and implement a practical cloud API complementary recommendation service for mashup creation. To address the above challenges, we first propose a cloud API complementary recommendation framework named FICR, which exploits feature interactions to mine implicit complementarity and models complementarity from the mashup side and the target cloud API side for scenario coverage. Then, we conduct performance optimization via precoding and batch size tuning to optimize FICR for real-time responses. Finally, we design a minimum-connection load balancing algorithm with resource early warning and build a distributed system for concurrent requests. Experiments conducted on real-world datasets show that FICR outperforms state-of-the-art baseline models in complementary recommendation scenarios, and demonstrate that the implemented service meets the real-time and concurrency requirements. The service will serve as a practical tool to help developers during mashup creation.

关键词： Concurrent computing Federated learning Mashups Precoding Load management Real-time systems Tuning

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Att-TCN: An Advertising Attribution Method Based on Temporal Convolutional Network with Attention Mechanism 8

Att-TCN: An Advertising Attribution Method Based on Temporal...

引用

8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Xie, Bowen Sun, Yi Lin, Zhaowen Ma, Baoquan Beijing University of Posts and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Technologies Beijing China National Engineering Laboratory for Industry Control System Information Security Technology Beijing China National Computer System Engineering Research Institute of China Beijing China

ISBN: (纸本)9781665450515

Advertising attribution is an effective method to measure the effect of various advertisements in the era of Internet advertising. It enables scientific tracking back and refined operation of advertising by quantitatively evaluating the contribution of different advertising channels to reach the target. It is challenging to predict conversion results based on users' journey data accurately, reflect the impact of ads on user conversions, and appropriately assign attribution values to advertising channels. To achieve the above objectives, we propose an advertising attribution method based on a temporal convolutional network with an attention mechanism (Att-TCN). By mining the hidden information in time and attribution channels using the attention mechanism, our proposed method can improve the accuracy of conversion prediction and thus more rationally assign attribution values across channels. Experimental validation on a real dataset obtained from an operational commercial advertising monitoring company shows that our proposed attribution method has better accuracy than other state-of-the-art attribution methods and gives an open-ended approach to attribution value assignment. © 2022 IEEE.

关键词： Marketing

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Evaluation of the Application of Hospital Management information system (SIMRS) in RSUD Dr. Kanujoso Djatiwibowo Using the HOT-Fit Method 3

Evaluation of the Application of Hospital Management Informa...

引用

3rd Borneo International Conference on Applied Mathematics and engineering 2020, BICAME 2020

作者： Setiorini, Adani Natasia, Sri R Wiranti, Yuyun Tri Ramadhan, Dean A Department of Information System Majoring in Mathematics and Information Technology Kalimantan Institute of Technology Balikpapan Indonesia Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia

Hospital Management information system (SIMRS) is a system that can process and integrate the entire hospital service process flow in the form of a network of coordination, reporting and administrative procedures to obtain accurate data and information. At the RSUD Dr. Kanujoso Djatiwibowo (RSKD) of Balikpapan City, the application of SIMRS experienced problems such as the high number of troubleshooting cases and the redundancy of patient medical record numbers. This case is consideredto inhibit the performance of hospital services related to efficiency. Based on this problem, the evaluation of SIMRS in the RSKD was carried out by using the constructed Human, Organization, technology-Fit (HOT-Fit) model. This model involves eight variables which consists of system Quality, information Quality, and Service Quality, system Use, User Satisfaction, Structure, and Environment, and Net Benefits. The results indicate that the proposed model either directly or indirectly affectsthe benefits obtained from implementing the SIMRS. Further implications of the results toward improvement of the system are provided. © Published under licence by IOP Publishing Ltd.

关键词： information management

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Design Thinking and Innovative Tool Use in Business Model

Design Thinking and Innovative Tool Use in Business Model

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Intelligent systems for Cybersecurity (ISCS), International Conference on

作者： Nitika Singhi Ankush Verma Atul Agrawal Irfan Khan Computer Science Engineering Shri Vaishnav Vidhyapeeth Vishwavidyalava Indore India Data Analytics and Information System Prestige Institute of Management & Research Indore India Computer Science Engineering Sushila Devi bansal College of Technology Indore India Computer Science Engineering Bansal Institute of Science and Technology Bhopal India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

In past few year designs thinking has worked on several new effective problem and has generated innovative solution for new business model. For the scenario of today's demand, to better understand how design thinking can work on different perspectives in business models and how it can bring new innovative solutions for any problem is essential However, studies of existing literature explore the application, potential, and challenges of Design Thinking towards the comprehensive aim of this paper, which regards business model innovation as a fundamental subject of Design Thinking. The paper provides practical tools for exploring Design Thinking along with various critical aspects for the researchers to explore how Design Thinking can be applied to innovative business models. The main focus of this paper is to explain the basics of Design Thinking, how to frame the mind through different techniques used, the Design Techniques used for a business model, the methodology of a design thinker, and different tools like BPM, Blue Work Live, Mind Mapping, Brainstorming, and RICPS.

关键词： Technological innovation Solution design Heuristic algorithms Design methodology Brain modeling Real-time systems Iterative methods

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Intent Classification of Nepali Factoid Questions

Intent Classification of Nepali Factoid Questions

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Sushil Timilsina Ashim Khadka Jianchu Lin Faculty of Science and Technology Gandaki College of Engineering and Science Pokhara Nepal Faculty of Science and Technology Nepal College of Information Technology Lalitpur Nepal Faculty of Computer and Software Engineer Huaiyin Institute of Technology Jiangsu China

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches.

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LiteCrypt: Enhancing IoMT Security with Optimized HE and Lightweight Dual-Authorization 30

LiteCrypt: Enhancing IoMT Security with Optimized HE and Lig...

引用

30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Xie, Qipeng Wang, Weizheng Huang, Yongzhi Zheng, Mengyao Shang, Shuai Jiang, Linshan Khan, Salabat Wu, Kaishun IoT Thrust Guang Zhou China Hong Kong University of Science and Technology Hong Kong City University of Hong Kong Computer Science Engineer Hong Kong Dsa Thrust Information Hub Guang Zhou China Harvard T.H. Chan School of Public Health Boston United States University of Electronic Science and Technology of China Chengdu Chengdu China National University of Singapore Institute of Data Science Singapore

ISBN: (纸本)9798331515966

The integration of 5G/6G networks with intelligent healthcare systems has enabled early disease detection through patient data monitoring. However, the Internet of Medical Things (IoMT) and remote healthcare services introduce significant privacy and security risks. In this paper, we propose LiteCrypt, which addresses these challenges by introducing an optimized Homomorphic Convolutional Neural Networks (HCNN) structure for secure inference and a lightweight Threshold Signature Scheme (TSS) based dual-authorization mechanism. To enhance the practicality of Homomorphic Encryption (HE)-based secure inference in telemedicine applications, LiteCrypt presents an optimized HCNN framework that ensures efficient and adaptable operations across multiple datasets. A high-performance GPU-accelerated HE engine is developed to address the computational demands of HE operations, enabling real-time processing of encrypted patient data. Besides, LiteCrypt introduces a novel TSS-based dual-authorization protocol, requiring consent from both the patient and the hospital to access patient data, thereby mitigating unauthorized access risks. The system adapts to a flexible 2-out-of-3 authorization scheme for emergencies, ensuring timely data retrieval while maintaining security. To overcome the initial challenge of prolonged computation time due to compute-intensive operations, In LiteCrypt, we utilized the lightweight TSS protocol, based on Oblivious Transfer (OT), which is designed for resource-constrained IoMT devices, reducing computation time from 11.9 to 0.11 seconds. Empirical validation demonstrates LiteCrypt's superior performance, achieving a 233-fold increase in processing speed, a 96% reduction in encrypted message size, and a 28-fold speed increase using GPUs. © 2024 IEEE.

关键词： Convolutional neural networks

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Traffic sign recognition based on deformable convolutions

Traffic sign recognition based on deformable convolutions

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International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Li Wenju Yu Jie Sha Liye Cui Liu School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

A traffic sign recognition model based on deformable convolution is proposed to address the low accuracy of existing traffic sign recognition algorithms for small and occluded targets. Firstly, replace some convolutions in the backbone network with deformable convolutions to adapt to different shape feature maps during feature extraction and improve the network's sensitivity to small targets; Secondly, in order to reduce the loss of information in the process of upsampling, the original Nearest-neighbor interpolation upsampling module is replaced by the sub-pixel convolution module in the feature fusion network to reduce the loss of information due to upsampling. Finally, design experiments on the TT100K dataset and test the advantages of this model in real-world scenarios. The experimental results show that the average accuracy of this model (mAP@0.5) It has reached 92.54%, with a Recall rate of 87.42%, which is 2.18% and 1.58% higher than the benchmark network YOLOv5n, respectively. The frame processing speed has reached 96.06.

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Acute lymphoblastic leukemia image segmentation based on modified HSV model

Acute lymphoblastic leukemia image segmentation based on mod...

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International Laser technology and Optics Symposium 2022, iLATOS 2022

作者： Najjar, Fallah H. Khudhair, Kifah T. Khudhair, Zaid Nidhal Alwan, Haneen H. Al-Khaykan, Ameer Department of Computer System Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Iraq Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Air Conditioning and Refrigeration Techniques Engineering Department Al- Mustaqbal University College Hillah Babylon51001 Iraq

Image segmentation is a critical step in computer-aided diagnosis that could speed up Leukemia detection. Leukemia is a cancer of the blood that has a reputation for being particularly lethal. Based on the immunohistochemical method, the leukocytes can be manually counted in a stained peripheral blood smear image to detect Acute Lymphoblastic Leukemia (ALL). Regrettably, the manual diagnosis process takes about 3 to 24 hours to complete, which is insufficient. This paper introduced a new and straightforward ALL image segmentation approach based on color image transformation. First, Leukemia, ALL-IDB1, ALL-IDB2, and ALL image datasets were used in this paper. The Leukemia dataset includes 208 ALL-IDB1 and ALL-IDB2 images, while The ALL dataset has 3256 images. Next, we use the HSV model to transform ALL images. In addition, we modified the HSV model by pre-processing the saturation channel for better results. Then, the pre-processed images were segmented based on a fixed threshold. After that, various metrics are utilized to measure the output of the proposed method. Finally, the proposed methodology is compared to currently used benchmarks. The proposed method outperforms previous approaches regarding accuracy, specificity, sensitivity, and time. In addition, results show that the proposed technique improves performance measures significantly. © Published under licence by IOP Publishing Ltd.

关键词： Image segmentation

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A Fair and Reliable Data Trading Scheme based on Blockchain 2

A Fair and Reliable Data Trading Scheme based on Blockchain

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2nd International Conference on computer Science and Blockchain, CCSB 2022

作者： Wu, Zhili Pan, Heng Si, Xueming Qian, Haiyang Cai, Fugang Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China Zhongyuan University of Technology The Frontier Information Technology Research Institute China Shanghai Jiao Tong University Department of Computer Science and Engineer Shanghai China

ISBN: (纸本)9798350331882

To solve the single point of failure and opaque transaction problems in the current centralized data trading model, we propose a blockchain-based data trading scheme. Firstly, we construct a blockchain-based data transaction model to provide users with peer-to-peer trusted transaction services. Then, we design a two-stage one-to-many data pricing method based on smart contracts and the Rubinstein model, which can guarantee the reasonableness of data transaction pricing and achieve a fair price between a single data demander and multiple data owners. Next, we suggest a forensic dispute arbitration method based on on-chain and off-chain data collaboration and a data integrity verification method, which aims to ensure the reliability of de-centralized data transactions and prevent malicious acts such as data owners providing false data, data requesters refusing to pay, and data stored in the cloud being tampered with. Finally, we perform experiments based on consortium blockchain. Experimental results show that the scheme of this paper has better functionality and performance than other schemes of data transactions. © 2022 IEEE.

关键词： Smart contract

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A novel medical image enhancement technique based on hybrid method

A novel medical image enhancement technique based on hybrid ...

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International Laser technology and Optics Symposium 2022, iLATOS 2022

作者： Khudhair, Kifah T. Najjar, Fallah H. Waheed, Safa Riyadh Al-Jawahry, Hassan M. Alwan, Haneen H. Al-Khaykan, Ameer Department of Information Technology Technical College of Management Al-Furat Al-Awsat Technical University Iraq Department of Computer System Techniques Technical Institute of Najaf Al-Furat Al-Awsat Technical University Iraq Faculty of Engineering School of Computing University Technology of Malaysia Johar Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq Air Conditioning and Refrigeration Techniques Engineering Department Al-Mustaqbal University College Hillah Babylon51001 Iraq

Medical images are a specific type of image that can be used to diagnose disease in patients. Critical uses for medical images can be found in many different areas of medicine and healthcare technology. Generally, the medical images produced by these imaging methods have low contrast. As a result, such types of images need immediate and fast enhancement. This paper introduced a novel image enhancement methodology based on the Laplacian filter, contrast limited adaptive histogram equalization, and an adjustment algorithm. Two image datasets were used to test the proposed method: The DRIVE dataset, forty images from the COVID-19 Radiography Database, endometrioma-11, normal-brain-MRI-6, and simple-breast-cyst-2. In addition, we used the robust MATLAB package to evaluate our proposed algorithm's efficacy. The results are compared quantitatively, and their efficacy is assessed using four metrics: Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Contrast to Noise Ratio (CNR), and Entropy (Ent). The experiments show that the proposed method yields improved images of higher quality than those obtained from state-of-the-art techniques regarding MSE, CNR, PSNR, and Ent metrics. © Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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