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7th International Conference on Future Networks and Distributed Systems, ICFNDS 2023

作者： Ramadan, Qusai Boukhers, Zeyd Alshaikh, Muath Lange, Christoph Jurjens, Jan Institute Of Software Engineering University Of Koblenz Germany Fraunhofer Institute For Applied Information Technology Fit Germany Uniklinik Köln Germany College Of Computing And Informatics Saudi Electronic University Saudi Arabia Rwth Aachen University Germany University Of Koblenz Germany Fraunhofer-Institute For Software And Systems Engineering Isst Germany

ISBN: (纸本)9798400709036

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking. © 2023 Owner/Author.

关键词： Electronic data interchange

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Automated Fabrication of Magnetic Soft Microrobots

arXiv

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arXiv 2025年

作者： Clancy, Kaitlyn Xie, Siwen Smith, Griffin Onaizah, Onaizah School of Biomedical Engineering McMaster University Hamilton Canada Department of Computing and Software McMaster University Hamilton Canada

The advent of 3D printing has revolutionized many industries and has had similar improvements for soft robots. However, many challenges persist for these functional devices. Magnetic soft robots require the addition of magnetic particles that must be correctly oriented. There is a significant gap in the automated fabrication of 3D geometric structures with 3D magnetization direction. A fully automated 3D printer was designed to improve accuracy, speed, and reproducibility. This design was able to achieve a circular spot size (voxels) of 1.6mm in diameter. An updated optical system can improve the resolution to a square spot size of 50µm by 50µm. The new system achieves higher resolution designs as shown through magneto-mechanical simulations. Various microrobots including’worm’,’gripper’ and’zipper’ designs are evaluated with the new spot size. Copyright © 2025, The Authors. All rights reserved.

关键词： Microfabrication

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Impact of Artificial Intelligence on Decision-Making in Information Systems

Impact of Artificial Intelligence on Decision-Making in Info...

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Advanced Research in computing (ICARC), International Conference on

作者： RWMR Dharani Rajasinghe Dept. of Software Engineering & Information Systems Faculty of Computing NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

关键词： Industries Surveys Ethics Adaptation models Technological innovation Explainable AI Law Decision making Data models Information systems

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Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study

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World Journal of Gastrointestinal Surgery 2024年第2期16卷 331-344页

作者： Miran Bezjak Ivan Stresec Branislav Kocman Stipislav Jadrijević Tajana Filipec Kanizaj Miro Antonijević Bojana Dalbelo Bašić Danko Mikulić Department of Surgery University Hospital MerkurZagreb 10000Croatia Department of Electronics MicroelectronicsComputer and Intelligent SystemsFaculty of Electrical Engineering and ComputingZagreb 10000Croatia Department of Gastroenterology University Hospital MerkurZagreb 10000Croatia Development of Software Nanobit Ltd.Zagreb 10000Croatia

BACKGROUND The growing disparity between the rising demand for liver transplantation(LT)and the limited availability of donor organs has prompted a greater reliance on older liver ***,utilizing livers from elderly donors has been associated with outcomes inferior to those achieved with grafts from younger *** accounting for additional risk factors,we hypothesize that the utili-zation of older liver grafts has a relatively minor impact on both patient survival and graft *** To evaluate the impact of donor age on LT outcomes using multivariate analysis and comparing young and elderly donor *** In the period from April 2013 to December 2018,656 adult liver transplants were performed at the University Hospital *** multivariate Cox propor-tional hazards models were developed to independently assess the significance of donor *** age was treated as a continuous *** approach involved univariate and multivariate analysis,including variable selection and assessment of interactions and ***,to exemplify the similarity of using young and old donor liver grafts,the group of 87 recipients of elderly donor liver grafts(≥75 years)was compared to a group of 124 recipients of young liver grafts(≤45 years)from the *** rates of the two groups were estimated using the Kaplan-Meier method and the log-rank test was used to test the differences between *** Using multivariate Cox analysis,we found no statistical significance in the role of donor age within the constructed *** when retained during the entire model development,the donor age's impact on survival remained insignificant and transformations and interactions yielded no substantial effects on *** insigni-ficance and low coefficient values suggest that donor age does not impact patient survival in our ***,there was no statistical evidence that the five developed models did not adhe

关键词： Liver transplantation Elderly donors Survival analysis Postoperative complications Cox proportional hazard models

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Distilling Task Specific Models from Ernie for Chinese Medical Intention Classification 9

Distilling Task Specific Models from Ernie for Chinese Medic...

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9th IEEE Smart World Congress, SWC 2023

作者： Wang, Zumin Xu, Chang Chen, Liming Qin, Jing Ji, Changqing School of Information Engineering Dalian University Dalian China School of Computing Ulster University Belfast United Kingdom School of Software Engineering Dalian University Dalian China School of Physical Science and Technology School of Information Engineering Dalian University Dalian China

ISBN: (纸本)9798350319804

The improvement of intention classification of medical query can effectively improve the performance of search engines and Question-Answering systems, and further improve medical services. The Pre-Trained language models has achieved good performance in various NLP tasks, but the slow reasoning speed and high requirements for storing computing resources of the model make it difficult for medical institutions to deploy relevant models. In this paper, we propose a knowledge distillation model based on ERNIE to solve the Chinese medical intention classification task, in order to provide a Small-Scale and more efficient intention classification model. We propose a Two-Stage framework, which is distilled on specific domain knowledge and tasks, and we use data augmentation methods to minimize the loss of accuracy caused by model compression. Specifically, we distill the Embedding layer and Transformer layer of the teacher model in the Domain-Knowledge-Specific-Distillation stage, so that our student model can better capture the general domain and Medical specific domain knowledge of the teacher model. In the Intent-Classification Task-specific Distillation stage, we also distilled the Prediction layer and added Soft-Label and Hard-Label to calculate the Prediction layer loss to ensure that the student model acquires knowledge of specific tasks. Considering the lack of labeled data available for training in the medical field and intention classification task, we use word exchange and whole entity masking methods to augment the labeled data and augment the generalization ability of student models. We conducted experiments on a publicly available CBLUE dataset, and the experimental results showed that our proposed 4-layer student model retained more than 98.6% of the language comprehension capabilities of the original 12-layer teacher model - ERNIE, while reducing the computational resources required and significantly accelerating reasoning speed. © 2023 IEEE.

关键词： Intent Classification Knowledge Distillation Medical NLP Pre-Trained Language Models

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CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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Computer Modeling in engineering & Sciences 2023年第4期135卷 45-64页

作者： Zhiyun Yang Qi Liu HaoWu Xiaodong Liu Yonghong Zhang School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK School of Automation Nanjing University of Information Science TechnologyNanjing210044China

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy *** relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather ***,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous *** address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar *** practical radar echo datasets were used involving the FREM and CIKM 2017 *** quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,*** particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

关键词： Radar echo extrapolation attention mechanism long short-term memory deep learning

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Integrating Private and Accountable Threshold Signature Into Ciphertext-Policy Attribute-Based Encryption Supporting Collaboration Decryption

Integrating Private and Accountable Threshold Signature Into...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Meixin Chen Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Cloud services are becoming increasingly popular in social life. Collaboration in the cloud is critical for teams in different locations and organizations. However, the current collaborative decryption scheme requires all members to be online in order to decrypt, which causes offline members to affect the progress of the project. In this paper, we propose a more flexible and reliable collaborative decryption solution for team collaboration scenarios in cloud services. Our solution, based on the Threshold Accountable Private Signature (TAPS) framework, offers higher security, practicality, and lower overhead. Unlike the existing scheme, our approach allows for decryption when the number of online members reaches a certain threshold. Additionally, our framework can track individual members instead of all members, ensuring continuity of collaboration. To demonstrate the practicality and efficiency of our framework, we implement and verify its usefulness through experiments using the Rust programming language.

关键词： Computer languages Collaboration Organizations Encryption Security Reliability Cybernetics

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LLMSmartSec: Smart Contract Security Auditing with LLM and Annotated Control Flow Graph

LLMSmartSec: Smart Contract Security Auditing with LLM and A...

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IEEE International Conference on Blockchain (Blockchain)

作者： Viraaji Mothukuri Reza M. Parizi James L. Massa Decentralized Science Lab College of Computing and Software Engineering Kennesaw State University Marietta GA USA

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Historically, the complexity of identifying vulnerabilities in smart contracts required human-intensive audits to supplement imprecise automated code scans. The growing smart contract market highlights the urgency for effective automated security auditing. Furthermore, new AI-powered coding assistants can introduce vulnerabilities that evade engineers, and autonomous AI can now write 100% code changes without human oversight. This research introduces LLMSmartSec, a new approach that accurately identifies and fixes smart contract vulnerabilities, leveraging the strengths of machine intelligence to operate at high speed, scale, and without fatigue. To develop LLMSmartSec, we first fine-tuned Open AI GPT-4 to understand Solidity, the programming language of Ethereum blockchain, and to micro-analyze smart contracts holistically from the viewpoints of the developer (LLMDev), auditor (LLMAudit), and ethical hacker (LLMeHack) and to identify vulnerabilities and generate code fixes for them. We used GPT-4 again to store the smart contract code in a Control Flow Graph (CFG) annotated with the code vulnerabilities and their fixes. Finally, we trained an LLMGraphAgent with open-source LLMs on the annotated CFG so that it can run locally, identifying vulnerabilities and fixes without costly calls to GPT-4. This research enhances the security of blockchain projects in Web3 by supplementing a costly human bottleneck with a low-cost automated security tool based upon the innovative creation of an annotated CFG with GPT-4.

关键词： Ethics Codes Smart contracts Fatigue Blockchains Security Flow graphs

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A Knowledge-graph based Method for Datum Reference Frame Reasoning 18

A Knowledge-graph based Method for Datum Reference Frame Rea...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Fu, Hongsheng Kong, Chao Cao, Yanlong Xu, Yuanping Jin, Jin Li, Tukun Luo, Junding Zhang, Zongzheng School of Mechanical Engineering Zhejiang University Zhejiang China School of Computing and Engineering University of Huddersfield Queensgate Huddersfield United Kingdom School of Software Engineering Chengdu University of Information Technology Chengdu China CO. Ltd. China Shandong Industrial Technology Research Institute of Zhejiang University Shandong China

In mechanical engineering, the datum is essential in the tolerance specification, which impacts assembly and functional requirements. The standard mechanical parts, such as shaft and gear, already have a formalized datum reference frame (DRF) design process. However, non-standard parts still mainly rely on manual design. To this end, a knowledge-graph-based DRF reasoning method for non-standard parts is proposed. In this paper, an OWL 2-based knowledge model is constructed according to industrial practice. It combines empirical knowledge, knowledge graphs, and ontology reasoning methods to perform DRF generation automatically and precisely. The finite element method is introduced into this work to evaluate reasoned DRF. The effectiveness and feasibility of the method are demonstrated via a case study. © 2024 The Authors. Published by Elsevier B.V.

关键词： Knowledge graph

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model 22

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zhang, Zhi Zhang, Shiwen Chen, Shuang Liang, Wei Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798331509712

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients' potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning. © 2024 IEEE.

关键词： Federated learning

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