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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Santos, Maíra Rocha de Sousa, Thainara Silva Mariano, Ari Melo Department of Administration University of Brasília Brazil Department of Production Engineering University of Brasília Brazil Department of Computer Science University of Brasilia Brazil DataLab-Data Lab University of Brasilia Brazil

This exploratory qualitative study sought to identify the main technological gaps faced by digital influencers from generations X, Y, and Z. Using interviews with open questions, perceptions of nine Brazilian digital influencers from different areas and age groups were collected. The interviews, with an average duration of 40 minutes, addressed two blocks: the influencers' profile and perception of the technologies used. The content analysis through a Similitude Tree created by the Iramuteq software revealed common gaps between generations, such as adaptation to new platforms, emotional control, search for engagement, and constant updating. Understanding these nuances across generations allows for tailored solutions to minimize technological challenges and promote digital inclusion and professional success for these women. Future studies should explore creating a structural equation model to test the standard dimensions found. © 2024 The Authors.

关键词： Brazil Digital Influencers Generations X, Y and Z Technology Tree of Similitude

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Collaboration-Aware Hybrid Learning for Knowledge Development Prediction 24

Collaboration-Aware Hybrid Learning for Knowledge Developmen...

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33rd ACM Web Conference, WWW 2024

作者： Chen, Liyi Qin, Chuan Sun, Ying Song, Xin Xu, Tong Zhu, Hengshu Xiong, Hui School of Data Science University of Science and Technology of China Hefei China Career Science Lab BOSS Zhipin Beijing China PBC School of Finance Tsinghua University Beijing China Guangzhou China Baidu Inc. Beijing China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400701719

In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge gaps and align their learning initiatives with evolving job requirements. However, it still confronts challenges in exploring the influence of collaborative networks on knowledge development and adapting to ecological situations in working environment. To this end, in this paper, we propose a Collaboration-Aware Hybrid Learning approach (CAHL) for predicting the future knowledge acquisition of employees and quantifying the impact of various knowledge learning patterns. Specifically, to fully harness the inherent rules of knowledge development, we first learn the knowledge co-occurrence and prerequisite relationships with an association prompt attention mechanism to generate effective knowledge representations through a specially-designed Job Knowledge Embedding module. Then, we aggregate the features of mastering knowledge and work collaborators for employee representations in another Employee Embedding module. Moreover, we propose to model the process of employee knowledge development via a Hybrid Learning Simulation module that integrates both collaborative learning and self learning to predict future-acquired job knowledge of employees. Finally, extensive experiments conducted on a real-world dataset clearly validate the effectiveness of CAHL. © 2024 ACM.

关键词： Forecasting

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural Computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph Neural Network 27

FL-GNN: Efficient Fusion of Fuzzy Neural Network and Graph N...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Du, Boyu Zhou, Jingya Liu, Ling She, Xiaolong School of Computer Science and Technology Soochow University China Engineering Lab.of Big Data and Intelligence of Jiangsu Province Soochow University China State Key Lab.for Novel Software Technology Nanjing University China School of Computer Science Georgia Institute of Technology United States

ISBN: (纸本)9781643685489

Graph Neural Networks (GNNs) have become an important graph feature learning paradigm that is extensively applied to graph inference ***, GNNs still have limitations in some aspects such as representation capability, interpretability, *** this end, this paper introduces a novel hybrid Fuzzy-Logic Graph Neural Network (FL-GNN) that synergistically combines Fuzzy Neural Network (FNN) and GNN to effectively capture and aggregate local information flows within the ***-GNN exhibits three distinctive ***, we incorporate a specialized structure fuzzy rule to enhance FL-GNN's graph inference capability, surpassing representative GNN ***, we augment the interpretability of FL-GNN by integrating analytical exploration methods from two perspectives: Fuzzy Inference System and Message Passing Algorithm (MPA).Lastly, we refine the structure of FL-GNN based on MPA to optimize its calculation complexity and consequently improve learning *** results show that FL-GNN outperforms existing representative graph neural networks for graph inference tasks. © 2024 The Authors.

关键词： Graph neural networks

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Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

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Plant Phenomics 2024年第2期6卷 349-363页

作者： Yandong Wu Weiliang Wen Shenghao Gu Guanmin Huang Chuanyu Wang Xianju Lu Pengliang Xiao Xinyu Guo Linsheng Huang National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Nongxin Science&Technology(Beijing)Co. LtdBeijing 100097China

The 3-dimensional(3D)modeling of crop canopies is fundamental for studying functional-structural plant *** studies often fail to capture the structural characteristics of crop canopies,such as organ overlapping and resource *** address this issue,we propose a 3D maize modeling method based on computational *** initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture.

关键词： intelligence modeling based canopies computational dimensional maize three

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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PROBLEMS: EASY TO SAY BUT DIFFICULT TO WRITE 24

PROBLEMS: EASY TO SAY BUT DIFFICULT TO WRITE

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24th International Conference on engineering Design, ICED 2023

作者： Giordano, Vito Consoloni, Marco Chiarello, Filippo Fantoni, Gualtiero School of Engineering Department of Energy Systems Land and Construction Engineering University of Pisa Italy School of Engineering Department of Civil and Industrial Engineering University of Pisa Italy B4DS - Business Engineering for Data Science Lab University of Pisa Italy

Patents are an invaluable source of data that can be beneficial for engineering Design (ED). Patenting is one of the main means for disclosing the inventive process. For this reason, the description of the problem solved should also be included in any patents. The ED literature lacks a proper definition of a problem, resulting in a fragmented scenario. Prior studies have employed Text Mining (TM) to extract problems from patents. We argue that TM can assist ED researchers in understanding how problems are articulated in text. Based on the literature, we propose two hypotheses: (1) problem-related text exhibits a negative sentiment polarity compared to other sections of patents;(2) problem-related keywords identified in the literature are predominantly used to describe problems rather than other aspects. We analyse Japanese patents to validate our hypotheses, since they explicit Problem and Solution in the abstract. Finally, we compare our results with a set of problem-related sentences extracted from USPTO patents. Our study reveals a higher positive sentiment in problem-related sentences compared to solution-related ones and highlights the inadequacy of using problem-related keywords alone to differentiate between the two. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Conceptual design

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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The Impact of Persuasion, Usefulness, and Credibility on User Intent in E-commerce Applications 11

The Impact of Persuasion, Usefulness, and Credibility on Use...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Lucas da Silva, Mariana Aires de Alencar Mariano, Ari Melo Santos, Maíra Rocha Collazos, Cristian Calderon Murillo, Patricia Daza Sandoval, Teresa Rocio Dalenz Loaiza-Torres, Jose Department of Production Engineering University of Brasília Brazil Department of Computer Science University of Brasilia Brazil DataLab-Data Lab University of Brasilia Brazil Universidad San Francisco Xavier de Chuquisaca Bolivia

This research aims to present priority actions to improve user experience and fashion consumer design. The Partial Least Square (PLS) technique was used to do this. One hundred thirteen responses were collected for the questionnaire, and an average composite reliability of 0.86 was obtained. The model explained Intention to Use by 37.4%, while Persuasion was explained by 27.9%. Persuasion was the variable that had the most significant impact on intention to use (26.88%), demonstrating that the more persuasive a website or application is, the more likely it is to be used. This is reflected in different segments, as evidenced by campaigns to raise awareness about AIDS and the use of condoms. In addition, usefulness proved to be a crucial factor in increasing persuasiveness (20.11%), indicating that the greater the utilitarian value of the app, the more persuasive it is. Credibility is also essential, influencing both persuasiveness (7.83%) and usefulness (10.16%), with credibility questions focused on verifying whether the honesty of information can influence purchase intent and increase the perception of usefulness and credibility of the website or app. © 2024 The Authors.

关键词： Marketplaces

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