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检索条件"丛书名=Communications in Computer and Information Science"

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Exploring the Innovation Capabilities of South African SMEs: A Principal Component Analysis Approach 4th

Exploring the Innovation Capabilities of South African SMEs:...

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4th International Conference on Society 5.0

作者： Weilbach, Lizette Smuts, Hanlie Boolaky, Aleesha Chittoo, Hemant Mauree-Narrainen, Diroubinee Padachi, Kesseven Ramphul, Needesh Univ Pretoria Pretoria South Africa Univ Technol Mauritius Port Louis Mauritius

ISBN: (纸本)9783031714115;9783031714122

Small and Medium-sized Enterprises (SMEs) play a critical role in the economic growth and innovation of any nation. Despite their importance, SMEs often face significant challenges that hinder their innovation processes, including financial limitations and restricted access to information and technology. Additionally, understanding how open innovation practices can sustain competitive advantages in fast-evolving global markets is essential. This study investigates the innovation capabilities in South African SMEs, focusing on their relative importance in driving innovation, highlighting their multifaceted approach to drive innovation and remain competitive. A survey of 220 South African SMEs evaluated 19 innovation capabilities across 8 dimensions using a 5-point Likert scale. Principal component analysis identified five key factor groupings: risk management, customer acquisition, knowledge management, business networks, and innovation/product enhancement. The findings reveal that SMEs strategically manage risks, leverage customer insights, and cultivate networks to enhance their innovation capabilities, thereby positioning themselves as competitive players. By pinpointing the primary drivers of innovation, this study proposes strategies to strengthen these capabilities in the dynamic global economic landscape.

关键词： SME innovation innovation capacity innovation capabilities principal component analysis

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SHGNN: A Substructure-Aware and High Expressive Graph Neural Network for Graph Classification 5th

SHGNN: A Substructure-Aware and High Expressive Graph Neural...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Sun, Jinyong Dong, Zhiwei Sun, Zhigang Wen, Yimin Zhao, Xiang Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Graph Neural Networks (GNNs), with their remarkable capabilities in learning graph features, have achieved success in graph classification tasks. However, the Message Passing GNNs (MP-GNNs), sharing the feature aggregation mechanism with the 1-Weisfeiler-Leman (1-WL) graph isomorphism test, suffer from limited expressiveness in downstream tasks, such as graph classification. To enhance the expressive power of GNNs, we propose a substructure-aware and high expressive graph neural network model. Firstly, Ego substructures are extracted from a graph and fed into a MP-GNN to obtain substructure-level features. Then, a count-sensitive neighborhood hashing method is introduced to encode the structural characteristics of Ego substructures, which are then injected into the corresponding substructure-level features to enhance the model's expressiveness. Next, an Ego-substructure-aware Transformer is utilized to extract global features from the graph, which use a token to fuse the interaction information among graphs' nodes, thereby generating graph-level feature vector. Finally, we apply a Multi-Layer Perceptron (MLP) followed by a softmax layer to classify graphs. Theoretical analysis demonstrates that the proposed SHGNN ismore powerful than 1-WL test. Experimental results show that, compared to the state-of-the-art graph deep learning methods, our model not only outperforms 1-WL in expressiveness, but also achieves higher classification accuracy.

关键词： graph classification graph neural networks substructure-aware neighborhood hashing Transformer

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Monitoring Traffic Congestion Using Trust-Based Smart Road Signs 12th

Monitoring Traffic Congestion Using Trust-Based Smart Road S...

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2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Abidi, Rihab Sahli, Nabil Ben Azzouna, Nadia Trojet, Wassim Hoblos, Ghaleb Univ Tunis ISG SMART Lab 41 Ave Liberte Tunis 2000 Tunisia Normandy Univ Unirouen Esigelec Irseem Technopole MadrilletAve Galilee F-76800 St Etienne Du Rouvray Normandie France German Univ Technol GUtech POB 1816 Muscat Oman Higher Coll Technol UAE POB 25026 Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031709654;9783031709661

The evolution of Intelligent Transportation Systems (ITSs) enabled the emergence of traffic management applications, with the aim to enhance the traffic flow and ease the congestion by monitoring the traffic. However, the efficiency of these applications resides on the accuracy of the shared traffic information. Accordingly, trust management models are applied to secure the Vehicular Ad-hoc NETwork (VANET) and to assess the reliability of the data shared within the vehicular network. In this paper, we propose a proof-of-concept of the trust framework proposed in [2]. The main objective is to observe the utility of applying trust management models to the intelligent transportation systems. The simulation results show that deploying trust-based Smart Road Signs (SRSs) helps to alleviate the traffic congestion around junctions by displaying the traffic state to users and offering them the opportunity to take alternative roads.

关键词： Intelligent transportation system Trust management Infrastructure-Based architecture

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Short-Term Prediction of Electricity Demand Using Deep Learning Models

Short-Term Prediction of Electricity Demand Using Deep Learn...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Gomez, Oscar Montano, Manuel Torres, Washington Carguachi, Jose Gutierrez, Dario Castellanos, German Univ Estatal Peninsula Santa Elena La Libertad Ecuador Univ Nacl Colombia Manizales Colombia Pontificia Univ Catolica Ecuador Ambato Ecuador

ISBN: (纸本)9783031834318;9783031834325

The present article performs short-term electricity demand prediction using deep learning tools for the Ecuadorian electrical system, specifically in the province of Santa Elena, Ecuador. The prediction was carried out using two neural network models: Long Short-Term Memory (LSTM) and Fully Connected Neural Network (FCNN). Additionally, exogenous variables such as temperature and relative humidity were incorporated. Historical data of exogenous variables were obtained from the NASA POWER vertical bar DAVe website with the aim of exploring the relationship between these variables and energy consumption. Subsequently, data processing was conducted, and both neural networks were trained. The results indicate robustness in electricity demand prediction for both architectures;however, the LSTM network exhibits significantly higher accuracy, evidenced by a Coefficient of Determination (R-2) of 0.986 compared to FCNN. These findings suggest that LSTM has an enhanced capacity to model and anticipate electricity demand, as it considers the correlation with exogenous variables that affect electricity consumption.

关键词： Deep Learning Energy Consumption FCNN LSTM Prediction

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A Model Towards Enabling Tutors to Fulfil a Role in Attenuating Source Code Plagiarism 53rd

A Model Towards Enabling Tutors to Fulfil a Role in Attenuat...

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53rd Southern African computer Lecturers Association Meeting

作者： Smit, Imelda North West Univ Unit Data Sci & Comp Vanderbijlpark South Africa

ISBN: (纸本)9783031824777;9783031824784

Source-code plagiarism has been a persistent problem in the academic computing environment, but its prevalence intensified due to the disruption caused by the emergency remote teaching and learning that the COVID-19 pandemic enforced. This is especially a concern in the context of teaching novice programmers to code. The pandemic compelled residential universities to accommodate students in ways previously unheard of, even in the context of non-residential establishments. Students found themselves in an environment where preparation for assessments was not necessary since they had access to material, peers and whatever help they could line up to pass. The pandemic is a distant memory, but academia will not return to its pre-Covid status, and to add to this situation, generative artificial intelligence emerged in 2023 as a playmate on this very playground. In this context, tutoring is of utmost importance when teaching novice programmers to code, and therefore it makes sense to learn from past approaches to guide this support function in future. With this premise in mind, an interpretivist perspective was adopted, and qualitative data were gathered, centering on focus group interviews, as well as interviews with tutors unavailable for focus group sessions. These tutors supported classes of introductory programming students over a period of three years. Within this setting, the lecturer-as-researcher reflects on mitigating source code plagiarism holistically. This paper has as its goal proposing a model to enable and support tutors in their role to attenuate source code plagiarism.

关键词： Academic integrity Novice programming Source code plagiarism Tutoring

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A Parallel Multi-threading Global Energy Balance for a Room Thermal Analysis in an Unsteady State 11th

A Parallel Multi-threading Global Energy Balance for a Room ...

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11th Latin American Conference on High Performance Computing

作者： Torres-Aguilar, Carlos Moreno-Bernal, Pedro Nesmachnow, Sergio Rossit, Diego Univ Juarez Autonoma Tabasco Cunduacan Mexico Univ Autonoma Estado Morelos Cuernavaca Morelos Mexico Univ Republica Montevideo Uruguay Univ Nacl CONICET Dept Engn INMABB Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783031800832;9783031800849

Unsteady-state heat transfer problems require considerable computation time for the numerical solution, due to the high spacetime dimensionality of realistic scenarios. This article presents a parallel multi-threading global energy balance method for the thermal evaluation of a room in unsteady state composed of different materials. The parallel numerical method applies the shared memory paradigm using a pool of sub-processes (threads) executing in different processing units (cores) for the thermal evaluation of a room in unsteady state using simulations. The experimental validation compares the proposed parallel method with an analytical solution reported in the literature for a case study considering climate conditions of Merida City in Yucatan, Mexico. Significant reductions are reported in the execution times and accurate performance results are obtained when using up to 16 computing resources.

关键词： Global energy balance energy efficiency High Performance Computing Multithreading OpenMP

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A Survey on Deep Learning-Based Medical Image Registration 5th

A Survey on Deep Learning-Based Medical Image Registration

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Xu, Ronghao Liu, Chongxin Liu, Shuaitong Huang, Weijie Zhang, Menghua Univ Jinan Sch Elect Engn Jinan 250000 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

In recent years, various methods have been proposed to address the fundamental task of medical image registration in medical image analysis. This paper systematically reviews the research progress in medical image registration using deep learning techniques. These methods utilize deep learning algorithms to achieve precise alignment of medical image data from different modalities, time points, or sources, thereby providing dependable help for clinical identification and care. This paper seeks to provide researchers and practitioners with the most current technological advances and potential applications in the realm of medical image registration.

关键词： Image registration Deep learning CNN Supervised Learning

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Does Simple Trump Complex? Comparing Strategies for Adversarial Robustness in DNNs 5th

Does Simple Trump Complex? Comparing Strategies for Adversar...

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5th Southern African Conference for Artificial Intelligence Research

作者： Brooks, William Davel, Marelie H. Mouton, Coenraad Northwest Univ Fac Engn Potchefstroom South Africa Ctr Artificial Intelligence Res Cape Town South Africa Natl Inst Theoret & Computat Sci Stellenbosch South Africa South African Natl Space Agcy Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Deep Neural Networks (DNNs) have shown substantial success in various applications but remain vulnerable to adversarial attacks. This study aims to identify and isolate the components of two different adversarial training techniques that contribute most to increased adversarial robustness, particularly through the lens of margins in the input space - the minimal distance between data points and decision boundaries. Specifically, we compare two methods that maximize margins: a simple approach which modifies the loss function to increase an approximation of the margin, and a more complex state-of-the-art method (Dynamics-Aware Robust Training) which builds upon this approach. Using a VGG-16 model as our base, we systematically isolate and evaluate individual components from these methods to determine their relative impact on adversarial robustness. We assess the effect of each component on the model's performance under various adversarial attacks, including AutoAttack and Projected Gradient Descent (PGD). Our analysis on the CIFAR-10 dataset reveals which elements most effectively enhance adversarial robustness, providing insights for designing more robust DNNs.

关键词： Deep Neural Networks Adversarial robustness Margins in input space Dynamics-Aware Robust Training Large margin loss Adversarial attacks

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Harnessing Technology for Mangrove Research in the Western Indian Ocean to Enhance Climate Change Resilience 4th

Harnessing Technology for Mangrove Research in the Western I...

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4th International Conference on Society 5.0

作者： Sunkur, Reshma Kantamaneni, Komali Bokhoree, Chandradeo Rathnayake, Upaka Fernando, Michael Univ Technol Mauritius Doctoral Sch La Tour Koenig Mauritius Univ Cent Lancashire United Nations SPIDER UK Reg Support Off Preston Lancs England Univ Cent Lancashire Sch Engn & Comp Preston Lancs England Atlantic Technol Univ Dept Civil Engn & Construct Fac Engn & Design Sligo 91 YW50 Ireland

ISBN: (纸本)9783031714115;9783031714122

Mangroves in the Western Indian Ocean face increasing threats from climate change such as rising sea levels and storm surges. To increase mangrove resilience in the region, there is a need to leverage technological innovations for research, management and monitoring purposes. The aim of this paper was thus to review the role of technology in supporting mangrove research regionally. A bibliometric analysis was conducted using Google Scholar for the period 2004-2024. From 31, 934 articles retrieved, 160 records were selected for the assessment and keyword co-occurrence was conducted on VosViewer. Remote sensing, GIS and modeling techniques were highlighted as key tools for monitoring mangrove health, extent and change over time. Community engagement through participatory strategies such as PGIS was noted as effective mangrove conservation and resilience building efforts. The keyword co-occurrence analysis showed that there was a strong link between mangrove, climate change and sea level rise suggesting the importance of mangroves in coastal resilience. By embracing advancements in technology and fostering collaboration between researchers, local communities and practitioners, coastal nations' resilience to climate change impacts can be enhanced by better managing and monitoring mangroves.

关键词： mangrove coastal management climate change

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Research on the Effectiveness of Wireless Networks Cellular communications Based on Digital Technologies 2nd

Research on the Effectiveness of Wireless Networks Cellular ...

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2nd International Conference on information Technologies and Their Applications

作者： Ibrahimov, Bayram G. Aliyev, Almaz Rafizade, Ulvi R. Azerbaijan Tech Univ Dept Radioelect & Aerosp Syst H Javid Ave 25 AZ-1073 Baku Azerbaijan Mil Def Univ Republ Azerbaijan Baku Azerbaijan Mingechaur State Univ Dept Informat Technol Zahid Khalilov 23 AZ-4015 Mingechaur Azerbaijan Azerbaijan Technol Univ Dept Comp Engn & Telecommunicat Sh I Khatai Ave 103 AZ-2011 Ganja Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

The advantages using the architectural concept of the Network 2030 technology focus group using the recommendations ITU-T, FG NET-2030 (FG NET-2030) for building highly efficient multiservice wireless packet-switched cellular networks when providing a wide range multimedia services are analyzed. A review of the results of the Network 2030 study, carried out by the ITU-T FG NET-2030 focus group to study the capabilities and principles of building fixed communication networks for the period up to 2030 and beyond, is reviewed and analyzed. The efficiency indicators wireless cellular networks based on digital technologies have been studied and performance quality criteria have been selected to ensure acceptable quality of service QoS (QoS, Quality of Service) and Quality of Experience (QoE, Quality of Experience) of useful and service traffic. Based on the research, a new approach has been developed to create a method for calculating performance indicators of multi-service wireless cellular networks using end-to-end digital technologies. A general block diagram model of a digital wireless communication system under the influence of various noise sources is proposed to study the reliability of the transmission and reception discrete messages. Based on the proposed calculation method, complex indicators communication quality such as throughput were studied, taking into account the self-similarity property of heterogeneous traffic, the probabilistic-time characteristics of networks, and the energy consumption hardware and software of mobile wireless networks. As a result of the study, analytical expressions were obtained for estimating the parameters of the output modulated communication channel, predicting efficiency in the time and frequency domains, signal-to-noise ratio per bit, as well as energy consumption indicators in mobile wireless communication networks.

关键词： Multiservice Network Hardware and Software Complex efficiency Probabilistic-Time Characteristics Efficiency Wireless Cellular Communication Networks

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