Tourism is admittedly perceived as a key driver for many national economies, displaying a large share in the GDP of many countries around the globe, including Greece. However, evolving trends of the sector and the not...
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Sustainability in procurement is gaining popularity and subsequent momentum globally. However, the aspect of social sustainability, particularly from a Nigerian perspective, is given little, if any focus. based on thi...
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the proceedings contain 68 papers. the topics discussed include: CLDM: a clothing landmark detector based on mask R- CNN;how to determine minimum support in association rule;towards interpretation of abstract instruct...
ISBN:
(纸本)9781450376655
the proceedings contain 68 papers. the topics discussed include: CLDM: a clothing landmark detector based on mask R- CNN;how to determine minimum support in association rule;towards interpretation of abstract instructions using declarative constraints in temporal logic;estimation of heat of formation for chemical systems using the lasso regression-based approach;exploiting deep neural networks for intention mining;performance evaluation for class center-based missing data imputation algorithm;digital shop floor management: a practical framework for implementation;comparison method for handling missing data in clinical studies;geospatial data sharing: preliminary studies on issues and challenges in natural disaster management;a distributed directed breadth-first search algorithm based on message-passing model for efficient line-of-sight computation;and research on product detection algorithm for intelligent refrigerator.
Withthe increasing numbers of clients connected to the Internet, the IPv4 address pool is nearly saturated. the industry introduces solutions of using IPv4/IPv6 dual-stack connection or NAT to mitigate the saturation...
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the inverted pendulum system is a typical nonlinear, multivariable, and strong coupling system. In this paper, the structure of the inverted pendulum is introduced in detail. the inverted pendulum produced by Quanser ...
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Networks are one of the most powerful structures for modeling problems in the real world. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding learns from feat...
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ISBN:
(纸本)9781665473316
Networks are one of the most powerful structures for modeling problems in the real world. Many machine learning algorithms, however, require that each input example is a real vector. Network embedding learns from feature representations of nodes and links in a network, and converts it to vectors. Community structure is an important feature of the network, which represents the relationship among nodes and attracts the attention of relevant researchers. Many algorithms have been developed to identify the community structure. these algorithms usually identify different communities in the network, generating different types of information. In this paper, we propose a "Community Splitter" model based on random walk and RNN (Recurrent Neural Networks) that combines the node information generated by multiple community detection algorithms to improve node representation and link prediction. Extensive experiments on nine real datasets demonstrate that our proposed Community Splitter model has a significant prediction power compared to state-of-the-art link prediction models.
the application of recommendation systems online services is becoming more and more extensive. However, most existing recommendation algorithms centralize multi-party information into a central processor, which may le...
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ISBN:
(纸本)9781665473316
the application of recommendation systems online services is becoming more and more extensive. However, most existing recommendation algorithms centralize multi-party information into a central processor, which may lead to the risk of privacy leakage. And many enterprises or institutions still have the problem that data cannot be shared. Federated learning has been introduced into recommendation algorithms for privacy- aware distributed learning. A typical federated learning is that each client uses local data to train a shared model, the server uses their gradient information to form a global model, and then each client updates. In this paper, we propose a federated deep recommendation algorithm called FedHe-mlp that applies a federated deep learning for data privacy protection, and combines heterogeneous information network (HIN) and matrix factorization technique for better prediction performance. First, each client obtains heterogeneous information through meta- paths, then we combine matrix factorization and heterogeneous information to mine the latent features and heterogeneous features of each client. Finally, We propose a deep neural network that considers features from multiple views. Extensive experiments on three public datasets demonstrate that FedHe- mlp can provide excellent convergence speed, recommendation accuracy, and communication efficiency while preserving data privacy.
In the era of digitalization, the efficiency of machine tools can be improved due to optimized machining simulations. However, actual simulations only consider the kinematics of the machine tool and not the influence ...
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ISBN:
(纸本)9781728143064
In the era of digitalization, the efficiency of machine tools can be improved due to optimized machining simulations. However, actual simulations only consider the kinematics of the machine tool and not the influence of the machining data, which are changed by experienced machine operators rather frequently. Moreover, these changes of machining data are not always properly communicated. For various reasons, this important knowledge of the machine operators remains tacit. Transferring tacit knowledge of the machine tool operators at the shop floor automatically to a database has not been possible so far. this paper describes a workflow for the acquisition of verified machining data and how to transfer them to a database.
Withthe continuous development of today9;s society, the ability to respond to unexpected events and emergencies is crucial for ensuring public safety and protecting property. However, traditional emergency respons...
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ISBN:
(数字)9798350307146
ISBN:
(纸本)9798350307153
Withthe continuous development of today's society, the ability to respond to unexpected events and emergencies is crucial for ensuring public safety and protecting property. However, traditional emergency response methods have some limitations and challenges, such as on-site operational risks, insufficient human resources, and difficulties in real-time decision-making. the use of mechanical equipment to perform specific tasks in unknown environments and scenarios or to analyze and make decisions based on the on-site conditions has become a trend. On this premise, this paper proposes a digital twin platform based on Unity3D software. the system collects real-time information in the real environment and feeds it back to the virtual platform. It uses a four-wheel, dual-drive intelligent car as the motion carrier, which carries a Six-degree of freedom industrial robotic arm to work synchronously, and uses an intelligent camera to detect environmental information, to realize the three-dimensional perception interaction between virtual and reality, and it can achieve remote control of real rescue robots by manipulating the robotic arm and its carrier in twin scenarios to replicate the actions of industrial equipment in real scenarios. the experimental results indicate that the system exhibits good performance and flexibility in emergency response. through remote control of industrial robotic arms and smart cars, as well as the use of digital twin models, the operators can respond to emergencies in a better way and make timely and accurate decisions, providing early warning as well as making search and rescue plans for emergency rescue to work.
Withthe development of smart mobile devices, location privacy has gained attention from both academia and industry. In recent years, a variety of location privacy definitions from different perspectives have been pro...
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ISBN:
(纸本)9781728165974
Withthe development of smart mobile devices, location privacy has gained attention from both academia and industry. In recent years, a variety of location privacy definitions from different perspectives have been proposed to quantify location privacy and compare location privacy protection mechanisms (LPPMs). these definitions, however, have some drawbacks. In this paper, we propose a location privacy metric for discrete location information which improves the quantification of distance between the prior and posterior distribution of an adversary who may hold background knowledge in differential privacy. Furthermore, we develop a non-convex optimization problem and construct a near-optimal mechanism. We evaluate our proposed metric by comparing it to the state-of-the-art definitions including Shokri's incorrectness, Andes's geo-indistinguishability and Dong's DPLO. We also evaluate our proposed mechanism withthe optimal mechanisms based on the afore mentioned existing definitions. We make experiments on both simulation and realworld dataset, and the results show that our proposed metric and mechanism have the ascendant position.
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