Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method. 相似文献
Heterogeneous information networks, which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects, are ubiquitous in the real world. In this paper, we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network, and we propose a new method named DEM short for Deep Entity Matching. In contrast to the traditional entity matching methods, DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching. Importantly, we incorporate DEM with the network embedding methodology, enabling highly efficient computing in a vectorized manner. DEM’s generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly. To illustrate its functionality, we apply the DEM algorithm to two real-world entity matching applications: user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks. Extensive experiments on real-world datasets demonstrate DEM’s effectiveness and rationality.
The rapid growth of data and the requirement of designers to track massive data to obtain design stimuli have posed challenges to conceptual design, thereby promoting the development of data-driven design. Concept networks precisely capture design information from a large volume of unstructured and heterogeneous textual data and saliently decrease time and labor cost for designers to read texts, which creates new opportunities for developing a smart product design system. To advance data-driven design, this study proposes the novel function-structure concept network (FSCN) construction method, which combines sentence parsing and word/phrase extraction to integrate functional and structural information. Furthermore, a network analysis method is proposed to explore design information associations that contain both explicit and implicit associations together and thereby recommend them simultaneously to designers as inspirational stimuli to support design ideation. This approach can enhance designers' capabilities to build associations between design information, conceive new design ideas during conceptual design, and increase creativity for solving design problems. The proposed FSCN construction and analysis method can be used as an auxiliary tool to visualize associations among design information so as to inspire idea generation in the early stage of conceptual design. An illustrative example was used to validate the practicability of the proposed methodology. The code of the proposed method is available at https://github.com/KWflyer/FSCN. 相似文献