作者机构：
[Zhou K.; Zhou S.; Liu J.] College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430023, China;[Zhou J.] College of Food Science and Engineering, Wuhan Polytechnic University, Wuhan, 430023, China;[Li G.] Enterprise School Joint Innovation Center, Qianjiang Jujin Rice Industry Co., Ltd., Hubei, China
会议名称：
15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020
会议时间：
23 October 2020 through 25 October 2020
关键词：
Information entropy theory;Multi-objective optimization model;Prediction equations;Residual analysis;Specialized raw material
期刊：
International Journal of Parallel, Emergent and Distributed Systems,2021年36(1):44-50 ISSN：1744-5760
通讯作者：
Kang Zhou
作者机构：
[Shuo Liu; Kang Zhou; Jiangrong Liu] Department of Math and Computer, Wuhan Polytechnic University, Wuhan, People’s Republic of China;[Huaqing Qi] Department of Economics and Management, Wuhan Polytechnic University, Wuhan, People’s Republic of China
通讯机构：
[Kang Zhou] D;Department of Math and Computer, Wuhan Polytechnic University, Wuhan, People’s Republic of China
期刊：
Communications in Computer and Information Science,2020年 1149: 31-43 ISSN：1865-0929
通讯作者：
Zhang, F.
作者机构：
[Jin Z.; Cui F.; Xu M.] Wuhan Digital and Engineering Institute, Wuhan, Hubei 430205, China;[Zhang F.] School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, Hubei 430023, China
通讯机构：
[Zhang, F.] S;School of Mathematics and Computer Science, China
会议名称：
13th Chinese Conference on Trusted Computing and Information Security, CTCIS 2019
会议时间：
24 October 2019 through 27 October 2019
会议论文集名称：
Trusted Computing and Information Security
摘要：
Recommender systems are widely used to provide users with items they may be interested in without explicitly searching. However, they suffer from low accuracy and scalability problems. Although existing clustering techniques have been incorporated to solve these inherent problems, most of them fail to achieve further improvement in recommendation accuracy because of ignoring the correlations between items and the different effects of item attributes on recommendation results. In this article, we propose a novel recommendation algorithm to alleviate these issues to a large extent. First of all, users and items are clustered into multiple cluster subsets based on user-item rating matrix and item attribute deriving from domain experts, respectively. Then we use a selection method relying on item attribute to mine candidate items and only their predictions will be calculated in the next step, which can save the computation time greatly. Furthermore, by weighting the predictions with TF-IDF (Term Frequency-Inverse Document Frequency) weights, the top-N recommendations are generated to the target user for return. Finally, comparative experiments on two real datasets demonstrate that this algorithm provides superior recommendation accuracy in terms of MAE (Mean Absolute Error) and RMSE (Root Mean Square Error).
作者机构：
[Xu, Xiangrui; Li, Yaqin; Yuan, Cao] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构：
[Yuan, Cao] W;Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
关键词：
Deep neural network;ownership verification;security and privacy;serial number;watermarking
摘要：
The power of deep learning and the enormous effort and money required to build a deep learning model makes stealing them a hugely worthwhile and highly lucrative endeavor. Worse still, model theft requires little more than a high-school understanding of computer functions, which ensures a healthy and vibrant black market full of choice for any would-be pirate. As such, estimating how many neural network models are likely to be illegally reproduced and distributed in future is almost impossible. Therefore, we propose an embedded & x2018;identity bracelet & x2019; for deep neural networks that acts as proof of a model & x2019;s owner. Our solution is an extension to the existing trigger-set watermarking techniques that embeds a post-cryptographic-style serial number into the base deep neural network (DNN). Called a DNN-SN, this identifier works like an identity bracelet that proves a network & x2019;s rightful owner. Further, a novel training method based on non-related multitask learning ensures that embedding the DNN-SN does not compromise model performance. Experimental evaluations of the framework confirm that a DNN-SN can be embedded into a model when training from scratch or in the student network component of Net2Net.
摘要：
Traffic network transportation optimization problem (TNTOP) has important applications in logistics distribution fields. In various disciplines, methods about the solutions-termed TNTOP can have shown promising performance from different types of detection, at different conditions. Due to the limitatioins of the calculation speed of traditonal algorithms, it is rare that a simple unmodified method provides complete techniques of tackling large-scale TNTOP. We use the term P systems to solve the above limitatioins. Specifically, it is a tissue-like P system with four cells based on particle swarm algorithm, referred to as MPSO. In this system, the modified prim algorithm and the position-updated mechanism are adopted to generate and update all particle individuals, velocity-updated mechanism and an exchange-tree strategy are adopted to balance exploration and exploitation processes. Besides, some special strategies are also added to this systems. Numerous experiments are presented to verify the performance of the MPSO. The results show that it can generate the individuals of higher quality in shorter computation time when comparing to other benchmark algorithms. These empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving TNTOP in terms of both quality and speed.
