Applying Deep Learning Technologies to Evaluate the Patent Quality with the Collaborative TrainingRead the full article
Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas in this fast moving field.
Chief Editor Dr Cai is an Associate Professor in the Department of Computer Science at Georgia State University, USA and an Associate Director at INSPIRE Center.
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Applying Deep Learning Technologies to Evaluate the Patent Quality with the Collaborative Training
As the country vigorously promotes the development of science and technology and tries to enhance independent innovation capabilities, more and more attention is paid on the protection of technology ownership. In recent years, China has developed rapidly in many scientific and technological fields, and the number of patent applications increased year by year. However, various patent quality problems including immature patent technology and low patent authorization rate appear. The indicators of patent quantification and quality evaluation are studied in this paper. First, we quantify the patent quality evaluation indicators and combine the content of the patent text to build a patent evaluation model. US patents with patent grade labels are used for training with multitask learning technology. Second, the evaluation model is transferred from the English patents to the Chinese patents, in which the active learning technology and transfer learning technology are used to minimize the work of manual labeling. Finally, a Chinese patent quality evaluation model based on collaborative training was designed and implemented. Methods used in this experiment have notably improved the prediction effect of the model and achieved a better migration effect. A large number of experimental results show that the Chinese patent quality evaluation model has achieved good evaluation results. This research uses deep learning and natural language processing technology to carry out research on patent quality evaluation models from different perspectives, to provide patent decision support for related companies, and to point out research directions for research institutions and patent inventors.
A Recommendation Model for College Career Entrepreneurship Projects Based on Deep Learning
The recommendation system is an active, personalized, and real-time technology platform proposed in the 1990s to solve the problem of information overload. The recommendation system can constantly adjust the recommendation results according to the real-time behaviors of users. In other words, if the user’s interest changes, the recommendation system can present different information to the user. Therefore, the recommendation system is the best way to solve the problem of information overload in entrepreneurial projects. Based on the ConvMF algorithm, this paper proposes an entrepreneurial project recommendation algorithm based on a deep neural network and matrix decomposition. A deep neural network was established for the extraction of the hidden features of entrepreneurial projects, and a convolution neural network was used to process the text description information of entrepreneurial projects. One-hot coding was used to process the regional characteristics and financing round characteristics of entrepreneurial projects, and word embedding was used to process the label features of entrepreneurial projects. The implicit features of users are extracted from the user’s rating matrix using matrix decomposition technology. Finally, recommendations are made according to the implicit characteristics of users and the items learned.
User-Level Membership Inference for Federated Learning in Wireless Network Environment
With the rise of privacy concerns in traditional centralized machine learning services, federated learning, which incorporates multiple participants to train a global model across their localized training data, has lately received significant attention in both industry and academia. Bringing federated learning into a wireless network scenario is a great move. The combination of them inspires tremendous power and spawns a number of promising applications. Recent researches reveal the inherent vulnerabilities of the various learning modes for the membership inference attacks that the adversary could infer whether a given data record belongs to the model’s training set. Although the state-of-the-art techniques could successfully deduce the membership information from the centralized machine learning models, it is still challenging to infer the member data at a more confined level, the user level. It is exciting that the common wireless monitor technique in the wireless network environment just provides a good ground for fine-grained membership inference. In this paper, we novelly propose and define a concept of user-level inference attack in federated learning. Specifically, we first give a comprehensive analysis of active and targeted membership inference attacks in the context of federated learning. Then, by considering a more complicated scenario that the adversary can only passively observe the updating models from different iterations, we incorporate the generative adversarial networks into our method, which can enrich the training set for the final membership inference model. In the end, we comprehensively research and implement inferences launched by adversaries of different roles, which makes the attack scenario complete and realistic. The extensive experimental results demonstrate the effectiveness of our proposed attacking approach in the case of single label and multilabel.
Resource Sharing of Smart City Based on Blockchain
The concept of smart city refers to the improvement of the quality of life of the city by making full use of idle resources by sharing. However, limited by the technical level, the current resource sharing system mostly adopts centralized data storage mode. Systems managed in this way are vulnerable to multiple threats. The tested blockchain technology with the characteristics of decentralization and tamper resistance can effectively prevent various risks. Starting with the architecture of blockchain intelligent contract, this paper puts forward a structural optimization factor model of intelligent contract. To optimize the structure of blockchain intelligent contract, the gas optimization theory is put forward by changing the order, reducing the use of costly EVM data fields, reducing redundant fields, and optimizing intelligent contract codes. Experimental analysis of the proposed model is carried out, and the effectiveness of the proposed method is verified by comparing the transaction execution time of cost calculation with the cost of executing gas, which can provide reference for the selection of intelligent contract organization structure of smart city resource sharing system.
The Hybrid Traffic Offloading Mode for Disaster-Resilient Communication Networks Based on User Mobility
Emergency communication systems play a major role in disaster-relief environments. In terms of the public safety research, the emergency relief communication system can provide a high system capacity for networks based on the development of Long-Term Evolution. However, in the event of a disaster, mass traffic information can cause congestion in the core network, and communications between relief workers may be limited. Consequently, spectrum efficiency can be very weak. This paper provides a hybrid traffic offloading mechanism combining Device-to-Device (D2D) and Local IP Access (LIPA) modes for the disaster-resilient network. With receiving power, the distance between relief workers and the distance between relief workers and the vehicular eNodeB (VeNB) as the LIPA/D2D switching criteria, the network can select an appropriate mode to prevent core network congestion. This paper also considers the effects of the mobility models (i.e., random walk and random direction) on the spectrum efficiency of the disaster-resilient communication system. The proposed hybrid LIPA/D2D traffic offloading mechanism can prevent the local communication traffic from flowing into the core network and significantly improve the system spectrum efficiency when the core network is under congestion. Therefore, the proposed mechanism can effectively improve the quality of the communication between relief workers served by the same VeNB for performing rescue operations. Moreover, the hybrid LIPA/D2D traffic offloading mechanism can be applied to the smart city and smart home in the future.
The Mobile Water Quality Monitoring System Based on Low-Power Wide Area Network and Unmanned Surface Vehicle
The increasingly serious water pollution problem makes efficient and information-based water quality monitoring equipment particularly important. To cover the shortcomings of existing water quality monitoring methods, in this paper, a mobile water quality monitoring system was designed based on LoRa communication and USV. In this system, the USV carrying water quality sensors was used as a platform. Firstly, the LoRa network is used to monitor water quality over a large area. Secondly, the unmanned surface vessel controls the position error within ±20 m and the velocity error within ±1 m/s based on the Kalman filter algorithm. Thirdly, the genetic algorithm based on improved crossover operators is used to determine the optimal operational path, which effectively improves the iterative efficiency of the classical genetic algorithm and avoids falling into local convergence. In the actual water surface test, its packet loss probability within a working range of 1.5 km was below 10%, and the USV could accurately navigate according to the preset optimal path. The test results proved that the system has a relatively large working range and high efficiency. This study is of high significance in water pollution prevention and ecological protection.