Due to the rapid development of smart city, Hybrid Information Centric-Networking (HICN) emerges as a promising technology to enable the power of smart city. One of the most important application is... Show More

Due to the rapid development of smart city, Hybrid Information Centric-Networking (HICN) emerges as a promising technology to enable the power of smart city. One of the most important application is the smart English translation, which becomes more and more popular with the process of Internationalization. In this work, we focus on studying the intelligent English translation is smart city using the HICN technology. Particularly, a method using collaborative machine learning and quality estimation technique is proposed, which sets a fixed threshold to filter pseudo-parallel data during unsupervised neural machine translation training. The quality estimation is used to evaluate and screen the pseudo-parallel data with high performance generated during reverse translation training. The results indicate that the proposed method outperforms the state-of-the-art methods.

In order to enhance the daily training for basketball, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball... Show More

In order to enhance the daily training for basketball, this paper establishes a human posture estimation framework by using monocular camera and wireless sensor network. First, the daily basketball training images are collected by monocular camera and transmitted through wireless sensor network. Second, the collected images are processed by an observation and reasoning model which is based on component and graph reasoning. The basketball player's posture is depicted by the rotation invariant features of edge field. The extracted features are used to learn a boosting classifier as the observation model. The experimental results show that the posture recognition rate can achieve more than 88% for basketball player's action.

In recent years, research on the QoS-aware service composition problem often assumes that each component service in the process to be solved is equally essential. They do not consider the impact of... Show More

In recent years, research on the QoS-aware service composition problem often assumes that each component service in the process to be solved is equally essential. They do not consider the impact of core component services and other component services on problem-solving, or even though their impact is considered, they are not fully considered. So this paper first proposes a diffractive method based on them. Considering the advantages of artificial bee colony (ABC) such as simplicity, this paper chooses it as the basic algorithm. In addition, with the continuous development of service ecosystem, it gradually formed a variety of domain features. They have an important influence on problem-solving, but the existing research has not explored this influence in-depth. Therefore, this paper digs deep into this influence. Given the characteristics of the problem to be solved in this paper, the S-ABCPC algorithm is designed. At last, experiments have proved the effectiveness of the method proposed in this paper. The impact factors of this method have been studied.

Emotion is an important research topic in the field of sports. The physiological changes caused by emotion have a great influence on the completion of sports. It cannot only fully mobilize the... Show More

Emotion is an important research topic in the field of sports. The physiological changes caused by emotion have a great influence on the completion of sports. It cannot only fully mobilize the organism and maximize the exercise potential, but also lead to muscle stiffness, movement deformation, or muscle contraction weakness. Furthermore, it can affect the completion of exercise. In order to ensure the athlete can keep the best competitive level, it is necessary to estimate the athlete's emotion before competition. This paper adopts the pulse wave signal to implement the emotion estimation for the athletes. First, the pulse wave signals are collected by using a portable sensor via mobile computing. Then, the collected pulse wave signals have noises removed by wavelet transform. Last, the denoised pulse wave signals are represented as the features in time domain and frequency domain to input into a trained classifier for determining the current emotion status. The experimental results show that the proposed method can recognize more than 90% of the abnormal emotions.

Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the... Show More

Data modeling of health-related data from Data Center (DC) has positive effects for health monitoring, disease prevention, and healthcare research. However, health-related data has the characteristics of huge, high-dimensional, and non-normalized, which are not beneficial to direct analysis, so data needs to be preprocessed before data modeling. This paper focuses on the features of health-related data, and outlier detection during data preprocessing is studied. Meanwhile, we propose an improved algorithm for health-related data based outlier detection. The experimental results reveal that the proposed outlier detection algorithm has a smaller running time, and more outliers are detected compared to three baselines. In addition, local importance based random forest feature selection algorithm is proposed to measure the importance of each feature. The experimental results indicate that the proposed algorithm can select optimal feature subset to apply health-related data.