A Crowdsourcing Mode of Tourism Customization Based on Sharing Economy

China’s latest innovations of Internet Economy are partly reflected in video living broadcast, shared bicycles etc. In recent years, tourism industry in China obtains rapid development by utilizing Internet. However, it is still difficult to meet the growing tourist demands. In order to solve this problem, in this paper, we put forward a Tourism Crowdsourcing Model (TCM), which utilizes the public creativity to meet the increasing demands for personalized tourism. Firstly, the main problems of the tourism industry are analyzed. Secondly, the pattern of TCM is elaborated, and a matching algorithm between the tourist requirements and the workers’ abilities is well designed to find the qualified service providers efficiently and accurately. Finally, an example is given to verify the feasibility and effectiveness of the TCM based on shared economy. The results shows that TCM has some significant advantages to satisfy the tourism personalized needs by motivating the public to participate in the tourism industry initiatively

Federated PAC-Bayesian Learning on Non-IID data

Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local data. This bound assumes unique prior knowledge for each client and variable aggregation weights. We also introduce an objective function and an innovative Gibbs-based algorithm for the optimization of the derived bound. The results are validated on real-world datasets

Association between triglyceride glucose index and H-type hypertension in postmenopausal women

BackgroundRecent studies have reported better predictive performance of triglyceride glucose (TyG) index for cardiovascular events in women, however, whether this association persists in postmenopausal women is inconclusive. We investigated the association between TyG index and H-type hypertension (HHT) in postmenopausal women.Methods1,301 eligible women with hypertension were included in this cross-sectional study. Concomitant homocysteine levels >10 μmol/L were defined as H-type hypertension. The TyG index was calculated as ln [triglycerides (mg/dl) × fasting glucose (mg/dl)/2]. Multivariable logistic regression models and restricted cubic spline models were used to assess the association between TyG index and H-type hypertension in postmenopausal women, and subgroup analyses were performed for potential confounders.ResultsOf the 1,301 hypertensive patients, 634 (48.7%) participants had H-type hypertension. In each adjusted model, TyG index was significantly associated with the risk of H-type hypertension. each 1-unit increase in TyG index was associated with an increased risk of H-type hypertension in all participants (OR = 1.6; 95% CI, 1.3–2.0; P < 0.001), and there was a linear relationship between TyG index and H-type hypertension (P for linear trend < 0.001).ConclusionTyG index is positively associated with H-type hypertension in postmenopausal women, suggesting that TyG index may be a promising marker for H-type hypertension. By controlling lipid levels and blood glucose levels, it may help prevent H-type hypertension in postmenopausal women

Association between body fat percentage and H-type hypertension in postmenopausal women

BackgroundPrevious studies have explored the relationship between body fat percentage (BFP) and hypertension or homocysteine. However, evidence on the constancy of the association remains inconclusive in postmenopausal women. The aim of this study was to investigate the association between BFP and H-type hypertension in postmenopausal women.MethodsThis cross-sectional study included 1,597 eligible female patients with hypertension. Homocysteine levels ≥10 mmol/L were defined as H-type hypertension. BFP was calculated by measuring patients' physical parameters. Subjects were divided into 4 groups according to quartiles of BFP (Q1: 33.4% or lower, Q2: 33.4–36.1%, Q3: 36.1–39.1%, Q4: >39.1%). We used restricted cubic spline regression models and logistic regression analysis to assess the relationship between BFP and H-type hypertension. Additional subgroup analysis was performed for this study.ResultsAmong 1,597 hypertensive patients, 955 (59.8%) participants had H-type hypertension. There were significant differences between the two groups in age, BMI, educational background, marital status, exercise status, drinking history, WC, TG, LDL, Scr, BUN, and eGFR (P < 0.05). The prevalence of H-type hypertension in the Q1 to Q4 groups was 24.9, 25.1, 24.9, and 25.1%, respectively. After adjusting for relevant factors, we found that the risk of H-type hypertension in the Q4 group had a significantly higher than the Q1 group (OR = 3.2, 95% CI: 1.3–7.5).ConclusionBFP was positively associated with the risk of H-type hypertension in postmenopausal women. Postmenopausal women should control body fat to prevent hypertension

Towards Efficient Communications in Federated Learning: A Contemporary Survey

In the traditional distributed machine learning scenario, the user's private data is transmitted between nodes and a central server, which results in great potential privacy risks. In order to balance the issues of data privacy and joint training of models, federated learning (FL) is proposed as a special distributed machine learning with a privacy protection mechanism, which can realize multi-party collaborative computing without revealing the original data. However, in practice, FL faces many challenging communication problems. This review aims to clarify the relationship between these communication problems, and focus on systematically analyzing the research progress of FL communication work from three perspectives: communication efficiency, communication environment, and communication resource allocation. Firstly, we sort out the current challenges existing in the communications of FL. Secondly, we have compiled articles related to FL communications, and then describe the development trend of the entire field guided by the logical relationship between them. Finally, we point out the future research directions for communications in FL

Adaptive visual–tactile fusion recognition for robotic operation of multi-material system

The use of robots in various industries is evolving from mechanization to intelligence and precision. These systems often comprise parts made of different materials and thus require accurate and comprehensive target identification. While humans perceive the world through a highly diverse perceptual system and can rapidly identify deformable objects through vision and touch to prevent slipping or excessive deformation during grasping, robot recognition technology mainly relies on visual sensors, which lack critical information such as object material, leading to incomplete cognition. Therefore, multimodal information fusion is believed to be key to the development of robot recognition. Firstly, a method of converting tactile sequences to images is proposed to deal with the obstacles of information exchange between different modalities for vision and touch, which overcomes the problems of the noise and instability of tactile data. Subsequently, a visual-tactile fusion network framework based on an adaptive dropout algorithm is constructed, together with an optimal joint mechanism between visual information and tactile information established, to solve the problem of mutual exclusion or unbalanced fusion in traditional fusion methods. Finally, experiments show that the proposed method effectively improves robot recognition ability, and the classification accuracy is as high as 99.3%

Towards All-around Knowledge Transferring: Learning From Task-irrelevant Labels

Deep neural models have hitherto achieved significant performances on numerous classification tasks, but meanwhile require sufficient manually annotated data. Since it is extremely time-consuming and expensive to annotate adequate data for each classification task, learning an empirically effective model with generalization on small dataset has received increased attention. Existing efforts mainly focus on transferring task-relevant knowledge from other similar data to tackle the issue. These approaches have yielded remarkable improvements, yet neglecting the fact that the task-irrelevant features could bring out massive negative transfer effects. To date, no large-scale studies have been performed to investigate the impact of task-irrelevant features, let alone the utilization of this kind of features. In this paper, we firstly propose Task-Irrelevant Transfer Learning (TIRTL) to exploit task-irrelevant features, which mainly are extracted from task-irrelevant labels. Particularly, we suppress the expression of task-irrelevant information and facilitate the learning process of classification. We also provide a theoretical explanation of our method. In addition, TIRTL does not conflict with those that have previously exploited task-relevant knowledge and can be well combined to enable the simultaneous utilization of task-relevant and task-irrelevant features for the first time. In order to verify the effectiveness of our theory and method, we conduct extensive experiments on facial expression recognition and digit recognition tasks. Our source code will be also available in the future for reproducibility