Point-of-Interest Recommendation Algorithm Based on User Similarity in Location-Based Social Networks

Location-based social network is rising recent years with the development of mobile internet, and point-of-interest (POI) recommendation is a hot topic of this field. Because the factors that affect the behavior of users are very complex, most of the research focuses on the context of the recommendation. But overall context data acquisition in practice is often difficult to obtain. In this paper, we have considered the most common collaborative recommendation algorithm based on user similarity, and discussed several methods of user similarity definition. Comparing the effect of different methods in the actual dataset, experimental results show among the factors including that social relation, check-in and geographical location the check-in is extremely important, so this work is of certain guiding significance to the actual applications

Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark

Dialogue topic shift detection is to detect whether an ongoing topic has shifted or should shift in a dialogue, which can be divided into two categories, i.e., response-known task and response-unknown task. Currently, only a few investigated the latter, because it is still a challenge to predict the topic shift without the response information. In this paper, we first annotate a Chinese Natural Topic Dialogue (CNTD) corpus consisting of 1308 dialogues to fill the gap in the Chinese natural conversation topic corpus. And then we focus on the response-unknown task and propose a teacher-student framework based on hierarchical contrastive learning to predict the topic shift without the response. Specifically, the response at high-level teacher-student is introduced to build the contrastive learning between the response and the context, while the label contrastive learning is constructed at low-level student. The experimental results on our Chinese CNTD and English TIAGE show the effectiveness of our proposed model

Intra-Familial Phenotypic Heterogeneity and Telomere Abnormality in von Hippel- Lindau Disease: Implications for Personalized Surveillance Plan and Pathogenesis of VHL-Associated Tumors

von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome with poor survival. The current recommendations have proposed uniform surveillance strategies for all patients, neglecting the obvious phenotypic varieties. In this study, we aim to confirm the phenotypic heterogeneity in VHL disease and the underlying mechanism. A total of 151 parent-child pairs were enrolled for genetic anticipation analysis, and 77 sibling pairs for birth order effect analysis. Four statistical methods were used to compare the onset age of patients among different generations and different birth orders. The results showed that the average onset age was 18.9 years earlier in children than in their parents, which was statistically significant in all of the four statistical methods. Furthermore, the first-born siblings were affected 8.3 years later than the other ones among the maternal patients. Telomere shortening was confirmed to be associated with genetic anticipation in VHL families, while it failed to explain the birth order effect. Moreover, no significant difference was observed for overall survival between parents and children (p = 0.834) and between first-born patients and the other siblings (p = 0.390). This study provides definitive evidence and possible mechanisms of intra-familial phenotypic heterogeneity in VHL families, which is helpful to the update of surveillance guidelines

Cognitive Deficits and Associated ERP N400 Abnormalities in FXTAS With Parkinsonism

Objective: To examine cognitive deficits and associated brain activity in fragile X-associated tremor/ataxia syndrome (FXTAS) patients with parkinsonism (FXTp+), in relation to FXTAS patients without parkinsonism (FXTp-), and normal elderly controls (NC).Methods: Retrospective reviews were performed in 65 FXTAS patients who participated in the event-related brain potential (ERP) study and also had either a videotaped neurological examination or a neurological examination for extrapyramidal signs. Parkinsonism was defined as having bradykinesia with at least one of the following: rest tremor, postural instability, hypermyotonia, or rigidity. Eleven FXTp+ patients were identified and compared to 11 matched FXTp- and 11 NC. Main ERP measures included the N400 congruity effect, N400 repetition effect, and the late positive component (LPC) repetition effect.Results: When compared with FXTp- and NC, the FXTp+ group showed more severe deficits in executive function, cued-recall, recognition memory, along with a significantly reduced N400 repetition effect (thought to index semantic processing and verbal learning/memory) which was correlated with poorer verbal memory. Across all patients, FMR1 mRNA levels were inversely correlated with delayed recall on the California Verbal Learning Test (CVLT).Interpretation: The findings of more prominent executive dysfunction and verbal learning/memory deficits in FXTp+ than FXTp- are consistent with findings in Parkinson’s disease (PD), and may indicate that concomitant and/or synergistic pathogenetic mechanisms associated with PD play a role in FXTAS. These results have implications not only for understanding the cognitive impairments associated with the parkinsonism subtype of FXTAS, but also for the development of new interventions for these patients

A High-Precision Method for 100-Day-Old Classification of Chickens in Edge Computing Scenarios Based on Federated Computing

Due to the booming development of computer vision technology and artificial intelligence algorithms, it has become more feasible to implement artificial rearing of animals in real production scenarios. Improving the accuracy of day-age detection of chickens is one of the examples and is of great importance for chicken rearing. This paper focuses on the problem of classifying the age of chickens within 100 days. Due to the huge amount of data and the different computing power of different devices in practical application scenarios, it is important to maximize the computing power of edge computing devices without sacrificing accuracy. This paper proposes a high-precision federated learning-based model that can be applied to edge computing scenarios. In order to accommodate different computing power in different scenarios, this paper proposes a dual-ended adaptive federated learning framework; in order to adapt to low computing power scenarios, this paper performs lightweighting operations on the mainstream model; and in order to verify the effectiveness of the model, this paper conducts a number of targeted experiments. Compared with AlexNet, VGG, ResNet and GoogLeNet, this model improves the classification accuracy to 96.1%, which is 14.4% better than the baseline model and improves the Recall and Precision by 14.8% and 14.2%, respectively. In addition, by lightening the network, our methods reduce the inference latency and transmission latency by 24.4 ms and 10.5 ms, respectively. Finally, this model is deployed in a real-world application and an application is developed based on the wechat SDK

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms

Fusion of Infrared and Visible Images Based on Optimized Low-Rank Matrix Factorization with Guided Filtering

In recent years, image fusion has been a research hotspot. However, it is still a big challenge to balance the problems of noiseless image fusion and noisy image fusion. In order to improve the weak performance and low robustness of existing image fusion algorithms in noisy images, an infrared and visible image fusion algorithm based on optimized low-rank matrix factorization with guided filtering is proposed. First, the minimized error reconstruction factorization is introduced into the low-rank matrix, which effectively enhances the optimization performance, and obtains the base image with good filtering performance. Then using the base image as the guide image, the source image is decomposed into the high-frequency layer containing detail information and noise, and the low-frequency layer containing energy information through guided filtering. According to the noise intensity, the sparse reconstruction error is adaptively obtained to fuse the high-frequency layers, and the weighted average strategy is utilized to fuse the low-frequency layers. Finally, the fusion image is obtained by reconstructing the pre-fused high-frequency layer and the pre-fused low-frequency layer. The comparative experiments show that the proposed algorithm not only has good performance for noise-free images, but more importantly, it can effectively deal with the fusion of noisy images

Fusion of Infrared and Visible Images Based on Three-Scale Decomposition and ResNet Feature Transfer

Image fusion technology can process multiple single image data into more reliable and comprehensive data, which play a key role in accurate target recognition and subsequent image processing. In view of the incomplete image decomposition, redundant extraction of infrared image energy information and incomplete feature extraction of visible images by existing algorithms, a fusion algorithm for infrared and visible image based on three-scale decomposition and ResNet feature transfer is proposed. Compared with the existing image decomposition methods, the three-scale decomposition method is used to finely layer the source image through two decompositions. Then, an optimized WLS method is designed to fuse the energy layer, which fully considers the infrared energy information and visible detail information. In addition, a ResNet-feature transfer method is designed for detail layer fusion, which can extract detailed information such as deeper contour structures. Finally, the structural layers are fused by weighted average strategy. Experimental results show that the proposed algorithm performs well in both visual effects and quantitative evaluation results compared with the five methods