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Author(s):  
Xi Cheng

AbstractTo solve the problem of low accuracy of traditional travel route recommendation algorithm, a travel route recommendation algorithm based on interest theme and distance matching is proposed in this paper. Firstly, the real historical travel footprints of users are obtained through analysis. Then, the user’s preferences of interest theme and distance matching are proposed based on the user’s stay in each scenic spot. Finally, the optimal travel route calculation method is designed under the given travel time limit, starting point, and end point. Experiments on the real data set of the Flickr social network showed that the proposed algorithm has a higher accuracy rate and recall rate, compared with the traditional algorithm that only considers the interest theme and the algorithm which only considers the distance matching.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Luisa Weiner ◽  
Andrea Guidi ◽  
Nadège Doignon-Camus ◽  
Anne Giersch ◽  
Gilles Bertschy ◽  
...  

AbstractThere is a lack of consensus on the diagnostic thresholds that could improve the detection accuracy of bipolar mixed episodes in clinical settings. Some studies have shown that voice features could be reliable biomarkers of manic and depressive episodes compared to euthymic states, but none thus far have investigated whether they could aid the distinction between mixed and non-mixed acute bipolar episodes. Here we investigated whether vocal features acquired via verbal fluency tasks could accurately classify mixed states in bipolar disorder using machine learning methods. Fifty-six patients with bipolar disorder were recruited during an acute episode (19 hypomanic, 8 mixed hypomanic, 17 with mixed depression, 12 with depression). Nine different trials belonging to four conditions of verbal fluency tasks—letter, semantic, free word generation, and associational fluency—were administered. Spectral and prosodic features in three conditions were selected for the classification algorithm. Using the leave-one-subject-out (LOSO) strategy to train the classifier, we calculated the accuracy rate, the F1 score, and the Matthews correlation coefficient (MCC). For depression versus mixed depression, the accuracy and F1 scores were high, i.e., respectively 0.83 and 0.86, and the MCC was of 0.64. For hypomania versus mixed hypomania, accuracy and F1 scores were also high, i.e., 0.86 and 0.75, respectively, and the MCC was of 0.57. Given the high rates of correctly classified subjects, vocal features quickly acquired via verbal fluency tasks seem to be reliable biomarkers that could be easily implemented in clinical settings to improve diagnostic accuracy.


2021 ◽  
Author(s):  
Inyoung Kim ◽  
Sang Yoon Byun ◽  
Sangyeup Kim ◽  
Sangyoon Choi ◽  
Jinsung Noh ◽  
...  

Analyzing B-cell receptor (BCR) repertoires is immensely useful in evaluating one's immunological status. Conventionally,repertoire analysis methods have focused on comprehensive assessment of clonal compositions, including V(D)J segment usage, nucleotide insertion/deletion, and amino acid distribution. Here, we introduce a novel computational approach that applies deep-learning based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that our new approach enables us to not only accurately cluster repertoires of COVID-19 patients and healthy subjects, but also efficiently track minute changes in immunity conditions as patients undergo a course of treatment over time. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved over 87% mean accuracy rate given a repertoire of CDR3 sequences.


2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Zhang ◽  
Yingjing Duan ◽  
Xiaoqing Gu

Starting from a pure-image perspective, using machine learning in emotion analysis methods to study artwork is a new cross-cutting approach in the field of literati painting and is an effective supplement to research conducted from the perspectives of aesthetics, philosophy, and history. This study constructed a literati painting emotion dataset. Five classic deep learning models were used to test the dataset and select the most suitable model, which was then improved upon for literati painting emotion analysis based on accuracy and model characteristics. The final training accuracy rate of the improved model was 54.17%. This process visualizes the salient feature areas of the picture in machine vision, analyzes the visualization results, and summarizes the connection law between the picture content of the Chinese literati painting and the emotion expressed by the painter. This study validates the possibility of combining deep learning with Chinese cultural research, provides new ideas for the combination of new technology and traditional Chinese literati painting research, and provides a better understanding of the Chinese cultural spirit and advanced factors.


2021 ◽  
Vol 12 ◽  
Author(s):  
Zhong Lin ◽  
Xinyu Song ◽  
Jingwen Guo ◽  
Feng Wang

Although research on peer feedback in second language teaching and learning has been developed from various perspectives over the past three decades, less is known about feedback in translation settings. This study reports the results of a quasi-experiment with advanced second language learners in a Chinese–English translation course. It examines how effective peer feedback is in improving the quality of translations. The following data were collected from 30 students: their initial translation drafts, the drafts with the feedback of their peers, and the final corrected translations. The whole process was facilitated by computer assistance and under anonymity. It was found that most students drew on direct or indirect corrective feedback while few students drew on metalinguistic corrective feedback. Text genres were also proved to impact the types and counts of peer feedback. An analysis of the accuracy rate of corrections after peer feedback showed that it had a positive impact on translation quality. The findings shed light on the applicability of peer feedback in other pedagogical activities.


2021 ◽  
pp. 004051752110342
Author(s):  
Yuki Karasawa ◽  
Mayumi Uemae ◽  
Hiroaki Yoshida ◽  
Masayoshi Kamijo

The clothing comfort sensation is a combination of complex components, including psychological and physiological responses. General linear analysis is not always sufficient for the evaluation of the clothing comfort sensation. The current study sought to predict the clothing comfort sensation of wearing an undershirt using an artificial neural network (ANN). We constructed ANN models with psychological sensation data and physiological response data as inputs, including electrocardiogram and thermo-physiological indicators, and the clothing comfort sensation as the output. For the input layer of the model, three conditions were used: the psychological response data only, the physiological response data only, and both the psychological and physiological data. The number of hidden layers in the models ranged from one to three, and the number of units in each hidden layer was changed when fixed values of 30, 60, and 90 were used, or according to the number of data points in the input conditions. The results revealed that, among the three conditions, the accuracy rate was higher when both psychological and physiological response data were used as input. The prediction results exhibited an accuracy rate of up to 85% for unknown test data. The results suggest that the method of evaluating the state of clothing comfort sensation when wearing an undershirt using psychophysiological response measurement was effective and that neural networks are useful for predicting the clothing comfort sensation.


Author(s):  
Jianfang Cao ◽  
Minmin Yan ◽  
Yiming Jia ◽  
Xiaodong Tian ◽  
Zibang Zhang

AbstractIt is difficult to identify the historical period in which some ancient murals were created because of damage due to artificial and/or natural factors; similarities in content, style, and color among murals; low image resolution; and other reasons. This study proposed a transfer learning-fused Inception-v3 model for dynasty-based classification. First, the model adopted Inception-v3 with frozen fully connected and softmax layers for pretraining over ImageNet. Second, the model fused Inception-v3 with transfer learning for parameter readjustment over small datasets. Third, the corresponding bottleneck files of the mural images were generated, and the deep-level features of the images were extracted. Fourth, the cross-entropy loss function was employed to calculate the loss value at each step of the training, and an algorithm for the adaptive learning rate on the stochastic gradient descent was applied to unify the learning rate. Finally, the updated softmax classifier was utilized for the dynasty-based classification of the images. On the constructed small datasets, the accuracy rate, recall rate, and F1 value of the proposed model were 88.4%, 88.36%, and 88.32%, respectively, which exhibited noticeable increases compared with those of typical deep learning models and modified convolutional neural networks. Comparisons of the classification outcomes for the mural dataset with those for other painting datasets and natural image datasets showed that the proposed model achieved stable classification outcomes with a powerful generalization capacity. The training time of the proposed model was only 0.7 s, and overfitting seldom occurred.


2021 ◽  
Vol 39 (7) ◽  
pp. 1069-1079
Author(s):  
Bilal Mohammed ◽  
Ekhlas K. Gbashi

Intrusion detection system is responsible for monitoring the systems and detect attacks, whether on (host or on a network) and identifying attacks that could come to the system and cause damage to them, that’s mean an IDS prevents unauthorized access to systems by giving an alert to the administrator before causing any serious harm. As a reasonable supplement of the firewall, intrusion detection technology can assist systems to deal with offensive, the Intrusions Detection Systems (IDSs) suffers from high false positive which leads to highly bad accuracy rate. So this work is suggested to implement (IDS) by using a Recursive Feature Elimination to select features and use Deep Neural Network (DNN) and Recurrent Neural Network (RNN) for classification, the suggested model gives good results with high accuracy rate reaching 94%, DNN was used in the binary classification to classify either attack or Normal, while RNN was used in the classifications for the five classes (Normal, Dos, Probe, R2L, U2R). The system was implemented by using (NSL-KDD) dataset, which was very efficient for offline analyses systems for IDS.                                                                                                   


2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guiyong Xu ◽  
Yang Xu ◽  
Sicong Zhang ◽  
Xiaoyao Xie

In the era of big data, convolutional neural network (CNN) has been widely used in the field of image classification and has achieved excellent performance. More and more researchers are beginning to combine deep neural networks with steganalysis to improve performance in recent years. However, most of the steganalysis algorithm based on the convolutional neural network has only run test against the WOW and S-UNIWARD algorithms; meanwhile, their versatility is insufficient due to long training time and the limit of image size. This paper proposes a new network architecture, called SFRNet, to solve these problems. The feature extraction and fusion layer can extract more features from the digital image. The RepVgg block is used to accelerate the inference and increase memory utilization. The SE block improves the detection accuracy rate because it can learn feature weights to make effective feature maps with significant weights and invalid or ineffective feature maps with small weights. Experimental results show that the SFRNet has achieved excellent performance in the detection accuracy rate against four state-of-the-art steganography algorithms in the spatial domain, e.g., HUGO, WOW, S-UNIWARD, and MiPOD, under different payloads. The SFRNet detection accuracy rate achieves 89.6% against S-UNIWARD algorithm with the payload of 0.4bpp and 72.5% at 0.2bpp. As the same time, the training time of our network is greatly reduced by 35% compared with Yedroudj-Net.


2021 ◽  
Vol 2021 (4) ◽  
pp. 420-440
Author(s):  
Nguyen Phong Hoang ◽  
Arian Akhavan Niaki ◽  
Phillipa Gill ◽  
Michalis Polychronakis

Abstract Although the security benefits of domain name encryption technologies such as DNS over TLS (DoT), DNS over HTTPS (DoH), and Encrypted Client Hello (ECH) are clear, their positive impact on user privacy is weakened by—the still exposed—IP address information. However, content delivery networks, DNS-based load balancing, co-hosting of different websites on the same server, and IP address churn, all contribute towards making domain–IP mappings unstable, and prevent straightforward IP-based browsing tracking. In this paper, we show that this instability is not a roadblock (assuming a universal DoT/DoH and ECH deployment), by introducing an IP-based website finger-printing technique that allows a network-level observer to identify at scale the website a user visits. Our technique exploits the complex structure of most websites, which load resources from several domains besides their primary one. Using the generated fingerprints of more than 200K websites studied, we could successfully identify 84% of them when observing solely destination IP addresses. The accuracy rate increases to 92% for popular websites, and 95% for popular and sensitive web-sites. We also evaluated the robustness of the generated fingerprints over time, and demonstrate that they are still effective at successfully identifying about 70% of the tested websites after two months. We conclude by discussing strategies for website owners and hosting providers towards hindering IP-based website fingerprinting and maximizing the privacy benefits offered by DoT/DoH and ECH.


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