Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy

Background: Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to health of female. Methods: In light of drug repositioning strategy, we trained and benchmarked multiple machine learning predictive models so as to predict potential effective antiviral drugs for HPV infection in this work. Based on antiviral-target interaction dataset, we generated high dimension feature set of drug-target interaction pairs and used the dataset to train and construct machine learning predictive models. Results: Through optimizing models, measuring models predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by United States Food and Drug Administration, and benchmarking different models predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naive Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 58 pairs of antiviral-HPV protein interactions from 846 pairs of antiviral-HPV protein associations. Conclusions: Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.

Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information

Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/testing. The proposed system was found to outperform a baseline and a previous state of the art in an offline, human-judged evaluation.

AIR-Act2Act: Human–human interaction dataset for teaching non-verbal social behaviors to robots

To better interact with users, a social robot should understand the users’ behavior, infer the intention, and respond appropriately. Machine learning is one way of implementing robot intelligence. It provides the ability to automatically learn and improve from experience instead of explicitly telling the robot what to do. Social skills can also be learned through watching human–human interaction videos. However, human–human interaction datasets are relatively scarce to learn interactions that occur in various situations. Moreover, we aim to use service robots in the elderly care domain; however, there has been no interaction dataset collected for this domain. For this reason, we introduce a human–human interaction dataset for teaching non-verbal social behaviors to robots. It is the only interaction dataset that elderly people have participated in as performers. We recruited 100 elderly people and 2 college students to perform 10 interactions in an indoor environment. The entire dataset has 5,000 interaction samples, each of which contains depth maps, body indexes, and 3D skeletal data that are captured with three Microsoft Kinect v2 sensors. In addition, we provide the joint angles of a humanoid NAO robot which are converted from the human behavior that robots need to learn. The dataset and useful Python scripts are available for download at https://github.com/ai4r/AIR-Act2Act . It can be used to not only teach social skills to robots but also benchmark action recognition algorithms.

Local Temporal Block Difference Pattern for Action Recognition in Surveillance Videos using Tree Based Classifiers

Intelligent video classification and prediction is a fundamental step towards effective retrieval system. Huge volume of video is available for navigation today and managing such video and prediction of the activity before its completion gains importance in video surveillance,human computer recognition, gesture recognition etc., An eminent Local Temporal Block Difference Pattern (LTBDP) is introducedwhich enable efficient feature extraction that could be given to Tree Classifiers like Random Forest and REPTree for further prediction.The proposed pattern has been evaluated on UT-interaction dataset which enable researchers to predict ongoing human actions in an efficient manner. Experimental results using LTBDP in Random Forest and REPTree classifier gives 85.6% and 66.45% accuracy respectively.

Human Detection and Segmentation Using Automatic Geodesic Active Contours for Vision Based Activity Recognition Applications

Vision-based activity recognition applications are in need of Automatic human detection and segmentation for surveillance purposes. Though there are many state-of-art methods in the literature, there still exist many challenges such as self-occlusion, illumination variations and sensitive to light conditions, appearance, and variations due to clothing. In this paper, a new novel framework for automatic detection and segmentation of the human region in a video sequence using Automatic Geodesic Active Contours is proposed. Normally geodesic active contours have static Region of coincidence but in this work, a dynamic Region of coincidence is proposed to draw the initial contours on the human region. To detect the human region the histogram of oriented gradients are computed and trained using SVM classifier. Once the human region is detected, the contour is drawn on the human region alone to segment the human region from the background. The proposed algorithm achieves an accuracy rate of 98%, 99% and 98% for KTH, Weizmann and U t-interaction dataset respectively.