Yoga is a 5000-year-old practice developed in ancient India by the Indus-Sarasvati civilization. The word yoga means deep association and union of mind with the body. It is used to keep both mind and body in equilibration in all flip-flops of life by means of asana, meditation, and several other techniques. Nowadays, yoga has gained worldwide attention due to increased stress levels in the modern lifestyle, and there are numerous methods or resources for learning yoga. Yoga can be practiced in yoga centers, through personal tutors, and can also be learned on one’s own with the help of the Internet, books, recorded clips, etc. In fast-paced lifestyles, many people prefer self-learning because the abovementioned resources might not be available all the time. But in self-learning, one may not find an incorrect pose. Incorrect posture can be harmful to one’s health, resulting in acute pain and long-term chronic concerns. In this paper, deep learning-based techniques are developed to detect incorrect yoga posture. With this method, the users can select the desired pose for practice and can upload recorded videos of their yoga practice pose. The user pose is sent to train models that output the abnormal angles detected between the actual pose and the user pose. With these outputs, the system advises the user to improve the pose by specifying where the yoga pose is going wrong. The proposed method was compared to several state-of-the-art methods, and it achieved outstanding accuracy of 0.9958 while requiring less computational complexity.

Modelling the joint trajectories of a biped locomotion correctly is a highly challenging problem due to the complex nature of biped locomotion. In human as well as in biped humanoids the periodic walking pattern which is known as gait, consists of both discrete (known as stance phase) and continuous phases (known as swing phase). The system is highly nonlinear and modelling using conventional kinematics has not proved to be very effective. Several compelling assumptions to make model simple makes it unrealistic. In this research work we present a computational model for prediction, formal verification and analyses of joint trajectories of bipedal locomotion using powerful theoretical computer science framework which is known as automata theory-more precisely here hybrid automata technique has been used for modelling. We argued that hybrid automata is more suitable to model a biped locomotion which is inherently hybrid (consisting of discrete and continuous phases) in nature. One of the major contributions of the paper is developing the hybrid automata model for automatic generation of joint trajectories for different sub phases of a bipedal gait. Other contributions include the development of generic equations for seven sub interval phases of a gait cycle which has been divided into eight equal parts and verification of its correctness by comparing the model with one existing standard model of OpenSim named as GAIT -2354 [1]. Through rigorous simulations we have established that the hip and knee joint trajectories generated by our hybrid automata based model closely matches with OpenSim model and that of captured data driven model of normal walking. Hence it is effective, robust and generic. We conclude that since the hybrid automata based model has a strong theoretical basis, it can be used for generating hip and knee trajectories for any biped robot for subsequent execution level control.

Scheduling extensive scientific applications that are deadline-aware (usually referred to as workflow) is a difficult task. This research provides a virtual machine (VM) placement and scheduling approach for effectively scheduling process tasks in the cloud environment while maintaining dependency and deadline constraints. The suggested model’s aim is to reduce the application’s energy consumption and total execution time while taking into account dependency and deadline limitations. To select the VM for the tasks and dynamically deploy/undeploy the VM on the hosts based on the jobs’ requirements, an energy-efficient VM placement (EVMP) algorithm is presented. Demonstrate that the proposed approach outperforms the existing PESVMC (power-efficient scheduling and VM consolidation) algorithm.

Cross channel scripting (XCS) is a common web application vulnerability, which is a variant of a cross-site scripting (XSS) attack. An XCS attack vector can be injected through network protocol and smart devices that have web interfaces such as routers, photo frames, and cameras. In this attack scenario, the network devices allow the web administrator to carry out various functions related to accessing the web content from the server. After the injection of malicious code into web interfaces, XCS attack vectors can be exploited in the client browser. In addition, scripted content can be injected into the networked devices through various protocols, such as network file system, file transfer protocol (FTP), and simple mail transfer protocol. In this paper, various computational techniques deployed at the client and server sides for XCS detection and mitigation are analyzed. Various web application scanners have been discussed along with specific features. Various computational tools and approaches with their respective characteristics are also discussed. Finally, shortcomings and future directions related to the existing computational techniques for XCS are presented.