The student feedback system is used to get student feedback about the staff through the web-based application. This Student Feedback System is used to determine whether students understand the teaching concepts or not, whether their performance is going well or poorly, and how they should direct their subsequent efforts. In this way, feedback is essential to scholars' learning and growth. Flask and MySQL technologies are used to develop this student feedback system. Flask is a web Framework used to develop the web application and MySQL is the Relational Database that is used to store the data and manipulate data. This student feedback system is very useful for schools and colleges to get staff feedback. Four characters are involved in this application. They are admin, hod, staff, and student, and then this application consists of the four important modules: the admin module, the hod module, the staff module, and the student module. The admin can add the HOD to each department, and the admin can view all the students' feedback. This process was managed by the Admin Module. The HOD can add the staff to their department, and the HOD can view only their department’s student feedback. This process was managed by the HOD module. The Staff can add the students and only can view their feedback. They cannot see other staff's feedback. This process was managed by the Staff Module. Finally, Students can only provide feedback to their staff. This process was handled by the student module. These are all the four important modules and characteristics of this application
Thecontrol system is designed to assist and provide support in order to fulfill the needs of elderly and disabled in home. Automation of the surrounding environment of a modern human being allows increasing his work efficiency and comfort. There has been a significant development in the area of an individual's routine tasks and those can be automated. In the present times, we can find most of the people clinging to their mobile phones and smart devices throughout the day. Hence with the help of his companion-a mobile phone, some daily household tasks can be accomplished by personifying the use of the mobile phone. Analyzing the current smart phone market, most users are opting for Android based phones. It has become a second name for a mobile phone in layman terms. Home Automation System has been designed for mobile phones having Android platform to automate an WIFI interfaced Arduino which controls a number of home appliances like lights, fans, bulbs and many more using on/off relay. This paper presents the automated approach of controlling the devices in a household that could ease the tasks of using the traditional method of the switch. The most famous and efficient technology for short range wireless communication-WIFI is used here to automate the system. The HAS system for Android users is a step towards the ease of the tasks by controlling one to twenty-four different appliances in any home environment.
Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic Adaptive Streaming over HTTP (DASH) becomes the defacto standard of video streaming and it is adopted by YouTube, Netflix, etc.Despite of the popularity, network traffic during video streaming shows identifiable pattern which brings threat to user privacy.In this paper, to proposea video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bit rate trend remains relatively stable because of the widely used Variable Bit-Rate(VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, to propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. To evaluate our attack method in different scenarios for various video content, segment lengths and quality levels. The experimental results show that the identification accuracy can reach up to 90%using only three minute continuous network traffic eavesdropping.
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