China
Africa
Explore chapters and articles related to this topic
Server-Side Technologies
Published in Akshi Kumar, Web Technology, 2018
Server-side scripting refers to the dynamic generation of web pages served up by the web server, as opposed to static web pages in the server storage that are served up to the web browser. In other words, some part of the content sent in response to an HTTP request is determined on the fly by a program that executes on the server after the HTTP request has been received and generates content as a result of the execution. For example, insertion of continuously changing content into a web page, such as news, weather, or stock quotes, and retrieval of data in response to query string parameters and insertion into a web page are perhaps the most common uses of server-side scripting. All server-side technologies share a common set of features: Read data submitted by the user.Generate HTML dynamically based on user input.Determine information about the client browser.Access database systems.Exploit the HTTP protocol.
HTML and Scripts
Published in Tom Hutchison, Paul Allen, Web Marketing for the Music Business, 2013
Whereas CGI is for server-side programming, often referred to as back-end programming, JavaScript is used for client-side programming, often referred to as front-end programming (although it can also be used for server side programming). Server side programming runs on the host’s server, whereas client side programming runs in the user’s browser. Wikipedia has the following paragraph to explain the difference: Server-side scripting is a web server technology in which a user’s request is fulfilled by running a script directly on the web server to generate dynamic HTML pages. It is usually used to provide interactive web sites that interface to databases or other data stores. This is different from client-side scripting where scripts are run by the viewing web browser, usually in JavaScript. The primary advantage to server-side scripting is the ability to highly customize the response based on the user’s requirements, access rights, or queries into data stores.
Enhancing User’s Self-Disclosure through Chatbot’s Co-Activity and Conversation Atmosphere Visualization
Published in International Journal of Human–Computer Interaction, 2022
Rafikatiwi Nur Pujiarti, Bumho Lee, Mun Yong Yi
We built a set of chatbot scenarios using Google’s Natural Language Understanding (NLU) platform called Dialogflow. For each experiment day, chat scenarios were pre-defined, so as to control the experiment setting. The chatbot was also equipped with Dialogflow’s SmallTalk ability to answer questions that users may ask outside the scope of the study, although the chabot’s ability is still limited. We built a web application chatbot using Python (server side) and JavaScript (client side). When the user said something, the client-side created a request to the server-side. The server-side took the user’s message. We used Flask, a Python-based web framework for the server-side. Then, the server called Dialogflow API to detect the user’s intent. Dialogflow matched user input with appropriate intent and returned the matched intent and its corresponding chatbot response to the server. The server then sent this dialog information to the client so that it could respond to the user. The user’s conversation log and dialog information were stored in the Firebase real-time database. Finally, we used Heroku to host the web application. The complete design structure of the system is depicted in Figure 1.
Algorithm for identifying clients based on dynamic MAC addresses in narrowly targeted secure networks using deep learning neural networks
Published in International Journal of Parallel, Emergent and Distributed Systems, 2021
Alexander Tyutyunnik, Ekaterina Lobaneva, Alexey Lazarev
To solve this problem, a unique neural network-based algorithm has been developed that allows application software packages to generate unique MAC addresses for both client and server. The algorithm uses bi-directional recurrent neural networks to acquire previous and predicted MAC address values at different points in time for subsequent analysis and decision-making on the possibility of unauthorised access. By paralleling the MAC address generation process, the proposed algorithm allows the generation of a unique fingerprint that is valid for a specific time interval. Thus, the sequence generated on the client side will be successfully identified on the server side, with each successive MAC address being matched on the client-server side. Also, by using a specific sample of training data, it is expected to be possible to ensure 95% uniqueness of the generated fingerprint. The aspect of restoring access in the event of a planned or unplanned reboot of the device was an important consideration – the side-by-side algorithm provided additional security and the ability to operate in conditions where there was an unstable power supply.
LIA: A Virtual Assistant that Can Be Taught New Commands by Speech
Published in International Journal of Human–Computer Interaction, 2019
LIA is composed of a client side running on Android, and a Java based server side. Figure 1 shows screen-shots of a user interacting with the Android app. LIA listens for a wake-up phrase (‘Jessica’), and then lights up, waiting for the user to give a voice command. The recording of the command is sent to the server side which, using an ASR service, is converted to text. The command is then processed and the client receives the response, which can either be text that is spoken back to the user, or something more complex such as opening an app. The user may also communicate with LIA using text (rather than speech). See https://tinyurl.com/LiaDemo for a demonstration of a user interacting with LIA. In this demonstration, LIA answers a factoid question, plays a song, sets a reminder, and learns a new command. See https://tinyurl.com/LiaDemo1 for a demonstration of LIA learning a complex new command in the email domain, and based upon its generalization method, executing this learned command with a different argument.