scholarly journals A Study on the Implementation of SIP based new Integrated Instant Messenger

2004 ◽  
Vol 11C (3) ◽  
pp. 371-378
Keyword(s):  
2005 ◽  
Vol 35 (3) ◽  
pp. 445-462 ◽  
Author(s):  
Melanie C. Green ◽  
Jessica Hilken ◽  
Hayley Friedman ◽  
Karly Grossman ◽  
Josephine Gasiewskj ◽  
...  

2021 ◽  
Vol 25 (2) ◽  
pp. 107-128
Author(s):  
Graham Pluck ◽  
◽  
Pablo Emilio Barrera Falconi ◽  
◽  
◽  
...  

Computational modeling and brain imaging studies suggest that sensitivity to rewards and behaviorist learning principles partly explain smartphone engagement patterns and potentially smartphone dependence. Responses to a questionnaire, and observational measures of smartphone use were recorded for 121 university students. Each participant was also tested with a laboratory task of reward sensitivity and a test of verbal operant conditioning. Twenty-three percent of the sample had probable smartphone addiction. Using multivariate regression, smartphone use, particularly the number of instant messenger services employed, was shown to be significantly and independently predicted by reward sensitivity (a positive relationship), and by instrumental conditioning (a negative relationship). However, the latter association was driven by a subset of participants who developed declarative knowledge of the response-reinforcer contingency. This suggests a process of impression management driven by experimental demand characteristics, producing goal-directed instrumental behavior not habit-based learning. No other measures of smartphone use, including the self-report scale, were significantly associated with the experimental tasks. We conclude that stronger engagement with smartphones, in particular instant messenger services, may be linked to people being more sensitive to rewarding stimuli, suggestive of a motivational or learning mechanism. We propose that this mechanism could underly problem smartphone use and dependence. It also potentially explains why some aspects of smartphone use, such as habitual actions, appear to be poorly measured by technology-use questionnaires. A serendipitous secondary finding confirmed that smartphone use reflected active self-presentation. Our ‘conditioning’ task-induced this behavior in the laboratory and could be used in social-cognition experimental studies.


2021 ◽  
Vol 5 (1) ◽  
pp. 45-54
Author(s):  
Imam Riadi ◽  
Rusydi Umar ◽  
Muhammad Irwan Syahib

Viber is one of the most popular social media in the Instant Messenger application category that can be used to send text messages, make voice calls, send picture messages and video messages to other users. As many as 260 million people around the world have used this application. Increasing the number of viber users certainly brings positive and negative impacts, one of the negative impacts of this application is the use of digital forensic crime. This research simulates and removes digital crime evidence from the viber application on Android smartphones using the National Institute of Standards Technology (NIST) method, which is a method that has work guidelines on forensic policy and process standards to ensure each investigator follows the workflow the same so that their work is documented and the results can be accounted for. This study uses three forensic tools, MOBILedit Forensic Express, Belkasoft and Autopsy. The results in this study show that MOBILedit Forensic Express gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. Belkasoft gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. For Autopsy does not give the expected results in the extraction process, in other words the Autopsy application gives zero results. It can be concluded that MOBILedit Forensic Express and Belkasoft have a good performance compared to Autopsy and thus this research has been completed and succeeded in accordance with the expected goals.


Data Mining ◽  
2013 ◽  
pp. 326-335
Author(s):  
Roberto Marmo

Research on social networks has advanced significantly due to wide variety of on-line social websites and very popular Web 2.0 application. Social network analysis views social relationships in terms of network and graph theory about nodes (individual actors within the network) and ties (relationships between the actors). Using web mining techniques and social networks analysis it is possible to process and analyze large amount of social data (such as blogtagging, online game playing, instant messenger etc.) and by this to discover valuable information from data. In this way, we can understand the social structure, social relationships and social behaviors. This new approach is also denoted as social network mining. These algorithms differ from established set of data mining algorithms developed to analyze individual records, because social network datasets are called relational due to centrality of relations among entities. This chapter also sets out a process to apply web mining.


2016 ◽  
pp. 97-113
Author(s):  
Michelle F. Wright

Children and adolescents have become active users of electronic technologies, with many of them blogging, watching videos, and chatting via instant messenger and social networking sites. Many of these activities have become a typical part of their lives. Electronic technologies have brought many conveniences to the lives of children and adolescents. Along with the opportunities associated with these technologies, children and adolescents are also susceptible to risks, including cyberbullying. Therefore, many researchers have become concerned with identifying which factors might predict children's and adolescents' involvement in these behaviors. Some predictors that researchers have focused on include age, gender, and ethnicity, but the findings were mixed. This chapter draws on research to review studies on the relationship of age, gender, and ethnicity to children's and adolescents' cyberbullying involvement and concludes with solutions and recommendations as well as future directions for research focused on these predictors and cyberbullying.


Sign in / Sign up

Export Citation Format

Share Document