On enhancing serial fusion based multi-biometric verification system - Hossain, Md; Chen, Jundong; Rahman, Khandaker (Applied Intelligence, 2018)
Design of a serial fusion based multi-biometric verification system requires fixing several parameters, such as reject thresholds at each stage of the architecture and the order in which each individual verifier is placed within the multi-stage system. Selecting the order of verifier is a crucial parameter to fix because of its high impact on verification errors. A wrong choice of verifier order might lead to tremendous user inconvenience by denying a large number of genuine users and might cause severe security breach by accepting impostors frequently. Unfortunately, this design issue has been poorly investigated in multi-biometric literature. In this paper, we address this design issue by performing experiments using three different serial fusion based multi-biometric verification schemes. We did our experiments on publicly available NIST multi-modal dataset. We tested 24 orders—all possible orders originated from four individual verifiers—on a four-stage biometric verification system. Our experimental results show that the verifier order “best-to-worst”, where the best performing individual verifier is placed in the first stage, the next best performing individual verifier is placed in the second stage, and so on, is the top performing order. In addition, we have proposed a modification to the traditional architecture of serial fusion based multi-biometric verification systems. With rigorous experiments on the NIST multi-modal dataset and using three serial fusion based multi-biometric verification schemes, we demonstrated that our proposed architecture significantly improves the performance of serial fusion based multi-biometric verification systems.
9 citations (Crossref) [2023-10-31] Citation Key Alias: hossainEnhancingSerialFusion2018a, lens.org/017-508-366-099-573, pop00310 tex.type: [object Object]
Keywords: Architecture; Biometric verification; Biometrics; Multi biometrics; Multi-biometric verification; Multi-modal dataset; Proposed architectures; Reject option; Serial fusion; Traditional architecture; Verifier order; dept.csc; peer-reviewed

Analyzing the sentiment correlation between regular tweets and retweets - Chen, Jundong; Hossain, Shafaeat; Zhang, Huan (Social Network Analysis and Mining, 2020)
Retweeting is an important way of information propagation on Twitter. In this paper, we investigate the sentiment correlation between regular tweets and retweets. We anticipate our investigation sheds a light on how the sentiment of regular tweets impacts the retweets of different sentiments. We propose a method for measuring the sentiment of tweets. We categorize the Twitter users into different groups by different norms, which are the follower count, the betweenness connectivity, a combination of follower count and betweenness centrality,and the amount of tweets. Then, we calculate the sentiment correlation for different groups to examine the influential factors for retweeting a message with a certain sentiment.We find that the users with higher betweenness centrality and higher tweets amount tend to exhibit a higher sentiment correlation. The users with medium-level followers_count show the highest sentiment correlation compared to the low-level and high-level followers_count. After combining the two factors of followers_count and betweenness centrality, we discover that specifically at low-level betweenness centrality the users with medium-level followers_count have the highest sentiment correlation. Our last observation is that the difference for correlation coefficients exists between different types of users. Our study on the sentiment correlation provides instructional information for modeling information propagation in human society. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.
21 citations (Crossref) [2023-10-31] tex.ids: chenAnalyzingSentimentCorrelation2020a tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85078901417 type: Article
Keywords: Betweenness; Betweenness centrality; Correlation coefficient; Human society; Influential factors; Information dissemination; Information propagation; Instructional information; Learning systems; Machine learning; Model informations; Sentiment analysis; Social network; Social networking (online); Twitter classification; dept.csc; peer-reviewed

An enhanced architecture for serial fusion based multi-biometric verification system - Hossain, Shafaeat; Chen, Jundong; Rahman, Khandaker Abir (International Symposium on Networks, Computers and Communications, 2018)
The traditional architecture of serial fusion based multi-biometric verification systems places an average performing or the worst performing individual verifier in the final stage. Because the final stage gives the verification decision using a single threshold and takes on the most confusing samples which are rejected by all previous stages, an average or the worst performing individual verifier may incur high verification errors in the final stage, which may negatively impact the performance of the whole system. Unfortunately, it is not possible to place a strong individual verifier in the final stage of a traditional architecture because if we place a strong individual verifier in the final stage, we will have to place a weak individual verifier in an earlier stage. Studies show that placing a weak individual verifier in an earlier stage worsens the performance of the whole system by giving more wrong decision earlier. Hence, the challenge is-how can we place the best performing individual verifier in the first stage and at the same time not place an average or the worst performing individual verifier in the final stage? In this paper, we address this challenge. We have come up with a very simple but effective solution. We have proposed a modification to the traditional architecture of serial fusion based multi-biometric verification systems. With rigorous experiments on the NIST multi-modal dataset and using three serial fusion based multi-biometric verification schemes, we demonstrated that our proposed architecture significantly improves the performance of serial fusion based multi-biometric verification systems. © 2018 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Architecture; Biometrics; Effective solution; Multi biometrics; Multi-modal dataset; Network architecture; Proposed architectures; Serial fusion; Traditional architecture; dept.csc

Sentiment analysis of the correlation between regular tweets and retweets - Chen, Jundong; Li, He; Wu, Zeju; Hossain, Md Shafaeat (2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), 2017)
In this paper, we study the influence from the sentiment of regular tweets on retweeting. We propose a method to calculate the sentiment score for each tweet and each Twitter user. This method enables us to place the tweets and retweets into the same time period to explore the sentiment factor. We adopt the correlation coefficient between the sentiment scores of regular tweets and those of retweets to measure the influence. We categorize the Twitter users in three different ways to investigate three factors, which are the number of followers, betweenness centrality and the types of accounts. Community detection and machine learning are integrated into our approach. We find that the difference for correlation coefficients exists between different levels of the number of followers, and different types of users. Our method sheds a light on better predicting the dynamics of tweets diffusion by including the sentiment factor into the prediction model. © 2017 IEEE.
6 citations (Crossref) [2023-10-31] Citation Key Alias: pop00210 tex.type: [object Object]
Keywords: Artificial intelligence; Betweenness centrality; Community detection; Correlation coefficient; Data mining; Learning systems; Prediction model; Sentiment analysis; Sentiment scores; Social networking (online); Time-periods; Twitter classification; dept.csc; machine learning; sentiment analysis; social network

A game theoretic analysis of the twitter follow-unfollow mechanism - Chen, Jundong; Hossain, Md Shafaeat; Brust, Matthias R.; Johnson, Naomi (Decision and Game Theory for Security, 2018)
Twitter users often crave more followers to increase their social popularity. While a variety of factors have been shown to attract the followers, very little work has been done to analyze the mechanism how Twitter users follow or unfollow each other. In this paper, we apply game theory to modeling the follow-unfollow mechanism on Twitter. We first present a two-player game which is based on the Prisoner’s Dilemma, and subsequently evaluate the payoffs when the two players adopt different strategies. To allow two players to play multiple rounds of the game, we propose a multi-stage game model. We design a Twitter bot analyzer which follows or unfollows other Twitter users by adopting the strategies from the multi-stage game. We develop an algorithm which enables the Twitter bot analyzer to automatically collect and analyze the data. The results from analyzing the data collected in our experiment show that the follow-back ratios for both of the Twitter bots are very low, which are 0.76%0.76%0.76\% and 0.86%0.86%0.86\%. This means that most of the Twitter users do not cooperate and only want to be followed instead of following others. Our results also exhibit the effect of different strategies on the follow-back followers and on the non-following followers as well.
2 citations (Crossref) [2023-10-31] Citation Key Alias: chenGameTheoreticAnalysis2018, pop00231 tex.type: [object Object]
Keywords: Decision theory; Game theoretic analysis; Game theory; Learning systems; Machine learning; Multi stage games; Social network; Social networking (online); Twitter bot; Twitter classification; Two-player games; dept.csc