Results | SCSU Bibliography

Liu, J., & Kim, C. S. (2012). Information seeking tasks: why do searchers feel them difficult? Proceedings of HCIR 2012. http://ils.unc.edu/hcir2012/hcir2012_submission_4.pdf

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Liu, J., & Kim, C. S. (2013). Why do users perceive search tasks as difficult? Exploring difficulty in different task types. Symposium on Human-Computer Interaction and Information Retrieval, 5. https://doi.org/10.1145/2528394.2528399

In this paper, we examined why information searchers perceive search tasks as difficult, and what factors/reasons make them perceive tasks as difficult. We also examined if task difficulty reasons vary across different tasks (task types). Data was collected through a controlled laboratory experiment in which tasks were designed following a classification scheme. A total of 32 undergraduate students participated, each was given 4 search tasks, and they were asked in questionnaires both before and after the tasks for task difficulty ratings and why they gave those ratings. We developed a coding scheme based on the difficulty reasons users gave, which covered various aspects of task, user, and user-task interaction. Difficulty reasons were categorized following this scheme. Results showed that searchers had some common reasons for task difficulty in different tasks, but most of the difficulty reasons varied across tasks. For each task, there were also common reasons for task difficulty, although there was some variation here as well. Task difficulty was also found to be negatively correlated with users topic knowledge, previous experience, and topic interest. Our findings help understand search task difficulty, as well as the relationships between task difficulty and task type, knowledge background, etc. These can also be helpful with experiment task design. © 2013 ACM.

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Liu, J., Kim, C. S., & Creel, C. (2013). ASIST - Why do users feel search task difficult (Vol. 50). Wiley. https://doi.org/10.1002/meet.14505001125

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Liu, J., Kim, C. S., & Creel, C. (2013). Why do users feel search task difficult. ASIST ’13 Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries, 50(1), 1–4. https://doi.org/10.1002/meet.14505001125

This paper reports on an examination of why information searchers find search tasks difficult and the specific reasons they feel difficult. Data was collected through a controlled laboratory experiment with 32 participants, each working with 4 search tasks. They were asked in questionnaires, both before and after the tasks, for task difficulty ratings and their reasoning for giving those ratings. We developed a coding scheme based on the difficulty reasons users gave, which covered various aspects of task, user, and user-task interaction. This helps us understand search task difficulty, as well as provides a scheme for task difficulty control in experiment task design.

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Liu, J., Kim, C. S., & Creel, C. (2015). Exploring search task difficulty reasons in different task types and user knowledge groups. Information Processing and Management, 51(3), 273–285. https://doi.org/10.1016/j.ipm.2014.10.001

Search task difficulty has been attracting much research attention in recent years, mostly regarding its relationship with searchers' behaviors and the prediction of task difficulty from search behaviors. However, it remains unknown what makes searchers feel the difficulty. A study consisting of 48 undergraduate students was conducted to explore this question. Each participant was given 4 search tasks that were carefully designed following a task classification scheme. Questionnaires were used to elicit participants' ratings on task difficulty and why they gave those ratings. Based on the collected difficulty reasons, a coding scheme was developed, which covered various aspects of task, user, and user-task interaction. Difficulty reasons were then categorized following this scheme. Results showed that searchers reported some common reasons leading to task difficulty in different tasks, but most of the difficulty reasons varied across tasks. In addition, task difficulty had some common reasons between searchers with low and high levels of topic knowledge, although there were also differences in top task difficulty reasons between high and low knowledge users. These findings further our understanding of search task difficulty, the relationship between task difficulty and task type, and that between task difficulty and knowledge level. The findings can also be helpful with designing tasks for information search experiments, and have implications on search system design both in general and for personalization based on task type and searchers' knowledge. (C) 2014 Elsevier Ltd. All rights reserved.

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Chowdhury, S., Katangur, A., Sheta, A., Psayadala, N. R., & Liu, S. (2023). Genetic Algorithm Based Service Broker Policy to find Optimal Datacenters in Cloud Services. 2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, 270–278. Scopus. https://doi.org/10.1109/ICCCBDA56900.2023.10154791

In modern-day computing, cloud services are widely used in every aspect of life. So, user satisfaction depends on the effectiveness and efficiency of cloud services. Service broker policy of the cloud maintains the effectiveness and efficiency of cloud services. Service broker policy provides the rules and norms based on which a data center is selected for a userbase request. This paper proposes a genetic algorithm-based service broker policy that provides the optimal sequence of data centers for different userbases based on their requirements. This research aims to find an optimal data center for userbases that can achieve user satisfaction by minimizing the cloud service's response time and data processing time. We have experimented with our proposed genetic algorithm-based service broker policy in the CloudAnalyst platform based on different real-world scenarios. Simulation results indicate that our proposed genetic algorithm outperforms existing traditional algorithms. © 2023 IEEE.

Sufian, R. S., Liu, T., De Téramond, G. F., Dosch, H. G., Brodsky, S. J., Deur, A., Islam, M. T., Ma, B.-Q., & HLFHS Collaboration. (2018). Nonperturbative strange-quark sea from lattice QCD, light-front holography, and meson-baryon fluctuation models. Physical Review D, 98(11). Scopus. https://doi.org/10.1103/PhysRevD.98.114004

We demonstrate that a nonzero strangeness contribution to the spacelike electromagnetic form factor of the nucleon is evidence for a strange-antistrange asymmetry in the nucleon's light-front wave function, thus implying different nonperturbative contributions to the strange and antistrange quark distribution functions. A recent lattice QCD calculation of the nucleon strange quark form factor predicts that the strange quark distribution is more centralized in coordinate space than the antistrange quark distribution, and thus the strange quark distribution is more spread out in light-front momentum space. We show that the lattice prediction implies that the difference between the strange and antistrange parton distribution functions, s(x)-s(x), is negative at small-x and positive at large-x. We also evaluate the strange quark form factor and s(x)-s(x) using a baryon-meson fluctuation model and a novel nonperturbative model based on light-front holographic QCD. This procedure leads to a Veneziano-like expression of the form factor, which depends exclusively on the twist of the hadron and the properties of the Regge trajectory of the vector meson which couples to the quark current in the hadron. The holographic structure of the model allows us to introduce unambiguously quark masses in the form factors and quark distributions preserving the hard scattering counting rule at large-Q2 and the inclusive counting rule at large-x. Quark masses modify the Regge intercept which governs the small-x behavior of quark distributions, therefore modifying their small-x singular behavior. Both nonperturbative approaches provide descriptions of the strange-antistrange asymmetry and intrinsic strangeness in the nucleon consistent with the lattice QCD result. © 2018 authors. Published by the American Physical Society.

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