�糵����

, assistant professor in Illinois Tech’s Department of Information Technology and Management, will have five papers, four of which are co-authored with students, published at upcoming conferences.

Zheng’s paper with Archana Subramaniyan, “Personality-Aware Collaborative Learning: Models and Explanations” will be published during the 33rd annual International Conference on Advanced Information Networking and Applications (AINA-2019) on March 27–29, 2019, in Matsue, Japan. A second paper, authored with Sridhar Srinivasan, “Mobile App and Malware Classifications By Mobile Usage with Time Dynamics,” will also be published during AINA-2019. Three of Zheng’s papers will be published during the Association for Computing Machinery (ACM)/Special Interest Group on Applied Computing’s (SIGAPP) 34th annual Symposium on Applied Computing on April 8–12, 2019, in Limassol, Cyprus. Those papers include: “Integrating Context-Awareness and Multi-Criteria Decision Making in Educational Learning” with co-authors Shephalika Shekhar, Alisha Anna Jose, and Sunil Kumar Rai; “Context-Aware Recommendations via Sequential Predictions” with Jose; and his sole-authored paper, “Utility-Based Multi-Criteria Recommender Systems.”

These publications are associated with the following research projects:

Project: Malware Analysis in Mobile Networks

• Publication: Yong Zheng, Sridhar Srinivasan, “Mobile App and Malware Classifications By Mobile Usage with Time Dynamics”, will be published during the 33rd annual (AINA-2019) on March 27–29, 2019, in Matsue, Japan.
• Description: Smartphones have become a popular target for cyberattacks. Malware can be embedded into the mobile applications or devices where they can further consume mobile resources, drain the battery, steal information, and so forth. We use the SherLock data which is a labeled smartphone dataset that captures ongoing attacks within the low-privileged monitorable features. We analyze the usage behaviors, discover temporal and usage patterns, and further examine multiple classification techniques to predict the type and the running state (i.e., benign and malicious) of the mobile applications with different combinations of usage and temporal features. Our experiments identified the best features and methods to detect malwares and we successfully incorporate time dynamics to build more effective models.
• Student Highlights: Sridhar Srinivasan is a Master student graduated in Fall, 2018. His expertise lies in data management and analytics. Recently, he got and accepted the Data Scientist position at , New Orleans, USA

Project: Personality-Aware Recommendations

• Publication: Zheng’s paper with Archana Subramaniyan, “Personality-Aware Collaborative Learning: Models and Explanations” will be published during the 33rd annual (AINA-2019) on March 27–29, 2019, in Matsue, Japan.
• Description: Personality traits have been demonstrated as one of the effective human factors in the process of decision making. Personality-aware recommendation models have been built for different applications. However, the models and research for educations are still under investigation. In this paper, we utilize the educational learning as a case study, exploit and summarize different approaches which take advantage of personality traits in collaborative personalized recommendations. Furthermore, we extend the existing personality-aware recommendation models and propose two other alternative recommendation algorithms which can utilize the personality traits. The empirical comparisons and studies over the educational data set demonstrate the effectiveness of our proposed recommendation models

In addition, three of Zheng’s papers will be published during the Association for Computing Machinery (ACM)/Special Interest Group on Applied Computing’s (SIGAPP) 34th annual on April 8–12, 2019, in Limassol, Cyprus. Those papers include: “Integrating Context-Awareness and Multi-Criteria Decision Making in Educational Learning” with co-authors Shephalika Shekhar, Alisha Anna Jose, and Sunil Kumar Rai; “Context-Aware Recommendations via Sequential Predictions” with Jose; and his sole-authored paper, “Utility-Based Multi-Criteria Recommender Systems.”

ACM SAC has become a primary forum for applied computer scientists, computer engineers, software engineers, and application developers from around the world to interact and present their work. Each year the event is organized as a number of specialist tracks on various areas of computer applications. The submissions are relatively competitive, and ACM SAC usually has an acceptance rate between 22% and 24%. Dr. Yong Zheng served as the chair for the track on recommender systems at the ACM SAC conference from 2017 to 2019.