24 JUN 2024

Awards at Columbia University in the City of New York, USA

Two 2024 BSc Computer Science Graduates (on Joint Bachelor's Degree Program between CityUHK and Columbia University) WANG Yucheng and WANG Xuezhen have won a number of awards at Columbia University in the City of New York, USA:

Mr Wang Xuezhen:

  • Jonathan L. Gross Award for Academic Excellence (an award honouring students who graduate at the top of their class with a track record of promising innovative contributions to computer science)
  • Valedictorian (GS Class of 2024)


Mr Wang Yucheng:

  • Russell C. Mills Award (cash prize given to a computer science major who has exhibited excellence in the area of computer science)


22 MAY 2024

17th F1Tenth Autonomous Grand Prix on the 2024 Cyber-Physical Systems and Internet-of-Things Week

The team FSM Speed supervised by Prof Wang Jianping has triumphed at the 17th F1Tenth Autonomous Grand Prix during the 2024 Cyber-Physical Systems and Internet-of-Things Week.  The team members include CS PhD student Deng Jinghuai and Research Assistants Hu Hua, Dong Xiaoyun, Huang Bingyuan and Li Jiahao

The competition, held from 14 May to 16 May, was renowned for its rigorous challenges, including intense practice session, time trial races, and head-to-head races on a brand-new demanding circuit.  The participants were required to build their race cars from scratch, implement their sophisticated algorithms, and constantly adjust parameters in response to ever-changing track conditions to reach the best performance. The entire competition demanded a robust understanding of vehicle engineering, ROS, localization and mapping, trajectory planning, and model predictive control.  After a heated competition against other top-tier teams, FSM Speed distinguished itself by enhancing the standard “follow the gap” algorithms, achieving superior speed and obstacle avoidance performance, ultimately securing their first-place finish in this highly competitive Grand Prix.

16 MAY 2024

Best Conference Paper Prize at the 25th IEEE International Conference on Industrial Technology (ICIT)

Prof Gerhard P. HANCKE, in collaboration with two CS PhD students Mr Dutliff BOSHOFF and Mr Raphael NKROW and CS Postdoc Dr Bruno SILVA, has been awarded the Best Conference Paper Prize at the 25th IEEE International Conference on Industrial Technology (ICIT) for their paper titled Physical Layer Key Sharing for an Off-The-Shelf UWB Module.  Their paper investigates the feasibility of deriving shared symmetric keys from ultra-wideband radio channels on COTS devices already used for real time location systems in industrial environments.

ICIT is one of the flagship conferences hosted by the IEEE Industrial Electronics Society, and received about 400 submissions in 2024 on topics related to industrial technology, including intelligent systems and control, factory communications and automation, data acquisition, signal processing and vision systems.  ICIT 2024 was held in Bristol, UK from 25 to 27 March 2024. 

26 APR 2024

Distinguished Paper Award at the 46th International Conference on Software Engineering (ICSE 2024)

Prof ZHAO Qingchuan, in collaboration with two CS PhD students CHEN Yongliang and TANG Ruoqin and four fellow scholars, have been awarded the Distinguished Paper Award at the 46th International Conference on Software Engineering (ICSE 2024) in Lisbon, Portugal.  This conference is a premier event in the field of software engineering.

The paper, titled “Attention! Your copied data is under monitoring: A systematic study of clipboard usage in Android apps“, addresses an issue in mobile operating systems.  The paper highlights the risks associated with insufficient access control on the clipboard, which can potentially expose data to threats as one app can access and store data copied in other apps, or even transmit it to remote servers.  The team proposed an automated tool, ClipboardScope, which leverages principled static program analysis to uncover clipboard data usage in mobile apps.  Furthermore, they identified a prevalent programming habit of using the SharedPreferences object to store historical data, which could become an unnoticeable privacy leakage channel.