In April 2018, Hyeongcheol Kim flew to Montreal for work. The young PhD student was excited — it was his first time in the Canadian city and the conference he was about to attend was one of the biggest in his field of computer science. What’s more, Montreal was only a three hour journey from Quebec City, a place he had glimpsed many times on the small screen.

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21 December 2020 – Information Systems PhD student Ahmad Asadullah won the Kauffman Best Student Paper Award at the International Conference on Information Systems (ICIS) 2020.

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Most pundits gazing into the crystal ball will likely shout two words in their prediction of healthcare’s future: precision medicine. Increasingly, there is growing recognition that tailoring treatments based on an individual’s lifestyle, genes, and environmental factors can yield much improved outcomes.

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Lettuce, mint and even tomatoes – Singaporeans may soon be able to grow these vegetables and more in their HDB flats.

Having witnessed “a deep psychological fear” when COVID-19 sparked panic buying here, Toby Fong and his team – superFARM – decided to bolster the nation’s food security. Their plan? Encourage green fingers through home-based farming.

“When we think about food security, it’s usually at a national level so it almost feels like the individual (is disconnected) from the entire food security equation,” said Toby, who graduated with a Master’s from NUS Architecture this year.

Under the “Make Our People Better” category, Toby, NUS Computing graduate Lim Hui Qi and NUS Arts and Social Sciences graduate Ong Jun Ren will design modular farming units that can fit into the smallest of homes. These units can also be customised for bigger spaces.

The plan is to transform niche hydroponics systems into functional mini-farms. In the next six months, half of their $50,000 funding will go to research such as field testing and online surveys, while the rest will be used for prototype development.

The team also wants to expand the individual’s role in food security to make sustainability a way of life.

“We want to recalibrate people’s attitude and behaviour to encourage responsible food consumption,” said Toby.

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9 December 2020 – Associate Professor He Bingsheng and his collaborators have won the IEEE Transactions on Parallel and Distributed Systems 2019 Best Paper award.

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8 December 2020 – Assistant Professor Jun Han, Computer Science PhD student Sriram Sami, and final-year undergraduates Yimin Dai (Computer Science) and Sean Rui Xiang Tan (Computer Engineering), as well as Assistant Professor Nirupam Roy from the University of Maryland, won the Best Poster Runner-Up Award at the 18th ACM Conference on Embedded Networked Sensor Systems (SenSys 2020).

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When your robot vacuum cleaner does its work around the house, beware that it could pick up private conversations along with the dust and dirt. Computer scientists from NUS have demonstrated that it is indeed possible to spy on private conversations using a common robot vacuum cleaner and its built-in Light Detection and Ranging (Lidar) sensor.

The novel method, called LidarPhone, repurposes the Lidar sensor that a robot vacuum cleaner normally uses for navigating around a home into a laser-based microphone to eavesdrop on private conversations.

The research team, led by Assistant Professor Jun Han from NUS Computer Science, and his doctoral student Mr Sriram Sami, managed to recover speech data with high accuracy. NUS students, Mr Dai Yimin and Mr Sean Tan Rui Xiang, as well as Assistant Professor Nirupam Roy from the University of Maryland, also contributed to this work.

Mr Sami shared, “The proliferation of smart devices – including smart speakers and smart security cameras – has increased the avenues for hackers to snoop on our private moments. Our method shows it is now possible to gather sensitive data just by using something as innocuous as a household robot vacuum cleaner. Our work demonstrates the urgent need to find practical solutions to prevent such malicious attacks.”'

The core of the LidarPhone attack method is the Lidar sensor, a device which fires out an invisible scanning laser, and creates a map of its surroundings. By reflecting lasers off common objects such as a dustbin or a takeaway bag located near a person’s computer speaker or television soundbar, the attacker could obtain information about the original sound that made the objects’ surfaces vibrate. Using applied signal processing and deep learning algorithms, speech could be recovered from the audio data, and sensitive information could potentially be obtained.

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Say "no" when your child asks to use your work laptop to do his schoolwork, or set up a different user account on the work laptop for different activities.

There are ways to reset habits and practices for a more digitally secure 2021 as working and e-learning from home become the new normal even after Covid-19, said panellists at The Straits Times Reset 2021 Webinar Series: Digitalisation And Cyber Security on Wednesday.

The panellists comprised of Associate Professor Steven Wong from the Singapore Institute of Technology, Mr David Koh, chief executive of the Cyber Security Agency of Singapore; Associate Professor Chang Ee-Chien from the National University of Singapore School of Computing; and Mr Benjamin Ang, head of the Cyber and Homeland Defence Programme at the Centre of Excellence for National Security, a policy research think-tank.

Prof Chang suggested segregating devices at home by individual or workflow. For example, as far as possible, children should use a different desktop or laptop from the ones their parents use for work.

"If that is not possible, then try to segregate by setting up different user accounts on a laptop. Even if you have your own machine, you can segregate accounts for work, for family, or for playing games," he said.

"Segregation is about setting up security parameters, so that when something happens within that parameter it will not spill over to other (areas)."

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NUS has honoured seven exceptional educators, researchers and professionals at the NUS University Awards 2020. The annual event recognises individuals for their outstanding contributions in the areas of education, research and service to the University, Singapore and the global community.

Professor Dong Jin Song from the NUS' School of Computing was given the University Research Recognition Award for developing a software verification framework that has more than 4,000 users from over 150 countries.

NUS President Professor Tan Eng Chye lauded the award winners for being role models for the university community. “Each award winner has exemplified the spirit of excellence with an indomitable spirit. They are truly esteemed individuals – beacons and pathfinders who inspire us to better ourselves and to scale new heights even in times of crisis. NUS is proud to celebrate their dedication and distinguished accomplishments,” he said.

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2 December 2020 – Four new faculty members have joined NUS Computing’s Department of Computer Science:

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In recent years, the Singapore government has tapped on facial recognition for various purposes as part of its ‘smart nation’ initiative. For instance, Changi Airport’s Terminal 4 uses facial recognition technology for various purposes such as passenger check-in, immigration and boarding, while GovTech launched a launched the "Lamppost-as-a-Platform" project, which outfits some 95,000 traditional lampposts in the country with a network of wireless sensors and cameras to support urban and transportation planning and operations.

Associate Professor Terence Sim from the School of Computing at the National University of Singapore stated in an exclusive interview with China-based news website The Paper that there are trends of facial recognition technology being abused, and that laws protecting such technology could be further strengthened. He also elaborated further on privacy issues regarding such technology.

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Every semester, Francis Yeoh spends part of his time in pitch slams. These are intense sessions where teams of students have five minutes to try and sell their start-up ideas. Yeoh, the Professorial Fellow for Entrepreneurship at the NUS School of Computing, and his colleagues listen carefully to the pitches before deciding which teams are worthy of a $10,000 grant.

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25 November 2020 – Associate Professor Hahn Jungpil and PhD in Information Systems graduate, Peng Jiaxu, recently won the Best Paper award in the Advances in Research Methods track at the International Conference on Information Systems (ICIS) 2020.

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25 November 2020 – Associate Professor Hahn Jungpil and Master’s in Information Systems graduate, Vasilii Zorin, recently won the Best Paper award in the Information Systems Development & Project Management track at the International Conference on Information Systems (ICIS) 2020.

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20 November 2020 – Computer Science professors Chan Mun Choon and Han Jun, along with their PhD students Nishant Budhdev and Nitya Lakshmanan, were recently listed in the GSMA Mobile Security Hall of Fame.

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18 November 2020 – Associate Professor Kan Min-Yen and Computer Engineering alumnus Nguyen Van Hoang won the Best Paper Full Research Paper Award at the 29th ACM International Conference on Information and Knowledge Management (CIKM2020), held online from 19 to 23 October.

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Stéphane Bressan and Christian Miniatura grew up in rival neighbourhoods of the naval garrison town of Toulon in southern France. They went to the same high school and the same college only a few years apart, but never were acquainted until 2006 when they were both working halfway across the world, at the National University of Singapore. Miniatura and Bressan became fast friends, meeting regularly to “put the world to rights” over French food and wine.

“One of our favourite debates was whether artificial intelligence can be useful to quantum physics,” says Bressan, an associate professor at the School of Computing. He was convinced that AI could lend a helping hand in solving some of physics’ longstanding problems. But Miniatura, a quantum physicist by training and the director of the Franco-Singaporean physics laboratory MajuLab, remained perplexed albeit intrigued at the possibility.

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National University of Singapore (NUS) researchers have developed a tool to safeguard against a new form of cyber attack that can recreate the data sets containing personal information used to train artificial intelligence (AI) machines.

The tool, called the Machine Learning (ML) Privacy Meter, has been incorporated into the developer toolkit that Google uses to test the privacy protection features of AI algorithms.

In recent years, hackers have figured out how to reverse-engineer and reconstruct database sets used to train AI systems through an increasingly common kind of attack called a membership inference (MI) attack.

Assistant Professor Reza Shokri, who heads the research team behind ML Privacy Meter, said such attacks involve hackers repeatedly asking the AI system for information, analysing the data for a pattern, and then using the pattern to guess if a data record was used to train the AI system.

Prof Shokri likened MI attacks to thieves probing for weak spots in a house's walls and doors with a needle before breaking in. "But the thief is not going to break in with the needle. Now that he knows (where the weak spots are), he is going to come with a hammer and break the wall," he said.

ML Privacy Meter helps AI developers through a scorecard showing how accurately attackers could recreate the original data sets and suggests techniques to guard against actual MI attacks. The Privacy Meter is the result of three years of work to create an easy-to-use tool which helps programmers see where the weak spots in their algorithms are.

Google started using the tool earlier this year. The tool is open-source, meaning that it can be used for free by other researchers or companies around the world.

"Our main focus was to build an easy-to-use interface for anybody who knows machine learning, but might not know anything about privacy and cyber attacks," said Prof Shokri, who is Iranian by birth and moved to Singapore in 2017. 

The NUS research team that developed the Machine Learning Privacy Meter also consists of master's student Mihir Khandekar, 24, doctoral student Chang Hongyan, 24, research assistant Aadyaa Maddi, 22, and doctoral student Rishav Chourasia, 24.

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5 November 2020 – Several NUS Computing professors were recently awarded funding from the MOE Academic Research Funding (AcRF) Tier 2 scheme. Among the grant recipients were Professor Dong Jin Song, Professor Wing-Kin Sung and Assistant Professor Lee Gim Hee, all from the Department of Computer Science.

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3 November 2020 – Professor Chua Tat Seng, Kwan Im Thong Hood Cho Temple Chair Professor at NUS Computing and Director of the NUS-Tsinghua Extreme Search Center (NExT++), won the Best Paper award at the ACM Multimedia Conference. The conference was held online from 12 – 16 October 2020, and is a leading international forum for researchers focusing on advancing the research and applications of multiple media such as images, text, audio, speech, music, sensor and social data.

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