Greening The Net

Life has gotten more digital than ever before. And the plusses of that for the environment has been clear…as in clear skies and clean air. What is not so clear however is this: when it comes to greenhouse gases, the Internet is responsible for 2% of global emissions. If the Internet were a country, it would be ranked as the sixth largest polluter in the world.

But the Internet is here to stay, so what can be done to make it greener? From individuals not hitting Reply All on emails or turning off Auto Play for videos, to data centres running on green energy and search engines giving back to the environment, host Prerna Pant looks at all the various ways we are Greening The ‘Net.

Pant also interviews Assistant Professor Trevor E. Carlson on how Internet usage is contributing to our carbon footprint.

CNA Insider, 20 January 2021


NUS scientists develop computational tool to help design safer devices

As the world embraces the Internet of Things (IoT), more and more everyday appliances are being connected to the Internet so that people can monitor those appliances remotely. While this makes our lives more convenient, there is a looming threat of cybercriminals using these devices to gain access to sensitive data.

Now, scientists from the National University of Singapore’s School of Computing (NUS Computing) have made it easier to guard against that. They have developed a software tool that can simulate hacker attacks, and which provide an automated way to protect the design. This helps designers create more secure computer chips.

The software works by simulating a physical hardware attack known as laser fault injection. To accomplish this on a real device, the cyber-criminal would first partially disassemble the hardware to gain access to its silicon chip without interrupting its operation. Then, they use a laser to generate a processor error. This throws the gates open, allowing them to extract data and security information.

Previously, it was expensive to protect chips against this kind of attack because they had to be tested manually. If the chip fails the test, the design must start over. The NUS software, called the Laser fault Attack Benchmark Suite or LABS, can now simulate attacks in a wide variety of situations and demonstrate how the chip reacts. All this can be done without having to manufacture a single chip. This helps chip designers figure out how to repel the attack, and even trick the attackers into thinking they have succeeded. With this software, chip manufacturers will be able to simulate any device, and results are available within minutes.

The NUS scientists, led by Assistant Professor Trevor E. Carlson and Professor Peh Li Shiuan, have made the software open source so researchers and the chip design community can use it, or help make it better.

India Education Diary, 28 December 2020

NUS News, 28 December 2020

Generation Grit: NUS undergrad with cerebral palsy plans on helping others like him

The road to university can be difficult for any student, but Mr Ng Jun Kang had to overcome daily challenges that others gave no thought to. Like getting to class, for instance, or taking notes. Or even getting a drink of water.

The 22-year-old first year Computer Science undergraduate at the National University of Singapore has spastic quadriplegic cerebral palsy, which was caused by a brain injury during birth.

Although his condition affects his muscle control, motor skills and his speech, it proved no obstacle to his achieving good grades and clinching scholarships. Quite the opposite, he argues.

"My condition has gifted me resilience and patience in everything that I do," he said.

The Straits Times, 23 December 2020

Beyond the classroom: Innovations that change the world

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.

The Straits Times, 14 December 2020

NUS News, 14 December 2020

Robot vacuum cleaners can be used by hackers to 'spy' on private conversations: NUS study

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.

AZO Robotics, 8 December 2020

South China Morning Post, 8 December 2020

CNA, 7 December 2020

TODAYOnline, 7 December 2020, 7 December 2020

Futurity, 7 December 2020

NUS News, 7 December 2020

Forbes, 22 November 2020

New practices needed to stay safe online in era of working from home

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)."

The Straits Times, 11 December 2020

Seven NUS professors lauded for their work and service

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.

The Straits Times, 7 December 2020

NUS News, 4 December 2020

60 years of facial recognition: The hidden perils behind Singapore’s ‘facial recognition era’

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.

The Paper, 27 November 2020

Hackers hijacking WhatsApp accounts by asking for security codes

When a secondary school friend contacted him out of the blue a few months ago asking for a verification code on WhatsApp, administrative executive Tan Jun Heng, 25, did not suspect anything was amiss.

His friend simply claimed to have "accidentally" sent the code to his number. But within seconds of sending the code, Mr Tan was automatically locked out of his own WhatsApp account. It had been hijacked.

Mr Tan and his friends are among a growing pool of WhatsApp users who have become victims of social hacking, where scammers use already hijacked social media accounts to contact victims by posing as their friends or family.

National University of Singapore's Associate Professor Chang Ee-Chien, whose research interests include data privacy, said the impersonation tactics used by hackers are "very low-tech, but very effective, as people tend to trust their friends or family".

With full access to their victim's account, hackers may then exploit the victim's personal relationships and ask for money from friends or family. Or, if they glean enough information about their victim's place of employment, they may also target the victim's workplace, added Prof Chang. 

However, experts say, there are preventive measures that users can take to prevent such attacks.

Ms Wong and AiSP executive committee member James Tan said setting up a two-step verification process on your WhatsApp account can prevent others from signing in to it. Users should not click on suspicious looking links, even if they are purportedly from friends or family, they added.

For impersonation scams, however, "the only solution is to not trust people", said Prof Chang. He added: "It is very important that you must presume that whoever is speaking to you on the other end is not your friend."

The Straits Times, 17 November 2020

The New Paper, 17 November 2020

NUS team develops tool that can assess vulnerability of AI systems to attacks

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.

CDO Trends, 18 November 2020

The Straits Times, 10 November 2020

NUS News, 10 November 2020

New $9m research programme for smart city solutions

NUS and ST Engineering are collaborating on a S$9 million, multi-year advanced digital technologies research programme to further their common goals of building a people-centric, smart future for Singapore and beyond.

Research efforts of this new programme will focus on technologies related to Smart City as well as Smart Maintenance, Repairs and Overhaul (MRO), covering five areas: resource optimisation and scheduling; prescriptive analytics; decision and sense-making; reasoning engine and machine learning; as well as digital twin. These research areas support ST Engineering’s focus on developing differentiated and people-centric, smart city solutions that meet the present and future needs of cities around the world. The interdisciplinary research areas are also aligned with NUS’ endeavours as a driving force behind smart city innovations, leveraging its deep expertise that spans multiple domains and faculties.

Professor Chen Tsuhan, NUS Deputy President (Research & Technology), said, “As Singapore advances its position as a Smart Nation, having the right enterprise architecture to support those goals will determine if true digital transformation can be achieved. Over the years, NUS and ST Engineering have enjoyed a close and productive relationship. This new collaboration will combine NUS’ expertise in the science of cities with ST Engineering's industry knowledge to co-create people-centric Smart City solutions that will form the foundational systems to bring about not just impactful, but radical, change to the lives of people in Singapore and the world.”

The Straits Times, 16 September 2020

NUS News, 16 September 2020


How hackers use sound to unlock the secrets of your front door key

A group of security researchers from the department of computer science at the National University of Singapore has created an attack model they call SpiKey to determine the key shape that will open any tumbler lock.

Soundarya Ramesh, Harini Ramprasad and Jun Han are the talented hackers behind SpiKey, which they say "significantly lowers the bar for an attacker," when compared to a more traditional lock-picking attack. The theoretical methodology is deceptively simple, listening for the sound of the key as it moves past tumbler pins in turn when the key is inserted in the lock.

The Singapore hackers use a simple smartphone to record the sound of the key being inserted, and withdrawn, with a smartphone and then observe the time between each tumbler pin click using their custom key reverse-engineering application. This forms the secret of the key, the fine-grained bitting depths which, the researchers report, can differ by as little as 15 milli-inches, or 0.381 millimeters.

"As SpiKey infers the shape of the key, it is inherently robust against anti-picking features in modern locks," the research paper states, "and grants multiple entries without leaving any traces."

Tech Xplore, 24 August 2020

Daily Mail, 24 August 2020

Forbes, 22 August 2020

The Telegraph, 22 August 2020

Science Alert, 21 August 2020

Mashable, 20 August 2020

Interesting Engineering, 20 August 2020

Schneier on Security, 20 August 2020

Gizmodo, 19 August 2020

Slash Gear, 19 August 2020

ACM News, 13 August 2020

Smart Nation scholars eager to help Singapore's digitalisation effort

He was only 11 when he learnt how to code and design his first computer game, a 2D car racing game, with a $25 software called Game Maker 8.1. Now 19, Mr Victor Loh will be joining the Government Technology Agency (GovTech) once he completes his studies at the National University of Singapore (NUS), where he is reading a double degree in computer science and statistics. The national serviceman is one of 15 Smart Nation scholarship awardees this year - selected from a pool of 723 applicants, an increase from 614 applicants last year.

Another Smart Nation scholar joining GovTech is Mr Kevin Foong, 21, a Year 1 computer science student at NUS. He became interested in artificial intelligence and cloud computing during his eight-month internship at a private software engineering company last year. Mr Foong believes technology can solve problems and cited GovTech's SafeEntry digital check-in system, which aids in contact tracing efforts against Covid-19, as an example.

The New Paper, 17 August 2020

Researchers give robots intelligent sensing abilities to carry out complex tasks

Using Intel’s neuromorphic chip, Loihi, researchers from the National University of Singapore (NUS) developed an artificial skin that allows robots to detect touch 1,000 times faster than the human sensory nervous system. The system can also identify the shape, texture and hardness of objects 10 times faster than the blink of an eye. The researchers believe this work could improve human-robot interaction, making things like caregiving robots and automated robotic surgery more feasible.

NUS said enabling a human-like sense of touch in robotics could significantly improve current functionality, offering the example of robotic arms fitted with artificial skin that could easily adapt to changes in goods manufactured in a factory, using tactile sensing to identify and grip unfamiliar objects with the right amount of pressure to prevent slipping.

Techgoondu, 28 July 2020

ACM TechNews, 24 July 2020

India Education Diary, 17 July 2020

TechSpot, 16 July 2020

InsideBigData, 16 July 2020

News Break, 15 July 2020

Yahoo News Singapore, 15 July 2020

Digital Trends, 15 July 2020 

Engadget, 15 July 2020

VentureBeat, 15 July 2020

ZDNet, 15 July 2020

SlashGear, 15 July 2020

SiliconANGLE, 15 July 2020

Tech Xplore, 15 July 2020

Silicon Republic, 15 July 2020

Computer Weekly, 15 July 2020

HPCwire, 15 July 2020

Computer Business Review Online, 15 July 2020

Robotics and Automation News, 15 July 2020

Communications of the ACM, 15 July 2020

NUS News, 15 July 2020

Restore privacy with visual distortion

New research by a team of NUS Computing professors is promising to restore privacy to individuals by making their online images unrecognisable to even the most advanced facial recognition technologies.

Led by Professor Mohan Kankanhalli, Dean of NUS Computing, the research team from NUS Computer Science has developed a technique that safeguards sensitive information in photos by making subtle changes that are almost imperceptible to humans, but render selected features undetectable by known algorithms.

Futurity, 27 July 2020

CDOTrends, 15 July 2020

CNA 93.8FM, 14 July 2020

Dark Reading, 9 July, 2020

The Straits Times, 1 July 2020, 1 July 2020

EurekAlert, 1 July 2020

Tech Xplore, 1 July 2020

Scienmag, 1 July 2020

India Education Diary, 1 July 2020

More options for NSF cyber specialists as Mindef and NUS tie-up for new work-learn programme

Full-time National Servicemen (NSF) who are cyber specialists can now take modules from NUS Computing’s Information Security programme, after the signing of a Memorandum of Understanding for a new Work-Learn Programme by Dean Mohan Kankanhalli and Defence Cyber Chief Brigadier-General Mark Tan.

The academic credits earned in the programme can be counted towards a full degree.

The Straits Times, 30 May 2020

The Straits Times Online, 29 May 2020

Your data, my business: Why data privacy is especially hazardous for startups

Data privacy issues are especially hazardous for startups, as many use data to the same extent, or more, as large corporations. Professor Mohan Kankanhalli, Dean of NUS Computing and director of the NUS Centre for Research in Privacy Technologies (N-CRiPT), shared reasons why data privacy may not be high on startups’ list of priorities. NUS Computing Associate Professor Terence Sim, who is a principal investigator at N-CRiPT, added that ensuring good security of data is the first line of defence to preserving privacy.

Assistant Professor Reza Shokri, who does research on data privacy at N-CRiPT, shared insights from a paper that he worked on with NUS Faculty of Law Associate Professor Daniel Seng, which looked at whether different types of machine learning algorithms complied with privacy regulations.

The Business Times, 25 January 2020

Track your health with sensor integrated into smartwatch? No sweat

A team of NUS researchers has come up with the pH Watch, an ‘add on’ to a wearable health monitoring gadget that allows users to assess their health condition from their sweat pH. NUS Computing Professor Peh Li Shiuan and her PhD student, Mr Ananta Narayanan Balaji from the Department of Electrical and Computer Engineering, were part of the research team.

The other team members include NUS Computing research fellow Dr Wang Bo, as well as PhD student Ms Chen Yuan and Assistant Professor Shao Huilin from the Department of Biomedical Engineering and the Institute for Health Innovation & Technology.

NUS News, 13 January 2020

The Straits Times, 12 January 2020

Giving food, friendship to homeless people

Since July 2014, NUS Computing alumnus and software developer Abraham Yeo has been giving food, drinks and companionship to the homeless in Singapore. Abraham co-founded an informal volunteer group, the Homeless Hearts of Singapore, to rally like-minded people together to conduct night walks and checks on the homeless.

The Straits Times, 19 December 2019

Lost? Eyes in the sky can tell you where you are

No matter how many times you’ve flown, sitting at the window seat and watching the world shrink away from view as the plane takes off never seems to grow old. Towering trees and skyscrapers become mere pixels, roads and rivers now thin winding ribbons, and vast tracts of land appear as tiny thumbnails below.

The familiar can become unrecognizable as we’re transported from the ground up into the air. People sometimes struggle with this change in perspective, and it turns out machines do too — especially those tasked with helping to make navigation easier.

Striving to create more accurate geolocation systems, researchers have in recent years been making use of satellite imagery. The underlying idea is simple: take the image in question and compare it with those from a database of geotagged satellite images. Find a match and you’ll be able to pinpoint your location. The snag, however, is that such ground-to-aerial matching — with its potential for use in navigation, autonomous vehicles, augmented reality and other applications — is incredibly challenging.

“It’s difficult because of a drastic change in viewpoints,” says Assistant Professor Gim Hee Lee, who studies computer vision and robotic perception at the National University of Singapore’s (NUS) School of Computing. “When you compare two images from satellite and street views, they're hardly recognisable.”

Cross-view matching, as it’s formally called, has gained increasing attention in recent years. Traditionally, geo-localisation involves comparing two images — a query one against a reference one — both taken from the ground view. This approach is relatively easy to implement but suffers from two main drawbacks. “Your reference map needs to be well-covered,” says Lee. “But it’s impossible to access every part of the world no matter how much money or manpower you have.”

Furthermore, reference images, often crowdsourced from sites such as Flickr, tend to be very biased. Images of popular places are often abundantly available while those of more isolated areas are lacking. “For example in Singapore, you see a lot of images that are focused on Gardens by the Bay, Marina Bay Sands, or the Merlion,” says Lee. “But if you want to navigate to NUS, then there will be very few images. Not to mention the heartlands like Clementi or Ang Mo Kio.”

Aerial view of a neighbourhood in Singapore

Employing satellite images can help overcome these issues. “We can easily access them, and they have worldwide coverage,” says Lee. Which explains why ground-to-aerial matching systems have become increasingly popular for geo-localisation in recent years.

Still, one big hurdle remains: how to overcome the drastic change in viewpoint when comparing an image taken on the ground to one taken up above.

Aggregating features

Spurred on by this challenge, Lee and his PhD student Sixing Hu began working on a possible solution in early 2017. What they came up with was the Cross-View Matching Network, or CVM-Net, a machine-learning based algorithm that makes ground-to-aerial geo-localisation possible.

“We exploit the very popular deep learning approach because it can extract features from images in a very powerful way,” says Lee. Feature extraction — the identification of features in a given image — is the first step of CVM-Net.

The second stage involves aggregating these features to form a unique signature for each image. “Just like how your thumbprint is unique to you, the signature is unique to the image,” he explains. The signature generated, recorded as a string of numbers, can then be compared against pre-computed, geotagged ones in the database of satellite images to determine the location in question.

Crucially, it’s the creation of this distinctive thumbprint that has made ground-to-aerial localisation possible. “This particular step actually makes the whole process more robust and rotationally invariant,” says Lee. In other words, aggregating features within a particular image to form a unique signature can be used to pinpoint its location, regardless of the illumination or orientation of the picture.

A moonshot

After training the CVM-Net model, the researchers tested its effectiveness using two large datasets. One involved nearly 9,000 image pairs, while the other close to a million. In both instances, CVM-Net outperformed all other geo-localisation approaches in terms of accurate identification.

The researchers then proceeded to do real-world testing. Using a car fitted with 12 infrared cameras offering views in four directions, the team drove around two test sites (one urban and the other rural) in Singapore. The tests demonstrated — for the first time ever — that by simply providing images or videos of your surroundings while in a moving vehicle, CVM-Net can tell you where you are in real-time.

NUS Computing Asst Prof Lee Gim Hee

The impact of Lee and Hu’s work has been far and wide-reaching. “All the subsequent research has followed what we are doing,” says Lee. “We became a benchmark that everybody has to follow in order to reach this kind of performance in ground-to-aerial geo-localisation.”

Work in the field is, however, far from over. “I don’t claim that we have solved the problem,” says Lee. “There are still a lot of other problems that remain.”

One thing he and other researchers are looking into is how to do semantic labeling. “Let’s say I show you a map, can you show me where all the road networks are? Or which ones are buildings?” he says.

Generalisation is another big issue in the field. “If you train your network on dataset from one geographic location, will it also work when you bring your car to another part of the world?” says Lee.

Despite the challenges that remain, Lee is proud of how far his team has come. “When we first began, I was quite skeptical. This was like a moonshot thing because it sounded almost impossible to do in reality,” he recalls. “But then we showed a proof-of-concept and CVM-Net actually worked on a real vehicle.”


Image-Based Geo-Localization Using Satellite Imagery