Computers Math

Music artists can find inspiration and new creative directions for their song writing with technology developed by Waterloo researchers.
LyricJam, a real-time system that uses artificial intelligence (AI) to generate lyric lines for live instrumental music, was created by members of the University’s Natural Language Processing Lab.
The lab, led by Olga Vechtomova, a Waterloo Engineering professor cross-appointed in Computer Science, has been researching creative applications of AI for several years.
The lab’s initial work led to the creation of a system that learns musical expressions of artists and generates lyrics in their style.
Recently, Vechtomova, along with Waterloo graduate students Gaurav Sahu and Dhruv Kumar, developed technology that relies on various aspects of music such as chord progressions, tempo and instrumentation to synthesize lyrics reflecting the mood and emotions expressed by live music.
As a musician or a band plays instrumental music, the system continuously receives the raw audio clips, which the neural network processes to generate new lyric lines. The artists can then use the lines to compose their own song lyrics. More

Advancements in natural language processing (NLP) enable computers to understand what humans say and help people communicate through tools like machine translation, voice-controlled assistants and chatbots.
But NLP research often only focuses on spoken languages, excluding the more than 200 signed languages around the world and the roughly 70 million people who might rely on them to communicate.
Kayo Yin, a master’s student in the Language Technologies Institute, wants that to change. Yin co-authored a paper that called for NLP research to include signed languages.
“Signed languages, even though they are a significant part of the languages used in the world, aren’t included,” Yin said. “There is a demand and an importance in having technology that can handle signed languages.”
The paper, “Including Signed Languages in Natural Language Processing,” won the Best Theme Paper award at this month’s 59th Annual Meeting of the Association for Computational Linguistics. Yin’s co-authors included Amit Moryossef of Bar-Ilan University in Israel; Julie Hochgesang of Gallaudet University; Yoav Goldberg of Bar-Ilan University and the Allen Institute for AI; and Malihe Alikhani of the University of Pittsburgh’s School of Computing and Information.
The authors wrote that communities relying on signed language have fought for decades both to learn and use those languages, and for them to be recognized as legitimate. More

Children’s heavy digital media use is associated with a risk of being overweight later in adolescence. Physical activity protects children from the adverse effects of digital media on their weight later in adolescence.
A recently completed study shows that six hours of leisure-time physical activity per week at the age of 11 reduces the risk of being overweight at 14 years of age associated with heavy use of digital media.
Obesity in children and adolescents is one of the most significant health-related challenges globally. A study carried out by the Folkhälsan Research Center and the University of Helsinki investigated whether a link exists between the digital media use of Finnish school-age children and the risk of being overweight later in adolescence. In addition, the study looked into whether children’s physical activity has an effect on this potential link.
The results were published in the Journal of Physical Activity and Health.
More than six hours of physical activity per week appears to reverse adverse effects of screen time
The study involved 4,661 children from the Finnish Health in Teens (Fin-HIT) study. The participating children reported how much time they spent on sedentary digital media use and physical activity outside school hours. The study demonstrated that heavy use of digital media at 11 years of age was associated with a heightened risk of being overweight at 14 years of age in children who reported engaging in under six hours per week of physical activity in their leisure time. In children who reported being physically active for six or more hours per week, such a link was not observed.
The study also took into account other factors potentially impacting obesity, such as childhood eating habits and the amount of sleep, as well as the amount of digital media use and physical activity in adolescence. In spite of the confounding factors, the protective role of childhood physical activity in the connection between digital media use in childhood and being overweight later in life was successfully confirmed.
Activity according to recommendations
“The effect of physical activity on the association between digital media use and being overweight has not been extensively investigated in follow-up studies so far,” says Postdoctoral Researcher Elina Engberg.
Further research is needed to determine in more detail how much sedentary digital media use increases the risk of being overweight, and how much physical activity is needed, and at what intensity, to ward off such a risk. In this study, the amount of physical activity and use of digital media was reported by the children themselves, and the level of their activity was not surveyed, so there is a need for further studies.
“A good rule of thumb is to adhere to the physical activity guidelines for children and adolescents, according to which school-aged children and adolescents should be physically active in a versatile, brisk and strenuous manner for at least 60 minutes a day in a way that suits the individual, considering their age,” says Engberg. In addition, excessive and extended sedentary activity should be avoided.
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What if a super-resolution imaging technique used in the latest 8K premium TVs is applied to scanning electron microscopy, essential equipment for materials research?
A joint research team from POSTECH and the Korea Institute of Materials Science (KIMS) applied deep learning to the scanning electron microscopy (SEM) to develop a super-resolution imaging technique that can convert a low-resolution electron backscattering diffraction (EBSD) microstructure images obtained from conventional analysis equipment into super-resolution images. The findings from this study were recently published in the npj Computational Materials.
In modern-day materials research, SEM images play a crucial role in developing new materials, from microstructure visualization and characterization, and in numerical material behavior analysis. However, acquiring high-quality microstructure image data may be exhaustive or highly time-consuming due to the hardware limitations of the SEM. This may affect the accuracy of subsequent material analysis, and therefore, it is paramount to overcome the technical limitations of the equipment.
To this, the joint research team developed a faster and more accurate microstructure imaging technique using deep learning. In particular, by using a convolutional neural network, the resolution of the existing microstructure image was enhanced by 4 times, 8 times, and 16 times, which reduces the imaging time up to 256 times compared to the conventional SEM system.
In addition, super-resolution imaging verified that the morphological details of the microstructure can be restored with high accuracy through microstructure characterization and finite element analysis.
“Through the EBSD technique developed in this study, we anticipate the time it takes to develop new materials will be drastically reduced,” explained Professor Hyoung Seop Kim of POSTECH who led the research.
This research was conducted with the support from the Mid-career Researcher Program of the National Research Foundation of Korea, the AI Graduate School Program of the Institute for Information & Communications Technology Promotion (IITP), and Phase 4 of the Brain Korea 21 Program of the Ministry of Education, and with the support from the Korea Materials Research Institute.
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The inner 24-hour cycles — or circadian rhythms — are key to maintaining human, plant and animal health, which could provide valuable insight into how broken clocks impact health.
Circadian rhythms, such as the sleep-wake cycle, are innate to most living organisms and critical to life on Earth. The word circadian originates from the Latin phrase ‘circa diem’ which means ‘around a day’.
Biologically, the circadian clock temporally orchestrates physiology, biochemistry, and metabolism across the 24-hour day-night cycle. This is why being out of kilter can affect our fitness levels, our health, or our ability to survive. For example, experiencing jet lag is a chronobiological problem — our body clocks are out of sync because the normal external cues such as light or temperature have changed.
The circadian clock isn’t unique to humans. In plants, an accurate clock helps to regulate flowering and is crucial to synchronising metabolism and physiology with the rising and setting sun. Understanding circadian rhythms can help to improve plant growth and yields, not to mention revealing new avenues for tackling human diseases.
Beyond plants
For this latest research, the team applied ML to predict complex temporal circadian gene expression patterns in model plant Arabidopsis thaliana. Taking newly generated datasets, published temporal datasets, and Arabidopsis genomes, the team of scientists trained ML models to make predictions about circadian gene regulation and expression patterns. More

A new study shows that artificial intelligence networks based on human brain connectivity can perform cognitive tasks efficiently.
By examining MRI data from a large Open Science repository, researchers reconstructed a brain connectivity pattern, and applied it to an artificial neural network (ANN). An ANN is a computing system consisting of multiple input and output units, much like the biological brain. A team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute trained the ANN to perform a cognitive memory task and observed how it worked to complete the assignment.
This is a unique approach in two ways. Previous work on brain connectivity, also known as connectomics, focused on describing brain organization, without looking at how it actually performs computations and functions. Secondly, traditional ANNs have arbitrary structures that do not reflect how real brain networks are organized.By integrating brain connectomics into the construction of ANN architectures, researchers hoped to both learn how the wiring of the brain supports specific cognitive skills, and to derive novel design principles for artificial networks.
They found that ANNs with human brain connectivity, known as neuromorphic neural networks, performed cognitive memory tasks more flexibly and efficiently than other benchmark architectures. The neuromorphic neural networks were able to use the same underlying architecture to support a wide range of learning capacities across multiple contexts.
“The project unifies two vibrant and fast-paced scientificdisciplines,” says Bratislav Misic, a researcher at The Neuro and the paper’s senior author. “Neuroscience and AI share common roots, but have recently diverged. Using artificial networks will help us to understand how brain structure supports brain function. In turn, using empirical data to make neural networks will reveal design principles for building better AI. So, the two will help inform each other and enrich our understanding of the brain.”
This study, published in the journal Nature Machine Intelligence on Aug. 9, 2021, was funded with the help of the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains, Healthy Lives initiative, the Natural Sciences and Engineering Research Council of Canada, Fonds de Recherche du Quebec — Santé, Canadian Institute for Advanced Research, Canada Research Chairs, Fonds de Recherche du Quebec — Nature et Technologies, and Centre UNIQUE (Union of Neuroscience and Artificial Intelligence).
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In response to the COVID-19 pandemic, educational institutions have had to quickly transition to remote learning and exam taking. This has led to an increase in the use of online proctoring services to curb student cheating, including restricted browser modes, video/screen monitoring, local network traffic analysis and eye tracking.
In a first-of-its-kind study, researchers led by Adam Aviv, an associate professor of computer science at the George Washington University, explored the security and privacy perceptions of students taking proctored exams. After analyzing user reviews of eight proctoring services’ browser extensions and subsequently performing an online survey of students, the researchers found: Exam proctoring browser extensions use a technique called “URL match patterns” to turn on whenever they find a given URL. These URL patterns match a wide variety of URLs, most associated with online course content. However, generic URL patterns (e.g., any URL that has /courses/ or /quizzes/) can also activate the browser extension regardless of whether the student is taking an exam. As a result, the data collection and monitoring features of proctoring browser extensions could be active on a number of websites, even when a student is not taking an exam. Students understood they would need to give up some privacy aspects in order to take exams safely from home during the pandemic. However, a large number of students had concerns about sharing personal information with proctoring companies in order to take an exam. These concerns include the process of identity verification, the amount of information collected by these companies and having to install third party online exam proctoring software on their personal computers. When reviewing exam proctoring browser extensions in the Google Chrome web store, there was a noticeable increase in February 2020 in the total number of ratings combined with a sharp decrease in the “star ratings” for these extensions. This likely indicates an extreme dislike for exam proctoring services.”Institutional support for third-party proctoring software conveys credibility and makes the exam proctoring software appear safer and less potentially problematic because students assume that institutions have done proper vetting of both the software and the methods employed by the proctoring services,” David Balash, a PhD student at GW and a lead researcher on the study, said. “We recommend that institutions and educators follow a principle of least monitoring when using exam proctoring tools by using the minimum number of monitoring types necessary, given the class size and knowledge of expected student behavior.”
“As many universities and colleges return to the classroom, students may be less willing to trade their privacy for personal safety going forward,” Rahel Fainchtein, a PhD student at Georgetown University and a lead researcher on the study, said. “However, at the same time, online exam proctoring technology appears here to stay.”
The paper, “Examining the Examiners: Students’ Privacy and Security Perceptions of Online Proctoring Services,” will be presented at the 17th Symposium on Usable Privacy and Security on August 10, 2021. In addition to Aviv, Balash and Fainchtein, the research team included Dongkun Kim and Darikia Shaibekova at GW and Micah Sherr at Georgetown.
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When you pick up a balloon, the pressure to keep hold of it is different from what you would exert to grasp a jar. And now engineers at MIT and elsewhere have a way to precisely measure and map such subtleties of tactile dexterity.
The team has designed a new touch-sensing glove that can “feel” pressure and other tactile stimuli. The inside of the glove is threaded with a system of sensors that detects, measures, and maps small changes in pressure across the glove. The individual sensors are highly attuned and can pick up very weak vibrations across the skin, such as from a person’s pulse.
When subjects wore the glove while picking up a balloon versus a beaker, the sensors generated pressure maps specific to each task. Holding a balloon produced a relatively even pressure signal across the entire palm, while grasping a beaker created stronger pressure at the fingertips.
The researchers say the tactile glove could help to retrain motor function and coordination in people who have suffered a stroke or other fine motor condition. The glove might also be adapted to augment virtual reality and gaming experiences. The team envisions integrating the pressure sensors not only into tactile gloves but also into flexible adhesives to track pulse, blood pressure, and other vital signs more accurately than smart watches and other wearable monitors.
“The simplicity and reliability of our sensing structure holds great promise for a diversity of health care applications, such as pulse detection and recovering the sensory capability in patients with tactile dysfunction,” says Nicholas Fang, professor of mechanical engineering at MIT.
Fang and his collaborators detail their results in a study appearing today in Nature Communications. The study’s co-authors include Huifeng Du and Liu Wang at MIT, along with professor Chuanfei Guo’s group at the Southern University of Science and Technology (SUSTech) in China. More