Weekly News Ukraine

Published on August 18th, 2017 | by

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10 Bits: the Data News Hotlist

This week’s list of data news highlights covers  August 12 – 18, 2017, and includes articles about a machine learning technique that could help improve fingerprint matching and Ukraine’s new beneficial ownership database.

1. Detecting When Doctors Don’t Wash Their Hands

Researchers at the Swiss Federal Institute of Technology have developed a system for detecting if healthcare staff in a hospital follow proper sanitation procedures with 75 percent accuracy. The researchers used cameras to track when hospital staff used hand sanitizer dispensers when entering patient rooms and then trained computer vision algorithms to identify when staff used the dispensers correctly.  

2. Matching Fingerprints with Machine Learning

Scientists from the U.S. National Institute for Standards and Technology (NIST) and Michigan State University have developed a machine learning method to improve automated fingerprint matching. Law enforcement agencies use a system called the Automated Fingerprint Identification System (AFIS) to match fingerprints, but to ensure accuracy the system requires humans to vet fingerprints for quality before analyzing them. The scientists trained a machine learning system on a dataset of 100 fingerprints rated for quality by 31 human experts to teach it to vet fingerprints. The system was more effective at identifying suitable fingerprints for AFIS than humans.

3. Teaching Machine Learning to Manage Spectrum

The U.S. Defense Advanced Research Projects Agency (DARPA) has launched the Radio Frequency Machine Learning Systems (RFMLS) program to advance research into machine learning systems that can help connected technologies better share radio frequency spectrum. RFMLS will focus on developing systems that can identify and prioritize radio frequency signals in civilian and military settings, differentiate between different kinds of data transmissions, and optimize a connected system to use radio frequency spectrum more effectively.

4. Reducing the Need for Buffering in Video Streaming

Researchers at the Massachusetts Institute of Technology have developed an AI system called Pensieve that can automatically adjust video streaming algorithms to allow for high-quality video streaming with less buffering time than traditional methods. Video streaming algorithms typically determine video quality by either matching video quality to connection speed or by ensuring that a certain amount of video preloads to reduce potential interruptions in playback. Pensieve is capable of merging elements of both, and in tests, users rated their experience watching video streamed using Pensieve 10 to 25 percent higher than other methods, while Pensieve used 10 to 30 percent less buffering.

5. Increasing Corporate Transparency in Ukraine

Ukraine’s Prime Minister Volodymyr Groysman has announced that beneficial ownership data about Ukrainian companies is now available on the country’s open data portal. Beneficial ownership data is information about who influences and benefits from a corporation’s actions, but who are not the legally designated owners of a corporation. Many countries do not require the disclosure of beneficial ownership data, which makes them attractive places for the formation of shell companies for illicit purposes, such as tax evasion and sanctions violations.

6. Keeping Machines Airborne with Machine Learning

Microsoft researchers have developed an AI system capable of keeping a motorless 12 pound aircraft aloft by identifying and using environmental factors to its advantage. The system can measure differences in wind direction and temperature to identify columns of rising warm air, called thermals, and steer the aircraft into it to stay airborne similar to how birds can soar in the air without flapping their wings for long periods of time.

7. Building a Genetic Atlas for Cancer Research

Researchers at the Royal Institute of Technology in Sweden have published an open-access repository of data about how thousands of genes associated with different cancers relate to patient survival and treatment options. The researchers used a supercomputer to map the genes of 8,000 tumor samples representing 17 types of human cancers, and by comparing this information against patient outcomes, were able to identify thousands of genes that influence patient survival. The researchers were also able to identify 32 genes found in 80 percent of tumors that could kill tumor growth without harming the patient, which could make the genes valuable targets for new drugs.

8. Spotting Counterfeit Goods with AI

New York University researchers have developed a system called Entrupy that uses microscopy and machine learning to differentiate between genuine and counterfeit goods with 98.5 percent accuracy. Entrupy analyzes microscoping characteristics of goods such as toys, electronics, and clothes to identify subtle physical characteristics, such as texture and manufacturing artifacts, and compares them against a dataset of 3 million microscopic images of similar goods to see if they match.

9. Tracking Hate Crimes with Machine Learning

Google News Lab and investigative journalism nonprofit ProPublica have developed a publicly accessible machine learning tool called the Documenting Hate News Index that analyzes news articles to populate a constantly-updating feed of news about hate crimes in the United States. The Documenting Hate Crimes Index automatically extracts relevant details from news stories such as names and locations to make it easier for journalists to track and study hate crimes.

10. Giving Smartphones 3D Vision

Qualcomm has developed smartphone camera technology called active depth sensing that allows a smartphone to create accurate 3D maps of what it sees. The technology emits infrared light from a camera and can detect the shapes and distance of objects based on how that  light gets distorted as it encounters objects. While this approach is not new, Qualcomm’s technology is accurate within a tenth of a millimeter.

Image: Max Pixel

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