Stefaan Verhulst

Mozilla: “Timely and open access to novel outputs is key to scientific research. It allows scientists to reproduce, test, and build on one another’s work — and ultimately unlock progress.

The most recent example of this is the research into COVID-19. Much of the work was published in open access journals, swiftly reviewed and ultimately improving our understanding of how to slow the spread and treat the disease. Although this rapid increase in scientific publications is evident in other domains too, we might not be reaping the benefits. The tools to parse and combine this newly created knowledge have roughly remained the same for years.

Today, Mozilla Fellow Kostas Stathoulopoulos is launching Orion — an open-source tool to illuminate the science behind the science and accelerate knowledge discovery in the life sciences. Orion enables users to monitor progress in science, visually explore the scientific landscape, and search for relevant publications.

Orion collects, enriches and analyses scientific publications in the life sciences from Microsoft Academic Graph.

Users can leverage Orion’s views to interact with the data. The Exploration view shows all of the academic publications in a three-dimensional visualization. Every particle is a paper and the distance between them signifies their semantic similarity; the closer two particles are, the more semantically similar. The Metrics view visualizes indicators of scientific progress and how they have changed over time for countries and thematic topics. The Search view enables the users to search for publications by submitting either a keyword or a longer query, for example, a sentence or a paragraph of a blog they read online….(More)”.

Article by Eliza McCullough: “….Instead of a smart city model that extracts from, surveils, and displaces poor people of color, we need a democratic model that allows community members to decide how technological infrastructure operates and to ensure the equitable distribution of benefits. Doing so will allow us to create cities defined by inclusion, shared ownership, and shared prosperity.

In 2016, Barcelona, for example, launched its Digital City Plan, which aims to empower residents with control of technology used in their communities. The document incorporates over 8,000 proposals from residents and includes plans for open source software, government ownership of all ICT infrastructure, and a pilot platform to help citizens maintain control over their personal data. As a result, the city now has free applications that allow residents to easily propose city development ideas, actively participate in city council meetings, and choose how their data is shared.

In the U.S., we need a framework for tech sovereignty that incorporates a racial equity approach: In a racist society, race neutrality facilitates continued exclusion and exploitation of people of color. Digital Justice Lab in Toronto illustrates one critical element of this kind of approach: access to information. In 2018, the organization gave community groups a series of grants to hold public events that shared resources and information about digital rights. Their collaborative approach intentionally focuses on the specific needs of people of color and other marginalized groups.

The turn toward intensified surveillance infrastructure in the midst of the coronavirus outbreak makes the need to adopt such practices all the more crucial. Democratic tech models that uplift marginalized populations provide us the chance to build a city that is just and open to everyone….(More)”.

Paper by Ciara Greene and Gillian Murphy: “Previous research has argued that fake news may have grave consequences for health behaviour, but surprisingly, no empirical data have been provided to support this assumption. This issue takes on new urgency in the context of the coronavirus pandemic. In this large preregistered study (N = 3746) we investigated the effect of exposure to fabricated news stories about COVID-19 on related behavioural intentions. We observed small but measurable effects on some related behavioural intentions but not others – for example, participants who read a story about problems with a forthcoming contact-tracing app reported reduced willingness to download the app. We found no effects of providing a general warning about the dangers of online misinformation on response to the fake stories, regardless of the framing of the warning in positive or negative terms. We conclude with a call for more empirical research on the real-world consequences of fake news….(More)”

Matthew Hutson at IEEE Spectrum: “…Researchers say they’ve learned a lot of lessons modeling this pandemic, lessons that will carry over to the next.

The first set of lessons is all about data. Garbage in, garbage out, they say. Jarad Niemi, an associate professor of statistics at Iowa State University who helps run the forecast hub used by the CDC, says it’s not clear what we should be predicting. Infections, deaths, and hospitalization numbers each have problems, which affect their usefulness not only as inputs for the model but also as outputs. It’s hard to know the true number of infections when not everyone is tested. Deaths are easier to count, but they lag weeks behind infections. Hospitalization numbers have immense practical importance for planning, but not all hospitals release those figures. How useful is it to predict those numbers if you never have the true numbers for comparison? What we need, he said, is systematized random testing of the population, to provide clear statistics of both the number of people currently infected and the number of people who have antibodies against the virus, indicating recovery. Prakash, of Georgia Tech, says governments should collect and release data quickly in centralized locations. He also advocates for central repositories of policy decisions, so modelers can quickly see which areas are implementing which distancing measures.

Researchers also talked about the need for a diversity of models. At the most basic level, averaging an ensemble of forecasts improves reliability. More important, each type of model has its own uses—and pitfalls. An SEIR model is a relatively simple tool for making long-term forecasts, but the devil is in the details of its parameters: How do you set those to match real-world conditions now and into the future? Get them wrong and the model can head off into fantasyland. Data-driven models can make accurate short-term forecasts, and machine learning may be good for predicting complicated factors. But will the inscrutable computations of, for instance, a neural network remain reliable when conditions change? Agent-based models look ideal for simulating possible interventions to guide policy, but they’re a lot of work to build and tricky to calibrate.

Finally, researchers emphasize the need for agility. Niemi of Iowa State says software packages have made it easier to build models quickly, and the code-sharing site GitHub lets people share and compare their models. COVID-19 is giving modelers a chance to try out all their newest tools, says Meyers, of the University of Texas. “The pace of innovation, the pace of development, is unlike ever before,” she says. “There are new statistical methods, new kinds of data, new model structures.”…(More)”.

Poster by Geoffrey Henry Siwo: The promise of artificial intelligence (AI) in medicine is advancing rapidly driven by exponential growth in computing speed, data and new modeling techniques such as deep learning. Unfortunately, advancements in AI stand to disproportionately benefit diseases that predominantly affect the developed world because the key ingredients for AI – computational resources, big data and AI expertise – are less accessible in the developing world. Our research on automated mining of biomedical literature indicates that adoption of machine learning algorithms in global health, for example to understand malaria, lags several years behind diseases like cancer.

 To shift these inequities, we have been exploring the use of crowdsourced data science challenges as a means to rapidly advance computational models in global health. Data science challenges involve seeking computational solutions for specific, well-defined questions from anyone in the world. Here we describe key lessons from our work in this area and the potential value of data science challenges in accelerating AI for global health.

In one of our first initiatives in this area – the Malaria DREAM Challenge – we invited data scientists from across the world to develop computational models that predict the in vitro and in vivo drug sensitivity of malaria parasites to artemisinin using gene expression datasets. More than 360 individuals drawn from academia, government and startups across 31 countries participated in the challenge. Approximately 100 computational solutions to the problem were generated within a period of 3 months. In addition to this sheer volume of participation, a diverse range of modeling approaches including artificial neural networks and automated machine learning were employed….(More)”.

Book by Bharat Vagadia: “Implications and opportunities for Economies, Society, Policy Makers and Business Leaders: “This book goes beyond the hype, delving into real world technologies and applications that are driving our future and examines the possible impact these changes will have on industries, economies and society at large. It details the actions governments and regulators must take in order to ensure these changes bring about positive benefits to the public without stifling innovation that may well be the future source of value creation. It examines how organisations in a world of digital ecosystems, where industry boundaries are blurring, must undertake radical digital transformation to survive and thrive in this new digital world. The reader is taken through a framework that critically examines (i) Digital Connectivity including 5G and IoT; (ii) Data Capture and Distribution which includes smart connected verticals; (iii) Data Integrity, Control and Tokenisation that includes cyber security, digital signatures, blockchain, smart contracts, digital assets and cryptocurrencies; (iv) Data Processing and Artificial Intelligence; and (v) Disruptive Applications which include platforms, virtual and augmented reality, drones, autonomous vehicles, digital twins and digital assistants…(More)”.

Book edited by Martin Paul Eve and Jonathan Gray: “The Open Access Movement proposes to remove price and permission barriers for accessing peer-reviewed research work—to use the power of the internet to duplicate material at an infinitesimal cost-per-copy. In this volume, contributors show that open access does not exist in a technological or policy vacuum; there are complex social, political, cultural, philosophical, and economic implications for opening research through digital technologies. The contributors examine open access from the perspectives of colonial legacies, knowledge frameworks, publics and politics, archives and digital preservation, infrastructures and platforms, and global communities.

he contributors consider such topics as the perpetuation of colonial-era inequalities in research production and promulgation; the historical evolution of peer review; the problematic histories and discriminatory politics that shape our choices of what materials to preserve; the idea of scholarship as data; and resistance to the commercialization of platforms. Case studies report on such initiatives as the Making and Knowing Project, which created an openly accessible critical digital edition of a sixteenth-century French manuscript, the role of formats in Bruno Latour’s An Inquiry into Modes of Existence, and the Scientific Electronic Library Online (SciELO), a network of more than 1,200 journals from sixteen countries. Taken together, the contributions represent a substantive critical engagement with the politics, practices, infrastructures, and imaginaries of open access, suggesting alternative trajectories, values, and possible futures…(More)”.

Book edited by John R. Vacca: “… the most complete guide for integrating next generation smart city technologies into the very foundation of urban areas worldwide, showing how to make urban areas more efficient, more sustainable, and safer. Smart cities are complex systems of systems that encompass all aspects of modern urban life. A key component of their success is creating an ecosystem of smart infrastructures that can work together to enable dynamic, real-time interactions between urban subsystems such as transportation, energy, healthcare, housing, food, entertainment, work, social interactions, and governance. Solving Urban Infrastructure Problems Using Smart City Technologies is a complete reference for building a holistic, system-level perspective on smart and sustainable cities, leveraging big data analytics and strategies for planning, zoning, and public policy. It offers in-depth coverage and practical solutions for how smart cities can utilize resident’s intellectual and social capital, press environmental sustainability, increase personalization, mobility, and higher quality of life….(More)”

Special issue by ZDNet exploring “how new technologies like AI, cloud, drones, and 5G are helping government agencies, public organizations, and private companies respond to the events of today and tomorrow…:

• How data is making the EMS to emergency room hand-off easier
• What data tells us about first responders dealing with COVID-19 cases
• GE Healthcare and Microsoft partner to help hospitals fight COVID-19
• Running to the fight: How Verizon brought hospital ship USNS Comfort online to aid NYC COVID-19 response
• How AI and ML is helping first responders
• Startup uses network automation to help hospitals solve wi-fi problems
• How drones can augment first responders
• Cloud platform modernizes 911 system by adding location data, chat and video to emergency calls
• Microsoft is dedicating significant tech resources to COVID-19 first responder organizations
• How fire departments can extinguish money, space, and data loss with the cloud
• Samsung demos MCPTX video call on AWS
• Best security and surveillance drones for business in 2020: Impossible Aerospace, Microdrones, DJI, and more“

Press Release: “Important questions are being raised about whether blockchain technologies can contribute to solving governance challenges in the mining, oil and gas sectors. This report seeks to begin addressing such questions, with particular reference to current blockchain applications and transparency efforts in the extractive sector.

It summarizes analysis by The Governance Lab (GovLab) at the New York University Tandon School of Engineering and the Natural Resource Governance Institute (NRGI). The study focused in particular on three activity areas: licensing and contracting, corporate registers and beneficial ownership, and commodity trading and supply chains.

Key messages:

• Blockchain technology could potentially reduce transparency challenges and information asymmetries in certain parts of the extractives value chain. However, stakeholders considering blockchain technologies need a more nuanced understanding of problem definition, value proposition and blockchain attributes to ensure that such interventions could positively impact extractive sector governance.
• The blockchain field currently lacks design principles, governance best practices, and open data standards that could ensure that the technology helps advance transparency and good governance in the extractive sector. Our analysis offers an initial set of design principles that could act as a starting point for a more targeted approach to the use of blockchain in improving extractives governance.
• Most blockchain projects are preliminary concepts or pilots, with little demonstration of how to effectively scale up successful experiments, especially in countries with limited resources.
• Meaningful impact evaluations or peer-reviewed publications that assess impact, including on the implications of blockchain’s emissions footprint, are still lacking. More broadly, a shared research agenda around blockchain could help address questions that are particularly ripe for future research.
• Transition to a blockchain-enabled system is likely to be smoother and faster in cases when digital records are already available than when a government or company attempts to move from an analog system to one leveraging blockchain.
• Companies or governments using blockchain are more likely to implement it successfully when they have a firm grasp of the technology, its strengths, its weaknesses, and how it fits into the broader governance landscape. But often these actors are often overly reliant on and empowering of blockchain technology vendors and startups, which can lead to “lock-in”, whereby the market gets stuck with an approach even though market participants may be better off with an alternative.
• The role played by intermediaries like financial institutions or registrars can determine the success or failure of blockchain applications….(More)”.