With the Telangana government declaring last year as the year of AI and numerous job opportunities in the IT sector, the scope for AI is growing.

Hyderabad: Digithon, the digital entity of Telangana Information Technology Association (TITA), is starting an internship programme in Artificial Intelligence (AI) from July 12. Those clearing the course will receive a certificate from University of Texas at Dallas (UTD), US.

With the Telangana government declaring last year as the year of AI and numerous job opportunities in the IT sector, the scope for AI is growing. TITA had earlier imparted training in AI to make the State’s youth job-ready. Over 50,000 applications were received for the course of which more than 2,000 individuals were imparted training in AI as part of the Telangana government’s ‘Year of the AI’ initiative.

About 96.7 per cent of individuals cleared the AI exam conducted by UTD, and of this 80 per cent of individuals landed jobs in the AI area. The TS government, in its official report on AI, recognised and appreciated the role of Digithon, the digital entity of Telangana Information Technology Association (TITA) in the application and imparting skills in AI. To continue the training programmes in 2021, TITA has come up with the internship programme.

TITA will roll out the ‘Artificial Intelligence & Machine Learning In-plant Training cum Internship Program’ from July 12 and the last date for enrolling in the course is July 10. Participants will be trained in AI projects such as facial recognition, chatbot, etc. The ensuing training session will take up projects based on machine learning (ML) and deep learning.

Participants will visit industry as part of the industrial tour and those clearing an exam post the eight-week AI programme will receive a certificate in AI & ML from the UTD.

TITA said, institutes offering programmes in AI usually charge a hefty fee ranging into lakhs of rupees. However, TITA is offering the same programme at a nominal charge of Rs 10,000 for the benefit of youth in the State. Individuals interested in taking up the AI programme can register themselves at bit.ly/digithon_academy.

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Patterns and anomalies in big data can help businesses target likely customers, reveal fraud or even predict drug interactions. Unfortunately, these patterns are often not easily observable. To extract the needles of useful information out of haystacks of data, data scientists need increasingly powerful methods of machine learning.

Dr. Aria Nosratinia, the Erik Jonsson Distinguished Professor of electrical and computer engineering at The University of Texas at Dallas, has received two grants from the National Science Foundation totaling $749,492 to uncover relationships hiding in big data via machine learning and to develop methods to keep data communications safe.

“The contribution of my lab is to expand the universe of tools and techniques so we can discover new connections in the data,” said Nosratinia, who is associate department head of electrical and computer engineering in the Erik Jonsson School of Engineering and Computer Science.

Many machine learning and data mining algorithms use graphs, which are simply lists of connections between people, groups or objects. Examples include “friend,” “like” or “follow” relationships in social networks, or the list of videos streamed or marked as favorites in a streaming subscription service.

These mountains of data hide useful information whose extraction belongs to an area known as graph inference. Graph inference has many interesting and useful applications—for example, suggesting movies in a streaming service based on viewing history or purchasing suggestions in online shopping. It also can reveal patterns in the spread of epidemics, or provide insights into the folding of proteins, which is important in understanding how proteins function.

Nosratinia’s work for the first time proposes and analyzes techniques to improve graph inference by absorbing nongraph information, whose efficient blending with graph information was previously not well understood. Examples of non-graph information include a person’s age and residence ZIP code, which are individual attributes.

“In almost every practical application involving graphs, there exist nongraph data of great relevance,” Nosratinia said. “The kind of work we do is further upstream, developing the mathematical models, theory and techniques, but it has widespread applications.”

In several published works, Nosratinia describes the mathematical models he and members of his lab have developed that can improve the estimation of the information hidden in the graph with the aid of side information. Nosratinia and co-author Hussein Saad Ph.D.”19, now a senior engineer with Qualcomm Inc., recently analyzed how to identify a small cluster or community hidden in a large graph. Their latest work appeared in the December 2020 issue of the journal IEEE Transactions on Information Theory.

The second component of Nosratinia’s research addresses data security. His work harnesses the natural variations of wireless channels to provide layers of security for data transmission. This area of work, known as physical layer security, aims to leverage the imperfections of the communication channel as a tool for security. Part of this research is aimed at developing techniques for making the presence of electronic communication undetectable to cybercriminals.

“To give a simple example, a password works by leveraging the difference between what is known by a legitimate user versus cybercriminals who want to steal information,” Nosratinia said. “Our work creates, amplifies and analyzes statistical asymmetry of information against adversaries in ways that do not involve passwords or keys, and uses them for securing communications.”

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A Clemson University faculty member will use an award from the National Science Foundation (NSF) to examine middle school students’ data science knowledge and practices through the lens of social issues and gauge students’ sense of empowerment to positively change communities through data science.

Golnaz Arastoopour Irgens, assistant professor of learning sciences in the Clemson University College of Education, said it is a common misconception that data is neutral or free from the influence of social issues or that data has no effect on social issues. She said it is often the case that technology informed by data science, such as search engines or facial recognition software, has been shown to either reinforce discrimination or mischaracterize minority groups.

Because humans design these forms of technology and many more make decisions based on them, a critical eye on how they are developed and how they are utilized becomes necessary. Arastoopour Irgens said it follows that the way we educate students to employ data science and utilize it falls short when social, ethical and political issues are not integrated into that education.

“Data analytics education is not something new, but data analytics practices have changed dramatically and the decisions that are made based on these data are now affecting people at much larger scales than ever before,” Arastoopour Irgens said. “This can be a problem when the population of computer scientists is mainly white and male; the viewpoints of other, non-dominant populations aren’t represented, so it’s important for our youth to recognize and engage with the changes in this area.”

Arastoopour Irgens and a team of graduate education students will use a new methodology, quantitative ethnography, which uses statistical tools to make sense of what learners are saying and doing as they engage with data science practices. This methodology will allow her to develop visual, dynamic models of how learners are connecting data science knowledge and practices to social, ethical and political issues in ways that are meaningful to them.

In order to measure the degree to which students feel empowered to enact change through data science, Arastoopour Irgens will combine and adapt existing surveys that measure political perceptions, civic engagement and agency with those that measure computing confidence, enjoyment, perceived usefulness, motivation to succeed and identity/belongingness. The research team will then use statistical models to note any changes in empowerment or attitudes toward computing after participation in an after-school program, which is a central aspect of the research.

This program will take place at five different sites across Greenville County. Arastoopour Irgens will co-design the after-school program with after-school staff, families, youth and community members to ensure it is aligned with their communities’ interests and values.

“Although formal schooling is a place where critical data education should be taught, this project will focus on broader community engagement and design without the constraints of schooling,” Arastoopour Irgens said. “We have already started a partnership with the after-school center and have held meetings with staff and conducted short activities and interviews with youth. We are confident that this collaboration will result in some powerful learning experiences and impacts on youth and their communities.”

Arastoopour Irgens said that few programs currently engage learners at an early age in data science education in which culturally relevant social, ethical and political issues are the focus. The project aims to address this gap for an audience primarily comprised of children of color or those living in poverty—populations who are underserved and underrepresented in science, technology, engineering and math (STEM).

Arastoopour Irgens said the project serves underrepresented youth by providing culturally relevant experiences that may ultimately motivate them to pursue STEM majors and careers. Youth involved who may not be part of an underrepresented population will also be positively impacted as they are made more aware of harmful traditions of technology development and consumption that marginalize others.

“We plan to post all of our materials on my research lab website and make them openly available and easily adaptable for other educators to use,” Arastoopour Irgens said. “My hope is to continue this work after this initial trial and that other educators can improve upon what we’ve done and adapt it to better fit the needs of their learner populations.”

The research is funded by NSF’s Building Capacity in STEM Education Research, which supports projects that build individuals’ capacity to carry out high-quality STEM education research that will enhance the nation’s STEM education enterprise and broaden the pool of researchers that can conduct fundamental research in STEM learning and learning environments and broaden participation in STEM fields and enhance STEM workforce development.

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Army researchers developed a reinforcement learning approach that allows swarms of unmanned aerial and ground vehicles to optimally accomplish various missions while minimizing performance uncertainty.

Swarming is a method of operations where multiple autonomous systems act as a cohesive unit by actively coordinating their actions.

Army researchers said future multi-domain battles will require swarms of dynamically coupled, coordinated heterogeneous mobile platforms to overmatch enemy capabilities and threats targeting U.S. forces.

The Army is looking to swarming technology to be able to execute time-consuming or dangerous tasks, said Dr. Jemin George of the U.S. Army Combat Capabilities Development Command’s Army Research Laboratory.

“Finding optimal guidance policies for these swarming vehicles in real-time is a key requirement for enhancing warfighters’ tactical situational awareness, allowing the U.S. Army to dominate in a contested environment,” George said.

Reinforcement learning provides a way to optimally control uncertain agents to achieve multi-objective goals when the precise model for the agent is unavailable; however, the existing reinforcement learning schemes can only be applied in a centralized manner, which requires pooling the state information of the entire swarm at a central learner. This drastically increases the computational complexity and communication requirements, resulting in unreasonable learning time, George said.

In order to solve this issue, in collaboration with Prof. Aranya Chakrabortty from North Carolina State University and Prof. He Bai from Oklahoma State University, George created a research effort to tackle the large-scale, multi-agent reinforcement learning problem. The Army funded this effort through the Director’s Research Award for External Collaborative Initiative, a laboratory program to stimulate and support new and innovative research in collaboration with external partners.

The main goal of this effort is to develop a theoretical foundation for data-driven optimal control for large-scale swarm networks, where control actions will be taken based on low-dimensional measurement data instead of dynamic models.

The current approach is called Hierarchical Reinforcement Learning, or HRL, and it decomposes the global control objective into multiple hierarchies – namely, multiple small group-level microscopic control, and a broad swarm-level macroscopic control.

“Each hierarchy has its own learning loop with respective local and global reward functions,” George said. “We were able to significantly reduce the learning time by running these learning loops in parallel.”

According to George, online reinforcement learning control of swarm boils down to solving a large-scale algebraic matrix Riccati equation using system, or swarm, input-output data.

The researchers’ initial approach for solving this large-scale matrix Riccati equation was to divide the swarm into multiple smaller groups and implement group-level local reinforcement learning in parallel while executing a global reinforcement learning on a smaller dimensional compressed state from each group.

Their current Hierarchical Reinforcement Learning scheme uses a decupling mechanism that allows the team to hierarchically approximate a solution to the large-scale matrix equation by first solving the local reinforcement learning problem and then synthesizing the global control from local controllers (by solving a least squares problem) instead of running a global reinforcement learning on the aggregated state. This further reduces the learning time.

Experiments have shown that compared to a centralized approach, HRL was able to reduce the learning time by 80% while limiting the optimality loss to 5%.

“Our current HRL efforts will allow us to develop control policies for swarms of unmanned aerial and ground vehicles so that they can optimally accomplish different mission sets even though the individual dynamics for the swarming agents are unknown,” George said.

George stated that he is confident that this research will be impactful on the future battlefield, and has been made possible by the innovative collaboration that has taken place.

“The core purpose of the ARL science and technology community is to create and exploit scientific knowledge for transformational overmatch,” George said. “By engaging external research through ECI and other cooperative mechanisms, we hope to conduct disruptive foundational research that will lead to Army modernization while serving as Army’s primary collaborative link to the world-wide scientific community.”

The team is currently working to further improve their HRL control scheme by considering optimal grouping of agents in the swarm to minimize computation and communication complexity while limiting the optimality gap.

They are also investigating the use of deep recurrent neural networks to learn and predict the best grouping patterns and the application of developed techniques for optimal coordination of autonomous air and ground vehicles in Multi-Domain Operations in dense urban terrain.

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We can say that we have reached exciting points in the images that can be created by artificial intelligence. New techniques developed in research laboratories provide more successful results in this field. A new AI algorithm that turns human faces into animals is evidence of this.

Educator and artificial intelligence researcher Xander Steenbrugge has developed an artificial intelligence algorithm that can turn the human face into animal face and then turn it back into animal form. It can be said that the developed algorithm has made transformations quite successfully (although not as successful as in science fiction movies …). This experiment is part of the Neural Synesthesia project, where Steenbrugge gained audiovisual experiences using algorithms and machine learning models.

Steenbrugge’s work was done using productive contentious networks (GAN) that “learn” from a dataset like many image transfer studies and then try to convert the target image into the source you feed. For example; deepfake videos are also made using GANs.

Steenbrugge uses a new set of artificial intelligence production model StarGAN v2 and 15,000 HD animal photos for the change called “humanimals”. It uses this data set together with human faces as educational data and passes it through a different model, StyleGAN v2. You can watch the fantastic results that come out here.

About the new algorithm, the researcher said, “I believe that creativity has become an interactive process between man and machine, and we witnessed the beginning of a new era in digital environment. commented.

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TROY, N.Y. — A researcher from Rensselaer Polytechnic Institute is suggesting that the coronavirus will peak in the Capital Region in the second half of May or early June.

Malik Magdon-Ismail tailored a robust machine learning model that can predict pandemic impact even in smaller cities, with 75% of the population in the Capital Region in New York remaining at home, the COVID-19 pandemic will peak locally in the second half of May. If the rate of people staying home drops to 50%, it will peak in early June, according to the model.

Magdon-Ismail said he tailored the models he is developing to work with sparse data points, like those available during the early phase in a pandemic or in smaller cities, which ordinarily make trend-spotting difficult.

“There are no simple, robust, general tools that, for example, officials in Albany could use to make projections,” said Magdon-Ismail, a professor of computer science, and expert in machine learning, data mining, and pattern recognition. “These models show that the projections vary enormously from one city to another. This knowledge could relieve some of the uncertainty that is around in developing policy.”

Using county data available through the New York State Department of Health and Mental Hygiene, Magdon-Ismail said he has developed models that can predict local aspects of the pandemic such as the rate of infections over time, the infectious force of the pandemic, the rate at which mild infections become serious, and estimates for asymptomatic infections. The research model is ongoing work and, given the time-sensitive nature of the work, earlier versions have been released on the arXiv preprint server, which is moderated but not peer-reviewed.

His model for the Capital Region — which incorporates the data from Albany, Rensselaer, Saratoga, and Schenectady counties up to April 10 — uses a total at-risk population of 855,000 to estimate that daily confirmed infections will peak at 1,490 on June 8 with 50% staying at home, or 750 on May 28 with 75% staying at home. The number of infections would total 58,000 or 29,000, respectively. Confirmed infections as of April 10 are approximately 1,000 and the model estimates 14,000 asymptomatic cases at that time, according to a news release.

“The machine gives you the model that best fits the data, but it turns out the best is usually a very fragile principle. There are lots of different models, lots of different explanations that are essentially as good,” Magdon-Ismail said in the news release. “To make the output robust, we consider the collection of models that have near-optimal levels of consistency with the data. I find a variety of models that fit the data, and then I use all of those models together to predict.”

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Oil and gas pipelines have become polarizing issues in Canada, but supporters and detractors alike can agree that the safer they are, the better. With news emerging last month that the Keystone pipeline leak in North Dakota may have affected almost 10 times the amount of land previously reported, the issue of pipeline failure takes on an added urgency.

Unfortunately, integrity and health are ongoing and serious problems for North America’s pipeline infrastructure. According to the US Department of Transportation (DOT), there have been more than 10,000 pipeline failures in that country alone since 2002. Complicating safety measures are the cost and intensity of labour required to monitor the health of the thousands of kilometres of pipelines that criss-cross Canada and the United States.

In a recent paper in the Journal of Pipeline Systems Engineering and Practice, researchers at Concordia and the Hong Kong Polytechnic University look at the methodologies currently used by industry and academics to predict pipeline failure — and their limitations.

“In many of the existing codes and practices, the focus is on the consequences of what happens when something goes wrong,” says Fuzhan Nasiri, associate professor in the Department of Building, Civil and Environmental Engineering at the Gina Cody School of Engineering and Computer Science.

“Whenever there is a failure, investigators look at the pipeline’s design criteria. But they often ignore the operational aspects and how pipelines can be maintained in order to minimize risks.”

Nasiri, who runs the Sustainable Energy and Infrastructure Systems Engineering Lab, co-authored the paper with his PhD student Kimiya Zakikhani and Hong Kong Polytechnic professor Tarek Zayed.

Safeguarding against corrosion

The researchers identified five failure types: mechanical, the result of design, material or construction defects; operational, due to errors and malfunctions; natural hazard, such as earthquakes, erosion, frost or lightning; third-party, meaning damage inflicted either accidentally or intentionally by a person or group; and corrosion, the deterioration of the pipeline metal due to environmental effects on pipe materials and acidity of oil and gas impurities. This last one is the most common and the most straightforward to mitigate.

Nasiri and his colleagues found that the existing academic literature and industry practices around pipeline failures need to further evolve around available maintenance data. They believe the massive amounts of pipeline failure data available via the DOT’s Pipeline and Hazardous Materials Safety Administration can be used in the assessment process as a complement to manual in-line inspections.

These predictive models, based on decades’ worth of data covering everything from pipeline diameter to metal thickness, pressure, average temperature change, location and timing of failure, could provide failure patterns. These could be used to streamline the overall safety assessment process and reduce costs significantly.

“We can identify trends and patterns based on what has happened in the past,” Nasiri says. “And you could assume that these patterns could be followed in the future, but need certain adjustments with respect to climate and operational conditions. It would be a chance-based model: given variables such as location and operational parameters as well as expected climatic characteristics, we could predict the overall chance of corrosion over a set time span.”

He adds that these models would ideally be consistent and industry-wide, and so transferrable in the event of pipeline ownership change — and that research like his could influence industry practices.

“Failure prediction models developed based on reliability theory should be realistic. Using historical data (with adjustments) gets you closer to what actually happens in reality,” he says.

“They can close the gap of expectations, so both planners and operators can have a better idea of what they could see over the lifespan of their structure.”

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You may have heard that Big Data is all the rage these days. Big Data this, Big Data that. Let’s acquire as much information as possible and try to wrestle with it to gain insights into everything from stock market performance to consumer preferences to health outcomes.

And it’s great, I have no problem at all with Big Data.

Many of the sciences have been playing the Big Data game for a long time, long before it became fashionable in silicon valley. In particular, physicists and astronomers have been grappling with large datasets for decades, as starlight pours into telescopes and particle explosions pour through colliders. They’ve developed a lot of sophisticated techniques for filtering through data, finding correlations and trying to make sense of what nature is whispering to us.

And a lot of those techniques are now being popularized and put to useful effect outside the science world, which is fantastic.

A lot of up-and-coming students are interested in careers in Big Data. They like the idea of trying to swim through massive data challenges to make the world a better place. They’re interested in fulfilling and lucrative careers (a rare combo) in places like Silicon Valley and New York.

And they’re not too picky about where they get their degrees from. An advanced degree in, say, astronomy that focuses on Big Data problems is pretty attractive on the job market. And it’s a fun problem to cut your teeth on in the Big Data world before moving on.

But it’s the “moving on” part that’s causing a little bit of worry in the astronomy and physics communities. The point of graduate school is to train the next generation of scientists. Now for the past few decades there’s been a bit of a problem that we’re turning out way too many PhD’s than there are open faculty positions, but still the facade continues that the point of the exercise – the reason for the close advisor and mentor relationships and all the training – is to craft an independent scientific researcher.

But what do you do when students aren’t interested in becoming an independent researcher? They’re not interested in a science career, they’re just using this degree as a stepping stone into a different one.

As science becomes more and more data heavy and leans more and more on Big Data techniques, the students who are naturally gifted or most talented or most passionate about Big Data aren’t going to end up in science, they’re going to end up in the industry.

And what does that leave with science? The leftovers? The Big Data B-Team?

For decades graduate schools across the country have operated under the mantra that they are training new scientists, even though there aren’t enough positions for all those trainees. But now the tables have been flipped, and a growing fraction of the incoming students aren’t interested in this career path anyway.

It could be the case that when it comes to Big Data, the academic institution of science has reaped what is sowed.

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• Executes projects in the field of advanced automated horticultural production systems with a focus on computer vision and machine learning applications; contributes to the development of sustainable (indoor) horticultural crop production systems.
• Develops new image analysis and control software for the detection of crop growth parameters of various crops, the quality detection of fresh products and crops and the detection of plant health and resilience; uses various camera techniques such as RGB, 3D, hyperspectral or thermal imaging.
• Applies modern machine learning methods such as deep learning and reinforcement learning, develops new AI algorithms for growing horticultural crops with a minimum of resources; deals with time series; uses various sensors; deals with data fusion.
• Leads and works in projects together with other academic and industrial partners.
• Conducts applied scientific research within the projects.
• Communicates relevant research results to science and practise.

Requirements:

We are looking for an enthusiastic and ambitious researcher computer vision and machine learning, with:

• Academic working and thinking on the field of computer vision and machine learning, study in e.g. artificial intelligence, natural or biomedical science, holds a PhD.
• Knowledge of various image analysis techniques, data analysis methods, classification and statistics.
• Knowledge of various modern machine learning techniques, such as deep learning and reinforcement learning methods.
• Experience with the structured development of complex software, programming in C++ and C#; experience with Halcon and / or LabView and / or Matlab is an advantage.
• Knowledge of lighting, optics and camera systems (2D, 3D) including calibration and control is an advantage; knowledge of various sensor types, data output and practical usage of such sensors is an advantage.
• Experience with the application of image processing and machine learning in the green environment and willingness to learn the challenges of horticulture.
• Willingness to combine theoretical knowledge with practical setups and implementation.
• Experience with project-based work and project leadership.
• Excellent knowledge of Dutch and English language.
• Driving License B.
• Is an enthusiastic independent researcher with good communication skills and team spirit.
• Is customer- and quality-oriented and stress tolerant.

Salary Benefits:

A challenging position with, depending on your experience, a competitive salary up to a maximum of € 4.934,- gross per month for a full working week of 36 hours in accordance with the Collective Labor Agreement for Wageningen Research (scale 11). At WUR you work in a (inter-)national leading organization in the field of research and education. This is a position for a duration of one year with the prospect of permanent employment if our cooperation is mutually satisfactory.

In addition, we offer:

• 8% holiday allowance;
• a fixed end-of-year bonus of 3%;
• excellent training opportunities and secondary employment conditions;
• flexible working hours and vacations can be determined in consultation with colleagues in such a way that an optimal balance between work and private life is possible;
• excellent pension plan through ABP;
• 171 vacation hours, the possibility to purchase extra and good supplementary leave schemes;
• a choice model to put together part of your employment conditions yourself, such as a bicycle plan;
• make use of the sports facilities on campus for a small fee.

Wageningen University & Research stimulates internal career opportunities and mobility with an active internal recruitment policy. There are ample opportunities for own initiative in a learning environment. We offer a versatile job in an international environment with varied activities in a pleasant and open working atmosphere.

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