We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C.

This thesis proposal makes the following technical contributions: (i) we provide a definition of the Dynamic Influence Maximization Under Uncertainty (or DIME) problem, which models the problem faced by homeless shelters accurately; (ii) we propose a novel Partially Observable Markov Decision Process (POMDP) model for solving the DIME problem; (iii) we design two scalable POMDP algorithms (PSINET and HEALER) for solving the DIME problem, since conventional POMDP solvers fail to scale up to sizes of interest; and (iv) we test our algorithms effectiveness in the real world by conducting a pilot study with actual homeless youth in Los Angeles. The success of this pilot (as explained later) shows the promise of using influence maximization for social good on a larger scale.

The potential of Artificial Intelligence (AI) to tackle challenging problems that afflict society is enormous, particularly in the areas of healthcare, conservation and public safety and security. Many problems in these domains involve harnessing social networks of under-served communities to enable positive change, e.g., using social networks of homeless youth to raise awareness about Human Immunodeficiency Virus (HIV) and other STDs. Unfortunately, most of these real-world problems are characterized by uncertainties about social network structure and influence models, and previous research in AI fails to sufficiently address these uncertainties. This thesis addresses these shortcomings by advancing the state-of-the-art to a new generation of algorithms for interventions in social networks. In particular, this thesis describes the design and development of new influence maximization algorithms which can handle various uncertainties that commonly exist in real-world social networks. These algorithms utilize techniques from sequential planning problems and social network theory to develop new kinds of AI algorithms. Further, this thesis also demonstrates the real-world impact of these algorithms by describing their deployment in three pilot studies to spread awareness about HIV among actual homeless youth in Los Angeles. This represents one of the first-ever deployments of computer science based influence maximization algorithms in this domain. Our results show that our AI algorithms improved upon the state-of-the-art by 160% in the real-world. We discuss research and implementation challenges faced in deploying these algorithms, and lessons that can be gleaned for future deployment of such algorithms. The positive results from these deployments illustrate the enormous potential of AI in addressing societally relevant problems.

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Bayesian Networks (BNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical structure of the DHS data with score-based, constraint-based, and hybrid structure learning algorithms. We investigate the effect of missing values, sample size, and knowledge-based constraints on each of the structure learning algorithms and assess their accuracy with multiple scoring functions. Weaknesses in the survey methodology and data available, as well as the variability in the BNs generated, mean that is not possible to learn a definitive causal BN from data. However, knowledge-based constraints are found to be useful in reducing the variation in the graphs produced by the different algorithms, and produce graphs which are more reflective of the likely influential relationships in the data. Furthermore, valuable insights are gained into the performance and characteristics of the structure learning algorithms. Two score-based algorithms in particular, TABU and FGES, demonstrate many desirable qualities; a) with sufficient data, they produce a graph which is similar to the reference graph, b) they are relatively insensitive to missing values, and c) behave well with knowledge-based constraints. The results provide a basis for further investigation of the DHS data and for a deeper understanding of the behaviour of the structure learning algorithms when applied to real-world settings.

This past Friday was arguably the biggest day of the year for retailers, particularly online retailers. Throughout last week, you probably received emails about massive Black Friday discounts. Some of you might have put together wish lists to check out at the stroke of midnight while others used your phones, to scout whether a 30% discount was worth the still hefty price tags. What you may be interested to know is that artificial intelligence (AI) has tailored your online experience. AI is a technology that makes machines smart.

It is striking when you find yourself living at major inflection points of history. After all, there is no such thing as quiet times. The 1990s, for instance, seem like a distant quiet time. But in that period, ethnic cleansing in Bosnia and Rwanda were happening. O.J. Simpson's "trial of the century" has faded into the mist of history.

Recently, scientist Gagandeep Kang had to forcefully remind a room full of senior colleagues -- all men -- that she was the chair and that they should speak only when their turn comes. This kind of thing happens all the time, and you become so inured to it that you don't realise it," she says. Kang is the first Indian woman to be elected as a fellow of the Royal Society, but even that, evidently, does not protect you from microaggressions from men. It is a reminder of the kind of bias that women in science have to deal with. Prejudice at many levels is one reason why there are far fewer women scientists than men in the higher echelons of science in India. A 2016-17 report, "Status of Women in Science Among Select Institutions in India: Policy Implications", supported by NITI Aayog, found that while women constitute over a third of science graduates and postgraduates, they make up only 15-20% of tenured faculty across research institutions and universities in India. "As a group, it is not easy for women to stay in science. Only 14% of scientists are women," science writers Nandita Jayaraj and Aashima Dogra write in their recent book, 31 Fantastic Adventures in Science: Women Scientists in India. However, there are women who have beaten odds and shattered stereotypes and glass ceilings. This special feature looks at nine such women who are doing critical work in science and technology in India. They work on an array of complex problems -- in fields ranging from quantum computation to paleoecology. Neuroscientist Vidita Vaidya is looking to decode how experiences and the environment affect the circuits in our brain, which might offer a clue to how we develop psychiatric disorders. Aditi Sen De, the first woman to receive the Shanti Swarup Bhatnagar Prize in physical sciences, is working on different aspects of quantum communication, a field that uses the laws of quantum physics to protect data. This is by no means an exhaustive list of exceptional women scientists, but they are representative of the brilliant minds that have striven and made it to the top and become exemplars. As Kang says, "If you see role models, you see areas you can aspire to.

File photo - A Microsoft delegate takes a picture during the launch of the Windows 10 operating system in Kenya's capital Nairobi, July 29, 2015. Some users love it while others hate it. Some experts estimate that Windows 10 dominates nearly 40 percent of the desktop OS market, handily surpassing the popularity of Windows 7. Speaking of, are you still using Windows 7? Microsoft is ending support for the 10-year-old operating system in January. Tap or click to learn how to bring your PC up to date before it's too late. Using the slogan "upgrade your world," Microsoft has described Windows 10 as the "final" version.

The graph represents a network of 2,418 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 27 November 2019 at 20:47 UTC. The requested start date was Monday, 25 November 2019 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 5-day, 4-hour, 57-minute period from Tuesday, 19 November 2019 at 20:03 UTC to Monday, 25 November 2019 at 01:01 UTC.

This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high-dimensional regime, where Euclidean distance becomes less informative. However, it is unknown whether popular methods, such as kernel k-means, are optimal in this regime. We consider the problem of high-dimensional Gaussian clustering and show that, with the exponential kernel function, the sufficient conditions for partial recovery of clusters using the NP-hard kernel k-means objective matches the known information-theoretic limit up to a factor of $\sqrt{2}$ for large $k$. It also exactly matches the known upper bounds for the non-kernel setting. We also show that a semi-definite relaxation of the kernel k-means procedure matches up to constant factors, the spectral threshold, below which no polynomial-time algorithm is known to succeed. This is the first work that provides such optimality guarantees for the kernel k-means as well as its convex relaxation. Our proofs demonstrate the utility of the less known polynomial concentration results for random variables with exponentially decaying tails in a higher-order analysis of kernel methods.