University of Johannesburg and CIRANO; 4 / 46 AEA 2019 - Atlanta SKEMA Introduction Introduction China's rapid development have spurred large migration from rural areas to urban areas Between 1990 and the end of 2015 the proportion of China's population living in urban areas jumped from 26% to 56% Currently estimated by census, there are more than 240 million rural-to-urban migrants and more than 160 million working in cities outside of their hukou.

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain.

Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume applicability of guideline recommendations concerning the generic goal of patient well-being, resolve conflicts among recommendations using patient's conditions and preferences, and then consider prioritised patient-centered goals to yield non-conflicting, goal-maximising and preference-respecting recommendations. We rely on the state-of-the-art Transition-based Medical Recommendation model for representing guideline recommendations and augment it with context given by the patient's conditions, goals, as well as preferences over recommendations and goals. We establish desirable properties of our approach in terms of sensitivity to recommendation conflicts and patient context.

Talking about Intel, one immediately thinks of computer science, since this American company manufactures world-renowned microprocessors for computers. It now aims to use artificial intelligence to protect elephants, the largest terrestrial mammals threatened with extinction due to poaching. These pachyderms are killed for their ivory tusks. It is a software that works with "intelligent" cameras. The project is supported by the National Geographic Society and the Leonardo DiCaprio Foundation.

Samsung Electronics will introduce its new products and solutions to its business partners around the world at Samsung Forum 2019. During the two-month event, strategic products including the 2019 QLED TV lineup as well as customized products for regional markets will be showcased. Based on'New Bixby,' Samsung's intelligence platform, Connected Solution will also be exhibited where global business partners can interact with various Samsung products. Starting with the European Forum, Samsung will invite media and partners from Europe, Southwest Asia and Latin America to Porto of Portugal from February 12th to 22nd. From March 7th to 11th, Samsung will host the Middle East and CIS (Commonwealth of Independent States) Forum in Antalya of Turkey.

The graph represents a network of 3,408 Twitter users whose tweets in the requested range contained "futureofwork ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 19 February 2019 at 14:05 UTC. The requested start date was Tuesday, 19 February 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 1-day, 11-hour, 23-minute period from Sunday, 17 February 2019 at 13:37 UTC to Tuesday, 19 February 2019 at 01:00 UTC.

Machine Learning as a Service Market to reach USD 16.13 billion by 2025 Machine Learning as a Service Market valued approximately USD 0.87 billion in 2017 is anticipated to grow with a healthy growth rate of more than 43.9% over the forecast period 2018-2025. Machine learning as a service is a significant range of solutions and services that are offered by cloud service providers. The tools offered by service providers include APIs, data visualization, natural language processing, face recognition, deep learning, and predictive analytics. The main benefit associated with these services is that the customers are able to quickly start with machine learning with no need to install or download any software on their servers. Enhancements in technology, growth in data volume and rise in IT spending in some of the developing regions are the major factors which are driving the growth in the global market.

The members of the Global University Leaders Forum community convened at the World Economic Forum Annual Meeting 2019 to discuss their role in our ever-changing world. Here are six topics that were top of the agenda as the members considered the future of the university and its role in society. Today data is omnipresent and often overwhelming. By way of example, Domo's Data Never Sleeps 6.0 reported that in 2018 Google conducted an average 3.8 million searches per minute. Though not all graduates will enter data-related fields, universities are starting to work towards increasing data literacy in their student body by adding data science courses and challenges for social science majors so that graduates can effectively communicate with their data-oriented peers and co-workers.

In 1950, Norbert Wiener's The Human Use of Human Beings was at the cutting edge of vision and speculation in proclaiming: But this was his book's denouement, and it has left us hanging now for 68 years, lacking not only prescriptions and proscriptions but even a well-articulated "problem statement." We have since seen similar warnings about the threat of our machines, even in the form of outreach to the masses, via films like Colossus: The Forbin Project (1970), The Terminator (1984), The Matrix (1999), and Ex Machina (2015). But now the time is ripe for a major update with fresh, new perspectives -- notably focused on generalizations of our "human" rights and our existential needs. Concern has tended to focus on "us versus them" (robots) or "gray goo" (nanotech) or "monocultures of clones" (bio). To extrapolate current trends: What if we could make or grow almost anything and engineer any level of safety and efficacy desired?

In the past decade, sparse principal component analysis has emerged as an archetypal problem for illustrating statistical-computational tradeoffs. This trend has largely been driven by a line of research aiming to characterize the average-case complexity of sparse PCA through reductions from the planted clique (PC) conjecture - which conjectures that there is no polynomial-time algorithm to detect a planted clique of size $K = o(N^{1/2})$ in $\mathcal{G}(N, \frac{1}{2})$. All previous reductions to sparse PCA either fail to show tight computational lower bounds matching existing algorithms or show lower bounds for formulations of sparse PCA other than its canonical generative model, the spiked covariance model. Also, these lower bounds all quickly degrade with the exponent in the PC conjecture. Specifically, when only given the PC conjecture up to $K = o(N^\alpha)$ where $\alpha < 1/2$, there is no sparsity level $k$ at which these lower bounds remain tight. If $\alpha \le 1/3$ these reductions fail to even show the existence of a statistical-computational tradeoff at any sparsity $k$. We give a reduction from PC that yields the first full characterization of the computational barrier in the spiked covariance model, providing tight lower bounds at all sparsities $k$. We also show the surprising result that weaker forms of the PC conjecture up to clique size $K = o(N^\alpha)$ for any given $\alpha \in (0, 1/2]$ imply tight computational lower bounds for sparse PCA at sparsities $k = o(n^{\alpha/3})$. This shows that even a mild improvement in the signal strength needed by the best known polynomial-time sparse PCA algorithms would imply that the hardness threshold for PC is subpolynomial. This is the first instance of a suboptimal hardness assumption implying optimal lower bounds for another problem in unsupervised learning.