We present ProxiModel, a novel event mining framework for extracting high-quality structured event knowledge from large, redundant, and noisy news data sources. The proposed model differentiates itself from other approaches by modeling both the event correlation within each individual document as well as across the corpus. To facilitate this, we introduce the concept of a proximity-network, a novel space-efficient data structure to facilitate scalable event mining. This proximity network captures the corpus-level co-occurence statistics for candidate event descriptors, event attributes, as well as their connections. We probabilistically model the proximity network as a generative process with sparsity-inducing regularization. This allows us to efficiently and effectively extract high-quality and interpretable news events. Experiments on three different news corpora demonstrate that the proposed method is effective and robust at generating high-quality event descriptors and attributes. We briefly detail many interesting applications from our proposed framework such as news summarization, event tracking and multi-dimensional analysis on news. Finally, we explore a case study on visualizing the events for a Japan Tsunami news corpus and demonstrate ProxiModel's ability to automatically summarize emerging news events.

FPGAs have become a popular choice for deploying deep learning architectures (DLA). There are many researchers that have explored the deployment and mapping of DLA on FPGA. However, there has been a growing need to do design-time hardware-software co-verification of these deployments. To the best of our knowledge this is the first work that proposes a 2-Level 3-Way (2L-3W) hardware-software co-verification methodology and provides a step-by-step guide for the successful mapping, deployment and verification of DLA on FPGA boards. The 2-Level verification is to make sure the implementation in each stage (software and hardware) are following the desired behavior. The 3-Way co-verification provides a cross-paradigm (software, design and hardware) layer-by-layer parameter check to assure the correct implementation and mapping of the DLA onto FPGA boards. The proposed 2L-3W co-verification methodology has been evaluated over several test cases. In each case, the prediction and layer-by-layer output of the DLA deployed on PYNQ FPGA board (hardware) alongside with the intermediate design results of the layer-by-layer output of the DLA implemented on Vivado HLS and the prediction and layer-by-layer output of the software level (Caffe deep learning framework) are compared to obtain a layer-by-layer similarity score. The comparison is achieved using a completely automated Python script. The comparison provides a layer-by-layer similarity score that informs us the degree of success of the DLA mapping to the FPGA or help identify in design time the layer to be debugged in the case of unsuccessful mapping. We demonstrated our technique on LeNet DLA and Caffe inspired Cifar-10 DLA and the co-verification results yielded layer-by-layer similarity scores of 99\% accuracy.

Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massive geo-social data is now readily available for exploitation by the recommendation system. In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities. We present an attention mechanism to model the influence of the target user $u_T$ in candidate user groups that satisfy the social constraints. It helps to retrieve the optimal user group and activity topic candidates, as well as explains the group decision-making process. Once the user group and topics are retrieved, a novel efficient spatial query algorithm SPA-DF is employed to determine the activity location under the constraints of the given user group and activity topic candidates. The proposed method is evaluated in real-world datasets and the experimental results show that the proposed model significantly outperforms baseline methods.

Fall armyworm, the larval life stage of a fall armyworm moth, impacts maize crops worldwide but particularly in countries like Uganda, where agricultural businesses employ 70% of the population. Studies show the potential impact is between 8.3 and 20.6 million tons per year, with the fallout amounting to between $2.48 million and $6.19 million per year. The threat of devastating losses prompted developers participating in a Google Developer Group in Mbale to create an Android app -- FlatButter -- that identifies signs of fall armyworm damage in maize crops. It's been featured on a national TV station in Uganda and highlighted by the Food Agricultural Organization of the United Nations, as well as by Google in a short film published today. "The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help," wrote Hansu Mobile and Intelligent Innovations CEO Nsubuga Hassan, who led the team that developed the app.

Pizza lunch will be provided at 12:15pm, with the talk following at 12:30pm. All students, alumni, staff and faculty are welcome to register for this free event using the registration link below. "Communication has been weaponized, used to provoke, mislead and influence the public in numerous insidious ways. Disinformation was just the first stage of an evolving trend of using information to subvert democracy, confuse rival states, define the narrative and control public opinion. Using large, unregulated, open environments that tech companies once promised would "empower" ordinary people, disinformation has spread rapidly across the globe.

Sign in to report inappropriate content. An incredible human story of the Ugandan developer, Nazirini Siraji, and the app created by her small team in Mbale, Uganda, with learning from their local Google Developer Group community. Harnessing the power of TensorFlow and Machine Learning, this free app helps farmers identify and treat Fall Armyworm, reducing the massive crop devastation currently impacting Uganda and Africa. Find your local GDG chapter https://goo.gle/33ER1Kd

Artificial Intelligence (AI) is the most important ethical issue of our age. It's certain that it will continue to play an ever-increasing role in all our lives. Last year, Facebook claimed it would be able to predict when we will die, along with other key life events, from marriages to deaths, based on social media activity. It's encouraging that some leading international data scientists are now keen to engage with philosophers and faith leaders as they start to deal with the ethical issues raised by AI. They know that people involved in faith have had thousands of years of practice discussing issues such as AI, which are hard to define but have a great impact on humanity.

Conversations about AI often focus on technology, but we shouldn't forget that most business outcomes are linked to relationships with people, said Warren Hero, chief digital officer at Webber Wentzel, a law firm headquartered in Johannesburg Webber Wentzel – recently recognized as the African law firm of the year – aims to build a completely digital practice. Hero was speaking at AfricaCom about his experience of applying AI in the legal profession to increase both efficiency and productivity. He revealed that in the first AI experiment the company ran, it was able to decrease the time required to deliver a specific service from seven months to just three weeks. "in the process, we were able to give our clients certainty about, first of all, the quality of the outcome. And, second of all, about the cost of that outcome," Hero said.

"I say this to everyone in the media world who I talk to," says Darren Atkins, wrapping up our phone interview: "Please, absolutely do not portray this as a hidden agenda to get rid of staff." Atkins is the Chief Technology Office for AI automation at East Suffolk and North Essex NHS Foundation Trust – group of hospitals employing more than 10,000 staff, who serve a quarter of a million people in the South East of England. "If this technology is applied in the wrong way, it can be very threatening," Atkins says. "Our main priority is to free up time for staff to do the work that they should be doing, rather than the work that has no value." Just over a year ago, Atkins led the deployment of virtual workers across his group of NHS hospitals – and according to him, it's been an unqualified success. Patients are missing fewer appointments and staff are happier.

Between 2013 and 2016, U.S. farmers and ranchers weathered a 45% dip in net farm income -- the largest since the Great Depression -- while the number of mouths to feed grew sharply by the day. The global population is expected to increase by 2.2 billion by 2050, and the world's farmers will have to grow about 70% more food than is now produced. If you ask Microsoft, the solution lies in technology. The tech giant's FarmBeats program, which launched in preview today on Azure Marketplace ahead of Ignite 2019, is a multi-year effort to bring robust data analytics to the agriculture sector. With a backend built on Azure and compatibility with hardware from a range of top manufacturers, it aims to promote what Ranveer Chandra, FarmBeats project lead and chief scientist at Azure Global, calls "data-driven" farming techniques. The International Food Policy Research Institute claims these can boost farm productivity by as much as 67% while reducing resource usage.