In this paper, we take a new approach for time of arrival geo-localization. We show that the main sources of error in metropolitan areas are due to environmental imperfections that bias our solutions, and that we can rely on a probabilistic model to learn and compensate for them. The resulting localization error is validated using measurements from a live LTE cellular network to be less than 10 meters, representing an order-of-magnitude improvement.

The internet uses routing tables to determine how and where data is sent and received. Without accurate and reliable tables, the internet would be like a highway system with no signs or signals to direct the traffic to the right places. Of course, cybercriminals find a way to corrupt just about everything that makes the internet work, and routing is no exception. IP hijacking, or BGP (Border Gateway Protocol) hijacking, is a process in which hackers and cybercriminals take over groups of IP addresses by corrupting the routing tables that use BGP. The purpose is to redirect traffic on the public internet or on private business networks to the hijackers' own networks where they can intercept, view, and even modify the packets of data.

I am the co-founder and CEO of Neurala Inc., a Boston-based company building Artificial Intelligence emulating brain function in software. Neurala's deep learning tech makes robots, drones, cars, consumer electronics, toys and smart devices more useful, engaging and autonomous. Neurala stems out of 10 years of research at Boston University Neuromorphics Lab, where as AI Professor I have pioneered the research and fielding of brain-inspired (also called Deep Learning, or Artificial Neural Networks) algorithms that allow robots and drones to perceive, navigate, interact and learn real-time in complex environments. Over my academic and industrial career, I have lectured and spoken at dozens of events and venues, including TEDx, keynote at Mobile World Congress Drone Summit, NASA, the Pentagon, GTC, InterDrone, Los Alamo National Lab, GE, Air Force Research Labs, HP, iRobot, Samsung, LG, Qualcomm, Huawei, Ericsson, BAE Systems, AI World, Mitsubishi, ABB and Accenture, among many others. My work has been featured in TIME, IEEE Spectrum, Fortune, CNBC, The Boston Globe, Xconomy, The Chicago Tribune, TechCrunch, VentureBeat, Nasdaq, Associated Press and many other media.

KT Corporation and Portugal-based WeDo Technologies have signed a Cooperation Agreement for AI-FMS (Artificial Intelligence based Fraud Management System) development and sales. KT's Deep Learning-based Artificial Intelligence (AI) module has been implemented and tested on WeDo's RAID FMS system. This AI module, trained with KT Big Data, has showed strong results for fraud detection and prevention, and has reportedly proved to be effective for a number of fraud use cases, with a high degree of accuracy. KT and WeDo plan to supply the AI-based International Revenue Share Fraud (AI-IRSF) module with the RAID platform to communication service providers (CSPs) by the end of 2019. KT's DL (Deep Learning) based AI module has been implemented and tested on WeDo's RAID FMS system.

As we prepare to enter the next decade, telecoms are being transformed by technology in a variety of ways. From artificial intelligence (AI) to the threat of cyberattack, here are the 7 biggest technology trends that will transform telecoms in 2020. The European Union's 5G action plan includes uninterrupted 5G coverage by 2025 for railways and major roads. In addition to being able to support a hundredfold increase in connected devices per each unit area, 5G will offer ultra-low latency, improved data rates and enable network slicing. This opens the door for new services, network operation and customer experience for telecom operators.

It is our pleasure to invite you to the 4th Huawei International Video Intelligence Forum based in Dublin, Ireland. At this summit you will hear from leading experts and scientists on the impact that AI and Machine Learning has on Visual Computational Networks, from behavior to AR/VR. Huawei has collaborated with leading Universities across the world and this forum is an ideal arena to explore this exciting Innovation. Leading speakers include top Researchers from Imperial College London, Queen Mary and Trinity College Dublin to name a few.

Prospect, build, and maintain business relationships within the Telecommunications and Cable Service Provider industries Identify and generate new business leads for the organization Achieve a personal sales target goals through new and existing business relationships Conduct technical product presentations with consultative selling skills with technical and non-technical clients Provide articulate business value propositions of the organization to clients through proven data analytics from Artificial Intelligence/Machine Learning predictive models Qualifications of Technical Business Executive: 7 years of experience within Business Development supporting Telecommunications and/or Cable Service Provider clients Strong understanding of information technology, technical product capabilities, and ability to speak technically with C-Level executives Extensive personal network with access to a rolodex of Telecommunications and/or Cable Service Provider clients Ability to build and maintain Tier 1 prospects and clients for the organization Excellent communication skills, written and orally, conducting Client meetings, workshops, and presentations Ability to travel nationwide and internationally (when needed) up to 50% of the time Compensation of Technical Business Executive: Up to $140,000 annually (based on experience) plus a comprehensive commission plan Medical, Dental, Vision and 401k benefits offered Paid Time Off, Holidays, and other benefits offered Please send only qualified resumes to Kevin Foster at [email protected] Keywords: Business development, technical business development, sales, sales engineer, account manager, account management, sales account manager, business development manager, business development management, artificial intelligence, machine learning, ai/ml, data analysis, telecommunications, telco, telecom, cable service provider, cable services, csp, information technology, IT, technical sales engineer, pre-sales engineer, presales engineer. Paid Time Off, Holidays, and other benefits offered Please send only qualified resumes to Kevin Foster at [email protected] Keywords: Business development, technical business development, sales, sales engineer, account manager, account management, sales account manager, business development manager, business development management, artificial intelligence, machine learning, ai/ml, data analysis, telecommunications, telco, telecom, cable service provider, cable services, csp, information technology, IT, technical sales engineer, pre-sales engineer, presales engineer.

As we prepare to enter the next decade, telecoms are being transformed by technology in a variety of ways. From artificial intelligence (AI) to the threat of cyberattack, here are the 7 biggest technology trends that will transform telecoms in 2020. The European Union's 5G action plan includes uninterrupted 5G coverage by 2025 for railways and major roads. In addition to being able to support a hundredfold increase in connected devices per each unit area, 5G will offer ultra-low latency, improved data rates and enable network slicing. This opens the door for new services, network operation and customer experience for telecom operators.

Deep tech is the newest catchphrase in the Indian startup ecosystem. A bunch of homegrown companies are using new-age technologies like artificial intelligence, machine learning, data analytics, cloud, and the internet-of-things (IoT) to solve real-world problems, and essentially, alter the way humans lead daily lives. On Day One of TechSparks 2019, YourStory's flagship annual conference, a panel of founders, investors, and technical heads gathered to take stock of the evolution of the local deep tech startups ecosystem. Swapan Rajdev, Co-Founder and CTO, Haptik (maker of AI chatbots, recently acquired by Reliance Jio) elaborated on how the growth of AI has spurred new jobs and roles. Gone are the days when Indian companies failed to make a mark in hardware.

--Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cybersecurity, opinion models, trust models, and/or social network analysis. However, SL and its variants have exposed limitations in predicting uncertain opinions in real-world dynamic network data mainly in threefold: (1) a lack of scalability to deal with a large-scale network; (2) limited capability to handle heterogeneous topological and temporal dependencies among node-level opinions; and (3) a high sensitivity with conflicting evidence that may generate counterintuitive opinions derived from the evidence. In this work, we proposed a novel deep learning (DL)- based dynamic opinion inference model while node-level opinions are still formalized based on SL meaning that an opinion has a dimension of uncertainty in addition to belief and disbelief in a binomial opinion (i.e., agree or disagree). The proposed DLbased dynamic opinion inference model overcomes the above three limitations by integrating the following techniques: (1) state-of-the-art DL techniques, such as the Graph Convolutional Network (GCN) and the Gated Recurrent Units (GRU) for modeling the topological and temporal heterogeneous dependency information of a given dynamic network; (2) modeling conflicting opinions based on robust statistics; and (3) a highly scalable inference algorithm to predict dynamic, uncertain opinions in a linear computation time. We validated the outperformance of our proposed DLbased algorithm (i.e., GCN-GRU-opinion model) via extensive comparative performance analysis based on four real-world datasets. In the decision making domain, including the fields of evidence and belief theories, reasoning or managing uncertainty has been studied since 1960s. The examples include Fuzzy Logic, Dempster-Shafer Theory (DST), Transferable Belief Model, and Dezert-Smarandache Theory [6]. These theories deal with uncertainty implicitly. In 1990's, as another variant of DST, Subjective Logic (SL) [16] is proposed to deal with a dimension of uncertainty in subjective opinions more explicitely. SL defines a binomial opinion (e.g., agree vs. disagree) with three dimensions, including belief, disbelief, and uncertainty.