The following keynote speakers have been confirmed for IEEE GLOBECOM 2019. Abstract: We are well into the "Internet of Things" era for the Internet. Billions of devices are expected and it is not uncommon to find a dozen or even a score of Internet-enabled devices in residences and offices around the world. These systems run on software - some of which has not been well tested for safety and security. We need to introduce and promote an ethic of software safety and extended maintenance to protect the users of these devices.

Take a look around and you will find a lot of self-proclaimed marketing professionals. However, there are only a few names who have actually made it to the top and gotten their art acknowledged. Today on Branex Talks, we are privileged to have such a gentleman with us. To date, he has helped businesses and startups founders scale their business. He has extensive experience working with various professionals in 70 countries, including Taiwan, Spain, Ireland, the US and the UK.

The context: The vast majority of Facebook's moderation is now done automatically by the company's machine-learning systems, reducing the amount of harrowing content its moderators have to review. In its latest community standards enforcement report, published earlier this month, the company claimed that 98% of terrorist videos and photos are removed before anyone has the chance to see them, let alone report them. So, what are we seeing here? The company has been training its machine-learning systems to identify and label objects in videos--from the mundane, such as vases or people--to the dangerous, such as guns or knives. Facebook's AI uses two main approaches to look for dangerous content. One is to employ neural networks that look for features and behaviors of known objects and label them with varying percentages of confidence (as we can see in the video above).

No one knows who gave Rahul Roy tuberculosis. Roy's charmed life as a successful trader involved traveling in his Mercedes C class between his apartment on the plush Nepean Sea Road in South Mumbai and offices in Bombay Stock Exchange. He cared little for Mumbai's weather. He seldom rolled down his car windows – his ambient atmosphere, optimized for his comfort, rarely changed. Historically TB, or "consumption" as it was known, was a Bohemian malady; the chronic suffering produced a rhapsody which produced fine art. TB was fashionable in Victorian Britain, in part, because consumption, like aristocracy, was thought to be hereditary. Even after Robert Koch discovered that the cause of TB was a rod-shaped bacterium – Mycobacterium Tuberculosis (MTB), TB had a special status denied to its immoral peer, Syphilis, and unaesthetic cousin, leprosy. TB became egalitarian in the early twentieth century but retained an aristocratic noblesse oblige. George Orwell may have contracted TB when he voluntarily lived with miners in crowded squalor to understand poverty. Unlike Orwell, Roy had no pretentions of solidarity with poor people. For Roy, there was nothing heroic about getting TB. He was embarrassed not because of TB's infectivity; TB sanitariums are a thing of the past. TB signaled social class decline. He believed rickshawallahs, not traders, got TB.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective accountability and regulation is stymied by a persistent lack of resources supporting public understanding of algorithms and artificial intelligence. Through interactions with a US-based civil rights organization and their coalition of community organizations, we identify a need for (i) heuristics that aid stakeholders in distinguishing between types of analytic and information systems in lay language, and (ii) risk assessment tools for such systems that begin by making algorithms more legible. The present work delivers a toolkit to achieve these aims. This paper both presents the Algorithmic Equity Toolkit (AEKit) Equity as an artifact, and details how our participatory process shaped its design. Our work fits within human-computer interaction scholarship as a demonstration of the value of HCI methods and approaches to problems in the area of algorithmic transparency and accountability.

The damaging effects of hate speech in social media are evident during the last few years, and several organizations, researchers and the social media platforms themselves have tried to harness them without great success. Recently, following the advent of deep learning, several novel approaches appeared in the field of hate speech detection. However, it is apparent that such approaches depend on large-scale datasets in order to exhibit competitive performance. In this paper, we present a novel, publicly available collection of datasets in five different languages, that consists of tweets referring to journalism-related accounts, including high-quality human annotations for hate speech and personal attack. To build the datasets we follow a concise annotation strategy and employ an active learning approach. Additionally, we present a number of state-of-the-art deep learning architectures for hate speech detection and use these datasets to train and evaluate them. Finally, we propose an ensemble model that outperforms all individual models.

Optimization algorithms inspired by the geometry of dissipative systems Alessandro Bravetti 1, Maria L. Daza-Torres 2, Hugo Flores-Arguedas 3, and Michael Betancourt 4 1 Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), Universidad Nacional Autónoma de México, A.P. 70-543, 04510 Ciudad de México, México alessandro.bravetti@iimas.unam.mx 2 Universidad de Guadalajara, Guadalajara, México, mdazatorres@cimat.mx 3 Centro de Investigación en Matemáticas (CIMAT), Guanajuato, México, hugo.flores@cimat.mx 4 Symplectomorphic LLC, New York, USA, betan@symplectomorphic.com Abstract Accelerated gradient methods are a powerful optimization tool in machine learning and statistics but their development has traditionally been driven by heuristic motivations. Recent research, however, has demonstrated that these methods can be derived as discretizations of dynamical systems, which in turn has provided a basis for more systematic investigations, especially into the structure of those dynamical systems and their structure preserving discretizations. In this work we introduce dynamical systems defined through a contact geometry which are not only naturally suited to the optimization goal but also subsume all previous methods based on geometric dynamical systems. These contact dynamical systems also admit a natural, robust discretization through geometric contact integrators. We demonstrate these features in paradigmatic examples which show that we can indeed obtain optimization algorithms that achieve oracle lower bounds on convergence rates while also improving on previous proposals in terms of stability. Keywords: optimization, accelerated gradient, geometric integrators, contact geometry 1 arXiv:1912.02928v1 Despite their practical utility and explicit convergence bounds, accelerated gradient methods have long been difficult to motivate from a fundamental theory.

Machine learning has started to be deployed in fields such as healthcare and finance, which propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -- Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks. Our 4PC protocol tolerating at most one malicious corruption is practically efficient as compared to the existing works. We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds. Our framework operates in the offline-online paradigm over rings and is instantiated in an outsourced setting for machine learning. Also, we propose conversions especially relevant to privacy-preserving machine learning. The highlights of our framework include using a minimal number of expensive circuits overall as compared to ABY3. This can be seen in our technique for truncation, which does not affect the online cost of multiplication and removes the need for any circuits in the offline phase. Our B2A conversion has an improvement of $\mathbf{7} \times$ in rounds and $\mathbf{18} \times$ in the communication complexity. In addition to these, all of the special conversions for machine learning, e.g. Secure Comparison, achieve constant round complexity. The practicality of our framework is argued through improvements in the benchmarking of the aforementioned algorithms when compared with ABY3. All the protocols are implemented over a 64-bit ring in both LAN and WAN settings. Our improvements go up to $\mathbf{187} \times$ for the training phase and $\mathbf{158} \times$ for the prediction phase when observed over LAN and WAN.

Recently there has been an increase in the studies on time - series data mining specifically time - series clustering due to the vast existe nce of time - series in various domains. The large volume of data in the form of time - series make s it necessary to employ various techniques such as clustering to understand the data and to extract information and hidden patterns. In the field of clustering specifically, time - series clustering, the most important aspects are the similarity measure used and the algorithm employed to conduct the clustering. In this paper, a new similarity measure for time - series clustering is developed based on a combination of a simple representation of time - series, slope of each segment of time - series, Euclidean distance and the so - called dynamic time warping. It is proved in this paper that the proposed distance measure is metric and thus indexing can be applied. For the task of clustering, the Particle Swarm Optimization algorithm is employed. The proposed similarity measure is compared to three existing measures in terms of various criteria used for the evaluation of clustering algorithms. The results indicate that the propo sed similarity measure outperforms the rest in almost every dataset used in this paper.