Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters' interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

The usability and practicality of any machine learning (ML) applications are largely influenced by two critical but hard-to-attain factors: low latency and low cost. Unfortunately, achieving low latency and low cost is very challenging when ML depends on real-world data that are highly distributed and rapidly growing (e.g., data collected by mobile phones and video cameras all over the world). Such real-world data pose many challenges in communication and computation. For example, when training data are distributed across data centers that span multiple continents, communication among data centers can easily overwhelm the limited wide-area network bandwidth, leading to prohibitively high latency and high cost. In this dissertation, we demonstrate that the latency and cost of ML on highly-distributed and rapidly-growing data can be improved by one to two orders of magnitude by designing ML systems that exploit the characteristics of ML algorithms, ML model structures, and ML training/serving data. We support this thesis statement with three contributions. First, we design a system that provides both low-latency and low-cost ML serving (inferencing) over large-scale and continuously-growing datasets, such as videos. Second, we build a system that makes ML training over geo-distributed datasets as fast as training within a single data center. Third, we present a first detailed study and a system-level solution on a fundamental and largely overlooked problem: ML training over non-IID (i.e., not independent and identically distributed) data partitions (e.g., facial images collected by cameras varies according to the demographics of each camera's location).

Artificial neural networks have exceeded human-level performance in accomplishing several individual tasks (e.g. voice recognition, object recognition, and video games). However, such success remains modest compared to human intelligence that can learn and perform an unlimited number of tasks. Humans' ability of learning and accumulating knowledge over their lifetime is an essential aspect of their intelligence. Continual machine learning aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn online from a non-stationary and never-ending stream of data. A key component of such a never-ending learning process is to overcome the catastrophic forgetting of previously seen data, a problem that neural networks are well known to suffer from. The work described in this thesis has been dedicated to the investigation of continual learning and solutions to mitigate the forgetting phenomena in neural networks. To approach the continual learning problem, we first assume a task incremental setting where tasks are received one at a time and data from previous tasks are not stored. Since the task incremental setting can't be assumed in all continual learning scenarios, we also study the more general online continual setting. We consider an infinite stream of data drawn from a non-stationary distribution with a supervisory or self-supervisory training signal. The proposed methods in this thesis have tackled important aspects of continual learning. They were evaluated on different benchmarks and over various learning sequences. Advances in the state of the art of continual learning have been shown and challenges for bringing continual learning into application were critically identified.

Abstract: Replacing static word embeddings with contextualized word representations has yielded significant improvements on many NLP tasks. However, just how contextual are the contextualized representations produced by models such as ELMo and BERT? Are there infinitely many context-specific representations for each word, or are words essentially assigned one of a finite number of word-sense representations? For one, we find that the contextualized representations of all words are not isotropic in any layer of the contextualizing model. While representations of the same word in different contexts still have a greater cosine similarity than those of two different words, this self-similarity is much lower in upper layers.

He said he was "shocked by resistance" to San Francisco's Proposition C, which called for higher taxes on businesses to raise funds for addressing homelessness. Jack Dorsey, CEO of Twitter and Square, is a prominent opponent, arguing the measure is unfair to Square and fintech startups. San Francisco voters approved Proposition C in November 2018, though the city has said it won't spend the money raised until legal questions are settled. On WeWork, whose plans for an initial public offering were derailed recently, Mr. Benioff said that "an IPO is like a spiritual cleanse." That's because going public earlier in its life forces a startup to do things like find a top CEO, he said.

Oscar-nominated actor Samuel L. Jackson is lending his iconic voice to Amazon's Alexa – profanities and all. During Amazon's event to unveil new products and services Wednesday, the online shopping giant announced that Jackson will be the first celebrity voice for its Alexa virtual assistant and was created using neural text-to-speech technology. There will be both an explicit version and a clean version when the feature launches later this year. The Alexa "skill" will cost 99 cents as an introductory offer. After the introductory period, the price will be $4.99, according to the product page.

Gluware Automation v3.6 extends the platform API capabilities including integrations with the Mist and Ansible platforms and introduces industry-first lifecycle management and infrastructure integration enhancements. The new version was developed in response to a growing list of global enterprise customers seeking to scale their network automation capabilities to work in their current environment and across more nodes to be faster within their complex networks. Unique in the industry, Gluware is the only solution that offers this level of Intent-Based network automation capabilities without coding or scripting and in support of a tapestry of automation solutions for multi-vendor, brownfield and greenfield networks. For the first time, companies can use the new Intelligent Model Discovery (IMD) Workflow in the Gluware platform to read in an existing device configuration. This puts the power of "infrastructure as code" into the hands of network engineers enabling powerful automation with no IT staff coding required.

Reach, the Daily Mirror and Daily Express publisher, has launched a brand-safety platform created by IBM that will hope to curb articles being unnecessarily blacklisted from advertising. The platform, called Mantis, uses IBM Watson's artificial-intelligence engine and machine learning to check whether content is appropriate. Reach began looking for a tech solution last year in response to a signficicant proportion of news content being blacklisted due to "less intuitive and less sophisticated solutions" currently on the market. The main four players offering third-party brand-safety solutions for publishers are ADmantX (which was hired by newspaper sales joint venture The Ozone Project earlier this year), DoubleVerify, Grapeshot and Integral Ad Science. Like ADmantX, Mantis uses natural language processing to decipher context in language.

ANN ARBOR, Mich.–(BUSINESS WIRE)–Automation experts--and those who want to explore how to grow their business with the latest trends and …