Between 2010 and 2050, the global road network is expected to grow by 60 percent. Many of the new roads will emerge in areas that are still undeveloped in Africa, South America or Asia, further limiting the habitats of endangered animals. Many roads are unplanned or even illegally built. In rainforests, which are very important for the global climate, illegal road construction already leads to deforestation, slash-and-burn and colonization.

We learn to lie as children, between the ages of two and five. By adulthood, we are prolific. We lie to our employers, our partners and, most of all, one study has found, to our mothers. The average person hears up to 200 lies a day, according to research by Jerry Jellison, a psychologist at the University of Southern California. The majority of the lies we tell are "white", the inconsequential niceties – "I love your dress!" – that grease the wheels of human interaction. But most people tell one or two "big" lies a day, says Richard Wiseman, a psychologist at the University of Hertfordshire. We lie to promote ourselves, protect ourselves and to hurt or avoid hurting others. The mystery is how we keep getting away with it. Our bodies expose us in every way. We stutter, stall and make Freudian slips. "No mortal can keep a secret," wrote the psychoanalyst in 1905.

Adaptive filters are applied in several electronic and communication devices like smartphones, advanced headphones, DSP chips, smart antenna, and teleconference systems. Also, they have application in many areas such as system identification, channel equalization, noise reduction, echo cancellation, interference cancellation, signal prediction, and stock market. Therefore, reducing the energy consumption of the adaptive filtering algorithms has great importance, particularly in green technologies and in devices using battery. In this thesis, data-selective adaptive filters, in particular the set-membership (SM) adaptive filters, are the tools to reach the goal. There are well known SM adaptive filters in literature. This work introduces new algorithms based on the classical ones in order to improve their performances and reduce the number of required arithmetic operations at the same time. Therefore, firstly, we analyze the robustness of the classical SM adaptive filtering algorithms. Secondly, we extend the SM technique to trinion and quaternion systems. Thirdly, by combining SM filtering and partial-updating, we introduce a new improved set-membership affine projection algorithm with constrained step size to improve its stability behavior. Fourthly, we propose some new least-mean-square (LMS) based and recursive least-squares based adaptive filtering algorithms with low computational complexity for sparse systems. Finally, we derive some feature LMS algorithms to exploit the hidden sparsity in the parameters.

AI is already delivering tangible benefits for organizations – but where is the technology headed next? How is artificial intelligence (AI) impacting customer experience (CX) now, and where is it going in the future? What new benefits is it unlocking for organizations and what will it mean for the workforce of tomorrow? These are the questions we posed to five of the industry's leading analysts during EmpowerCX Americas 2019, Sitel Group's annual client event. "Artificial intelligence is already helping contact centers improve two key metrics – reduce cost and improve customer satisfaction," states Juan Gonzalez, Research Director for Frost & Sullivan in Latin America.

The 5th Huawei Asia-Pacific Innovation Day was held in Chengdu, China. This year's Innovation Day is themed "Innovation Enables Asia-Pacific Digitization". More than 200 representatives from government, industry and academia of Asia-Pacific countries and regions got together to discuss innovative 5G technologies and applications, sustainable development, as well as technology, humanity, and nature. As a ubiquitous technology, 5G is the cornerstone of a smart world in which everything is connected. Today, as we usher in the 5G era, we are also at a critical stage of digital transformation across industries worldwide.

A New Research on the Global Emotion Artificial Intelligence Market was conducted across a variety of industries in various regions to produce more than 150 page reports. This study is a perfect blend of qualitative and quantifiable information highlighting key market developments, industry and competitors' challenges in gap analysis and new opportunities and may be trending in the Emotion Artificial Intelligence market. Some are part of the coverage and are the core and emerging players being profiled IBM, Microsoft, Eyesight Technologies, Affectiva, NuraLogix, gestigon GmbH, Crowd Emotion, Beyond Verbal, nViso, Cogito Corporation, Kairos. Import and export policies that can have an immediate impact on the global Emotion Artificial Intelligence market. This study includes EXIM * related chapters for all relevant companies dealing with the Emotion Artificial Intelligence market and related profiles and provides valuable data in terms of finances, product portfolio, investment planning and marketing and business strategy. The study is a collection of primary and secondary data that contains valuable information from the major suppliers of the market.

Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in the context of machine listening (Task 1b of the DCASE 2019 Challenge). We propose a novel approach to learn domain-invariant classifiers in an end-to-end fashion by enforcing equal hidden layer representations for domain-parallel samples, i.e. time-aligned recordings from different recording devices. No classification labels are needed for our domain adaptation (DA) method, which makes the data collection process cheaper.

We provide the construction of a set of square matrices whose translates and rotates provide a Parseval frame that is optimal for approximating a given dataset of images. Our approach is based on abstract harmonic analysis techniques. Optimality is considered with respect to the quadratic error of approximation of the images in the dataset with their projection onto a linear subspace that is invariant under translations and rotations. In addition, we provide an elementary and fully self-contained proof of optimality, and the numerical results from datasets of natural images.

Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged. In medical imaging, there is a high heterogeneity of distributions among images based on the equipment that generate them and their parametrization. This heterogeneity triggers a common issue in machine learning called domain shift, which represents the difference between the training data distribution and the distribution of where a model is employed. A high domain shift tends to implicate in a poor performance from models. In this work, we evaluate the extent of domain shift on three of the largest datasets of chest radiographs. We show how training and testing with different datasets (e.g. training in ChestX-ray14 and testing in CheXpert) drastically affects model performance, posing a big question over the reliability of deep learning models.