In this Book we argue that the fruitful interaction of computer vision and belief calculus is capable of stimulating significant advances in both fields. From a methodological point of view, novel theoretical results concerning the geometric and algebraic properties of belief functions as mathematical objects are illustrated and discussed in Part II, with a focus on both a perspective 'geometric approach' to uncertainty and an algebraic solution to the issue of conflicting evidence. In Part III we show how these theoretical developments arise from important computer vision problems (such as articulated object tracking, data association and object pose estimation) to which, in turn, the evidential formalism is able to provide interesting new solutions. Finally, some initial steps towards a generalization of the notion of total probability to belief functions are taken, in the perspective of endowing the theory of evidence with a complete battery of estimation and inference tools to the benefit of all scientists and practitioners.

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing. Keywords: P systems, Neural-symbolic integration, Membrane computing 1. Introduction In the last years, the scientific community has paid more and more attention to artificial neural networks due to the doubtless success of such devices in many real-world problems.

Everything started from a conversation with a friend who one day during lunch told me "How stupid and limited Siri is. I ask her a question, and she gave me an answer. And it is so boring that I stopped using her for searching stuff. I use her only for weather and other shortcuts." "Why would you say so? Technology came a long way nowadays. You have the entire world in a metal container" I told him.

Artificial intelligence research has a lot to learn from nature. My work links biology with computation every day, but recently the rest of the world was reminded of the connection: The 2018 Nobel Prize in Chemistry went to Frances Arnold together with George Smith and Gregory Winter for developing major breakthroughs that are collectively called "directed evolution." One of its uses is to improve protein functions, making them better catalysts in biofuel production. Another use is entirely outside chemistry – outside even the traditional life sciences. That might sound surprising, but many research findings have very broad implications.

In September, after one of the most violent wildfire seasons in California history, Gov. Jerry Brown signed a bill that would allow utility companies to charge customers for future legal settlements from the 2017 wildfires--even if they were the utility company's fault. It's meant to prevent Pacific Gas & Electric Co. from going bankrupt; as the AP reported, the company would have to pay billions of dollars if investigators found the company's equipment at fault for sparking the Tubbs Fire that killed 22 people in Santa Rose and made thousands more homeless. Consumer advocates have criticized it as a bailout, with utilities having to augment their efforts to reduce the risk of fires. Furthermore, the law requires to pay the entire cost of accidental fires if they fail to properly maintain electrical transmission stations and distribution power lines. After yet another devastating summer of flames raging through California's parched forests, utility executives are forced to figure out one thing: How can they limit the danger of electrical transmission equipment and distribution power lines igniting vegetation and sparking yet another devastating wildfire?

The future of the oil and gas industry is not as bleak as many are predicting it to be, with technology set to play a major role in how the industry is set to evolve, said Abdul Nasser Al Mughairbi, group SVP of digital at Adnoc. Speaking in a conference session at the 38th Gitex Technology Week exhibition, he noted that the speed of technology development has never been faster or its potential impact as powerful. "We don't see oil as ending anytime soon; we might not use it as an energy source much in the future, but oil and gas will continue to play an indispensable role in fuelling global energy demand," he said. He added: "Big data is going to be the foundation of all our future digital transformation strategies. Our studies show that 80 per cent of oil and gas data will be unstructured by 2020. We see opportunities created by new technologies everywhere, ranging from optimising our production capacity to enhancing our drilling performance."

Abstract-- Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN subnetworks on separate data subsets, and (b) an inner-layer parallel training for each subnetwork. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneousaware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy. Index Terms--Big data, bi-layered parallel computing, convolutional neural networks, deep learning, distributed computing. Convolutional Neural Network (CNN) algorithm is an important branch of DL.

Although developments in the field of artificial intelligence began around the 1950s, its capacities have significantly increased in the recent years. Owing to factors such as the development of faster computers, availability of open-source software and the access to vast amounts of computational data, AI has now branched into machine learning (ML), probabilistic predictions, chaos theory and evolutionary computation. Investing in artificial intelligence courses, therefore, prepares people to undertake not just one, but many AI applications. AI and ML already play a pivotal role in our day-to-day life, even without our realization. Netflix and Amazon use machine-learning algorithms to recommend shows and products based on our usage history.

Reolink has effectively filled a void with its Argus line of affordable, no-frills, wireless indoor/outdoor security cameras. The Argus Pro is its latest offering, but it's not a premium model as you might expect given the name. In fact, it lacks a few features of the original Argus cameras--though you likely won't miss them--and, at just $100, it actually costs a bit less. Having reviewed the Argus 2, using the Argus Pro was a case of deja vu all over again. The cameras have virtually identical features: 1080p live streaming and recording, a 130-degree field of view, two-way audio, and PIR motion detection.

In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power control is employed. Due to the presence of interference, determining the optimal power control is a non-convex problem but can be solved via monotonic optimization framework. However, monotonic optimization is an iterative algorithm and can often entail considerable computational complexity, making it not suitable for real-time applications. To address this, we propose a learning-based approach that treats the input and output of a resource allocation algorithm as an unknown nonlinear mapping and a deep neural network (DNN) is employed to learn this mapping. This learned mapping via DNN can provide the optimal power level quickly for given channel conditions.