Semantic image segmentation is one of fastest growing areas in computer vision with a variety of applications. In many areas, such as robotics and autonomous vehicles, semantic image segmentation is crucial, since it provides the necessary context for actions to be taken based on a scene understanding at the pixel level. Moreover, the success of medical diagnosis and treatment relies on the extremely accurate understanding of the data under consideration and semantic image segmentation is one of the important tools in many cases. Recent developments in deep learning have provided a host of tools to tackle this problem efficiently and with increased accuracy. This work provides a comprehensive analysis of state-of-the-art deep learning architectures in image segmentation and, more importantly, an extensive list of techniques to achieve fast inference and computational efficiency. The origins of these techniques as well as their strengths and trade-offs are discussed with an in-depth analysis of their impact in the area. The best-performing architectures are summarized with a list of methods used to achieve these state-of-the-art results.

The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over $T$ time periods with an \emph{unknown} demand function of posted price and personalized information. At each time $t$, the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of \emph{anticipating} $(\varepsilon, \delta)$-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for $d$-dimensional personalized information, our algorithm achieves the expected regret at the order of $\tilde{O}(\varepsilon^{-1} \sqrt{d^3 T})$, when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $\tilde{O}(\sqrt{d^2T} + \varepsilon^{-2} d^2)$

Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various applications. This paper reviews and comments on the past, present and future of normalization methods in the context of DNN training. We provide a unified picture of the main motivation behind different approaches from the perspective of optimization, and present a taxonomy for understanding the similarities and differences between them. Specifically, we decompose the pipeline of the most representative normalizing activation methods into three components: the normalization area partitioning, normalization operation and normalization representation recovery. In doing so, we provide insight for designing new normalization technique. Finally, we discuss the current progress in understanding normalization methods, and provide a comprehensive review of the applications of normalization for particular tasks, in which it can effectively solve the key issues.

While traditional methods have focused on parametric models informed by domain expertise - such as autoregressive (AR) [6], exponential smoothing [7, 8] or structural time series models [9] - modern machine learning methods provide a means to learn temporal dynamics in a purely data-driven manner [10]. With the increasing data availability and computing power in recent times, machine learning has become a vital part of the next generation of time series forecasting models. Deep learning in particular has gained popularity in recent times, inspired by notable achievements in image classification [11], natural language processing [12] and reinforcement learning [13]. By incorporating bespoke architectural assumptions - or inductive biases [14] - that reflect the nuances of underlying datasets, deep neural networks are able to learn complex data representations [15], which alleviates the need for manual feature engineering and model design. The availability of open-source backpropagation frameworks [16, 17] has also simplified the network training, allowing for the customisation for network components and loss functions.

The Artificial Intelligence (AI) In Fintech Market report predicts promising growth and development during the period 2020-2027. The Artificial Intelligence (AI) In Fintech Market survey report represents vital statistical data represented in an organized format such as graphs, charts, tables, and figures to provide a detailed understanding of the Artificial Intelligence (AI) In Fintech Market in a simple manner. The report covers an in-depth analysis of the Artificial Intelligence (AI) In Fintech market and offers key insights on current and emerging trends, market drivers, and market insights offered by industry experts. The report examines the impact of COVID-19 on market growth. The study provides comprehensive coverage of the impact of the COVID-19 pandemic on the Artificial Intelligence (AI) In Fintech market and its key segments.

Learning embeddings from large-scale networks is an open challenge. Despite the overwhelming number of existing methods, is is unclear how to exploit network structure in a way that generalizes easily to unseen nodes, edges or graphs. In this work, we look at the problem of finding inductive network embeddings in large networks without domain-dependent node/edge attributes. We propose to use a set of basic predefined local encodings as the basis of a learning algorithm. In particular, we consider the degree frequencies at different distances from a node, which can be computed efficiently for relatively short distances and a large number of nodes. Interestingly, the resulting embeddings generalize well across unseen or distant regions in the network, both in unsupervised settings, when combined with language model learning, as well as in supervised tasks, when used as additional features in a neural network. Despite its simplicity, this method achieves state-of-the-art performance in tasks such as role detection, link prediction and node classification, and represents an inductive network embedding method directly applicable to large unattributed networks.

Tackling the information retrieval gap between non-technical database end-users and those with the knowledge of formal query languages has been an interesting area of data management and analytics research. The use of natural language interfaces to query information from databases offers the opportunity to bridge the communication challenges between end-users and systems that use formal query languages. Previous research efforts mainly focused on developing structured query interfaces to relational databases. However, the evolution of unstructured big data such as text, images, and video has exposed the limitations of traditional structured query interfaces. While the existing web search tools prove the popularity and usability of natural language query, they return complete documents and web pages instead of focused query responses and are not applicable to database systems. This paper reports our study on the design and development of a natural language query interface to a backend relational database. The novelty in the study lies in defining a graph database as a middle layer to store necessary metadata needed to transform a natural language query into structured query language that can be executed on backend databases. We implemented and evaluated our approach using a restaurant dataset. The translation results for some sample queries yielded a 90% accuracy rate.

This book comprehensively presents a novel approach to the systematic security hardening of software design models expressed in the standard UML language. It combines model-driven engineering and the aspect-oriented paradigm to integrate security practices into the early phases of the software development process. To this end, a UML profile has been developed for the specification of security hardening aspects on UML diagrams. In addition, a weaving framework, with the underlying theoretical foundations, has been designed for the systematic injection of security aspects into UML models. The work is organized as follows: chapter 1 presents an introduction to software security, model-driven engineering, UML and aspect-oriented technologies.

Nvidia's GeForce RTX 3090 delivers exhilarating graphics prowess--the fastest possible gaming frame rates at extreme resolutions, and outstanding performance in professional applications. At $1,500, it's either a hard pass or a no-brainer, depending on how you plan to use it. You can read every nitty-gritty detail in our comprehensive review of Nvidia's GeForce RTX 3090 Founders Edition. But if you don't feel like sifting through thousands of words of technical and testing details, here are the five key things you need to know. Yes, the GeForce RTX 3090 offers the "ultimate gaming experience" that Nvidia promised.

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.