Virtually all verification techniques using formal methods rely on the availability of a formal specification, which describes the design requirements precisely. However, formulating specifications remains a manual task that is notoriously challenging and error-prone. To address this bottleneck in formal verification, recent research has thus focussed on automatically generating specifications for formal verification from examples of (desired and undesired) system behavior. In this survey, we list and compare recent advances in mining specifications in Linear Temporal Logic (LTL), the de facto standard specification language for reactive systems. Several approaches have been designed for learning LTL formulas, which address different aspects and settings of specification design. Moreover, the approaches rely on a diverse range of techniques such as constraint solving, neural network training, enumerative search, etc. We survey the current state-of-the-art techniques and compare them for the convenience of the formal methods practitioners.

It therefore takes seriously constraints on a theory of meaning coming from the cognitive structure of human concepts, from the need to learn words, and from the connection between meaning, perception, action, and nonlinguistic thought. The theory treats meanings, like phonological structures, as articulated into substructures or tiers: a division into an algebraic Conceptual Structure and a geometric/ topological Spatial Structure; a division of the former into Propositional Structure and Information Structure; and possibly a division of Propositional Structure into a descriptive tier and a referential tier. All of these structures contribute to word, phrase, and sentence meanings. The ontology of Conceptual Semantics is richer than in most approaches, including not only individuals and events but also locations, trajectories, manners, distances, and other basic categories. Word meanings are decomposed into functions and features, but some of the features and connectives among them do not lend themselves to standard definitions in terms of necessary and sufficient conditions.

Sentiment analysis is an important tool to automatically understand the user-generated content on the Web. The most fine-grained sentiment analysis is concerned with the extraction and sentiment classification of aspects and has been extensively studied in recent years. In this work, we provide an overview of the first step in aspect-based sentiment analysis that assumes the extraction of opinion targets or aspects. We define a taxonomy for the extraction of aspects and present the most relevant works accordingly, with a focus on the most recent state-of-the-art methods. The three main classes we use to classify the methods designed for the detection of aspects are pattern-based, machine learning, and deep learning methods.

The Serbian language is a Slavic language spoken by over 12 million speakers and well understood by over 15 million people. In the area of natural language processing, it can be considered a low-resourced language. Also, Serbian is considered a high-inflectional language. The combination of many word inflections and low availability of language resources makes natural language processing of Serbian challenging. Nevertheless, over the past three decades, there have been a number of initiatives to develop resources and methods for natural language processing of Serbian, ranging from developing a corpus of free text from books and the internet, annotated corpora for classification and named entity recognition tasks to various methods and models performing these tasks.

Federated Learning (FL) is an efficient distributed machine learning paradigm that employs private datasets in a privacy-preserving manner. The main challenges of FL is that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID). Due to limited communication bandwidth and unstable availability of such devices in a mobile network, only a fraction of end devices (also referred to as the participants or clients in a FL process) can be selected in each round. Hence, it is of paramount importance to utilize an efficient participant selection scheme to maximize the performance of FL including final model accuracy and training time. In this paper, we provide a review of participant selection techniques for FL.

Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on “braintumor” website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.

AI is seeping into different industries, slowly remolding the global competitive landscape. However, most business leaders still don't know how machine intelligence will impact their businesses. EY recently published a brief, which focuses the current state of AI. We interviewed Nigel Duffy, EY Global Innovation AI leader who co-authored the document with Chris Mazzei, EY Global Innovation Technologies Leader and Global Chief Analytics Officer. The brief frames the current state of AI well: "Most organizations aren't exploiting the potential of AI; they are just at the beginnings of their AI journeys.

If you missed that thread and there's something you want to mention, post it and I'll put it up. Lots of categories didn't have an entry. You can also make a category yourself. "and we all realized what a pain in the ass Tensorflow was and how it didn't need to be that way. In the academic community, it certainly to me feels like pytorch has become the dominant framework (probably not backed up by actual stats...

Recently, Tokyo terminated its traditional visual-identification work, which had been used for 20 years, and shifted to a new automated system. This article introduces the Fixed Assets Change Judgment (FACJ) system and its core tool, RealScape. RealScape automatically detects changes in the height and color of buildings based on three-dimensional analysis of aerial photographs. It employs a unique pixelby-pixel stereo processing method and enables a foot-level precision for each building. RealScape automatically detects changes in the height and color of buildings based on three-dimensional analysis of aerial photographs. The three-dimensional analysis employs a pixel-by-pixel stereo processing method that calculates the height of each pixel in aerial photographs and thus enables precise difference detection between previous and current aerial photographs. Since then, it has been used at its tax bureau every year to calculate the municipality's fixed-asset tax. After the success in Tokyo, other major city governments, including Osaka and Sapporo, have followed suit. The Japanese fixed-property tax is imposed by municipalities on the owners of land, buildings, and depreciation assets (all hereinafter referred to as "fixed assets") on January 1 of every year by calculating the tax sum according to current asset values.

This article reports on an extensive survey and analysis of research work related to machine learning as it applies to automated planning over the past 30 years. Major research contributions are broadly characterized by learning method and then descriptive subcategories. Survey results reveal learning techniques that have extensively been applied and a number that have received scant attention. We extend the survey analysis to suggest promising avenues for future research in learning based on both previous experience and current needs in the planning community. Within the AI research community, machine learning is viewed as a potentially powerful means of endowing an agent with greater autonomy and flexibility, often compensating for the designer's incomplete knowledge of the world that the agent will face and incurring low overhead in terms of human oversight and control.