Reasoning semantically in first-order logic is notoriously a challenge. This paper surveys a selection of semantically-guided or model-based methods that aim at meeting aspects of this challenge. For first-order logic we touch upon resolution-based methods, tableaux-based methods, DPLL-inspired methods, and we give a preview of a new method called SGGS, for Semantically-Guided Goal-Sensitive reasoning. For first-order theories we highlight hierarchical and locality-based methods, concluding with the recent Model-Constructing satisfiability calculus.

Feature selection is a crucial preprocessing step in data analytics and machine learning. Classical feature selection algorithms select features based on the correlations between predictive features and the class variable and do not attempt to capture causal relationships between them. It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system. Consequently, causality-based feature selection has gradually attracted greater attentions and many algorithms have been proposed. In this paper, we present a comprehensive review of recent advances in causality-based feature selection. To facilitate the development of new algorithms in the research area and make it easy for the comparisons between new methods and existing ones, we develop the first open-source package, called CausalFS, which consists of most of the representative causality-based feature selection algorithms (available at https://github.com/kuiy/CausalFS).

Intelligent software is replacing humans doing repetitive and routine machine-focused jobs such as switchboard operators, front-desk hotel assistants, typists, manufacturing production and packaging operatives. We are even starting to see robots take and deliver orders at restaurants, provide banking assistance, serve coffee and drive cars. Increasingly, artificial intelligence (AI) is encroaching into knowledge worker roles. In the legal sector, AI is carrying out routine contract review work. In the financial and insurance sectors, intelligent apps are creating new business models that replace many of the activities of sales representatives, by cross-selling to consumers through the app, for example.

ABSTRACT Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification have evolved enormously over the past few decades. Sketch recognitio n is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in curren t trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of se nsitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classificat ion models . But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match com posite sketches to human images which includes component - based representation approach, automatic composite sketch recognition technique etc. INTRODUCTION As per the researches carried out, a complete face recognition system includes two patterns of face detection and face recognition: 1) Structural similarity and 2) individual local differences of human faces. Therefore, it is required to extract the features of the face through the face detection process. The evolution of face recognition is due to its technical challenges and huge potential application in video surveillance, identity authorization, multimedia applications, home and office security, law enforcement and different human - computer interaction activities. Facial recognition technology (FRT) is one of the most controversial new tools. It was first devel oped in the 1960s.

In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task. Variants of GCN are used in multiple state-of-the-art approaches and therefore it is important to understand the specifics and limitations of GCN-based models. Despite serious efforts, we were not able to fully reproduce the results from the original paper and after a thorough audit of the code provided by authors, we concluded, that their implementation is different from the architecture described in the paper. In addition, several tricks are required to make the model work and some of them are not very intuitive. We provide an extensive ablation study to quantify the effects these tricks and changes of architecture have on final performance. Furthermore, we examine current evaluation approaches and systematize available benchmark datasets. We believe that people interested in KG matching might profit from our work, as well as novices entering the field

A survey of UK decision makers has found the interest and support for the use of robotic technology in the workplace has seen a notable increase since 2018, suggesting the use of artificial intelligence (AI) in office is set to grow. Conducted by software provider Advanced and detailed in its Annual Trends Survey for 2019-20, the survey focuses on perceptions around workplace robotic technology, including AI and robotic process automation (RPA). It found that more decision makers now see robotic technology as a benefit to the workplace, with 77% of the 1,000 decision makers surveyed now saying they would be happy to work alongside such technologies if it meant that manual processes were reduced. This is a significant jump from last year's survey, where the number stood at just 65%. AI is now also the technology that the most people would like to see in their daily lives, with 38% placing it as the top priority.

Mobile is now the first and primary channel for consumers to interact with various products and services. Gradual and consistent advancement in the technology industry has created a necessity for enterprises and businesses to incorporate various features that supports and enhances the usability for the next breed of mobile users. Next Generation applications may require a new, improved and innovative approach to development that also helps in the rapid and exponential growth of businesses. The latest technological trends like Artificial Intelligence, Internet of Things, AR / VR, and cloud-driven mobile app development have gained significant popularity in recent years. Because of that developers are more focused on leveraging these cutting-edge technologies by offering more robust and scalable next-generation mobile apps that exceed the standards of what businesses and customers expect.

Time series classification problems have drawn increasing attention in the machine learning and statistical community. Closely related is the field of functional data analysis (FDA): it refers to the range of problems that deal with the analysis of data that is continuously indexed over some domain. While often employing different methods, both fields strive to answer similar questions, a common example being classification or regression problems with functional covariates. We study methods from functional data analysis, such as functional generalized additive models, as well as functionality to concatenate (functional-) feature extraction or basis representations with traditional machine learning algorithms like support vector machines or classification trees. In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners. Additionally, we provide a software framework in R for functional data analysis for supervised learning, including machine learning and more linear approaches from statistics. This allows convenient access, and in connection with the machine-learning toolbox mlr, those methods can now also be tuned and benchmarked.

--Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair classifiers based on decision trees even though they have proven very efficient. In an up-to-date comparison of state-of- the-art classification algorithms in tabular data, tree boosting outperforms deep learning [1]. For this reason, we have developed a novel approach of adversarial gradient tree boosting. The objective of the algorithm is to predict the output Y with gradient tree boosting while minimizing the ability of an adversarial neural network to predict the sensitive attribute S . The approach incorporates at each iteration the gradient of the neural network directly in the gradient tree boosting. We empirically assess our approach on 4 popular data sets and compare against state-of- the-art algorithms. The results show that our algorithm achieves a higher accuracy while obtaining the same level of fairness, as measured using a set of different common fairness definitions. I NTRODUCTION Machine learning models are increasingly used in decision making processes. In many fields of application, they generally deliver superior performance compared with conventional, deterministic algorithms. However, those models are mostly black boxes which are hard, if not impossible, to interpret.

As deep learning models become more and more popular in real-world business applications and training datasets grow very large, machine learning (ML) infrastructure is becoming a critical issue in many companies. To help you stay aware of the latest research advances in ML infrastructure, we've summarized some of the most important research papers recently introduced in this area. As you read these summaries, you will be able to learn from the experience of the leading tech companies, including Google, Microsoft, and LinkedIn. The papers we've selected cover data labeling and data validation frameworks, different approaches to distributed training of ML models, a novel approach to tracking ML model performance in production, and more. If you'd like to skip around, here are the papers we've summarized: If these accessible AI research analyses & summaries are useful for you, you can subscribe to receive our regular industry updates below.