Artificial Intelligence is machine intelligence or ability to think and process information like natural human intelligence in order to create expert systems with human intelligence (reasoning, learning, and problem solving) with help from science and technology disciplines such as Mathematics, Engineering, Biology, Computer Science, Linguistics and Psychology. The term intelligence, literally, means the ability to acquire and apply knowledge and skills. The term Artificial Intelligence ( Artificial Intelligence) is pretty self-explanatory. It is the ability to acquire and apply knowledge and skills artificially. In 1956, a group of researchers from different disciplines of technology gathered for the summit called Dartmouth Summer Research Project.

ABSTRACT: This paper investigates the detection and diagnosis of brush seizing faults in the spindle positioning servo drive of a high-precision machining centre using a recently developed time–frequency pattern classification technique known as selective regional correlation (SRC). It is shown that SRC is capable of significantly enhancing the resolution of fault diagnosis when compared to conventional correlation-based techniques. The performance of this approach is evaluated using three time–frequency transformation techniques: the short-time Fourier transform (STFT), continuous wavelet transform (CWT) and S-Transform. In addition, three different 2D windows are used to isolate features for use with SRC: a rectangular (boxcar) window, a Gaussian window and a Kaiser window. The results have indicated that SRC is a promising tool for machine condition monitoring (MCM).

In coming years, the most intelligent organizations will need to blend technology-enabled insights with a sophisticated understanding of human judgment, reasoning, and choice. Those that do this successfully will have an advantage over their rivals. To succeed in the long run, businesses need to create and leverage some kind of sustainable competitive edge. This advantage can still derive from such traditional sources as scale-driven lower cost, proprietary intellectual property, highly motivated employees, or farsighted strategic leaders. But in the knowledge economy, strategic advantages will increasingly depend on a shared capacity to make superior judgments and choices. Intelligent enterprises today are being shaped by two distinct forces. The first is the growing power of computers and big data, which provide the foundation for operations research, forecasting models, and artificial intelligence (AI). The second is our growing understanding of human judgment, reasoning, and choice.

Formal query generation aims to generate correct executable queries for question answering over knowledge bases (KBs), given entity and relation linking results. Current approaches build universal paraphrasing or ranking models for the whole questions, which are likely to fail in generating queries for complex, long-tail questions. In this paper, we propose SubQG, a new query generation approach based on frequent query substructures, which helps rank the existing (but nonsignificant) query structures or build new query structures. Our experiments on two benchmark datasets show that our approach significantly outperforms the existing ones, especially for complex questions. Also, it achieves promising performance with limited training data and noisy entity/relation linking results. 1 Introduction Knowledge-based question answering (KBQA) aims to answer natural language questions over knowledge bases (KBs) such as DBpedia and Freebase. Formal query generation is an important component in many KBQA systems (Bao et al., 2016; Cui et al., 2017; Luo et al., 2018), especially for answering complex questions. Given entity and relation linking results, formal query generation aims to generate correct executable queries, e.g., SP ARQL queries, for the input natural language questions. An example question and its formal query are shown in Figure 1.

Encryption has always been a battle line in cyberspace. Attackers try to break it; defenders reinforce it. The next front in that struggle is something known as homomorphic encryption, which scrambles data not just when it is at rest or in transit, but when it is being used. The idea is to not have to decrypt sensitive financial or healthcare data, for example, in order to run computations with it. Defenders are trying to get ahead of attackers by locking down data wherever it lies.

Measuring of text complexity is a needed task in several domains and applications (such as NLP, semantic web, smart education and etc.). The Semantic layer of a text is more tacit than its syntactic structure and as a result, calculation of semantic complexity is more difficult. Whereas there are famous and powerful academic and commercial syntactic complexity measures, the problem of measuring Semantic complexity is a challenging one, yet. In this article, we introduce the DAST model which stands for Deciding About Semantic Complexity of a Text. In this model, an intuitionistic approach to semantics lets us have a well-defined definition for semantic of a text and its complexity: we consider semantic and meaning as a lattice of intuitions. Semantic complexity is defined as the result of a calculation on this lattice. A set theoretic formal definition of semantic complexity, as a 6-tuple formal system, is provided. By using this formal system, a method for measuring semantic complexity is presented. The evaluation of the proposed approach is done by a detailed example and a case study, a set of eighteen human-judgment experiments and a corpus-based evaluation. The results show that DAST model is capable of deciding about semantic complexity of a text. Furthermore, Analysis of the experiment results leads us to introduce a Markovian model for the process of common-sense multi-steps semantic-complexity reasoning in people. The Experiments-result demonstrates that our method consistently outperforms the random baseline in terms of better precision and accuracy.

A common representation of information about relations of objects and attributes in knowledge domains are data-tables. The structure of such information can be analysed using Formal Concept Analysis (FCA). Attribute exploration is a knowledge acquisition method from FCA that reveals dependencies in a set of attributes with help of a domain expert. However, in general no single expert is capable (time- and knowledge-wise) of exploring knowledge domains alone. Therefore it is important to develop methods that allow multiple experts to explore domains together. To this end we build upon results on representation of incomplete knowledge [2, 8-10], adapt the corresponding version of attribute exploration to fit the setting of multiple experts and suggest formalizations for key components like expert knowledge, interaction and collaboration strategy. Furthermore we discuss ways of comparing collaboration strategies and suggest avenues for future research.

Visual knowledge bases such as Visual Genome power numerous applications in computer vision, including visual question answering and captioning, but suffer from sparse, incomplete relationships. All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each. Hiring human annotators is expensive, and using textual knowledge base completion methods are incompatible with visual data. In this paper, we introduce a semi-supervised method that assigns probabilistic relationship labels to a large number of unlabeled images using few labeled examples. We analyze visual relationships to suggest two types of image-agnostic features that are used to generate noisy heuristics, whose outputs are aggregated using a factor graph-based generative model. With as few as 10 labeled examples per relationship, the generative model creates enough training data to train any existing state-of-the-art scene graph model. We demonstrate that our method outperforms all baseline approaches on scene graph prediction by5.16 recall@100 for PREDCLS. In our limited label setting, we define a complexity metric for relationships that serves as an indicator (R^2 = 0.778) for conditions under which our method succeeds over transfer learning, the de-facto approach for training with limited labels.

The palapes cadets are one of the uniform organizations in UiTM Perlis for extra-curricular activities. The palapes cadets arrange their organization in a hierarchy according to grade. Senior uniform officer (SUO) is the highest rank, followed by a junior uniform officer (JUO), sergeant, corporal, lance corporal, and lastly, cadet officer, which is the lowest rank. The palapes organization has several methods to measure performance toward promotion to a higher rank, whether individual performance or in a group. Cadets are selected for promotion based on demonstrated leadership abilities, acquired skills, physical fitness, and comprehension of information as measured through standardized testing. However, this method is too complicated when manually assessed by a trainer or coach. Therefore, this study will propose an expert system, which is one of the artificial intelligence techniques that can recognize the readiness and progression of a palapes cadet.

Learning embeddings of entities and relations existing in knowledge bases allows the discovery of hidden patterns in data. In this work, we examine the geometrical space's contribution to the task of knowledge base completion. We focus on the family of translational models, whose performance has been lagging, and propose a model, dubbed HyperKG, which exploits the hyperbolic space in order to better reflect the topological properties of knowledge bases. We investigate the type of regularities that our model can capture and we show that it is a prominent candidate for effectively representing a subset of Datalog rules. We empirically show, using a variety of link prediction datasets, that hyperbolic space allows to narrow down significantly the performance gap between translational and bilinear models.