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.

This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a method that combines a knowledge-based approach and a reinforcement learning (RL) technique into one method for intelligent problem solving. The proposed approach focuses on multi-expert knowledge acquisition, with the reinforcement learning being applied as a conflict resolution strategy aimed at integrating the knowledge of multiple exerts into one knowledge base. The article describes the KB-RL approach in detail and applies the reported method to one of the most challenging problems of current Artificial Intelligence (AI) research, namely playing a strategy game. The results show that the KB-RL system is able to play and complete the full FreeCiv game, and to win against the computer players in various game settings. Moreover, with more games played, the system improves the gameplay by shortening the number of rounds that it takes to win the game. Overall, the reported experiment supports the idea that, based on human knowledge and empowered by reinforcement learning, the KB-RL system can deliver a strong solution to the complex, multi-strategic problems, and, mainly, to improve the solution with increased experience.

Prior work has demonstrated that question classification (QC), recognizing the problem domain of a question, can help answer it more accurately. However, developing strong QC algorithms has been hindered by the limited size and complexity of annotated data available. To address this, we present the largest challenge dataset for QC, containing 7,787 science exam questions paired with detailed classification labels from a fine-grained hierarchical taxonomy of 406 problem domains. We then show that a BERT-based model trained on this dataset achieves a large (+0.12 MAP) gain compared with previous methods, while also achieving state-of-the-art performance on benchmark open-domain and biomedical QC datasets. Finally, we show that using this model's predictions of question topic significantly improves the accuracy of a question answering system by +1.7% P@1, with substantial future gains possible as QC performance improves.

Natural language understanding (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through the QA task and focus on the aspects that make it a challenge for the current state-of-the-art technology. This thesis is organized into three main parts: In the first part, we explore multiple formalisms to improve existing machine comprehension systems. We propose a formulation for abductive reasoning in natural language and show its effectiveness, especially in domains with limited training data. Additionally, to help reasoning systems cope with irrelevant or redundant information, we create a supervised approach to learn and detect the essential terms in questions. In the second part, we propose two new challenge datasets. In particular, we create two datasets of natural language questions where (i) the first one requires reasoning over multiple sentences; (ii) the second one requires temporal common sense reasoning. We hope that the two proposed datasets will motivate the field to address more complex problems. In the final part, we present the first formal framework for multi-step reasoning algorithms, in the presence of a few important properties of language use, such as incompleteness, ambiguity, etc. We apply this framework to prove fundamental limitations for reasoning algorithms. These theoretical results provide extra intuition into the existing empirical evidence in the field.

With the increasing complexity of CPSs, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the MAB-EX framework for building self-explainable systems that leverage requirements- and explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet un-explainable behavior be detected by the system.