ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based commonsense knowledge representation by capturing semantic facets like locations and purposes, and composite concepts, i.e., subgroups and related aspects of subjects. In this demo, we present a web portal that allows users to understand its construction process, explore its content, and observe its impact in the use case of question answering. The demo website and an introductory video are both available online.

Approximation fixpoint theory (AFT) provides an algebraic framework for the study of fixpoints of operators on bilattices and has found its applications in characterizing semantics for various classes of logic programs and nonmonotonic languages. In this paper, we show one more application of this kind: the alternating fixpoint operator by Knorr et al. for the study of the well-founded semantics for hybrid MKNF knowledge bases is in fact an approximator of AFT in disguise, which, thanks to the power of abstraction of AFT, characterizes not only the well-founded semantics but also two-valued as well as three-valued semantics for hybrid MKNF knowledge bases. Furthermore, we show an improved approximator for these knowledge bases, of which the least stable fixpoint is information richer than the one formulated from Knorr et al.'s construction. This leads to an improved computation for the well-founded semantics. This work is built on an extension of AFT that supports consistent as well as inconsistent pairs in the induced product bilattice, to deal with inconsistencies that arise in the context of hybrid MKNF knowledge bases. This part of the work can be considered generalizing the original AFT from symmetric approximators to arbitrary approximators.

Artificial Intelligence Platforms use machines to execute various tasks that can be done by human beings also. These machines can simulate the cognitive functions of human minds at ease. Some of these cognitive functions performed by artificial intelligence are learning, problem-solving capability, reasoning, etc. Moreover, general intelligence and social intelligence can be a part of Artificial Intelligence programming also. The application of Artificial Intelligence (AI) platforms includes the mechanisms of a different expert system like speech recognition ability, natural language processing, and understanding capability, machine vision, etc. These AI platforms can solve uncommon tasks and reduces the workload of human beings efficiently.

The constant increase in the amount and complexity of information obtained from IT data networkelements, for its correct monitoring and management, is a reality. The same happens to data net-works in electrical systems that provide effective supervision and control of substations and hydro-electric plants. Contributing to this fact is the growing number of installations and new environmentsmonitored by such data networks and the constant evolution of the technologies involved. This sit-uation potentially leads to incomplete and/or contradictory data, issues that must be addressed inorder to maintain a good level of monitoring and, consequently, management of these systems. Inthis paper, a prototype of an expert system is developed to monitor the status of equipment of datanetworks in electrical systems, which deals with inconsistencies without trivialising the inferences.This is accomplished in the context of the remote control of hydroelectric plants and substationsby a Regional Operation Centre (ROC). The expert system is developed with algorithms definedupon a combination of Fuzzy logic and Paraconsistent Annotated Logic with Annotation of TwoValues (PAL2v) in order to analyse uncertain signals and generate the operating conditions (faulty,normal, unstable or inconsistent / indeterminate) of the equipment that are identified as importantfor the remote control of hydroelectric plants and substations. A prototype of this expert systemwas installed on a virtualised server with CLP500 software (from the EFACEC manufacturer) thatwas applied to investigate scenarios consisting of a Regional (Brazilian) Operation Centre, with aGeneric Substation and a Generic Hydroelectric Plant, representing a remote control environment.

Machine learning applications have become ubiquitous. Their applications from machine embedded control in production over process optimization in diverse areas (e.g., traffic, finance, sciences) to direct user interactions like advertising and recommendations. This has led to an increased effort of making machine learning trustworthy. Explainable and fair AI have already matured. They address knowledgeable users and application engineers. However, there are users that want to deploy a learned model in a similar way as their washing machine. These stakeholders do not want to spend time understanding the model. Instead, they want to rely on guaranteed properties. What are the relevant properties? How can they be expressed to stakeholders without presupposing machine learning knowledge? How can they be guaranteed for a certain implementation of a model? These questions move far beyond the current state-of-the-art and we want to address them here. We propose a unified framework that certifies learning methods via care labels. They are easy to understand and draw inspiration from well-known certificates like textile labels or property cards of electronic devices. Our framework considers both, the machine learning theory and a given implementation. We test the implementation's compliance with theoretical properties and bounds. In this paper, we illustrate care labels by a prototype implementation of a certification suite for a selection of probabilistic graphical models.

Artificial intelligence, commonly referred to as AI, represents both a risk and a benefit to the security of society, according to Bruce Schneier, security technologist, researcher, and lecturer at Harvard Kennedy School. Schneier made his remarks about the risks of AI in an afternoon keynote session at the 2021 RSA Conference on May 17. Hacking for Schneier isn't an action that is evil by definition; rather, it's about subverting a system or a set of rules in a way that is unanticipated or unwanted by a system's designers. "All systems of rules can be hacked," Schneier said. "Even the best-thought-out sets of rules will be incomplete or inconsistent, you'll have ambiguities and things that designers haven't thought of, and as long as there are people who want to subvert the goals in a system, there will be hacks." Schneier highlighted a key challenge with hacking that is conducted by some form of AI: it might be difficult to detect.

Machine learning techniques are gaining attention in the context of intrusion detection due to the increasing amounts of data generated by monitoring tools, as well as the sophistication displayed by attackers in hiding their activity. However, existing methods often exhibit important limitations in terms of the quantity and relevance of the generated alerts. Recently, knowledge graphs are finding application in the cybersecurity domain, showing the potential to alleviate some of these drawbacks thanks to their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies. We discuss the application of machine learning on knowledge graphs for intrusion detection and experimentally evaluate a link-prediction method for scoring anomalous activity in industrial systems. After initial unsupervised training, the proposed method is shown to produce intuitively well-calibrated and interpretable alerts in a diverse range of scenarios, hinting at the potential benefits of relational machine learning on knowledge graphs for intrusion detection purposes.

In the meantime, a wide variety of terminologies, motivations, approaches and evaluation criteria have been developed within the scope of research on explainable artificial intelligence (XAI). Many taxonomies can be found in the literature, each with a different focus, but also showing many points of overlap. In this paper, we summarize the most cited and current taxonomies in a meta-analysis in order to highlight the essential aspects of the state-of-the-art in XAI. We also present and add terminologies as well as concepts from a large number of survey articles on the topic. Last but not least, we illustrate concepts from the higher-level taxonomy with more than 50 example methods, which we categorize accordingly, thus providing a wide-ranging overview of aspects of XAI and paving the way for use case-appropriate as well as context-specific subsequent research.

A lot of relevant works have been completed to provided different kinds of method to properly handle information offered which promotes the development of information industry. The representatives of the corresponding theories are soft theory [1-5], Z-numbers [6-9], D-numbers [10-14], fuzzy theory [15-18], Dempster-Shafer evidence theory [19-23] and some other mixed theories [24-26]. And the effectiveness of these theories are verified in many practical applications, like risk evaluation [27-29], pattern classification [30], optimization [31-34] and decision making [35-38]. Moreover, due to the rapid progress of quantum computing, some researchers come up with the idea that traditional information management can be transferred to the level of quantum. Some meaningful works about the topic are complex mass function [39-43] and quantum information theory [44-47]. In this paper, the proposed method is based on the quantum model of mass function [47]. In order to avoid the deviation which may caused by the original quantum evidences, a dual check system is designed to ensure the authenticity of the original judgments which utilizes the concept of Z-number [9]. Besides, because of the introduction of the time interval, a specially devised rule is proposed to appropriately decide the importance of different relationships of incidents, which is a kind of expert system under some restrictions. The contributions of the proposed method can be listed as: (1) The second dual check system can help avoid the deviation produced by the original evidences to help provide more effective results.

We introduce an NLP toolkit based on object-oriented knowledge base and multi-level grammar base. This toolkit focuses on semantic parsing, it also has abilities to discover new knowledge and grammar automatically, new discovered knowledge and grammar will be identified by human, and will be used to update the knowledge base and grammar base. This process can be iterated many times to improve the toolkit continuously.