International Journal of Reasoning-based Intelligent Systems (9 papers in press)
A Metaphorical Situation Annotation Framework
by Mehdi Fesharaki, Abdolhamid Fetanat, Mehdi Amjadi
Abstract: Recent advances in cognitive sciences show that metaphorical thinking plays an essential role in human cognition. Metaphors are basic building blocks that construct a human minds understanding of the world. However, formal models of cognition, intelligence and even natural language have mostly ignored metaphorical thinking. Consequently, formal processing machines and intelligent agents developed based on them cannot process metaphors and thus, cannot use human minds metaphorical capabilities. There are formal models of metaphorical reasoning mainly developed in psychology that can be used as the basis for metaphorical intelligent systems. These models describe metaphorical reasoning as a process comprised of two logically distinct phases: metaphorical structure mapping and metaphorical knowledge transfer. They even propose simple algorithms for implementing metaphorical reasoning. In this paper, we propose a formal framework that provides tools and methods for a metaphorical communication between human and machine agents in a situation awareness environment, based on the formal models of metaphorical reasoning. The framework leaves the first phase of metaphorical reasoning to human minds and the second phase to machine agents. Then, it utilizes the metaphorical semantic tags as the mediators between the two phases of metaphor processing. Therefore, the metaphorical tags enable the sharing of metaphorical wisdom of human agents with machine agents. The framework implements metaphorical tags and their processing schemes based on common semantic technologies and thus, is an easily implementable extension to existing semantic applications.
Keywords: Metaphorical Reasoning; semantic web; situation awareness; tag-based annotation; human-computer interaction.
Analysis of IDS Alerts by Generalizing Features and Discovering Emerging Patterns
by Mahdi Maleki, Seyed Mansour Shahidi
Abstract: One of the significant problems in using intrusion detection systems is the high volume of low-level Alerts. In this paper, an appropriate analysis of cyber alerts has been used to reduce low-level alerts utilizing a range of available features of attacks. It has also benefited from the discovery of emerging patterns to improve situational awareness in cyber attacks. Moving to different levels of generalization and extraction of rules; based on Attribute-Oriented Induction and emerging patterns is a remarkable achievement of this. To evaluate the proposed method, a new CICIDS2017 database is used to eliminate the defects of the previous datasets. The results show a decrease in alerts at the rate of 99% at the lowest generalization level and an average of 25% at other generalization levels. In addition to normal traffic, 14 different types of attacks have been identified. The Dos Hulk attack has the highest frequency with 8.16%, and the Heartbleed attack having the lowest frequency with 0.0004% frequency. On average, 18 overlap (TO-EP) pattern, 63 relatively subsumption-overlap pattern (SO-EP), and 92 similar (SIM-EP) pattern have been extracted at four generalization levels.
Keywords: Intrusion Detection System; Feature Generalization; Multidimensional Data Mining; OLAP; Multistage Attacks.
Many-valued tableau calculi for decision logic based on approximation regions in VPRS
by Yotaro Nakayama, Seiki Akama, Tetsuya Murai
Abstract: Rough sets theory is studied to manage uncertain and inconsistent information. While the Pawlak's decision logic of rough sets is based on classical two-valued logic, this causes inconvenience for the various reasoning. In this paper, we propose many-valued logics, especially a three-valued logic, as the deduction system for the decision logic of rough sets. To enhance the decision logic from classical bivalent logic to three-valued logic, we adopt variable precision rough set (VPRS). As a deductive basis for three-valued decision logic, we define a consequence relation based on three-valued semantics to constructing a deduction system with the semantic tableau. We show to deal with two types of the third value of three-valued semantics one is unknown, and the other is inconsistent using Belnap's four-valued interpretation.
Keywords: many-valued logic; tableau calculi; decision logic; variable precision rough set; VPRS; knowledge representation.
A Bayesian network approach to handle uncertainty in Web Ontology Language
by Sonika Malik, Sarika Jain
Abstract: In information management systems ontology play a vital role. An ontological application should include a mechanism for handling uncertainty. Ontological innovations are to become the web's future in the coming period, but there are still some features such as exceptions, vulnerability and default values missing. Ontological languages such as OWL and RDF are by necessity distinct in nature, so ambiguous details cannot be treated. In this research, article uncertainty is handled in the ontology by Bayesian network. A probabilistic model of uncertainty available in the knowledge base is the Bayesian network. The Bayesian network is a conceptual model that is ideal for the representation and analysis of ambiguity and information found in data. The probability of uncertainty can be used for many real-life scenarios in the knowledge-base. We also introduced defaults and exceptions along with uncertainties to enhance performance and improve the OWL features. The source code is then translated to a jar-file with maven and it can be used in Protégé itself.
Keywords: ontology; uncertainty; Bayesian network; unit of knowledge.
Special Issue on: Intelligent Information Technologies and Agriculture
Frost forecast - a practice of machine learning from data
by Liya Ding, Yosuke Tamura, Kosuke Noborio, Kazuki Shibuya
Abstract: Among the efforts in frost forecast using machine learning techniques, a well-adopted method is to first apply time series forecast for the lowest temperature at future time points, such as the next a few days, and then apply predictive model to predict the event of frost at these time points using corresponding temperature forecasted. According to the domain understanding, there exists some 'cause-effect' between environment factors, including temperature and others, and the occurrence of frost in a few hours' period. A new modelling concept has been proposed by Ding et al. to capture such cause-effect. Preliminary experiments showed encouraging results with a sample of minute-level sensor data collected in Ikuta campus of Meiji University. In this article, as a continuation of the work, we shall further discuss methods of modelling, including causal models and associative models, and propose a framework of hybrid system in supporting frost forecast of short-term (e.g., a few hours) as well as that of relatively longer periods (e.g., a few days). More experiments are provided, and the issues of performance evaluation are discussed.
Keywords: frost forecast; machine learning; prediction; time series forecasting; cause-effect.
Development of IoT-based smart agriculture monitoring system for red radish plants production
by Ari Aharari, Chunsheng Yang
Abstract: The world population is increasing at a fast rate, and as results need for food is also growing briskly. The traditional method of agriculture is not sufficient enough to cover the needs of the market. On the other hand, the aging of agricultural workers has progressed rapidly, and the successor problem is becoming more serious. Under such circumstances are coming out also new farmers that will help the beginner to agriculture. However, the establishment of farming technology has become a significant management challenge for new farmers. In this paper, we focused on automation in agriculture by applying IoT technologies. The proposed system is utilising to monitor the environmental information during the experiment of producing the red radish. The sensor data is analysed to find the relation between ecological parameters and the growth results. The result of the proposed system was satisfactory as the first step in much deep measure development.
Keywords: smart agriculture; internet of things; red radish.
Special Issue on: ICEST'19 Reasoning in Engineering Systems Natural and Artificial Intelligence
Low-cost energy-efficient air quality monitoring system using sensor network
by Mare Srbinovska, Aleksandra Krkoleva Mateska, Vesna Andova, Maja Celeska Krstevska, Tomislav Kartalov
Abstract: The air pollution has a significant impact on human's health and global environment. The air quality significantly decreased over the past few years. One of the methods for air pollution reduction is by installing green walls, green roofs or by implementing green buildings in urban areas as plants have capabilities to absorb the particulate matter through their leaves. The main goals of this paper are to present system for air quality monitoring using sensor network technology that can be easily deployed in polluted areas and to examine the influence of the experimental green wall setup to particulate matter more precisely PM10 and PM2.5 concentrations in Skopje, Republic of North Macedonia. Furthermore, the paper presents the preliminary results of the ongoing experiment developed to evaluate the impact of green walls in reduction of air polluting particles' concentrations. The air quality monitoring system can be easily replicated on other locations in the urban areas of Skopje.
Keywords: air quality monitoring system; green walls; sensor network; particulate matter.
A comparative performance analysis of different machine learning techniques for SNR prediction in microcell and picocell wireless environment
by Nikola Sekulović, Miloš Stojanović, Aleksandra Panajotović, Miloš Banđur
Abstract: Knowledge of propagation channel conditions enables adaptive data transmission which improves the quality and efficiency of communication system. Wireless channels are characterised by highly dynamic time-varying nature. This means that information regarding propagation channel condition obtained by channel estimation can become outdated because of delay caused by processing and feedback phases. In fast fading environments, prediction of channel based on channel states in previous moments can ensure timely information. In this paper, a comparative performance analysis of an echo state network (ESN), an extreme learning machine (ELM) and least squares support vector machines (LS-SVM) for prediction of wireless channel conditions for single-input single-output (SISO) systems in microcellular and picocellular environments is carried out. Normalised mean squared error (NMSE) and time consumption are used as performance indicators. The experimental results on measured values for signal-to-noise ratio (SNR) show that all models have better and comparable prediction accuracy in microcell environment, while prediction framework based on the ESN outperforms the others in picocell environment.
Keywords: channel prediction; echo state network; extreme learning machines; ELMs; least squares support vector machines; LS-SVMs; microcellular environment; picocellular environment.
Use of infrared radiometry in temperature measurement of plant leaf
by Hristo I. Hristov, Kalin L. Dimitrov, Stanyo V. Kolev
Abstract: Through our present work we will show the importance of infrared radiometry in conducting various plant studies. We will look at the factors that affect temperature measurements and their significance. We will analyse how changing the distance between a radiometer and an object of study affects the heat flux entering the radiometer aperture from an object, and then how changing the distance between them affects the total remaining heat flux entering the radiometer aperture. We will analyse these processes in different temperatures and with different surface areas of the object of study. We will observe the change of the flux entering from the object of study and the change in the total remaining heat flux entering the radiometer aperture, when the scene is made of insulation material. We will draw conclusions about the significance of the distance between the thermal camera and the object of study.
Keywords: infrared radiometry; infrared thermography; agriculture; solid angle; thermal radiation; emission coefficient; surface temperature; plants.