Visual analysis of sensor logs in smart spaces: Activities vs. situations

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions. The proposed pipeline is employed to automatically extract models to be reused for ambient intelligence. In this paper, we present an user evaluation aimed at demonstrating the effectiveness of the approach, by comparing it wrt. a relevant state-of-the-art visual tool, namely SITUVIS

A Spatio-Temporal Framework for Managing Archeological Data

Space and time are two important characteristics of data in many domains. This is particularly true in the archaeological context where informa- tion concerning the discovery location of objects allows one to derive important relations between findings of a specific survey or even of different surveys, and time aspects extend from the excavation time, to the dating of archaeological objects. In recent years, several attempts have been performed to develop a spatio-temporal information system tailored for archaeological data. The first aim of this paper is to propose a model, called Star, for repre- senting spatio-temporal data in archaeology. In particular, since in this domain dates are often subjective, estimated and imprecise, Star has to incorporate such vague representation by using fuzzy dates and fuzzy relationships among them. Moreover, besides to the topological relations, another kind of spatial relations is particularly useful in archeology: the stratigraphic ones. There- fore, this paper defines a set of rules for deriving temporal knowledge from the topological and stratigraphic relations existing between two findings. Finally, considering the process through which objects are usually manually dated by archeologists, some existing automatic reasoning techniques may be success- fully applied to guide such process. For this purpose, the last contribution regards the translation of archaeological temporal data into a Fuzzy Temporal Constraint Network for checking the overall data consistency and reducing the vagueness of some dates based on their relationships with other ones

Aspects of dealing with imperfect data in temporal databases

In reality, some objects or concepts have properties with a time-variant or time-related nature. Modelling these kinds of objects or concepts in a (relational) database schema is possible, but time-variant and time-related attributes have an impact on the consistency of the entire database. Therefore, temporal database models have been proposed to deal with this. Time itself can be at the source of imprecision, vagueness and uncertainty, since existing time measuring devices are inherently imperfect. Accordingly, human beings manage time using temporal indications and temporal notions, which may contain imprecision, vagueness and uncertainty. However, the imperfection in human-used temporal indications is supported by human interpretation, whereas information systems need extraordinary support for this. Several proposals for dealing with such imperfections when modelling temporal aspects exist. Some of these proposals consider the basis of the system to be the conversion of the specificity of temporal notions between used temporal expressions. Other proposals consider the temporal indications in the used temporal expressions to be the source of imperfection. In this chapter, an overview is given, concerning the basic concepts and issues related to the modelling of time as such or in (relational) database models and the imperfections that may arise during or as a result of this modelling. Next to this, a novel and currently researched technique for handling some of these imperfections is presented

ARF : an Automated Real-Time Fuzzy Logic Threat Evaluation System.

Intrusion Detection has emerged as a powerful component of network security systems. A wide range of hardware and software components exist to meet most basic security needs on all platforms. These systems log system usage that could be considered as a breach of security in many networks. However, signature based intrusion detection systems have one catastrophic downfall, in that the number of alerts being logged can quickly outgrow the amount of resources necessary to investigate this anomalous behavior. This thesis explores the use of a fuzzy logic based analysis engine that gives an overall threat level of an intrusion detection sensor, prioritizing alerts that are the most threatening. This application gives security personnel a launching point to determine where security holes exist and a snapshot of the threats that exist in a system. The fuzzy logic system is based on a set of membership functions that define certain metrics from an alert dataset and a set of rules that determine a threat level based on the defined metrics. This application functions as a proof of concept prototype for an administrative tool that can analyze multiple sensors across multiple networks and give a reasonable output of the threat level across a series of intrusion detection sensors on a network. Initial testing indicates promising performance results for testing the threat level of a remote sensor using this methodology

Tracking Data Provenance of Archaeological Temporal Information in Presence of Uncertainty

The interpretation process is one of the main tasks performed by archaeologists who, starting from ground data about evidences and findings, incrementally derive knowledge about ancient objects or events. Very often more than one archaeologist contributes in different time instants to discover details about the same finding and thus, it is important to keep track of history and provenance of the overall knowledge discovery process. To this aim, we propose a model and a set of derivation rules for tracking and refining data provenance during the archaeological interpretation process. In particular, among all the possible interpretation activities, we concentrate on the one concerning the dating that archaeologists perform to assign one or more time intervals to a finding to define its lifespan on the temporal axis. In this context, we propose a framework to represent and derive updated provenance data about temporal information after the mentioned derivation process. Archaeological data, and in particular their temporal dimension, are typically vague, since many different interpretations can coexist, thus, we will use Fuzzy Logic to assign a degree of confidence to values and Fuzzy Temporal Constraint Networks to model relationships between dating of different findings represented as a graph-based dataset. The derivation rules used to infer more precise temporal intervals are enriched to manage also provenance information and their following updates after a derivation step. A MapReduce version of the path consistency algorithm is also proposed to improve the efficiency of the refining process on big graph-based datasets

Opportunities and Challenges of Applying Artificial Intelligence in the Financial Sectors and Startups during the Coronavirus Outbreak

Purpose: The main goal of this article is the comprehensive study of the applications of artificial intelligence in financial sectors in addition to startups and its impacts on such cases along with Covid19. Methodology: we have tried to study the applications of artificial intelligence in different areas especially financial fields such as accounting, auditing, management, capital market, banking etc. On the other hand, we have studied the impacts of artificial intelligence on startups during Covid-19 too. Findings: The results showed that AI can be a powerful tool in financial fields such as investment advice, asset allocation, fraud detection, portfolio management and etc. and startups such as increasing production and productivity, time management, data management and analysis and etc. during the Covid-19 outbreaks and it can decrease the harmful effects of Coronavirus. Thus, timely actions can be taken. Originality/Value: The main contribution of this paper is a comprehensive and specialized look at the discussion of the applications of artificial intelligence in the field of finance as well as startups during Covid19. We have tried to consider subjects and contents which cover most of the paper

Development of the Availability Concept by Using Fuzzy Theory with AHP Correction, a Case Study: Bulldozers in the Open-Pit Lignite Mine

Availability is one of the most used terms in maintainability engineering. This concept is used to denote: The quality of service of an engineering system, i.e., machines, weak points' analysis, asset management, as well as making decisions in the process of life cycle management. Availability is an overall indicator and contains partial indicators that are oriented towards reliability, maintenance, and logistical support. Availability presents a variable value and changes in time and space. Usually, availability is shown as the coefficient of time use of the machine. This approach is not good enough because it does not go into the structure of the availability itself and requires a high level of IT support in system monitoring. In this sense, this paper will use the fuzzy theory and the corresponding analytic hierarchy process (AHP) multi-criteria analysis to present a conceptual and mathematical model for the assessment of availability based on expert judgment. The model will be shown in the case study (on the example) of bulldozers working in the open-pit lignite mine