INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Wide area detection system: Conceptual design study

An integrated sensor for traffic surveillance on mainline sections of urban freeways is described. Applicable imaging and processor technology is surveyed and the functional requirements for the sensors and the conceptual design of the breadboard sensors are given. Parameters measured by the sensors include lane density, speed, and volume. The freeway image is also used for incident diagnosis

Lying to identity: analysis of latencies from interviews.

openDetecting liars of personal identities is becoming an increasingly important goal. However, an obstacle to this endeavor is that deceivers can prepare a "lie script" prior to investigative interviews, producing narratives that are indistinguishable from those of truth tellers. To overcome this limitation, specific interview techniques have been developed that pose cognitive disadvantages for deceivers, such as including unexpected questions alongside control and expected questions. Unexpected questions can be considered a "rehearsal averting strategy" since liars cannot anticipate and prepare responses in advance. Consequently, when confronted with unexpected questions, liars are compelled to generate an immediate deceptive statement, inhibit the truth, and replace it with a fabricated narrative, while ensuring that the deception remains undetectable to the interviewer. This process of information reconstruction leads to increased response times and error rates for unexpected questions. Even truth tellers will experience an increase in cognitive load when responding to unexpected questions, but their responses, based on genuine memory traces, will be more comparable. The purpose of this study is to assess whether it is possible to discriminate between identity liars and truth tellers by analyzing response times and errors obtained from face-to-face interviews that implement unexpected questions.Detecting liars of personal identities is becoming an increasingly important goal. However, an obstacle to this endeavor is that deceivers can prepare a "lie script" prior to investigative interviews, producing narratives that are indistinguishable from those of truth tellers. To overcome this limitation, specific interview techniques have been developed that pose cognitive disadvantages for deceivers, such as including unexpected questions alongside control and expected questions. Unexpected questions can be considered a "rehearsal averting strategy" since liars cannot anticipate and prepare responses in advance. Consequently, when confronted with unexpected questions, liars are compelled to generate an immediate deceptive statement, inhibit the truth, and replace it with a fabricated narrative, while ensuring that the deception remains undetectable to the interviewer. This process of information reconstruction leads to increased response times and error rates for unexpected questions. Even truth tellers will experience an increase in cognitive load when responding to unexpected questions, but their responses, based on genuine memory traces, will be more comparable. The purpose of this study is to assess whether it is possible to discriminate between identity liars and truth tellers by analyzing response times and errors obtained from face-to-face interviews that implement unexpected questions

Investigation of low-cost infrared sensing for intelligent deployment of occupant restraints

In automotive transport, airbags and seatbelts are effective at restraining the driver and passenger in the event of a crash, with statistics showing a dramatic reduction in the number of casualties from road crashes. However, statistics also show that a small number of these people have been injured or even killed from striking the airbag, and that the elderly and small children are especially at risk of airbag-related injury. This is the result of the fact that in-car restraint systems were designed for the average male at an average speed of 50 km/hr, and people outside these norms are at risk. Therefore one of the future safety goals of the car manufacturers is to deploy sensors that would gain more information about the driver or passenger of their cars in order to tailor the safety systems specifically for that person, and this is the goal of this project. This thesis describes a novel approach to occupant detection, position measurement and monitoring using a low-cost thermal imaging based system, which is a departure from traditional video camera-based systems, and at an affordable price. Experiments were carried out using a specially designed test rig and a car driving simulator with members of the public. Results have shown that the thermal imager can detect a human in a car cabin mock up and provide crucial real-time position data, which could be used to support intelligent restraint deployment. Other valuable information has been detected such as whether the driver is smoking, drinking a hot or cold drink, using a mobile phone, which can help to infer the level of driver attentiveness or engagement

Visually Guided Control of Movement

The papers given at an intensive, three-week workshop on visually guided control of movement are presented. The participants were researchers from academia, industry, and government, with backgrounds in visual perception, control theory, and rotorcraft operations. The papers included invited lectures and preliminary reports of research initiated during the workshop. Three major topics are addressed: extraction of environmental structure from motion; perception and control of self motion; and spatial orientation. Each topic is considered from both theoretical and applied perspectives. Implications for control and display are suggested

Who Controls the Past Controls the Future - Life Annotation in Principle and Practice

The fields of the Semantic Web and Ubiquitous Computing are both relatively new fields within the discipline of Computer Science. Yet both are growing and have begun to overlap as people demand ever-smaller computers with persistent access to the internet. The Semantic Web has the potential to become a global knowledge store duplicating the information on the Web, albeit in a machine-readable form. Such a knowledge base combined with truly ubiquitous systems could provide a great benefit for humans. But what of personal knowledge? Information is generally of more use when linked to other information. Sometimes this information must be kept private, so integrating personal knowledge with the Semantic Web is not desirable. Instead, it should be possible for a computer system to collect and store private knowledge while also being able to augment it with public knowledge from the Web, all without the need for user effort. This thesis begins with a review of both fields, indicating the points at which they overlap. It describes the need for semantic annotation and various processes through which it may be achieved. A method for annotating a human's life using a combination of personal data collected using an ubiquitous system and public data freely available on the Semantic Web is suggested and conceptually compared to human memory. Context-aware computing is described along with its potential to annotate the life of a human being and the hypothesis that today's technology is able to carry out this task is presented. The work then introduces a portable system for automatically logging contextual data and describes a study which used this system to gather life annotations on one specific individual over the course of two years. The implementation of the system and its use is documented and the data collected is presented and evaluated. Finally the thesis offers the conclusion that one type of contextual data is not enough to answer most questions and that multiple forms of data need to be merged in order to get a useful picture of a person's life. The thesis concludes with a brief look into the future of the Semantic Web and how it has the potential to assist in achieving better results in this field of study

Intelligent automotive thermal comfort control

Mobility has become a substantial part in our society. Since we spend a lot of our available time on the road, we expect the automotive environment to provide similar comfort levels than residential buildings. Within this context, this research thesis especially focuses on automotive thermal comfort control. The automotive cabin is a very special environment, which is characterized by extreme inhomogeneity and overall transient behavior. Thermal comfort is a very vague and a very subjective term, which depends on physiological and psychological variables. Theories for thermal comfort in transient environments have not been fully established yet and researchers are still busy with its investigation. At present, automotive industry relies on extensive thermal comfort models, manikins and powerful simulation tools to assess and control thermal comfort. This thesis studies the application of artificial intelligence and proposes a blackbox approach which aims for extracting thermal comfort knowledge directly from human's interaction with the HVAC controls. This methodology avoids the use of human physiological and psychological thermal comfort models and does not require any a-priori knowledge. A novel comfort acquisition tool has been developed and has been integrated into a research vehicle in order to gather the required data for system learning. Data has been collected during spring, autumn and summer conditions in Southern Africa. Methods of data mining have been applied and an intelligent implementation using artificial neural networks has been proposed. The achieved results are promising and allow for about 87 perecent correct classification. It is concluded that methods of artificial intelligence perform well and are far superior compared to conventional approaches. These methods can be used as a powerful tool for the development process of vehicle air-conditioning controls and have great potential for time and cost reduction

30th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems. The amount and complexity of information itself, the number of abstraction levels of information, and the size of databases and knowledge bases are continuously growing. Conceptual modelling is one of the sub-areas of information modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers