Designing Adaptive Spatio-temporal Information Systems for Natural Hazard Risks with ASTIS

Adaptive on-line information systems are able to adapt or to personalize the hyper-document overall structure and content, the navigation help and the layout according to a user’s model. SWAN aims at designing an adaptive web server for on-line information systems about nautical publications. According to a task model, concept filtering and spatial filtering are used to select the relevant information space and to sort its content.KeywordsTask ModelInformation UnitAdaptive User InterfaceInformation ViewInformation Retrieval TaskThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Nowadays the safety of computer networks becomes more important than other characteristics. The services which are used by a huge number of users entice hacker. Especially, online payment services are often the main target of malicious software. In the article, the computer virus SpyEye spread via Internet resources is considered. The Petri-object model of propagation process is created with the use of Petri-object simulation software written in Java. The model reproduces the events of damaging website, infecting user’s resource, sending information about vulnerabilities and sending personal data. It was suggested that users are connected when they use the same website. The investigation of the spreading time for different kinds of users’ connections topology is presented. It was considered not only well-known star and chain topologies but also their combinations and the topology with random connections. Simulation results confirm the virus spreading time dependence on user’s connections topology.

The integration of Artificial Intelligence (AI) into Information Systems (IS) design is significantly reshaping traditional development processes, introducing automation, intelligent decision-making, and advanced data analysis capabilities. This systematic review explores the current landscape of AI-driven IS design, focusing on key AI techniques—such as machine learning, natural language processing, and generative models—that are increasingly applied across various stages of system development. The paper examines how these AI technologies are enhancing requirement engineering, system modeling, and process optimization. It also evaluates the benefits of AI in improving system efficiency, decision-making, and user experiences, while addressing challenges such as data quality, technical expertise, and ethical concerns. Finally, the review looks toward the future of AI in IS design, highlighting emerging trends such as low-code platforms and explainable AI. The findings emphasize the need for interdisciplinary collaboration and the development of transparent, responsible AI frameworks to fully realize the potential of intelligent, adaptive, and user-centric information systems.

Clustering of search results has been shown to be advantageous over the simple list presentation of search results. However, in most clustering interfaces, the clusters are not adaptive to a user’s interaction with the clustering results, and the important question “how to optimize the benefit of a clustering interface for a user” has not been well addressed in the previous work. In this paper, we study how to exploit a user’s clickthrough information to adaptively reorganize the clustering results and help a user find the relevant information more quickly. We propose four strategies for adapting clustering results based on user actions. We propose a general method to simulate different kinds of users and linearize the cluster results so that we can compute regular retrieval measures. The simulation experiments show that the adaptation strategies have different performance for different types of users; in particular, they are effective for “smart users” who can correctly recognize the best clusters, but not effective for “dummy users” who follow system’s ranking of results. We further conduct a user study on one of the four adaptive clustering strategies to see if an adaptive clustering system using such a strategy can bring users better search experience than a static clustering system. The results show that there is generally no significant difference between the two systems from a user’s perspective.

Adaptive Information Systems (AdIS) are systems responsive to environmental changes or changes in a ship's systems. In this paper the potential of shipboard AdIS to decrease an officer's excessive workload are examined. The workload of the Officer Of the Watch (OOW) consists of tasks being initiated by the OOW and by external inputs. Sometimes the external inputs, particularly those requiring low priority actions, actually distract the OOW and increase the workload. Consequently an overload may be reduced by delaying low priority information, thus delaying the actions they could initiate. To estimate the applicability of AdIS, a model has been developed using a discrete event simulation software, consisting of three main modules: environment, AdIS and the OOW. The simulation has been run with a traffic environment comparable to those existing in the Dover Strait. A comparison between the OOW workload with and without AdIS has been estimated, indicating that during demanding navigation AdIS can significantly reduce the overload time. In areas similar to the Dover Strait the overload time can be reduced by a third.

The problem of automation of the processes of information transmission and processing in adaptive information systems is considered. An analysis of existing approaches to solving this problem showed the prospects of using neural network technologies. A neural network method for processing and transmitting information in adaptive information systems is formulated. The method includes a formalized description of a neural network data channel - a software tool for analysis, data processing and selection of data transfer protocol. The main stages of the proposed method are outlined: classification of the structures of the source data, their transformation, data processing, selection of the necessary protocol for transmitting information. Each of the stages is implemented through neural networks of various architectures. The theoretical rationale of the possibility of using the neural network method is given, obtained on the basis of the proof of a number of theorems. The novelty of the proposed method consists in the transition from an analytical solution of the problems of classification, processing and data transfer to an automated approach using machine learning technologies. The practical significance of the neural network method is to reduce the complexity of the implementation of information processing and transmission processes, to increase the level of automation in the organization of intermodular interaction. The implementation of the neural network method has been assessed using a number of software complexity assessment metrics. The application, virtues and failings of the developed method are analyzed.

Impact of wind farms on disturbance detection and classification in distributed generation using modified Adaline network and an adaptive neuro-fuzzy information system

On the field of battle, a continual flow of information is necessary to achieve information superiority. The required information potentially originates from numerous information producers. Unfortunately, due to dynamic and often dangerous conditions, these information producers can be incapacitated or destroyed. To be successful, a battlefield information system must provide a continuous flow of information and thus adaptability and fault tolerance are key features. In this paper, we show how we build adaptive and fault tolerance information systems using an organizational model. We introduce our organizational model and provide details pertaining to its use in the development of a battlefield information system.

Content in collaborative systems, such as Online Social Networks, is often co-owned by multiple users with different privacy expectations, leading to possible multiuser privacy conflicts. In order to address these conflicts, we argue that users should be supported by trustworthy agents that adhere to the following criteria: (i) concealment of privacy preferences, such that only necessary information is shared with others; (ii) equity of treatment, such that different kinds of users are supported equally; (iii) collaboration of users, such that a group of users can support each other in agreement and (iv) explainability of actions, such that users know why certain information about them was shared to reach a decision.

The following paper describes the implementation of three different controllers for a wheelchair. It was built for improving people's life that could have disabilities associated with functions regarding muscle strength, power of muscle of all limbs, tone of muscle of all limbs and endurance of all muscles of the body. Using three programmed methods that allow the user to have full control over the wheelchair. By acquiring voltage signals generated in eye's movements and using Neuro-Fuzzy Networks for differentiating each one, the wheelchair is able of being controlled by eye's movements. Figuring out about different kinds of user's needs and environments, the wheelchair includes voice control in spaces where noise levels are less than 40dB. The voice commands are detected using the Speech Recognition software which is a default program installed on Microsoft Windows operative system. The eye and voice controls were designed using LabVIEW and the intelligent control toolkit (ICTL). There were developed virtual simulators on C# code that enable the patient to have a previous training for having a better wheelchair control and performance. The system was tested on a person with cerebral palsy.

The nature of explanation dialogues is examined and an expert system that provides acceptable and appropriate answers and explanations to the questions posed by different kinds of users is designed. An empirical study and analysis of natural dialogues between experts, novices and partial experts is described. From this analysis a theory of explanation dialogues (extended schema-based theory, or EST) is developed. In EST questions are interpreted by combining information from different, semantically related question types that together best capture the essence and meaning of the question. This theory is applied to the design of an architecture and computational model for interpreting questions and generating explanations. The expert system, EXPLAIN, understands the nature of the question and is able to take account of the previous dialogue. The system can tailor its responses to an individual user's characteristics, including level of expertise and depth of knowledge in the domain. >

The long-tail phenomenon tells us that there are many items in the tail. However, not all tail items are the same. Each item acquires different kinds of users. Some items are loved by the general public, while some items are consumed by eccentric fans. In this paper, we propose a novel metric, item eccentricity, to incorporate this difference between consumers of the items. Eccentric items are defined as items that are consumed by eccentric users. We used this metric to analyze two real-world datasets of music and movies and observed the characteristics of items in terms of eccentricity. The results showed that our defined eccentricity of an item does not change much over time, and classified eccentric and noneccentric items present significantly distinct characteristics. The proposed metric effectively separates the eccentric and noneccentric items mixed in the tail, which could not be done with the previous measures, which only consider the popularity of items.

Today it is not easy to imagine what would be the future of user experience (UX) design in the world of Artificial Intelligence (AI). Referring to the sector of autonomous vehicles the paper aims to explore the changes that will be brought by artificial intelligence to the innovative sector of the self-driving car. According to these transformation car interiors and passengers experiences will become very different from the actual one. The aim of the research is, starting from different kind of passengers needs (Human Centered Design approach) individuating essential factors to design autonomous cars interiors and in particular to design innovative interfaces for a better communication with the passengers and a high level of living experience for different kind of users.

Different categories of drinking houses, it is claimed, are historically related to different kinds of users and usages. Industrialism, however, entrenched the ubiquitous pub amidst the song, sin and insobriety of the poor and the new working-class urban proletariat. The public house thus became a focus of social freedoms and sexual licence, as well as a locus of more formal elements of rational recreation. Yet the public house was subject to social controls of a ferocious kind and not simply licensing constraints, and some discussion is developed in examining the class basis of recreation and leisure controls. Attention finally turns to viewing the public house as an intermediate context in a relational sense, in an historical sense and in a time sense, and one deserving further sociological attention. Historically, different types of public drinking houses have generally developed out of distinct categories of social usage. The inn developed from the coaching tradition of providing refreshment and lodgings for travellers. Such houses tended over time to foster recreation and itinerant entertainers and to be a focus of not just wealthy travellers but plyers of wares and trades-but always perhaps within the context of a dominant use by the relatively wealthy. Such drinking houses catered for catholic drinking tastes-spirits, wines and beers. The taverns were originally concerned with selling wine, the availability of wine itself being boosted by the cultivation of vines in the medieval monasteries. Such taverns, by the late eighteenth century, were virtually indistinguishable from inns and met the needs of the casual drinker, catering for a wide range of users. The alehouse was the traditional village drinking house; it had none of the accommodation or the formal entertainment patterns of the inn. It was effectively the local community drinking and meeting place and was

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times.