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We claim that computing forms of consensus among several agents about their solutions to past problems can play a useful pre-treatment role in case-based reasoning. Intuitively, we define a consensus as a subset of the plain accumulation of all the agents' individual past discovered solutions such that every agent can agree on all the information in this subset. A consensus can be expected to form...
As the notion of context-awareness evolves in different paradigms, the development of context-aware systems involves several processes. These processes include, inter alia, context modeling and reasoning as well as adaptation. In [1], we presented an approach that consists of context modeling and reasoning hand in hand based on relational concept analysis and descriptive logic, respectively. An essential...
In this work, given the context of the driver, of the vehicle and of the environment, our objective is to correctly recognize the traffic situation and provide the driver with the corresponding assistance by providing notification or alert about the situation or the infraction that was committed, or acting directly on the vehicle. To do so, we need to consider the signal processing related to these...
Over the course of Dave Hall's career, he highlighted various concerns associated with the implementation of data fusion methods. Many of the issues included the role of uncertainty in data fusion, practical implementation of sensor fusion systems, and incorporating new technology into information fusion designs. These thoughts were communicated through technical books and Handbook collections of...
This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe...
Case-Based Reasoning (CBR) interests the scientific community, whom are concerned with scalability in knowledge representation and processing. CBR systems scale far better than rule-based systems. Rule-based systems are limited by the need to know the rules of engagement, which is practically unobtainable. The work presented in this paper pertains to knowledge generalization based on randomization...
Situation identification has become a major issue for situation-aware ubiquitous learning environments. The identification process aims to infer learner's situation by aggregating detected context information pieces. Most recent situation identification approaches are basically focused on crisp ontological modelling and reasoning. Given the fact that crisp ontology is not able to deal with context...
Fuzzy information granulation is key to many applications. Fuzzy Inference Systems (FIS) are an important technology in control systems and reasoning. ANFIS, FIS trained with Adaptive Networks, are a promising machine learning method to tackle the so-called data deluge. The heterogeneity and lack of quality control in big-data, however, lead to inhomogeneous, dynamically changing, non-partitioning...
There are numerous automated strategies in the context of decision making, each with their own strengths and weaknesses. However, the general utility of a decision support systems relates to its expressive capabilities regarding human-like reasoning. In the ongoing effort to bolster decision support, this work aims to enrich fuzzy integrals with the power to model partial absorption operators. Starting...
A lot of IT workers suffer from stress in doing their work and there are a few counselors to help them. To cope with this problem, an enhanced context respectful counseling agent is proposed. This counseling agent extracts emotional words from clients' utterances throughout their dialogue to detect their changes and provides clients with such changes as dialogue summary. If no change is detected,...
In many applications domains, time modeling has a fundamental role. Allen temporal relations are one of the most used and known formalisms for modeling and handling temporal data. However, classical Allen relations deal only with crisp time information, but time is often subjective and fuzzy. This paper discusses a disjunctive view of temporal relations between fuzzy time intervals. This approach...
Algorithmic composition is the process of creating musical material by means of formal methods. As a consequence of its design, algorithmic composition systems are (explicitly or implicitly) described in terms of parameters. Thus, parameter space exploration plays a key role in learning the system's capabilities. However, in the computer music field, this task has received little attention. This is...
In this paper a preferential extension of the Halpern-Shoham logic (HS) is proposed. The proposed extension (PHS) may be exploited for a representation of both temporal relations and preferences, which adds some “rationality” for temporal reasoning. The next, a new interval-based fibred semantics will be proposed for some subsystem of PHS with temporal modalities restricted to 〈L〉-operator, denoted...
For the matching relation between the logistics cloud service and logistics task in cloud logistics systems, the description method of logistics cloud service based on context was proposed. Firstly, build the concept model of logistics cloud service, then designed cloud logistics domain context ontology and analyzed the description of logistics cloud service by introducing the definition of context,...
Nowadays, online social network are becoming more, more an integral part of our daily life. Users often spend a large amount of time in their profile, updating their status, interacting with other members of the network, hence a massive amount of content are posted over years by users which may include sensitive information are still available online which raises new challenges to data privacy. Specifying...
Context modeling restricts the development of context awareness. Most of existing context modeling techniques are closely related to the applications, which limits reusing of contextaware computing systems. Context Space Modeling(CSM) is not application-specified, but it ignores the time information in the context cannot accurate to represent the dynamic contexts. Address the above problems, the Context...
The use of computers and the Internet have changed classic methods of teaching and Learning, introducing the concept of distance Learning as a great opportunity for studying unfettered by constraints of time and space [1]. In addition, acquisition of new skills and knowledge is not only affected by an individual's mental schemes or beliefs, but also by their interaction, cooperation and collaboration...
Adaptations need to be considered at design-time (adapting complex systems to new technologies, reengineering due to new regulations etc.), but also during runtime (e.g. new emerging functional and non-functional requirement, context-specific decisions). Objective: I use SPLs as a strategy for coping with uncertainty and adapting to change, where conventionally change occurs in the requirements of...
Pervasive computing vision considers the growing concentration of small computers merged into everyday objects to enhance environment with added-value services. In this vision computing systems are numerous, heterogeneous, and dynamic, yet seamlessly integrated. The complexity of pervasive environments can lead to conflicts between systems, but it can also be a strength if systems manage to collaborate...
Urban search and rescue (USAR) missions can benefit a great deal from teams of mobile robots endowed with advanced perception capabilities. To effectively collaborate with humans, these robots should have situation awareness about their robotic and human teammates, for intuitive decision making. Moreover, robots should be able to contextually share information so that humans can benefit from augmented...
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