Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

Towards AI-governance in psychosocial care: A systematic literature review analysis

With increased digitalization and e-government services, Artificial Intelligence (AI) gained momentum. This paper focuses on AI-governance in Child Social Care field, exploring how aspects of individual, family/community factors are embedded in organizational level, especially when dealing with children resilience and wellbeing. A three-level based review has been conducted. In the first part we explored the interlink between individual factors associated to either resilience or wellbeing are connected to community and governance level where a new conceptual model is provided. In the second phase, we conducted an in-depth systematic literature review using PRISMA review protocol where new categorizations of identified literature with respect to individual, family and community levels in child social care field were suggested, while in the third phase, a review of relevant AI-initiatives in Europe and USA was performed. Finally, a comprehensive discussion of the literature review outcomes was carried out and a new updated conceptual model was provided.© 2023 The Author(s). Published by Elsevier Ltd on behalf of Prof JinHyo Joseph Yun. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Learning Community Group Concept Mapping: Fall 2014 Outreach and Recruitment, Spring 2015 Case Management and Service Delivery. Final Reports

Beginning in 2014, the Federal Government provided funding to New York State as part of an initiative to improve services that lead to sustainable outcomes for youth receiving Supplemental Security Income (SSI) benefits. As part of the NYS PROMISE initiative, Concept Systems, Inc. worked with the Learning Community to develop learning needs frameworks using the Group Concept Mapping methodology (GCM). This GCM project gathers, aggregates, and integrates the specific knowledge and opinions of the Learning Community members and allows for their guidance and involvement in supporting NYS PROMISE as a viable community of practice. This work also increases the responsiveness of NYS PROMISE to the Learning Community members’ needs by inspiring discussion during the semi-annual in-person meetings. As of the end of year two, two GCM projects have been completed with the PROMISE Learning Community. These projects focused on Outreach and Recruitment and Case Management and Service Delivery. This report discusses the data collection method and participation in both GCM projects, as well as providing graphics, statistical reports, and a summary of the analysis. In this report we refer to the Fall 2014 project as Project 1, and the Spring 2015 project as Project 2

Facility Location Decision for Global Entrepreneurial Small-to-Medium Enterprises Using Similarity Coefficient-based Clustering Algorithms

Decisions on location selection are critical for the survival of small-to-medium entrepreneurial organizations from the time they are established until later stages of operation and expansion. The selection of location for small and medium entrepreneurial businesses requires a selection strategy that incorporates relevant factors, quantifies these factors and develops a methodology that analyzes data for better decision-making. In the era of globalization where borders have become easier to transcend, many small ventures tend to choose more attractive international markets as a potential location for their operations where they can obtain higher returns on their investment. Thus, significant changes in the location decision process of the small and medium entrepreneurial companies have received great attention in the literature about small firms with global orientation as a response to the international entrepreneurship phenomenon. Therefore, consideration should be given to factors and attributes that reinforce the appeal of the international market to new businesses. These factors and attributes will provide the decision maker with an effective methodology for data analysis that will provide a framework for decision-making in the selection of locations for the entrepreneurial organization. In this research, the most frequent and critical attributes to select the best location for the entrepreneurial firms (globally) are extracted from relevant literature. Then, a similarity-based cluster analysis approach is introduced to quantify these attributes based on the existing data of economic metrics, such as technological advancement, expenditures on education, expenditures on research and development, the quality of the labor force, unemployment rates, domestic competitiveness, etc. Subsequently, the resulting outcomes are used to identify groups of prospective sites that fit the needs of the entrepreneurial firm. Last, the validity of the adopted methodology will be tested via numerical examples

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn

Machine learning of molecular motifs in soft supramolecular systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network