Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

The Critical Role of Public Charging Infrastructure

Editors: Peter Fox-Penner, PhD, Z. Justin Ren, PhD, David O. JermainA decade after the launch of the contemporary global electric vehicle (EV) market, most cities face a major challenge preparing for rising EV demand. Some cities, and the leaders who shape them, are meeting and even leading demand for EV infrastructure. This book aggregates deep, groundbreaking research in the areas of urban EV deployment for city managers, private developers, urban planners, and utilities who want to understand and lead change

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

24th International Conference on Information Modelling and Knowledge Bases

In the last three decades information modelling and knowledge bases have become essentially important subjects not only in academic communities related to information systems and computer science but also in the business area where information technology is applied. The series of European – Japanese Conference on Information Modelling and Knowledge Bases (EJC) originally started as a co-operation initiative between Japan and Finland in 1982. The practical operations were then organised by professor Ohsuga in Japan and professors Hannu Kangassalo and Hannu Jaakkola in Finland (Nordic countries). Geographical scope has expanded to cover Europe and also other countries. Workshop characteristic - discussion, enough time for presentations and limited number of participants (50) / papers (30) - is typical for the conference. Suggested topics include, but are not limited to: 1. Conceptual modelling: Modelling and specification languages; Domain-specific conceptual modelling; Concepts, concept theories and ontologies; Conceptual modelling of large and heterogeneous systems; Conceptual modelling of spatial, temporal and biological data; Methods for developing, validating and communicating conceptual models. 2. Knowledge and information modelling and discovery: Knowledge discovery, knowledge representation and knowledge management; Advanced data mining and analysis methods; Conceptions of knowledge and information; Modelling information requirements; Intelligent information systems; Information recognition and information modelling. 3. Linguistic modelling: Models of HCI; Information delivery to users; Intelligent informal querying; Linguistic foundation of information and knowledge; Fuzzy linguistic models; Philosophical and linguistic foundations of conceptual models. 4. Cross-cultural communication and social computing: Cross-cultural support systems; Integration, evolution and migration of systems; Collaborative societies; Multicultural web-based software systems; Intercultural collaboration and support systems; Social computing, behavioral modeling and prediction. 5. Environmental modelling and engineering: Environmental information systems (architecture); Spatial, temporal and observational information systems; Large-scale environmental systems; Collaborative knowledge base systems; Agent concepts and conceptualisation; Hazard prediction, prevention and steering systems. 6. Multimedia data modelling and systems: Modelling multimedia information and knowledge; Contentbased multimedia data management; Content-based multimedia retrieval; Privacy and context enhancing technologies; Semantics and pragmatics of multimedia data; Metadata for multimedia information systems. Overall we received 56 submissions. After careful evaluation, 16 papers have been selected as long paper, 17 papers as short papers, 5 papers as position papers, and 3 papers for presentation of perspective challenges. We thank all colleagues for their support of this issue of the EJC conference, especially the program committee, the organising committee, and the programme coordination team. The long and the short papers presented in the conference are revised after the conference and published in the Series of “Frontiers in Artificial Intelligence” by IOS Press (Amsterdam). The books “Information Modelling and Knowledge Bases” are edited by the Editing Committee of the conference. We believe that the conference will be productive and fruitful in the advance of research and application of information modelling and knowledge bases. Bernhard Thalheim Hannu Jaakkola Yasushi Kiyok

Flood Risk Assessment, Management and Perceptions in a Changing World

Floods are a global challenge that is increasing due to changes in climate and human populations. Using an interdisciplinary approach, this work contributes novel methodologies and knowledge to three key challenges associated with floods. The first chapter builds upon the atmospheric and statistical sciences to provide a general methodology for climate informed approaches to projecting long-term flood events based on large-scale ocean-atmospheric processes. The second chapter builds upon the engineering, decision analysis, and economics disciplines to integrate climate-informed projections with decision-scaling, a decision-making under uncertainty framework, to further flood risk management. The third chapter builds upon the social sciences to provide new knowledge on flood risk perception and mitigation in West Africa. The outcomes of this work contribute important advancements to addressing the clear and urgent need for solutions to floods around the world

A software classification scheme

Reusing code is one approach to software reusability. Code is the end product of the software lifecycle. It is delivered in a low level representation that is difficult to reuse unless an almost perfect match exists between available features and required specifications. There is a need to organize large inventories of software such that reusable code is easy to locate and exchange. The relative success in the reuse of code fragments reported by some software factories is due in part to their capacity to encapsulate domain specific functions and create specialized libraries of components classified by these locally standardized functions.A general software classification scheme that organizes reusability related attributes and common functions from different domains is proposed as a partial solution to the software reusability problem. For the problem of selecting from similar, potentially reusable. components, a partial solution based on evaluation of common characteristics is also proposed. A library system is presented that integrates the proposed classification scheme with an evaluation mechanism based on inherent component attributes, programming languages characteristics and reuser experience.The fundamental contribution of this dissertation is a formal treatment of a faceted scheme for software classification leading to better understanding of reusability at the code level. This approach has been prototyped in a library system for the semi-automatic classification of software components. Analysis were performed to evaluate the classification scheme. The results show the potential of the scheme in organizing collections of code fragments, in improving retrieval, and in simplifying the classification process. Tests of the evaluation mechanism showed positive correlation with evaluations conducted by potential reusers

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research