In this paper we describe our ongoing work on SustaInno; an open-source search repository of innovations related to sustainability. SustaInno utilizes advanced information retrieval and text processing methods on technical innovations (initially patent data) to provide its users with practical, applicable, and detailed solutions to their sustainability related challenges. For example, problems like urban heat islands and rainwater waste are of major concern to most urban cities. Using our repository, decision makers can get quite in-depth solutions on practical approaches to address these and many other problems. The novelty of our work stems from three main factors: (1) such a repository does not exist,(2) it is focused on sustainability innovations which are of great importance for the creation of sustainable living environment, and (3) it provides a set of open-source tools and open-access datasets that could accelerate the dissemination of knowledge about sustainability.

This paper uses natural language processing to create the first machine-coded democracy index, which I call Automated Democracy Scores (ADS). The ADS are based on 42 million news articles from 6,043 different sources and cover all independent countries in the 1993-2012 period. Unlike the democracy indices we have today the ADS are replicable and have standard errors small enough to actually distinguish between cases. The ADS are produced with supervised learning. Three approaches are tried: a) a combination of Latent Semantic Analysis and tree-based regression methods; b) a combination of Latent Dirichlet Allocation and tree-based regression methods; and c) the Wordscores algorithm. The Wordscores algorithm outperforms the alternatives, so it is the one on which the ADS are based. There is a web application where anyone can change the training set and see how the results change: democracy-scores.org

We present the Bayesian Echo Chamber, a new Bayesian generative model for social interaction data. By modeling the evolution of people's language usage over time, this model discovers latent influence relationships between them. Unlike previous work on inferring influence, which has primarily focused on simple temporal dynamics evidenced via turn-taking behavior, our model captures more nuanced influence relationships, evidenced via linguistic accommodation patterns in interaction content. The model, which is based on a discrete analog of the multivariate Hawkes process, permits a fully Bayesian inference algorithm. We validate our model's ability to discover latent influence patterns using transcripts of arguments heard by the US Supreme Court and the movie "12 Angry Men."

Dr. Lucien Mehl, born 1919 in Paris, studied at the University, Paris where he obtained his degrees in Philosophy and Law, and a Diploma of Advanced Studies in Political Economy and at the National School of Administration. He is now'Maitre des Requetesi to the Council of State and Director of external training at the National School of Administration. He is a member of the International Fiscal Association, the International Cybernetics Association and the French Operational Research Society. He has published a number of articles on administrative science, law, cybernetics and operational research. INTRODUCTION I. It may seem an ambitious step to try to apply mechanization or automation to the legal sciences. However, a machine for processing information can be an effective aid in searching for sources of legal information, in developing legal argument, in preparing the decision of the administrator or judge, and finally in checking the coherence of solutions arrived at.

Dr. Francois Paycha, born at Narbonne, studied medicine at the University of Montpellier. His first researches were concerned with the embryology of the eye, later using the distribution of radioactive phosphorus P32 to study the structure of the tissues and for the detection of tumours. He was then appointed to the National Centre of Scientific Research. While in charge of a hospital clinic, he noted the considerable differences in the diagnoses of conscientious and knowledgeable practitioners and those advanced by the hospital. In view of the special need for exact diagnosis in medicine he made a study of the causes of these differences. After theoretical research, he made the first "Medical Memory' in 1953 with the help of Bull and later of I.B.M. He studied the structure of a three-symbol logic which is applicable to medical' problems and in general. After a year in the service of Prof. G. E. Jayle, he abandoned pure research and entered industry. SUMMARY I am going to analyse ...

Biology seems to be a science in its own right, or set of sciences having common aims, and so it should have its own language and explanatory concepts; yet when any specifically biological concept is suggested and used as an explanatory concept it seems to be unsatisfactory and even mystical. There are many biological concepts of this kind: Purpose, Drive, elan vital, Entelechy, Gestalten.* Physicists and engineers seem, on the other hand, to have clearly defined concepts having great power within biology.

If ability to perform complex calculations were a sufficient criterion, then even a conventional digital computor could lay claim to more intelligence than any of usand perhaps we had better let it make away with the word and be done with it.

In either view, typical examples are closer where (only) positive examples ought to exceptional cases, case memory, analogy, to the prototype, and atypical examples and be.

In this short note, we discuss several aspects of "dimensions" and the related construct of "factors". We concentrate on those aspects that are relevant to articles in this special issue, especially those dealing with the analysis of the wild animal cases discussed in Berman and Hafner's 1993 ICAIL article. We review the basic ideas about dimensions, as used in HYPO, and point out differences with factors, as used in subsequent systems like CATO. Our goal is to correct certain misconceptions that have arisen over the years.

In this paper we discuss a hybrid approach combining Case-Based Reasoning (CBR) .