Based on integrated infrastructure of resource sharing and computing in distributed environment, cloud computing involves the provision of dynamically scalable and provides virtualized resources as services over the Internet. These applications also bring a large scale heterogeneous and distributed information which pose a great challenge in terms of the semantic ambiguity. It is critical for application services in cloud computing environment to provide users intelligent service and precise information. Semantic information processing can help users deal with semantic ambiguity and information overload efficiently through appropriate semantic models and semantic information processing technology. The semantic information processing have been successfully employed in many fields such as the knowledge representation, natural language understanding, intelligent web search, etc. The purpose of this report is to give an overview of existing technologies for semantic information processing in cloud computing environment, to propose a research direction for addressing distributed semantic reasoning and parallel semantic computing by exploiting semantic information newly available in cloud computing environment.

To date, most studies on spam have focused only on the spamming phase of the spam cycle and have ignored the harvesting phase, which consists of the mass acquisition of email addresses. It has been observed that spammers conceal their identity to a lesser degree in the harvesting phase, so it may be possible to gain new insights into spammers' behavior by studying the behavior of harvesters, which are individuals or bots that collect email addresses. In this paper, we reveal social networks of spammers by identifying communities of harvesters with high behavioral similarity using spectral clustering. The data analyzed was collected through Project Honey Pot, a distributed system for monitoring harvesting and spamming. Our main findings are (1) that most spammers either send only phishing emails or no phishing emails at all, (2) that most communities of spammers also send only phishing emails or no phishing emails at all, and (3) that several groups of spammers within communities exhibit coherent temporal behavior and have similar IP addresses. Our findings reveal some previously unknown behavior of spammers and suggest that there is indeed social structure between spammers to be discovered.

State-of-the-art link prediction utilizes combinations of complex features derived from network panel data. We here show that computationally less expensive features can achieve the same performance in the common scenario in which the data is available as a sequence of interactions. Our features are based on social vector clocks, an adaptation of the vector-clock concept introduced in distributed computing to social interaction networks. In fact, our experiments suggest that by taking into account the order and spacing of interactions, social vector clocks exploit different aspects of link formation so that their combination with previous approaches yields the most accurate predictor to date.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.

The RoboCup Rescue Robot and Simulation competitions have been held since 2000. The experience gained during these competitions has increased the maturity level of the field, which allowed deploying robots after real disasters (for example, Fukushima Daiichi nuclear disaster). This article provides an overview of these competitions and highlights the state of the art and the lessons learned.

The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.

The Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) was held October 8-12, 2012, at Stanford University in Palo Alto, California. The conference included a research and industry track as well as a demonstration program. The conference featured 16 technical papers, 16 posters, and one demonstration, along with invited speakers, the StarCraft Ai competition, a newly-introduced Doctoral Consortium, and 5 workshops. This report summarizes the activities of the conference.

The deadline for the discounted AAAI rate is June AAAI-13 invites short papers on latebreaking for volunteer applications is May 1, 19, 2013. A special student rate has al-developments in the field of 2013.

We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real world geographic data, lifestyle-dependent driving patterns and vehicle models to create an agent-based model of the drivers. We use real world statistics of gas price fluctuation to create scenarios of temporal and spatial distribution of gas prices. The price of the information is determined on a case-by-case basis through a simple negotiation model. The trader and the customers are adapting their negotiation strategies based on their historical profits. We are interested in the general properties of the emerging information market: the amount of realizable profit and its distribution between the trader and customers, the business strategies necessary to keep the market operational (such as promotional deals), the price elasticity of demand and the impact of pricing strategies on the profit.

Management of security policies has become increasingly difficult given the number of domains to manage, taken into consideration their extent and their complexity. Security experts has to deal with a variety of frameworks and specification languages used in different domains that may belong to any Cloud Computing or Distributed Systems. This wealth of frameworks and languages make the management task and the interpretation of the security policies so difficult. Each approach provides its own conflict management method or tool, the security expert will be forced to manage all these tools, which makes the field maintenance and time consuming expensive. In order to hide this complexity and to facilitate some security experts tasks and automate the others, we propose a security policies aligning based on ontologies process; this process enables to detect and resolve security policies conflicts and to support security experts in managing tasks.