Resource Description and Access is the new content standard coming Spring 2013, with national libraries using RDA effective March 30, 2013. Libraries need to address training for staff in all departments on how to interpret, catalogue and use RDA records.
There have been a number of steganography embedding techniques proposed over the past few years. In turn, there has been great interest in steganalysis techniques as the embedding techniques improve. Specifically, universal steganalysis techniques have become more attractive since they work independently of the embedding technique. In this work, we examine the effectiveness of a basic universal technique that relies on some knowledge about the cover media, but not the embedding technique. We consider images as a cover media, and examine how a single technique that we call steganographic sanitization performs on 26 different steganography programs that are publicly available on the Internet. Our experiments are completed using a number of secret messages and a variety of different levels of sanitization. However, since our intent is to remove covert communication, and not authentication information, we examine how well the sanitization process preserves authentication information such as watermarks and digital fingerprints.
In this work we discuss our efforts to use the ubiquity of smart phone systems and the mobility they provide to stream historical information about your current place on the earth to the end user. We propose the concept of timescapes to portray this historical significance of where they are standing and allow a brief travel through time. By combining GPS location, with a rich media interpretation of existing historical documents, historical facts become an on-demand resource available to travellers, school children, historians and any interested 3rd party. To our knowledge this is the first introduction of the term timescape to be used in the context of historical information pull. Copyright
New threats to networks are constantly arising. This justifies protecting network assets and mitigating the risk associated with attacks. In a distributed environment, researchers aim, in particular, at eliminating faulty network entities. More specifically, much research has been conducted on locating a single static black hole, which is defined as a network site whose existence is known a priori and that disposes of any incoming data without leaving any trace of this occurrence. However, the prevalence of faulty nodes requires an algorithm able to (a) identify faulty nodes that can be repaired without human intervention and (b) locate black holes, which are taken to be faulty nodes whose repair does require human intervention. In this paper, we consider a specific attack model that involves multiple faulty nodes that can be repaired by mobile software agents, as well as a virus v that can infect a previously repaired faulty node and turn it into a black hole. We refer to the task of repairing multiple faulty nodes and pointing out the location of the black hole as the Faulty Node Repair and Dynamically Spawned Black Hole Search. Wefirst analyze the attack model we put forth. We then explain (a) how to identify whether a node is either (1) a normal node or (2) a repairable faulty node or (3) the black hole that has been infected by virus v during the search/repair process and, (b) how to perform the correct relevant actions. These two steps constitute a complex task, which, we explain, significantly differs from the traditional Black Hole Search. We continue by proposing an algorithm to solve this problem in an asynchronous ring network with only one whiteboard (which resides in a node called the homebase). We prove the correctness of our solution and analyze its complexity by both theoretical analysis and experiment evaluation. We conclude that, using our proposed algorithm, b + 4 agents can repair all faulty nodes and locate the black hole infected by a virus v within finite time. Our algorithm works even when the number of faulty nodes b is unknown a priori.
Semantic scene classification is a challenging problem in computer vision. In this paper, we present a novel multi-level active learning approach to reduce the human annotation effort for training robust scene classification models. Different from most existing active learning methods that can only query labels for selected instances at the target categorization level, i.e., the scene class level, our approach establishes a semantic framework that predicts scene labels based on a latent object-based semantic representation of images, and is capable to query labels at two different levels, the target scene class level (abstractive high level) and the latent object class level (semantic middle level). Specifically, we develop an adaptive active learning strategy to perform multi-level label query, which maintains the default label query at the target scene class level, but switches to the latent object class level whenever an "unexpected" target class label is returned by the labeler. We conduct experiments on two standard scene classification datasets to investigate the efficacy of the proposed approach. Our empirical results show the proposed adaptive multi-level active learning approach can outperform both baseline active learning methods and a state-of-the-art multi-level active learning method.
The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, that is, searching the best element first. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we have previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion, and found that it performs well in a wide range of cases. However, previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, the History Heuristic, and the Killer Moves technique. We find that, in a wide range of two-player and multi-player games, at various points in the game’s progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.