A primary well-controlled study of 900 participants found that personal presentation choices in professional emails (non-content changes like Profile Avatar 8 Signature) impact the recipient’s perception of the sender’s personality and the quality of the email itself. By understanding the role these choices play, employees can gain better control over how they influence the recipient of their messages. Results further indicate that although some variations can positively impact the recipient’s view of the sender, these same variations often also have negative side effects. This implies that many seemingly innocuous presentation decisions should be made in the context of who is receiving the email, and if these effects negatively impact the content of the message. For example, although statements in a Signature about the email having been written on a phone are included to preemptively apologize for typing mistakes, this causes the sender to appear less agreeable, less conscientious, and less open, and the email itself appears less well written and more poorly formatted. This is surprising given that the email itself was not changed in the study.

We present an evaluation of Panopticon, a video surrogate system, as an online eLearning support search tool for finding information within video lectures. A comparison was made with a standard video player (YouTube) in two scenarios with two classes of users: revision students and independent learners. Results showed that users of Panopticon were significantly faster at finding information within the lecture videos than users of the YouTube player. It was also found that videos predominantly featuring a talking lecturer took longest to navigate, presenting design implications for lectures to be uploaded to open eLearning platforms.

Significant requirements for a large digital preservation system are the system scalability, its ability to store and service heterogeneous digital holdings, and the evolvability over time of both the technologies comprising the system and the data formats in its storage. The use of information technology by the government, business corporations, academic institutions, and the general public results in staggering amounts of digital-born materials requiring long-term preservation and access. Organizations in different domains have to cope with the daunting task of storing and providing access to the growing amount of digital data. In many cases data heterogeneity, which ranges from office automation and geospatial images, to multimedia artifacts, adds to the challenge. In response to these challenges, we propose a Content Server Architecture Pattern. Content Server Architecture Pattern can be applied for instantiation of data-centric architecture, service-centric architecture, storage technology-centric architecture or an architecture that is some hybrid of that set to implement a Content Server Repository portion of a digital preservation system. A key characteristic of Content Server Architecture Pattern is its versatility that allows tailoring of the architecture of a digital preservation system to specific business needs.

For many probability distributions of interest, it is quite difficult to obtain samples efficiently. Often, Markov chains are employed to obtain approximately random samples from these distributions. The primary drawback to traditional Markov chain methods is that the mixing time of the chain is usually unknown, which makes it impossible to determine how close the output samples are to having the target distribution. The authors present a novel protocol, the randomness recycler (RR), that overcomes this difficulty. Unlike classical Markov chain approaches, an RR-based algorithm creates samples drawn exactly from the desired distribution. Other perfect sampling methods such as coupling from the past, use existing Markov chains, but RR does not use the traditional Markov chain at all. While by no means universally useful, RR does apply to a wide variety of problems. In restricted instances of certain problems, it gives the first expected linear time algorithms for generating samples. The authors apply RR to self-organizing lists, the Ising model, random independent sets, random colorings, and the random cluster model.

Abstract We present two algorithms for uniformly sampling from theproper colorings of a graph using k colors. We use exactsampling from the stationary distribution of a Markov chain with states that are the k-colorings of a graph with maxi-mum degree \\Delta. As opposed to approximate sampling algo-rithms based on rapid mixing, these algorithms have termination criteria that allow them to stop on some inputs muchmore quickly than in the worst case running time bound. For the first algorithm we show that when k? \\Delta (\\Delta + 2), thealgorithm has an upper limit on the expected running time that is polynomial. For the second algorithm we show thatfor k? r\\Delta; where r is an integer that satisfies r r? n,the running time is polynomial. Previously, Jerrum showed that it was possible to approximately sample uniformly inpolynomial time from the set of k-colorings when k * 2\\Delta;but our algorithm is the first polynomial time exact sampling algorithm for this problem. Using approximate sampling,Jerrum also showed how to approximately count the number of k-colorings. We give a new procedure for approx-imately counting the number of k-colorings that improvesthe running time of the procedure of Jerrum by a factor of (m=n) 2 when k * 2\\Delta, where n is the number of nodes inthe graph to be colored and m is the number of edges. Inaddition, we present an improved analysis of the chain of