When failure is not an option

Some organizations seem to have purged "human error", operating highly complex and hazardous technological systems essentially without mistakes. How do they do it?     

When secret use is not so secret

The concept of secret use has been a difficult element in establishing whether the validity of a patent may be challenged. Its difficulty is entrenched in the uncertainty of interpretation of the construction of the legislation and the meaning that the legislature imparts on it. One of the main cases in Australian patent law that has tested the issue of secret use is Azuko Pty Ltd. v. Old Digger Pty Ltd. (2001) 52 IPR 75. The significance of this decision is the differing approaches to the issue of secret use. This case demonstrates the various practical and policy implications that arise out of the differing views from the judgment. This paper will discuss the practical and policy implications raised by the differing views of the judges.     

When ignorance helps: Graphical multicast cost sharing games

In non-cooperative games played on highly decentralized networks the assumption that each player knows the strategy adopted by any other player may be too optimistic or even infeasible. In such situations, the set of players of which each player knows the chosen strategy can be modeled by means of a social knowledge graph in which nodes represent players and there is an edge from i to j if i knows j. Following the framework introduced in [7], we study the impact of social knowledge graphs on the fundamental multicast cost sharing game in which all the players want to receive the same communication from a given source in an undirected network. In the classical complete information case, such a game is known to be highly inefficient, since its price of anarchy can be as high as the total number of players ρ. We first show that, under our incomplete information setting, pure Nash equilibria always exist only if the social knowledge graph is directed acyclic (DAG). We then prove that the price of stability of any DAG is at least and provide a DAG lowering the classical price of anarchy to a value between and log²ρ. If specific instances of the game are concerned, that is if the social knowledge graph can be selected as a function of the instance, we show that the price of stability is at least , and that the same bound holds also for the price of anarchy of any social knowledge graph (not only DAGs). Moreover, we provide a nearly matching upper bound by proving that, for any fixed instance, there always exists a DAG yielding a price of anarchy less than 4. Our results open a new window on how the performances of non-cooperative systems may benefit from the lack of total knowledge among players.     

When a usability test is not the answer

The author makes the point that a usability test does not tell you if something has been left out of a software interface (e.g., from a menu). She proposes instead task analysis, which is a generic name for a set of observational techniques in which one studies the tasks people have created for themselves. Task-analysis data are very useful for determining the functions a system should provide, how the functions should be implemented, typical task times and error rates which help set measurable objectives for improving task performance, and whether a system is used successfully in an operational environment. Planning, conducting the study, analyzing the data, and choosing the right method are discussed     

When standard RANSAC is not enough: cross-media visual matching with hypothesis relevancy

The same scene can be depicted by multiple visual media. For example, the same event can be captured by a comic image or a movie frame; the same object can be represented by a photograph or by a 3D computer graphics model. In order to extract the visual analogies that are at the heart of cross-media analysis, spatial matching is required. This matching is commonly achieved by extracting key points and scoring multiple, randomly generated mapping hypotheses. The more consensus a hypothesis can draw, the higher its score. In this paper, we go beyond the conventional set-size measure for the quality of a match and present a more general hypothesis score that attempts to reflect how likely is each hypothesized transformation to be the correct one for the matching task at hand. This is achieved by considering additional, contextual cues for the relevance of a hypothesized transformation. This context changes from one matching task to another and reflects different properties of the match, beyond the size of a consensus set. We demonstrate that by learning how to correctly score each hypothesis based on these features we are able to deal much more robustly with the challenges required to allow cross-media analysis, leading to correct matches where conventional methods fail.     

Representing partial ignorance

This paper advocates the use of nonpurely probabilistic approaches to higher-order uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decision-driven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decision-driven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decision-making as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decision-making and a non-Bayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in non-Bayesian settings are reviewed, and the difference between focusing on a reference class and revising due to the arrival of new information is pointed out. A comparison of Bayesian and non-Bayesian revision modes is discussed on a classical example     

照片的生命之旅

上传到计算机,用看图软件欣赏照片,用编辑软件修饰优化照片效果,把自己的得意之作与亲朋好友分享,把最有意义的照片冲洗或打印出来放在镜框里,或寄给远方的父母亲……这里我们会从看图软件的选择、照片优化处理技巧、照片共享方法、打印机选择,Web冲印等方面对照片诞生后的“成     

When not losing is better than winning: Abstraction and refinement for the full μ-calculus

This work presents a novel game-based approach to abstraction-refinement for the full μ-calculus, interpreted over 3-valued semantics.A novel notion of non-losing strategy is introduced and exploited for refinement. Previous works on refinement in the context of 3-valued semantics require a direct algorithm for solving a 3-valued model checking game. This was necessary in order to have the information needed for refinement available on one game board. In contrast, while still considering a 3-valued model checking game, here we reduce the problem of solving the game to solving two 2-valued model checking (parity) games. In case the result is indefinite (don’t know), the corresponding non-losing strategies, when combined, hold all the information needed for refinement. This approach is beneficial since it can use any solver for 2-valued parity games. Thus, it can take advantage of newly developed such algorithms with improved complexity.     

简单培训:在线学习就足够了

在如今人员精简的商业社会里，每年365天、每天的24小时都会有服务需求．生产力水平一直不停地提高．经济也在持续发展．这些因素使得时间变得至关重要．如今的社会“时间就是金钱”已经是再也明显不过了。因此．要让公司节省出时间去培训员工已经变得越来越困难。     

When good enough software is best

“You can get it fast; you can have it cheap; you can get it right. Pick two”. That sign could be displayed on the wall of every software development organization; and yet most of our customers want all three. The author tackles this dilemma. He contends that we don't rationally establish a proper balance among the critical project parameters: cost, schedule, staffing, functionality and quality. Our customers want us to optimize all these parameters, even when this is clearly impossible     

When is partial trace equivalence adequate?

Two processes arepartial trace equivalent iff they can perform the same sequences of actions in isolation. Partial trace equivalence is perhaps the simplest possible notion of process equivalence. In general, it is too simple: it is not usually an adequate semantics. We investigate the circumstances under which it is adequate, which are surprisingly rich. We give two substantial classes of languages for which partial traces are adequate. In one class, partial trace equivalence suffices for total correctness, and operations such as true sequencing are possible; but all processes are determinate and silent moves are not possible. The other class — which includes many standard process calculi, such as CCS and CSP — admits indeterminacy and silent moves, but partial traces only suffice for partial correctness and true sequencing is not definable.Supported by NSF grant (CCR-9003441)     

When is adaptive better than optimal?

Given a stationary process, let us predict it using a first-order predictor whose single coefficient is adapted to the current observations using a constant gain identification algorithm. We investigate the prediction error variance as a function of the adaptation gain i.e., the length of the memory (the number of observations) of the identification scheme. An infinite-memory corresponds to the asymptotically constant optimal predictor and a finite memory to a locally adaptive time varying predictor. We show that, in some specified situations, the prediction error variance associated with the finite memory adaptation scheme is smaller that the optimal variance. This can only occur if the model is misspecified i.e., the structure of the optimal predictor is too simple