Defending against the propagation of active worms

Active worms propagate across networks by employing the various target discovery techniques. The significance of target discovery techniques in shaping a worm’s propagation characteristics is derived from the life cycle of a worm. The various target discovery techniques that could be employed by active worms are discussed. It is anticipated that future active worms would employ multiple target discovery techniques simultaneously to greatly accelerate their propagation. To accelerate a worm’s propagation, the slow start phase in the worm’s propagation must be shortened by letting the worm infect the first certain percentage of susceptible hosts as soon as possible. Strategies that future active worms might employ to shorten the slow start phase in their propagation are studied. Their respective cost-effectiveness is assessed. A novel active defense mechanism is proposed, which could be an emerging solution to the active worm problem. Our major contributions in this article are first, we found the combination of target discovery techniques that can best accelerate the propagation of active worms; second, we proposed several strategies to shorten a worm’s slow start phase in its propagation and found the cost-effective hit-list size and average size of internally generated target lists; third, we proposed a novel active defense mechanism and evaluated its effectiveness; and fourth, we proposed three novel discrete time deterministic propagation models of active worms.     

Autopoiesis, the immune system, and adaptive information filtering

Adaptive information filtering is a challenging and fascinating problem. It requires the adaptation of a representation of a user’s multiple interests to various changes in them. We tackle this dynamic problem with Nootropia, a model inspired by the autopoietic view of the immune system. It is based on a self-organising antibody network that reacts to user feedback in order to define and preserve the user interests. We describe Nootropia in the context of adaptive, content-based document filtering and evaluate it using virtual users. The results demonstrate Nootropia’s ability to adapt to both short-term variations and more radical changes in the user’s interests, and to dynamically control its size and connectivity in the process. Advantages over existing approaches to profile adaptation, such as learning algorithms and evolutionary algorithms are also highlighted.

Research of internet worm warning system based on system identification

In this paper, by analyzing the worm’s propagation model, we propose a new worm warning system based on the method of system identification, and use recursive least squares algorithm to estimate the worm’s infection rate. The simulation result shows the method we adopted is an efficient way to conduct Internet worm warning.     

Content-based image retrieval using fuzzy perceptual feedback

In this paper, a new framework called fuzzy relevance feedback in interactive content-based image retrieval (CBIR) systems is introduced. Conventional binary labeling scheme in relevance feedback requires a crisp decision to be made on the relevance of the retrieved images. However, it is inflexible as user interpretation of visual content varies with respect to different information needs and perceptual subjectivity. In addition, users tend to learn from the retrieval results to further refine their information requests. It is, therefore, inadequate to describe the user’s fuzzy perception of image similarity with crisp logic. In view of this, we propose a fuzzy relevance feedback approach which enables the user to make a fuzzy judgement. It integrates the user’s fuzzy interpretation of visual content into the notion of relevance feedback. An efficient learning approach is proposed using a fuzzy radial basis function (FRBF) network. The network is constructed based on the user’s feedbacks. The underlying network parameters are optimized by adopting a gradient-descent training strategy due to its computational efficiency. Experimental results using a database of 10,000 images demonstrate the effectiveness of the proposed method.

A powerful relevance feedback mechanism for content-based 3D model retrieval

The technique of relevance feedback has been introduced to content-based 3D model retrieval, however, two essential issues which affect the retrieval performance have not been addressed. In this paper, a novel relevance feedback mechanism is presented, which effectively makes use of strengths of different feature vectors and perfectly solves the problem of small sample and asymmetry. During the retrieval process, the proposed method takes the user’s feedback details as the relevant information of query model, and then dynamically updates two important parameters of each feature vector, narrowing the gap between high-level semantic knowledge and low-level object representation. The experiments, based on the publicly available 3D model database Princeton Shape Benchmark (PSB), show that the proposed approach not only precisely captures the user’s semantic knowledge, but also significantly improves the retrieval performance of 3D model retrieval. Compared with three state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval effectiveness only with a few rounds of relevance feedback based on several standard measures.

Presence in the age of social networks: augmenting mediated environments with feedback on group activity

The present study aimed at increasing behavioral engagement in groups of networked people by providing feedback on the group activity. Each participant logged into an on-line virtual environment for four subsequent treasure-hunting sessions along with other nine players. During the game, all players communicated dyadically through textual chats, and searched for the treasures in the virtual environment. In two conditions, the participants received a visual feedback depicting the communication activity with the group based on social network analysis indices (i.e. ‘centrality’ or ‘reciprocity’). Feedback was not provided in the third condition. The underlying assumption was that if the group activity becomes more visible to the individual user through the feedback, then the behavioral engagement with the group increases. The resulting behavioral engagement was measured with two techniques, one based on the amount of messages exchanged and one based on self-reported measures. The results show that feedback improved the exchange of messages with respect to the control condition and that this effect was only partially captured by self-reported measures.

A multi-agent platform for content-based image retrieval

Efficient and possibly intelligent image retrieval is an important task, often required in many fields of human activity. While traditional database indexing techniques exhibit a remarkable performance in textual information retrieval current research in content-based image retrieval is focused on developing novel techniques that are biologically motivated and efficient. It is well known that humans have a remarkable ability to process visual information and to handle the volume and complexity of such information quite efficiently. In this paper, we present a content-based image retrieval platform that is based on a multi-agent architecture. Each agent is responsible for assessing the similarity of the query image to each candidate image contained in a collection based on a specific primitive feature and a corresponding similarity criterion. The outputs of various agents are integrated using one of several voting schemes supported by the system. The system’s performance has been evaluated using various collections of images, as well as images obtained in specific application domains such as medical imaging. The initial evaluation has yielded very promising results.

Independent query refinement and feature re-weighting using positive and negative examples for content-based image retrieval

Query refinement and feature re-weighting are the two core techniques underlying the relevance feedback of content-based image retrieval. Most existing relevance feedback mechanisms generally model the user’s query target with a single query point and weight each extracted feature with a single importance factor. A designed estimation procedure then estimates the best query point and all importance factors by optimizing a formulated criterion which measures the goodness of the estimation. This formulated criterion simultaneously encapsulates all positive and negative examples supplied from the user’s feedback. Under such formulation, the positive and negative examples may contribute contradictorily to the criterion and sometimes may introduce higher difficulty in attaining a good estimation. In this paper, we propose a different statistical formulation to estimate independently two pairs of query points and feature weights from the positive examples and negative examples, respectively. These two pairs then define the likelihood ratio, a criterion term used to rank the relevance of all database images. This approach simplifies the criterion formulation and also avoids the mutual impeditive influence between positive examples and negative examples. The experimental results demonstrate that the proposed approach outperforms some other related approaches.

Hitchcock’s (2001) treatment of singular and general causation

Hitchcock (2001a) argues that the distinction between singular and general causation conflates the two distinctions ‘actual causation vs. causal tendencies’ and ‘wide vs. narrow causation’. Based on a recent regularity account of causation I will show that Hitchcock’s introduction of the two distinctions is an unnecessary multiplication of causal concepts.

Capture local information in shape representation

Research in content-based image retrieval has been around for over a decade. While the research community has successfully exploited content features such as color and texture, finding an effective shape representation and measure remains a challenging task. The shape feature is particularly crucial for the success of content-based systems as it carries meaningful semantics of the objects of interest and fits more naturally into humans’ perception of similarity. In this paper, we present our approach to use the shape feature for image retrieval. First, we introduce an effective image decomposition method called Crawling Window (CW) to distinguish the outline of each object in the image. Second, to represent each individual shape, we propose a novel representation model called component Distance Distribution Function and its measure. Traditionally, an object is represented by a set of points on the shape’s contour. Our idea is to first compute the distance between each point and the center of the object. The distance values for all points form a signal, which we call Distance Distribution Function (DDF). Each DDF is then divided into component DDFs (cDDF) by taking local signal information into account. Finally, a transformation technique is employed to generate the feature vector for each cDDF. All vectors from the cDDFs in circular order construct the final shape representation. The model is invariant to position, scaling, rotation and starting point. The similarity measure model based on the new representation is also introduced. Our extensive experiments show that our models are more effective than the existing representation model, both in the shape and the image level.

Turing’s Responses to Two Objections

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical thinking about the nature of mind.

An examination of private intermediaries’ roles in software vulnerabilities disclosure

Software vulnerability disclosure has generated much interest and debate. Recently some private intermediaries have entered this market. This paper examines the effects of such private intermediaries on optimal timing of disclosure policy made by public intermediaries and vendors’ reactions. Our analysis of private intermediaries’ role suggests that public intermediary’s optimal disclosure time does not change with private intermediary’s participation. However, a vendor’s patch time increases when the probability of information leakage is low, if not non-existent. In other words, private intermediaries’ service decreases a vendor’s willingness to deliver quick patches. Empirical evidence with 1493 vulnerability observations from CERT/CC and other 326 different vulnerability observations from iDefense provided support for our analytical results.

Unpacking Tasks: The Fusion of New Technology with Instructional Work

This paper discusses how a new technology (designed to help pupils with learning about Shakespeare’s Macbeth) is introduced and integrated into existing classroom practices. It reports on the ways through which teachers and pupils figure out how to use the software as part of their classroom work. Since teaching and learning in classrooms are achieved in and through educational tasks (what teachers instruct pupils to do) the analysis explicates some notable features of a particular task (storyboarding one scene from the play). It is shown that both ‘setting the task’ and ‘following the task’ have to be locally and practically accomplished and that tasks can operate as a sense-making device for pupils’ activities. Furthermore, what the task ‘is’, is not entirely established through the teacher’s initial formulation, but progressively clarified through pupils’ subsequent work, and in turn ratified by the teacher.

Evolving strategy for a probabilistic game of imperfect information using genetic programming

We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.

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Russell’s Structuralism and the Supposed Death of Computational Cognitive Science

John Searle believes that computational properties are purely formal and that consequently, computational properties are not intrinsic, empirically discoverable, nor causal; and therefore, that an entity’s having certain computational properties could not be sufficient for its having certain mental properties. To make his case, Searle’s employs an argument that had been used before him by Max Newman, against Russell’s structuralism; one that Russell himself considered fatal to his own position. This paper formulates a not-so-explored version of Searle’s problem with computational cognitive science, and refutes it by suggesting how our understanding of computation is far from implying the structuralism Searle vitally attributes to it. On the way, I formulate and argue for a thesis that strengthens Newman’s case against Russell’s structuralism, and thus raises the apparent risk for computational cognitive science too.

Policy-based Management: A Historical Perspective

This paper traces the history of policy-based management and how it evolved from the first security models dating back to the late 1960’s until today’s more elaborate frameworks, languages, and policy-based management tools. The focus will be on providing a synthesized chronicle of the evolution of ideas and research trends rather than on surveying the various specification formalisms, frameworks, and application domains of policy-based management.

The effects of transparency on trust in and acceptance of a content-based art recommender

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study’s results.

Does Japan really have robot mania? Comparing attitudes by implicit and explicit measures

Japan has more robots than any other country with robots contributing to many areas of society, including manufacturing, healthcare, and entertainment. However, few studies have examined Japanese attitudes toward robots, and none has used implicit measures. This study compares attitudes among the faculty of a US and a Japanese university. Although the Japanese faculty reported many more experiences with robots, implicit measures indicated both faculties had more pleasant associations with humans. In addition, although the US faculty reported people were more threatening than robots, implicit measures indicated both faculties associated weapons more strongly with robots than with humans. Despite the media’s hype about Japan’s robot ‘craze,’ response similarities suggest factors other than attitude better explain robot adoption. These include differences in history and religion, personal and human identity, economic structure, professional specialization, and government policy. Japanese robotics offers a unique reference from which other nations may learn.

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The Metaphilosophy of Information

This article mounts a defence of Floridi’s theory of strongly semantic information against recent independent objections from Fetzer and Dodig-Crnkovic. It is argued that Fetzer and Dodig-Crnkovic’s objections result from an adherence to a redundant practice of analysis. This leads them to fail to accept an informational pluralism, as stipulated by what will be referred to as Shannon’s Principle, and the non-reductionist stance. It is demonstrated that Fetzer and Dodig-Crnkovic fail to acknowledge that Floridi’s theory of strongly semantic information captures one of our deepest and most compelling intuitions regarding informativeness as a basic notion. This modal intuition will be referred to as the contingency requirement on informativeness. It will be demonstrated that its clarification validates the theory of strongly semantic information as a novel, and non ad hoc solution to the Bar-Hillel-Carnap semantic paradox.

A cross-modal approach for karaoke artifacts correction

Karaoke singing is a popular form of entertainment in several parts of the world. Since this genre of performance attracts amateurs, the singing often has artifacts related to scale, tempo, and synchrony. We have developed an approach to correct these artifacts using cross-modal multimedia streams information. We first perform adaptive sampling on the user’s rendition and then use the original singer’s rendition as well as the video caption highlighting information in order to correct the pitch, tempo and the loudness. A method of analogies has been employed to perform this correction. The basic idea is to manipulate the user’s rendition in a manner to make it as similar as possible to the original singing. A pre-processing step of noise removal due to feedback and huffing also helps improve the quality of the user’s audio. The results are described in the paper which shows the effectiveness of this multimedia approach.