A simple and fast exact clustering algorithm defined for complex networks and based on the properties of primes

In this paper a new clustering method based on primes is proposed. This method define a nodes cluster of any complex network, considering the nodes with same input/output number and same number of paths with equal length, so all the network nodes with analogous functions will be possible to identify. The clustering algorithm proposed, results very efficient because it is defined on simple computations with primes. For example, with our algorithm the analysis of a network with 500 nodes and 124750 connections is performed in 80 seconds on Pentium 4 with CPU 2Ghz and 1Gb ram. Keywords: Complex network, clustering method, graph theory, unidirectional/bidirectional network, complete path

Dynamic, Task-Related and Demand-Driven Scene Representation

Humans selectively process and store details about the vicinity based on their knowledge about the scene, the world and their current task. In doing so, only those pieces of information are extracted from the visual scene that is required for solving a given task. In this paper, we present a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approaches, our system is able to acquire information selectively according to the demands of the given task and based on the system’s knowledge. The proposed control mechanism decides which properties need to be extracted and how the independent processing modules should be combined, based on the knowledge stored in the system’s long-term memory. Additionally, it ensures that algorithmic dependencies between processing modules are resolved automatically, utilizing procedural knowledge which is also stored in the long-term memory. By evaluating a proof-of-concept implementation on a real-world table scene, we show that, while solving the given task, the amount of data processed and stored by the system is considerably lower compared to processing regimes used in state-of-the-art systems. Furthermore, our system only acquires and stores the minimal set of information that is relevant for solving the given task

Digital Forensics Tools Integration

As technology has become pervasive in our lives we record our daily activities both intentionally and unintentionally. Because of this, the amount of potential evidence found on digital media is staggering. Investigators have had to adapt and change their methods of conducting investigations to address the data volume. Digital forensics examiners current process consists of performing string searches to identify potential evidentiary items. Items of interest must then go through association, target comparison, and event reconstruction processes. These are manual and time consuming tasks for an examiner. This thesis presents a user interface that combines both the string searching capabilities that begin an investigation with automated correlation and abstraction into a single timeline visualization. The capability to improve an examiner\u27s process is evaluated on the tools ability to reduce the number of results to sort through while accurately presenting key items for three use cases

Task- and Knowledge-Driven Scene Representation: A Flexible On-Demand System Architecture for Vision

In this thesis a flexible system architecture is presented along with an attention control mechanism allowing for a task-dependent representation of visual scenes. Contrary to existing approaches, which measure all properties of an object, the proposed system only processes and stores information relevant for solving a given task. The system comprises a short- and long-term memory, a spatial saliency algorithm and multiple independent processing routines to extract visual properties of objects. Here, the proposed control mechanism decides which properties need to be extracted and which processing routines should be coupled in order to effectively solve the task. This decision is based on the knowledge stored in the long-term memory of the system. An experimental evaluation on a real-world scene shows that, while solving the given task, the computational load and the amount of data stored by the system are considerably reduced compared to state-of-the-art systems.Die Umgebung des Menschen ist voller visueller Details. Diese immense Menge an Information kann, unter der Annahme von begrenzten Verarbeitungs- und Speicherresourcen, nur teilweise aufgenommen und gespeichert werden. Daraus ergibt sich die Notwendigkeit einer selektiven Verarbeitung, die, je nach Aufgabenstellung, zu einer unterschiedlichen Repräsentation der visuellen Szene führt. Psychophysische Experimente zeigen, dass dabei die erfasste Umgebung nicht nur örtlich, sondern auch im Merkmalsraum selektiv bearbeitet wird, dass heißt es wird nur die visuelle Information aufgenommen, die für das Lösen der jeweiligen Aufgabe erforderlich ist. Im Rahmen dieser Arbeit werden eine flexible Systemarchitektur und eine Kontrollstruktur zur aufgabenbezogenen Szenenrepräsentation vorgestellt. Im Gegensatz zu existierenden Arbeiten ermöglicht dieser Ansatz eine selektive Informationsaufnahme. Die vorgeschlagene Architektur enthält neben einem Lang- und Kurzzeitgedächtnis sowie einer Aufmerksamkeitskarte auch mehrere Verarbeitungsmodule zur Merkmalsextraktion. Diese Verarbeitungsmodule sind spezialisiert auf die Extraktion eines Merkmals und arbeiten unabhängig voneinander. Sie können jedoch je nach Aufgabenstellung dynamisch miteinander gekoppelt werden um gezielt die benötigte Information aus der Szene zu extrahieren. Die Entscheidung, welche Information benötigt wird und welche Module zur Extraktion dieser Merkmale gekoppelt werden müssen, trifft die im Rahmen der Arbeit entwickelte Kontrollstruktur, welche das gespeicherte Wissen des Systems und die gestellte Aufgabe berücksichtigt. Weiterhin stellt die Kontrollstruktur sicher, dass algorithmische Abhängigkeiten zwischen den Verarbeitungsmodulen unter Zuhilfenahme von systemimmanentem Prozesswissen automatisch aufgelöst werden. Die hier vorgestellte Systemarchitektur und die ebenfalls vorgeschlagene Kontrollstruktur werden experimentell anhand einer realen Tischszene evaluiert. Bei den durchgeführten Experimenten zeigt sich, dass bei Lösung einer gestellten Aufgabe die Menge der vom System verarbeiteten und gespeicherten Informationen deutlich reduziert wird. In der Folge werden die Anforderungen an die Verarbeitungs- und Speicherressourcen ebenfalls deutlich reduziert. Diese Arbeit leistet damit einen Beitrag zur aufgabenbezogenen Repräsentation von visuellen Szenen, da nur noch die Information verarbeitet und gespeichert wird, die tatsächlich zur Lösung der Aufgabe erforderlich ist.The visual environment of humans is full of details. This incredible amount of data can neither be processed nor stored when assuming a limited computational power and memory capacity. Consequently, a selective processing is necessary, which leads to different representations of the same scene depending on the given task. Psychophysical experiments show that both the spatial domain as well as the feature domain are parsed selectively. In doing so, only those information are extracted from the visual scene that are required to solve a given task. This thesis proposes a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approaches, the resulting system is able to acquire information selectively according to the demands of the given task. This system comprises both a short-term and a long-term memory, a spatial saliency algorithm and multiple visual processing modules used to extract visual properties of a focused object. At this, the different visual processing modules operate independently and are specialized in extracting only a single visual property. However, the dynamic coupling of multiple processing modules allows for the extraction of specific more complex features that are relevant for solving the given task. Here, the proposed control mechanism decides which properties need to be extracted and which processing modules should be coupled. This decision is based on the knowledge stored in the long-term memory of the system. Additionally, the control mechanism ensures that algorithmic dependencies between processing modules are resolved automatically, utilizing procedural knowledge which is also stored in the long-term memory. A proof-of-concept system is implemented according to the system architecture and the control mechanism presented in this thesis. The experimental evaluation using a real-world table scene shows that, while solving the given task, the amount of data processed and stored by the system is considerably lower compared to processing regimes used in state-of-the-art systems. This in turn leads to a noticeable reduction of the computational load and memory demand. In doing so, the present thesis contributes to a task-dependent representation of visual scenes, because only those information are acquired and stored that are relevant for solving the given task

A heuristic information retrieval study : an investigation of methods for enhanced searching of distributed data objects exploiting bidirectional relevance feedback

A thesis submitted for the degree of Doctor of Philosophy of the University of LutonThe primary aim of this research is to investigate methods of improving the effectiveness of current information retrieval systems. This aim can be achieved by accomplishing numerous supporting objectives. A foundational objective is to introduce a novel bidirectional, symmetrical fuzzy logic theory which may prove valuable to information retrieval, including internet searches of distributed data objects. A further objective is to design, implement and apply the novel theory to an experimental information retrieval system called ANACALYPSE, which automatically computes the relevance of a large number of unseen documents from expert relevance feedback on a small number of documents read. A further objective is to define a methodology used in this work as an experimental information retrieval framework consisting of multiple tables including various formulae which anow a plethora of syntheses of similarity functions, ternl weights, relative term frequencies, document weights, bidirectional relevance feedback and history adjusted term weights. The evaluation of bidirectional relevance feedback reveals a better correspondence between system ranking of documents and users' preferences than feedback free system ranking. The assessment of similarity functions reveals that the Cosine and Jaccard functions perform significantly better than the DotProduct and Overlap functions. The evaluation of history tracking of the documents visited from a root page reveals better system ranking of documents than tracking free information retrieval. The assessment of stemming reveals that system information retrieval performance remains unaffected, while stop word removal does not appear to be beneficial and can sometimes be harmful. The overall evaluation of the experimental information retrieval system in comparison to a leading edge commercial information retrieval system and also in comparison to the expert's golden standard of judged relevance according to established statistical correlation methods reveal enhanced system information retrieval effectiveness