Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolving Intelligent Systems: Methodology and Applications

From theory to techniques, the first all-in-one resource for EIS There is a clear demand in advanced process industries, defense, and Internet and communication (VoIP) applications for intelligent yet adaptive/evolving systems. Evolving Intelligent Systems is the first self- contained volume that covers this newly established concept in its entirety, from a systematic methodology to case studies to industrial applications. Featuring chapters written by leading world experts, it addresses the progress, trends, and major achievements in this emerging research field, with a strong emphasis on the balance between novel theoretical results and solutions and practical real-life applications. Explains the following fundamental approaches for developing evolving intelligent systems (EIS): the Hierarchical Prioritized Structure the Participatory Learning Paradigm the Evolving Takagi-Sugeno fuzzy systems (eTS+) the evolving clustering algorithm that stems from the well-known Gustafson-Kessel offline clustering algorithm Emphasizes the importance and increased interest in online processing of data streams Outlines the general strategy of using the fuzzy dynamic clustering as a foundation for evolvable information granulation Presents a methodology for developing robust and interpretable evolving fuzzy rule-based systems Introduces an integrated approach to incremental (real-time) feature extraction and classification Proposes a study on the stability of evolving neuro-fuzzy recurrent networks Details methodologies for evolving clustering and classification Reveals different applications of EIS to address real problems in areas of: evolving inferential sensors in chemical and petrochemical industry learning and recognition in robotics Features downloadable software resources Evolving Intelligent Systems is the one-stop reference guide for both theoretical and practical issues for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.
Fast, distributed optimization strategies for resource allocation in networks

FAST, DISTRIBUTED OPTIMIZATION STRATEGIES FOR RESOURCE ALLOCATION IN NETWORKS Michael C. Zargham Ali Jadbabaie Many challenges in network science and engineering today arise from systems composed of many individual agents interacting over a network. Such problems range from humans interacting with each other in social networks to computers processing and exchanging information over wired or wireless networks. In any application where information is spread out spatially, solutions must address information aggregation in addition to the decision process itself. Intelligently addressing the trade off between information aggregation and decision accuracy is fundamental to finding solutions quickly and accurately. Network optimization challenges such as these have generated a lot of interest in distributed optimization methods. The field of distributed optimization deals with iterative methods which perform calculations using locally available information. Early methods such as subgradient descent suffer very slow convergence rates because the underlying optimization method is a first order method. My work addresses problems in the area of network optimization and control with an emphasis on accelerating the rate of convergence by using a faster underlying optimization method. In the case of convex network flow optimization, the problem is transformed to the dual domain, moving the equality constraints which guarantee flow conservation into the objective. The Newton direction can be computed locally by using a consensus iteration to solve a Poisson equation, but this requires a lot of communication between neighboring nodes. Accelerated Dual Descent (ADD) is an approximate Newton method, which significantly reduces the communication requirement. Defining a stochastic version of the convex network flow problem with edge capacities yields a problem equivalent to the queue stability problem studied in the backpressure literature. Accelerated Backpressure (ABP) is developed to solve the queue stabilization problem. A queue reduction method is introduced by merging ideas from integral control and momentum based optimization. iv
RSIM Reference Manual: Version 1.0

Acknowledgments We thank other past and present members of the RSIM group for their contributions. Hazim Abdel-Shaa contributed parts of the memory system simulator code and documentation. Murthy Durbhakula provided valuable support in setting up the RSIM distribution. Jonathan Hall worked on initial versions of the memory system simulator. Tracy Harton supported our development eort over the last two y ears. We are also grateful to the Rice Parallel Processing Testbed (RPPT) group. Signiicant parts of the RSIM memory and network system are based on code from RPPT, a project led by Prof. and involving several graduate students.
The Package migtlm: Multiple interval genetical trait loci mapping

The package migtlm is for genetic trait loci (GTL) mapping in experimental crosses using the approach of multiple interval mixture generalized linear models developed by Chen and Liu (2007). Currently, the package contains functions for GTL mapping with backcross design only. The development of the full package containing functions for both backcross and intercross into a library as an extention of R/qtl is underway. The R codes of the package are contained in the file migtlm.R . T he package contains two major functions: migtlm and mimglm. The usage of the two functions are described below:
ANALYZING THE IMPORTANCE OF GROUP STRUCTURE IN THE GOOGLE PAGERANK ALGORITHM

Google bombs have been used by various groups with different motivations to bring a desired page to the top in a Google search result. Some people bomb other pages for fun, while others may use it to broadcast their thoughts or even advertisements. However, the act creates spam on the search results in Google and may not reflect the views of the general public. Such bombs have been used in the past to attack various prominent web sites, including the White House. With limited number of participants to an attack, the goal is to raise the pagerank of a page as much as possible. We will show in this thesis that the best possible architecture for a Google Bomb is an uncoordinated one. This optimal architecture not only makes an attack extra easy to create, it also make the attack hard to detect due to the lack of interconnections between the participants. The result will be a bunch of “random” pages creating an undesired consequence.
Informed Browsing of Digital Image Collections

The proliferation of digital cameras and their low operating costs lead to an abundance of digital photos that are not only laborious to organize but also lack the tangibility of their physical counterparts. Informed Browsing tries to ease the organization of a large media collection by applying automatic analysis methods. Tabletop displays allow for a more direct handling of virtual objects and facilitate multi-user interaction and communication. In the course of my diploma thesis I developed and implemented an application, flux, that takes concepts from Informed Browsing and puts them in a tabletop interface to combine a convenient, physical treatment of photos with the advantages of digital information: manipulability and easier organization. This work presents the basic motivation, the idea of Informed Browsing and its roots in the photowork process, related work in the same field, the design process with multiple iterations and user feedback in form of a focus group and the implementation with selected aspects and the hardware and technology side.
Software watermarking using fixed size encoding and random dummy method insertion

Recently, the rise of software piracy has become rampant and a major concern among software developers. The global software industry lost about USD 50 billion in 2008. One of the techniques that can be used to discourage piracy is watermarking, by embedding developer’s watermark into software which can later be extracted to prove ownership. During the last few years, different algorithms were produced and developed to hide the watermark inside software. This study analyzes software watermarking algorithms that exist in the literature and then identified a dummy method algorithm is suitable for watermarking. In addition, this study enhances dummy method insertion technique in embedding and recognizing the watermark in Java class files. The enhancement includes fixed size encoding scheme and random dummy method insertion. The proposed fixed size encoding scheme used hash function that can produce a fixed size watermark bit sequences. Random dummy method insertion selects a dummy method at random from a collection of dummy methods. Finally, this study analyzes the enhancement of dummy method insertion technique using two different measures, namely data-rate and resilience of the watermarking algorithm. In term of data rate, the results show that encoded watermark for proposed encoding scheme is always fixed even though size of watermark character is increased. In term of resilience, experimental results show no similarity between class files and thus survived from collusion attack compared to previous method.
Comparative Ecology: A Computational Perspective

A long-standing dream in the field of artificial intelligence has been to use evolutionary processes to produce systems of greater competence than those we can directly design. This paper compares different evolutionary models such as biological ecosystems, markets, and EURISKO-with respect to this goal. This comparison suggests that a form of ecosystem here termed a direct market (as opposed to the indirect market of human society) is a promising basis for computational ecosystems . Related papers [I,II) in this book elaborate a vision of direct computational markets termed agoric open systems.
Diagnostic assessment without cut-offs: modelling bovine digital dermatitis infection.

Bovine digital dermatitis (BDD) is an epidermitis which is a leading cause of infectious lameness. The only recognised diagnostic procedure is foot inspection, which is labour-intensive to carry out, is subjective and has limited diagnostic sensitivity. Serology is a potentially more sensitive indicator of infection. A reliable and repeatable ELISA was used to test animals from a cross-sectional study on 8 representative dairy farms in the UK. No ‘Gold Standard’ was assumed to exist. To make fullest use of the information inherent in the ELISA and covariate data, a model using Bayesian statistics was developed which did not impose a cut-off but instead estimated a probability of infection for each individual animal. By modelling BDD in this way, a more detailed and informative analysis of the farm-level distribution of infection could be performed. By extending the model to enable predictive inference, the results of this work can be generally applied.
Animating Real Objects With Image-Based Illumination

We describe a new paradigm for three-dimensional computer graphics, using projectors to graphically animate physical objects in the real world. The idea is to replace a physical object­ with its inherent color, texture, and material properties-with a neutral object and projected im­ agery, reproducing the original (or alternative) appearance directly 011 the object. Because the approach is to effectively "lift" the visual properties of the object into the projector, we call the projectors shader lamps. We address the central issue of complete and continuous illumination of non-trivial physical objects using multiple projectors and present a set of new techniques that makes the process of illumination practical. We demonstrate the viability of these techniques through a variety of table-top applications, and describe preliminary results to reproduce life­ sized virtual spaces.
Data placement in shared-nothing parallel database systems

Due to their better price/performance ratios and scalability, "Shared-Nothing" computer systems are beginning to displace traditional mainframe computers for the largest database and transaction processing tasks in commercial applications. A Shared-Nothing (SN) system contains several independent processing nodes--each node containing its own processor and secondary storage--interconnected by a common network. In this dissertation, we address the problem of placing the set of relations (or tables) in a relational database across the nodes of an SN parallel computer system. Given a database workload, our objective is to find a placement that optimizes the performance of the workload, measured in terms of throughput or response time. The data placement (DP) problem is studied in terms of two primary issues, viz. declustering and assignment. Declustering deals with determining the appropriate number of horizontal partitions (or, equivalently, nodes) for each table in the database. It enables the parallel processing of individual database operations that access a table. Assignment deals with the allocation of the table partitions to nodes in the system. A variety of DP algorithms, based on combinatorial optimization techniques and direct heuristics, are considered to solve the DP problem in an integrated fashion. In particular, the DP algorithms employ intelligent heuristics, based on disjoint assignment of table partitions and load balancing, to solve the assignment sub-problem. A number of experiments are conducted to compare the performance of the various DP algorithms. Details of a comprehensive analytical cost evaluation model, used in these experiments, are provided. The results of the various experiments illustrate the importance of performing a good data placement in order to obtain better performance from the SN database system. Algorithms based on a modified combinatorial optimization method called recursive optimization are found to be most suited for data placement. The experiments also show that an algorithm that uses a heuristic approach to find an initial placement and follows it with one combinatorial optimization step, compares quite favorably with the recursive optimization algorithms. Among assignment heuristics, the disjoint assignment method is best suited for optimizing response times while the load balancing method is best suited for optimizing throughput.
Feature selection from huge feature sets in the context of computer vision

Most learning systems use hand-picked sets of features as input data for their learning algorithms. This is particularly true of computer vision systems, where the number of features that can be computed over an image is, for practical purposes, limitless. Unfortunately, most of these features are irrelevant or redundant to a given task, and no feature selection algorithm to date can handle such large feature sets. Moreover, many standard feature selection algorithms perform poorly when faced with many irrelevant and redundant features. This work addresses the feature selection problem by proposing a three-step algorithm. The first step uses an algorithm based on the well known algorithm called Relief [54] to remove irrelevance; the second step clusters features using K-means to remove redundancy; and the third step is a standard feature selection algorithm. This three-step algorithm is shown to be more effective than standard feature selection algorithms for data with lots of irrelevance and redundancy. In another experiment a data set with 4096 features was reduced to 5% of its original size with very little information loss. In addition, we modify Relief to remove its bias against non-monotonic features and use correlation as the distance measure for K-means.
PROFILING THE PROFILER: EXPLORING THE ROLE OF CUES IN THE DEVELOPMENT OF EXPERTISE WITHIN THE DOMAIN OF OFFENDER PROFILING

Cues have been identified as important precursors to successful decision-making amongst expert practitioners. Consequently, the use of xpert cues in novice training programs is an attractive option for skill development. This thesis adopted the naturalistic approach to decision-making theory and research to investigate potential differences in cue activation (and presumably use) across two stages of expertise (expert and novice). The aim of the research was to determine whether it is possible to improve novice decision-making performance based on a modelling of expert cue activation. The research used the practice of Offender Profilingas a domain for investigating cue-use across expertise and involved three studies. Study 1 employed an interview strategy to identify a number of potential concepts of interest to expert and novice profilers when formulating decisions. Using content and thematic analyses, the findings yielded a number of crime-related and offender-related concept labels to be used as a basis for discriminating between expert and novice cue activation in Study 2. Study 2 incorporated two phases of research, the first of which (Study 2a) presented pairs of the concept labels identified in Study 1 as part of a Paired-Concept Association Task(P-CAT), which recorded participants’ response latency in recognising associations between the concepts. The second phase (Study 2b) involved the distribution of a survey to further test participants’ perceptions of the associations between concepts in relation to their frequency of use, strength, diagnosticity, and domain specificity. The findings of Study 2 revealed that the P-CAT was able to accurately discriminate between expert and novice cue activation and yielded a number of cue-based associations that were recognised/activated consistently and rapidly by, predominantly, the expert profilers. Further, the experts’ perceptions of the associations reflected their response latencies. However, novice perceptions did not differentiate between associations, suggesting that the ability to discriminate between cues is a function of expertise. Finally, Study 3 investigated whether
Web Page Classification based on Document Structure

The web is a huge repository of information and there is a need for categorizing web documents to facilitate the search and retrieval of pages. Existing algorithms rely solely on the text content of the web pages for classification. However, the web has a lot of information contained in structure, images, video etc present in the document. In this paper, we propose a method for automatic classification of web pages into a few broad categories based on the structure of the web document and the characteristics of images present in it.
Virtual Texturing with WebGL

Until recently, achieveing hardware accelerated 3D content on web sites have only been accessible through third party plugins. The new HTML5 standard eliminates this restriction by adding native 3D rendering through the WebGL API. This technology brings established desktop applications online, bridging the gap between software platforms. This thesis investigates how to implement Virtual Texturing for web browsers using WebGL and how to maintain high performance. Virtual Texturing is a recent approach to texture mapping that enables support for theoretically infinite image dimensions. In this thesis, Virtual Texturing is implemented with the help of several elements new to the HTML5 specification, such as Web Workers and Web Sockets. The thesis provides an introduction to the required browser features, an overview of relevant WebGL features,as well as a performance analysis. Results show that Virtual Texturing in WebGL is a viable approach for websites with high resource demands.
An Introduction to Formal Specification and Verification using EHDM

This report is a tutorial on formal speci cation and veri cation using Ehdm. The Ehdm speci cation language is very expressive, based on a stronglytyped higher-order logic, enriched with elements of the Hoare (relational) calculus. The type system provides subtypes, dependent types, and certain forms of type-polymorphism. Modules are used to structure large speci cations and support hierarchical development. The language has a complete formal semantic characterization and is supported by a fully mechanized speci cation and veri cation environment that has been used to develop large speci cations and perform very hard formal veri cations. The tutorial uses simple examples to describe the Ehdm language, methodology, and tools. The rst examples illustrate the basic ideas of speci cation and theorem proving in Ehdm. We then introduce the ideas of testing speci cations, of horizontal and vertical hierarchy, and of consistency and conservative extension. Later chapters cover more advanced topics including subtypes, higher-order logic, proofs by induction, and program veri cation using Hoare logic. The tutorial is illustrated throughout with self-contained examples of Ehdm speci cations and proofs, all of which have been mechanically checked. An Introduction to Formal Speci cation and Veri cation Using EHDM John Rushby, Friedrich von Henke, and Sam Owre Computer Science Laboratory SRI International 333 Ravenswood Avenue Menlo Park, CA 94025 Technical Report CSL-91-2 February 1991
Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages

Simultaneous localization and mapping (SLAM), also known as concurrent mapping and localization (CML), is an important topic or robotics files. This method produces a real-time map of an environment and finds the current position of a robot on that map. This method is generally used to solve the problem of “Where am I? ” for localization, “Where do I go? ” for goal determination, and “How do I go there? ” for robot motion planning. Recently, the number of studies in this area has increased rapidly and expanded to different areas. In this paper analyzes SLAM or CML, which is currently a hot topic in the field of robotic research. In addition, this paper describes methods for solving SLAM problems, presents evaluation methods for SLAM, analyzes recent research on SLAM worldwide, and studies the academic importance of SLAM. This paper also reviews the use of SLAM for humanoid robots and aims to address the issue of the significance of SLAM engine in the future of stereo vision on humanoid robots.
Information systems development and data modelling: conceptual and philosophical foundations

Data modelling was hypothesised to be the salvation of an organisation's data problems. This book aims to analyse the problems encountered and to present a comparative philosophical study of the various approaches. The authors explore the epistemology, ontology and rationality of each modelling approach, and describe the underlying assumptions embedded in them.