Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Data Mining Tools

DATA MINING TECHNIQUES AND APPLICATIONS Introduction Trends Data Mining Techniques and Applications Data Mining for Cyber Security: Intrusion Detection Data Mining for Web: Web Page Surfing Prediction Data Mining for Multimedia: Image Classification Organization of This Book Next Steps Data Mining Techniques Introduction Overview of Data Mining Tasks and Techniques Artificial Neural Networks Support Vector Machines Markov Model Association Rule Mining (ARM) Multiclass Problem Image Mining Summary Data Mining Applications Introduction Intrusion Detection Web Page Surfing Prediction Image Classification Summary DATA MINING TOOL FOR INTRUSION DETECTION Data Mining for Security Applications Overview Data Mining for Cyber Security Current Research and Development Summary and Directions Dynamic Growing Self-Organizing Tree Algorithm Overview Our Approach DGSOT Discussion Summary and Directions Intrusion Detection Results Overview Dataset Results Complexity Validation Discussion Summary and Directions DATA MINING TOOL FOR WEB PAGE SURFING PREDICTION Web Data Management and Mining Overview Digital Libraries E-Commerce Technologies Semantic Web Technologies Web Data Mining Summary and Directions Effective Web Page Prediction Using Hybrid Model Overview Our Approach Feature Extraction Domain Knowledge and Classifier Reduction Summary Multiple Evidence Combination for WWW Prediction Overview Fitting a Sigmoid after SVM Fitting a Sigmoid after ANN Output Dempster-Shafer for Evidence Combination Dempster's Rule for Evidence Combination Using Dempster-Shafer Theory in WWW Prediction Summary and Directions WWW Prediction Results Overview Terminology Data Processing Experiment Setup Results Discussion Summary and Directions DATA MINING TOOL FOR IMAGE CLASSIFICATION Multimedia Data Management and Mining Overview Managing and Mining Multimedia Data Management and Mining Text, Image, Audio, and Video Data Summary and Directions Image Classification Models Overview Example Models Image Classification Summary Subspace Clustering and Automatic Image Annotation Introduction Proposed Automatic Image Annotation Framework The Vector Space Model Clustering Algorithm for Blob Token Generation Construction of the Probability Table AutoAnnotation Experimental Setup Evaluation Methods Results Summary Enhanced Weighted Feature Selection Introduction Aggressive Feature Weighting Algorithm Experiment Results Summary and Directions Image Classification and Performance Analysis Introduction Classifiers Evidence Theory and KNN Experiment Results Discussion Summary and Directions Summary and Directions Overview Summary of This Book Directions for Data Mining Tools Where Do We Go from Here Appendix: Data Management Systems: Developments and Trends Overview Developments in Database Systems Status, Vision, and Issues Data Management Systems Framework Building Information Systems from the Framework Relationship between the Texts Summary and Directions Index References appear at the end of each chapter.
Phytotoxin Produced by Pseudomonas glumue Kurita et Tabei, a Causal Bacterium of the Grain and Seedling Rot of Rice

The phytotoxin produced by Pseudomonas glumae, the causal agent of bacterial grain and seedling rot of rice, was purified and characterized. The ultraviolet and visible absorption spectrum of the phytotoxin exhibited two maxima at 260nm and 399nm in 80% aqueous methanol. In the infrared absorption spectrum, main bands at 2,800 3,00Ocm-‘, 1,68Ocm-‘, 1,540cm. ’ and 1,05Ocm-’ were observed, suggesting the presence of a methyl group, a ketone group, a benzene ring and a carbon- hydrogen bond, respectively. The molecular weight of this substance was determined as 193 and the molecular formula was decided as C7H7Ns02 by a high resolution EI mass spectrometry. The phytotoxin was isolated noi only from the PS broth, but also from rice seedling infected with the causal bacterium. This phytotoxin might be related to the pathogenicity of P. glumae.
Adaptive detection and tracking using multimodal information

This thesis describes work on fusing data from multiple sources of information, and focuses on two main areas: adaptive detection and adaptive object tracking in automated vision scenarios. The work on adaptive object detection explores a new paradigm in dynamic parameter selection, by selecting thresholds for object detection to maximise agreement between pairs of sources. Object tracking, a complementary technique to object detection, is also explored in a multi-source context and an efficient framework for robust tracking, termed the Spatiogram Bank tracker, is proposed as a means to overcome the difficulties of traditional histogram tracking. As well as performing theoretical analysis of the proposed methods, specific example applications are given for both the detection and the tracking aspects, using thermal infrared and visible spectrum video data, as well as other multi-modal information sources.
Secure Coding Schemes and Code Design for the Wiretap Channel

As wireless networks continue to flourish worldwide, the issues of security play an increasingly important role in those communication networks. Unlike the traditional security services, which are based on some cryptographic schemes in the application layer, the information theoretic security approach opens a promising direction to achieve the secrecy communication in the Physical layer. This PhD thesis builds upon the informationtheoretic security foundations to analyse and develop novel reliable and secure coding schemes and the code design technique in the Physical layer. First, the reliability and the security of the orthogonal codes using in Wyner’s coding structure have been analysed in the Gaussian wiretap channel. Both the analytic and the simulation results show that the orthogonal codes can achieve reliable and perfect secret communication in degraded Gaussian wiretap channel in wideband regime. Second, a simplified encoder for the syndrome coding scheme has been proposed, which makes the scheme practical for the codes with long lengths of the parity bits. An efficient recursive evaluation of the equivocation for the traditional syndrome coding scheme has been described, which gives an insight to construct the best codes. The best code design technique has been proposed for the syndrome coding scheme, as measured by an information secrecy metric: the equivocation. It is shown that the Best Equivocation Codes exhibit better security properties than the best known codes, when used in conjunction with syndrome coding schemes. Third, the security performance of the random codes in the traditional syndrome coding scheme has been studied. Besides the Monte Carlo analysis, a theoretical way has been proposed to evaluate the average equivocation of the random linear codes. It is shown that random codes with large values of m, the number of parity bits, may be analysed theoretically even though Monte Carlo analysis is impractical. The results give that as the code length increases, the information rate is getting closer to the secrecy capacity. Fourth, two chain based syndrome coding schemes have been proposed in the binary symmetric wiretap channel. It has been shown that the chain based syndrome coding
The RAGS reference manual

A replaceable, sterilizable, cell growth assembly comprising a chamber structure of gas permeable, liquid impermeable material having an inner surface to which cells are attachable. The chamber is of tubing configuration and is disposed in a plurality of layers in stacked relation. Spacer structure between the layers of tubing define a plurality of flow passages between the layers and enable a gaseous environment to bathe the majority of the external surface area of the tubing layers. The chamber also includes an inlet conduit for introducing culture media for flow through the plural layers of the chamber structure and an outlet conduit for receiving culture media from the chamber structure, and coupling structure for detachably connecting the inlet conduit to a source of culture media and the outlet conduit to an appropriate culture media receptacle.
Formation and Organization of Receptive fields, with an input Environment Composed of Natural Scenes

iii Acknowledgments I would like to thank Professor Leon N Cooper for urging me to examine the eeect of introducing a more realistic visual environments, and for asking me to make sense of some of the network models, and see how they relate to the BCM model. I would like to thank him also for sharing with me his ability to gure out what is important in a complex detailed situation, I hope some of it may have rubbed oo on me. I like to express my appreciation to Professor Eitan Domany who was my advisor at the Weizmann Institute, and who rst introduced me to the world of scientiic research, from him I learned the importance of a good understanding of the fundamentals. I Would also like to thank all the members of the Institute of Brain and Neural Systems, and in particular to, Yong Liu with whom I cooperated a lot in the last three years, to Nathan Intrator and Mike Perrone for many fruitful discussions, to Charlie Law who was very helpful in setting up the natural images data base, and for Roger Blumberg for carefully reading a draft of this thesis. My thanks to Prooesors Bob Pelcovits David Gottlieb and to Igor Bukharev for help and comments on some of this work. I would express my deepest thanks to my family; Rutie who helped even in the hardest times, though she would have preferred I did something comprehensible, to Ofer cause he is the cutest, and most curious kid in the world, and most of all to my parents who brought me up with a lot of love, and who encouraged my curiosity from the earliest age I can remember.
Crowdsourcing for Speech Processing: Applications to Data Collection, Transcription and Assessment

Provides an insightful and practical introduction to crowdsourcing as a means of rapidly processing speech dataIntended for those who want to get started in the domain and learn how to set up a task, what interfaces are available, how to assess the work, etc. as well as for those who already have used crowdsourcing and want to create better tasks and obtain better assessments of the work of the crowd. It will include screenshots to show examples of good and poor interfaces; examples of case studies in speech processing tasks, going through the task creation process, reviewing options in the interface, in the choice of medium (MTurk or other) and explaining choices, etc.Provides an insightful and practical introduction to crowdsourcing as a means of rapidly processing speech data.Addresses important aspects of this new technique that should be mastered before attempting a crowdsourcing application.Offers speech researchers the hope that they can spend much less time dealing with the data gathering/annotation bottleneck, leaving them to focus on the scientific issues. Readers will directly benefit from the books successful examples of how crowd- sourcing was implemented for speech processing, discussions of interface and processing choices that worked and choices that didnt, and guidelines on how to play and record speech over the internet, how to design tasks, and how to assess workers.Essential reading for researchers and practitioners in speech research groups involved in speech processing
Decision models in the design of adaptive educational hypermedia systems

Several efforts have been reported in literature aiming to support the Adaptation Model (AM) design in Adaptive Educational Hypermedia Systems (AEHS) with either guidance for the direct definition of adaptation rules, or semi-automated mechanisms which generate the AM via the implicit definition of such rules. The main drawback of the direct definition of adaptation rules is that there can be cases during the run-time execution of AEHS where no adaptation decision can be made, due to insufficiency and/or inconsistency of the defined adaptation rule sets. The goal of the semi-automated, decision-based approaches is to generate a continuous decision function that estimates the desired AEHS response, aiming to overcome the above mentioned problem. However, such approaches still miss a commonly accepted framework for evaluating their performance. In this chapter, we review the design approaches for the definition of the AM in AEHS and discuss a set of performance evaluation metrics proposed by the literature for validating the use of decision-based approaches.
Fusion of Asynchronous, Parallel, Unreliable Data Streams

Abstract : This report documents progress in an investigation of the fusion of parallel data streams composed of dissimilar data types of varying reliability, arriving at different times. The technique will serve to determine resemblance between objects, in this case, people, which can be described by a stream of data expressed as state vectors. Objects described by similar state vectors will group together. The data fusion methodology is based on the use of multidimensional scaling to evaluate data composed of attribute vectors of high-spatial dimensionality. A focus problem has been used for development of the technique, identification of high-value individuals (HVIs). The technique applies to other problems as well; the identification of HVIs is only one topical application. Data available to field operators and analysts includes traditional identification information,biometric identity files, situational and observational data, archival data, and intelligence information. These data may be used to classify individuals in terms of resemblance to key groups and cue an analyst to whether they are HVIs. A proof of concept of the methodology has been conducted with promising results.
Graph Construction Through Laplacian Function Optimization

I study a variety of topics relating to the graph construction technique proposed in ‘Fitting a Graph to Vector Data’ (Daitch, Kelner, Spielman, 2009). This technique attempts to construct a graph that optimizes how closely each vertex can be approximated as a weighted average of its neighbors. I describe a slightly modified graph construction technique and explore its e↵ectiveness for classification and regression tasks. I also present some interesting experimental results that seem to confirm the conjecture from ‘Fitting a Graph to Vector Data’ that the average degree of the constructed graphs is somehow correlated with the underlying dimensionality of our data. I give some ideas about how we can improve learning over our graphs and how we can explore the relationship between the optimization function used to construct the graphs and the optimization functions used in the learning algorithms applied to the graphs. Finally, I present some preliminary work looking at the structure of the optimization problem used to construct our graphs. This work eventually helped lead to a counter-example of the conjecture in the original paper that a data set in general position has a unique optimal graph. I will present the full extent of the work on the uniqueness problem in a forthcoming report with Christopher Musco, with whom I collaborated on this part of the project.
Semantic Provenance: Modeling, Querying, and Application in Scientific Discovery

Sahoo, Satya Sanket. Ph.D., Department of Computer Science and Engineering, Wright State University, 2010. Semantic Provenance: Modeling, Querying, and Application in Scientific Discovery Provenance metadata, describing the history or lineage of an entity, is essential for ensuring data quality, correctness of process execution, and computing trust values. Traditionally, provenance management issues have been dealt with in the context of workflow or relational database systems. However, existing provenance systems are inadequate to address the requirements of an emerging set of applications in the new eScience or Cyberinfrastructure paradigm and the Semantic Web. Provenance in these applications incorporates complex domain semantics on a large scale with a variety of uses, including accurate interpretation by software agents, trustworthy data integration, reproducibility, attribution for commercial or legal applications, and trust computation. In this dissertation, we introduce the notion of “semantic provenance” to address these requirements for eScience and Semantic Web applications. In addition, we describe a framework for management of semantic provenance by addressing the three issues of, (a) provenance representation, (b) query & analysis, and (c) scalable implementation. First, we introduce a foundational model of provenance called Provenir to serve as an upper-level reference ontology to facilitate provenance interoperability. Second, we define a classification scheme for provenance queries based on the query characteristics and use this scheme to define a set of specialized provenance query operators. Third, we describe the implementation of a highly scalable query engine to support the provenance query operators, which uses a new class of materialized views based on the Provenir ontology, called Materialized Provenance Views (MPV), for query optimization. We also define a novel provenance tracking approach called Provenance Context Entity (PaCE) for the Resource Description Framework (RDF) model used in Semantic Web applications. PaCE, defined in terms of the Provenir ontology, is an effective and scalable approach for RDF provenance tracking in comparison to the currently used RDF reification vocabulary. Finally, we describe the application of the semantic provenance framework in biomedical and oceanography research projects.
A JAVA FUNCTION VISUALIZER

Visualization is an important tool for student learning. The Function Visualizer provides a resource that permits the visualization of function calls for any set of Java functions. It provides a control panel where the user specifies the speed of the visualization and the parameter values for the initial function call. When the visualization begins, lines of code are highlighted in a function window as they are executed and when another function is called, a new window appears on top of the previous one, producing a visualization of the function call stack. This tool provides a flexible and intuitive visualization aid for the teaching of recursion. INTRODUCTION Visualization is an important classroom tool. All teachers use it to help their students “see” difficult concepts. A recent overview paper [1] listed many motivating factors for the use of visualization including better learning, attracting student attention, faster coverage of topics, increased student understanding, encouraging under-prepared students, facilitating debugging, and using classroom time efficiently. There are many opportunities for visualization in Computer Science instruction and it is natural to use the computer to generate such visualizations dynamically and interactively. Work in Computer Science instructional visualization has been focused on two specific areas: program visualization and algorithm animation. A taxonomy of the former category is found in [5] and
Primary prevention of cardiovascular disease: evaluation of an individual-based strategy

Deelname aan een webbased preventief gezondheidsonderzoek met gerichte leefstijladviezen voor patienten met hart- en vaatziekten leidt niet tot een meetbaar leefstijleffect. Wel was sprake van een afname van het risico op hart- en vaatziekte. Die afname is bijna 18 procent onder deelnemers met een hoog risico, en met zo’n 5 procent onder alle deelnemers na zes maanden follow-up. Verbetering van primaire preventiestrategieen kan hart- en vaatziekten voorkomen. Ersen Colkesen onderzocht het effect van een webbased preventief gezondheidsonderzoek met gerichte leefstijladviezen. Hij keek naar inhoud, methoden voor risicoberekening, wetenschappelijk gefundeerde keuze van interventies gebaseerd op richtlijnen, en naar bereik en effectiviteit van het programma in een bedrijfsgeneeskundige setting. Deelname leidde niet tot een meetbaar leefstijleffect. Dergelijke programma’s verbeteren wellicht de primaire preventie, maar gerandomiseerde studies met een langere follow-up moeten het uiteindelijke bewijs leveren.
Preemptive Placement and Routing for In-Field FPGA Repair

Preemptive Placement and Routing for In-Field FPGA Repair Joshua E. Jensen Department of Electrical and Computer Engineering, BYU Master of Science With the growing density and shrinking feature size of modern semiconductors, it is increasingly difficult to manufacture defect free semiconductors that maintain acceptable levels of reliability for long periods of time. These systems are increasingly susceptible to wearout by failing to meet their operational specifications for an extended period of time. The reconfigurability of FPGAs can be used to repair post-manufacturing faults by configuring the FPGA to avoid a damaged resource. This thesis presents a method for preemptively preparing to repair FPGA devices with wear-out faults by precomputing a set of repair circuits that, collectively, can repair a fault found in any logic block of the FPGA. This approach relies on logic placement and routing to create “repair” circuits that avoid specific logic blocks. These repairs can be used when a specific resource has failed. New placement and routing algorithms are proposed for generating such repair circuits. The number of repairs needed to create a complete repair set depends heavily on the utilization of the FPGA resources. The algorithms are tested against several benchmarks and with multiple area constraints for each benchmark. Using this work, on average 20 repair configurations were needed to repair 99% of permanent faults.