Miracle-dz/UniFAR-FTUs huggingface dataset:
Stream: A Framework For Data Stream Modeling in R

In recent years, data streams have become an increasingly important area of research. Common data mining tasks associated with data streams include classiﬁcation and clustering. Due to both the size and the dynamic nature of data streams, it is often diﬃcult to obtain real-time stream data without the overhead of setting up an infrastructure that will generate data with speciﬁc properties. We have built the framework in R , a popular tool for data mining and statistical analysis with the intent that researchers will be able to easily integrate our framework into their existing work. In this paper we introduce the implementation of stream , an R package that provides an intuitive interface for experimenting on data streams and their applications. stream is a general purpose tool that can model data streams and perform data mining tasks on the generated data. It allows the researcher to control speciﬁc behaviors of the streams so that they create scenarios that may not be easily reproducible in the real-world, such as the merging and splitting of clusters. Additionally, it has the ability to replay the requested data for other data mining tasks if needed, or read data streams from other sources and incorporate them into the framework.
MONITORING HEALTH BY DETECTING DRIFTS AND OUTLIERS IN PATTERNS OF AN INHABITANT IN A SMART HOME

All Rights Reserved iii ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my supervising professor Dr. Diane Cook for providing me with continuous encouragement, inspiration and confidence. Her support and guidance played a big part in my thesis. Many thanks to the faculty at UTA who gave me knowledge and valuable advice that I hope to carry all my life. I would like to especially thank Dr. and Dr. Alp Aslandogan. I thank my old school teachers for helping me grow interest in computer science. I thank all my friends at UTA and back home. I am also grateful to the many friends and colleague from AI Lab who gave me good advice, especially The elderly, along with people with disabilities or chronic illness, are most often dependent on some kind of formal or informal care. They are forced to move to a place where they can be cared for. Automatic health monitoring allows them to maintain their independence and continue living at home longer by continuously providing key health and activity information to caregivers. In this thesis, we present a novel technique, called the Health Monitoring System (HMS), which is a data-driven automated monitoring system for detecting changes in the patterns of activities/inactivity, health data and the living environment. HMS classifies these changes as drifts and outliers. These changes reflect short and long term lifestyle trends as well as any sudden changes v in the living patterns of the inhabitant. HMS uses domain knowledge to determine the importance of a change and reports them to the caregivers in an easy-to-understand format.
PARSEVAL : a workbench for queueing networks parallel simulation

This report describes PARSEVAL (PARalellizing Simulations for performance EVALuation), an experimental distributed simulation workbench dedicated to queueing systems and running on a network of Transputers attached to a Sun workstation. It uses an implementation of the conservative method proposed by Chandy and Misra for the synchronization of processes. After a brief description of the structure of PARSEVAL, the different parts that make it up and the implementation of the conservative mechanism, we give some considerations on its performance compared to the sequential simulator QNAP2. It is shown that speed-up depends on the structure of the model to be simulated and the hardware configuration used. Finally, we present current and planned developments around this workbench. Key-words: Discrete event simulation, Distributed Simulation, Queueing networks
Game Challenge: A Factorial Analysis Approach

Video games that customize to a player's experience level and abilities have the potential to allow a broader range of players to become engaged and maintain interest as they progress in experience level. A game that uniquely customizes the player's experience could attract additional demographics to gaming, which will result in a distinct edge in marketability and potential revenue. This thesis examines a subsection of adaptive gaming systems from the perspective of identifying game factors that alter the level of di culty. Our focus is to provide a solution useful to both research and commercial gaming communities by developing a system that simulates results o ine yet can be integrated into online play. While online performance is the main goal of an adaptive system, the o ine simulation provides several bene ts. O ine simulation allows the elimination of insigni cant factors from inclusion in the training and evolution phase of machine learning algorithms. In addition it provides commercial games with a useful tool or method for performing game balancing and level tuning. To test our approach we designed a test-bed version of the game PacMan. The experimental testbed alters environment variables to evaluate their e ect on a set of selected response variables. Observing the results of several response variables provides the potential to represent multiple player states, though our focus is on controlling the di culty for a player. The testbed will simulate the actions of both Pac-Man and the ghosts over a variety of di erent settings and strategies. The evaluation of a factor's signi cance and its e ect size are calculated using a factorial analysis approach. This method allows the identi cation of factors relevant to both individual strategies, and the set of all player strategies. Finally, as a proof of concept for both the online and adaptation prospects of this method, we developed a prototype adaptive system. Utilizing the relevant factor e ects calculated in the factorial analysis, the prototype adapts to control the progress of the game towards targeted response variable intervals.
Register Allocation for Modulo Scheduled Loops: Strategies, Algorithms and Heuristics

register allocation, modulo scheduling, software pipelining, instruction scheduling, code generation, instruction-level parallelism, multiple operation issue, VLIW processors, "ery Long Instruction word processors Software pipelining is an important instruction scheduling technique for efficiently overlapping successive iterations of loops and executing them in parallel. This technical report studies the task of register allocation for software pipelined loops, both with and without hardware features that are specifically aimed at supporting software pipelines. Register allocation for software pipelines presents certain novel problems leading to unconventional solutions, especially in the presence of hardware support. This technical report formulates these novel problems and presents a number of alternative solution strategies. These alternatives are comprehensively tested against over one thousand loops to determine the best register allocation strategy, both with and without the hardware support for software pipelining.
Title: Usability Patterns for Applications on the World Wide Web

A leading usability expert, Jakob Nielsen, enjoys pointing out that there are millions of web sites and millions of web pages, and only thousands of usability engineers and user interface designers. [Nielsen99a] These patterns and others like them endeavor to establish helpful guidelines for developers to improve usability for their users. By satisfying users’ expectations, productivity is improved and the users’ enjoyment is enhanced.
Storage systems and ZFS in FenixOS

In this project report we propose an architecture for a storage infrastructure for Fenix OS. Fenix OS is a research operating system that is developed at DTU Informatics. Some of the proposed architecture is evaluated by building a prototype implementation. The proposed architecture is modeled after the OpenSolaris ZFS implementation. The choice of ZFS as a base for the storage infrastructure for Fenix OS is based on studies of the storage stacks of the OpenSolaris 10 and Linux 2.6. 11 kernels. The project report will provide chapters that present the results of our studies of these legacy storage stacks. The report will also provide an introduction to the concepts and features available in the OpenSolaris ZFS implementation, as well as a study of the architecture of the ZFS stack. Based on these studies of ZFS, we present our own proposal for a storage infrastructure for Fenix OS. The proposal is evaluated by an prototype implementation that is able to read a Storage Pool created by OpenSolaris. We propose a way to interface the page cache in Fenix OS, but due to limitations in the Fenix OS kernel, the prototype implementation does not include caching.
Learning to Classify Texts Using Positive and Unlabeled Data

In traditional text classification, a classifier is built using labeled training documents of every class. This paper studies a different problem. Given a set P of documents of a particular class (called positive class) and a set U of unlabeled documents that contains documents from class P and also other types of documents (called negative class documents), we want to build a classifier to classify the documents in U into documents from P and documents not from P. The key feature of this problem is that there is no labeled negative document, which makes traditional text classification techniques inapplicable. In this paper, we propose an effective technique to solve the problem. It combines the Rocchio method and the SVM technique for classifier building. Experimental results show that the new method outperforms existing methods significantly.
An Evolutionary Approach to the Semantic Web

Proposals for creating a semantic web have been around at least since 1995 [Dobson and Burrill, 1995]. A wide range of semantic markup languages have been proposed including RDF, N3, SHOE, DAML, and OIL. Yet, in contrast with the explosive growth of HTML, the semantic web has been slow to materialize. Below, we attempt to explain why by articulating several hypotheses. We then sketch our own approach which seeks to facilitate gradual evolution from HTML to a semantic web. People have not been tagging their web pages because they have had no reason to do so.1 After all, HTML became popular only after MOSAIC came along. Tagging will be driven by applications that consume the tags and result in immediate, tangible satisfaction for the author; Instant gratification is a requirement for rapid adoption. Another factor to consider is ease of authoring. HTML spread like wild fire due to its simplicity; users did not need documentation. Instead, cut-and-paste sufficed to create the average home page. To proliferate, the tagging scheme has to be simple to understand and convenient to author. Clean syntax is key; usability is essential. Moreover, Selfdocumenting tags whose approximate meaning is apparent from their name, and are documented by very brief text, will proliferate before tags whose semantics requires elaborate specification. Unlike 1993, we now have upwards of a billion HTML pages. Backward compatibility is essential: HTML pages will be tagged without disrupting the standard HTML-based browsing and searching activities. In addition, whereas much of the research on the semantic web has focused on enabling SQL-style queries and powerful agents, we believe that tagging will impact browsing and searching before they support more “semantically intense” applications. Finally, we believe in Local tags. That is, local communities will adopt tagging schemes long before these spread across multiple organizations. We are testing our tagging scheme (and our working hypotheses) by attempting to gradually evolve a miniature semantic web in the University of Washington Computer Science Department, complete with a simple set of tags, instant gratification “apps. ”, and authoring tools. Because of our emphasis on local tagging, we have deferred committing to a mechanism for “semantic interoperability” such as XML namespaces in RDF or the useontology tag in SHOE. However, we anticipate leveraging the ontology-matching mechanisms outlined in [Doan et al., 2001]. Our tagging scheme was designed to embody the above hypotheses. We introduce it via a simple example in Figure 1. The tags are self-documenting, but two aspects are noteworthy. First, nested tags indicate attribute information. For example, <office hours> is an attribute of each <instructor>. Second, for convenience we enable taggers to tag an entire list or table by specifying a simple regular expression at the top.
Hidden Markov Models and Their Mixtures

Hidden Markov Models and Their Mixtures by Christophe Couvreur Diplôme d' etudes approfondies en math ematiques Facult e des sciences { D epartement de math ematiques, Universit e catholique de Louvain Prof. Jean-Marie Rolin, Advisor Hidden Markov models (HMMs) form a class of stochastic processes which have been applied successfully to a wide variety of practical problems. Hidden Markov models are based on an unobserved (or hidden) discrete Markov chain fXng which describes the evolution of the state of a system. Given a realization fxng of the state process, the observed variables fYng are conditionally independent, with the distribution of each Yn function of the corresponding state xn only. Solutions to the three basic hidden Markov modeling problems are presented: computation of the likelihood of a realization yN 0 = (y0; y2; : : : ; yN ) given a model, estimation of the corresponding unobserved state sequence XN 0 = (X0;X1; : : : ;XN ), and computation of the maximum likelihood estimate of the HMM parameters. A review of the HMM literature covering a wide range of applications is also provided. Inference issues for HMMs are discussed, including the description of the properties of the maximum likelihood estimates and a presentation of other estimation methodologies. Particular attention is devoted to the classi cation of HMMs (multiple point hypotheses testing). The new concept of mixture of HMMs is introduced. Various estimation and classi cation problems for mixtures of HMMs are investigated, with special care taken of the \decomposition of mixtures" question. Some preliminary numerical results are presented. Finally, directions for future research are proposed.
Using Fine-Grained Skill Models to Fit Student Performance with

The ASSISTment online tutoring system was used by over 600 students during the school year 2004-2005. Each student used the system as part of their math classes 1-2 times a month, doing on average over 100+ state-test items, and getting tutored on the ones they got incorrect. The ASSISTment system has 4 different skill models, each at different grain-size involving 1, 5, 39 or 106 skills. Our goal in the paper is to develop a model that will predict whether a student will get correct a given item. We compared the performance of these models on their ability to predict a student state test score, after the state test was "tagged" with skills for the 4 models. The best fitting model was the 39 skill model, suggesting that using finer-grained skills models is useful to a point. This result is pretty much the same as that which was achieved by Feng, Heffernan, Mani, & Heffernan (in press), who were working simultaneously, but using mized-effect models instead of Bayes networks. We discuss reasons why the finest-grained model might not have been able to predict the data the best. Implications for large scale testing are discussed.
RCS: The NBS Real-Time Control System

The National Bureau o f Standards, Indus t r ia l Systems Div is ion designed the Real time Control System where high level goals are decomposed through a succession o f l eve l s , each producing s t r i n g s o f simpler comnands t o the next lower leve l . The bottom l e v e l generates the dr i ve s igna ls t o the robot, gr ipper, and other actuators. Each cont ro l l e v e l i s a separate process wi th a l im i ted scope o f responsibi l i ty , independent o f the de ta i l s a t other levels, thus prov id ing a foundation f o r fu tu re modular, ''plug compatible " hardware and software f o r robots and r e a l -t i mesensory i n t e r a c t i v e control appl icat ions. To aid i n specifying the required task decomposition and task processing, a programing language and program development environment were inplemnted. Programs a t each cont ro l l e v e l are expressed as s t a t e tables, and the p rog ram ing environment permits the generation, edi t ing, emulation, and eva lua t i on o f these s t a t e tables. The con t ro l system i s completely in te rac t i ve , a l lowing the system t o run freely, o r be single stepped t o any l e v e l o f de ta i l .
Power analysis attacks and countermeasures for cryptographic algorithms

Power analysis attacks against smartcard implementations of cryptographic algorithms are reviewed and analyzed. The types of attacks that are possible, the severity of these attacks and solutions to prevent these attacks are investigated. Results from monitoring smartcard power signals are reported, and new techniques to model, and consequently, strengthen existing power analysis attacks are provided. Three new attacks, specifically aimed against the modular exponentiation algorithm of public-key cryptosystems are described. Some vulnerabilities in elliptic-curve cryptosystems are also given. New, more advanced power analysis attacks that use synchronized sampling, total power analysis, correlation analysis, and higher-order methods are also shown to be practical. Software and hardware countermeasures to these attacks are briefly discussed. Finally, a specific countermeasure that uses random masks is employed to secure the Advanced Encryption Standard (AES) finalist algorithms. The performance and memory requirements of these new implementations are reported. Experimental results confirm the effectiveness and practicality of many of the newly proposed attacks.
User’s Behavior Classification Model for Smart Houses Occupant Prediction

This paper deals with the smart house occupant prediction issue based on daily life activities. Based on data provided by non intrusive sensors and devices, our approach uses supervised learning technics to predict the house occupant. We applied Support Vector Machines (SVM) classifier to build a Behavior Classification Model (BCM) and learn the users’ habits when they perform activities for predicting and identifying the house occupant. To test the model, we have analyzed the early morning routine activity of six users at the DOMUS apartment and two users of the publicly available dataset of the Washington State University smart apartment tesbed. The results showed a high prediction precision and demonstrate that each user has his own manner to perform his morning activity, and can be easily identified by just learning his habits.
The Census of antique works of art and architecture known to the Renaissance

The Census of Antique Works of Art and Architecture known to the Renaissance is a joint venture of the Warburg Institute in London and the Bibliotheca Hertziana, an institute of the German Max Planck-Gesellschaft, in Rome. With the generous support of the Getty Art History lnformation Program over about ten years the two Institutes have started to build a scholarly database including all ancient monuments, which were known during the period from 1400 to around 1550, i.e. from architecture and sculpture to applied arts which are documented in visual or written sources of the Renaissance, such as drawings, prints, statuettes or printed guidebooks, letters, manuscripts etc. The project was founded in 1946 by Fritz Saxl, then Director of the Warburg Institute, together with Karl Lehmann-Hartleben, then Director of the Institute of Fine Arts in New York, and Richard Krautheimer, who was then working, in collaboration with his wife Trude Krautheimer-Hess, on their monograph of the Fiorentine sculptor Lorenzo Ghiberti. Initially the Census collected only information on figurative monuments. When the Hertziana joined in 198i the project was extended to include architecture, and new aspects, springing from recent developments in scholarly approaches to the Nachleben of the antique, were added. The project could be continued and computerized thanks to the generous support of AHIP since 1982. Over 10,500 computer records on ancient MONUMENTS and over 20,500 records on RENAISSANCE DOCUMENTS have so far been entered into the Census database; the present version of the interactive dual videodisk linked to the database contains 25,000 images. The Census provides for example information to archaeologists on the ancient and modem history of famous monuments like the Pantheon, the Colosseum, the Laocoon, the Apollo Belvedere in Rome and even on the Parthenon in Athens as well as on less famous artefacts like coins, pots or architectural fragments, on ancient buildings or statues which are no longer preserved today like the Septizodium or the so-called Letto di Policleto. Findspots of bases, capitals or cornices or of pieces of sculpture are recorded. The information on the provenance of antique works allows the reconstruction of entire Renaissance Collections which previously required a great deal of research. The sequence of previous states of preservation of ancient works of art can be visually followed almost like in a slow motion picture by e.g. displaying the series of the various representations of a piece of sculpture one after another.
Application Specific Sandboxing for Win32/Intel Binaries

Comparing the system call sequence of a network application against a sandboxing policy is a popular approach to detecting control-hijacking attack, in which the attacker exploits such software vulnerabilities as buffer overflow to take over the control of a victim application and possibly the underlying machine. The long-standing technical barrier to the acceptance of this system call monitoring approach is how to derive accurate sandboxing policies for Windows applications whose source code is unavailable. In fact, many commercial computer security companies take advantage of this fact and fashion a business model in which their users have to pay a subscription fee to receive periodic updates on the application sandboxing policies, much like anti-virus signatures. This paper describes the design, implementation and evaluation of a sandboxing system called BASS that can automatically extract a highly accurate application-specific sandboxing policy from a Win32/X86 binary, and enforce the extracted policy at run time with low performance overhead. BASS is built on a binary interpretation and analysis infrastructure called BIRD, which can handle application binaries with dynamically linked libraries, exception handlers and multithreading, and has been shown to work correctly for a large number of commercially distributed Windows-based network applications, including IIS and Apache. The throughput and latency penalty of BASS for all the applications we have tested except one is under 8
3D on the WEB and Virtual Humans

It is now possible to explore 3D worlds by using a classical WEB navigator as easily as it is to view 2D HTML pages. The capability to use 3D was previously restricted to powerful machines, it is no more the case. 3D provides a new interface to communicate. The Definition of realistic virtual humans in those worlds produces more intuitive interface for neophyte users. In this paper, we propose a set of tools to define virtual humans and bring them to life by using a standard 3D language for the WEB.