ppxscal/embedding_network huggingface dataset:
Learning data driven representations from large collections of multidimensional patterns with minimal supervision
Authors: S. Shankar Sastry
Venue:  (2008)

Traditionally, taking experimental measurements of a physical or biological phenomenon was an expensive, laborious and very slow process. However, significant advances in device technologies and computational techniques have sharply reduced the costs of data collection. Capturing thousands of images of developing biological organisms, or recording enormous amounts of video footage from a network of cameras monitoring an observation space, or obtaining a large number of neural measurements of brain signal patterns via non-invasive devices are some of the examples of such data proliferation. Analyzing such large volumes of multi-dimensional data through expert supervision is neither scalable nor cost-effective. In this context, there is a need for systems that complement the expert user by learning meaningful and compact representations from large collections of multidimensional data (images, videos etc.) with minimal supervision. In this dissertation, we present minimally supervised solutions to two such scenarios generally encountered. The first scenario arises when a large set of labeled noisy observations are available from a given class (or phenotype) with an unknown generative model. An interesting challenge here is to estimate the underlying generative model and the distribution over the distortion parameters that map the observed examples to the generative model. For example, this is the scenario encountered while attempting to construct high-throughput data-driven spatial gene expression atlases from many thousands of noisy images of Drosophila melanogaster imaginal discs. We discuss improvements to an existing information theoretic approach for joint pattern alignment (JPA) in order to address such high-throughput scenarios. Along with the discussion of the assumptions, advantages and limitations of our approach (Chapter 2), we show how this framework can be applied to a variety of applications (Chapters 3, 4, 5). The second scenario arises when there are observations available from multiple classes (phenotypes) without any labels. An interesting challenge here is to estimate a data driven organizational hierarchy that facilitates efficient retrieval and easy browsing of the observations. For example, this is the scenario encountered while organizing large collections of unlabeled activity videos based on the spatio-temporal patterns, such as actions of human beings, embedded in the videos. We show how some insights from computer vision and data-compression can be efficiently leveraged to provide a high-speed and robust solution to the problem of content-based hierarchy estimation (based on action similarity) for large video collections with minimal user supervision (Chapter 6). We demonstrate the usefulness of our approach on a benchmark dataset of human action videos.
Block-based motion estimation analysis for lip reading user authentication systems
Authors: Khaled Alghathbar, Hanan A. Mahmoud
Venue: WSEAS Transactions on Information Science and Applications (2009)

This paper proposes a lip reading technique for speech recognition by using motion estimation analysis. The method described in this paper represents a sub-system of the Silent Pass project. Silent Pass is a lip reading password entry system for security applications. It presents a user authentication system based on password lip reading. Motion estimation is done for lip movement image sequences representing speech. In this methodology, the motion estimation is computed without extracting the speaker's lip contours and location. This leads to obtaining robust visual features for lip movements representing utterances. Our methodology comprises of two phases, a training phase and a recognition phase. In both phases an n × n video frame of the image sequence for an utterance (can be an alphanumeric character, word or a sentence in more complicated analysis) is divided into m × m blocks. Our method calculates and fits eight curves for each frame. Each curve represents motion estimation of this frame in a specific direction. These eight curves are representing set of features of a specific frame and are extracted in an unsupervised manner. The feature set consists of the integral values of the motion estimation. These features are expected to be extremely effective in the training phase. The feature sets are used to characterize specific utterances with no additional acoustic feature set. A corpus of utterances and their motion estimation features are built in the training phase. The recognition phase is accomplished by extracting the feature set, from the new image sequence of lip movement of an utterance, and compare it to the corpus using the mean square error metric for recognition.
Tracking Files in the Kepler Provenance Framework
Authors: Pierre Mouallem, Roselyne Barreto, Scott Klasky, Norbert Podhorszki, Mladen Vouk
Venue: Proceedings of the 21st International Conference on Scientific and Statistical Database Management (2009)

Workflow Management Systems (WFMS), such as Kepler, are proving to be an important tool in scientific problem solving. They can automate and manage complex processes and huge amounts of data produced by petascale simulations. Typically, the produced data need to be properly visualized and analyzed by scientists in order to achieve the desired scientific goals. Both run-time and post analysis may benefit from, even require, additional meta-data --- provenance information. One of the challenges in this context is the tracking of the data files that can be produced in very large numbers during stages of the workflow, such as visualizations. The Kepler provenance framework collects all or part of the raw information flowing through the workflow graph. This information then needs to be further parsed to extract meta-data of interest. This can be done through add-on tools and algorithms. We show how to automate tracking specific information such as data files locations.
Applying genetic algorithms to decision making in autonomic computing systems
Authors: Andres J. Ramirez, David B. Knoester, Betty H.C. Cheng, Philip K. McKinley
Venue: Proceedings of the 6th international conference on Autonomic computing (2009)

Increasingly, applications need to be able to self-reconfigure in response to changing requirements and environmental conditions. Autonomic computing has been proposed as a means for automating software maintenance tasks. As the complexity of adaptive and autonomic systems grows, designing and managing the set of reconfiguration rules becomes increasingly challenging and may produce inconsistencies. This paper proposes an approach to leverage genetic algorithms in the decision-making process of an autonomic system. This approach enables a system to dynamically evolve reconfiguration plans at run time in response to changing requirements and environmental conditions. A key feature of this approach is incorporating system and environmental monitoring information into the genetic algorithm such that specific changes in the environment automatically drive the evolutionary process towards new viable solutions. We have applied this genetic-algorithm based approach to the dynamic reconfiguration of a collection of remote data mirrors, with the goal of minimizing costs while maximizing data reliability and network performance, even in the presence of link failures.
Working together - a VR based approach for cooperative digital design review
Authors: Christian Knöpfle
Venue: Proceedings of the working conference on Advanced visual interfaces (2002)

In this paper we will present an approach how to support collaborative work between several experts in a design review based on CAD data. The design review is an important part of the design process of a new product, because its objective is to ensure the quality of the final product. During a design review several experts discuss the current state of the work, trying to locate errors in the design, develop improvements or find solutions for unsolved problems. It is evident that the quality of the achieved results of such a review is directly linked to the quality of the collaboration between the involved experts. Apart from social issues, efficient collaboration is heavily influenced by technical aspects, e.g. which kind of media is used. Since pure digital data representations of the design is commonplace today, new metaphors have to be developed which allow a group of people to work together simultaneously and intuitively on the same data model. In this paper we will introduce a virtual reality based approach to collaborative working in a design review scenario. Our work is based on a pen and paper paradigm, which allows people to sketch and draw their ideas as well as annotations on the digital model in the same way they would do it using a pen and a piece of paper. Furthermore we will show how private spaces can be realized and how the metaphor can be integrated in an overall framework, which is usable for digital design reviews.
Iris Identification Using Geometrical Wavelets
Authors: Mariusz Rachubiński
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

This paper present personal identification and verification based on iris pattern and propose a new algorithm for iris feature extraction. The algorithm is based on texture analysis using wavelet transform. Iris code is generated using representation of the wavelet coefficients based on a wedgelet dictionary. Preliminary results on CASIA iris image database confirm the effectiveness of this method and encourage further research.
Profit analysis and simulation in motor insuarance
Authors: Noriszura Ismail, Abdul Aziz Jemain
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

This study suggests the use of profit analysis and simulation for assessing the price of motor insurance. Utilization of profit analysis and simulation on insurance pricing has several advantages; they may identify the distributions that agree or disagree with the actual experience, they may predict the performance of pricing models under alternative scenarios, and they may be applied to assess the performance of pricing models by allowing random variabilities in the underlying distribution of such models.
A spatial decision support system for land-use structure optimization
Authors: Li Xiaoli, Yingyi Chen, Li Daoliang
Venue: WSEAS Transactions on Computers (2009)

This article describes a decision support system for land-use structure optimization and land-use allocation. This system was established for the rural land managers to explore their land use options. It integrated database technology, expert system technology and spatial decision support system technology. The DSS consist of four components: a geographic information system (GIS), land use modules, a graphical user interface and land use planning tools. The linear programming, fuzzy clustering, and other land-use structure optimization algorithms are implemented on the ArcEngine software platform. The system has been applied in Beijing Pinggu area. The results suggest that this system can be a useful tool to support management decisions.
AGAMOS: A Graph-Based Approach to Modulo Scheduling for Clustered Microarchitectures
Authors: Alex Aletà, Josep M. Codina, Jesús Sánchez, Antonio González, David Kaeli
Venue: IEEE Transactions on Computers (2009)

This paper presents AGAMOS, a technique to modulo schedule loops on clustered microarchitectures. The proposed scheme uses a multilevel graph partitioning strategy to distribute the workload among clusters and reduces the number of intercluster communications at the same time. Partitioning is guided by approximate schedules (i.e., pseudoschedules), which take into account all of the constraints that influence the final schedule. To further reduce the number of intercluster communications, heuristics for instruction replication are included. The proposed scheme is evaluated using the SPECfp95 programs. The described scheme outperforms a state-of-the-art scheduler for all programs and different cluster configurations. For some configurations, the speedup obtained when using this new scheme is greater than 40 percent, and for selected programs, performance can be more than doubled.
Adaptive Fusion of Gait and Face for Human Identification in Video
Authors: Xin Geng, Liang Wang, Ming Li, Qiang Wu, Kate Smith-Miles
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

Most work on multi-biometric fusion is based on static fusion rules which cannot respond to the changes of the environment and the individual users. This paper proposes adaptive multi-biometric fusion, which dynamically adjusts the fusion rules to suit the real-time external conditions. As a typical example, the adaptive fusion of gait and face in video is studied. Two factors that may affect the relationship between gait and face in the fusion are considered, i.e., the view angle and the subject-to-camera distance. Together they determine the way gait and face are fused at an arbitrary time. Experimental results show that the adaptive fusion performs significantly better than not only single biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX.
A simpler unified analysis of budget perceptrons
Authors: Ilya Sutskever
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing if the number of errors is large. The Forgetron and the Randomized Budget Perceptron algorithms overcome this problem by restricting the number of support vectors the Perceptron is allowed to have. These algorithms have regret bounds whose proofs are dissimilar. In this paper we propose a unified analysis of both of these algorithms by observing that the way in which they remove support vectors can be seen as types of L2-regularization. By casting these algorithms as instances of online convex optimization problems and applying a variant of Zinkevich's theorem for noisy and incorrect gradient, we can bound the regret of these algorithms more easily than before. Our bounds are similar to the existing ones, but the proofs are less technical.
The Exploit: A Theory of Networks
Authors: Alexander R. Galloway, Eugene Thacker
Venue: Electronic Mediations (2007)

The Exploit is that rare thing: a book with a clear grasp of how networks operate that also understands the political implications of this emerging form of power. It cuts through the nonsense about how 'free' and 'democratic' networks supposedly are, and it offers a rich analysis of how network protocols create a new kind of control. Essential reading for all theorists, artists, activists, techheads, and hackers of the Net. McKenzie Wark, author of A Hacker Manifesto The network has become the core organizational structure for postmodern politics, culture, and life, replacing the modern eras hierarchical systems. From peer-to-peer file sharing and massive multiplayer online games to contagion vectors of digital or biological viruses and global affiliations of terrorist organizations, the network form has become so invasive that nearly every aspect of contemporary society can be located within it. Borrowing their title from the hacker term for a program that takes advantage of a flaw in a network system, Alexander R. Galloway and Eugene Thacker challenge the widespread assumption that networks are inherently egalitarian. Instead, they contend that there exist new modes of control entirely native to networks, modes that are at once highly centralized and dispersed, corporate and subversive. In this provocative book-length essay, Galloway and Thacker argue that a whole new topology must be invented to resist and reshape the network form, one that is as asymmetrical in relationship to networks as the network is in relation to hierarchy. Alexander R. Galloway is associate professor of culture and communications at New York University and the author of Gaming: Essays on Algorithmic Culture (Minnesota, 2006) and Protocol: How Control Exists after Decentralization. Eugene Thacker is associate professor of new media at the Georgia Institute of Technology and the author of Biomedia (Minnesota, 2004) and The Global Genome: Biotechnology, Politics, and Culture.
Interacting meaningfully with machine learning systems: Three experiments
Authors: Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret Burnett, Thomas Dietterich, Erin Sullivan, Jonathan Herlocker
Venue: International Journal of Human-Computer Studies (2009)

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple ''right/wrong'' judgments. If the users themselves could work hand-in-hand with machine learning systems, the users' understanding and trust of the system could improve and the accuracy of learning systems could be improved as well. We conducted three experiments to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study that investigated users' willingness to interact with machine learning reasoning, and what kinds of feedback users might give to machine learning systems. We then investigated the viability of introducing such feedback into machine learning systems, specifically, how to incorporate some of these types of user feedback into machine learning systems, and what their impact was on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human-computer collaboration via on-the-spot interactions as a promising direction for machine learning systems and users to collaboratively share intelligence.
MCITP: Microsoft Windows Vista Desktop Support Consumer Study Guide Exam 70-623
Authors: Eric Johnson, Eric Beehler
Venue:  (2007)

If you are a support professional who sets up, maintains, and troubleshoots desktop applications on Windows Vista, now you can build your skills and prepare for exam 70-623, MCITP: Supporting and Troubleshooting Applications on a Windows Vista Client for Consumer Support Technicians, the required exam for achieving certification as an MCITP: Consumer Support Technician. Inside this comprehensive study guide you'll find full coverage of all exam objectives, practical hands-on exercises, real-world scenarios, challenging review questions, and more.
Increasing Participation of Females and Underrepresented Minorities in Computing
Authors: Peggy Doerschuk, Jiangjiang Liu, Judith Mann
Venue: Computer (2009)

Retaining and recruiting women and underrepresented minorities in computing is an important issue for computing educators.
Analysis of Meaning Making in Online Learning
Authors: Daniel Suthers, Nathan Dwyer, Richard Medina, Ravi Vatrapu
Venue: Proceeding of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding (2006)

This paper reports on our efforts to deepen the analysis of online collaborative learning. Most studies of online learning use quantitative methods that assign meaning to contributions in isolation and aggregate over many sessions, obscuring the actual procedures by which participants accomplish learning through the affordances of online media. Methods for studying the interactional construction of meaning are available, but have largely been developed for brief episodes of face-to-face data, and do not scale well to online learning where media resources, time scale, and synchronicity all differ. In order to resolve this tradeoff, we are developing an analytic method that scales up sequential and interactional analysis to longer term distributed and asynchronous interactions. The paper describes applications to data derived from asynchronous interaction of dyads and small groups. Our long-term objective is to obtain a deep understanding of how learning is accomplished in technology-mediated interactions that take place at multiple time scales in different media.
Fine and Wilf words for any periods II
Authors: R. Tijdeman, L. Q. Zamboni
Venue: Theoretical Computer Science (2009)

Given positive integers n, and p"1,...,p"r, we establish a fast word combinatorial algorithm for constructing a word w=w"1...w"n of length n, with periods p"1,...,p"r, and on the maximal number of distinct letters. Moreover, we show that the constructed word, which is unique up to word isomorphism, is a pseudo-palindrome - i.e. it is a fixed point of an involutory antimorphism.
Implementing a 1GHz four-issue out-of-order execution microprocessor in a standard cell ASIC methodology
Authors: Wei-Wu Hu, Ji-Ye Zhao, Shi-Qiang Zhong, Xu Yang, Elio Guidetti, Chris Wu
Venue: Journal of Computer Science and Technology (2007)

This paper introduces the microarchitecture and physical implementation of the Godson-2E processor, which is a four-issue superscalar RISC processor that supports the 64-bit MIPS instruction set. The adoption of the aggressive out-of-order execution and memory hierarchy techniques help Godson-2E to achieve high performance. The Godson-2E processor has been physically designed in a 7-metal 90nm CMOS process using the cell-based methodology with some bit-sliced manual placement and a number of crafted cells and macros. The processor can be run at 1GHz and achieves a SPEC CPU2000 rate higher than 500.
A Comparison of Performance of Sequential Learning Algorithms on the Task of Named Entity Recognition for Indian Languages
Authors: Awaghad Ashish Krishnarao, Himanshu Gahlot, Amit Srinet, D. S. Kushwaha
Venue: Proceedings of the 9th International Conference on Computational Science: Part I (2009)

We have taken up the issue of named entity recognition of Indian languages by presenting a comparative study of two sequential learning algorithms viz. Conditional Random Fields (CRF) and Support Vector Machine (SVM). Though we only have results for Hindi, the features used are language independent, and hence the same procedure could be applied to tag the named entities in other Indian languages like Telgu, Bengali, Marathi etc. that have same number of vowels and consonants. We have used CRF++ for implementing CRF algorithm and Yamcha for implementing SVM algorithm. The results show a superiority of CRF over SVM and are just a little lower than the highest results achieved for this task. This can be attributed to the non-usage of any pre-processing and post-processing steps. The system makes use of the contextual information of words along with various language independent features to label the Named Entities (NEs).