ppxscal/embedding_network huggingface dataset:
Analyzing a class of pseudo-random bit generator through inductive machine learning paradigm
Authors: Shri Kant, Shehroz S. Khan
Venue: Intelligent Data Analysis (2006)

Random number generation is an integral part of strong cipher systems. If a pseudo-random sequence can be predicted with better than chance probability then the generator is considered to be cryptographically weak. This paper deals with next bit prediction of pseudo-random binary sequences generated by Linear Feedback Shift Register (LFSR) and LFSR-based Pseudo-Random Bit Generators (PRBG), using inductive Machine Learning (ML) paradigm, namely C4.5 the most common and widely used inductive data mining algorithm. This machine learning technique has been introduced to convert the theoretical prediction problem into a classification problem, which we coined as Classificatory Prediction problem. We further extended the use of this technique to predict next bit without having any knowledge of subsequent bits of the PRBG and can be termed as true Next Bit Predictor. The technique used is independent of the parameters and domain knowledge of the pseudo-random bit generators. The present study is a comprehensive extension of the work done by Hernandez et al. [15]. We performed meticulous experiments (over wide range of LFSRs) and came out with a more explanatory analysis. Our classificatory prediction results paved the way for the evolution of the next bit prediction model.
A generative model for parsing natural language to meaning representations
Authors: Wei Lu, Hwee Tou Ng, Wee Sun Lee, Luke S. Zettlemoyer
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

In this paper, we present an algorithm for learning a generative model of natural language sentences together with their formal meaning representations with hierarchical structures. The model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning. We introduce dynamic programming techniques for efficient training and decoding. In experiments, we demonstrate that the model, when coupled with a discriminative reranking technique, achieves state-of-the-art performance when tested on two publicly available corpora. The generative model degrades robustly when presented with instances that are different from those seen in training. This allows a notable improvement in recall compared to previous models.
Theory-Practice Interplay in Machine Learning --- Emerging Theoretical Challenges
Authors: Shai Ben-David
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

Theoretical analysis has played a major role in some of the most prominent practical successes of statistical machine learning. However, mainstream machine learning theory assumes some strong simplifying assumptions which are often unrealistic. In the past decade, the practice of machine learning has led to the development of various heuristic paradigms that answer the needs of a vastly growing range of applications. Many useful such paradigms fall beyond the scope of the currently available analysis. Will theory play a similar pivotal role in the newly emerging sub areas of machine learning?In this talk, I will survey some such application-motivated theoretical challenges. In particular, I will discuss recent developments in the theoretical analysis of semi-supervised learning, multi-task learning, "learning to learn", privacy-preserving learning and more.
Biomedical data acquisition and transmission infrastructure in the HEARTFAID platform
Authors: Domenico Conforti, Domenico Costanzo, Christos Biniaris, Andrew Marsh
Venue: Proceedings of the 2009 IEEE International Workshop on Medical Measurements and Applications (2009)

Chronic heart failure (CHF) is a complex cardiovascular syndrome whose management requires a complex clinical program involving the acquisition, integration and interpretation of heterogeneous biomedical data and information. Within the EU FP6 project HEARTFAID, an integrated platform of services has been developed to assist CHF stakeholders in their routine workflow and to provide an optimal management of CHF patients, by exploiting the most advanced technologies and instruments for diagnostic data processing. In this paper, we describe the platform component responsible for the acquisition and transmission of biomedical data from several medical devices and sensors.
Boosting unsupervised relation extraction by using NER
Authors: Ronen Feldman, Benjamin Rosenfeld
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

Web extraction systems attempt to use the immense amount of unlabeled text in the Web in order to create large lists of entities and relations. Unlike traditional IE methods, the Web extraction systems do not label every mention of the target entity or relation, instead focusing on extracting as many different instances as possible while keeping the precision of the resulting list reasonably high. URES is a Web relation extraction system that learns powerful extraction patterns from unlabeled text, using short descriptions of the target relations and their attributes. The performance of URES is further enhanced by classifying its output instances using the properties of the extracted patterns. The features we use for classification and the trained classification model are independent from the target relation, which we demonstrate in a series of experiments. In this paper we show how the introduction of a simple rule based NER can boost the performance of URES on a variety of relations. We also compare the performance of URES to the performance of the state-of-the-art KnowItAll system, and to the performance of its pattern learning component, which uses a simpler and less powerful pattern language than URES.
RECON: capturing mobile and ubiquitous interaction in real contexts
Authors: Kasper Løvborg Jensen
Venue: Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (2009)

Evaluating the user experience of mobile and ubiquitous applications is a challenging task. They are becoming increasingly complex and tightly interwoven into the fabric of everyday life and thus cannot easily be taken out of context and evaluated in controlled experimental environments. Methods for studying the user experience of such applications in the field tend to be cumbersome and expensive with regard to time and manpower, and they generally do not scale well with number of users and duration of studies. RECON addresses these challenges and provides an addition to the methodological and practical toolbox from which researchers and practitioners can draw when conducting field experiments. It facilitates automated capture of general usage and application specific interaction augmented with information about the context in which it occurs. RECON enables researchers and practitioners to conduct large scale remote studies of mobile and ubiquitous applications in real contexts.
Processing Hexagonal Images in a Virtual Environment
Authors: Sonya Coleman, Bryan Scotney, Bryan Gardiner
Venue: Proceedings of the 15th International Conference on Image Analysis and Processing (2009)

For many years the concept of using hexagonal pixels for image capture has been investigated, and several advantages of such an approach have been highlighted. Recently there has been a renewed interest in the use of hexagonal images, representation of architectures for such images and general hexagonal image processing. Therefore, we present multiscale hexagonal gradient operators, developed within the finite element framework, for use directly on hexagonal pixel-based images. We demonstrate these operators using two environments: a virtual hexagonal environment and the direct use of simulated hexagonal pixel-based images. In both scenarios, we evaluate the proposed operators and compare them with the use of standard image processing operators on typical square pixel-based images, demonstrating improved results in the case of simulated hexagonal pixel-based images.
A distributed approach to passive localization for sensor networks
Authors: Rahul Biswas, Sebastian Thrun
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

Sensors that know their location, from microphones to vibration sensors, can support a wider arena of applications than their location unaware counterparts. We offer a method for sensors to determine their own location relative to one another by using only exogenous sounds and the differences in the arrivals of these sounds at different sensors. We present a distributed and computationally efficient solution that offers accuracy on par with more active and computationally intense methods.
The Research of Detection System of Financial Service's Network Data Behavior
Authors: Zhang Zhibin, Gao Bo, Du Weifeng, Gao Yan
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

This article has analyzed the origin of the network behavior deviated from the norm, and it has proposed a system of network data behavior of examination. This system is between the firewall and the boundary router. It is responsible to monitor the network data stream about the financial service, and it makes the message return to original state effectively. It catches the suspicious network activity and dynamically discovers existent security problem on the network based on analyzing the information packet in the application layer.
VCC: A Practical System for Verifying Concurrent C
Authors: Ernie Cohen, Markus Dahlweid, Mark Hillebrand, Dirk Leinenbach, Michał Moskal, Thomas Santen, Wolfram Schulte, Stephan Tobies
Venue: Proceedings of the 22nd International Conference on Theorem Proving in Higher Order Logics (2009)

VCC is an industrial-strength verification environment for low-level concurrent system code written in C. VCC takes a program (annotated with function contracts, state assertions, and type invariants) and attempts to prove the correctness of these annotations. It includes tools for monitoring proof attempts and constructing partial counterexample executions for failed proofs. This paper motivates VCC, describes our verification methodology, describes the architecture of VCC, and reports on our experience using VCC to verify the Microsoft Hyper-V hypervisor.
Implications of Graphics on Usability and Accessibility for the Voter
Authors: Benjamin Smith, Sharon Laskowski, Svetlana Lowry
Venue: Proceedings of the 2nd International Conference on E-Voting and Identity (2009)

This paper explores the impact of graphics on the usability and accessibility of voting systems. Graphical elements, as part of voting systems, include both photographs and party logos that indicate specific candidates or political parties, informational icons such as arrows and alert symbols, and animations or other video. After an overview of the history of graphics on ballots, usability and accessibility issues concerning graphics are discussed in detail. The question of whether certain types of graphics would help people with cognitive disabilities vote is then considered in light of research and best practices for usability and accessibility.
A Dynamic Packet Management in a Protocol Processor
Authors: Yul Chu, Amit Uppal, Jae Sok Son
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

This paper studies a networking packet management for a protocol processor in a network terminal. The network terminal is to handle high-speed data streams, more than 10 Gb/s. We developed an algorithm, which distributes the packet losses evenly among the different applications. Our experimental results show that the proposed algorithm improves the packet loss ratio more than 15% compared to conventional dynamic algorithm.
Pseudo-differential operators associated with Laguerre hypergroups
Authors: Miloud Assal
Venue: Journal of Computational and Applied Mathematics (2009)

In this paper we consider the generalized shift operator generated from the Laguerre hypergroup; by means of this, pseudo-differential operators are investigated and Sobolev-boundedness results are obtained.
Scalable network-layer defense against internet bandwidth-flooding attacks
Authors: Katerina Argyraki, David R. Cheriton
Venue: IEEE/ACM Transactions on Networking (TON) (2009)

In a bandwidth-flooding attack, compromised sources send high-volume traffic to the target with the purpose of causing congestion in its tail circuit and disrupting its legitimate communications. In this paper, we present Active Internet Traffic Filtering (AITF), a network-layer defense mechanism against such attacks. AITF enables a receiver to contact misbehaving sources and ask them to stop sending it traffic; each source that has been asked to stop is policed by its own Internet service provider (ISP), which ensures its compliance. An ISP that hosts misbehaving sources either supports AITF (and accepts to police its misbehaving clients), or risks losing all access to the complaining receiver--this is a strong incentive to cooperate, especially when the receiver is a popular public-access site. We show that AITF preserves a significant fraction of a receiver's bandwidth in the face of bandwidth flooding, and does so at a per-client cost that is already affordable for today's ISPs; this per-client cost is not expected to increase, as long as botnet-size growth does not outpace Moore's law. We also show that even the first two networks that deploy AITF can maintain their connectivity to each other in the face of bandwidth flooding. We conclude that the network-layer of the Internet can provide an effective, scalable, and incrementally deployable solution against bandwidth-flooding attacks.
A Model of Data Reduction Based on Tensor Field
Authors: Xiangliang Li, Fanzhang Li
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

There has been much interest in the use of mutilinear algebra as a general technique of dimension reduction of large amounts of Tensor data in recent years. Some tensor based methods has been successfully applied to image processing signal processing and web search. The natural forms described by tensor are kept in arbitrary coordinates and according to this invariability of tensor it is able to deal with data under the framework of tensor field. In this paper a tensor field learning model combining available algorithms of dimension reduction based on tensor is presented. The model reveals that some tensor features could be acquired from the original data and the representation of reduction data is obtained on the basis of tensor field transformation implemented by an algorithm. An example refer to reduction of image data is given and the result shows the remarkable effect of the transformation algorithm.
Abstract verbs
Authors: Richard Power
Venue: Proceedings of the Eleventh European Workshop on Natural Language Generation (2007)

Existing generation systems use verbs almost exclusively to describe actions/events or to ascribe properties. In doing so, they achieve a direct concrete style of the kind often recommended in style manuals. However in many genres, including academic writing, it is common to find verbs expressing abstract relationships, with events etc. pushed down into nominalisations. This paper illustrates two important classes of abstract verb, one expressing discourse relations, the other expressing participant roles, and discusses some theoretical and practical reasons for studying such verbs and including them in generation systems.
Ubiquitous Intelligence in Agent Mining
Authors: Longbing Cao, Dan Luo, Chengqi Zhang
Venue: Agents and Data Mining Interaction: 4th International Workshop, ADMI 2009, Budapest, Hungary, May 10-15,2009, Revised Selected Papers (2009)

Agent mining, namely the interaction and integration of multi-agent and data mining, has emerged as a very promising research area. While many mutual issues exist in both multi-agent and data mining areas, most of them can be described in terms of or related to ubiquitous intelligence. It is certainly very important to define, specify, represent, analyze and utilize ubiquitous intelligence in agents, data mining, and agent mining. This paper presents a novel but preliminary investigation of ubiquitous intelligence in these areas. We specify five types of ubiquitous intelligence: data intelligence, human intelligence, domain intelligence, network and web intelligence, organizational intelligence, and social intelligence. We define and illustrate them, and discuss techniques for involving them into agents, data mining, and agent mining for complex problem-solving. Further investigation on involving and synthesizing ubiquitous intelligence into agents, data mining, and agent mining will lead to a disciplinary upgrade from methodological, technical and practical perspectives.
Optimized image resizing using seam carving and scaling
Authors: Weiming Dong, Ning Zhou, Jean-Claude Paul, Xiaopeng Zhang
Venue: ACM Transactions on Graphics (TOG) (2009)

We present a novel method for content-aware image resizing based on optimization of a well-defined image distance function, which preserves both the important regions and the global visual effect (the background or other decorative objects) of an image. The method operates by joint use of seam carving and image scaling. The principle behind our method is the use of a bidirectional similarity function of image Euclidean distance (IMED), while cooperating with a dominant color descriptor (DCD) similarity and seam energy variation. The function is suitable for the quantitative evaluation of the resizing result and the determination of the best seam carving number. Different from the previous simplex-mode approaches, our method takes the advantages of both discrete and continuous methods. The technique is useful in image resizing for both reduction/retargeting and enlarging. We also show that this approach can be extended to indirect image resizing.
Tightly coupling speech recognition and search
Authors: Taniya Mishra, Srinivas Bangalore
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers (2009)

In this paper, we discuss the benefits of tightly coupling speech recognition and search components in the context of a speech-driven search application. We demonstrate that by incorporating constraints from the information repository that is being searched not only improves the speech recognition accuracy but also results in higher search accuracy.
Time course analysis of microarray data for the pathway of reproductive development in female rainbow trout
Authors: Y. Liu, J. Verducci, I. Schultz, S. Hook, J. Nagler, G. Craciun, K. Sundling, W. Hayton
Venue: Statistical Analysis and Data Mining (2009)

New statistical procedures are introduced to investigate gene activity in support of the hypothalamus&ndash;pituitary&ndash;gonad&ndash;liver signaling network that provides the neuroendocrine regulation for reproduction in female, oviparous fishes. The methods include Shrunken Centroid Ordering by Orthogonal Projections (SCOOP) and a robust encoding of B-splines via Friedman's Generalized Elastic Net (GEN). SCOOP orders genes according to a novel criterion that balances discriminatory and correlative information. It is particularly useful in the present context where genes are only partially annotated, relevant networks are not known a priori, and the sample size is adequate for finding natural clusters. In this application, microarray measurements of gene expression were made in the pituitary, liver, and ovary of female rainbow trout (Oncorhynchus mykiss) over the course of their 1 year spawning cycle, and new methods were developed to detect systematic changes in potential gene networks. B-splines were fitted to gently smooth the estimates of expression versus time and provide a common framework for analysis. Unlike other methods, SCOOP selected not only the genes whose curves vary the most over time, but also genes closely correlated with these. This tended to recognize genes that may be part of an active network, but whose expressions undergo more modest fluctuations. To compensate for the high degree of uncertainty in fitting B-splines to individual genes, GEN methods were used to provide robust fits, and these were summarized through a novel GEN-transform as variable importance measures (VIMs). Clustering of genes via VIMs produced much more stable results than directly via B-spline coefficients, and the mean time course pattern of each cluster provided biologists with a reliable summary from which to interpret systematic patterns. Ultimately, the genes selected by SCOOP and clustered though the GEN-transform strongly suggested supportive pathways involving immunology, muscle contraction, reproduction, protein transport, metabolism, and reduction-oxidation. Copyright &copy; 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 192&ndash;208, 2009
Scenario-based orchestration of Web 2.0 applications in university teaching and learning processes: a case study
Authors: Angela Carell, Isabel Schaller
Venue: International Journal of Web Based Communities (2009)

In the context of Computer-Supported Collaborative Learning (CSCL), Web 2.0 applications are increasingly being used for Technology-Enhanced Learning (TEL). The paper presents the main principles of Web 2.0 and develops the hypotheses derived from the central aspect of collaborative learning. These hypotheses refer to the significance of Web 2.0 for the technical support of various collaborative teaching&#47;learning scenarios within the TEL approach. With an example scenario, the paper demonstrates how Web 2.0 applications can be orchestrated to support a CSCL scenario in face-to-face classes and the effects that this will have on the cooperative learning process of students.
Analysis of Gait Using a Treadmill and a Time-of-Flight Camera
Authors: Rasmus R. Jensen, Rasmus R. Paulsen, Rasmus Larsen
Venue: Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging (2009)

We present a system that analyzes human gait using a treadmill and a Time-of-flight camera. The camera provides spatial data with local intensity measures of the scene, and data are collected over several gait cycles. These data are then used to model and analyze the gait. For each frame the spatial data and the intensity image are used to fit an articulated model to the data using a Markov random field. To solve occlusion issues the model movement is smoothened providing the missing data for the occluded parts. The created model is then cut into cycles, which are matched and through Fourier fitting a cyclic model is created. The output data are: Speed, Cadence, Step length and Range-of-motion. The described output parameters are computed with no user interaction using a setup with no requirements to neither background nor subject clothing.