ppxscal/embedding_network huggingface dataset:
From task model to multi-channel access: services integration in the UBI-learn platform
Authors: Sarra Kaddouci, Thomas Vantroys, Vincent Chevrin
Venue: Proceedings of the sixth conference on IASTED International Conference Web-Based Education - Volume 2 (2007)

The growing parallel development of mobile learning and of service oriented architecture approaches can modify the way to conceive, deploy and use learning management system. One of the main problems in combining these approaches is in the integration of learning services while taking into account the user interactions. The Ubi-Learn platform aims to provide an intermediation between services and user accessibility to the LMS. In this paper, we explain the methodology we follow to integrate services. For this, we take the example of an e-Portfolio service. We show the entire life-cycle departing from the task models to the multi-channel access.
Integrating Semantic Knowledge into Text Similarity and Information Retrieval
Authors: Christof Muller, Iryna Gurevych, Max Muhlhauser
Venue: Proceedings of the International Conference on Semantic Computing (2007)

This paper studies the influence of lexical semantic knowledge upon two related tasks: ad-hoc information retrieval and text similarity. For this purpose, we compare the performance of two algorithms: (i) using semantic relatedness, and (ii) using a conventional extended Boolean model [12]. For the evaluation, we use two different test collections in the German language: (i) GIRT [5] for the information retrieval task, and (ii) a collection of descriptions of professions built to evaluate a system for electronic career guidance in the information retrieval and text similarity task. We found that integrating lexical semantic knowledge improves performance for both tasks. On the GIRT corpus, the performance is improved only for short queries. The performance on the collection of professional descriptions is improved, but crucially depends on the preprocessing of natural language essays employed as topics.
Adding Semantics to Formal Data Specifications to Automatically Generate Corresponding Voice Data-Input Applications
Authors: Fadi Hanna, Richard Frost
Venue: Proceedings of the International Conference on Semantic Computing (2007)

This paper introduces a new method for the automatic translation of formal data specifications to voice data-input applications. The objective is to automatically generate Voice XML applications that correspond to the structure of XML DTD specifications. A new formalization called Grammar-DTD (G-DTD) is introduced as an extended version of DTD. G-DTD is obtained by adding semantics to specify valid values of the DTD elements and attributes. The additional semantics are necessary to carry out the automatic translation. This work facilitates the rapid creation of voice applications.
Annotation Based Multimedia Streaming Over Wireless Networks
Authors: R. Cornea, A. Nicolau, N. Dutt
Venue: Proceedings of the 2006 IEEE/ACM/IFIP Workshop on Embedded Systems for Real Time Multimedia (2006)

The relatively high power consumption of wireless network interfaces represents an important detriment in multimedia streaming for mobile devices. The IEEE 802.11 built-in power saving mode was designed for transfers of different nature and is not able to take advantage of the short idle intervals and continuous, periodic transmissions inherent in multimedia streaming. We propose an annotation based approach to wireless network power management that analyzes the variations in data transfer bandwidth during playback and uses the results to buffer data into larger burst transmissions with longer idle periods when the network card is transitioned into a lower power, sleep mode. Annotations allow for energy savings of up to 75% for the network interface, with practically no quality degradation or packet loss, only a small delay due to the buffer
Depth estimation for ranking query optimization
Authors: Karl Schnaitter, Joshua Spiegel, Neoklis Polyzotis
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

A relational ranking query uses a scoring function to limit the results of a conventional query to a small number of the most relevant answers. The increasing popularity of this query paradigm has led to the introduction of specialized rank join operators that integrate the selection of top tuples with join processing. These operators access just "enough" of the input in order to generate just "enough" output and can offer significant speed-ups for query evaluation. The number of input tuples that an operator accesses is called the input depth of the operator, and this is the driving cost factor in rank join processing. This introduces the important problem of depth estimation, which is crucial for the costing of rank join operators during query compilation and thus for their integration in optimized physical plans. We introduce an estimation methodology, termed Deep, for approximating the input depths of rank join operators in a physical execution plan. At the core of Deep lies a general, principled framework that formalizes depth computation in terms of the joint distribution of scores in the base tables. This framework results in a systematic estimation methodology that takes the characteristics of the data directly into account and thus enables more accurate estimates. We develop novel estimation algorithms that provide an efficient realization of the formal Deep framework, and describe their integration on top of the statistics module of an existing query optimizer. We validate the performance of Deep with an extensive experimental study on data sets of varying characteristics. The results verify the effectiveness of Deep as an estimation method and demonstrate its advantages over previously proposed techniques.
Interfacing life: a year in the life of a research lab
Authors: Yuri Ivanov
Venue: Proceedings of the 9th international conference on Multimodal interfaces (2007)

Humans perceive life around them through a variety of sensory inputs. Some, such as vision, or audition, have high information content, while others, such as touch and smell, do not. Humans and other animals use this gradation of senses to know how to attend to what's important. In contrast, it is widely accepted that in tasks of monitoring living spaces the modalities with high information content hold the key to decoding the behavior and intentions of the space occupants. In surveillance, video cameras are used to record everything that they can possibly see in the hopes that if something happens, it can later be found in the recorded data. Unfortunately, the latter proved to be harder than it sounds. In our work we challenge this idea and introduce a monitoring system that is built as a combination of channels with varying information content. The system has been deployed for over a year in our lab space and consists of a large motion sensor network combined with several video cameras. While the sensors give a general context of the events in the entire 3000 square meters of the space, cameras only attend to selected occurrences of the office activities. The system demonstrates several monitoring tasks which are all but impossible to perform in a traditional camera-only setting. In the talk we share our experiences, challenges and solutions in building and maintaining the system. We show some results from the data that we have collected for the period of over a year and introduce some other successful and novel applications of the system.
Nazarov's uncertainty principles in higher dimension
Authors: Philippe Jaming
Venue: Journal of Approximation Theory (2007)

In this paper we prove that there exists a constant C such that, if S,@S are subsets of R^d of finite measure, then for every function f@?L^2(R^d),@!"R"^"d|f(x)|^2dx==1 a result of Nazarov [Local estimates for exponential polynomials and their applications to inequalities of the uncertainty principle type, Algebra i Analiz 5 (1993) 3-66 (in Russian); translation in St. Petersburg Math. J. 5 (1994) 663-717] in dimension d=1.
Method for identifying transcription factor binding sites in yeast
Authors: Huai-Kuang Tsai, Grace Tzu-Wei Huang, Meng-Yuan Chou, Henry Horng-Shing Lu, Wen-Hsiung Li
Venue: Bioinformatics (2006)

Motivation: Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit. Results: We develop a TFBS identification method, TFBSfinder, which utilizes several data sources, including DNA sequences, phylogenetic information, microarray data and ChIP-chip data. For a TF, TFBSfinder rigorously selects a set of reliable target genes and a set of non-target genes (as a background set) to find overrepresented and conserved motifs in target genes. A new metric for measuring the degree of conservation at a binding site across species and methods for clustering motifs and for inferring position weight matrices are proposed. For synthetic data and yeast cell cycle TFs, TFBSfinder identifies motifs that are highly similar to known consensuses. Moreover, TFBSfinder outperforms well-known methods. Availability: Contact: whli@uchicago.edu Supplementary information: Supplementary data are available on Bioinformatics online.
Research on Bayesian Network Structure Learning Based on Rough Set
Authors: Yu-ling Li, Qi-zong Wu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

Rough Set theory and method is one kind of effective method for dealing with complicated system, but it fails to contain the theory and mechanism handling imprecise or uncertain data. So, it has strong complementarities with Bayesian network theory. The paper puts forward a kind of Bayesian network structure learning method combining Rough Set theory with Bayesian network. Inclusion theory of Rough Set is used to mine cause and effect associated rules which determine arc and its direction between Bayesian network variables. At the same time, mining arithmetic of associated rules is presented in the paper .Finally, it shows rationality and validity of the approach through experiment analysis.
Microarchitecture configurations and floorplanning co-optimization
Authors: Changbo Long, Lucanus J. Simonson, Weiping Liao, Lei He
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2007)

Microarchitecture configurations and floorplanning are keys to boost throughput, and they are strongly related. In this paper, we propose a new method to optimize them simultaneously. We first concentrate on floorplanning under given microarchitecture configurations. In addition to the objectives of conventional floorplanning methods, we minimize the throughput degradation caused by pipelined global interconnects based on efficient yet accurate models for microarchitecture throughput over pipeline stages of global interconnects. Our results show that an accurate trajectory piecewise-linear (TPWL) model incurs more offline setup time to obtain 13% better throughput than a rough access ratio-based model, and both models lead to much better throughput (up to 64% higher) compared with conventional floorplanning methods. We then build a unified throughput model parameterized for pipelined global interconnects and microarchitecture configurations based on the TPWL method and apply this model to efficiently explore over one million microarchitecture configurations and corresponding floorplan variations. We obtain microarchitecture configurations and floorplans with throughput 26.9% better than manually chosen microarchitecture followed by automatic floorplanning in a very recent paper.
Proceedings of the 2007 ACM workshop on Storage security and survivability
Venue: Workshop On Storage Security And Survivability (2007)

Welcome to Storage Security and Survivability 2007! This workshop brings together leading edge research on storage security, on the disk and over the network, and storage recoverability and repair. This year focused especially on open source storage systems. We received 12 submissions and after much discussion, accepted 7 of the submissions which would most stimulate workshop discussion and lead to new advances in storage. We thank each and every submitter for their hard work researching and preparing their submissions; without them there would be no workshop. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Concurrent VLSI Architectures
Authors: C. L. Seitz
Venue: IEEE Transactions on Computers (1984)

This tutorial paper addresses some of the principles and provides examples of concurrent architectures and designs that have been inspired by VLSI technology. The circuit density offered by VLSI provides the means for implementing systems with very large numbers of computing elements, while its physical characteristics provide an incentive to organize systems so that the elements are relatively loosely coupled. One class of computer architectures that evolve from this reasoning include an interesting and varied class of concurrent machines that adhere to a structural model based on the repetition of regularly connected elements. The systems included under this structural model range from 1) systems that combine storage and logic at a fine grain size, and are typically aimed at computations with images or storage retrieval, to 2) systems that combine registers and arithmetic at a medium grain size to form computational or systolic arrays for signal processing and matrix computations, to 3) arrays of instruction interpreting computers that use teamwork to perform many of the same demanding computations for which we use high-performance single process computers today.
Opening the sensornet black box
Authors: Jung Il Choi, Jung Woo Lee, Megan Wachs, Philip Levis
Venue: ACM SIGBED Review (2007)

We argue that the principal cause of sensornet deployment and development difficulty is an inability to observe a network's internal operation. We further argue that this lack of visibility is due to the activity and resource constraints enforced by limited energy. We present the Mote Network (MNet) architecture, which elevates visibility to be its dominant design principle. We propose a quantitative metric for network visibility and explain why network isolation and fairness are critical concerns. We describe the Fair Waiting Protocol (FWP), MNet's single-hop protocol and show how its fairness and isolation can improve throughput and efficiency. We present the Pull Collection Protocol as a case study in designing multihop protocols in the architecture.
Functional Transformation in Simplification of Multivalued Switching Functions
Authors: Z. G. Vranesic
Venue: IEEE Transactions on Computers (1972)

A concept of functional transformation of multivalued switching functions, used to obtain simplified implementations is presented. Given some function f, a transformed function g is generated from f by permuting some or all of the truth values. Then, implementing g and performing a reverse permutation, a simpler implementation for f is obtained. A P matrix technique is given to facilitate determination of the required permutation.
Mutually beneficial learning with application to on-line news classification
Authors: Lei Wu, Zhiwei Li, Mingjing Li, Wei-Ying Ma, Nenghai Yu
Venue: Proceedings of the ACM first Ph.D. workshop in CIKM (2007)

There are three common challenges in real-world classification applications, i.e. how to use domain knowledge, how to resist noisy samples and how to use unlabeled data. To address these problems, a novel classification framework called Mutually Beneficial Learning (MBL) is proposed in this paper. MBL integrates two learning steps together. In the first step, the underlying local structures of feature space are discovered through a learning process. The result provides necessary capability to resist noisy samples and prepare better input for the second step where a consecutive classification process is further applied to the result. These two steps are iteratively performed until a stop condition is met. Different from traditional classifiers, the output of MBL consists of two components: a common classifier and a set of rules corresponding to local structures. In application, a test sample is first matched with the discovered rules. If a matched rule is found, the label of the rule is assigned to the sample; otherwise, the common classifier will be utilized to classify the sample. We applied the MBL to online news classification, and our experimental results showed that MBL is significantly better than Naïve Bayes and SVM, even when the data is noisy or partially labeled.
Non-Linear Approaches for the Classification of Facial Expressions at Varying Degrees of Intensity
Authors: Jane Reilly, John Ghent, John McDonald
Venue: Proceedings of the International Machine Vision and Image Processing Conference (2007)

The research discussed in this paper documents a comparative analysis of two nonlinear dimensionality reduction techniques for the classification of facial expressions at varying degrees of intensity. These nonlinear dimensionality reduction techniques are Kernel Principal Component Analysis (KPCA) and Locally Linear Embedding (LLE). The approaches presented in this paper employ psychological tools, computer vision techniques and machine learning algorithms. In this paper we concentrate on comparing the performance of these two techniques when combined with Support Vector Machines (SVMs) at the task of classifying facial expressions across the full expression intensity range from near-neutral to extreme facial expression. Receiver Operating Characteristic (ROC) curve analysis is employed as a means of comprehensively comparing the results of these techniques.
A System to Automatically Analyze Assembled Programs
Authors: V. Hayward
Venue: IEEE Transactions on Software Engineering (1983)

An original system to perform an automatic analysis of assembled programs is presented. Executable programs are analyzed from the description of the machine on which they run and are translated into an intermediate language taking into account the particularities of the considered machine. The system was primarily designed as the first step of a project for transferring programs from one machine to another. The final goal of the project is to achieve an even utilization of computer resources for a real-time controlled robot, on the basis of partially dedicated processors. At the present time, the actual implementation provides a tool for studying the theoretical aspect of machine-level program analysis. Nevertheless, other applications can be found in program debugging and assembled program validation.
A Unified Method for the Reconstruction of Sampled Data
Authors: A. E. Durling
Venue: IEEE Transactions on Computers (1973)

Many digital systems have need for continuous-type output for oscilloscope display, process control, or hybrid computation. This paper presents a generalization and unification of the theory of extrapolation and reconstruction of sampled data. This generalization allows implementation of a recursive algorithm or digital filter in conjunction with the continuous data reconstruction. The general procedure is demonstrated for the implementation of an nth-order recursion relation using polynomial or exponential data reconstruction requiring only a single resetting integrator for the general nth-order reconstructor.
Unateness Properties of and-Exclusive-or Logic Circuits
Authors: L. T. Fisher
Venue: IEEE Transactions on Computers (1974)

Reordering the terms of a Reed-Muller or ring sum expansion of a switching function expressed in terms of the Boolean ring operations AND and EXCLUSIVE OR in a more natural way exploits the similarities between these expressions and unate functions and displays mathematical structure which apparently has not been noted before. McNaughton's n orderings on the n cube appear in a new setting and lead quickly to simple but geometrically satisfying theorems dealing with a matrix of coefficients of all 2n ring sum expressions for various polarities of inputs. The structure of these matrices for minterms, implicants, and functions are shown to have simple and attractive forms. An algorithm is presented which allows simple determination of a ring sum realization using logic array notation, and which can also be used to find minimum cost polarities. A second algorithm allows nonexhaustive and near-optimal handling of functions with DON'T CARE conditions.
Architecture of a New Microprocessor
Authors: B. L. Peuto
Venue: Computer (1979)

Increased capabilities, architectural compatibility, and clearly defined interfaces were the chief architectural goals of Zilog's new Z8000 microprocessor family. Here is an account of how those goals were met for two members of that familythe Z8000 CPU and the MMU.
A semisupervised support vector machines algorithm for BCI systems
Authors: Jianzhao Qin, Yuanqing Li, Wei Sun
Venue: Computational Intelligence and Neuroscience (2007)

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.
Effects of context violation on execution result in local-lag algorithm
Authors: Lei Guo, Ling Chen, Gencai Chen
Venue: Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology (2007)

Local-lag algorithm is an efficient way to maintain consistency for replicated continuous applications which have a high requirement on accuracy. However, local-lag algorithm will lead to context violation, which may induce the execution result of an operation to be violated (i.e., result violation). In this paper, the definitions of context violation and result violation are presented and the relationship between these two violations is analyzed. Besides, a game named X-Spaceship, which adopts the local-lag algorithm, is employed to study the effects of context violation on the execution result in a particular system. The experiment results indicate that the probability of result violation happening increases fast with lag value when lag value ranges from 0 ms to 200 ms, when lag value exceeds 200 ms, it exceeds 0.7 in some situations.