ppxscal/embedding_network huggingface dataset:
Fault-Driven Re-Scheduling For Improving System-level Fault Resilience
Authors: Yawei Li, Prashasta Gujrati, Zhiling Lan, Xian-he Sun
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

The productivity of HPC system is determined not only by their performance, but also by their reliability. The conventional method to limit the impact of failures is checkpointing. However, existing research shows that such a reactive fault tolerance approach can only improve system productivity marginally. Leveraging the recent progress made in the field of failure prediction, we propose fault-driven rescheduling (FARS) to improve system resilience to failures, and investigate the feasibility and effectiveness of utilizing failure prediction to dynamically adjust the placement of active jobs (e.g. running jobs) in response to failure prediction. In particular, a rescheduling algorithm is designed to enable effective job adjustment by evaluating performance impact of potential failures and rescheduling on user jobs. The proposed FARS complements existing research on fault-aware scheduling by allowing user jobs to avoid imminent failures at runtime. We evaluate FARS by using actual workloads and failure events collected from production HPC systems. Our preliminary results show the potential of FARS on improving system resilience to failures.
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms
Authors: Jaume Amores, Nicu Sebe, Petia Radeva
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

We present a novel approach for retrieval of object categories based on a novel type of image representation: the Generalized Correlogram (GC). In our image representation, the object is described as a constellation of GCs where each one encodes information about some local part and the spatial relations from this part to others (i.e., the part's context). We show how such a representation can be used with fast procedures that learn the object category with weak supervision and efficiently match the model of the object against large collections of images. In the learning stage, we show that by integrating our representation with Boosting the system is able to obtain a compact model that is represented by very few features, where each feature conveys key properties about the object's parts and their spatial arrangement. In the matching step, we propose direct procedures that exploit our representation for efficiently considering spatial coherence between the matching of local parts. Combined with an appropriate data organization such as Inverted Files, we show that thousands of images can be evaluated efficiently. The framework has been applied to different standard databases and we show that our results are favorably compared against state-of-the-art methods in both computational cost and accuracy
Minimal Negative Gate Networks
Authors: K. Nakamura
Venue: IEEE Transactions on Computers (1972)

A negative gate is a gate that can realize an arbitrary negative function (or monotone decreasing function) and a positive gate is one that can realize an arbitrary positive function (or monotone increasing function). This paper discusses methods of realizing a given logical function or a given set of logical functions using a minimum number of negative gates alone or using a minimum number of negative and positive gates.
Author's Reply2
Authors: J. En
Venue: IEEE Transactions on Computers (1979)

Without proof Paaske3states that "... En1cannot be true." Paaske's statement is based on his qualitative assumption that channel error is "much more likely" to occur than a hardware error in the decoder. The author will show that En's statements are still true under Paaske's assumption. Previous correspondence was cited by Paaske and claimed to be "the best way..." to judge a
The island confinement method for reducing search space in local search methods
Authors: H. Fang, Y. Kilani, J. H. Lee, P. J. Stuckey
Venue: Journal of Heuristics (2007)

Typically local search methods for solving constraint satisfaction problems such as GSAT, WalkSAT, DLM, and ESG treat the problem as an optimisation problem. Each constraint contributes part of a penalty function in assessing trial valuations. Local search examines the neighbours of the current valuation, using the penalty function to determine a "better" neighbour valuation to move to, until finally a solution which satisfies all the constraints is found. In this paper we investigate using some of the constraints as "hard" constraints, that are always satisfied by every trial valuation visited, rather than as part of a penalty function. In this way these constraints reduce the possible neighbours in each move and also the overall search space. The treating of some constraints as hard requires that the space of valuations that are satisfied is "connected" in order to guarantee that a solution can be found from any starting position within the region; thus the concept of islands and the name "island confinement method" arises. Treating some constraints as hard provides new difficulties for the search mechanism since the search space becomes more jagged, and there are more deep local minima. A new escape strategy is needed. To demonstrate the feasibility and generality of our approach, we show how the island confinement method can be incorporated in, and significantly improve, the search performance of two successful local search procedures, DLM and ESG, on SAT problems arising from binary CSPs.
How to shift bias: Lessons from the baldwin effect
Authors: Peter Turney
Venue: Evolutionary Computation (1996)

An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their bias as they search for a hypothesis. Algorithms that shift bias in this manner are not as well understood as classical algorithms. In this paper, we show that the Baldwin effect has implications for the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in 1896 to explain how phenomena that might appear to require Lamarckian evolution (inheritance of acquired characteristics) can arise from purely Darwinian evolution. Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We explore a variation on their model, which we constructed explicitly to illustrate the lessons that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias.
Adding Behavior to Models
Authors: Jose E. Rivera, Antonio Vallecillo
Venue: Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference (2007)

Domain Specific Languages (DSLs) play a cornerstone role in Model-Driven Software Development for represent- ing models and metamodels. DSLs are usually defined in terms of their abstract and concrete syntax only. This al- lows the rapid and inexpensive development of DSLs and their associated tools (e.g., editors), but does not allow the representation of their behavioral semantics, something es- pecially important for model operations like simulation and verification. In this paper we explore the use of Maude as a formal notation for describing models and metamodels, including the specification of their dynamic behavior.
Timing- / Power-Optimization for Digital Logic Based on Standard Cells
Authors: H. T. Vierhaus, H. Rossmann, S. Misera
Venue: Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (2007)

Physical design for digital ICs, based on standard cells, has long been performed without explicit consideration of timing and power related to interconnects. With delays on wires more and more dominating the delays on logic paths, such design styles are becoming obsolete. What is needed is an iterative inclusion of power and timing aspects into the design flow for placement and routing, which yields economic results without multiple loops in the design flow. Basic problems and solutions in the implementation of such a design flow are presented.
Proceedings of the 2nd international conference on Pragmatic web
Venue: ACM International Conference Proceeding Series; Vol. 280 (2007)

We're delighted to welcome you to the 2nd International Conference on the Pragmatic Web. Following last year's inaugural meeting, it is exciting to see continued interest in this fascinating concept. At last year's conference, almost every speaker seemed to open with the words "I'm not sure what the Pragmatic Web is, but here's my take on it..." Clearly, the concept resonates in different ways with different people, and we will see an equally colourful spectrum of contributions over the next two days. As an emergent community, clearly we are not running a mega-conference yet---but holding a small, inclusive and welcoming meeting that is not too hasty to reject unfamiliar ideas should be taken as a feature, not a bug! Balancing that, we were of course still concerned to maintain quality, so that engaging with the people and papers repays the effort. Out of the 20 full paper submissions received, the programme before you has 8 full papers, and 2 short papers, mostly reviewed by three or four people, and subsequently revised.
Hybrid Ontology Integration for Distributed System
Authors: Ye Liang, Hong Bao, Hongzhe Liu
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

The heterogeneity of information raises several challenges for systems to search and retrieve information mutually. In the paper, a hybrid ontology integration framework is proposed. The implementation of the framework in distributed Xu Beihong's Galleries is discussed in detail. The emphasis is the mapping of local database schema and local ontology and the integration of local ontologies and global ontology. The experiments which involve simulated local museums in the lab of the Information Technology Institute of Beijing Union University of China prove the approach is efficient.
Compiler Construction for Lockstep Execution of Multithreaded Processors
Authors: Huong Giang Nguyen, Seok Joong Hwang, Seon Wook Kim
Venue: Proceedings of the 7th IEEE International Conference on Computer and Information Technology (2007)

Embedded applications intrinsically have high degree of parallelism, but it is difficult to exploit the parallelism due to resource constraint of embedded platforms. In or- der to overcome the problem, we introduced a promising processor solution to support parallel thread execution with pretty good performance while consuming small hardware resources. We call this processor as Multithread Lockstep Execution Processor (MLEP). Since each iteration of par- allel loops performs the same sequence of instructions at most time while manipulating different data, we only need to partially duplicate a pipeline resource to support the mul- tithreading. This architecture makes it possible that parallel threads execute synchronously in a lockstep manner. How- ever, because of providing a totally different kind of thread execution, it sometime makes programmers confused when parallelizing code for the processor. In this paper, we intro- duce a compiler infrastructure for our architecture in detail with discussion of how to support OpenMP APIs and how to integrate the Omni OpenMP compiler with our backend code generator. Also, for verification of our compiler sys- tem, we show that our code generation scheme delivers the same performance as handed codes.
Design and implementation of an isolated sandbox with mimetic internet used to analyze malwares
Authors: Shinsuke Miwa, Toshiyuki Miyachi, Masashi Eto, Masashi Yoshizumi, Yoichi Shinoda
Venue: Proceedings of the DETER Community Workshop on Cyber Security Experimentation and Test on DETER Community Workshop on Cyber Security Experimentation and Test 2007 (2007)

Recent viruses, worms, and bots, called malwares, often have anti-analysis functions such as mechanisms that confirm connectivity to certain Internet hosts and detect virtualized environments. We discuss how malwares can be kept alive in an analyzing environment by disabling their anti-analyzing mechanisms. To avoid any impacts to/from the Internet, we conclude that analyzing environments should be disconnected from the Internet but must be able to make malwares believe that they are connected to the real Internet. We also conclude that, for executing environments to analyze anti-virtualization malwares, they should not be virtualized but must be as easily re-constructable as a virtualized environment. To reconcile these cross-purposes, we designed an isolated sandbox that consists of a mimetic Internet and renewable actual nodes. We implemented a prototype system and conducted an experiment to test the efficiency of our sandbox.
Incorporating non-motion cues into 3D motion segmentation
Authors: Amit Gruber, Yair Weiss
Venue: Computer Vision and Image Understanding (2007)

We address the problem of segmenting an image sequence into rigidly moving 3D objects. An elegant solution to this problem in the case of orthographic projection is the multibody factorization approach in which the measurement matrix is factored into lower rank matrices. Despite progress in factorization algorithms, their performance is still far from satisfactory and in scenes with missing data and noise, most existing algorithms fail. In this paper we propose a method for incorporating 2D non-motion cues (such as spatial coherence) into multibody factorization. We show the similarity of the problem to constrained factor analysis and use the EM algorithm to find the segmentation. We show that adding these cues improves performance in real and synthetic sequences.
Model-based Programming Environment of Embedded Software for MPSoC
Authors: Soonhoi Ha
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

A noble model-based programming environment of embedded software for MPSoC is proposed. By defining a common intermediate code (CIC), it separates modeling of the software and implementation optimized for target architecture. It also allows us to use diverse models for initial specification. Another feature is to provide multi-phase debugging capabilities: at the modeling stage, at the code generation stage, and at the simulation stage. Preliminary experiments with a Divx player confirm the feasibility and validity of the proposed technique.
European activities in wireless multimedia communications
Authors: Hamid Aghvami
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (1998)

There are two system concepts for future mobile communication systems being developed and standardized in Europe. These are Universal Mobile Telecommunications System (UMTS) and Mobile Broadband System (MBS). UMTS is being standardized by the European Telecommunications Standards Institute (ETSI) through its various technical committee Special Mobile Groups (SMGs). UMTS is intended to provide a wide range of mobile services up to 2 Mb/s to the users. MBS is aimed at providing a range of broadband services with transmission at up to 155 Mb/s. The standardization of MBS has not yet been commenced [1].
Parametric active contours for object tracking based on matching degree image of object contour points
Authors: Qiang Chen, Quan-Sen Sun, Pheng-Ann Heng, De-Shen Xia
Venue: Pattern Recognition Letters (2008)

A parametric active contour model is presented for object tracking based on matching degree image of object contour points. We first construct a matching degree image according to object contour points, and track the object using parametric active contours. This paper presents a new feature matching approach and a new directional filter. Assuming that the motion of objects is small in this paper, we constrain the motion of object contour within the contour vicinity defined by a band, which is constructed by the generation method of narrow band of level set method. Experimental results demonstrate that our method can effectively track rigid and non-rigid objects. We apply the proposed tracking method for face tracking, outer contour segmentation of left ventricle magnetic resonance (MR) images, and brain stem segmentation of Chinese visible human datasets, which demonstrates that our method is feasible for practical applications.
Learning algorithm of wavelet network based on sampling theory
Authors: Zhiguo Zhang
Venue: Neurocomputing (2007)

To avoid overfitting, the sampling theory is applied to training of wavelet networks. A new algorithm is proposed based on the limited band of wavelet networks, in which the input weights are decided by the sampling period or the frequency band of target function instead of sample errors. The wavelet networks trained by our new algorithm have global convergence, avoidance of local minimum and ability to approximate band-limited functions. Our new algorithm is also extended to learn from noisy data. The theorems prove that the wavelet network trained by our new algorithm is just an ideal low-pass filter, which removes the high-frequency noise in training data. In the simulation, we compare the performance of new algorithm with that of regularization technology. The results show that our algorithm is more robust to the variance of noise and removes high-frequency noise more effectively.
Computation of local radius of information in SM-IBC identification of nonlinear systems
Authors: Mario Milanese, Carlo Novara
Venue: Journal of Complexity (2007)

System identification consists in finding a model of an unknown system starting from a finite set of noise-corrupted data. A fundamental problem in this context is to asses the accuracy of the identified model. In this paper, the problem is investigated for the case of nonlinear systems within the Set Membership-Information Based Complexity framework of [M. Milanese, C. Novara, Set membership identification of nonlinear systems, Automatica 40(6) (2004) 957-975]. In that paper, a (locally) optimal algorithm has been derived, giving (locally) optimal models in nonlinear regression form. The corresponding (local) radius of information, providing the worst-case identification error, can be consequently used to measure the quality of the identified model. In the present paper, two algorithms are proposed for the computation of the local radius of information: The first provides the exact value but requires a computational complexity exponential in the dimension of the regressor space. The second is approximate but involves a polynomial (quadratic) complexity.
Social Bookmarking for Scholarly Digital Libraries
Authors: Umer Farooq, Yang Song, John M. Carroll, C. Lee Giles
Venue: IEEE Internet Computing (2007)

Social bookmarking services have recently gained popularity among Web users. Whereas numerous studies provide a historical account of tagging systems, the authors use their analysis of a domain-specific social bookmarking service called CiteULike to reflect on two metrics for evaluating tagging behavior: tag growth and tag reuse. They examine the relationship between these two metrics and articulate design implications for enhancing social bookmarking services. The authors also briefly reflect on their own work on developing a social bookmarking service for CiteSeer, an online scholarly digital library for computer science.
An Automatic Generator for Compiler Testing
Authors: F. Bazzichi
Venue: IEEE Transactions on Software Engineering (1982)

A new method for testing compilers is presented. The compiler is exercized by compilable programs, automatically generated by a test generator. The generator is driven by a tabular description of the source language. This description is in a formalism which nicely extends context-free grammars in a context-dependent direction, but still retains the structure and readability of BNF. The generator produces a set of programs which cover all grammatical constructions of the source language, unless user supplied directives instruct otherwise. The programs generated can also be used to evaluate the performance of different compilers of the same source language.
A Measure of Computational Work
Authors: L. Hellerman
Venue: IEEE Transactions on Computers (1972)

The computational work of a process is measured in terms of the information in a memory for its table-lookup implementation. This measure is applied first to simple logical and arithmetic processes, and then more complicated processes comprising organizations (called synergisms) of several subprocesses. The computational advantages of Cartesian, compositional, and sequential synergisms are investigated and illustrated by means of the work measure. The relation between the work of a process and the work capacity of a facility on which it is implemented is examined, and a concept of efficiency of implementations is formulated. A few areas for further investigation are outlined.
Suppression and failures in sensor networks: a Bayesian approach
Authors: Adam Silberstein, Gavino Puggioni, Alan Gelfand, Kamesh Munagala, Jun Yang
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

Sensor networks allow continuous data collection on unprecedented scales. The primary limiting factor of such networks is energy, of which communication is the dominant consumer. The default strategy of nodes continually reporting their data to the root results in too much messaging. Suppression stands to greatly alleviate this problem. The simplest such scheme is temporal suppression, in which a node transmits its reading only when it has changed beyond some e since last transmitted. In the absence of a report, the root can infer that the value remains within &plusmn;&epsilon; hence, it is still able to derive the history of readings produced at the node. The critical weakness of suppression is message failure, to which sensor networks are particularly vulnerable. Failure creates ambiguity: a non-report may either be a suppression or a failure. Inferring the correct values for missing data and learning the parameters of the underlying process model become quite challenging. We propose a novel solution, BaySail, that incorporates the knowledge of the suppression scheme and application-level redundancy in Bayesian inference. We investigate several redundancy schemes and evaluate them in terms of in-network transmission costs and out-of-network inference efficacy, and the trade-off between these. Our experimental evaluation shows application-level redundancy outperforms retransmissions and basic sampling in both cost and accuracy of inference. The BaySail framework shows suppression schemes are generally effective for data collection, despite the presence of failures.
Predicting properties of congestion events for a queueing system with fBm traffic
Authors: Yasong Jin, Soshant Bali, Tyrone E. Duncan, Victor S. Frost
Venue: IEEE/ACM Transactions on Networking (TON) (2007)

In packet networks, congestion events tend to persist, producing large delays and long bursts of consecutive packet loss resulting in perceived performance degradations. The length and rate of these events have a significant effect on network quality of service (QoS). The packet delay resulting from these congestion events also influences QoS. In this paper a technique for predicting these properties of congestion events in the presence of fractional Brownian motion (fBm) traffic is developed.