ppxscal/embedding_network huggingface dataset:
Predicting link quality using supervised learning in wireless sensor networks
Authors: Yong Wang, Margaret Martonosi, Li-Shiuan Peh
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (2007)

Routing protocols in sensor networks maintain information on neighbor states and potentially many other factors in order to make informed decisions. Challenges arise both in (a) performing accurate and adaptive information discovery and (b) processing/analyzing the gathered data to extract useful features and correlations. To address such challenges, this paper explores using supervised learning techniques to make informed decisions in the context of wireless sensor networks. We investigate the design space of both offline learning and online learning and use link quality estimation as a case study to evaluate their effectiveness. For this purpose, we present MetricMap, a metric-based collection routing protocol atop MintRoute that derives link quality using classifiers learned in the training phase, when the traditional ETX approach fails. The offline learning approach is evaluated on a 30-node sensor network testbed, and our results show that MetricMap can achieve up to 300% improvement over MintRoute in data delivery rate for high data rate situations, with no negative impact on other performance metrics. We also explore the possibility of using online learning in this paper.
Distributed inter-domain lightpath provisioning in the presence of wavelength conversion
Authors: Q. Liu, N. Ghani, N. S. V. Rao, A. Gumaste, M. L. Garcia
Venue: Computer Communications (2007)

Dense wavelength division multiplexing (DWDM) has become the dominant transport layer technology for next-generation backbone networks due to its unprecedented capacity scalability. As a result, there is a pressing need to investigate lightpath provisioning in multi-domain DWDM networks. Although inter-domain provisioning has been well-studied for packet/cell-switching networks, the wavelength dimension (along with wavelength conversion) presents many added challenges. To address these concerns, a detailed GMPLS-based hierarchical routing framework for provisioning transparent/translucent/opaque multi-domain DWDM networks is presented. The scheme adapts topology abstraction to hide internal domain state so as to resolve routing scalability and security issues. Specifically a novel full-mesh topology abstraction scheme is developed for full wavelength conversion domains, i.e., to disseminate additional wavelength converter state. Related inter-domain lightpath RWA and signaling schemes are also tabled. Performance analysis results are then presented to demonstrate the effectiveness of the proposed mechanisms along with directions for future research work.
A Large-scale and Decentralized Infrastructure for Content-based Publish/Subscribe Services
Authors: Xiaoyu Yang, Yingwu Zhu, Yiming Hu
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

Publish/Subscribe model has become a prevalent paradigm for building distributed event delivering systems. Content-based publish/subscribe allows high expresses in subscriptions and thus is more appropriate for content dissemination. However, the scalability has remained a challenge in the design of distributed content-based publish/ subscribe systems due to the expensive matching and delivering cost of content-based events. In this paper we propose HyperSub, an infrastructure built on top of distributed hash table (DHT) for efficient content-based data distribution. Based on efficient subscription installation, event publication and event delivery mechanisms, Hyper- Sub can provide a scalable platform to simultaneously support any numbers of pub/sub schemes with different number of attributes. There are three key features in the design of HyperSub: (1) a locality-preserving hashing mechanism which partitions the content space into content zones. Subscriptions and events are mapped to the content zones for efficiently matching; (2) an efficient event delivery algorithm which exploits the embedded trees in the underlying DHT to deliver events to the corresponding subscribers; (3) light-weighted load balancing mechanisms to adjust the load among peers and ensure that no peer is unduly loaded.
Analysis of Random Mobility Models with Partial Differential Equations
Authors: Michele Garetto, Emilio Leonardi
Venue: IEEE Transactions on Mobile Computing (2007)

In this paper we revisit two classes of mobility models which are widely used to represent users’ mobility in wireless networks: Random Waypoint (RWP) and Random Direction (RD). For both models we obtain systems of partial differential equations which describe the evolution of the users’ distribution. For the RD model, we show how the equations can be solved analytically both in the stationary and transient regime adopting standard mathematical techniques. Our main contributions are i) simple expressions which relate the transient duration to the model parameters; ii) the definition of a generalized random direction model whose stationary distribution of mobiles in the physical space corresponds to an assigned distribution.
Becoming Visually Familiar
Authors: M. Castrillon-Santana, O. Deniz-Suarez, J. Lorenzo-Navarro, D. Hernandez-Sosa
Venue: Proceedings of the 14th International Conference on Image Analysis and Processing (2007)

Automatic face recognition has been mainly tackled by matching a new image to a set of previously computed identity models. The literature describes approximations where those identity models are based on a single sample or a set of them. However, face representation keeps being a topic of great debate in the psychology literature, with some recent results suggesting the use of an average image. In this paper, instead of restricting our system to a fixed and precomputed classifier, the system learns iteratively based on the experience extracted from each meeting. The experiments presented introduce the use of an exemplar average based approach. The results show similar performance to an approach based on the use of multiple exemplars per identity, but reducing storage and processing cost. The process is done autonomously, using an automatic face detection system that meets people, excepting the supervision provided by a human to confirm or correct each meeting classification suggested by the system.
The Most Important Service-Oriented Antipatterns
Authors: Jaroslav Kral, Michal Zemlicka
Venue: Proceedings of the International Conference on Software Engineering Advances (2007)

Service-oriented architecture (SOA) is now the most popular software engineering concept. Software related antipatterns - commonly used seemingly good but in fact wrong solutions - can have adverse consequences of varying importance. It implies that the use of an antipattern can and should be viewed as a risky event. It follows that some principles of risk management can be used. We propose a method based on slightly simplified procedures of risk management and assessment. Using the tool we give a short list of the most risky antipatterns, i.e. antipatterns occurring very often and having crucial consequences. We discuss these crucial SOA-related antipatterns: No Legacy (development from scratch), Standardization Paralysis, Business Process for Ever (Full Automation), Sand Pile (too fine grained services), On-Line Only (No Batch Systems). The discussion of antipatterns is based on a longterm experience with service-oriented and service-oriented like (e.g. process control) systems and on the analysis of practice.
Melding Software Systems from Reusable Building Blocks
Authors: G. E. Kaiser
Venue: IEEE Software (1987)

This declarative language takes the best features from the three most popular reusability approaches, but overcomes their flaws. It supports language independence, component composition, and tailoring.
An Empirical Assessment of the Perception of Computer Security between US and Korea: Focused on Rootkits
Authors: Ha Jin Hwang
Venue: Proceedings of the 5th ACIS International Conference on Software Engineering Research, Management & Applications (2007)

The surveys were conducted from 400 students of five korea and three U.S. universities to compare their knowledge and experience with various forms of malware.They provide an empirical assessment of the cross-cultural similarities and differences between students in the two countries. The variables examined include knowledge of computer viruses, spyware, and rootkits as well as perceptions of the damage that can result from various computer malware.While the two groups are similar with respect to their relative familiarity of rootkits compared with that of spyware and viruses, and in terms of how they perceive the malware knowledge of their peers, they exhibit significant differences in self-reported perceptions of rootkit familiarity. U.S. students report higher levels for all tested malware types, including the fictional "Trilobyte" virus. These comparisons reveal that little is known about rootkits today. However, there is hope for an accelerated rootkit awareness because of the rapid assimilation of Spyware knowledge in recent years.
Co-evolutionary learning of neural networks
Authors: Qiangfu Zhao
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (1998)

Compared with the conventional approaches, the evolutionary algorithms (EAs) are more efficient for system design in the sense that EAs can provide higher opportunity for obtaining the global optimal solution. However, in most existing EAs, an individual corresponds directly to a possible solution, and a large amount of computations is required for designing large-scaled systems. To solve this problem, this paper proposes a co-evolutionary algorithm (CEA). The basic idea is to divide and conquer&colon; divide the system into many small homogeneous modules, define an individual as a module, find many good individuals using existing EAs, and put them together again to form the whole system. To make the study more concrete, we focus the discussion on the evolutionary learning of neural networks for pattern recognition. Experimental results are provided to show the procedure and the performance of the CEA.
Context-aware statistical debugging: from bug predictors to faulty control flow paths
Authors: Lingxiao Jiang, Zhendong Su
Venue: Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (2007)

Effective bug localization is important for realizing automated debugging. One attractive approach is to apply statistical techniques on a collection of evaluation profiles of program properties to help localize bugs. Previous research has proposed various specialized techniques to isolate certain program predicates as bug predictors. However, because many bugs may not be directly associated with these predicates, these techniques are often ineffective in localizing bugs. Relevant control flow paths that may contain bug locations are more informative than stand-alone predicates for discovering and understanding bugs. In this paper, we propose an approach to automatically generate such faulty control flow paths that link many bug predictors together for revealing bugs. Our approach combines feature selection (to accurately select failure-related predicates as bug predictors), clustering (to group correlated predicates), and control flow graph traversal in a novel way to help generate the paths. We have evaluated our approach on code including the Siemens test suite and rhythmbox (a large music management application for GNOME). Our experiments show that the faulty control flow paths are accurate, useful for localizing many bugs, and helped to discover previously unknown errors in rhythmbox
A Privacy Preserving Access Control Scheme using Anonymous Identification for Ubiquitous Environments
Authors: Nguyen Ngoc Diep, Sungyoung Lee, Young-Koo Lee, HeeJo Lee
Venue: Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2007)

Comparedtoallemergingissues,privacyisprobably the most prominent concern when it comes tojudgingtheeffectsofawidespreaddeploymentofubiquitous computing. On one hand, service providerswanttoauthenticatelegitimateusersandmakesurethey are accessingtheir authorized servicesin alegalway. On the other hand, users prefer not to expose anysensitiveinformationtoanybody.Theywanttohavecomplete control on their personal data, without beingtrackeddownforwherevertheyare,wheneverandwhatevertheydo.Inthispaper,weintroduceananonymousidentificationauthenticationandaccesscontrolschemetosecureinteractionsbetweenusersandservicesinubiquitousenvironments.TheschemeusesanonymoususerID,sensitivedatasharingmethod,andaccountmanagementtoprovidealightweightauthenticationwhilekeepingusersanonymouslyinteracting withtheservicesin asecureand flexible way.
Performance evaluation and experimental assessment: conscience or curse of database research?
Authors: Ioana Manolescu, Stefan Manegold
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

Performance, performance and performance used to be the three things that really mattered in database research. Most of our published works indeed include an experimental evaluation of the proposed techniques. However, such evaluations are sometimes seen as a "must-have" eating up the valuable space where one could describe new ideas. The experimental evaluations end up being short, lacking important information to interpret and/or reproduce the results, and often end without clear conclusion.
Semi-parametric nonlinear regression and transformation using functional networks
Authors: Enrique Castillo, Ali S. Hadi, Beatriz Lacruz, Rosa E. Pruneda
Venue: Computational Statistics Data Analysis (2008)

Functional networks are used to solve some nonlinear regression problems. One particular problem is how to find the optimal transformations of the response and/or the explanatory variables and obtain the best possible functional relation between the response and predictor variables. After a brief introduction to functional networks, two specific transformation models based on functional networks are proposed. Unlike in neural networks, where the selection of the network topology is arbitrary, the selection of the initial topology of a functional network is problem driven. This important feature of functional networks is illustrated for each of the two proposed models. An equivalent, but simpler network may be obtained from the initial topology using functional equations. The resultant model is then checked for uniqueness of representation. When the functions specified by the transformations are unknown in form, families of linear independent functions are used as approximations. Two different parametric criteria are used for learning these functions: the constrained least squares and the maximum canonical correlation. Model selection criteria are used to avoid the problem of overfitting. Finally, performance of the proposed method are assessed and compared to other methods using a simulation study as well as several real-life data.
MCC: Planning the Revolution in Software
Authors: W. Myers
Venue: IEEE Software (1985)

MCC's Software Technology Program aims to make a dramatic improvement in the productivity of the software development process and the quality of the product. 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
Decentralized adaptive recurrent neural control structure
Authors: Victor H. Benitez, Edgar N. Sanchez, Alexander G. Loukianov
Venue: Engineering Applications of Artificial Intelligence (2007)

This paper presents a novel decentralized variable structure neural control approach for large-scale uncertain systems, which is developed using recurrent high-order neural networks (RHONN). It is assumed that each subsystem belongs to a class of block-controllable nonlinear systems whose vector fields includes interconnection terms, which are bounded by nonlinear functions. A decentralized RHONN structure and the respective learning law are proposed in order to approximate online the dynamical behavior of each nonlinear subsystem. The control law, which is able to regulate and to track the desired reference signals, is designed using the well-known variable structure theory. The stability of the whole system is analyzed via the Lyapunov methodology. The applicability of the proposed decentralized identification and control algorithm is illustrated via simulations as applied to an interconnected double inverted pendulum.
Modeling simple genetic algorithms
Authors: Michael D. Vose
Venue: Evolutionary Computation (1995)

The infinite-and finite-population models of the simple genetic algorithm are extended and unified, The result incorporates both transient and asymptotic GA behavior. This leads to an interpretation of genetic search that partially explains population trajectories. In particular, the asymptotic behavior of the large-population simple genetic algorithm is analyzed.
Static and Dynamic Characteristics of XPL Programs
Authors: W. G. Alexander
Venue: Computer (1975)

One use of performance measurement techniques is in the study of operational characteristics of programs written in high-level programming languages. Information derived from such studies can be used to construct benchmark programs and synthetic workloads,1,2detect inefficiencies in programming language implementation, and suggest possible improvements in the design of computers.3,9,10Our main interest is in the latter area: the discovery of primitive operations, implied by the semantics of a programming language, that can be added to the firmware or hardware of a computer to improve overall system performance. These computer architecture optimization techniques have been applied in several studies3,9and have been used commercially to design efficient pseudo machines for the Burroughs B1700.10,12
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
Authors: Gavin C. Cawley, Nicola L. C. Talbot
Venue: The Journal of Machine Learning Research (2007)

While the model parameters of a kernel machine are typically given by the solution of a convex optimisation problem, with a single global optimum, the selection of good values for the regularisation and kernel parameters is much less straightforward. Fortunately the leave-one-out cross-validation procedure can be performed or a least approximated very efficiently in closed form for a wide variety of kernel learning methods, providing a convenient means for model selection. Leave-one-out cross-validation based estimates of performance, however, generally exhibit a relatively high variance and are therefore prone to over-fitting. In this paper, we investigate the novel use of Bayesian regularisation at the second level of inference, adding a regularisation term to the model selection criterion corresponding to a prior over the hyper-parameter values, where the additional regularisation parameters are integrated out analytically. Results obtained on a suite of thirteen real-world and synthetic benchmark data sets clearly demonstrate the benefit of this approach.
Simulaton of Microprocessor Emulation Using GASP-PL/I
Authors: M. S. Jayakumar
Venue: Computer (1977)

As microprocessors find wider and wider application in system design, it is becoming increasingly important to simulate these elements in large integrated systems. At Southern Illinois University, NASA-sponsored work on avionics system design has motivated related work in the simulation of microprocessor systems. In particular, an effort was undertaken to simulate an Intel 3000 chip set, which was in turn emulating a PDP-8/A.
Software Aging Forecasting Model of Service-Oriented Application Server Based on Wavelet Network with Adaptive Genetic Algorithm
Authors: Hai Ning Meng, Yong Qi, Di Hou, Yao Zhang, Liang Liu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

Web services are gaining acceptance as a standards-based approach for integrating loosely coupled services. Achieving high levels of reliability and availability of service-oriented application server in spite of service or infrastructure failures poses new challenges. According to the characteristic of performance parameters of service-oriented application sever, a new software aging forecasting model based on wavelet network is proposed. The structure and parameters of wavelet network are optimized with genetic algorithm and evolutionary programming. The objective is to observe and model the existing systematic parameter data series of service-oriented application server to forecast software aging. The experiments are carried out to validate the efficiency of the proposed model and show that the aging forecasting model based on wavelet network with adaptive genetic algorithm is superior to the BP neural network model and wavelet network model in the aspects of convergence rate and forecasting precision.
Discovering authorities in question answer communities by using link analysis
Authors: Pawel Jurczyk, Eugene Agichtein
Venue: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (2007)

Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.
Sorting on STAR
Authors: H. S. Stone
Venue: IEEE Transactions on Software Engineering (1978)

This paper gives timing comparisons for three sorting algorithms written for the CDC STAR computer. One algorithm is Hoare's Quicksort, which is the fastest or nearly the fastest sorting algorithm for most computers. A second algorithm is a vector version of Quicksort that takes advantage of the STAR's vector operations. The third algorithm is an adaptation of Batcher's sorting algorithm, which makes especially good use of vector operations but has a complexity of N (log N)2 as compared to a complexity of N log N for the Quicksort algorithms. In spite of its worse complexity, Batcher's sorting algorithm is competitive with the serial version of Quicksort for vectors up to the largest that can be treated by STAR. Vector Quicksort outperforms the other two algorithms and is generally preferred. These results indicate that unusual instruction sets can introduce biases in program execution time that counter results predicted by worst-case asymptotic complexity analysis.
Monocular reconstruction of human translation in motion sequence by MTA
Authors: Li Zhang, Ling Li
Venue: Proceedings of the 5th international conference on Computer graphics and interactive techniques in Australia and Southeast Asia (2007)

A motion trend analysis method is proposed to reconstruct human motion from un-calibrated monocular video. In this paper, relocation of human subject is the main concern, which is one of the most difficult parts in motion reconstruction and critically affects the accuracy of human posture generation. Numerical studies are conducted on experiments from synthetic and real video data to demonstrate the correctness of the recovered human translation in the proposed system.