ppxscal/embedding_network huggingface dataset:
A Self-stabilizing Link-Cluster Algorithm in Mobile Ad Hoc Networks
Authors: Doina Bein, Ajoy K. Datta, Chakradhar R. Jagganagari, Vincent Villain
Venue: Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks (2005)

We design a self-stabilizing cluster routing algorithm based on the link-cluster architecture of wireless ad hoc networks. The network is divided into clusters. Each cluster has a single special node, called clusterhead that contains the routing information about inter and intra-cluster communication. The proposed algorithm assumes that all nodes have unique IDs. The algorithm achieves two tasks. First, the set of special nodes (clusterheads) is elected such that it models the link-cluster architecture: any node belongs to a single cluster, is within two hops of the clusterhead, and knows the direct neighbor on the shortest path toward the clusterhead. Second, the routing tables are maintained by the clusterheads to store information about nodes both within and outside the cluster. There are two advantages of maintaining routing tables only in the clusterheads. First, as no two neighboring nodes are clusterhead (as per the link-cluster architecture), there exists no consistency problems. Second, since other nodes are responsible for forwarding only, they use less power. So, when the CH runs out of power, some neighboring node will be available to take on the task.
Flexible IO Services in the Kepler Grid Workflow System
Authors: David Abramson, Jagan Kommineni, Ilkay Altintas
Venue: Proceedings of the First International Conference on e-Science and Grid Computing (2005)

Existing Grid workflow tools assume that individual components either communicate by passing files from one application to another, or are explicitly linked using interprocess communication pipes. When files are used it is usually difficult to overlap reader and write execution. On the other hand, interprocess communication primitives are invasive and require substantial modification to existing code. We have built a library, called GriddLeS, that combines the advantages of both approaches without any changes to the application code. GriddLeS overloads conventional file IO operations, and supports either local, remote or replicated file access, or direct communication over pipes. In this paper we discuss how GriddLeS can be combined with workflow packages and show how flexible and powerful virtual applications can be constructed rapidly. A large atmospheric science case study is discussed.
Power and Size Optimized Multi-Sensor Context Recognition Platform
Authors: Nagendra B. Bharatula, Mathias Stager, Paul Lukowicz, Gerhard Troster
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

This paper presents a miniaturized low-power platform for real-time activity recognition. The wearable sensor system comprises of accelerometers, a microphone, a light sensor and signal processing units. The recognition is performed with low-power features and a decision tree classi- fier. Power measurements show that our 4.15×2.75 cm2, 9 gram platform consumes less than 3mW and can perform continuous classification and result transmission for 129 hours on a small lithium-polymer battery.
An Efficient Algorithm for the Optimal Placement of Wireless Extension Points in RectilinealWireless Local Area Networks
Authors: Aaron So, Ben Liang
Venue: Proceedings of the Second International Conference on Quality of Service in Heterogeneous Wired/Wireless Networks (2005)

Wireless Extension Points (EPs) are immobile devices that relay data between the Access Point and the mobile users in a heterogeneous Wireless Local Area Network (WLAN). In this paper, we investigate the optimal placement of EPs such that the throughput capacity of a rectilineal WLAN is maximized. Two channel models are studied, based on the Shannon capacity bound and IEEE 802.11 specifications. We propose an efficient EP placement algorithm that determines the optimal locations of a fixed number of EPs. Our results show that, for a wide range of system parameters, the optimally placed EPs can significantly increase the network throughput capacity. Moreover, we study how the number of EPs, transmission power, path loss exponent, channel models and traffic characteristics affect the optimal EP placement and expected throughput capacity of the network.
Distributed Real-Time Processing for Humanoid Robots
Authors: Toshihiro Matsui, Hirohisa Hirukawa, Yutaka Ishikawa, Nobuyuki Yamasaki, Satoshi Kagami, Fumio Kanehiro, Hajime Saito, Tetsuya Inamura
Venue: Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2005)

A humanoid robot is a real-time system controlled by a complex computer system that requires huge computing power for perception and planning, high energy efficiency for self-contained control, reduction of physical dimensions, and high reliability. This paper proposes a distributed architecture for the humanoid robot control substituting conventional centralized control architectures. In addition to the parallelism that provides scalable computing power at low clock namely at low energy, the distributed architecture contributes to reliable operations by replacing many fragile analog signal wires with a digital network with redundant routes. In order to accomplish a real-time control over the network, RMTP (Responsive Multi-Threaded Processor)for parallel and real-time computation has been newly designed. RMTP can synchronize more than thirty nodes distributed over a robot body in less than 5 micro second with a real-time network called the Responsive Link (RL). Architectures of RMTP, RL and Linux-based real-time system software are presented.
Building Compact Classifier for Large Character Set Recognition Using Discriminative Feature Extraction
Authors: Ching-Lin Liu, Ryuji Mine, Masashi Koga
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

In this paper, we propose an approach to building compact classifier for camera-based printed japanese recognition on mobile phones. We design feature vector prototypes using learning vector quantization(LV Q) for achieving high accuracy, while the complexity is lowered by the line ar dimensionality reduction. The descriminative feature extraction(DFE) strategy, which optimizes both subspace axes and classifier parameters, is shown to yield high classification accuracy even on low dimensional subspace. On a 120D subspace, a 4,344-class classifier consumes only 613KB storage, and an accuracy of 99.41% was obtained on test set.
Improving flexibility on host discovery for pervasive computing middlewares
Authors: Emerson Loureiro, Loreno Oliveira, Hyggo Almeida
Venue: Proceedings of the 3rd international workshop on Middleware for pervasive and ad-hoc computing (2005)

The vision of pervasive or ubiquitous computing, conceived by Mark Weiser, foresees a world where computing is embedded in every day objects. Such objects interact with each other to perform actions on behalf of the user. As one of the mains of pervasive computing is to disappear from human perception, applications embedded in electronic devices must be proactive. This indicates that they should guess the needs of the users in order to provide them with anytime, anywhere relevant information. This characteristic must be achieved by acquiring information not only about the user, but also about the environment where he/she is immersed. This kind of information can be obtained by interacting with nearby hosts, trough a variety of networking protocols. Applications can take advantage of this characteristic by performing host discovery through heterogeneous networks, possibly at the same time. However, due to memory and storage constraints, it is not reasonable to embed all of such protocols in a mobile device. Therefore, we propose a flexible mechanism for performing host discovery in pervasive environments, allowing the insertion and removal of these networking protocols whenever needed.
Protection mechanisms for application service hosting platforms
Authors: Xuxian Jiang, Dongyan Xu, R. Eigenmann
Venue: Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid (2004)

The application service hosting platform (ASHP) has recently received tremendous attention from both industry and academia. An ASHP provides a shared high-performance infrastructure to host different application services (AS), outsourced by application service providers (ASP). In this paper, we focus on the protection of ASHP which has inherent requirement of sharing, openness, and mutual isolation. In contrast to a dedicated server platform, which is analogous with a private house, an ASHP is like an apartment building, involving the 'host' - the ASHP infrastructure, and the 'tenants' - the AS. Strong protection and isolation must be provided between the host and the tenants, as well as between different tenants. Unfortunately, traditional OS architecture and mechanisms are not adequate to provide strong ASHP protection. In this paper we first make the case for a new OS architecture based on the virtual OS technology. We then present three protection mechanisms we have developed in SODA, our ASHP architecture. The mechanisms include: (1) resource isolation between AS; (2) virtual switching and firewalling between AS; and (3) kernelized intrusion detection and logging for each AS. For (3), we have developed a system called Kernort inside the virtual OS kernel. Kernort detects network intrusions in real-time and logs AS activities even when the AS has been compromised. Moreover for the privacy of AS, logs are encrypted by Kernort so that the 'landlord' (namely ASHP owner) cannot view them without authorization. We are applying SODA to iShare, an Internet-based distributed resource sharing platform.
An FPGA Design of a Unified Hash Engine for IPSec Authentication
Authors: Esam Khan, M. Watheq El-Kharashi, Fayez Gebali, Mostafa Abd-El-Barr
Venue: Proceedings of the Fifth International Workshop on System-on-Chip for Real-Time Applications (2005)

Hash functions are important security primitives used for authentication and data integrity. Among the most popular hash functions are MD5, SHA-1, and RIPEMD-160 that are used in conjunction with HMAC for IPSec. These three hash functions are based on an older one, MD4. Therefore, they have some similarities that can be exploited for designing a unified engine to perform the three hash functions. A unified engine design proves useful since the three algorithms are to be used by same implementation on the same core for authentication and data integrity using HMAC for IPSec. In this work, we design a SoC with a unified hash engine that can be reconfigured at runtime to perform one of the three hash functions. The results of our work show that the proposed engine has a balance between area and throughput compared to previous works.
Subtractive and mmse algorithms for noise reduction in cochlear implants
Authors: Felipe De Jesus Toledo-Sanchez
Venue:  (2004)

Cochlear implants (CI) have improved enormously from four decades ago. The development of CI has grown in parallel with advances in technology. CI users benefited from this technological evolution, but as in many other fields of science different obstacles have appeared. CI users commonly complain about the performance degradation when exposed to noisy environments. This problem has been studied in the literature but it has not been until recently that interest and dedication have been increased. Not many solutions have been proposed to improve the performance of CI under noisy environments. In this matter, speech enhancement has been the most important field as it provides different solutions for the underlying problem. Several speech enhancement methods have been broadly studied in the literature including: spectral subtraction, Wiener filtering, and subspace. We introduce in this dissertation new methods for speech enhancement oriented for CI applications to overcome the limitations in noisy environments. Although, in the literature it is commonly assumed noise and speech signals being Gaussian distributed; we focus our attention on a different approach. Our assumption considers the speech envelope signals being Gamma or Laplacian distributed while the noise maintains the same traditional assumption (Gaussian). The noise involved in our study is multi-taker babble, which requires more complex methods to mitigate its effects on speech signals. The proposed algorithms are suitable for currently used strategies in CI applications, but our study focuses primarily on the constant interleaved strategy (CIS) with promising results. Additionally, the proposed algorithms may be embedded in current CI strategies providing strong advantages over conventional methods for speech enhancement in CI applications.
Multiple random walk and its application in content-based image retrieval
Authors: Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying Ma, Changshui Zhang
Venue: Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval (2005)

In this paper, we propose a transductive learning method for content-based image retrieval: Multiple Random Walk (MRW). Its basic idea is to construct two generative models by means of Markov random walks, one for images relevant to the query concept and the other for the irrelevant ones. The goal is to obtain the likelihood functions of both classes. Firstly, MRW generates two random walks with virtual absorbing boundaries, and uses the absorbing probabilities as the initial estimation of the likelihood functions. Then it refines the two random walks through an EM-like iterative procedure in order to get more accurate estimation of the likelihood functions. Class priors are also obtained in this procedure. Finally, MRW ranks all the unlabeled images in the database according to their posterior probabilities of being relevant. By using both labeled and unlabeled data, MRW can be seen as a transductive learning method, which has been demonstrated to outperform inductive ones by previous research work. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of MRW over state-of-the-art techniques.
Fuzzy pogroupoids
Authors: Hee Sik Kim, J. Neggers
Venue: Information Sciences: an International Journal (2005)

In this paper we show that, for given a pogroupoid (X, ċ), the associated poset (X, ≤) is (C2 + 1)-free if and only if the relation ⊳µ is transitive for any fuzzy subset µ of X. Also we determine the set C(X, ċ) of fuzzy subsets µ such that µ(xċy) = µ(yċx) for all x,y ∈ X. Furthermore, with a given finite poset (X, ≤) or the associated pogroupoid (X, ċ) we may then associate a polynomial whose coefficients count the number of congruence classes mod(X,ċ) of fuzzy subsets of X along with another polynomial invariant of interest.
Making Formal Methods Work
Authors: Anthony Hall
Venue: Proceedings of the Third IEEE International Conference on Software Engineering and Formal Methods (2005)

As we have got better at programming, we have come to recognize that the real problems of software development cannot be solved by thinking about program code. We need to address the problems of complex and shifting user requirements in a world of rapid application development, commercial off-theshelf components and concurrent engineering. Formal methods must help us do this if they are to play a significant role in real projects.
Gaussian Mixture Models Based on the Frequency Spectra for Human Identification and Illumination Classification
Authors: Sinjini Mitra, Marios Savvides
Venue: Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies (2005)

The importance of Fourier domain phase in human face identification is well-established ([7]). It therefore seems natural that identification tools based on phase features should be very efficient. In this paper we introduce a model-based approach using Gaussian mixture models (GMM) based on phase for performing human identification. Identification is performed using a MAP estimate and we show that we are able to achieve misclassification error rates as low as 2% on a database with 65 individuals with extreme illumination variations. The proposed method is easily adaptable to deal with other distortions such as expressions and poses, and hence this establishes its robustness to intra-personal variations. Finally we demonstrate that GMM based on the Fourier domain magnitude is effective for illumination normalization, so that near perfect identificationis obtained using the reconstructed illumination-free images.
Advance Reservation and Co-Allocation Protocol for Grid Computing
Authors: Dean Kuo, Mark Mckeown
Venue: Proceedings of the First International Conference on e-Science and Grid Computing (2005)

This paper specifies an advance reservation and co-allocation protocol for grid computing, and a simple framework for supporting well defined charging models. The protocol supports arbitrary level of nesting, that is a reservation may be to a virtual resource (a set of resources). The work is motivated by the need for advance reservation in the RealityGrid project. However the protocol is a generic protocol that can reserve any type of resource (e.g. clusters, network, and storage) and for any grid project where users need to reserve one or more resources in advance.
Is trust robust?: an analysis of trust-based recommendation
Authors: John O'Donovan, Barry Smyth
Venue: Proceedings of the 11th international conference on Intelligent user interfaces (2006)

Systems that adapt to input from users are susceptible to attacks from those same users. Recommender systems are common targets for such attacks since there are financial, political and many other motivations for influencing the promotion or demotion of recommendable items [2].Recent research has shown that incorporating trust and reputation models into the recommendation process can have a positive impact on the accuracy and robustness of recommendations. In this paper we examine the effect of using five different trust models in the recommendation process on the robustness of collaborative filtering in an attack situation. In our analysis we also consider the quality and accuracy of recommendations. Our results caution that including trust models in recommendation can either reduce or increase prediction shift for an attacked item depending on the model-building process used, while highlighting approaches that appear to be more robust.
Optimization of nitride films for linear pre-metal dielectric process
Authors: Nam-Hoon Kim, Yong-Jin Seo, Sung-Woo Park, Do-Won Lee, Sang-Yong Kim, Eui-Goo Chang, Woo-Sun Lee
Venue: Microelectronic Engineering (2005)

With applications in current semiconductor manufacturing, the characteristics of nitride films were investigated for the optimization of pre-metal dielectric (PMD) linear nitride process. For the purpose of this study, the deposit condition of nitride films was divided into four areas such as protected overcoat (PO) nitride process, baseline process, low hydrogen process, and low hydrogen process with high stress, respectively. The correlation between boro-phospho-silicate-glass (BPSG) depositions and their densification was also examined and Fourier transform infrared spectroscopy (FTIR) area method was used to analyze the change of Si-H and Si-NH-Si bonding density. In addition, the generation of cracks on the wafer edge was evaluated after BPSG densification. The stress changes of nitride film as a function of radio frequency (RF) power variation were used to determine the quality of the deposited films. Resultantly, the low hydrogen process was recommended as an optimized condition for linear PMD nitride film.
Functional Vectors Generation for RT-Level Verilog Descriptions Based on Path Enumeration and Constraint Logic Programming
Authors: Tun Li, Yang Guo, GongJie Liu, SiKun Li
Venue: Proceedings of the 8th Euromicro Conference on Digital System Design (2005)

This paper presents a novel method for automatic functional vectors generation from RT-Level HDL descriptions based on path coverage and constraint solving. Compared with existing method, the advantage of this method includes: 1) it avoids generating redundant constraints, which will accelerate the test generation process. 2) it solves the problem of how to propagate the internal values to the primary inputs with decision models. 3) it can handle various HDL description styles, and various styles of designs. Experimental results conduct on several practical designs show that our method can efficiently improve the functional vectors generation process. The prototype system has been applied to verify RTL description of a real 32-bits microprocessor core and complex bugs remained hidden in the RTL descriptions are detected.
A Hierarchical Classifier Using New Support Vector Machine
Authors: Yu-Chiang Wang, David Casasent
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

A binary hierarchical classifier is proposed to solve the multi-class classification problem. We also require rejection of non-target inputs, which thus producing a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is considered at each node in the hierarchy, since it offers good generalization and rejection ability. Using this hierarchical SVRDM classifier with magnitude Fourier transform features, initial recognition and rejection test results on simulated infra-red data are excellent.
Visualizing Hierarchies and Collection Structures with Fractal Trees
Authors: TeongJoo Ong, John J. Leggett, Unil Yun
Venue: Proceedings of the Sixth Mexican International Conference on Computer Science (2005)

This paper addresses the need for better information access to digital library collections. Without proper visualization tools, organizational and structural information is often lost or suppressed by digital library interfaces. We discuss a 2D fractal tree visualization tool that can be used to more accurately present the structure, organization and interrelation of collection metadata.
Locality-based Profile Analysis for Secondary Intrusion Detection
Authors: Mian Zhou, Robert Lee, Sheau-Dong Lang
Venue: Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks (2005)

While a firewall at the perimeter of a local network provides the first line of defense against attackers, many intrusion incidents result from successful penetration of the firewall. The compromise of one computer puts the entire network at risk. We propose a distributed personal Intrusion Detection System (IDS) that provides local anomaly detection as well as centralized traffic analysis. The system first builds profiles for normal network activity and then labels as suspicious any events that deviate from the normal profiles. The normal profiles are based on variations in connection-based behavior at each individual host. Deviations at each host are recorded using a local weight assignment scheme and then further processed by the central analyzer to build a weighted link graph representing the overall network abnormality. As local networks become more vulnerable to inside attack, our system reinforces security to prevent corruption from the inside.