ppxscal/embedding_network huggingface dataset:
Many-to-Many Disjoint Path Covers in Hypercube-Like Interconnection Networks with Faulty Elements
Authors: Jung-Heum Park, Hee-Chul Kim Hyeong-Seok Lim
Venue: IEEE Transactions on Parallel and Distributed Systems (2006)

A many-to-many k-disjoint path cover (k-DPC) of a graph G is a set of k disjoint paths joining k distinct source-sink pairs in which each vertex of G is covered by a path. We deal with the graph G_0 \oplus G_1 obtained from connecting two graphs G_0 and G_1 with n vertices each by n pairwise nonadjacent edges joining vertices in G_0 and vertices in G_1. Many interconnection networks such as hypercube-like interconnection networks can be represented in the form of G_0 \oplus G_1 connecting two lower dimensional networks G_0 and G_1. In the presence of faulty vertices and/or edges, we investigate many-to-many disjoint path coverability of G_0 \oplus G_1 and (G_0 \oplus G_1) \oplus (G_2 \oplus G_3), provided some conditions on the Hamiltonicity and disjoint path coverability of each graph G_i are satisfied, 0 \leq i \leq 3. We apply our main results to recursive circulant G(2^m,4) and a subclass of hypercube-like interconnection networks, called restricted HL-graphs. The subclass includes twisted cubes, crossed cubes, multiply twisted cubes, Möbius cubes, Mcubes, and generalized twisted cubes. We show that all these networks of degree m with f or less faulty elements have a many-to-many k{\hbox{-}}{\rm{DPC}} joining any k distinct source-sink pairs for any k \geq 1 and f \geq 0 such that f+2k\leq m-1.
Codes and sofic constraints
Authors: Marie-Pierre Béal, Dominique Perrin
Venue: Theoretical Computer Science (2005)

We study the notion of a code in a sofic subshift. We first give a generalization of the Kraft-McMillan inequality to this case. We then prove that the polynomial of the alphabet in an irreducible sofic shift divides the polynomial of any finite code which is complete for this sofic shift. This settles a conjecture from Reutenauer.
Isoperimetric Graph Partitioning for Image Segmentation
Authors: Leo Grady
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

Spectral graph partitioning provides a powerful approach to image segmentation. We introduce an alternate idea that finds partitions with a small isoperimetric constant, requiring solution to a linear system rather than an eigenvector problem. This approach produces the high quality segmentations of spectral methods, but with improved speed and stability.
An infrastructure for managing context information in pervasive computing environments
Authors: Mohamed Essam Khedr
Venue:  (2004)

The increasing popularity of mobile devices, such as mobile phones and personal digital assistants, and advances in wireless networking technologies, are enabling new classes of applications that raise challenging problems to application developers. These applications have to be aware of the variations in the execution context such as location, time, user activities, and device capabilities in order to tune and adapt their intended functionalities. We argue that developing and managing applications that are context-aware would be extremely hard, tedious, and error-prone if not supported by a computing infrastructure. This is because application developers would have to deal with issues such as context interpretation, context reasoning, and context adaptability, and consequently would be distracted from the actual requirements of the applications they are developing. We investigate the principles of ontologies, negotiation, and approximate reasoning, and their usage to support context-awareness and dynamic adaptation for applications in pervasive environments. These principles are integrated in our proposed infrastructure, which offers application developers a set of APIs and computing components to facilitate the process of developing and managing context-aware applications. The APIs that the infrastructure provides allow users to manipulate the information encoded in their semantic profiles and to negotiate context information causing the infrastructure to tailor its behaviour to applications' need. Accordingly, the infrastructure reasons and makes automated decisions that are based on this negotiated contextual information to achieve adaptability and to cope with the frequent changes in the environment. To manifest the effectiveness of these principles in developing and managing context-aware applications, we discuss the architecture and implementation of an agent-based context-aware infrastructure that implements these principles, and report on performance and usability results obtained from a thorough evaluation of the infrastructure.
Modeling the Random Drift of Micro-Machined Gyroscope with Neural Network
Authors: Wang Hao, Weifeng Tian
Venue: Neural Processing Letters (2005)

In this paper a new combined method was applied to model the random drift of a micro-electro-mechanical system (MEMS) gyro to enhance its performance. The gyro is used to set up a micro-inertial -measurement unit (MIMU) for its low cost, low power consumption and small dimensions. To improve the MIMU's performance, we model the gyro's random drift by a statistic method. Given the paucity of the knowledge of fabrication of the gyro, we select a neural network model instead of making a delicate physical-mathematical model. Since the gyro we used is a tuning fork micro-machined sensor with large random drift, the modeling performance is affected by the randomness inherent in the output data when neural network approach is applied. Therefore, radial basis network structure, which was successfully applied to model temperature drift of fiber optical gyros, was chosen to build the model and the grey neural network. Compared with autoregressive model, the standard error of the gyro's random drift is reduced dramatically by radial basis model and grey radial basis model.
Fine-Grained Activity Recognition by Aggregating Abstract Object Usage
Authors: Donald J. Patterson, Dieter Fox, Henry Kautz, Matthai Philipose
Venue: Proceedings of the Ninth IEEE International Symposium on Wearable Computers (2005)

In this paper we present results related to achieving finegrained activity recognition for context-aware computing applications. We examine the advantages and challenges of reasoning with globally unique object instances detected by an RFID glove. We present a sequence of increasingly powerful probabilistic graphical models for activity recognition. We show the advantages of adding additional complexity and conclude with a model that can reason tractably about aggregated object instances and gracefully generalizes from object instances to their classes by using abstraction smoothing. We apply these models to data collected from a morning household routine.
Mobility Helps Peer-to-Peer Security
Authors: Srdjan Čapkun, Jean-Pierre Hubaux
Venue: IEEE Transactions on Mobile Computing (2006)

We propose a straightforward technique to provide peer-to-peer security in mobile networks. We show that far from being a hurdle, mobility can be exploited to set up security associations among users. We leverage on the temporary vicinity of users, during which appropriate cryptographic protocols are run. We illustrate the operation of the solution in two scenarios, both in the framework of mobile ad hoc networks. In the first scenario, we assume the presence of an offline certification authority and we show how mobility helps to set up security associations for secure routing; in this case, the security protocol runs over one-hop radio links. We further show that mobility can be used for the periodic renewal of vital security information (e.g., the distribution of hash chain/Merkle tree roots). In the second scenario, we consider fully self-organized security: Users authenticate each other by visual contact and by the activation of an appropriate secure side channel of their personal device; we show that the process can be fuelled by taking advantage of trusted acquaintances. We then show that the proposed solution is generic: It can be deployed on any mobile network and it can be implemented either with symmetric or with asymmetric cryptography. We provide a performance analysis by studying the behavior of the solution in various scenarios.
Topics in combinatorial game theory: partizan versions of nim, the hypercube description and results on computation in the field of nimbers
Authors: Jason Lee
Venue:  (2005)

The ancient game of Nim and its complete solution by Bouton is one of the cornerstones of Combinatorial Game Theory. It has led to a large class of games (that are essentially equivalent to Nim), and much elegant structure has evolved. The work of Conway is of particular importance in this regard. This dissertation deals with some topics that stem from the game of Nim. Different versions of Nim are explored, and some further mathematical technique is presented. In Chapter 2, we discuss several versions of Partizan Nim, where the rules are modified to restrict what the players can do. Briefly stated, one player &ldquo;owns the black heaps of stones&rdquo; and the other player &ldquo;owns the white heaps.&rdquo; In turn, each player may remove a nonzero number of stones from any of his own heaps according to a certain rule. Otherwise, stones are removed from an opponent's heap according to a different rule. Results are given for several rule sets. For one version, in particular, we obtain the outcome class and the canonical form of sums. In Chapter 3, we describe geometric and algorithmic methods for performing addition and multiplication on the field of nimbers. In particular, we discuss the notion of using group actions on hypercubes to facilitate mental addition, and to illustrate certain aspects of computation on the field. These methods are used to simplify an analysis of the Turning Corners.
A Hybrid Movie Recommender System Based on Neural Networks
Authors: Christina Christakou, Andreas Stafylopatis
Venue: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (2005)

Recently, there has been a lot of speculation among the members of the artificial intelligence community concerning the way AI can help with the problem of successful information search in the reservoirs of knowledge of Internet. Recommender systems provide a solution to this problem by giving individualized recommendations. Content-based and Collaborative Filtering are usually applied to predict these recommendations. A combination of the results of these two techniques is used in this work in order to construct a system that provides more precise recommendations concerning movies. The MovieLens data set was used to test the proposed hybrid system.
Local Norm Features based on ridgelets Transform
Authors: Oriol Ramos Terrades, Ernest Valveny
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

We propose a set of shape descriptors for image retrieval of graphic documents based on the ridgelets transform, which can be seen as a combination of the Radon transform and the wavelets transform. It is especially well suited to detect linear features, the most relevant features in graphic documents. It also provides a multiscale representation, useful for indexing and retrieval purposes. From the ridgelets representation of an image, we have defined a set of local norm descriptors based on computing a norm over some specific areas of the image. This kind of descriptors are very flexible since we can define different sets of descriptors just by changing such areas of influence in the image. We have also defined a combination of descriptors at several scales of decomposition in order to improve retrieval results.
Placement and Routing in 3D Integrated Circuits
Authors: Cristinel Ababei, Yan Feng, Brent Goplen, Hushrav Mogal, Tianpei Zhang, Kia Bazargan, Sachin Sapatnekar
Venue: IEEE Design Test (2005)

Advanced manufacturing and packaging techniques are permitting a glimpse at the near-future, where wires can go in three dimensions, and ICs made in diverse processes can be assembledtogether--sandwich like--to achieve ever higher levels of integration. This article focuses on a specific CAD problem in connection with 3D ICs: how to partition, place, and wire a design subject to various constraints on power, timing, and manufacturability.
Protein structure prediction: sequence to structure alignment generation and atomic potential development
Authors: Jian Qiu
Venue:  (2005)

In template-based modeling of protein structures, the generation of the alignment between the target and the template is a critical step that significantly affects the accuracy of the final model. In this study, I propose an alignment algorithm SSALN that learns substitution matrices and position-specific gap penalties from a database of structurally aligned protein pairs. In addition to the amino acid sequence information, secondary structure and solvent accessibility information of a position are used to derive substitution scores and position-specific gap penalties. In the CASP5 and CASP6 test sets, SSALN generates more accurate alignments than sequence alignment methods. LOOPP server prediction based on an SSALN alignment is ranked the best for target T0280_1 in CASP6. SSALN is also compared with several threading methods and sequence alignment methods on the ProSup benchmark. SSALN has the highest alignment accuracy among the methods compared. On the Fischer's benchmark, SSALN performs better than CLUSTALW and GenTHREADER, and generates more alignments with >50%, >60% or >70% accuracy than FUGUE. In protein structure prediction, multiple models are often generated and a potential is used to distinguish native-like (homologous) models from misfolded (decoy) models. Here I present a procedure to develop atomic potentials for recognition of protein folds. The potentials are based on pairwise interactions (close in distance in the structure) between heavy (non-hydrogen) atoms. One or three distance steps are used to describe the range of interactions between a pair. Training is carried out with the mathematical programming approach on the decoy sets of Baker, of Levitt, and of our own design. Recognition is required not only for decoy-native structural pairs but also for pairs of decoy and homologous structures. Performance is tested on the template-based modeling test sets of CASP5 targets, and on two ab initio decoy test sets from the Skolnick's laboratory and from the Moult's laboratory. The newly derived potentials have significant recognition capacity that is comparable to the best atomic potential compared using a significantly smaller number of parameters.
Distance-based LocalizedMobile IP MobilityManagement
Authors: Chang Woo Pyo, Jie Li, Hiroyuki Morikawa
Venue: Proceedings of the 8th International Symposium on Parallel Architectures,Algorithms and Networks (2005)

As a mobility management extension of basic Mobile IP, localized mobility management (LMM) has been proposed for supporting local registrations of mobile nodes. The existing LMM protocols may suffer from high signaling traffic and long service delay caused by that a single and central gateway foreign agent (GFA) services all registrations and packet deliveries for mobile nodes roaming within a local network (i.e., domain). This paper introduces two distributed LMM methods to distribute the functions of the central GFA into other foreign agents. One is the distancebased distributed LMM method, which allows all routers to configure their own networks consisting of several routers within a distance from themselves. Each router acts as the temporary GFA on its domain while the central GFA still exists. Another is the distance-based fully distributed LMM method, in which there is not the central GFA in a domain while all routers can act as GFAs. We present the speci- fications and basic protocol operations of both distributed LMM methods for the system implementation. Performance evaluation and comparison are conducted by comprehensive simulation. It shows clearly trade-off and the performance gain by the proposed distributed LMM methods. These results may be useful for the design and implementation of the mobile Internet.
A Novel Method for Early Software Quality Prediction Based on Support Vector Machine
Authors: Fei Xing, Ping Guo, Michael R. Lyu
Venue: Proceedings of the 16th IEEE International Symposium on Software Reliability Engineering (2005)

The software development process imposes major impacts on the quality of software at every development stage; therefore, a common goal of each software development phase concerns how to improve software quality. Software quality prediction thus aims to evaluate software quality level periodically and to indicate software quality problems early. In this paper, we propose a novel technique to predict software quality by adopting Support Vector Machine (SVM) in the classification of software modules based on complexity metrics. Because only limited information of software complexity metrics is available in early software life cycle, ordinary software quality models cannot make good predictions generally. It is well known that SVM generalizes well even in high dimensional spaces under small training sample conditions. We consequently propose a SVM-based software classification model, whose characteristic is appropriate for early software quality predictions when only a small number of sample data are available. Experimental results with a Medical Imaging System software metrics data show that our SVM prediction model achieves better software quality prediction than some commonly used software quality prediction models.
Classification of Pathology Data Using a Probabilistic (Bayesian) Model
Authors: Howard Robin, John S. Eberhardt III, Wayne D. Muller, Robert Clark, Jenny Kam
Venue: Proceedings of the 18th International Conference on Systems Engineering (2005)

BACKGROUND: Applying dynamic statistical modeling to clinical laboratory data can help the clinician make more effective use of this essential information. We applied a probabilistic (Bayesian) classifier to a set of 645 breast tumor samples for which we had extensive clinical and pathological information. METHODS: The study included 645 formalin-fixed, paraffin embedded breast cancer samples observed from 1999 to 2003 and originally submitted to the Sharp Hospital system (San Diego, CA) for pathologic evaluation and breast predictive and prognostic studies. The data was analyzed with a Bayesian network utilizing DecisionQ FasterAnalytics. RESULTS: The model eliminated age and biopsy site as predictive markers and identified relationships between Ki67 proliferative index, tumor size, and Combined Nottingham Histologic Grade (CNHG) as well as between CNHG and ploidy, differentiation, tumor type, and estrogen-progesterone status. We performed cross-validation analysis to statistically validate the structure of the model. We calculated ROC curves and the area under the curve ranged from 68.5% to 91.2% with a mean of 80.1%; the positive predictive value of the model ranged from 55.6% to 82.6% with a mean of 69.2%. DISCUSSION: Our Bayesian model supports decision-making in complex disease populations by illustrating key relationships and providing probability estimates. The structure of the network allows it to support different types of clinical decisions by a variety of medical specialists. CONCLUSION: The Bayesian analysis tool we have developed assists the clinician and researcher in using prior knowledge, outcomes experience, and pathology data to hopefully make more meaningful diagnostic and treatment decisions in an evidence-based validated framework.
Self-Learning Histograms for Changing Workloads
Authors: Xiao-jing Li, Bo Zhou, Jin-xiang Dong
Venue: Proceedings of the 9th International Database Engineering & Application Symposium (2005)

The increasing complexity of DBMSs and their workloads has made it a difficult and time-consuming task to manage their performance manually. Autonomic computing has emerged as a promising approach to deal with this complexity by making DBMSs self-managed. Automatic statistics management, as an important part of autonomic computing, is especially necessary in decision-support systems. In this paper, we introduce a novel technique for automatic statistics management called Self-Learning Histograms (SLH), which can adapt to workload and data distribution changes by automatically building and maintaining itself using query feedback information. Query feedback is encoded as deducible rules and the histogram can be viewed as a set of these rules. Through deducing among rules, more accurate statistics can be inferred and damages to results of former tunings are avoided. Selectivity estimation based on validity of rules greatly lowered estimation errors. Extensive experiments showed the effectiveness of SLH.
Validating cardiac echo diagnosis through video similarity
Authors: Tanveer Syeda-Mahmood, Dulce Ponceleon, Jing Yang
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

Video data is increasingly being used in medical diagnosis. Due to the quality of the video and the complexities of underlying motion captured, it is difficult for an in-experienced physician/radiologist to describe motion abnormalities in a crisp way, leading to possible errors in diagnosis. In this paper, we present a method of capturing video similarity and its use for diagnosis verification during decision support. Specifically, we describe the motion information in videos using average velocity curves. Second-order motion statistics are extracted from average velocity curves and serve as features for computing video similarity. Given a new video sample already labeled with a diagnosis, a neighborhood of similar videos is assembled from the training set and their diagnosis labels are used to verify the diagnosis.
New Proposition for Intelligent Systems Design: Artificial Understanding of the Images as the Next Step of Advanced Data Analysis after Automatic Classification and Pattern Recognition
Authors: Ryszard Tadeusiewicz, Marek R. Ogiela
Venue: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (2005)

In this paper there will be presented the new opportunities for applying linguistic algorithms of pattern recognition for computer understanding of image semantic content in intelligent information systems. A successful obtaining of the crucial semantic information of the image - especially medical - may contribute considerably to the creation of new intelligent cognitive information systems. Thanks to the new algorithms of cognitive resonance between stream of the data extracted from the image and expectations taken from the representation of the medical knowledge, we can understand the merit content of the image even if the form of the image is very different from any known pattern. It seems that in the near future the technique of automatic understanding of images may become one of the effective tools for semantic interpreting, and intelligent storing of the visual data in scattered databases. In this article we will try proving that structural techniques may be applied in the case of tasks related to automatic classification and machine perception of the semantic meaning of selected classes of medical patterns.