ppxscal/embedding_network huggingface dataset:
Deploying Mobile Nodes to Connect Wireless Sensor Networks Using Novel Algorithms
Authors: Chang Wu Yu, Elvis Chen, Chun-Cheng Fang
Venue: Proceedings of the International Conference on Wireless Algorithms,Systems and Applications (2007)

Wireless sensor networks receive lots of attention due to its promising techniques and wide-ranging applications in recent years. The kind of network occasionally becomes disconnected due to initial uneven deployments or unpredictable failures or run out of battery of sensor nodes. However, sensor nodes with mobility then can be used in an addition deployment to reconnect the disconnected sensor networks. Theoretically, the augmenting geometric graph problem is defined here to model this kind of connectivity issues. The work proposes two novel algorithms: the graph-oriented algorithm and the divide-and-conquer algorithm to connect disconnected networks by using as less as possible mobile nodes. The first algorithm highly exploits traditional graph and geometry techniques including Fermat point, convex hull, nearest neighbor, minimum cost spanning tree, and graph contraction. Adopting a quite different approach, the second algorithm resolves the problem by dividing the deployed area and merging sub-solutions recursively. With respect to complexity issue, the graph-oriented algorithm takes O(n3) time; on the other hand, the divide-and-conquer algorithm requires O(n log n) time, where n is the size of vertex set of the given graph G=(V, E). These proposed two algorithms have low time complexity and can be implemented in a centralized sensor network.
Visual Object Class Recognition combining Generative and Discriminative Methods
Authors: Bernt Schiele
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

We describe various approaches capable of simultaneous recognition and localization of multiple object classes using a combination of generative and discriminative methods. A first approach uses a novel hierarchical representation allows to represent individual images as well as various objects classes in a single similarity invariant model. The recognition method is based on a codebook representation where appearance clusters built from edge based features are shared among several object classes. A probabilistic model allows for reliable detection of various objects in the same image. A second approach uses a dense representation and a topic distribution model to obtain an intermediate and general representation that is shared across object categories. Combined with discriminative methods these systems show excellent performance on several object categories.
Robust tracking and remapping of eye appearance with passive computer vision
Authors: Carlo Colombo, Dario Comanducci, Alberto Del Bimbo
Venue: ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) (2007)

A single-camera iris-tracking and remapping approach based on passive computer vision is presented. Tracking is aimed at obtaining accurate and robust measurements of the iris/pupil position. To this purpose, a robust method for ellipse fitting is used, employing search constraints so as to achieve better performance with respect to the standard RANSAC algorithm. Tracking also embeds an iris localization algorithm (working as a bootstrap multiple-hypotheses generation step), and a blink detector that can detect voluntary eye blinks in human-computer interaction applications. On-screen remapping incorporates a head-tracking method capable of compensating for small user-head movements. The approach operates in real time under different light conditions and in the presence of distractors. An extensive set of experiments is presented and discussed. In particular, an evaluation method for the choice of layout of both hardware components and calibration points is described. Experiments also investigate the importance of providing a visual feedback to the user, and the benefits gained from performing head compensation, especially during image-to-screen map calibration.
Improving fuzzy clustering of biological data by metric learning with side information
Authors: Michele Ceccarelli, Antonio Maratea
Venue: International Journal of Approximate Reasoning (2008)

Semi Supervised methods use a small amount of auxiliary information as a guide in the learning process in presence of unlabeled data. When using a clustering algorithm, the auxiliary information has the form of Side Information, that is a list of co-clustered points. Recent literature shows better performance of these methods with respect to totally unsupervised ones even with a small amount of Side Information. This fact suggests that the use of Semi Supervised methods may be useful especially in very difficult and noisy tasks where little a priori information is available, as is the case of data deriving from biological experiments. The two more frequently used paradigms to include Side Information into clustering are Constrained Clustering and Metric Learning. In this paper we use a Metric Learning approach as a way to improve the classical fuzzy c-means clustering through a two steps procedure: first a series of metrics (one for each cluster) that satisfy a randomly generated set of constraints are learnt based on the data; then a generalized version of the fuzzy c-means (with the metrics computed in the previous step) is executed. We show the benefits and the limitations of this method using real world datasets and a modified version of the Partition Entropy index.
Hard Disk Drives: The Good, the Bad and the Ugly!
Authors: Jon Elerath
Venue: Queue (2007)

HDDs (hard-disk drives) are like the bread in a peanut butter and jelly sandwich&mdash;sort of an unexciting piece of hardware necessary to hold the &ldquo;software.&rdquo; They are simply a means to an end. HDD reliability, however, has always been a significant weak link, perhaps the weak link, in data storage. In the late 1980s people recognized that HDD reliability was inadequate for large data storage systems so redundancy was added at the system level with some brilliant software algorithms, and RAID (redundant array of inexpensive disks) became a reality. RAID moved the reliability requirements from the HDD itself to the system of data disks. Commercial implementations of RAID range from n+1 configurations (mirroring) to the more common RAID-4 and RAID-5, and recently to RAID-6, the n+2 configuration that increases storage system reliability using two redundant disks (dual parity). Additionally, reliability at the RAID group level has been favorably enhanced because HDD reliability has been improving as well.
A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI
Authors: Jinn-Yi Yeh, J. C. Fu
Venue: Expert Systems with Applications: An International Journal (2008)

Magnetic resonance imaging (MRI) segmentation has been implemented by many clustering techniques, such as k-means, fuzzy c-means (FCM), learning-vector quantization (LVQ) and fuzzy algorithms for LVQ (FALVQ). Although these algorithms have been successful in applying MRI segmentation, obtaining the right number of clusters and adapting to different cluster characteristics are still not satisfactorily addressed. This report proposes an optimization technique, a hierarchical genetic algorithm with a fuzzy learning-vector quantization network (HGALVQ), to segment multi-spectral human-brain MRI. Evaluation of this approach is based on a real case with human-brain MRI of an individual suffering from meningioma. The HGALVQ is verified by the comparison with other popular clustering algorithms such as k-means, FCM, FALVQ, LVQ, and simulated annealing. Experimental results show that HGALVQ not only returns an appropriate number of clusters and also outperforms other methods in specificity.
Engaging every student in large laptop-enhanced courses
Authors: Roy P. Pargas
Venue: Proceedings of the sixth conference on IASTED International Conference Web-Based Education - Volume 2 (2007)

This paper proposes that with the right hardware and software tools, it is possible to: (1) engage students during lecture with short in-class exercises to help them to learn course material immediately, (2) provide students with feedback on how they are doing on a particular topic, (3) conduct frequent (even daily) assessment of each student in order to identify students who are not catching on, and (4) to conduct short programming exercises in lecture or lab, exercises that are easy to evaluate and can give valuable clues as to who can program and who cannot. We show how we use a web-based tool called MessageGrid in a laptop-enhanced computer science class to implement student activities that both engage the students and reap the benefits of peer-instruction and peer-collaboration. The exercises we describe and the pedagogical techniques we employ can be used in CS1, CS2, CS4, and other computer science classes.
A Gossip Protocol to Support Service Discovery with Heterogeneous Ontologies in MANETs
Authors: Andronikos Nedos, Kulpreet Singh, Raymond Cunningham, Siobhon Clarke
Venue: Proceedings of the Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (2007)

Service discovery is vital in enabling interoperability of dis- tributed service-based applications. In mobile ad hoc net- works (MANETs), discovery must cope not only with transient communication but also with an environment in which mobile nodes are autonomous and connectivity is opportunistic. The use of syntactic service interfaces in MANETs requires a priori agreement on interface names, limiting node autonomy and the range of provided services. A more flexible discovery mecha- nism can be provided with the use of ontologies. Since the use of a single ontology to describe all services also requires a pri- ori agreement on a common semantic representation, a more realistic assumption is the use of heterogeneous ontologies. However, this assumption poses many technical challenges by requiring a mechanism to match the different ontologies and make provided services available to all nodes. This paper presents a model to support such semantic service discovery in MANETs. A core part of the model is the distributed ap- proach to ontology matching and service discovery. We rely on the use of a novel gossip protocol to randomly disseminate on- tology concepts, thereby creating a semantic overlay network. We present the gossip protocol and an evaluation demonstrat- ing its scalability and discovery properties.
Simulating Open-Via Defects
Authors: Stefan Spinner, Jie Jiang, Ilia Polian, Piet Engelke, Bernd Becker
Venue: Proceedings of the 16th Asian Test Symposium (2007)

Open-via defects are a major systematic failure mechanism in nanoscale manufacturing processes. We present a flow for simulating open-via defects. Electrical parameters are extracted from the layout and technology data and represented in a way which allows efficient simulation on gate level. The simulator takes oscillation caused by open-via defects into account and quantifies its impact on defect coverage. The flow can be employed for manufacturing test as well as for defect diagnosis.
Improving Genetic Algorithms with Solution Space Partitioning and Evolution Refinements
Authors: Ay-Hwa Andy Liou, Tzong-Heng Chi, I-Jun Yu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

Irregular Sum Problem (ISP) is an issue resulted from mathematical problems and graph theories. It has the characteristic that when the problem size is getting bigger, the space of the solution is also become larger. Therefore, while searching for the feasible solution, the larger the question the harder the attempt to come up with an efficient search. We propose a new genetic algorithm, called the Incremental Improving Genetic Algorithm (IIGA), which is considered efficient and has the capability to incrementally improve itself from partial solutions. The initial solutions can be constructed by satisfying the constraints in stepwise fashion. The effectiveness of IIGA also comes from the allowing of suitable percentage of illegal solutions during the evolution for increasing the effectiveness of searching. The cut-point of the genetic coding for generating the descendants has carefully planned so that the algorithm can focus on the key factors for the contradiction and has the chances to fix it. After comparing the results of IIGA and usual genetic algorithm among different graphs, we found and shown that the performance of IIGA is truly better.
Short Distance Wireless, Dense Networks, and Their Opportunities
Authors: Jan M. Jan M. Rabaey, Y. H. Chee, David Chen, Luca de Nardis, Simone Gambini, Davide Guermandi, Michael Mark, Nathan Pletcher
Venue: Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (2007)

The availability of wireless transceivers transmitting over ranges from few microns to less than half a meter opens the door for a wide range of exciting new applications, ranging from seamless system assembly, smart surfaces, healthcare monitoring and intelligent machinery and components. However, the implementation challenges in terms of size and power for most of these applications are pushing the limits. Fortunately, by exploring the wide range of options offered to the designer, extremely small and virtually zero-power transceivers are feasible. This paper discusses the opportunities, challenges and options of short distance wireless, and illustrates the proposed techniques with several design examples. In addition, the challenges that emerge when trying to embed these nodes into very dense networks are explored. Special consideration is given to the issues of distributed synchronization, localization and robust communication.
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Authors: Heinz Mühlenbein, Dirk Schlierkamp-Voosen
Venue: Evolutionary Computation (1993)

In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported.
A Steganographic Algorithm in Uncompressed Video Sequence Based on Difference between Adjacent Frames
Authors: Changyong Xu, Xijian Ping
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

A steganographic algorithm for embedding secret bits into uncompressed video sequence was proposed in this paper. The difference between adjacent frames, which is called motion component, indicates the motion of video, and is not obviously affected by video compression. So motion component was used to embed secret bits for increasing the capability of resisting video compression. Motion component was firstly calculated on a frame by frame basis, and then it was two-level wavelet decomposed, and the secret bits were embedded into wavelet coefficients. For the purpose of little degrading video quality, the secret bits were only embedded into low frequency coefficients that within the region which has large moving speed. Experimental results show that the proposed algorithm not only can keep a good visual and statistical imperceptibility, but also has a considerable embedding capacity. Moreover, the algorithm has a good flexibility to MPEG encoding. Even after MPEG encoding, the secret bits can be extracted with a high accuracy.
Performance Impact of Advance Reservations from the Grid on Backfill Algorithms
Authors: Bo Li, Dongfeng Zhao
Venue: Proceedings of the Sixth International Conference on Grid and Cooperative Computing (2007)

Advance Reservations are important to guarantee the Quality of Services of Grid Jobs. However, the influence of AR from the Grid on local parallel schedulers is not clearly known. EASY backfill and Conservative backfill are two important parallel scheduling algorithms. This paper examined their procedures to support AR, evaluated their performances impacted from AR by using discrete event simulations with real trace from the parallel workload archive. Results show that as AR request increases, EASY backfill is preferable with higher utilization and lower average expansion factor and average weighted response time for both of local and AR Grid jobs. The use of conservative backfill typically provides both of local jobs and AR Grid jobs improved predictability and certainty, at the cost of lower utilization and degraded performance for both of local and AR Grid jobs.
A Synchronous Detection of the Road Boundary and Lane Marking for Intelligent Vehicles
Authors: Weina Lu, Haifang Wang, Qingzhu Wang
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

To prevent an intelligent vehicle from departing the lane in the vision-based navigation, an integrated method based on image processing is proposed to detect the road boundary and lane marking synchronously in structural road environment. The feature of the road boundary is extracted by means of gradient magnitude and gradient direction of pixels. And the lane marking feature is extracted by self-adaptive threshold segmenting with region connectivity analyzing. The characteristic points of both the road boundary and lane marking are matched to the straight or crooked road models by least-squares fit. With the circular calling of detecting and tracking blocks for mass image sequences, the whole process shows a real time and high antinoise capability. All the algorithms in the paper have been tested by the videos captured from real road scenes, and the experimental results proved that the detecting method is efficient, stable and accurate.
Movement Prediction Using Bayesian Learning for Neural Networks
Authors: Sherif Akoush, Ahmed Sameh
Venue: Proceedings of the Second International Conference on Systems and Networks Communications (2007)

A technique for reducing the wireless cost of tracking mobile users with uncertain parameters is developed in this paper. Such uncertainty arises naturally in wireless networks, since an efficient user tracking is based on a prediction of its future call and mobility parameters. The conventional approach based on dynamic tracking is not reliable in the sense that inaccurate prediction of the user mobility parameters may significantly reduce the tracking efficiency. Unfortunately, such uncertainty is unavoidable for mobile users, especially for a burst mobility patterns. In this paper, we present a novel hybrid Bayesian neural network model for predicting locations on Cellular Networks (can also be extended to other wireless networks such as WI-FI and WiMAX). We investigate different parallel implementation techniques on mobile devices of the proposed approach and compare it to many standard neural network techniques such as: Back-propagation, Elman, Resilient, Levenberg-Marqudat, and One-Step Secant models. Bayesian learning for Neural Networks predicts location better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationships being learned. The result of Bayesian training is a posterior distribution over network weights. We use Markov chain Monte Carlo methods (MCMC) to sample N values from the posterior weights distribution.
Software Product Lines Beyond Software Development
Venue: Proceedings of the 11th International Software Product Line Conference (2007)

The impact of software product lines to software development and maintenance costs has been well documented. Reduced time to market and number of people required to develop and support a company's software products constitute some of these savings. However, the actual savings model is more than just savings within the software development community. In fact, the savings realized directly by the software community may not be the biggest savings in the company. At Cummins Inc., we have realized impacts outside of embedded software development because of the use of software product lines. Service tool development costs for a product within the product line is 10% of those products outside the product line. Other impacts in areas like customer engineering, technician efficiency, calibration development, service engineering, management, and customer satisfaction are often harder to quantify, but these areas reap positive benefits from the product line architecture. This brings more of a business level to product line engineering.
Sharing introductory programming curriculum across disciplines
Authors: Dianne P. Bills, Roxanne L. Canosa
Venue: Proceedings of the 8th ACM SIGITE conference on Information technology education (2007)

Originally there was one computing curriculum, computer science, which provided a "one-size-fits-all" education in programming and computing in general. Today, computing education has diverged into an array of sub-discipline areas as educators try to meet the changing computing needs of business and industry. Information technology, software engineering, computer engineering, and information systems have emerged from computer science as distinct computing disciplines. Plus, additional "micro-disciplines" are quickly emerging: games and networking from information technology, for example. The foundation skill for all computing disciplines is programming. However as computing technologies advance, discipline-specific differences increase. Each computing sub-discipline needs to approach programming from a slightly different viewpoint to meet student expectations of being highly marketable and employer expectations of quick productivity. How can colleges and universities economically meet the competing demands for a focused computing education while maintaining a strong foundation in programming fundamentals. This paper discusses how an introductory programming sequence can be designed with a common base to support multiple computing sub-disciplines as well as differentiated to address the specific, focused needs of a given sub-discipline. We identify both commonalities that support economy of scale and important differences that distinguish sub-discipline curricula.
UMLtoCSP: a tool for the formal verification of UML/OCL models using constraint programming
Authors: Jordi Cabot, Robert Clarisó, Daniel Riera
Venue: Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (2007)

We present UMLtoCSP, a tool for the formal verification of UML/OCL models. Given a UML class diagram annotated with OCL constraints, UMLtoCSP is able to automatically check several correctness properties, such as the strong and weak satisfiability of the model or the lack of redundant constraints. The tool uses Constraint Logic Programming as the underlying formalism and the constraint solver ECLiPSe as the verification engine.