ppxscal/embedding_network huggingface dataset:
Design and performance evaluation of combined first-fit task allocation and migration strategies in mesh multiprocessor systems
Authors: Lee Kee Goh, Bharadwaj Veeravalli
Venue: Parallel Computing (2008)

In this paper, we consider the problem of processor allocation on mesh-based multiprocessor systems. We employ the idea of using migration to minimize fragmentation and the overall processing time of the tasks. In our schemes, we consider the use of task migration whenever required to improve the problem of fragmentation. To this end, we propose three efficient schemes to improve the performance of first-fit allocation strategies commonly used in practice. The first scheme, called the first-fit mesh-bifurcation (FFMB) scheme, attempts to start the search for a free submesh from either the bottom-left corner or the top-left corner of the mesh so as to reduce the amount of fragmentation in the mesh. The next two schemes, called the online dynamic compaction-single corner (ODC-SC) and online dynamic compaction-four corners (ODC-FC) schemes, use task migration to improve the performance of existing submesh allocation strategies. We perform rigorous simulation experiments based on practical workloads as reported in the literature to quantify all our proposed schemes and compare them against standard schemes existing in the literature. Based on the results, we make clear recommendations on the choice of the strategies.
Number-theoretic interpretation and construction of a digital circle
Authors: Partha Bhowmick, Bhargab B. Bhattacharya
Venue: Discrete Applied Mathematics (2008)

This paper presents a new interpretation of a digital circle in terms of the distribution of square numbers in discrete intervals. The number-theoretic analysis that leads to many important properties of a digital circle succinctly captures the original perspectives of digital calculus and digital geometry for its visualization and characterization. To demonstrate the capability and efficacy of the proposed method, two simple algorithms for the construction of digital circles, based on simple number-theoretic concepts, have been reported. Both the algorithms require only a few primitive operations and are completely devoid of any floating-point computation. To speed up the computation, especially for circular arcs of high radii, a hybridized version of these two algorithms has been given. Experimental results have been furnished to elucidate the analytical power and algorithmic efficiency of the proposed approach. It has been also shown, how and why, for sufficiently high radius, the number-theoretic technique can expedite a circle construction algorithm.
Dynamics based control with PSRs
Authors: Ariel Adam, Zinovi Rabinovich, Jeffrey S. Rosenschein
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version of the DBC for PSR model, EMT-PSR, and demonstrate how this algorithm can be applied to solve several control problems. We then provide some classifications and requirements of PSR environment models that are necessary for the EMT-PSR algorithm to operate.
Differential Cryptanalysis of the Stream Ciphers Py, Py6 and Pypy
Authors: Hongjun Wu, Bart Preneel
Venue: Proceedings of the 26th annual international conference on Advances in Cryptology (2007)

Py and Pypy are efficient array-based stream ciphers designed by Biham and Seberry. Both were submitted to the eSTREAM competition. This paper shows that Py and Pypy are practically insecure. If one key is used with about 216 IVs with special differences, with high probability two identical keystreams will appear. This can be exploited in a key recovery attack. For example, for a 16-byte key and a 16-byte IV, 223 chosen IVs can reduce the effective key size to 3 bytes. For a 32-byte key and a 32-byte IV, the effective key size is reduced to 3 bytes with 224 chosen IVs. Py6, a variant of Py, is more vulnerable to these attacks.
Context Information for Human Behavior Analysis and Prediction
Authors: J. Calvo, M. A. Patricio, C. Cuvillo, L. Usero
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

This work is placed in the context of computer vision and ubiquitous multimedia access. It deals with the development of an automated system for human behavior analysis and prediction using context features as a representative descriptor of human posture. In our proposed method, an action is composed of a series of features over time. Therefore, time sequential images expressing human action are transformed into a feature vector sequence. Then the feature is transformed into symbol sequence. For that purpose, we design a posture codebook, which contains representative features of each action type and define distances to measure similarity between feature vectors. The system is also able to predict next performed motion. This prediction helps to evaluate and choose current action to show.
Improved K-Means Clustering Algorithm
Authors: Zhe Zhang, Junxi Zhang, Huifeng Xue
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

K-means algorithm is widely used in spatial clustering. It takes the mean value of each cluster centroid as the Heuristic information, so it has some disadvantages: sensitive to the initial centroid and instability. The improved clustering algorithm referred to the best clustering centriod which is searched during the optimization of clustering centroid. That increased the searching probability around the best centroid and improved the stability of the algorithm. The experiment on two groups of representative dataset proved that the improved K-means algorithm performs better in global searching and is less sensitive to the initial centroid.
Single Sound Source SLAM
Authors: Rodrigo Munguía, Antoni Grau
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

Bearing-only Simultaneous Localization and Mapping (SLAM) is a partially observable SLAM problem, in wich the sensor used for perceiving the robot`s enviroment, provides only-angular information respect to the landmarks, and therefore does not give enough information to compute the full state of a landmark from a single observation. In this context, vision-based systems have also gained a great interest in the robotics community. Nevertheless the use of "sound sources" as map's features have been very little explored in SLAM. In this work a method for performing SLAM with sound sources is presented. A robot capable of sense bearing information respect to an external sound source with modest angular acuity (-10&deg;) is considered.At the robot trajectory start, the sound source position is unknown; while the robot moves, the position of the sound source and the robot position in a global coordinate frame are both estimated. Experimental results with simulations and with a real robot demonstrate that tracking a unique source sound is enough to reasonably correct the odometry information provided by the encoders.
Mathematical modeling of the interaction between terrorism and counter-terrorism and its policy implications
Authors: Alvin M. Saperstein
Venue: Complexity (2008)

It was developed from a presentation made at the Jan Tinbergen Conference of the Peace Science Society, in Amsterdam, June 26&ndash;28, 2006.
Optimization of Hybrid Electric Cars by Neuro-Fuzzy Networks
Authors: Fabio Massimo Frattale Mascioli, Antonello Rizzi, Massimo Panella, Claudia Bettiol
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

In this paper, the problem of the optimization of energetic flows in hybrid electric vehicles is faced. We consider a hybrid electric vehicle equipped with batteries, a thermal engine (or fuel cells), ultracapacitors and an electric engine. The energetic flows are optimized by using a control strategy based on the prediction of short-term and medium-term vehicle states (energy consumption, vehicle load, current route, traffic flow, etc.). The prediction will be performed by a neuro-fuzzy control unit, where the predictive model exploits the robustness of fuzzy logic in managing the said uncertainties and the neural approach as a data driven tool for non-linear control modeling.
Parametric fuzzy study for effects analysis of age on PWR containment cooling system
Authors: Antônio César Ferreira Guimarães, Celso Marcelo Franklin Lapa
Venue: Applied Soft Computing (2008)

This paper presents an alternative parametric fuzzy study to subside aging impact analysis on the performance and availability of safety industrial systems. To this sense, a fuzzy fault tree computational system analysis, named of FuzzyFTA, was adopted. FuzzyFTA is a computing system that uses fault tree and fuzzy logic to determine fault event fuzzy importance and associated fuzzy uncertainty measures. The main goal of this approach makes possible, elapsed many years of plant operation, the gray determination of the system critical components and top event occurrence probability. A typical four loops pressurized water reactor (PWR) containment cooling system (CCS) was selected to examine the feasibility of the computational system approach in a real case study. Thus, a commonly found containment spray system and a fan cooler system were modeled. Failure rates, obtained from the operation of a PWR along 20 years, were accounted in the model. The bathtub curve characteristics presents in these failure rates permit the determination of the time-dependent effects of aging degradation more important to the general uncertainty of system. Thus, the system unavailability, considering the component failure rates increase by aging, can be calculated.
Novel Approaches in Psychiatric Genomics
Authors: Maja Bucan
Venue: Proceedings of the 7th international workshop on Algorithms in Bioinformatics (2007)

A central challenge in biomedical research remains identification of genetic factors influencing human behavior and susceptibility to common disease. Recent advances in genotyping technologies, coupled with the fundamental information provided by the Human Genome and International HapMap Projects, permit characterization and analysis of high-density SNP genotype data for thousands of individuals. Using genotype data obtained on the Illumina HumanHap550 SNP platform, for 1000 individuals (in 170 autism multiplex families from Autism Genetic Resource Exchange, 120 HapMap samples and 320 neurologically normal children from Children's Hospital of Philadelphia) we performed a pilot study to identify genetics risk factors for autism susceptibility. Using these data we illustrate that novel statistical and computational biology approaches provide additional insights than can not be observed when using simple association tests. These novel approaches include: a) phylogenetic analysis of population structure; b) pathway-based analysis of genome-wide association data c) selection of variants based on a priori potential to affect phenotypes and d) analysis of copy number variation (CNVs) in autistic and healthy (control) children. In addition, our results demonstrate that high-density SNP genotyping array can be used to detect copy number variants (CNV) at an unprecedentedly high resolution and that regions with a high density of CNVs in many cases correspond to syntenic breakpoints, providing further support for the importance of evolutionary analysis of structural variation to disease and disease susceptibility.This is a joint work with Junhyong Kim (Penn), Mingyao Li (Penn) and Hakon Hakonarson (CHOP). 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
A note on the correctness of the causal ordering algorithm
Authors: Denver Dash, Marek J. Druzdzel
Venue: Artificial Intelligence (2008)

In this paper we examine in detail the algorithm of Simon [H.A. Simon, Causal ordering and identifiability, in: W.C. Hood, T.C. Koopmans (Eds.), Studies in Econometric Method. Cowles Commission for Research in Economics, Monograph No. 14, John Wiley & Sons, Inc., New York, 1953, pp. 49-74, Chapter III], called the causal ordering algorithm (COA), used for constructing the ''causal ordering'' of a system given a complete specification of the system in terms of a set of ''structural'' equations that govern the variables in the system. This algorithm constructs a graphical characterization of the model in a form that we call a partial causal graph. Simon argued in [H.A. Simon, Causal ordering and identifiability, in: W.C. Hood, T.C. Koopmans (Eds.), Studies in Econometric Method. Cowles Commission for Research in Economics, Monograph No. 14, John Wiley & Sons, Inc., New York, 1953, pp. 49-74, Chapter III] and subsequent papers that a graph so generated explicates causal structure among variables in the model. We formalize this claim further by proving that any causal model based on a one-to-one correspondence between equations and variables must be consistent with the COA.
Incentivizing outsourced computation
Authors: Mira Belenkiy, Melissa Chase, C. Chris Erway, John Jannotti, Alptekin Küpçü, Anna Lysyanskaya
Venue: Proceedings of the 3rd international workshop on Economics of networked systems (2008)

We describe different strategies a central authority, the boss, can use to distribute computation to untrusted contractors. Our problem is inspired by volunteer distributed computing projects such as SETI@home, which outsource computation to large numbers of participants. For many tasks, verifying a task's output requires as much work as computing it again; additionally, some tasks may produce certain outputs with greater probability than others. A selfish contractor may try to exploit these factors, by submitting potentially incorrect results and claiming a reward. Further, malicious contractors may respond incorrectly, to cause direct harm or to create additional overhead for result-checking. We consider the scenario where there is a credit system whereby users can be rewarded for good work and fined for cheating. We show how to set rewards and fines that incentivize proper behavior from rational contractors, and mitigate the damage caused by malicious contractors. We analyze two strategies: random double-checking by the boss, and hiring multiple contractors to perform the same job. We also present a bounty mechanism when multiple contractors are employed; the key insight is to give a reward to a contractor who catches another worker cheating. Furthermore, if we can assume that at least a small fraction h of the contractors are honest (1% - 10%), then we can provide graceful degradation for the accuracy of the system and the work the boss has to perform. This is much better than the Byzantine approach, which typically assumes h > 60%.
Development and application of miniaturized scanners for laser beam micro-welding
Authors: F. Schmitt, M. Funck, A. Boglea, R. Poprawe
Venue: Microsystem Technologies (2008)

Nowadays, flexible production systems for laser joining of micro parts require high dynamics and precision, as well as increased flexibility regarding the working space. The combination of miniaturized scanner-based laser joining systems and high-performance assembly systems is a new approach for a high accuracy compact micro assembly system with an increased flexibility. Using highly dynamic oscillation techniques leads to a stabilization of the welding process of difficult-to-weld materials, e.g. of zinc-containing alloys on the one hand, and to an increased process velocity and reduction of the joining geometry, e.g. for the welding of plastic materials on the other. Hence, a miniaturized laser processing optics is being developed featuring an integrated beam deflection for laser beam micro welding of metal as well as plastic materials. The requirements on this laser processing optics, the choice of a suitable beam deflection system and the design and simulation of the miniaturized optics are presented and discussed. Furthermore, results of laser beam micro welding applications are presented. These results directly influence the design of new miniaturized laser processing optics. By means of the integration of highly dynamic scanner devices further process improvements can be achieved for laser beam micro welding.
Extending the Radiological Workplace Across the Borders
Authors: Hanna Pohjonen, Peeter Ross, Johan (Hans) Blickman
Venue: Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers (2008)

Emerging technologies are transforming the workflows in healthcare enterprises. Today, several vendors offer holistic web-based solutions for radiologists, radiographers and clinicians - a single platform for all users. Besides traditional web, streaming technology is also emerging to the radiological practice in order for improving security and enabling the use of low network bandwidths.The technology does not set limitations any more: today, the digital workplace knows no boundaries; remote reporting, off-hour coverage, virtual radiologists are all ways to offer imaging services in a non-traditional way. The challenge, however, is to provide trust over distance - across organizational or even national boundaries. In the following three different aspects important in building trust in remote reporting are discussed: 1) organizational change issues, 2) continuous feedback and 3) legal implications.
A Design of Reward Function Based on Knowledge in Multi-agent Learning
Authors: Bo Fan, Jiexin Pu
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

The design of reward function is the key to build reinforcement learning system. With the analysis and research of the reinforcement learning and Markov games, an improved reward function is presented, which includes both the goal information based on task and learner's action information based on its domain knowledge. According with this reinforcement function, reinforcement learning integrates the external environment reward and the internal behavior reward so that learner can perform better. The results of the experiment illuminates the reward function involving domain knowledge is better than the traditional reward function in application.
A Multiclass Machine Learning Approach to Credit Rating Prediction
Authors: Yun Ye, Shufen Liu, Jinyu Li
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Corporate credit ratings are important financial indicators of investment risks. Traditional credit rating models employ classical econometrics methods with heteroscedasticity adjustments across various industries. In this paper, we propose using machine learning techniques in predicting corporate ratings and demonstrate, empirically, that multiclass machine learning algorithms outperform traditional econometrics models in exact, 1-notch, or 2-notch away rating predictions. We use three years of CompuStat data from four very different industries and compare corporate credit rating prediction tasks across linear regression, ordered probit model, bagged decision tree with Laplace smoothing, multiclass support vector machines (SVM), and multiclass proximal support vector machines (PSVM). Our findings show that with the proper multiclass and heteroscedasticity adjustments, the computationally inexpensive multiclass PSVM can be utilized in making viable automated corporate credit rating systems for today’s vast marketplace.
Combining Web Usage Mining and XML Mining in a Real Case Study
Authors: Federico Michele Facca
Venue: From Web to Social Web: Discovering and Deploying User and Content Profiles: Workshop on Web Mining, WebMine 2006, Berlin, Germany, September 18, 2006. Revised Selected and Invited Papers (2007)

In this paper we report our first extended experiments on Conceptual Web log generation and XML Mining over generated Conceptual logs. Conceptual logs are XML Web server log containing rich information about the structure of a Web site and its content. Furthermore they can be automatically generated starting from a proper logging facility and a conceptual application model. This allows an easier analysis of the results of the mining process, thanks to the rich information provided and allows to perform the data mining process at different levels of abstraction. In this work we use WebML as conceptual model, and XMINE as mining tool; nevertheless the underlying idea is of general validity and can be applied to any other conceptual modeling framework and mining technique.
Ensuring Well-Behaved Usage of APIs through Syntactic Constraints
Authors: Martin Feilkas, Daniel Ratiu
Venue: Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension (2008)

Libraries are the most wide spreaded form of software reuse. In order to properly use a library API, its clients should fulfill a series of (many times implicit) assumptions made by the API programmers. Failing to fulfill these assumptions leads to a misuse of the library and thereby to defects in the client's code. In this paper we present a method for checking a well-behaved usage of an API through a set of context-sensitive syntactic constraints over the API clients. These constraints restrict the set of programs that can be written with an API only to programs that fulfill the API assumptions and thereby represent a well-behaved and valid usage of the API. In this paper we present a set of typical assumption classes made by API providers about their clients. We define a framework for formalizing the context-sensitive constraints over the API client code and propose a typical constraint for each class of assumptions. Thereby we provide a mechanism that allows the provider of an API to describe the knowledge of how an API is intended to be used in an automatically checkable form. We present our experience with parts of the Java Standard APIs.
Interleaved Alldifferent Constraints: CSP vs. SAT Approaches
Authors: Frédéric Lardeux, Eric Monfroy, Frédéric Saubion
Venue: Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications (2008)

In this paper, we want to handle multiple interleaved Alldiff constraints from two points of view: a uniform propagation framework with some CSP reduction rules and a SAT encoding of these rules that preserves the reduction properties of CSP.
Evaluation of Spectral-Based Methods for Median Graph Computation
Authors: Miquel Ferrer, Francesc Serratosa, Ernest Valveny
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

The median graph is a useful tool to cluster a set of graphs and obtain a prototype of them. The spectral graph theory is another approach to represent graphs and find "good" approximate solutions for the graph-matching problem. Recently, both approaches have been put together and a new representation has emerged, which is called Spectral-Median Graphs. In this paper, we summarize and compare two techniques to synthesize a Spectral-Median Graph: one is based on the correlation of the modal matrices and the other one is based on the averaging of the spectral modes. Results show that, although both approaches obtain good prototypes of the clusters, the first one is slightly more robust against the noise than the second one.