ppxscal/embedding_network huggingface dataset:
Using CHI-scores to reward honest feedback from repeated interactions
Authors: Radu Jurca, Boi Faltings
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

Online communities increasingly rely on reputation information to foster cooperation and deter cheating. As rational agents can often benefit from misreporting their observations, explicit incentives must be created to reward honest feedback. Reputation side-payments (e.g., agents get paid for submitting feedback) can be designed to make truth-telling optimal. In this paper, we present a new side-payment scheme adapted for settings where agents repeatedly submit feedback. We rate the feedback set of an agent, rather than individual reports. The CHI-Score of the feedback set is computed based on a Chi-square independence test that assesses the correlation between the agent's feedback and the feedback submitted by the rest of the community. The mechanism has intuitive appeal and generates significantly lower costs than existing incentive-compatible reporting mechanisms.
The complexity of model checking concurrent programs against CTLK specifications
Authors: Alessio Lomuscio, Franco Raimondi
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

This paper presents complexity results for model checking concurrent programs against temporal-epistemic formulae. We apply these results to evaluate the complexity of verifying programs by means of two model checkers for multi-agent systems: MCMAS and Verics.
Tracking of Multiple Humans in Meetings
Authors: Bo Wu, Ram Nevatia
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Tracking conferees in meeting rooms is important for many applications. In this paper, we present an approach based on single-frame head-shoulder detection to track multiple humans in meetings. The responses of a multiple view head-shoulder detection system are taken as the observation of the human hypotheses. Trajectory initialization and termination are fully automatic and rely on the evidence collected from the detection responses. An object is tracked by data association if its corresponding detection response can be found; otherwise it is tracked by a meanshift style tracker. Finally the tracked hypotheses are verified by evidence collected from body part movements. The system is evaluated on two meeting video corpora.
Introducing Lean Principles with Agile Practices at a Fortune 500 Company
Authors: Emma Parnell-Klabo
Venue: Proceedings of the conference on AGILE 2006 (2006)

Why does the introduction of Agile software practices within a large corporation pose a difficult and challenging prospect? How can Lean principles mitigate development challenges and enhance your success? What does an approach born in manufacturing have to offer to software development? This experience report sets out to answer these questions and offer insights into achieving a smoother transition to Agile through the application of Lean tools and principles.
Detection in colored non-Gaussian noise using cumulants
Authors: B. M. Sadler, G. B. Giannakis
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

The authors consider detection in colored linear non-Gaussian noise of unknown covariance. Higher-order cumulants of non-Guassian processes are estimated in the presence of unknown deterministic and/or Gaussian signals. This allows either parametric or non-parametric estimation of the covariance of the non-Gaussian process via its cumulants. The approach to detection is to augment methods based on second-order statistics using cumulants. A solution is proposed for detection of deterministic signals based on matched filters which incorporate cumulants, where the resulting solutions are valid under either detection hypothesis. This allows for single record detection and obviates the need for prewhitening using noise-only training records. Detection of random Gaussian signals in non-Gaussian noise is also considered.
Course on Simulation in Information Technology: the Global System for Mobile communications
Authors: A. Donder, M. Beck, M. Bossert, J. Hess, U. Ketzer, W. G. Teich
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

Simulation in Information Technology is a course on system simulation that is offered by the Department of Information Technology to graduate students which are majoring in communications engineering. The course imparts a fundamental knowledge of simulation tools and of mobile communication systems. The simulation tool which is used in the course is COSSAP (Communication Systems Simulation and Analysis Package) and the considered communication system is GSM (Global System for Mobile communications). The authors give an introduction into COSSAP, into GSM and especially into the course structure. In addition, some simulation results are given, i.e. the improvement of soft decision decoding versus hard decision decoding.
The balance between Aesthetics, Usability and Corporate Identity: Graphic User Interface design within a commercial company
Authors: A. E. J. Debije-Meessen, J. A. H. Jansen
Venue: Proceedings of the conference on Information Visualization (2006)

This paper describes the search for a balance between aesthetics, usability and corporate identity in product development. This case study shows that usability has been integrated in the process for some time now. Aesthetics and corporate identity is relatively new and not yet incorporated at the same level. Over the past three years we developed a new software style called Ocean. For the first time, as a starting point of the design process, the design team created an aesthetic concept. During the following design process, small, splintered checks are carried out on usability and on perception. However, no overall test is carried out to evaluate the perception of the product as a whole. Do our users experience the software product as professional, independent and human? Do they recognise the Océ brand they know from earlier marketing campaigns? Looking back on the Ocean design process, aesthetics proved itself to be a valid requirement. Expanding our tests with professional checks on perception would complete the embedding of aesthetics in our design process. This is a bridge that still needs to be crossed. To be able to cross this bridge we, as graphic user interface designers, need proper evaluation methods, better information and more case studies. In other words, we need backup from the Information Visualisation field in order to achieve this acceptance.
Semi-Supervised Classification Using Linear Neighborhood Propagation
Authors: Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong Wang
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors' labels, we develop a novel approach, called Linear Neighborhood Propagation (LNP), to learn the linear construction weights and predict the labels of the unlabeled data from the labeled data. The main procedure is composed of two steps: (1) it computes the local neighborhood geometry according to the image information by solving a constrained least squares problem, (2) the obtained geometry will be used to predict the labels of unlabeled data by solving a linearly constrained quadratic optimization problem. LNP has at least three characteristics. Firstly, it is much practical in that only one free parameter, the size of neighborhood, requires user-tuning. Secondly it can be easily extended to out-of-sample examples. Finally the labels achieved from LNP can be sufficiently smooth with respect to the intrinsic structure collectively revealed by the labeled and unlabeled points. Many promising experimental results, such as object recognition, data ranking and interactive image segmentation, demonstrate the effectiveness and efficiency of the proposed approach.
Outlier Detection Using Random Walks
Authors: H. D. K. Moonesignhe, Pang-Ning Tan
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

The discovery of objects with exceptional behavior is an important challenge from a knowledge discovery standpoint and has attracted much attention recently. In this paper, we present a stochastic graph-based algorithm, called OutRank, for detecting outlying objects. In our method, a matrix is constructed using the similarity between objects and used as the adjacency matrix of the graph representation. The heart of this approach is the Markov model that is built upon this graph, which assigns an outlier score to each object. Using this framework, we show that our algorithm is more powerful than the existing outlier detection schemes and can effectively address the inherent problems of such schemes. Empirical studies conducted on both real and synthetic data sets show that significant improvements in detection rate and a lower false alarm rate are achieved using our proposed framework.
Genesis: A Scalable Self-Evolving Performance Management Framework for Storage Systems
Authors: Kristal T. Pollack, Sandeep M. Uttamchandani
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

data-center environment, the administrator needs to understand the root-cause of the issue. The growing trend of system virtualization, combined with the need to support end-to-end performance goals for enterprise applications, have made root-cause analysis a nontrivial problem - administrators are required to manually parse all hardware events, configuration modifications, and changes in access characteristics, across all tiers of the IO path from application servers to the disks. We propose a framework that assists storage administrators with root-cause analysis in distributed systems. GENESIS consists of three key modules: Abnormality Detection, Snapshot Generation, and Diagnosis. The Abnormality Detection module uses clustering algorithms to create and constantly evolve the normality models of measurable parameters in components. The Snapshot Generator is triggered by a combination of abnormality detection and policies to take compact snapshots of the system state for analysis whenever a significant change occurs. The Diagnosis module parses the snapshots and shortlists the root-cause for the administrator using knowledge about the impact of the run-time changes on IO performance. We have implemented an initial proof-of-concept of GENESIS in GPFS (a high performance distributed file-system) and validated its operation for several interesting real-world scenarios. Encouraged by the results, we are currently deploying our prototype in an existing data center environment.
A Comparative Study of Algorithms for Finding Web Communities
Authors: Hidehiko Ino, Mineichi Kudo, Atsuyoshi Nakamura
Venue: Proceedings of the 21st International Conference on Data Engineering Workshops (2005)

Recently, researches on extraction of densely connected subgraphs, which are called communities, from the graph representing link structure inWWW, are very popular. However, few methods guarantee that extracted subgraphs satisfy community conditions which are strictly defined. In this paper, we consider the problem of extracting subgraphs that strictly satisfy the community conditions proposed in [3]. It is known that finding all such communities is computationally hard. As methods that possibly find many communities efficiently, we experimentally compared two methods, a method with a Gomory-Hu tree construction and a method with calculating edge-betweenness. We also proposed evaluation criterion for ranking found communities.
Blind equalization using second-order statistics
Authors: K. H. Afkhamie, Zhi-Quan Luo
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02 (1995)

When a message signal is transmitted through a linear dispersive system, the system output may contain severe intersymbol interference (ISI). The removal of the ISI without the aid of training signals is referred to as blind equalization. We present a new algorithm that achieves blind equalization of possibly nonminimum phase channels, based only on the second-order statistics of the source symbols. Source symbols may have an arbitrary distribution; specifically, they do not have to be independently identically distributed (i.i.d.). This is an extension to previous work done by Tong, Xu and Kailath (1994). Simulations show that the new algorithm compares favorably to the algorithm given by Tong, Xu and Kailath.
Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections
Authors: Alexey Tsymbal, Mykola Pechenizkiy, Padraig Cunningham, Seppo Puuronen
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

In the real world concepts and data distributions are often not stable but change with time. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the target concept. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined. In this paper we consider the use of an ensemble integration technique that helps to better handle concept drift at the instance level. Our experiments with real-world antibiotic resistance data demonstrate that dynamic integration of classifiers built over small time intervals can be more effective than globally weighted voting which is currently the most commonly used integration approach for handling concept drift with ensembles.
Detecting identity-based attacks in wireless networks using signalprints
Authors: Daniel B. Faria, David R. Cheriton
Venue: Proceedings of the 5th ACM workshop on Wireless security (2006)

Wireless networks are vulnerable to many identity-based attacks in which a malicious device uses forged MAC addresses to masquerade as a specific client or to create multiple illegitimate identities. For example, several link-layer services in IEEE 802.11 networks have been shown to be vulnerable to such attacks even when 802.11i/1X and other security mechanisms are deployed. In this paper we show that a transmitting device can be robustly identified by its signalprint, a tuple of signal strength values reported by access points acting as sensors. We show that, different from MAC addresses or other packet contents, attackers do not have as much control regarding the signalprints they produce. Moreover, using measurements in a testbed network, we demonstrate that signalprints are strongly correlated with the physical location of clients, with similar values found mostly in close proximity. By tagging suspicious packets with their corresponding signalprints, the network is able to robustly identify each transmitter independently of packet contents, allowing detection of a large class of identity-based attacks with high probability.
How We Process Words: The Marketing of WP Software
Authors: Amy Wohl
Venue: IEEE Annals of the History of Computing (2006)

This personal note focuses on the market for PC word processing software and how it established a pattern of a popular product dominating the market until overtaken and pushed aside by its successor.
Musician Children Detect Pitch Violations in Both Music and Language Better than Nonmusician Children: Behavioral and Electrophysiological Approaches
Authors: Cyrille Magne, Daniele Schön, Mireille Besson
Venue: Journal of Cognitive Neuroscience (2006)

The idea that extensive musical training can influence processing in cognitive domains other than music has received considerable attention from the educational system and the media. Here we analyzed behavioral data and recorded event-related brain potentials (ERPs) from 8-year-old children to test the hypothesis that musical training facilitates pitch processing not only in music but also in language. We used a parametric manipulation of pitch so that the final notes or words of musical phrases or sentences were congruous, weakly incongruous, or strongly incongruous. Musician children outperformed nonmusician children in the detection of the weak incongruity in both music and language. Moreover, the greatest differences in the ERPs of musician and nonmusician children were also found for the weak incongruity: whereas for musician children, early negative components developed in music and late positive components in language, no such components were found for nonmusician children. Finally, comparison of these results with previous ones from adults suggests that some aspects of pitch processing are in effect earlier in music than in language. Thus, the present results reveal positive transfer effects between cognitive domains and shed light on the time course and neural basis of the development of prosodic and melodic processing.
Learning acyclic decision trees with Functional Dependency Network and MDL Genetic Programming
Authors: Wing-Ho Shum, Kwong-Sak Leung, Man-Leung Wong
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2006)

One objective of data mining is to discover parent-child relationships among a set of variables in the domain. Moreover, showing parents' importance can further help to improve decision makings' quality. Bayesian Network (BN) is a useful model for multi-class problems and can illustrate parent-child relationships with no cycle. But it cannot show parents' importance. In contrast, decision trees state parents' importance clearly, for instance, the most important parent is put in the first level. However, decision trees are proposed for single-class problems only, when they are applied to multi-class ones, they are likely to produce cycles representing tautologic. In this paper, we propose to use MDL Genetic Programming (MDLGP) and Functional Dependency Network (FDN) to learn a set of acyclic decision trees [9]. The FDN is an extension of BN; it can handle all of discrete, continuous, interval and ordinal values; it guarantees to produce decision trees with no cycle; its learning search space is smaller than decision trees'; and it can represent higher-order relationships among variables. The MDLGP is a robust Genetic Programming (GP) proposed to learn the FDN. We also propose a method to derive acyclic decision trees from the FDN. The experimental results demonstrate that the proposed method can successfully discover the target decision trees, which have no cycle and have the accurate classification results.
Automata Construct with Genetic Algorithm
Authors: Vit Fabera, Vlastimil Janes, Maria Janesova
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

A finite state machine (FSM) is a behavioural model of digital hardware. The "revolutionary programming" algorithm was created in 60's by Fogel and it was the first attempt to construct a FSM by evolutionary principle, but only with usage a mutation operator. Lucas and Reynolds tried to construct FSMs without output function using genetic algorithm (for purpose of lexical analysers). This contribution describes experiments with a standard genetic algorithms (SGA) on more general FSMs (Moore and Mealy). It compares simple GA with those, where the mutation affected by the probability. The minimisation is built-in genetic algorithm. The algorithm is tested on several examples.
Analysis of supply chains using system dynamics, neural nets, and eigenvalues
Authors: Luis Rabelo, Magdy Helal, Chalermmon Lertpattarapong
Venue: Proceedings of the 36th conference on Winter simulation (2004)

Supply chain management is a critically significant strategy that enterprises depend on in meeting the challenges of today's highly competitive and dynamic business environments. An important aspect of supply chain management is how enterprises can detect the supply chain behavioral changes due to endogenous and/or exogenous influences and to predict such changes and their impacts in the short and long term horizons. A methodology for addressing this problem that combines system dynamics and neural networks analysis is proposed in this paper. We use neural networks' pattern recognition abilities to capture a system dynamics model and analyze simulation results to predict changes before they take place. We also describe how eigenvalue analysis can be used to enhance the understanding of the problematic behaviors. A case study in the electronics manufacturing industry is used to illustrate the methodology.
An Agent-Based Mechanism for Autonomous Multiple Criteria Auctions
Authors: Marie-Jo Bellosta, Sylvie Kornman, Daniel Vanderpooten
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

This paper aims at defining an agent-based mechanism for autonomous multiple criteria reverse English auctions which are conducted by software agents that negotiate on behalf of buyers and sellers. We propose models for buyer and seller agents where agents' preference relations can be based on various aggregation models. We introduce pricing functions to let seller agents interpret the buyer agent request and associate a price to their bids in order to make them acceptable to the buyer as well as to their own commercial constraints.
Comparison of Early Cortical Networks in Efficient and Inefficient Visual Search: An Event-Related Potential Study
Authors: Ute Leonards, Julie Palix, Christoph Michel, Vicente Ibanez
Venue: Journal of Cognitive Neuroscience (2003)

Functional magnetic resonance imaging studies have indicated that efficient feature search (FS) and inefficient conjunction search (CS) activate partially distinct frontoparietal cortical networks. However, it remains a matter of debate whether the differences in these networks reflect differences in the early processing during FS and CS. In addition, the relationship between the differences in the networks and spatial shifts of attention also remains unknown. We examined these issues by applying a spatio-temporal analysis method to high-resolution visual event-related potentials (ERPs) and investigated how spatio-temporal activation patterns differ for FS and CS tasks. Within the first 450 msec after stimulus onset, scalp potential distributions (ERP maps) revealed 7 different electric field configurations for each search task. Configuration changes occurred simultaneously in the two tasks, suggesting that contributing processes were not significantly delayed in one task compared to the other. Despite this high spatial and temporal correlation, two ERP maps (120&ndash;190 and 250&ndash;300 msec) differed between the FS and CS. Lateralized distributions were observed only in the ERP map at 250&ndash;300 msec for the FS. This distribution corresponds to that previously described as the N2pc component (a negativity in the time range of the N2 complex over posterior electrodes of the hemisphere contralateral to the target hemifield), which has been associated with the focusing of attention onto potential target items in the search display. Thus, our results indicate that the cortical networks involved in feature and conjunction searching partially differ as early as 120 msec after stimulus onset and that the differences between the networks employed during the early stages of FS and CS are not necessarily caused by spatial attention shifts.