ppxscal/embedding_network huggingface dataset:
An on Demand Business Context to Improve Software Development Process Based on Business Knowledge
Authors: Miguel Ángel Sánchez, Ana Fermoso, Luís Joyanes
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

On Demand Business concept, from IBM, shows how to coordinate the transformation of the enterprise organization and its technological infrastructure to bridge the gap between business and technology. This problem appears in most of information systems. This concept is based on Service Oriented Architecture (SOA) to improve the connection between business services and software services.If companies can reach this On Demand Business context they could improve the software development process by applying standards like Model Driven Architecture (MDA), to separate business logic from software and technological platforms, and Business Process Management (BPM) to define business processes. In this article we propose a new approach that combines On Demand Business with MDA, BPM and SOA to improve software development process starting on business knowledge.The most important contribution of this approach to Knowledge Society is the method used to connect business knowledge with technological resources using a well defined architecture.
The Morris Worm: A Fifteen-Year Perspective
Authors: Hilarie Orman
Venue: IEEE Security and Privacy (2003)

The Morris worm was the first worm to hit the Internet and caused a disruption never seen before. In the 15 years since its appearance, have we learned our lessons about computer security?
Gesture Recognition with Hidden Markov Models to Enable Multi-modal Haptic Feedback
Authors: Vadim Frolov, Barbara Deml, Gunter Hannig
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

The ability to recognize and classify human gestures bears a huge potential of improving haptic feedback in virtual environments: when it is possible to predict how a user wants to explore or manipulate his environment both the visual and the auditive feedback may be adapted in order to enhance the immersiveness of haptic displays. Within this work a software approach is suggested that allows for such a real-time classification of gestures in a continuous data stream. A visually based tracking system is used to record the hand movements, while Hidden Markov models are applied to analyze the data. After presenting the methodological background and the software implementation, the outcome of an evaluation study is discussed. It reveals satisfying results of gesture classification and, what is particularly important, the recognition is fast enough to enable multi-modal haptic feedback.
Effective Visualization of Information Diffusion Process over Complex Networks
Authors: Kazumi Saito, Masahiro Kimura, Hiroshi Motoda
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Effective visualization is vital for understanding a complex network, in particular its dynamical aspect such as information diffusion process. Existing node embedding methods are all based solely on the network topology and sometimes produce counter-intuitive visualization. A new node embedding method based on conditional probability is proposed that explicitly addresses diffusion process using either the IC or LT models as a cross-entropy minimization problem, together with two label assignment strategies that can be simultaneously adopted. Numerical experiments were performed on two large real networks, one represented by a directed graph and the other by an undirected graph. The results clearly demonstrate the advantage of the proposed methods over conventional spring model and topology-based cross-entropy methods, especially for the case of directed networks.
Using Cost-Sensitive Learning to Determine Gene Conversions
Authors: Mark J. Lawson, Lenwood Heath, Naren Ramakrishnan, Liqing Zhang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Gene conversion, a non-reciprocal transfer of genetic information from one sequence to another, is a biological process whose importance in affecting both short-term and long-term evolution cannot be overemphasized. Knowing where gene conversion has occurred gives us important insights into gene duplication and evolution in general. In this paper we present an ensemble-based learning method for predicting gene conversions using two different models of reticulate evolution. Since detecting gene conversion is a rare-class problem, we implement cost-sensitive learning in the form of a generated cost matrix that is used to modify various underlying classifiers. Results show that our method combines the predictive power of different models and is able to predict gene conversion more accurately than any of the two studied models. Our work provides a useful framwork for future improvement of gene conversion predictions through multiple models of gene conversion.
Computer Education
Authors: D. Irwin
Venue: Computer (1975)

This issue of Computer addresses some of the major problems that face those who must educate-and employ-the computer scientists and engineers who will graduate over the next 5 to 10 years.
A Feedback Based Adaptive Marking Algorithm for Assured Service
Authors: Fanjun Su, Chunxue Wu, Guoqiang Sun
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

We propose an algorithm to realize proportional bandwidth allocation in DiffServ networks. According to the feedback information, ingress routers adjust Committed Information Rates (CIRs) of different aggregates using AIMD way to let the network reach an exactly subscribed state. Based on CIRs, packets are marked. Simulations with ns2 show that proportional bandwidth allocation under different network conditions can be achieved.
The Power of Data
Authors: Monica Marinucci
Venue: Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge (2008)

From raw data to knowledge discovery, the ability to store, handle, manipulate, distribute and replicate data and information provides a tremendous asset to organisations, both in the commercial and scientific and academic sector. As IT infrastructures become more and more reliable, dynamic and flexible, applications and services require a modular, distributed, functionality-driven and secure environment.Continual improvements and advancements are needed to meet these requirements and Oracle constantly innovates and pushes the limits of the technology to make data, information and knowledge available and exploitable. For Oracle to continue leading the database sector and to contribute to the development of ICT, it is crucial to engage with the R D sector, both industrial and scientific, where existing and new challenges can be explored and novel and ground-breaking solutions found.The talk will explore some of the latest directions and developments in the database field and describe how Oracle contributes to them partnering up with other leading organisations in different sectors. Highlights from some of the R D projects across Europe Oracle is part of will be also presented. 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
Learning Classifier System Approach to Natural Language Grammar Induction
Authors: Olgierd Unold
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

This paper describes an evolutionary approach to the problem of inferring non-stochastic context-free grammar (CFG) from natural language (NL) corpora. The approach employs Grammar-based Classifier System (GCS). GCS is a new version of Learning Classifier Systems in which classifiers are represented by CFG in Chomsky Normal Form. GCS has been tested on the NL corpora, and it provided comparable results to the pure genetic induction approach, but in a significantly shorter time. The efficient implementation for grammar induction is very important during analysis of large text corpora.
Sensitivity derivatives for flexible sensorimotor learning
Authors: M. N. Abdelghani, T. P. Lillicrap, D. B. Tweed
Venue: Neural Computation (2008)

To learn effectively, an adaptive controller needs to know its sensitivity derivatives---the variables that quantify how system performance depends on the commands from the controller. In the case of biological sensorimotor control, no one has explained how those derivatives themselves might be learned, and some authors suggest they are not learned at all but are known innately. Here we show that this knowledge cannot be solely innate, given the adaptive flexibility of neural systems. And we show how it could be learned using forms of information transport that are available in the brain. The mechanism, which we call implicit supervision, helps explain the flexibility and speed of sensorimotor learning and our ability to cope with high-dimensional work spaces and tools.
Two Extensions of Data Assimilation by Field Alignment
Authors: Sai Ravela
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Classical formulations of data-assimilation perform poorly when forecast locations of weather systems are displaced from their observations. They compensate position errors by adjusting amplitudes, which can produce unacceptably "distorted" states. Motivated by cyclones, in earlier work we show a new method for handling position and amplitude errors using a single variational objective. The solution could be used with either ensemble or deterministic methods. In this paper, extension of this work in two directions is reported. First, the methodology is extended to multivariate fields commonly used in models, thus making this method readily applicable. Second, an application of this methodology to rainfall modeling is presented.
A C-Language Binding for PSL
Authors: Ping Hang Cheung, Alessandro Forin
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

In recent years we have seen an increase in the complexity of embedded system design and in the difficulties of their verification. As a result, engineers have been trying to verify the specifications at a higher level of abstraction. In this paper we present an automated tool which is able to perform runtime verification of a programs logical properties asserted by the programmer. The idea is to leverage the Assertion Based Verification language PSL, which is widely used by hardware engineers, extending it to the software verification of C language programs. The properties expressed in a simple subset of PSL are evaluated by the tool during full system simulation. Like in hardware Assertion Based Verification, the tool can handle both safety properties (absence of bad events) and liveness properties (good events eventually happen). The liveness property is not widely supported in existing verification tools.
Multi-resolution Image Parametrization in Stepwise Diagnostics of Coronary Artery Disease
Authors: Matjaž Kukar, Luka Šajn, Ciril Grošelj, Jera Grošelj
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

Coronary artery disease is one of the world's most important causes of early mortality, so any improvements of diagnostic procedures are highly appreciated. In the clinical setting, coronary artery disease diagnostics is typically performed in a sequential manner. The four diagnostic levels consist of evaluation of (1) signs and symptoms of the disease and ECG (electrocardiogram) at rest, (2) ECG testing during a controlled exercise, (3) myocardial perfusion scintigraphy, and (4) finally coronary angiography (which is considered as the "gold standard" reference method). In our study we focus on improving diagnostic performance of the third diagnostic level (myocardial perfusion scintigraphy). This diagnostic level consists of series of medical images that are easily obtained and the imaging procedure represents only a minor threat to patients' health. In clinical practice, these images are manually described (parameterized) and subsequently evaluated by expert physicians. In our paper we present an innovative alternative to manual image evaluation --- an automatic image parametrization on multiple resolutions, based on texture description with specialized association rules, and image evaluation with machine learning methods. Our results show that multi-resolution image parameterizations equals the physicians in terms of quality of image parameters. However, by using both manual and automatic image description parameters at the same time, diagnostic performance can be significantly improved with respect to the results of clinical practice.
Secret-Ballot Receipts: True Voter-Verifiable Elections
Authors: David Chaum
Venue: IEEE Security and Privacy (2004)

A new kind of receipt sets a far higher standard of security by letting voters verify the election outcome—even if all election computers and records were compromised. The system preserves ballot secrecy, while improving access, robustness, and adjucation, all at lower cost.
A Biphase-Bayesian-Based Method of Emotion Detection from Talking Voice
Authors: Jangsik Cho, Shohei Kato, Hidenori Itoh
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

This paper propose a Bayesian-based method of emotion detection from talking voice. Development of a entertainment robot and joyful communication between human and robot have given us the motivation for a computational method for robot to detect its dialogist's emotion from his talking voice. The method is based on the Bayesian networks which represent the dependence and its strength between dialogist's utterance and his emotion, by using a Bayesian modeling for prosodic feature quantities extracted from emotionally expressive voice data. In this paper, we propose a biphase inference method using the Bayesian networks. This inference method has two steps: to reduce the choice of emotion at the first step and to infer a certain emotion reliably from little choice at the second step. The paper also reports some empirical reasoning performance of this method.
Similarity of XML schema definitions
Authors: Irena Mlýnková
Venue: Proceeding of the eighth ACM symposium on Document engineering (2008)

In this paper we propose a technique for evaluating similarity of XML Schema fragments. Firstly, we define classes of structurally and semantically equivalent XSD constructs. Then we propose a similarity measure that is based on the idea of edit distance utilized to XSD constructs and enables one to involve various additional similarity aspects. In particular, we exploit the equivalence classes and semantic similarity of element/attribute names. Using experiments we show the behavior and advantages of the proposal.
The key locus of common response inhibition network for no-go and stop signals
Authors: Dongming Zheng, Tatsuro Oka, Hirokazu Bokura, Shuhei Yamaguchi
Venue: Journal of Cognitive Neuroscience (2008)

Response inhibition is one of the highest evolved executive functions of human beings. Previous studies revealed a wide variety of brain regions related to response inhibition, although some of them may not be directly related to inhibition but to task-specific effects or noninhibitory cognitive functions such as attention, response competition, or error detection. Here, we conducted event-related functional magnetic resonance imaging studies in which all subjects performed both stop-signal and go/no-go tasks in order to explore key neural correlates within the response inhibition network irrelevant to task designs and other cognitive processes. The successful inhibition in the stop-signal and go/no-go tasks, respectively, activated a set of predominantly right-lateralized hemispheric cortices. The common inhibitory regions across the two tasks included the right middle prefrontal cortex in addition to the right middle occipital cortex. Correlation analysis was carried out within these areas between intensity of activation and behavioral performance in the two tasks. Only the region located in the middle prefrontal cortex showed significant correlations in both tasks. We believe this region is the key locus for execution of response inhibition in the distributed inhibitory neural network.
Proximity classification for mobile devices using wi-fi environment similarity
Authors: Alessandro Carlotto, Matteo Parodi, Carlo Bonamico, Fabio Lavagetto, Massimo Valla
Venue: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments (2008)

This paper describes an algorithm to compute lists of people and devices that are physically nearby to a mobile user based on the analysis of signals from existing wireless networks. The system evaluates proximity by classifying the degree of similarity of the Wi-Fi scan data through a statistical Gaussian Mixture Model. It recognizes when the devices are in the same area, and, in this case, it distinguishes three proximity levels: High (e.g. same room), Medium (e.g. same floor) and Low (e.g. same building). The algorithm can be deployed on a remote server that receives Wi-Fi scanning data (including MAC addresses and signal strength) from mobile devices. The server estimates proximity by extracting a set of features from each received pair of Wi-Fi data, feeding them to the GMM model and selecting the category with greatest probability. The method presented in the paper does not require calibration and leverages on existing Wi-Fi signals, while obtaining a percentage of correct discrimination among three levels near to 90%.
On-line expert FDI system
Authors: Octavian Grigore-Müler
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

In common applications of control systems the fault would only be detected when the output signal, previously set up, were out of the values' range, and it couldn't be any chance to change it under the actual commands. At this moment, due to the chosen strategy, in most cases the system shuts down, occurrence that must be avoided in any aircraft control system. In this paper an on-line expert fuzzy FDI system for execution element of a B747 automatic flight control system (AFCS) is illustrated.
An Optimal Strategy Improvement Algorithm for Solving Parity and Payoff Games
Authors: Sven Schewe
Venue: Proceedings of the 22nd international workshop on Computer Science Logic (2008)

This paper presents a novel strategy improvement algorithm for parity and payoff games, which is guaranteed to select, in each improvement step, an optimal combination of local strategy modifications. Current strategy improvement methods stepwise improve the strategy of one player with respect to some ranking function, using an algorithm with two distinct phases: They first choose a modification to the strategy of one player from a list of locally profitable changes, and subsequently evaluate the modified strategy. This separation is unfortunate, because current strategy improvement algorithms have no effective means to predict the global effect of the individual local modifications beyond classifying them as profitable, adversarial, or stale. Furthermore, they are completely blind towards the cross effect of different modifications: Applying one profitable modification may render all other profitable modifications adversarial. Our new construction overcomes the traditional separation between choosing and evaluating the modification to the strategy. It thus improves over current strategy improvement algorithms by providing the optimal improvement in every step, selecting the best combination of local updates from a superset of all profitable and stale changes.
A Novel Artificial Neural Network Based on Hybrid PSO-BP Algorithm in the Application of Adaptive PMD Compensation System
Authors: Ying Chen, Qiguang Zhu, Zhiquan Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

An artificial neural network (ANN) based on hybrid algorithm combining particle swarm optimization (PSO) algorithm with back-propagation (BP) algorithm has been introduced to compensate the polarization mode dispersion (PMD) in the ultra-high speed optical communication system. The hybrid algorithm, also referred to as PSO-BP algorithm, has been adopted to train the weights of ANN, and it can make use of not only strong global searching ability of the PSO algorithm, but also strong local searching ability of the BP algorithm. In the proposed algorithm, a heuristic way was adopted to give a transition from particle swarm search to gradient descending search. The experimental results show that the hybrid algorithm is better than the Adaptive PSO algorithm and BP algorithm in convergent speed and convergent accuracy. And in the PMD compensation system, the ANN is used to optimize the degree of polarization (DOP) signal, which can achieve the random stochastic PMD compensation adaptively. Simulation results show the opening of eye diagram can be improved obviously.
Perspective Use and Perspective Shift in Spatial Dialogue
Authors: Juliana Goschler, Elena Andonova, Robert J. Ross
Venue: Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space (2008)

Previous research has shown variability in spatial perspective and the occurrence of perspective shifts to be common in monologic descriptions of spatial relationships, and in route directions, in particular. Little is known, however, about preferences and the dynamics of use of route vs. survey perspectives as well as perspective shifts in dialogue. These were the issues we addressed in a study of dialogic interaction where one participant instructed the other on how to navigate a wheelchair avatar in a shared environment towards a goal. Although there was no clear preference for one of the two perspectives overall, dialogues tended to evolve from an early incremental, local, ego-based strategy towards a later more holistic, global, and environment-oriented strategy in utterance production. Perspective mixing was also observed for a number of reasons, including the relative difficulty of spatial situations and changes across them, navigation errors by the interlocutor, and verbal reactions by the interlocutor.
Concerning Olga, the Beautiful Little Street Dancer: Adjectives as Higher-Order Polymorphic Functions
Authors: Walid S. Saba
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

In this paper we suggest a typed compositional semantics for nominal compounds of the form [Adj Noun] that models adjectives as higher-order polymorphic functions, and where types are assumed to represent concepts in an ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language. In addition to [Adj Noun] compounds our proposal seems also to suggest a plausible explanation for well known adjective ordering restrictions.