ppxscal/embedding_network huggingface dataset:
Podcasting: A new technological tool to facilitate good practice in higher education
Authors: Vicenc Fernandez, Pep Simo, Jose M. Sallan
Venue: Computers Education (2009)

The literature has frequently highlighted the usefulness of podcasting in higher education; however, there is an important gap between the theory on good practice in higher education and empirical studies about podcasting. With this in mind, we carried out an empirical study on an undergraduate degree course in Information Systems Management. The study consisted of the creation and broadcast of 13 podcasts, distributed over four months in which ninety distance students took part. The analysis follows the suggestions proposed in previous literature about the evaluation of technologies in a university learning environment. The findings, discussed within the framework of principles for good practice in higher education, suggest some interesting issues in distance courses, such as: (1) podcasting is a powerful tool as a complement to the traditional resources on a course, but not a substitute for them; (2) the characteristics of podcasting increase the impression of permanent contact between students and teachers, increasing students' motivation; (3) the use of podcasting allows for a diverse range of student skills and learning methods. Other secondary findings are discussed and some suggestions for future research are proposed at the end of this paper.
Cache Line Impact on 3D PDE Solvers
Authors: Masaaki Kondo, Mitsugu Iwamoto, Hiroshi Nakamura
Venue: Proceedings of the 4th International Symposium on High Performance Computing (2009)

Because performance disparity between processor and main memory is serious, it is necessary to reduce off-chip memory accesses by exploiting temporal locality. Loop tiling is a well-known optimization which enhances data locality. In this paper, we show a new cost model to select the best tile size in 3D partial differential equations. Our cost model carefully takes account of the effect of cache line. We present performance evaluation of our cost models. The evaluation results reveal the superiority of our cost model to other cost models proposed so far.
Multi step ahead prediction of north and south hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model
Authors: Sanjay L. Badjate, Sanjay V. Dudul
Venue: WSEAS Transactions on Information Science and Applications (2009)

Multi-Step ahead prediction of a chaotic time series is a difficult task that has attracted increasing interest in recent years. The interest in this work is the development of nonlinear neural network models for the purpose of building multistep ahead prediction of North and South hemisphere sunspots chaotic time series. In the literature there is a wide range of different approaches but their success depends on the predicting performance of the individual methods. Also the most popular neural models are based on the statistical and traditional feed forward neural networks. But it is seen that this kind of neural model may present some disadvantages when long-term prediction is required. In this paper focused time lagged recurrent neural network (FTLRNN) model with gamma memory is developed not only for short-term but also for long-term prediction which allows to obtain better predictions of northern and southern chaotic time series in future. The authors experimented the performance of this FTLRNN model on predicting the dynamic behavior of typical northern and southern sunspots chaotic time series. Static MLP model is also attempted and compared against the proposed model on the performance measures like mean squared error (MSE), Normalized mean squared error (NMSE) and Correlation Coefficient (r). The standard back propagation algorithm with momentum term has been used for both the models. The various parameters like number of hidden layers, number of processing elements in the hidden layer, step size, the different learning rules, the various transfer functions like tanh, sigmoid, linear-tanh and linear sigmoid, different error norms L1, L2 (Euclidean), L3, L4, L5 and L∞, and different combination of training and testing samples are exhaustively varied and experimented for obtaining the optimal values of performance measures. The obtained results indicates the superior performance of estimated dynamic FTLRNN based model with gamma memory over the static MLP NN in various performance metrics. In addition, the output of proposed FTLRNN neural network model with gamma memory closely follows the desired output for multi- step ahead prediction for all the chaotic time series considered in the study.
Statistical tissue characterization of coronary plaque by ASSOM using intravascular ultrasound method
Authors: Ryosuke Kubota, Mami Kunihiro, Noriaki Suetake, Eiji Uchino, Genta Hashimoto, Takafumi Hiro, Masunori Matsuzaki
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

Tissue characterization of plaque is very important for a diagnosis of the acute coronary syndromes. The conventional methods however can not perform a good characterization, because the features employed for characterization are not particular for each tissue. In this paper, we propose a novel tissue characterization method by using an adaptive subspace self-organizing map (ASSOM). ASSOM can produce various features from an intravascular ultrasound (IVUS) radiofrequency signal. The acquired features are suitable for the tissue characterization, because the overlap of the distributions of the acquired features is much smaller than the one, e.g., by an integrated backscatter (IB) analysis or by a traditional Fourier spectrum analysis. A tissue is characterized by using the statistical information of the features acquired by ASSOM. Through the application to the tissue characterization of the real IVUS signal, the performance of the proposed method has been verified by comparing it with the conventional methods.
End-to-end utilization control for aperiodic tasks in distributed real-time systems
Authors: Yong Liao, Xu-Dong Chen, Guang-Ze Xiong, Qing-Xin Zhu, Nan Sang
Venue: Journal of Computer Science and Technology (2007)

An increasing number of DRTS (Distributed Real-Time Systems) are employing an end-to-end aperiodic task model. The key challenges of such DRTS are guaranteeing utilization on multiple processors to achieve overload protection, and meeting the end-to-end deadlines of aperiodic tasks. This paper proposes an end-to-end utilization control architecture and an IC-EAT (Integration Control for End-to-End Aperiodic Tasks) algorithm, which features a distributed feedback loop that dynamically enforces the desired utilization bound on multiple processors. IC-EAT integrates admission control with feedback control, which is able to dynamically determine the QoS (Quality of Service) of incoming tasks and guarantee the end-to-end deadlines of admitted tasks. Then an LQOCM (Linear Quadratic Optimal Control Model) is presented. Finally, experiments demonstrate that, for the end-to-end DRTS whose control matrix G falls into the stable region, the IC-EAT is convergeut and stable. Moreover, it is capable of providing better QoS guarantees for end-to-end aperiodic tasks and improving the system throughput.
A Frame-Activity-Based Dynamic Frame-Skipping Transcoder
Authors: Yan Zheng, Shibao Zheng
Venue: Wireless Personal Communications: An International Journal (2009)

Nowadays band-limited terminals are very popular in wireless networking environments, with the result that frame-skipping technology is applied to reduce frame rate for video transmission. In this paper, we propose a dynamic frame-skipping transcoding scheme, where a frame being skipped or not is based on its activity. Whether a frame has relatively drastic motion activities or soft motion activities is evaluated among a certain number of its neighboring frames, which keeps the continuity of the transcoded sequence logically. To reduce the drift errors caused by locating overlapping blocks during the motion vector re-construction process in multi-frame skipping, we propose a new efficient overlapping block location method. A transcoder structure with the proposed dynamic frame-skipping scheme is also designed for hardware application. Simulation shows that the proposed dynamic frame-skipping scheme obtains lower complexity and higher performance, which is especially useful for real-time transcoding.
Design, Simulation and Implementation of a Fuzzy-PID Controller for Controlling a DC-DC Converter
Authors: Mohammad Jafari, Zahra Malekjamshidi
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

This paper presents the design, simulation and implementation of hybrid powerful Fuzzy-PID (FZ-PID) controller. The arithmetic of PID control is simple and the stability of it is good. However, the dynamic characteristics of PID control are poor [3]. The fuzzy logic control method is flexible and adaptive and does not rely on a mathematical model of the system and can deal with the nonlinearity of parameters and uncertain problems [1]. This general-purpose FZ-PID regulates the output voltage of a Full Bridge dc-dc converter and experimentally tested. This controller based on a qualitative description of the system to be controlled and its implementation is relatively simple. Experimental results show that this controller can guarantee a small variation of voltage in working point and keep these parameters in safe and stable condition and an improved large-signal response. Finally Results of such a control system is compared with a pure fuzzy controller and results show that new hybrid FZ-PID controller provides better system responses and control potentialities.
Initial fieldwork for LWAZI: a telephone-based spoken dialog system for rural South Africa
Authors: Tebogo Gumede, Madelaine Plauché
Venue: Proceedings of the First Workshop on Language Technologies for African Languages (2009)

This paper describes sociological fieldwork conducted in the autumn of 2008 in eleven rural communities of South Africa. The goal of the fieldwork was to evaluate the potential role of automated telephony services in improving access to important government information and services. Our interviews, focus group discussions and surveys revealed that Lwazi, a telephone-based spoken dialog system, could greatly support current South African government efforts to effectively connect citizens to available services, provided such services be toll free, in local languages, and with content relevant to each community.
A method to automatically adapt the step size for numerical solving of ordinary differential equations
Authors: C. Berbente, S. Berbente
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

Accurate numerical solving of differential equations requires a step size modification in order to control the error amplification. In a standard strategy this implies to repeat the calculations with different step size, then compare the obtained results and finally to adapt a sufficiently small step size. A consequence of such a strategy is the increased computation time, especially for functions with very large gradients. In this paper another way to adapt the step size to the local function slope is proposed. A rotation of the system coordinates in connection with certain change of variables is proved to be very efficient.
Dynamics of Rule Revision and Strategy Revision in Legislative Games
Authors: Moshe Looks, Ronald P. Loui, Barry Z. Cynamon
Venue: Proceeding of the 2005 conference on Legal Knowledge and Information Systems: JURIX 2005: The Eighteenth Annual Conference (2005)

Many legislative games of interest defy classical assumptions and techniques; they tend to be open-ended, with weakly defined objectives, and either noncompetitive or pseudo-competitive. We introduce a conceptual and mathematical framework for grappling with such systems. Simulation results are presented for basic specifications of the framework that exhibit a number of qualitative phenomena overlapping with real-world dynamics across a broad spectrum of settings, including aspects of financial regulation and academic decision procedures, that as we demonstrate, may be viewed through the lens of our framework.
The impact of computer mediated communication systems monitoring on organizational communications content
Authors: Rosann Webb Collins, Richard P. Will
Venue:  (2008)

Employer monitoring of communications is prevalent and on the rise due in part to the Sarbanes-Oxley Act, the Health Insurance Privacy Protection Act, and other legislation in the U.S. and other countries. However, the critical effect of this new activity on what is communicated in companies has not been assessed. This dissertation examines the impacts of computer mediated communication systems monitoring on neutral, incriminating and exculpatory content, as well as the overall volume of communications issued on monitored and non-monitored computer mediated communication systems. Incriminating communication is cataloged in a hazard communications taxonomy for this investigation. A controlled laboratory experiment has subjects participate in an instant messaging discussion on a topic for which they are likely to be aware of information that is incriminating to their organization, or its members, or both. Consistent with self awareness theory, monitored subjects engage in significantly less overall and neutral communication. They volunteer fewer high intensity hazard communications, but are less likely to curtail low intensity hazard communications. They issue denials about more incriminating topics. Contributions to research include theory development, especially in the area of standard selection; application of self-awareness theory to the new domain of computer mediated communications monitoring; a research framework; a taxonomy and coding scheme for the new hazard communications constructs; and a relative standards influence instrument and methodology for use in studying competing standards. Implications for corporate monitoring and communications policies are discussed, and a research agenda is outlined.
On interdependence of secrets in collaboration networks
Authors: Sara Miner More, Pavel Naumov
Venue: Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge (2009)

The paper proposes Logic of Secrets in Collaboration Networks, a formal logical system for reasoning about a set of secrets established over a fixed configuration of communication channels. The system's key feature, a multi-channel relation called independence, is a generalization of a two-channel relation known in the literature as nondeducibility. The main result is the completeness of the proposed system with respect to a semantics of secrets.
Proportional Fair Scheduling Based on Genetic Algorithms for Multi-user MIMO Systems
Authors: Peng Shang, Gang Su, Guangxi Zhu, Li Tan
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

For the resource allocation and scheduling of downlink multi-user multiple input multiple output (MU-MIMO) system, a multi-user proportional fair scheduling scheme based on genetic algorithms (GA) is proposed. By adding some good-gene individuals to the initial population and keeping its gene stable, the convergence of GA is greatly accelerated. Specifically, the base station exploits Block Diagonalization (BD) precoding technique to eliminate the inter-user interference. To guarantee the fairness while maintaining the throughput performance, a subset of users is selected to serve at one time slot. Moreover, the impact of feedback error on the channel state information is analyzed. Simulation results show that both schemes can achieve a good tradeoff between fairness and throughput with low computational complexity compared to other scheduling schemes.
A lower bound on the sample size needed to perform a significant frequent pattern mining task
Authors: Stéphanie Jacquemont, François Jacquenet, Marc Sebban
Venue: Pattern Recognition Letters (2009)

During the past few years, the problem of assessing the statistical significance of frequent patterns extracted from a given set S of data has received much attention. Considering that S always consists of a sample drawn from an unknown underlying distribution, two types of risks can arise during a frequent pattern mining process: accepting a false frequent pattern or rejecting a true one. In this context, many approaches presented in the literature assume that the dataset size is an application-dependent parameter. In this case, there is a trade-off between both errors leading to solutions that only control one risk to the detriment of the other one. On the other hand, many sampling-based methods have attempted to determine the optimal size of S ensuring a good approximation of the original (potentially infinite) database from which S is drawn. However, these approaches often resort to Chernoff bounds that do not allow the independent control of the two risks. In this paper, we overcome the mentioned drawbacks by providing a lower bound on the sample size required to control both risks and achieve a significant frequent pattern mining task.
Structures and distributions in morphology learning
Authors: Charles D. Yang
Venue:  (2008)

One of the great challenges in linguistics and cognitive science is to understand the nature of the mental representation of language. The precise mechanisms of the mind are unknown, but can be modeled through observation and experimentation. By viewing the mind as a computational device that receives input (primary linguistic data) and produces output (the development of grammatical speech) during language acquisition, one can reason about what representations and algorithms must be internal to the learner. In this thesis, I investigate the acquisition of morphology. The principal challenges are how to learn a theory in the presence of sparse data, and in a manner that can provide explanations for the developmental processes in child language acquisition. The main idea underlying this work is that a consideration of the different aspects of language acquisition places strong constraints on cognitively plausible representations and algorithms that are internal to the learner. To develop a model of morphology acquisition, I pursue three lines of work: First, I formulate a cognitively-oriented computational framework for studying language acquisition that consists of four components: the linguistic representation, the statistical distribution of the input data, the observed behavior of the human learner, and the performance of the learning algorithm. All four components and their interactions are important for understanding language acquisition. Second, I examine the statistical distributions of morphology in naturally occurring corpora and discuss their implications for acquisition and theories of morphology. The Zipfian distribution of morphological inflections favors a rule-based model of morphology, where rules are learned one at a time by relating them to the morphological base. This provides an explanation of children's incremental acquisition of morphology. Third, to provide empirical support for this theory of acquisition, I implement unsupervised algorithms for the induction of morphology and lexical categories from text corpora. A rule-based model of morphology called the base-and-transforms model is learned, which consists of lexical categories, morphological base forms, and rules that convert base forms to other inflections. Morphological base forms play an important role in bootstrapping the acquisition of morphological relations, and simplify lexical category induction through distributional analysis.
Intelligent data analysis applied to debug complex software systems
Authors: Emilio Serrano, Jorge J. Gómez-Sanz, Juan A. Botía, Juan Pavón
Venue: Neurocomputing (2009)

The emergent behavior of complex systems, which arises from the interaction of multiple entities, can be difficult to validate, especially when the number of entities or their relationships grows. This validation requires understanding of what happens inside the system. In the case of multi-agent systems, which are complex systems as well, this understanding requires analyzing and interpreting execution traces containing agent specific information, deducing how the entities relate to each other, guessing which acquaintances are being built, and how the total amount of data can be interpreted. The paper introduces some techniques which have been applied in developments made with an agent oriented methodology, INGENIAS, which provides a framework for modeling complex agent oriented systems. These techniques can be regarded as intelligent data analysis techniques, all of which are oriented towards providing simplified representations of the system. These techniques range from raw data visualization to clustering and extraction of association rules.
Fuzzy Immune PID Temperature Control of HVAC Systems
Authors: Desheng Liu, Zhiru Xu, Qingjun Shi, Jingguo Zhou
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

In this paper, a new nonlinear fuzzy-immune proportional-integral derivative (PID) controller is proposed. This controller consists of a PID controller and a basic immune proportional controller in cascaded connection, the nonlinear function of the immune proportional controller is realized by using fuzzy reasoning. The simulations are done on a first-order system with varying parameters and time delay. The superior performance of the proposed controller is demonstrated through an experimental example.