ppxscal/embedding_network huggingface dataset:
Asymmetric Stereoscopic Video Encoding Algorithm Based on Joint Compensation Prediction
Authors: He Wenge, Liu Feng
Venue: Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 02 (2009)

Stereoscopic video bring people more actual 3D scene feeling, while it also makes bitrates increase largely. To reduce bitrates is highly emphasized in stereoscopic video encoding. In the current stereo video coding scheme, the two views channels have the same spatial resolution. In order to raise the coding efficiency, an asymmetric stereoscopic video encoding algorithm of based on joint Compensation Prediction is proposed in this paper, using different spatial resolution of the two view channels. And in encoding the lower images phase, a low complexity prediction mode is proposed. Experimental result indicates that this proposed algorithm is more efficient than JMVM algorithm while the coding complexity is also dropped
Hardware-in-the-Loop Simulation of Pure Electric Vehicle Control System
Authors: Yang Zhu, Hao Hu, Guoqing Xu, Zhiguo Zhao
Venue: Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics (2009)

Hardware-in-the-loop (HIL) testing and simulation is increasingly being used in the test and design of vehicle control system. Utilizing Matlab/Simulink with dSPACE, a hardware-in-the-loop simulation platform for the control system of pure electric vehicle is built. HIL simulation of vehicle control units, especially the vehicle management system, are carried out based on CAN bus communication. A control-monitor interface for HIL testing is set up using ControlDesk to observe and measure input/output signals. In order to examine the reliability of HIL platform, primary simulation models built through Matlab/Simulink are replaced by real components, including electric motor and Ni-H battery, with true sensors from practical vehicles. Result confirms a good stability of the HIL platform, which can be flexible to allow extension and reconfiguration.
Design a personalized e-learning system based on item response theory and artificial neural network approach
Authors: Ahmad Baylari, Gh. A. Montazer
Venue: Expert Systems with Applications: An International Journal (2009)

In web-based educational systems the structure of learning domain and content are usually presented in the static way, without taking into account the learners' goals, their experiences, their existing knowledge, their ability (known as insufficient flexibility), and without interactivity (means there is less opportunity for receiving instant responses or feedbacks from the instructor when learners need support). Therefore, considering personalization and interactivity will increase the quality of learning. In the other side, among numerous components of e-learning, assessment is an important part. Generally, the process of instruction completes with the assessment and it is used to evaluate learners' learning efficiency, skill and knowledge. But in web-based educational systems there is less attention on adaptive and personalized assessment. Having considered the importance of tests, this paper proposes a personalized multi-agent e-learning system based on item response theory (IRT) and artificial neural network (ANN) which presents adaptive tests (based on IRT) and personalized recommendations (based on ANN). These agents add adaptivity and interactivity to the learning environment and act as a human instructor which guides the learners in a friendly and personalized teaching environment.
First Specifications of an Information System for Urban Soundscape
Authors: Sylvie Servigne, Robert Laurini, Myoung-Ah Kang, Ki Joune Li
Venue: Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02 (1999)

Sounds are very important in our daily life with a twofold attitude for any citizen. In one hand, when music, sounds are considered as enhancing the quality of life, but in the other hand, traffic noise deteriorates the quality of life. Due to those contradictory characteristics, the new concept of soundscape tries to combine both positive and negative aspects of the auditory environment. Presently, and more and more in the future, any urban planning activities try and must try to diminish noise levels everywhere in the cities and also outside, for instance at the vicinity of airports.Local authorities daily receive complaints regarding noise. But they generally come from very quiet precincts, and practically never from very noisy zones such as along busy highways. So, it is very important to provide the urban decision-makers an objective tool to compare not only noise levels but also soundscape, and to simulate the auditory impacts of any new urban developments.The goal of this paper will be to give the first elements of the design of an information system dedicated to auditory information for cities. We will successively described the importance of urban soundscape, the structure of the information system, some element for the visualization of sounds.
The effect of borderline examples on language learning
Authors: Katia Lida Kermanidis
Venue: Journal of Experimental Theoretical Artificial Intelligence (2009)

Imbalanced training sets, where one or more classes are heavily underrepresented in the training data compared to the others, prove to be problematic when trying to classify new instances that belong to a rare class. In the present article, class imbalance occurs in the datasets of three language learning applications: automatic identification of verb complements, automatic recognition of semantic entities and learning taxonomic relations. All three applications are tested on Modern Greek text corpora; verb complement identification is applied to English corpora also, and comparative results are presented. The difference in statistical behaviour between the classes may be attributed to the low pre-processing level of the corpora, and/or the automatic nature of the pre-processing phase, and/or the exceptional occurrence of the linguistic information of interest in the data. Two different approaches are experimented with in order to deal with the problem: one-sided sampling (OSS) of the dataset and classification using support vector machines (SVMs). OSS removes redundant, noisy and misleading instances of the majority class, reducing thereby the training set size, while SVMs, without removing any instances, take into account only examples that appear close to the borderline region between the classes. The better results achieved by OSS in all test cases lead to some very interesting observations regarding the impact of the various training instances on classification, depending on their position in the feature-vector space.
Exploring factors that influence computer science introductory course students to persist in the major
Authors: Lecia J. Barker, Charlie McDowell, Kimberly Kalahar
Venue: ACM SIGCSE Bulletin (2009)

This paper describes an exploratory study to identify which environmental and student factors best predict intention to persist in the computer science major. The findings can be used to make decisions about initiatives for increasing retention. Eight indices of student characteristics and perceptions were developed using the research-based Student Experience of the Major Survey: student-student interaction; student-faculty interaction; collaborative learning opportunities; pace/workload/prior experience with programming; teaching assistants; classroom climate/pedagogy; meaningful assignments; and racism/sexism. A linear regression revealed that student-student interaction was the most powerful predictor of students' intention to persist in the major beyond the introductory course. Other factors predicting intention to persist were pace/workload/prior experience and male gender. The findings suggest that computer science departments interested in increasing retention of students set structured expectations for student-student interaction in ways that integrate peer involvement as a mainstream activity rather than making it optional or extracurricular. They also suggest departments find ways to manage programming experience gaps in CS1.
An application of a new meta-heuristic for optimizing the classification accuracy when analyzing some medical datasets
Authors: Huy Nguyen Anh Pham, Evangelos Triantaphyllou
Venue: Expert Systems with Applications: An International Journal (2009)

Medical data mining has recently become one of the most popular topics in the data mining community. This is due to the societal importance of the field and also the particular computational challenges posed in this domain of data mining. However, current medical data mining approaches oftentimes use identical costs or just ignore them for the different cases of classification errors. Thus, their outcome may be unexpected. This paper applies a new meta-heuristic approach, called the Homogeneity-Based Algorithm (or HBA), for optimizing the classification accuracy when analyzing some medical datasets. The HBA first expresses the objective as an optimization problem in terms of the error rates and the associated penalty costs. These costs may be dramatically different in medical applications as the implications of having a false-positive and a false-negative case may be tremendously different. When the HBA is combined with traditional classification algorithms, it enhances their prediction accuracy. It does so by using the concept of homogenous sets. Five medical datasets, obtained from the machine learning data repository at the University of California, Irvine (UCI), USA, were tested. Some computational results indicate that the HBA, when it is combined with traditional methods, can significantly outperform current stand-alone data mining approaches.
A Grid-Scan Localization Algorithm for Wireless Sensor Network
Authors: Zhongjiang Yan, Yilin Chang, Zhong Shen, Ying Zhang
Venue: Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 02 (2009)

Location estimation is an essential issue for wireless sensor networks (WSN). In this paper a new localization estimation algorithm for WSN is proposed which is based on the Grid-Scan method. In the proposed algorithm, the node collects its 1-hop and 2-hop anchors before the localization algorithm is waked up. Firstly, it obtains the intersection region of the 1-hop anchors' transmission area as the estimative region of its location. Then, the region is divided into grid array, and each grid is judged by examining the distance between any of the 1-hop and 2-hop anchors and the grid if it possibly is that grid the sensor located in. Finally, the estimated position is got by averaging locations of the possible grids. Simulation results show that the proposed algorithm achieves better accuracy than DLE.
Evaluating CMPs and Their Memory Architecture
Authors: Chris Jesshope, Mike Lankamp, Li Zhang
Venue: Proceedings of the 22nd International Conference on Architecture of Computing Systems (2009)

Many-core processor architectures require scalable solutions that reflect the locality and power constraints of future generations of technology. This paper presents a CMP architecture that supports automatic mapping and dynamic scheduling of threads leaving the binary code devoid of any explicit communication. The thrust of this approach is to produce binary code that is divorced from implementation parameters, yet, which still gives good performance over future generations of CMPs. A key component of this abstract processor architecture is the memory system. This paper evaluates the memory architectures, which must maintain performance across a range of targets.
Improvement of speech recognition for robots using blind signal separation
Authors: Daniel Bicher, Olaf Kroll-Peters, Thebin Lee, Natascha Tiotuico, Mathias Wilhelm
Venue: Proceedings of the 8th conference on Signal processing, computational geometry and artificial vision (2008)

For modern entertainment and service robots, it is desirable to establish good verbal communication with humans. This communication may easily be influenced or even completely suppressed by noise. An often neglected source of noise is that produced by the robot motors. In this paper we will show how to extract the motor noise of a robot, separate it from the desired input, and how to improve the robustness of speech recognition using Blind Source Separation.
Concurrent neural classifiers for pattern recognition in multispectral satellite imagery
Authors: Victor-Emil Neagoe, Gabriel-Eduard Strugaru
Venue: Proceedings of the 12th WSEAS international conference on Computers (2008)

We investigate multispectral satellite image classification using the neural model previously proposed by the first author called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of self-organizing neural network modules. For comparison, we evaluate the performances of several statistical classifiers (Bayes, 1-NN, and K-means). The implemented neural versus statistical classifiers are evaluated using a LANDSAT 7 ETM+ image. One takes in considerations both the interband and intraband pixel correlation using a 63-dimensional representation of the 7-band pixels. There is a subset containing labeled pixels, corresponding to seven thematic categories. The best experimental result leads to the recognition rate of 99.11%.
You must be joking...: the poetry of science
Authors: Michalis Faloutsos
Venue: ACM SIGCOMM Computer Communication Review (2009)

They say that music and mathematics are intertwined. I am not sure this is true, but I always wanted to use the word intertwined. The point is that my call for poetry received a overwhelmingly enthusiastic response from at least five people. My mailbox was literally flooded (I have a small mailbox). This article is a tribute to the poetry of science, or, as I like to call it, the Poetry of Science. You will be amazed.
Hiding distinguished ones into crowd: privacy-preserving publishing data with outliers
Authors: Hui (Wendy) Wang, Ruilin Liu
Venue: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology (2009)

Publishing microdata raises concerns of individual privacy. When there exist outlier records in the microdata, the distinguishability of the outliers enables their privacy to be easier to be compromised than that of regular ones. However, none of the existing anonymization techniques can provide sufficient protection to the privacy of the outliers. In this paper, we study the problem of anonymizing the micro-data that contains outliers. We define the distinguishability-based attack by which the adversary can infer the existence of outliers as well as their private information from the anonymized microdata. To defend against the distinguishability-based attack, we define the plain k-anonymity as the privacy principle. Based on the definition, we categorize the outliers into two types, the ones that cannot be hidden by any plain k-anonymous group (called global outliers) and the ones that can (called local outliers). We propose the algorithm to efficiently anonymize local outliers with low information loss. Our experiments demonstrate the efficiency and effectiveness of our approach.
A New Method for Optical Vector-Matrix Multiplier
Authors: Mei Li, Hua-can He, Yi Jin
Venue: Proceedings of the 2009 International Conference on Electronic Computer Technology (2009)

As a general purpose system in optical computing, optical vector-matrix multiplier (OVMM) can be applied in a variety of fields. In 2007 a novel ternary logic optical processor has been developed in our lab. We combine light intensity and polarization to denote ternary notation and use liquid crystal array to complete the state transformation among ternary values. Here we describe a new method to realize binary vector-matrix multiplication using this optical processor and the advantages of this method are discussed in comparison with the traditional one.
Solving similarity joins and range queries in metric spaces with the list of twin clusters
Authors: Rodrigo Paredes, Nora Reyes
Venue: Journal of Discrete Algorithms (2009)

The metric space model abstracts many proximity or similarity problems, where the most frequently considered primitives are range and k-nearest neighbor search, leaving out the similarity join, an extremely important primitive. In fact, despite the great attention that this primitive has received in traditional and even multidimensional databases, little has been done for general metric databases. We solve two variants of the similarity join problem: (1) range joins: Given two sets of objects and a distance threshold r, find all the object pairs (one from each set) at distance at most r; and (2) k-closest pair joins: Find the k closest object pairs (one from each set). For this sake, we devise a new metric index, coined List of Twin Clusters (LTC), which indexes both sets jointly, instead of the natural approach of indexing one or both sets independently. Finally, we show how to use the LTC in order to solve classical range queries. Our results show significant speedups over the basic quadratic-time naive alternative for both join variants, and that the LTC is competitive with the original list of clusters when solving range queries. Furthermore, we show that our technique has a great potential for improvements.
Exploring potential usability gaps when switching mobile phones: an empirical study
Authors: Aiko Fallas Yamashita, Wolmet Barendregt, Morten Fjeld
Venue: Proceedings of the 21st British CHI Group Annual Conference on HCI 2007: People and Computers XXI: HCI...but not as we know it - Volume 1 (2007)

The present study explores potential usability gaps when users switch from a familiar to an unfamiliar mobile phone interface. A within-subject experiment was performed in which nine users familiar with Sony-Ericsson T630 and nine familiar with Nokia 7250 performed tasks on both phones. On average, test subjects spent more time on finishing tasks with an unfamiliar phone than with a familiar one. For two of the four tasks, there was a significant difference in completion time between the first-time Nokia users and the first-time Sony-Ericsson users. The tasks of adding a contact to the address book and sending an SMS to a contact in the address book were performed more quickly by new Nokia users than by new Sony-Ericsson users. The subjective difficulty ranking also showed that first-time Nokia users found the new phone easier to use than first-time Sony-Ericsson users did. Hierarchical Task Analysis is used as a potential explanation, and three other theories that relate to these findings are presented: mental models, habit errors, and emotional attachment.
Issue Mechanism for Embedded Simultaneous Multithreading Processor
Authors: Chengjie Zang, Shigeki Imai, Steven Frank, Shinji Kimura
Venue: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2008)

Simultaneous Multithreading (SMT) technology enhances instruction throughput by issuing multiple instructions from multiple threads within one clock cycle. For in-order pipeline to each thread, SMT processors can provide large number of issued instructions close to or surpass than using out-of-order pipeline. In this work, we show an efficient issue logic for predicated instruction sequence with the parallel flag in each instruction, where the predicate register based issue control is adopted and the continuous instructions with the parallel flag of '0' are executed in parallel. The flag is pre-defined by a compiler. Instructions from different threads are issued based on the round-robin order. We also introduce an Instruction Queue skip mechanism for thread if the queue is empty. Using this kind of issue logic, we designed a 6 threads, 7-stage, in-order pipeline processor. Based on this processor, we compare round-robin issue policy (RR(T1-Tn)) with other policies: thread one always has the highest priority (PR(T1)) and thread one or thread n has the highest priority in turn (PR(T1-Tn)). The results show that RR(T1-Tn) policy outperforms others and PR(T1-Tn) is almost the same to RR(T1-Tn) from the point of view of the issued instructions per cycle.
A Generalized Approach to Construct Benchmark Problems for Dynamic Optimization
Authors: Changhe Li, Shengxiang Yang
Venue: Proceedings of the 7th International Conference on Simulated Evolution and Learning (2008)

There has been a growing interest in studying evolutionary algorithms in dynamic environments in recent years due to its importance in real applications. However, different dynamic test problems have been used to test and compare the performance of algorithms. This paper proposes a generalized dynamic benchmark generator (GDBG) that can be instantiated into the binary space, real space and combinatorial space. This generator can present a set of different properties to test algorithms by tuning some control parameters. Some experiments are carried out on the real space to study the performance of the generator.
Group actions on gröbner bases of saturated zero-dimensional binomial ideals
Authors: Massimo Caboara, Fabrizio Caruso
Venue: ACM Communications in Computer Algebra (2009)

Fully saturated zero-dimensional binomial ideals (lattice ideals) describe lattices on Zn that are of interest in cryptanalysis. Gröbner basis computation for these ideals are computationally very intensive. We sketch a modified version of Buchberger's algorithm for lattice ideals that are closed with respect to a group action on their indeterminates. The main idea is to exploit the symmetries of such ideals to avoid performing S-polynomial reductions that are "equivalent" with respect to the group action. Using our techniques the number of S-polynomials to be considered can be reduced by a factor up to the order of the group.
A Model Based RL Admission Control Algorithm for Next Generation Networks
Authors: Silvano Mignanti, Alessandro Di Giorgio, Vincenzo Suraci
Venue: Proceedings of the 2008 The Second International Conference on Next Generation Mobile Applications, Services, and Technologies (2008)

In this paper we study the call admission control problem to optimize the network operators' revenue guaranteeing the quality of service to the end users. We consider a network scenario where each class of service is characterized by a different constant bit rate and an associated revenue. We formulate the problem as a Semi-Markov Decision Process, and we use a model based Reinforcement Learning approach. Other traditional algorithms require an explicit knowledge of the state transition models while our solution learns it on-line. We will show how our policy provides better solution than a classic greedy algorithm.
Sliding mode control design for uncertain delay systems with partial actuator degradation
Authors: Yugang Niu, Xingyu Wang
Venue: International Journal of Systems Science (2009)

This article is concerned with the design of sliding mode control for uncertain state-delayed systems with partial actuator degradation. In the systems under consideration, there are parameter uncertainties in their state matrices and possible variations in the output of the actuators. In this work, a model of actuator faults is adopted such that both normal operation and partial actuator degradation are covered. A sufficient condition is derived such that the resultant sliding mode control system is asymptotically stable despite partial actuator degradation. Furthermore, it is shown that when a couple of linear matrix inequalities are feasible, the design of the sliding mode controller can be easily obtained via convex optimisation.