ppxscal/embedding_network huggingface dataset:
The von Neumann Syndrome and the CS Education Dilemma
Authors: Reiner Hartenstein
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

Computing the von Neumann style is tremendously inefficient because multiple layers of massive overhead phenomena often lead to code sizes of astronomic dimensions, thus requiring large capacity slow off-chip memory. The dominance of von-Neumann-based computing will become unaffordable during next decade because of growing very high energy consumption and increasing cost of energy. For most application domains a von-Neumann-based parallelization does not scale well, resulting in the escalating many-core programming crisis by requiring complete remapping and re-implementation--often promising only disappointing results. A sufficiently large population of manycore-qualified programmers is far from being available. Efficient solutions for the many-core crisis are hardly possible by fully instruction-stream-based approaches. Several HPC celebrities call for a radical re-design of the entire computing discipline. The solution is a dual paradigm approach, which includes fundamental concepts known already for a long time from Reconfigurable Computing. Whistle blowing is overdue, since these essential qualifications for our many-core future and for low energy computing are obstinately ignored by CE, CS and IT curriculum task forces. This talk also sketches a road map. 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
Automated Model Building: From Finite to Infinite Models
Authors: Nicolas Peltier
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

We propose a method for using existing finite model builders for constructing infinite models of first-order formulae. The considered interpretations are represented by tree tuple automata. Our approach is based on formula transformation. It is proven to be sound (i.e. all the constructed interpretations are models of the original formula) and complete for the considered class of interpretations (i.e. a model is eventually built for any formula having a model representable by a tree automaton).
On the Computational Power of Flip-Flop Proteins on Membranes
Authors: Shankara Narayanan Krishna
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

P Systems with proteins on membranes were introduced recently by A.Paun and B. Popa, in an effort to bridge the gap between membrane computing and brane calculi. In this variant, one considers multisets of objects inside the membranes as well as proteins on the membranes. The action of the proteins on the objects is classified broadly into 5 categories. In this paper, we study the computational power of these actions and come up with upper and lower bounds in terms of computational power for some of them.
Adaptively Embedding Binary Data in an Image
Authors: Ching-Chiuan Lin, Nien-Lin Hsueh, Wen-Hsiang Shen
Venue: Fundamenta Informaticae (2008)

In this paper, we propose an embedding algorithm, of high visual quality, that can adaptively embed a binary message into an image. The binary message to be embedded is divided into two segments, each of which is then decomposed into n + 1 or n types of sub-messages (where n &ges; 3 enables each pixel to embed a sub-message), respectively, according to the desired embedding capacity. Embedding is done by leaving the pixel value unchanged or changing it into one of its n or n &minus; 1 neighboring values according to the type of the sub-message. From the results of this study, each pixel may not embed a fixed number of message bits and the adjustment of the pixel value is minimal, thus the image quality is significantly improved by adaptively decomposing the message into sub-messages and embedding them into the host image.
Kohonen networks as hydroacoustic signatures classifier
Authors: Andrzej Zak
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

The paper presents the technique of artificial neural networks used as classifier of hydroacoustic signatures generated by moving ship. In the paper firstly the method of feature extraction from hydroacoustic signatures using calculation of Mel-Frequency Cepstral Coefficients was discussed. Next the method of feature matching using for purpose of object classification basing on hydroacoustic signatures was described. The technique of artificial neural networks especially Kohonen networks which belongs to group of self organizing networks where chosen to solve the research problem of classification. The choice was caused by some advantages of mentioned kind of neural networks like: they are ideal for finding relationships amongst complex sets of data, they have possibility to self expand the set of answers for new input vectors. To check the correctness of classifier work the research in which the number of right classification for presented and not presented before hydroacoustic signatures were made. Some results of research were presented on this paper.
Ant Colony Optimization with Castes
Authors: Oleg Kovářík, Miroslav Skrbek
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

Ant Colony Optimization (ACO) is a nature inspired metaheuristic for solving optimization problems. We present a new general approach for improving ACO adaptivity to problems, Ant Colony Optimization with Castes (ACO+C). By using groups of ants with different characteristics, known as castes in nature, we can achieve better results and faster convergence thanks to possibility to utilize different types of ant behaviour in parallel. This general principle is tested on one particular ACO algorithm: ${\cal MAX}-{\cal MIN}$ Ant System solving Symmetric and Asymmetric Travelling Salesman Problem. As experiments show, our method brings a significant improvement in the convergence speed as well as in the quality of solution for all tested instances.
A Navigation System for Brain Surgery Using Computer Vision Technology
Authors: Jiann-Der Lee, Chung-Wei Lin, Chung-Hsien Huang, Shin-Tseng Lee, Chien-Tsai Wu
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

In this paper, a surgical navigation system based on stereo-vision has been proposed. With the help of a projector, surface point data of a patient are firstly captured from two cameras by using the stereo-vision technique. Next, these point data is then registered by another surface point data obtained from the patient's pre-stored CT images using Feature-added ICP algorithm. For surgical navigation, a stereo-vision-based tracker is also designed in this system. In this manner, the anatomical information obtained from CT is fused on the images captured by the cameras and the tracker can be mounted to any surgical device for further surgical applications. Compared to conventional image guided surgeries, the proposed system has the advantage of fusing the visual and anatomical information such that it is more accurate and convenient.
When the Spatial Networks Split?
Authors: Joanna Natkaniec, Krzysztof Kułakowski
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

We consider a three dimensional spatial network, where N nodes are randomly distributed within a cube L×L×L. Each two nodes are connected if their mutual distance does not excess a given cutoff a. We analyse numerically the probability distribution of the critical density $\rho_c=N(a_c/L)^3$, where one or more nodes become separated; ρ c is found to increase with N as N 0.105, where N is between 20 and 300. The results can be useful for a design of protocols to control sets of wearable sensors.
Learning Similarity Functions from Qualitative Feedback
Authors: Weiwei Cheng, Eyke Hüllermeier
Venue: Proceedings of the 9th European conference on Advances in Case-Based Reasoning (2008)

The performance of a case-based reasoning system often depends on the suitability of an underlying similarity (distance) measure, and specifying such a measure by hand can be very difficult. In this paper, we therefore develop a machine learning approach to similarity assessment. More precisely, we propose a method that learns how to combine given local similarity measures into a global one. As training information, the method merely assumes qualitative feedback in the form of similarity comparisons, revealing which of two candidate cases is more similar to a reference case. Experimental results, focusing on the ranking performance of this approach, are very promising and show that good models can be obtained with a reasonable amount of training information.
An OSGI-Based Model for Remote Management of Residential Gateways
Authors: Mario Ibáñez, Natividad Martínez Madrid, Ralf Seepold, Willem Willigenburg, Harold Balemans
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

Devices connected to the home network demand in many cases services which are located outside the home network. For this reason one of the most important elements in the home network is the Residential Gateway. It will manage all subscribed services, work as central point to control all devices of the home network and provide an integration platform for various internal and external services. This paper proposes a solution based on OSGI and TR-069 to support auto-configuration of a residential gateway located in home network.
Built-In Self-Test Trends in Motorola Microprocessors
Authors: R. Daniels, William Bruce
Venue: IEEE Design Test (1985)

The first built-in self-test feature in a Motorola sidered a ?wart? until a RAM test application recast it as a ? feature.?Though the BIST approach?an idea conceived as a way to reduce production costs for the MC6805 family?did not meet its majordesign objective, the experience provided impetus for the development of BIST techniques for the MC6804P2, which met mostof the objectives intended for the MC6805P2. The Motorola microprocessor family has come to incorporate a growing number oftestability features; current devices typically employ a combination of BIST and other techniques. If present trends continue,transistor counts for microprocessor-related parts should approach 10 million within 10 years. The authors argue that structureddesign techniques offer the most promising prospects for solving the design and test problems resulting from this increasein complexity.
Multi-person Tracking Strategies Based on Voxel Analysis
Authors: C. Canton-Ferrer, J. Salvador, J. R. Casas, M. Pardàs
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

This paper presents two approaches to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited to generate a discrete 3D binary representation of the foreground objects in the scene. Color information obtained from a zenithal camera view is added to this 3D information. The first tracking approach implements heuristic association rules between blobs labelled according to spatiotemporal connectivity criteria. Association rules are based on a cost function which considers their placement and color histogram. In the second approach, a particle filtering scheme adapted to the incoming 3D discrete data is proposed. A volume likelihood function and a discrete 3D re-sampling procedure are introduced to evaluate and drive particles. Multiple targets are tracked by means of multiple particle filters and interaction among them is modeled through a 3D blocking scheme. Evaluation over the CLEAR 2007 database yields quantitative results assessing the performance of the proposed algorithm for indoor scenarios.
Introducing a Two Dimensional Measure for Watermarking Capacity in Images
Authors: Farzin Yaghmaee, Mansour Jamzad
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

One of the most important parameters in evaluating a watermarking algorithm is its capacity. Of course capacity has a paradoxical relation with two other important parameters: image quality and robustness. Generally, watermarking capacity is expressed by bit per pixel (bpp) unit measure. This measure does not show what the side effects would be on image quality and capacity. Therefore, we proposed a two dimensional measure named capacity curve which shows the effects of capacity on visual quality. In this measure we can find the amount of degradation by a numerical value for each modification in pixels. Our experimental results are compatible with previous works in this field.
Appliance Design for Pervasive Computing
Authors: George Roussos
Venue: IEEE Pervasive Computing (2003)

The First International Conference on Appliance Design offered the opportunity for computer scientists, electronic engineers, designers, architects, and business strategists to discuss and to blend all the perspectives of design physical, functional, interaction, graphical, and information of pervasive computing systems and infrastructures.
Meeting the data challenge: curriculum development for parallel data systems
Authors: Thomas J. Hacker, John A. Springer
Venue: Proceedings of the 9th ACM SIGITE conference on Information technology education (2008)

The emergence of commodity-based high performance computing systems and low-cost storage systems in concert with the continued proliferation of data has created a significant need for technologists with expertise in parallel data systems. The training in this area, though, falls outside the traditional boundaries of the data management curriculum. In this paper, we describe our efforts in developing a new course focused on parallel data systems, which exploit the power of high performance computing and commodity hardware to deliver high throughput and well-scaled storage systems. We describe in detail the trends and forces driving the need for this course, the topics to be covered in this course, the data laboratory to be used with the course, assessment methods to measure student progress, and desired learning outcomes for the course.
A Method for Automatic Membership Function Estimation Based on Fuzzy Measures
Authors: Grzegorz Nieradka, Bohdan Butkiewicz
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

Estimation of membership function is one of the most important problems in the application of fuzzy sets. This paper presents one of approaches to this problem. A method for estimation of membership function is proposed, based on fuzzy measures: fuzzy entropy and fuzzy index. Examples of generating membership function in the field of image processing are shown.The method presented in this paper can be used in other fields of computer sciences, where statistical data are available.
Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms
Authors: Simon Price, Peter Flach
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

Integrating heterogeneous data from sources as diverse as web pages, digital libraries, knowledge bases, the Semantic Web and databases is an open problem. The ultimate aim of our work is to be able to query such heterogeneous data sources as if their data were conveniently held in a single relational database. Pursuant to this aim, we propose a generalisation of joins from the relational database model to enable joins on arbitrarily complex structured data in a higher-order representation. By incorporating kernels and distances for structured data, we further extend this model to support approximate joins of heterogeneous data. We demonstrate the flexibility of our approach in the publications domain by evaluating example approximate queries on the CORA data sets, joining on types ranging from sets of co-authors through to entire publications.
Trusting Groups in Coalition Formation Using Social Distance
Authors: Peter Shaw, Paul Sage, Peter Milligan
Venue: Proceedings of the 5th international conference on Autonomic and Trusted Computing (2008)

In environments where distributed team formation is key, and defections are possible, the use of trust as social capital allows social norms to be defined and compared. An agent can use this information, when invited to join a group or coalition, to decide whether or not its utility will be increased by joining. In this work a social network approach is used to define and reason about the relationships contained in the agent community. Previous baseline work is extended with two decision making mechanisms. These are compared by simulating an abstract grid-like network, and preliminary results are reported.
Improving application and understanding of experience packages through learning spaces
Authors: Eric Ras
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

Understanding and applying software engineering experience packages is often problematic because people lack the necessary background knowledge or because the information in an experience package is not described adequately. The learning space approach automatically enriches experience packages with additional learning content and contextual information. The results of a two-factorial counter-balanced within-subject experiment with 19 students reveal that learning spaces significantly improve (p
Regularization Paths for ν-SVM and ν-SVR
Authors: Gaëlle Loosli, Gilles Gasso, Stéphane Canu
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

This paper presents the &nu;-SVM and the &nu;-SVR full regularization paths along with a leave-one-out inspired stopping criterion and an efficient implementation. In the &nu;-SVR method, two parameters are provided by the user: the regularization parameter C and &nu; which settles the width of the &nu;-tube. In the classical &nu;-SVM method, parameter &nu; is an lower bound on the number of support vectors in the solution. Based on the previous works of [1,2], extensions of regularization paths for SVM and SVR are proposed and permit to automatically compute the solution path by varying &nu; or the regularization parameter.
Automated module assignment in stacked-Vdd designs for high-efficiency power delivery
Authors: Yong Zhan, Sachin S. Sapatnekar
Venue: ACM Journal on Emerging Technologies in Computing Systems (JETC) (2008)

With aggressive reductions in feature sizes and the integration of multiple functionalities on the same die, bottlenecks due to I/O pin limitations have become a critical issue in today's VLSI designs, especially for 3D IC technologies. To alleviate the pin limitation problem, a stacked-Vdd circuit paradigm has recently been proposed in the literature. However, for a circuit designed using this paradigm, a significant amount of power may be wasted if modules are not carefully assigned to different Vdd domains. In this article, we present a partition-based algorithm for efficiently assigning modules at the floorplanning level, so as to reuse currents between Vdd domains and minimize the power wasted during the operation of the circuit. Experimental results on both 3D and 2D ICs show that compared with assigning modules to different Vdd domains using enumeration and simulated annealing, our algorithm can generate circuits with competitive power and IR noise performance, while being orders of magnitude faster.
A Fuzzy Hahn-Banach Theorem
Authors: Wesley Kotzé, Andrew Pinchuck
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

A fuzzy version of the Hahn-Banach theorem is proved based on the classical result. A comparison is also drawn with an earlier published result in this connection.
Self-Organizing Neural Grove: Efficient Multiple Classifier System with Pruned Self-Generating Neural Trees
Authors: Hirotaka Inoue
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

Multiple classifier systems (MCS) have become popular during the last decade. Self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of the simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNT. In an earlier paper, we proposed a pruning method for the structure of the SGNT in the MCS to reduce the computation cost. In this paper, we propose a novel pruning method for more effective processing and we call this model as self-organizing neural grove (SONG). The pruning method is constructed from an on-line pruning method and an off-line pruning method. Experiments have been conducted to compare the SONG with an unpruned MCS based on SGNT, an MCS based on C4.5, and k-nearest neighbor method. The results show that the SONG can improve its classification accuracy as well as reducing the computation cost.
Processing the Mixed Properties of Light Verb Constructions
Authors: Jong-Bok Kim, Kyung-Sup Lim
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

One of the most widely used constructions in Korean is the so-called light verb construction (LVC) involving an active-denoting verbal noun (VN) together with the light verb ha-ta `do'. This paper first discusses the argument composition of the LVC, mixed properties of VNs which have provided a challenge to syntactic analyses with a strict version of X-bar theory. The paper shows the mechanism of multiple classification of category types with systematic inheritance can provide an effective way of capturing these mixed properties. An implementation of the analysis within the LKB (Linguistics Knowledge Building) system proves its feasibility and efficiency.
Improving Chinese Pronominal Anaphora Resolution by Extensive Feature Representation and Confidence Estimation
Authors: Tyne Liang, Dian-Song Wu
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

Pronominal anaphora resolution denotes antecedent identification for anaphoric pronouns expressed in discourses. Effective resolution relies on the kinds of features to be concerned and how they are appropriately weighted at antecedent identification. In this paper, a rich feature set including the innovative discourse features are employed so as to resolve those commonly-used Chinese pronouns in modern Chinese written texts. Moreover, a maximum-entropy based model is presented to estimate the confidence for each antecedent candidate. Experimental results show that our method achieves 83.5% success rate which is better than those obtained by rule-based and SVM-based methods.
An Extremely Simple Reinforcement Learning Rule for Neural Networks
Authors: Xiaolong Ma
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

In this paper we derive a simple reinforcement learning rule based on a more general form of REINFORCE formulation. We test our new rule on both classification and reinforcement problems. The results have shown that although this simple learning rule has a high probability of being stuck in local optimum for the case of classification tasks, it is able to solve some global reinforcement problems (e.g. the cart-pole balancing problem) directly in the continuous space.