ppxscal/embedding_network huggingface dataset:
Network Structure Cascade for Reversible Logic
Authors: Zhijin Guan, Xiaolin Qin, Yiqing Zhang, Quan Shi
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

A reversible logic function maps each of the 2^m input patterns to a unique output pattern. The network cascade structure problem is to realize a reversible function by a cascade of primitive reversible gates. We present a method that synthesizes a network with family of Toffoli gates. The cascade algorithm finds cascade of Toffoli gates with no backtracking and minimal look-ahead, and apply transformations that reduce the number of gates in the network. We have synthesized all three input and output reversible functions and compare our results to the other optimal results. We also present the results of applying our synthesis method to obtain networks for number of benchmark function.
Visualization-informed noise elimination and its application in processing high-spatial-resolution remote sensing imagery
Authors: Yu Qian, Fang Qiu, Jie Chang, Kang Zhang
Venue: Computers Geosciences (2008)

Noise removal is perhaps one of the most fundamental and challenging tasks for extracting useful information from a spatial data set. One of the challenges is that there is no general agreement on the definition of noise that can be universally applied to all different domains. This paper proposes a novel technique called Visualization-Informed Noise Elimination (VINE) to support a customized noise removal through incorporation of domain knowledge. The VINE technique consists of three steps of consecutive operations. First, a k-mutual neighbor graph is derived from a spatial data set to model the spatial proximity among data points. Next, a fast partitioning method is employed to reassemble graph nodes into groups. Last, a 3-dimensional (3D) visualization model is created to provide a layered view of the partitioned data, which allows an informed identification and elimination of noise by tailoring to the requirements of a specific domain. The flexibility and customizability provided by this novel technique ensures an effective differentiation of noise from valid data and demonstrates various advantages over traditional methods with improved results. When adapted in post-classification smoothing of high-spatial-resolution remotely sensed images, this approach was able to discover and reassign noise (such as shadows often seen in high-spatial-resolution images) to its proper target class. By incorporating domain knowledge and making use of spatial contextual information, the VINE technique could produce results significantly superior to existing approaches such as majority filter and size-based noise removal.
Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis
Authors: Valentin Dinu, Hongyu Zhao, Perry L. Miller
Venue: Journal of Biomedical Informatics (2007)

Genome-wide association studies can help identify multi-gene contributions to disease. As the number of high-density genomic markers tested increases, however, so does the number of loci associated with disease by chance. Performing a brute-force test for the interaction of four or more high-density genomic loci is unfeasible given the current computational limitations. Heuristics must be employed to limit the number of statistical tests performed. In this paper we explore the use of biological domain knowledge to supplement statistical analysis and data mining methods to identify genes and pathways associated with disease. We describe Pathway/SNP, a software application designed to help evaluate the association between pathways and disease. Pathway/SNP integrates domain knowledge-SNP, gene and pathway annotation from multiple sources-with statistical and data mining algorithms into a tool that can be used to explore the etiology of complex diseases.
A multi-channel MAC implementation for wireless sensor networks
Authors: Youngmin Kim, Hyojeong Shin, Hojung Cha
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

Sensor nodes are typically battery powered and operate in unattended environments. Minimizing the energy consumption of sensor nodes is, therefore, important to prolong the network life time. Since the radio is a main energy consumer, most of the Medium Access Control (MAC) protocols for wireless sensor networks (WSNs) have focused on energy efficiency. With widespread use of sensor network applications, network bandwidth required by the applications tends to increase and even multiple applications may run in a sensor node. For these reasons, maximizing the bandwidth of WSNs has become a major consideration in MAC design.
Securing Designs against Scan-Based Side-Channel Attacks
Authors: Jeremy Lee, Mohammad Tehranipoor, Chintan Patel, Jim Plusquellic
Venue: IEEE Transactions on Dependable and Secure Computing (2007)

Traditionally, the only standard method of testing that has consistently provided high fault coverage has been scan test due to the high controllability and high observability this technique provides. The scan chains used in scan test not only allow test engineers to control and observe a chip, but these properties also allow the scan architecture to be used as a means to breach chip security. In this paper, we propose a technique, called Lock & Key, to neutralize the potential for scan-based side-channel attacks. It is very difficult to implement an all inclusive security strategy, but by knowing the attacker, a suitable strategy can be devised. The Lock & Key technique provides a flexible security strategy to modern designs without significant changes to scan test practices. Using this technique, the scan chains are divided into smaller subchains. With the inclusion of a test security controller, access to subchains are randomized when being accessed by an unauthorized user. Random access reduces repeatability and predictability making reverse engineering more difficult. Without proper authorization, an attacker would need to unveil several layers of security before gaining proper access to the scan chain in order to exploit it. The proposed Lock & Key technique is design independent while maintaining a relatively low area overhead.
Positive solutions of m-point boundary value problems for second order differential equations with an advanced argument
Authors: Yanping Guo, Yunhai Wang, Changlong Yu
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

In this paper, by using the fixed point index method, we establish the existence of at least one or at least two positive solutions to m-point boundary value problem for the second order differential equation with an advanced argument { u^" (t) + a(t)f(u(h(t))) = 0, t \in (0,1),u(0) = 0, \sum\limits_{i = 1}^{m - 2} {k_i u(\xi _i ) = u(1),}where 0 = \xi_0 \le \xi_1 \le \cdot \cdot \cdot \le \xi_{m-2} \le \xi_{m-1} = 1.
A Highly Efficient Multi-objective Optimization Evolutionary Algorithm
Authors: Bojin Zheng
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

Multi-objective Optimization Evolutionary Algorithms (MOEAs) are effective for solving Multi-objective Optimization Problems. Here a new algorithm is proposed and is compared with some famous MOEAs at the state of the art. The experimental results imply that the approximated Pareto Fronts which are obtained by this algorithm are better than the approximated Pareto Fronts by SPEA2, NSGAII etc. when dealing with the chosen test problems within satisfactory computational time.
Efficiently determining a locally exact shortest path on polyhedral surfaces
Authors: Shi-Qing Xin, Guo-Jin Wang
Venue: Computer-Aided Design (2007)

In this paper, we present an efficient visibility-based algorithm for determining a locally exact shortest path (LESP) from a source point to a destination point on a (triangulated) polyhedral surface. Our algorithm, of a finitely-iterative scheme, evolves an initial approximately shortest path into a LESP. During each iteration, we first compute the exact shortest path restricted on the current face sequence according to Fermat's principle which affirms that light always follows the shortest optical path, and then optimize the face sequence where the path is not locally shortest on the polyhedral surface. Since the series of paths we obtained are monotonic decreasing in length, the algorithm gives a LESP which is shorter than the initial path, at conclusion. For comparison, we use various methods to provide an initial path. One of the methods is Dijkstra's algorithm, and the others are the Fast Marching Method (FMM) and its improved version. Our intention for improvement is to overcome the limitation of acute triangulations in the original version. To achieve this goal, we classify all the edges into seven types according to different wavefront propagation manners, and dynamically determine the type of each edge for controlling the subsequent wavefront expansion. Furthermore, we give two approaches for backtracing the approximately shortest paths directed at the improved FMM. One exploits the known propagation manners of the edges as well as the Euler's method. This is another contribution in this paper.
A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis
Authors: Seral Özşen, Sadık Kara, Fatma Latifoğlu, Salih Güneş
Venue: Computer Methods and Programs in Biomedicine (2007)

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.
Performance Predictions for General-Purpose Computation on GPUs
Authors: Weiguo Liu, Wolfgang Muller-Wittig, Bertil Schmidt
Venue: Proceedings of the 2007 International Conference on Parallel Processing (2007)

Using modern graphics processing units for no-graphics high performance computing is motivated by their enhanced programmability, attractive price/performance ratio and incredible growth in speed. Although the pipeline of a modern graphics processing unit (GPU) permits high throughput and more concurrency, they bring more complexities in analyzing the performance of GPU-based applications. In this paper, we identify factors that determine performance of GPU-based applications. We then classify them into three categories: data-linear, data-constant and computation-dependent. According to the characteristics of these factors, we propose a performance model for each factor. These models are then used to predict the performance of bio-sequence database scanning application on GPUs. Theoretical analyses and measurements show that our models can achieve precise performance predictions.
Decor: a tool for the detection of design defects
Authors: Naouel Moha, Yann-Gael Guéhéneuc
Venue: Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (2007)

Software engineers often need to identify design defects, recurring design problems that hinder the development process, to improve and assess the quality of their systems. However, this is diÂ±cult because of the lack of specifications and tools. We propose Decor, a method to specify design defects systematically and to generate automatically detection algorithms. With this method, software engineers analyse and specify design defects at a high-level of abstraction using a unified vocabulary and dedicated language for generating detection algorithms
Separating Models of Learning from Correlated and Uncorrelated Data
Authors: Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andrew Wan
Venue: The Journal of Machine Learning Research (2007)

We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possible examples. A recent paper by Bshouty et al. (2003) shows that the class of polynomial-size DNF formulas is efficiently learnable in this random walk model; this result suggests that the Random Walk model is more powerful than comparable standard models of learning from independent examples, in which similarly efficient DNF learning algorithms are not known. We give strong evidence that the Random Walk model is indeed more powerful than the standard model, by showing that if any cryptographic one-way function exists (a universally held belief in cryptography), then there is a class of functions that can be learned efficiently in the Random Walk setting but not in the standard setting where all examples are independent.
Searchius: A Collaborative Search Engine
Authors: Athanasios Papagelis, Christos Zaroliagis
Venue: Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science (2007)

Searchius is a collaborative search engine that produces search results based solely on user provided web-related data. We discuss the architecture of this system and how it compares to current state-of-the-art search engines. We show that the global users' preference over pages can be efficiently used as a metric of page quality, and that the inherent organization of the collected data can be used to discover related URLs. We also conduct an extensive experimental study, based on the web related data of 36483 users, to analyze the qualitative and quantitative characteristics of user collected URL collections, to investigate how well the users URL collections cover the web and discover the characteristics that affect the quality of the search results under the proposed setting.
Microsystems PLZ: A Family of System Programming Languages for Microprocessors
Authors: C. Bass
Venue: Computer (1978)

There is an art to the practice of any science, a point argued cogently by Donald E. Knuth,1author of the series of volumes deliberately titled The Art of Computer Programming. If "science" is knowledge which has been logically arranged and systematically codified, then "art" refers to the use of personal skill, guided by a sense of aesthetics, in applying these organized principles, whether they describe engineering, physics, mathematics, or computer programming. For, at a given stage in the translation of an art into the organized body of the corresponding science, that which is still art contains intuitive and aesthetic factors which defy precise formalization. Computer programming- with its scope extending from "arty" foklore to science-based automatic code generation and verification-is a prime example of these subtle differences. Now widely called computer science, the art of programming continues to challenge and often baffle the most scientific of managers and "computer scientists." 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
Interaction based on function of a table in real world with "Kage no Sekai"
Authors: Yu Uchida, Mami Naito, Shiho Hirayama, Masa Inakage
Venue: Proceedings of the 2nd international conference on Digital interactive media in entertainment and arts (2007)

Our research is about an interaction based on the function of a table in real world. A tabletop type display is very popular these days, and most of it focuses on the process of presenting information and the interaction with people. These, however, are forgetting the function as a table. The function of a table is related to various purposes in using tabletop such as studying, and it is necessary for our daily life. Therefore in this research, we looked at the characteristics of the table. The content "Kage no Sekai" (Figure1) provides information through shadows that are not distracting while work. This enables the users to keep working without being so much conscious of the digital world. Moreover, it is fully possible for this content to be placed in daily use such as information desks, for it consists of only light and shadow.
CONFIGR: A vision-based model for long-range figure completion
Authors: Gail A. Carpenter, Chaitanya Sai Gaddam, Ennio Mingolla
Venue: Neural Networks (2007)

CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the ''early vision'' stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. In the CONFIGR algorithm, the smallest independent image unit is the visible pixel, whose size defines a computational spatial scale. Once the pixel size is fixed, the entire algorithm is fully determined, with no additional parameter choices. Multi-scale simulations illustrate the vision/recognition system. Open-source CONFIGR code is available online, but all examples can be derived analytically, and the design principles applied at each step are transparent. The model balances filling-in as figure against complementary filling-in as ground, which blocks spurious figure completions. Lobe computations occur on a subpixel spatial scale. Originally designed to fill-in missing contours in an incomplete image such as a dashed line, the same CONFIGR system connects and segments sparse dots, and unifies occluded objects from pieces locally identified as figure in the initial recognition stage. The model self-scales its completion distances, filling-in across gaps of any length, where unimpeded, while limiting connections among dense image-figure pixel groups that already have intrinsic form. Long-range image completion promises to play an important role in adaptive processors that reconstruct images from highly compressed video and still camera images.
A Novel Strategy of Coalition Evolvement Based on Interest
Authors: Xiang Yin, Jianguo Jiang, Na Xia
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

Cooperating and sharing resources by creating coalitions of agents are important ways for autonomous agents to execute tasks and to maximize their payoff. However coalition formation is a complex process which usually causes a mass of computation and communication. In this paper, we focus on situations where time is limited and coalitions may be formed as soon as possible. To address this problem, we have developed a novel strategy of coalition evolvement based on fuzzy interest and provide it with a modification rule. By theoretical analysis, we show the strategy is simple, efficient and easy to implement.
Software pipelining of loops by the method of modulo scheduling
Authors: N. I. V'Yukova, V. A. Galatenko, S. V. Samborskii
Venue: Programming and Computing Software (2007)

Software pipelining is an efficient method of loop optimization that allows for parallelism of operations related to different loop iterations. Currently, most commercial compilers use loop pipelining methods based on modulo scheduling algorithms. This paper reviews these algorithms and considers algorithmic solutions designed for overcoming the negative effects of software pipelining of loops (a significant growth in code size and increase in the register pressure) as well as methods making it possible to use some hardware features of a target architecture. The paper considers global-scheduling mechanisms allowing one to pipeline loops containing a few basic blocks and loops in which the number of iterations is not known before the execution.
An architecture for xml information retrieval in a peer-to-peer environment
Authors: Judith Winter, Oswald Drobnik
Venue: Proceedings of the ACM first Ph.D. workshop in CIKM (2007)

XML has become a widely accepted standard for modelling, storing, and exchanging structured documents. Taking advantage of the document structure can result in improving the retrieval performance of XML-documents notably. A growing number of these documents are stored in Peer-to-Peer networks, which are promising self-organizing infrastructures. Documents are distributed over the Peer-to-Peer network by either being stored locally on individual peers or by being assigned to collections such as Digital Libraries. Current search methods for XML-documents in Peer-to-Peer networks lack the use of Information Retrieval techniques for vague queries and relevance detection. Our work aims for the development of a search engine for XML-documents, where Information Retrieval methods are enhanced by using structural information. Documents and global index are distributed over a Peer-to-Peer Network, building a virtually unlimited storage space. In this paper, a conceptual architecture for XML Information Retrieval in Peer-to-Peer networks is proposed. Based on this general architecture, a component-structured architecture for a concrete search engine is presented, which uses an extension of the Vector Space Model to compute relevance for dynamic XML-documents.
A Snapshot of the State of Practice in Software Development for Medical Devices
Authors: Christian Denger, Raimund L. Feldmann, Martin Host, Christin Lindholm, Forrest Shull
Venue: Proceedings of the First International Symposium on Empirical Software Engineering and Measurement (2007)

The medical device industry is a constantly growing domain which makes use of more and more software products. Given the importance to this industry of dependable software components, rigorous software engineering techniques would seem to have an important role to play. However, in a recent survey of the industry we found a lower than expected rate of adoption of certain sound software engineering practices. To ensure and improve the quality of developed software products, whether they are stand-alone applications or embedded in complex systems, both the medical device industry as well as software engineers have to take action. Our survey is a first step in this direction and may help in identifying appropriate programs and future research topics.