ppxscal/embedding_network huggingface dataset:
Demand Assignment Multiple Access Schemes in Broadcast Bus Local Area Networks
Authors: M. Fine
Venue: IEEE Transactions on Computers (1984)

Local area communications networks based on packet broadcasting techniques provide simple architectures and efficient and flexible operation. Various ring systems and CSMA contention bus systems have been in operation for several years. More recently, a number of distributed demand assignment multiple access (DAMA) schemes suitable for broadcast bus networks have emerged which provide conflict-free broadcast communications by means of various scheduling techniques. Among these schemes, the Token-Passing Bus Access method uses explicit tokens, i.e., control messages, to provide the required scheduling. Others use implicit tokens, whereby stations in the network rely on information deduced from the activity on the bus to schedule their transmissions. In this paper we present many implicit-token DAMA schemes in a unified manner grouped according to their basic access mechanisms, and compare them in terms of performance and other important attributes.
A Statistical Method for People Counting in Crowded Environments
Authors: Massimiliano Bozzoli, Luigi Cinque
Venue: Proceedings of the 14th International Conference on Image Analysis and Processing (2007)

In this paper we present the results of a two-years research project on automatic people counting in public crowded environments. The aim of the proposed system is to estimate the number of people passing through a gate in a public area such as a metro or a railway station. The problem is particularly challenging due to both the presence of crowd which makes it difficult the use of previous systems based on detection of isolated passengers and to the high level of statistic accuracy requested by traffic monitoring applications (error rate less then 5%).
Optimization of the AUC Criterion for Rule Subset Selection
Authors: Celso Y. Ishida, Aurora T. R. Pozo
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

The area under the ROC curve (AUC) is considered a relevant criterion to deal with imbalanced data, misclassi- fication costs and noisy data. Based on this preference, we present an algorithm for rule subset selection. The algo- rithm builds a Pareto Front using the Sensitivity and Speci- ficity criteria selecting rules from a large set of rules. An empirical study is carried out to verify the influence of the Apriori Parameter in Pareto Front Elite Algorithm. We com- pare our results with other rule induction algorithms and the results show that the new algorithm obtains a set of rules with greater values of the AUC.
Quality Assurance in Future Development Environments
Authors: J. P. Cavano
Venue: IEEE Software (1987)

SQA will evolve into a broader software quality technology, shifting from a passive process to an active one, from fault detection to fault avoidance.
LQRD: An Improved ECN Algorithm
Authors: Jun Lai, Wu Ye, Sui-li Feng
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

In this paper the performance of the gateway using ECN (Explicit Congestion Notification) is analyzed firstly. Study shows that the recommendation in the protocol that gateway drops packets when the average queue size exceeds the upper threshold brings too low throughput. However, if it is be substituted for marking packets, which increases average delay and induces parts of fail connections for heavy loads, the throughput will improve greatly. To make sure high throughput, low delay and robust connectivity for long queue length, this paper presents an improved algorithm named LQRD (Long Queue Random Drop). The algorithm drops packets with a certain probability when the average queue size exceeds the upper threshold to limit the increase of queue length. The drop probability is computed according to buffer size, link capacity, average queue length and average queue delay to reflect load changing. Then we analyze the solved model of the LQRD algorithm by using differential equation. And at last, some simulation experiments show the algorithm has good performance.
A Wideband CMOS LC-VCO Using Variable Inductor
Authors: K. Ohashi, Y. Ito, Y. Yoshihara, K. Okada, K. Masu
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

This paper proposes a wide-range tunable CMOS voltage controlled oscillator (VCO). VCO uses an on-chip variable inductor and switched capacitors as variable elements. The VCO was fabricated using a standard 0.18 mum CMOS process with five metal layers. The oscillation frequency can be tuned from 1.28 GHz to 2.75 GHz with tuning range of 72 %.
Answering English Questions using Foreign-Language, Semi-Structured Sources
Authors: Boris Katz, Gary Borchardt, Sue Felshin, Yuan Shen, Gabriel Zaccak
Venue: Proceedings of the International Conference on Semantic Computing (2007)

Despite continuing advances in machine translation technology, users who lack familiarity with particular foreign languages have no good way to find information in those languages. In this paper, we present a technical framework and implemented system that answers English questions on the basis of information in foreign-language, semi-structured sources such as websites. This work helps users locate, with high precision, relevant segments of foreign-language information, and then makes use of existing machine translation services to present that information in English. The resulting technology extends an approach embodied in the START information access system and its supporting Omnibase uniform data access system, and it has been applied to several Chinese and Arabic websites.
iTrails: pay-as-you-go information integration in dataspaces
Authors: Marcos Antonio Vaz Salles, Jens-Peter Dittrich, Shant Kirakos Karakashian, Olivier René Girard, Lukas Blunschi
Venue: Proceedings of the 33rd international conference on Very large data bases (2007)

Dataspace management has been recently identified as a new agenda for information management [17, 22] and information integration [23]. In sharp contrast to standard information integration architectures, a dataspace management system is a data-coexistence approach: it does not require any investments in semantic integration before querying services on the data are provided. Rather, a dataspace can be gradually enhanced over time by defining relationships among the data. Defining those integration semantics gradually is termed pay-as-you-go information integration [17], as time and effort (pay) are needed over time (go) to provide integration semantics. The benefits are better query results (gain). This paper is the first to explore pay-as-you-go information integration in dataspaces. We provide a technique for declarative pay-as-you-go information integration named iTrails. The core idea of our approach is to declaratively add lightweight 'hints' (trails) to a search engine thus allowing gradual enrichment of loosely integrated data sources. Our experiments confirm that iTrails can be efficiently implemented introducing only little overhead during query execution. At the same time iTrails strongly improves the quality of query results. Furthermore, we present rewriting and pruning techniques that allow us to scale iTrails to tens of thousands of trail definitions with minimal growth in the rewritten query size.
A novel anti-collusion coding scheme tailored to track linear collusions
Authors: Kannan Karthik, Dimitrios Hatzinakos
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

A set of semi-fragile watermarksW_F\tau = {V1, V2,.., V_v} can be used as building blocks for constructing any digital fingerprint. One such family, is obtained by modulating the sign bits alone of selective DCT-AC coefficients, where, each V_j represents the positions of a disjoint subset of modulated coefficients. We show that a linear collusion of K \le n sign fingerprinted images neatly translates into a majority bit vote of K corresponding binary strings and in this binary output, '1' represents survival of a particular block V_j and '0' its erasure. By design, each block V_j is unperturbed by specific collusion patterns and so by facilitating complementary coverage of each newly added block V_j+1, a compact anti-collusion code (ACC) for tracking linear collusions can be constructed.
A Tool for Automated Performance Testing of Java3D Applications in Agile Environments
Authors: Xueling Shu, Frank Maurer
Venue: Proceedings of the International Conference on Software Engineering Advances (2007)

Following the agile philosophy that all core features of a system need an automated test harness, performance requirements also need such a check when they are essential for the success of a project. The purpose of this paper is to describe a tool, J3DPerfUnit, which supports automated performance testing for Java3D applications in agile environments. We elicited tool requirements from domain experts through a survey and evaluated J3DPerfUnit using code from our partner's bioinformatics project and Java3D official tutorials. The evaluation pointed out that the tool is effective in detecting performance problems and in identifying where they come from when loading/unloading a 3D object.
Statistical convergence in fuzzy normed linear spaces
Authors: C. Şençı.men, S. Pehlı.van
Venue: Fuzzy Sets and Systems (2008)

In this paper we introduce the notions of statistically convergent sequence and statistically Cauchy sequence in a fuzzy normed linear space (FNS) and establish some basic facts. We also define statistical limit points and statistical cluster points of a sequence in an FNS and investigate the relations between these concepts.
The Assignment Statement in Hardware Description Languages
Authors: H. F. Jordan
Venue: Computer (1977)

The basic character of a programming language or a hardware design language is determined by the data types on which it operates and how changes in their values are made. Few of the papers on specific hardware design languages have included a discussion of this basic part, of the languages' semantics. Often a heuristic discussion based on examples is taken as the most rapid way of conveying the flavor of a new language. Since the range of language applications is often limited by the available data types and the transformations which may be applied to them, a careful analysis of this area of language structure is essential. It is not the intent of this paper to define another hardware description language. Instead, we want to propose a basic set of data types and assignment statements which should be available in any general-purpose hardware description language.
Named entity recognition in Vietnamese using classifier voting
Authors: Pham Thi Xuan Thao, Tran Quoc Tri, Dinh Dien, Nigel Collier
Venue: ACM Transactions on Asian Language Information Processing (TALIP) (2007)

Named entity recognition (NER) is one of the fundamental tasks in natural-language processing (NLP). Though the combination of different classifiers has been widely applied in several well-studied languages, this is the first time this method has been applied to Vietnamese. In this article, we describe how voting techniques can improve the performance of Vietnamese NER. By combining several state-of-the-art machine-learning algorithms using voting strategies, our final result outperforms individual algorithms and gained an F-measure of 89.12. A detailed discussion about the challenges of NER in Vietnamese is also presented.
Design of Embedded Software Based on Rough Set and Neural Network
Authors: Jin-Cherng Lin, Kuo-Chiang Wu
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

At the time of designing embedded software, too many variables in the original requirement specifications may cause the design work very difficult. This paper tries to use the knowledge reduction in the rough set to simplify the design of embedded software. We first get raw data from requirement specifications or use cases and then turn raw data into rough set by the process of the discretization. Next, extract knowledge (or decision rules) from the rough set by the knowledge reduction. Finally, employing the neural network theory learned these decision rules from the embedded software. In brief, rough set theory is able to extract the decision rules from the raw data, and then the neural network is capable of learning these decision rules to help development team works out.
Rough Set Research on Rule Extraction in Information Table
Authors: E. Xu, Shaocheng Tong, Liangshan Shao, Yongjun Li, Dianke Jiao
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03 (2007)

The problem of extracting rules from information table has been studied by use of rough set theory. According to the corresponding relationship and the indiscernible relationship, the concepts of classification quality, discernible vector. Through the discernible vector, we can scan the discernible and obtain the core attribute set and the importance of every attribute. A reduced attribute set can be acquired through combing the score attribute set and the important attribute selected from the different attribute set in the discernible vector with the constraints of classification quality. Finally, delete redundant attribute value and obtain the concise rules. The illustration and experiment results indicate that the method is effective and efficient for rule extraction.
Enabling assisted strategic negotiations in actual-world procurement scenarios
Authors: Jesús Cerquides, Maite López-Sánchez, Antonio Reyes-Moro, Juan A. Rodríguez-Aguilar
Venue: Electronic Commerce Research (2007)

In the everyday business world, the sourcing process of multiple goods and services usually involves complex negotiations (via telephone, fax, etc.) that include discussion of product and service features. Nowadays, this is a high-cost process due to the scarce use of tools that streamline this negotiation process and assist purchasing managers' decision-making. With the advent of Internet-based technologies, it has become feasible the idea of tools enabling low-cost, assisted, fluid, on-line dialogs between buyer enterprises and their providers wherever they are located. Consequently, several commercial systems to support on-line negotiations (e-sourcing tools) have been released. It is our view that there is still a need for these systems to incorporate effective decision support. This article presents the foundations of Quotes, a commercial sourcing application developed by iSOCO that, in addition to cover the whole sequence of sourcing tasks, incorporates decision support facilities based on Artificial Intelligence (AI) techniques that successfully address previous limitations within a single and coherent framework. The paper focuses on the computational realization of sourcing tasks along with the decision support facilities they require. While supported negotiation events are Request for Quotations/Proposals (RFQs/RFPs) and reverse auctions, decision support facilities include offer generation, offer comparison, and optimal bid set computation (winner determination) in combinatorial negotiations. Additionally, the paper presents a compound of experiences and lessons learned when using Quotes for real sourcing processes.
Gaussian Hierarchical Bayesian Clustering Algorithm
Authors: Rafael Eduardo Ruviaro Christ, Edwin Villanueva Talavera, Carlos Dias Maciel
Venue: Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications (2007)

This paper presents the Gaussian Hierarchical Bayesian Clustering algorithm (GHBC). A new method for agglom- erative hierarchical clustering derived from the HBC algo- rithm. GHBC has several advantages over traditional ag- glomerative algorithms. (1) It reduces the limitations due time and memory complexity. (2) It uses a bayesian pos- terior probability criterion to decide on merging clusters (modeling clusters as Gaussian distributions) rather than ad-hoc distance metrics. (3) It automatically finds the par- tition that most closely matches the data using Bayesian In- formation Criterion (BIC). Finally, experimental results on synthetic and real data show that GHBC can cluster data as the best classical agglomerative and partitional algorithms.
Improved Rough Set Model Based on Set Pair Connection Degree
Authors: Yi Xu, Long-shu Li, Xue-jun Li
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

In order to apply the classical rough set theory in the incomplete information system, equivalence relation has been extended, such as rough Set model based on set pair connection degree. But as the binary relation defined in this model has some limitations, inconsistent problems will happen in the process of decision making and when null values are too many the performance is not desirable. In this paper, the reason of these limitations generation is analyzed, and in view of these limitations, a new binary relation based on set pair connection degree, called generalized set pair similarity relation, is proposed. Based on this a more generalized rough set model is presented. Finally, the compare of the generalized rough set mode with some existing extension of rough set models is given. By an example, it is demonstrated that the new model is simpler and more effective when processing incomplete information system.
A dualistic approach to bounding the chromatic number of a graph
Authors: Jaroslav Nešetřil, Claude Tardif
Venue: European Journal of Combinatorics (2008)

We give a new and more direct proof of the characterization theorem for finitary homomorphism dualities of directed graphs. This result may be viewed as a characterization of Gallai-Hasse-Roy-Vitaver type theorems. We exhibit infinitely many examples where this general setting improves the bounds for chromatic number of graphs and we relate this to extremal problems for oriented graphs.
Layer-Based Representation of Polyhedrons for Point Containment Tests
Authors: Wencheng Wang, Jing Li, Hanqiu Sun, Enhua Wu
Venue: IEEE Transactions on Visualization and Computer Graphics (2008)

This paper presents the layer-based representation of polyhedrons and its use for point-in-polyhedron tests. In the representation, the facets and edges of a polyhedron are sequentially arranged, and so the binary search algorithm is efficiently used to speed up inclusion tests. In comparison with conventional representation for polyhedrons, the layer-based representation we propose greatly reduces the storage requirement because it represents much information implicitly, though it still has a storage complexity O(n). It is simple to implement, and robust for inclusion tests because many singularities are erased in constructing the layer-based representation. Incorporating an octree structure for organizing polyhedrons, our approach can run at a speed comparable with BSP-based inclusion tests, and at the same time greatly reduce storage and preprocessing time in treating large polyhedrons. We have developed an efficient solution for point-in-polyhedron tests with the time complexity varying between O(n) and O(log n), depending on the polyhedron shape and the constructed representation, and less than O(log^3 n) in most cases. The time complexity of preprocess is between O(n) and O(n^2), varying with polyhedrons, where n is the edge number of a polyhedron.
Automating Logic Rectification by Approximate SPFDs
Authors: Yu-Shen Yang
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

In the digital VLSI cycle, a netlist is often modified to correct design errors, perform small specification changes or implement incremental rewiring-based optimization operations. Most existing automated logic rectification tools use a small set of predefined logic transformations when they perform such modifications. This paper first shows that a small set of predefined transformations may not allow rectification to exploit the full potential of the design. Then, it proposes an automated simulation-based methodology to "approximate" sets of pairs of functions to be distinguished (SPFDs) and avoid the memory/time explosion problem. This representation is used by a SAT-based algorithm that devises appropriate logic transformations to fix a design. The SAT method is later complemented by a greedy one that improves on runtime performance. An extensive suite of experiments documents the added potential of the proposed rectification methodology.
Deploying Wireless Sensor Networks under Limited Mobility Constraints
Authors: Sriram Chellappan, Wenjun Gu, Xiaole Bai, Dong Xuan, Bin Ma, Kaizhong Zhang
Venue: IEEE Transactions on Mobile Computing (2007)

In this paper, we study the issue of sensor networks deployment using limited mobility sensors. By limited mobility, we mean that the maximum distance that sensors are capable of moving to is limited. Given an initial deployment of limited mobility sensors in a field clustered into multiple regions, our deployment problem is to determine a movement plan for the sensors to minimize the variance in number of sensors among the regions, and simultaneously minimize the sensor movements. Our methodology to solve this problem is to transfer the nonlinear variance/movement minimization problem into a linear optimization problem through appropriate weight assignments to regions. In this methodology, the regions are assigned weights corresponding to the number of sensors needed. During sensor movements across regions, larger weight regions are given higher priority compared to smaller weight regions, while simultaneously ensuring minimum number of sensor movements. Following the above methodology, we propose a set of algorithms to our deployment problem. Our first algorithm is the Optimal Maximum Flow based (OMF) centralized algorithm. Here, the optimal movement plan for sensors is obtained based on determining the minimum cost maximum weighted flow to the regions in the network. We then propose the Simple Peak-Pit based distributed (SPP) algorithm that uses local requests and responses for sensor movements. Using extensive simulations, we demonstrate the effectiveness of our algorithms from the perspective of variance minimization, number of sensor movements and messaging overhead under different initial deployment scenarios.
Unsupervised Nonlinear Dimensionality Reduction Based on Tensor Tangent Space Alignment
Authors: Lei Guo, Chengwen Yu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

Nonlinear dimensionality reduction from points underlying low dimension manifolds with outliers in high dimensional space is a challenge problem. In this paper, we proposed a robust dimensionality reduction method which can learn the low dimensional embeddings of manifold from input high dimensional data with large percentage of outliers. The proposed method named tensor tangent space alignment operates locally in neighborhoods and integrates tensor voting for nonlinear manifold inference and an improved tangent space alignment method for dimensionality reduction perfectively. We demonstrate the robust effectiveness of our method on several datasets with different noise levels.