ppxscal/embedding_network huggingface dataset:
Approximate Clustering of Noisy Biomedical Data
Authors: Krzysztof Boryczko, Marcin Kurdziel
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

Classical clustering algorithms often perform poorly on data harboring background noise, i.e. large number of observations distributed uniformly in the feature space. Here, we present a new density-based algorithm for approximate clustering of such noisy data. The algorithm employs Shared Nearest Neighbor Graphs for estimating local data density and identification of core points, which are assumed to indicate locations of clusters. Partitioning of core points into clusters is performed by means of Mutual Nearest Neighbor distance measure. This similarity measure is sensitive to changes in local data density, and is thus useful for discovering clusters that differ in this respect. Performance of the presented algorithm was demonstrated on three data sets, two synthetic and one real world. In all cases, meaningful clustering structures were discovered.
A new segmentation system for brain MR images based on fuzzy techniques
Authors: S. R. Kannan
Venue: Applied Soft Computing (2008)

This work concerns a new method called fuzzy membership C-means (FMCMs) for segmentation of magnetic resonance images (MRI), and an efficient program implementation of it to the segmentation of MRI. Classical unsupervised clustering methods including the FCM by Bezdek, suffer many problems that can be partially treated with a proper rule to construct the initial membership matrix to clusters. This work develops a specific method to construct the initial membership matrix to clusters in order to improve the strength of the clusters. The new FMCM is tested on a set of benchmarks and then the application to the segmentation of MR images is presented and compared with the results obtained using FCM.
A Robust Sampling-Based Framework for Privacy Preserving OLAP
Authors: Alfredo Cuzzocrea, Vincenzo Russo, Domenico Saccà
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

A robust sampling-based framework for privacy preserving OLAP is introduced and experimentally assessed in this paper. The most distinctive characteristic of the proposed framework consists in adopting an innovative privacy OLAP notion, which deals with the problem of preserving the privacy of OLAP aggregations rather than the one of data cube cells, like in conventional perturbation-based privacy preserving OLAP techniques. This results in a greater theoretical soundness, and lower computational overheads due to processing massive-in-size data cubes. Also, the performance of our privacy preserving OLAP technique is compared with the one of the method Zero-Sum, the state-of-the-art privacy preserving OLAP perturbation-based technique, under several perspectives of analysis. The derived experimental results confirm to us the benefits deriving from adopting our proposed framework for the goal of preserving the privacy of OLAP data cubes.
Abstract Semantics by Observable Contexts
Authors: Filippo Bonchi
Venue: Proceedings of the 4th international conference on Graph Transformations (2008)

The operational behavior of interactive systems is usually given in terms of transition systems labeled with actions, which, when visible, represent both observations and interactions with the external world. The abstract semantics is given in terms of behavioral equivalences, which depend on the action labels and on the amount of branching structure considered. Behavioural equivalences are often congruences with respect to the operations of the language, and this property expresses the compositionality of the abstract semantics.
A Control Packet Minimized Routing Protocol for Ad-Hoc Wireless Networks
Authors: Youn-Sik Hong
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

AODV routing protocol is intended for use by mobile nodes in ad-hoc wireless networks. As the degree of node mobility becomes high, however, the number of RREQ and RREP messages during the route discovery process increase so rapidly. The unexpected increases in the number of control packets cause the destination node to decrease the packet receiving rate. Besides, the overall energy consumption for the network can be increased. Thus, we propose a novel method of adaptively controlling the occurrences of the control packets based on AIAD (additive increase additive decrease) under a consideration of the current network status. We have tested our proposed method with both the conventional AODV and the method using timestamp based on the three performance metrics; i.e., node mobility, node velocity, and node density, to compare their performances.
Reducing SVR Support Vectors by Using Backward Deletion
Authors: Masayuki Karasuyama, Ichiro Takeuchi, Ryohei Nakano
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III (2008)

Support Vector Regression (SVR) is one of the most famous sparse kernel machines which inherits many advantages of Support Vector Machines (SVM). However, since the number of support vectors grows rapidly with the increase of training samples, sparseness of the SVR is sometimes insufficient. In this paper, we propose two methods which reduce the SVR support vectors using backward deletion. Experiments show our method can dramatically reduce the number of support vectors without sacrificing the generalization performance.
Clustering Massive Text Data Streams by Semantic Smoothing Model
Authors: Yubao Liu, Jiarong Cai, Jian Yin, Ada Wai-Chee Fu
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality. In this paper, we firstly give an improved semantic smoothing model for text data stream environment. Then we use the improved semantic model to improve the clustering quality and present an online clustering algorithm for clustering massive text data streams. In our algorithm, a new cluster statistics structure, cluster profile, is presented in which the semantics of text data streams are captured. We also present the experimental results illustrating the effectiveness of our technique.
Short Communication: Protein ligand interaction database (PLID)
Authors: A. Srinivas Reddy, H.S. Durga Amarnath, Raju S. Bapi, G. Madhavi Sastry, G. Narahari Sastry
Venue: Computational Biology and Chemistry (2008)

A comprehensive database named, protein ligand interaction database (PLID), is created with 6295 ligands bound to proteins extracted from the protein data bank (PDB). This is by far the most comprehensive database of physico-chemical properties, quantum mechanical descriptors and the residues present in the active site of proteins. It is a publicly available web-based database (via the Internet) at http://203.199.182.73/gnsmmg/databases/plid/.
A Method of Graphics Composition Using Differential SVG Documents
Authors: Motohiro Matsuda, Kazunari Ito, Martin J. Dürst, Kôiti Hasida
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

This paper describes a novel method extracting editing differences between pairs of SVG graphics documents. These differences are extracted based on the analysis of the tree structure of SVG, and are generalized in order to abstract from the specifics of targets and document structure. The generalized differences can then be applied to other SVG graphics, resulting in new, heretofore unavailable graphics. We show the effectiveness of our method with experiments involving a variety of SVG documents.
New Distinguishers Based on Random Mappings against Stream Ciphers
Authors: Meltem Sönmez Turan, Çağdaş Çalık, Nurdan Buz Saran, Ali Doğanaksoy
Venue: Proceedings of the 5th international conference on Sequences and Their Applications (2008)

Statistical randomness testing plays an important role in security analysis of cryptosystems. In this study, we aim to propose a new framework of randomness testing based on random mappings. Considering the probability distributions of coverage and ρ-lengths, we present three new distinguishers; (i) coverage test, (ii) ρ-test and (iii) DP-coverage test and applied them on Phase III Candidates of eSTREAM project. We experimentally observed some statistical weaknesses of Pomaranch using the coverage test.
Collision Prevention for Exploiting Spatial Reuse in Ad Hoc Network Using Directional Antenna
Authors: Pu Qingxian, Won-Joo Hwang
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

To improve the performance of a wireless Ad Hoc network at the MAC layer, one way is to prevent collision and allow spatial reuse among neighbor nodes as much as possible. In this paper, we present a novel MAC protocol that prevents collision and supports spatial reuse. The proposed MAC protocol uses a control window between RTS/CTS packets and DATA/ACK packets which allows neighbor nodes to exchange control packets inserted to realize spatial reuse. Simulation result shows the advantages of our proposal in comparison with existing MAC in terms of network throughput and end-to-end delay.
Classification and Quantification Based on Image Analysis for Sperm Samples with Uncertain Damaged/Intact Cell Proportions
Authors: Lidia Sánchez, Víctor González, Enrique Alegre, Rocío Alaiz
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Classifying damaged-intact cells in a semen sample presents the peculiarity that the test class distribution is unknown. This paper studies under which design conditions the misclassification rate is minimum for the uncertainty region of interest (ratio of damaged cells lower than 20%) and (b) deals with quantifying the proportion of damaged/intact sperm cells in a given sample based on computer vision and supervised learning. We have applied a discrete wavelet transform to the spermatozoa head images and computed the mean and standard deviation (WSF) and four Haralick descriptors (WCF). Using a backpropagation neural network, the error rate averaged over distributions in the region of interest is 4.85% with WCF. The assessment of several quantification methods shows the conditions under which the Adjusted Count method leads to an overall mean absolute error of 3.2 and the Classify Count method yields 2.4, both with WCF features. Deviations of this order are considered reasonable for this field.
Design and Performance Evaluation of High Efficient TCP for HBDP Networks
Authors: Taejoon Park, Mankyu Park, Jaeyong Lee, Byungchul Kim
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

While the legacy TCP is the most commonly used and reliable transport protocol in the Internet, it is not suitable for massive data transfers in high bandwidth delay product networks. To cope with this problem, we propose high efficient TCP congestion control mechanism that can provide efficient data transfer in HBDP networks. When there is some available bandwidth and it satisfies a certain condition, the congestion window grows rapidly. Otherwise, it maintains a linear growth of the congestion avoidance phase similar to the legacy TCP congestion avoidance algorithm. Based on the relationship of the current and minimum round trip time, the proposed method selects between the linear and rapid growth phases of the congestion window update during the congestion avoidance period. To prevent packet loss during the exponential growth phase, proposed method uses not only the end-to-end delay information, but also the estimated bandwidth of the bottleneck node.
The Ultimate in Instant Gratification
Authors: John S. Quarterman
Venue: IEEE Security and Privacy (2003)

In 1973, Larry Niven published a short story called "Flash Crowd" that imagines teleportation as the most widely used transportation mechanism. Today's technology offers something almost comparable: smart mobs brought together via the Internet.
The (k,l) Coredian Tree for Ad Hoc Networks
Authors: Amit Dvir, Michael Segal
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

In this paper, we present new efficient strategy for constructing a wireless tree network containing n nodes of diameter Δ while satisfying the QoS requirements such as bandwidth and delay. Given a tree network T, a coredian path is a path in T that minimizes the centdian function, ak-coredian tree is a subtree of T with k leaves that minimizes the centdian function, and a (k, l)-coredian tree is a subtree of T with k leaves and diameter l at most thatminimizes the centdian function. The (k, l)-coredian tree can serve as a backbone for a network, where intermediate nodes belong to the backbone and the leaves serve as the heads of the clusters covering the rest of the network. We show that a coredian path can be constructed at O(Δ)time with O(n) messages and a k-coredian tree can be constructed at O(kΔ) time with O(kn) messages. While a(k, l)-coredian tree can be constructed at O(n2) time with O(n2) messages. A simulation is presented for various values of n and k.
Solutions of two-factor models with variable interest rates
Authors: Jinglu Li, C. B. Clemons, G. W. Young, J. Zhu
Venue: Journal of Computational and Applied Mathematics (2008)

The focus of this work is on numerical solutions to two-factor option pricing partial differential equations with variable interest rates. Two interest rate models, the Vasicek model and the Cox-Ingersoll-Ross model (CIR), are considered. Emphasis is placed on the definition and implementation of boundary conditions for different portfolio models, and on appropriate truncation of the computational domain. An exact solution to the Vasicek model and an exact solution for the price of bonds convertible to stock at expiration under a stochastic interest rate are derived. The exact solutions are used to evaluate the accuracy of the numerical simulation schemes. For the numerical simulations the pricing solution is analyzed as the market completeness decreases from the ideal complete level to one with higher volatility of the interest rate and a slower mean-reverting environment. Simulations indicate that the CIR model yields more reasonable results than the Vasicek model in a less complete market.
Adaptive Search Region Fast Area-Based Stereo Correspondence
Authors: Longyuan Guo, Yongquan Xia, Jingyu Yang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

Traditionally, the search region is based one pipolar line and the maximum disparity constraints in area-based stereo correspondence methods. The search region is invariable with this method. The reduction of search region can increase the performance of the matching process, in the context of the execution time and accuracy. According to disparity gradient theory, the paper presents a fast area-based correspondence whose search region is variable. The core of the method is that the search region of each point in one image is relation to itself disparity gradient and former result. The paper apply disparity gradient to the whole image. While others similarity papers focus on feature-based correspondence. The proposed approach is compared with the traditional area-based matching method with invariable search region. The experiment result indicates that the proposed algorithm is the higher matching speed and the less calculation than the ones based on invariable search region.
Mixing Lossy and Perfect Fifo Channels
Authors: P. Chambart, Ph. Schnoebelen
Venue: Proceedings of the 19th international conference on Concurrency Theory (2008)

We consider asynchronous networks of finite-state systems communicating via a combination of reliable and lossy fifo channels. Depending on the topology, the reachability problem for such networks may be decidable. We provide a complete classification of network topologies according to whether they lead to a decidable reachability problem. Furthermore, this classification can be decided in polynomial-time.
A Novel Architecture for the Classification and Visualization of Sequential Data
Authors: Jorge Couchet, Enrique Ferreira, André Fonseca, Daniel Manrique
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

This paper introduces a novel architecture to efficiently code in a self-organized manner, data from sequences or a hierarchy of sequences. The main objective of the architecture proposed is to achieve an inductive model of the sequential data through a learning algorithm in a finite vector space with generalization and prediction properties improved by the compression process. The architecture consists of a hierarchy of recurrent self-organized maps with emergence which performs a fractal codification of the sequences. An adaptive outlier detection algorithm is used to automatically extract the emergent properties of the maps. A visualization technique to help the analysis and interpretation of data is also developed. Experiments and results for the architecture are shown for an anomaly intrusion detection problem.
Trusting spam reporters: A reporter-based reputation system for email filtering
Authors: Elena Zheleva, Aleksander Kolcz, Lise Getoor
Venue: ACM Transactions on Information Systems (TOIS) (2008)

Spam is a growing problem; it interferes with valid email and burdens both email users and service providers. In this work, we propose a reactive spam-filtering system based on reporter reputation for use in conjunction with existing spam-filtering techniques. The system has a trust-maintenance component for users, based on their spam-reporting behavior. The challenge that we consider is that of maintaining a reliable system, not vulnerable to malicious users, that will provide early spam-campaign detection to reduce the costs incurred by users and systems. We report on the utility of a reputation system for spam filtering that makes use of the feedback of trustworthy users. We evaluate our proposed framework, using actual complaint feedback from a large population of users, and validate its spam-filtering performance on a collection of real email traffic over several weeks. To test the broader implication of the system, we create a model of the behavior of malicious reporters, and we simulate the system under various assumptions using a synthetic dataset.
Locality Sensitive Information Brokerage in Distributed Sensor Networks
Authors: Hong Lu, Andrew (Anxiao) Jiang, Steve Liu
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

In sensor network applications, sensors often need to retrieve data from each other. Information brokerage is a scheme that stores data (or index files of data) at rendezvous nodes, so that every sensor can efficiently finds the data it needs. A very useful property for information brokerage is locality sensitivity, which means that a sensor close the original source of the data should also be able to retrieve the data with a small communication cost. Given the locality sensitivity requirement, the key is to design an information brokerage scheme that minimizes the storage cost. In this paper, we present a locality sensitive information brokerage scheme. It is designed for general locality-sensitive requirements, which include the linear data-retrieval cost (a frequently studied scenario) as a special case. We also prove that for a large class of networks, in the scenario of linear data-retrieval cost, our scheme achieves the asymptotically optimal storage cost. The result also proves the optimality of a few other schemes in the literature.
A Value Estimation Method for Feature-Oriented Requirements Tracing
Authors: Sangim Ahn, Kiwon Chong
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Traceability links are useful for requirements change impact analysis, requirements conflict analysis, and requirements consistency checking. However, establishing and maintaining traceability links places a big burden since complex systems have especially yield an enormous number of various artifacts. We propose a value estimation method for feature-oriented requirements tracing. It uses a feature as intermediate catalysis to generate traceability links between user requirements and implementation artifacts, by priority level after analyzing each feature's value. In addition, we present value evaluation parameters including requirements importance, requirements risk, cost & efforts, and templates for value analysis.
Using TTCN for Radio Conformance Test Systems
Authors: Javier Poncela-González, Juan Gómez-Salvador, Carlos Valero-Roldán, Unai Fernández-Plazaola
Venue: Proceedings of the 13th international forum on Design for Dependable Systems (2007)

While protocol conformance testing methodology is a well formalized field, radio testing methodology still relies on natural language specifications. This paper proposes an improvement on the quality of radio test specifications via the use of formal notation TTCN. This approach, and the fact that protocol and radio conformance testing share most of the underlying concepts, enables the use of a generic architecture for implementations of both types of testers, resulting in a reduction of the development efforts. This architecture has been validated with the implementation of radio test cases for the UMTS technology.
Approximating the Traffic Grooming Problem with Respect to ADMs and OADMs
Authors: Michele Flammini, Gianpiero Monaco, Luca Moscardelli, Mordechai Shalom, Shmuel Zaks
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

We consider the problem of switching cost in optical networks, where messages are sent along lightpaths. Given lightpaths, we have to assign them colors, so that at most g lightpaths of the same color can share any edge (g is the grooming factor). The switching of the lightpaths is performed by electronic ADMs (Add-Drop-Multiplexers) at their endpoints and optical ADMs (OADMs) at their intermediate nodes. The saving in the switching components becomes possible when lightpaths of the same color can use the same switches. Whereas previous studies concentrated on the number of ADMs, we consider the cost function - incurred also by the number of OADMs - of f(¿) = ¿|OADMs| + (1 ¿ ¿)|ADMs|, where 0 ≤ ¿ ≤ 1. We concentrate on chain networks, but our technique can be directly extended to ring networks. We show that finding a coloring which will minimize this cost function is NP-complete, even when the network is a chain and the grooming factor is g = 2, for any value of ¿. We then present a general technique that, given an r-approximation algorithm working on particular instances of our problem, i.e. instances in which all requests share a common edge of the chain, builds a new algorithm for general instances having approximation ratio r ¿logn ¿. This technique is used in order to obtain two polynomial time approximation algorithms for our problem: the first one minimizes the number of OADMs (the case of ¿= 1), and its approximation ratio is 2 ¿logn ¿; the second one minimizes the combined cost f(¿) for 0 ≤ ¿< 1, and its approximation ratio is $2 \sqrt{g}~\lceil \log n \rceil$.