ppxscal/embedding_network huggingface dataset:
Analysis of and techniques for privacy preserving data mining
Authors: Yuliang Zheng, Xintao Wu
Venue:  (2007)

Privacy is often considered as a social, moral or legal concept. As Internet and e-commerce have prospered nowadays, privacy has become one of the most important issues in IT and has received increasing attention from enterprises, consumers and legislators. Although various techniques, such as randomization-based methods, cryptographic-based methods, and database inference control etc. have been developed, many key problems still remain open in this area. Especially, new privacy and security issues have been identified, and the scope of the privacy has been expanded. An essential problem under the context is tradeoffs between the data utility and the disclosure risk. Since previous research only conducted empirical evaluations or limited analysis for existing randomization techniques, a more solid theoretical analysis is needed. This dissertation investigates different perturbation models in randomization-based privacy preserving data mining. Among them, the additive-noise-based model and the projection-based-model are primary tools. For the additive-noise-based perturbation, the explicit relation between noise and mining accuracy has not been carefully studied. We first propose an improved strategy to reconstruct the data based on the representative method. Then we develop explicit bounds of reconstruction error. Both the upper bound and the lower bound provide a guideline to balance the privacy/accuracy tradeoff. We also discuss other potential threats to the privacy based on our defined measure for quantifying the privacy. For the projection-based perturbation, properties of different models and possible disclosures within those models are analyzed in detail. Particularly, we propose an A-priori Knowledge-based ICA attack (AK-ICA) which is effective against all the existing projection models. Due to the vulnerabilities in previous randomization models, a general-location-model-based approach is proposed. It first builds a statistical model to fit the real data with both categorical and numerical types of variables, then generates a synthetic data set for mining by tuning parameters of the model instead of perturbing particular individual values. Since the search space of parameters of the model is much smaller than that of data and all information which attackers can derive is contained in those parameters, this approach is expected to be more effective and efficient. This dissertation investigates privacy issues of the numerical data in this model, wherein the disclosure is analyzed and controlled in different scenarios.
An $\mbox{O}(n^{1.75})$ Algorithm for L(2,1)-Labeling of Trees
Authors: Toru Hasunuma, Toshimasa Ishii, Hirotaka Ono, Yushi Uno
Venue: Proceedings of the 11th Scandinavian workshop on Algorithm Theory (2008)

An L(2,1)-labeling of a graph G is an assignment f from the vertex set V(G) to the set of nonnegative integers such that |f(x) ¿ f(y)| ¿ 2 if x and y are adjacent and |f(x) ¿ f(y)| ¿ 1 if x and y are at distance 2 for all x and y in V(G). A k-L(2,1)-labeling is an assignment f:V(G)¿{0,...,k}, and the L(2,1)-labeling problem asks the minimum k, which we denote by ¿(G), among all possible assignments. It is known that this problem is NP-hard even for graphs of treewidth 2. Tree is one of a few classes for which the problem is polynomially solvable, but still only an $\mbox{O}(\Delta^{4.5} n)$ time algorithm for a tree T has been known so far, where Δ is the maximum degree of T and n = |V(T)|. In this paper, we first show that an existent necessary condition for ¿(T) = Δ + 1 is also sufficient for a tree T with $\Delta=\Omega(\sqrt{n})$, which leads a linear time algorithm for computing ¿(T) under this condition. We then show that ¿(T) can be computed in $\mbox{O}(\Delta^{1.5}n)$ time for any tree T. Combining these, we finally obtain an time algorithm, which substantially improves upon previously known results.
Neural Network Approach for Designing One- and Two-Dimensional Quasi-Equiripple FIR Digital Filters
Authors: Xiaohua Wang, Yigang He, Yulou Peng
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A quasi-equiripple one- and two-dimensional linear-phase FIR digital filters design approach is proposed based on a novel neural network optimization technique. Its goal is to minimize the weighted square-error function in the frequency domain. The design solution is presented as a parallel algorithm to approximate the desired frequency response specification, and the weight coefficients are updated according to the error function. Thus, the proposed approximation method can avoid the overshoot phenomenon which may happen near the pass-band and stop-band edges of the designed filter, and may make a fast calculation of the filter's coefficients possible. Several optimal design examples are given to illustrate the effectiveness of the proposed approach.
Data Estimation in Sensor Networks Using Physical and Statistical Methodologies
Authors: Yingshu Li, Chunyu Ai, Wiwek P. Deshmukh, Yiwei Wu
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

Wireless Sensor Networks (WSNs) are employed in many applications in order to collect data. One key challenge is to minimize energy consumption to prolong network lifetime. A scheme of making some nodes asleep and estimating their values according to the other active nodes’ readings has been proved energy-efficient. For the purpose of improving the precision of estimation, we propose two powerful estimation models, Data Estimation using Physical Model (DEPM) and Data Estimation using Statistical Model (DESM). DEPM estimates the values of sleeping nodes by the physical characteristics of sensed attributes, while DESM estimates the values through the spatial and temporal correlations of the nodes. Experimental results on real sensor networks show that the proposed techniques provide accurate estimations and conserve energy efficiently.
Caravela: Semantic Content Management with Automatic Information Integration and Categorization (System Description)
Authors: David Aumüller, Erhard Rahm
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

Semantic web content management poses much manual work onto the community. To reduce this labour we have devised Caravela, a generic approach to dynamic content integration and automatic categorization. Content and documents of different types can be integrated from diverse semi-structured sources and categorized along multiple dimensions. Automatic linking provides dynamic categorizations at no user cost. We illustrate our approach by an online bibliography categorizing scientific research publications.
Weighted Fuzzy Feature Matching for Region-Based Medical Image Retrieval: Application to Cerebral Hemorrhage Computerized Tomography
Authors: Shaofeng Jiang, Wufan Chen, Qianjin Feng, Suhua Yang
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

In this paper, we focus on retrieval for cerebral hemorrhage Computerized Tomography images based on Weighted Fuzzy Feature Matching (WFFM). We first apply an improved Expectation Maximization (EM) algorithm to segment the images into regions, and then extract the texture features of each region with Gabor filters. To improve the robustness of retrieval system against segmentation-related uncertainties, WFFM maps the intensity features of each region into fuzzy features with the exponential membership functions. Based on fuzzy features, regions between images are matched and the texture features serve as weighting factors when calculating the similarities between the images. Experiments show that the retrieval method performs better than some similar methods in the application to retrieve cerebral hemorrhage CT images.
Speculative Synchronization: Programmability and Performance for Parallel Codes
Authors: Jose F. Martinez, Josep Torrellas
Venue: IEEE Micro (2003)

Proper synchronization is vital to ensuring that parallel applications execute correctly. A common practice is to place synchronization conservatively so as to produce simpler code in less time. Unfortunately, this practice frequently results in suboptimal performance because it stalls threads unnecessarily. Speculative synchronization overcomes this problem by allowing threads to speculatively execute past active barriers, busy locks, and unset flags. The result is high performance.
Brief paper: Output feedback variable structure control design with an H2 performance bound constraint
Authors: Han Ho Choi
Venue: Automatica (Journal of IFAC) (2008)

In this paper, we give an LMI-based method for designing an output feedback variable structure control (VSC) system with an H"2 constraint. The uncertain system model under consideration may have nonmatching disturbance input signals. Nonmatching disturbance input signal reduction is performed via H"2 norm minimization. In terms of a constrained Riccati inequality we give a sufficient condition for the existence of a linear sliding surface and a compensator guaranteeing an H"2 constraint, and we show that the control parameters can be characterized by using the solution to the given inequality condition. We also give a simple LMI-based design algorithm of an output feedback VSC law guaranteeing an H"2 constraint. Finally, by using a design example we show that our method can supplement the existing results.
Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing Maps
Authors: Frank-Michael Schleif, Thomas Villmann, Barbara Hammer
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

Analysis and visualization of high-dimensional clinical proteomic spectra obtained from mass spectrometric measurements is a complicated issue. We present a wavelet based preprocessing combined with an unsupervised and supervised analysis by Self-Organizing Maps and a fuzzy variant thereof. This leads to an optimal encoding and a robust classifier incorporating the possibility of fuzzy labels.
On External Measures for Validation of Fuzzy Partitions
Authors: Alessandro G. Nuovo, Vincenzo Catania
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

The procedure of evaluating the results of a clustering algorithm is know under the term cluster validity. In general terms, cluster validity criteria can be classified in three categories: internal, external and relative. In this work we focus on the external criteria, which evaluate the results of a clustering algorithm based on a pre-specified structure S, that pertains to the data but which is independent of it. Usually S is a crisp partition (i.e. the data labels), and the most common approach for external validation of fuzzy partitions is to apply measures defined for crisp partitions to fuzzy partitions, using crisp partitions derived (hardened) from them. In this paper we discuss fuzzy generalizations of two well known crisp external measures, which are able to assess the quality of a partition U without the hardening of U. We also define a new external validity measure, that we call DNC index, useful for comparing a fuzzy U to a crisp S. Numerical examples based on four real world data sets are given, demonstrating the higher reliability of the DNC index.
Comparing Datasets Using Frequent Itemsets: Dependency on the Mining Parameters
Authors: Irene Ntoutsi, Yannis Theodoridis
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

Comparison between sets of frequent itemsets has been traditionally utilized for raw dataset comparison assuming that frequent itemsets inherit the information lying in the original raw datasets. In this work, we revisit this assumption and examine whether dissimilarity between sets of frequent itemsets could serve as a measure of dissimilarity between raw datasets. In particular, we investigate how the dissimilarity between two sets of frequent itemsets is affected by the minSupport threshold used for their generation and the adopted compactness level of the itemsets lattice, namely frequent itemsets, closed frequent itemsets or maximal frequent itemsets. Our analysis shows that utilizing frequent itemsets comparison for dataset comparison is not as straightforward as related work has argued, a result which is verified through an experimental study and opens issues for further research in the KDD field.
An effective defect inspection system for polarized film images using image segmentation and template matching techniques
Authors: Young-Geun Yoon, Seok-Lyong Lee, Chin-Wan Chung, Sang-Hee Kim
Venue: Computers and Industrial Engineering (2008)

In this paper, we present an effective defect inspection system that identifies film defects and determines their types in order to produce polarized films for TFT-LCD (thin film transistor - liquid crystal display). The proposed system is designed and implemented to find defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects using template matching techniques. We extract features of the defects such as shape and texture, and compare them to the features of referential defect images stored in a template database. Experimental results using the proposed system show that it identifies defects of test images effectively (Recall=1.00, Precision=0.95) and efficiently (Average response time=0.64s), and also achieves a high correctness in determining the types (Recall=0.95, Precision=0.96) for five classes of defects. In addition the experiment shows that our system is fairly robust with respect to the rotational transformation, achieving the desirable property of the rotation invariance.
Set Operations for L-Fuzzy Sets
Authors: Jouni Järvinen
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

In this paper, we introduce the operations of union, intersection, and complement for preorder-based fuzzy sets. The given operations are even capable of dealing with fuzzy sets that have membership degrees coming from different preordered sets. This enables us to handle the difficult situation in which one has different people giving judgements and they all like to use their own language and expressions.
An Information Hiding Scheme Using a Pattern-Based Compression Algorithm
Authors: Farhad Keissarian
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

This paper presents a data hiding scheme that hides the data in the compression domain of a digital image. The pattern-based compression algorithm used for hiding the information, compresses the image according to the visual activity of individual image blocks. The key point of the proposed scheme is to first identify the smooth area of the host image and then embed the data in these areas. Experimental results confirm that the proposed scheme can embed a large amount of data in the compressed file while maintaining satisfactory image quality.
A Time-Frequency Analysis Method Using Stochastic Resonance
Authors: Zhijin Zhao, Yao Yao, Chunyun Xu
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Time-frequency analysis has been widely used in non-stationary signal analysis, and the reassignment method can improve the performance of the time-frequency analysis. But they can not analyze signals at low signal-to-noise ratio. The stochastic resonance system can increase signal-to-noise ratio. Using this feature this paper proposes a time-frequency analysis method applying stochastic resonance as a pre-processing system. It means that a signal is pre-processed by stochastic resonance system first, and then is analyzed using time-frequency distribution and reassignment method. The simulation results show that the proposed method can analyze signals very well under low signal-to-noise ratio. When signal-to-noise ratio is -11dB, the reassignment spectrogram using stochastic resonance provides a higher time-frequency concentrate and improves the readability of spectrogram; the reassignment scalogram using stochastic resonance has the good performance.
An Electronic Cash System Based on Group Blind Signatures
Authors: Constantin Popescu
Venue: Informatica (2006)

One important requirement of electronic cash systems is the anonymity of customers. Unconditional anonymity is also very well suited to support criminals in blackmailing. Maitland and Boyd proposed at ICICS 2001 an offline electronic cash system based on a group signature scheme. Their scheme cannot be used to solve blackmailing and other anonymity problems such as money laundering and illegal purchases. Chen, Zhang and Wang suggested an offline electronic cash scheme to prevent blackmailing by using the group blind signature. In their payment system, they used a group signature scheme of Camenisch and Stadler for large groups which is not secure. In this paper we improve these electronic cash systems to prevent blackmailing, money laundering and illegal purchases by using a secure coalition-resistant group blind signature scheme.
Cases in Robotic Soccer
Authors: Hans-Dieter Burkhard, Ralf Berger
Venue: Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development (2007)

Soccer playing robots are a well established test bed for the development of artificial intelligence for use in real environments. The challenges include perception, decision making and acting in a dynamic environment with only unreliable and partial information. Behaviors and skills for such environments must be optimized by experiences. Case Based Reasoning provides an excellent framework for learning as discussed in this paper.
CPXe: Web Services for Internet Imaging
Authors: Timothy Thompson, Rick Weil, Mark D. Wood
Venue: Computer (2003)

The Common Picture eXhange environment is a highly interoperable service delivery framework that leverages the Web services paradigm to give providers access to an expanded market and offer consumers a broad range of digital imaging services. Multiple providers can register their services in a central directory and precisely characterize their offerings using an extensible catalog and order model as well as a comprehensive dictionary of terms. A service locator mechanism lets consumers easily select vendors offering the products and features they desire.
Optimal balking strategies and pricing for the single server Markovian queue with compartmented waiting space
Authors: Antonis Economou, Spyridoula Kanta
Venue: Queueing Systems: Theory and Applications (2008)

We consider the single server Markovian queue and we assume that arriving customers decide whether to enter the system or balk based on a natural reward-cost structure, which incorporates their desire for service as well as their unwillingness to wait.We suppose that the waiting space of the system is partitioned in compartments of fixed capacity for a customers. Before making his decision, a customer may or may not know the compartment in which he will enter and/or the position within the compartment in which he will enter. Thus, denoting by n the number of customers found by an arriving customer, he may or may not know ¿ n/a ¿+1 and/or (n mod a)+1.We examine customers' behavior under the various levels of information regarding the system state and we identify equilibrium threshold strategies. We also study the corresponding social and profit maximization problems.
Beginning Ubuntu LTS Server Administration: From Novice to Professional, Second Edition, 2nd edition
Authors: Sander van Vugt
Venue:  (2008)

Beginning Ubuntu LTS Server Administration, Second Edition is the touchstone companion book for anyone implementing Ubuntu LTS Server. Author Sander van Vugt introduces the most recent server edition of Ubuntu Linux, the most popular Linux distribution, to power users, system administrators, and programmers and demonstrates the power and flexibility that the HardyHeron Ubuntu Server Long Term Support Edition offers for small networks and corporate systems alike. This book covers: Installing and configuring Ubuntu to ensure secure installations Virtual Ubuntu and LDAP configuration Full network and web services management What youll learn Administer, install, and configure the 5year HardyHeron Ubuntu Server Long Term Support Edition of the most popular Linux distribution in the world. Use the new installation and GUI tools. Familiarize yourself, whether you are a power user or system administrator, with LDAP administration. Build a network infrastructure with Ubuntu Server. Harden Ubuntu to withstand new security threats. Use virtual services. Who is this book for This book will suit Linux system administrators of all levels. It also lets power users who want to familiarize themselves with network and web services relevant to their work acquire uptodate specialized Ubuntu skills. About the Apress Beginning Series The Beginning series from Apress is the right choice to get the information you need to land that crucial entrylevel job. These books will teach you a standard and important technology from the ground up because they are explicitly designed to take you from novice to professional. Youll start your journey by seeing what you need to knowbut without needless theory and filler. Youll build your skill set by learning how to put together realworld projects step by step. So whether your goal is your next career challenge or a new learning opportunity, the Beginning series from Apress will take you thereit is your trusted guide through unfamiliar territory!
Content-oriented composite service negotiation with complex preferences
Authors: Reyhan Aydoǧan
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: doctoral mentoring program (2008)

In e-commerce, for some cases the service requested by the consumer cannot be fulfilled by the producer. In such cases, service consumers and producers need to negotiate their service requirements and offers. Whereas some multiagent negotiation approaches treat the price as the primary construct for negotiation, we consider that the service content is as much important as the price. Therefore, this study mainly focuses on the content of the service described in a common ontology accessed by both agents for common understanding. Acquiring user's preferences and acting upon these preferences are crucial tasks for a consumer agent as far as the negotiation is concerned. Since the size of complete preference information increases exponentially with the number of attributes and size of domain, it is required to keep these preferences in a compact way. There are a variety of ways of representing preferences and using these structures for automatic generation of consumer's request. This research develops an automated negotiation approach in which the consumer takes the preferences of the user in an efficient way and uses these preferences in the generation of request. For this purpose, we design several strategies to generate requests to take the best offer by the producer. On the other side, in order to obtain a more effective negotiation results the producer tries to learn the consumer preferences from the bid exchanges incrementally in order to refine its offer over time. Furthermore, for some complicated services desired by the consumer, a single producer by itself may not meet the consumer's needs. In such cases, the system should allow consumers negotiating with multiple service producers as far as composite services are concerned.