ppxscal/embedding_network huggingface dataset:
A stochastic model for the analysis of a two-machine flexible manufacturing cell
Authors: Majid M. Aldaihani, Mehmet Savsar
Venue: Computers and Industrial Engineering (2005)

This paper presents a stochastic model to determine the performance of a flexible manufacturing cell (FMC) under variable operational conditions, including random machining times, random loading and unloading times, and random pallet transfer times. The FMC under study consists of two machines, pallet handling system, and a loading/unloading robot. After delivering the blanks by the pallet to the cell, the robot loads the first machine followed by the second. Unloading of a part starts with the machine that finishes its part first, followed by the next machine. When the machining of all parts on the pallet is completed, the handling system moves the pallet with finished parts out and brings in a new pallet with blanks. A model with these characteristics turns out to be a Markov chain with a transition matrix of size 5n+3, where n is the number of parts on the pallet. In this paper, we present exact numerical solutions and economic analysis to evaluate FMC systems, to determine optimal pallet capacity and robot speed that minimize total FMC cost per unit of production.
On estimation accuracy of desynchronization attack channel parameters
Authors: Shankar Sadasivam, Pierre Moulin
Venue: IEEE Transactions on Information Forensics and Security (2009)

In this paper, we study some fundamental performance limits of blind data hiding against desynchronization attacks. These attacks are modeled in addition to independent Gaussian noise to the marked signal, followed by linear, time-invariant filtering. We study a joint estimator-decoder which estimates the desynchronization attack parameters and uses these estimates in the decoding step. We propose a coding scheme based on distortion-compensated quantization index modulation and derive the estimation accuracy of the attack parameters via Fisher information and a Cramér-Rao type bound. For illustration purposes, we report estimation and decoding results on several attacks, including classical ones (scaling and fractional shifts) and some new ones. The results are in close agreement with our bounds and tightly quantify the performance loss due to desynchronization and the influence of code block length. Thus our results demonstrate the high performance of a joint estimation-decoding approach.
Steganalysis Based on Regression Model and Bayesion Network
Authors: Xiao Yi Yu, Aiming Wang
Venue: Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 01 (2009)

In this paper, we propose a feature generation and classification approach for universal steganalysis based on Genetic Algorithm (GA) and higher order statistics. The GA is utilized to select a subset of candidate features, a subset of candidate transformations to generate new features. The Logistic Regression Model and Bayesion Network Model are then used as the classifier. Experimental results show that the GA based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm.
Modeling and performance analysis of TCP over grid-OBS networks
Authors: Shuping Peng, Zhengbin Li, Zhenrong Zhang, Yongqi He, Anshi Xu
Venue: IEEE Communications Letters (2009)

Owing to the fine-granularity and the separation of control and data plane, OBS technology has the potential to support Grid applications. In this letter, a general analytical model is established to analyze the impacts of the influential factors of both TCP and OBS layers on the performance of Grid-OBS networks all-roundly. The extensive simulation studies are also conducted. Both the analytical and simulation results show the same trends.
Watermarking robustness evaluation based on perceptual quality via genetic algorithms
Authors: Giulia Boato, Valentina Conotter, Francesco G. B. De Natale, Claudio Fontanari
Venue: IEEE Transactions on Information Forensics and Security (2009)

This paper presents a novel and flexible benchmarking tool based on genetic algorithms (GA) and designed to assess the robustness of any digital image watermarking system. The main idea is to evaluate robustness in terms of perceptual quality, measured by weighted peak signal-to-noise ratio. Through a stochastic approach, we optimize this quality metric, by finding the minimal degradation that needs to be introduced in a marked image in order to remove the embedded watermark. Given a set of attacks, chosen according to the considered application scenario, GA support the optimization of the parameters to be assigned to each processing operation, in order to obtain an unmarked image with perceptual quality as high as possible. Extensive experimental results demonstrate the effectiveness of the proposed evaluation tool.
A Novel Double 3D Digital Watermarking Scheme
Authors: Kong Xiangzeng, Yao Zhiqiang
Venue: Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 01 (2009)

In this paper, a watermarking approach for the 3D models is proposed by embedding double watermarks. It is designed for the sender to check the copyright ownership (robustness) and for the receiver to verify the authentication of the received data (frangibility). The watermark is embedded at one time to possess the robust and fragile attributes by perturbing the distance between the vertices of the model to the center of the model. We keep a balance appropriately between the strength of the watermark signal and the local geometric features of the model. We also introduce a vertex-weighted skill in the watermark extraction process that makes watermark detection more robust against attacks. Experiments show that this approach not only is able to withstand common attacks on 3D models such as polygon mesh simplifications, addition of random noise, model cropping, translation, rotation, scaling as well as a combination of these attacks, but also can detect and locate tampered vertices.
Mutual information of multipath channels with imperfect channel information
Authors: S. H. Song, Q. T. Zhang
Venue: IEEE Transactions on Communications (2009)

Analysis of mutual information is important to wireless transmission over fading channels, and is usually done by assuming perfect channel state information (CSI) available at the receiver. In many practical applications, however, the CSI must be estimated by sending a limited number of pilot symbols and thus, can suffer from considerable inaccuracy. The influence of CSI inaccuracy on achievable mutual information, though analyzed in the past, is not well understood. The difficulty arises from the presence of a product term of signal and channel estimation error in the received signal model, and the lack of appropriate tools to determine its probability density function. This situation forces the adoption of a bounding technique in current literature by treating the product term either as a signal component or as a noise component. In this paper, we take a different methodology by accurately fitting the received signal with the multivariate Pearson's type-7 (MPT-7) distribution. The new results are simple in expression, very accurate as evidenced by simulation, and capable of directly revealing the dependence of the achievable mutual information on a particular channel estimator.
Proceeding of the 2nd ACM workshop on Social web search and mining
Venue: Conference on Information and Knowledge Management (2009)

It is our great pleasure to welcome you to the 2nd ACM Workshop on Social Web Search & Mining -- SWSM'09. This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports on leading edge issues of searching and mining of the social web and social networks. The scope of the workshop includes, but not limited to web mining, social network analysis, semantic web, information retrieval, and natural language processing. Moreover, SWSM'09 plans to provide with delegates theoretical analyses as well as practical system perspectives on issues related to web search and mining. The call for papers attracted 17 submissions from Asia, Europe, and the United States. The program committee accepted 8 full papers and 1 short paper that cover a variety of topics, including social network modeling and analysis, social influence analysis, influence analysis, and semantic social network. In addition, the program a keynote speech by Dr. Ravi Kumar from Yahoo! Research. We hope that these proceedings will serve as a valuable reference to further the research in social network search and mining.
Business logic for geoprocessing of distributed geodata
Authors: Christian Kiehle
Venue: Computers Geosciences (2006)

This paper describes the development of a business-logic component for the geoprocessing of distributed geodata. The business logic acts as a mediator between the data and the user, therefore playing a central role in any spatial information system. The component is used in service-oriented architectures to foster the reuse of existing geodata inventories. Based on a geoscientific case study of groundwater vulnerability assessment and mapping, the demands for such architectures are identified with special regard to software engineering tasks. Methods are derived from the field of applied Geosciences (Hydrogeology), Geoinformatics, and Software Engineering. In addition to the development of a business logic component, a forthcoming Open Geospatial Consortium (OGC) specification is introduced: the OGC Web Processing Service (WPS) specification. A sample application is introduced to demonstrate the potential of WPS for future information systems. The sample application Geoservice Groundwater Vulnerability is described in detail to provide insight into the business logic component, and demonstrate how information can be generated out of distributed geodata. This has the potential to significantly accelerate the assessment and mapping of groundwater vulnerability. The presented concept is easily transferable to other geoscientific use cases dealing with distributed data inventories. Potential application fields include web-based geoinformation systems operating on distributed data (e.g. environmental planning systems, cadastral information systems, and others).
A performance analysis model for IEEE 802.11e EDCA under saturation condition
Authors: Jae Young Lee, Hwang Soo Lee
Venue: IEEE Transactions on Communications (2009)

An accurate model of IEEE 802.11e EDCA for a performance analysis that captures all major QoS features of the standard is introduced in this paper. Using the model, we derive the saturation throughput, and validate the accuracy of the proposed model through comparisons with other models via simulations.
Use of neural networks and decision fusion for lithostratigraphic correlation with sparse data, Mono-Inyo Craters, California
Authors: M. Bursik, G. Rogova
Venue: Computers Geosciences (2006)

We explore the use of multiple artificial neural networks combined within the framework of the Dempster-Shafer Theory of Evidence to construct a hybrid information processing system for the correlation of tephra layers. The working hypothesis is that the system can correctly correlate tephra layers from one site to another even when data are sparse. The collection and analysis of data appropriate for utilization in standard statistical techniques aiding correlation is costly and time consuming. Given this state of affairs, here we employ a hybrid pattern recognition approach, which allows us to produce a recognition result with a relatively small amount of data. We used the major tephra-fall layers within one eruption sequence, the North Mono eruption, Mono Craters, CA, USA, to determine whether the system can be trained to distinguish layers on a bed-by-bed basis. The beds are distinguished in the field by the fraction of pumice, grading, zoning, thickness, and size of large pumice or lithic fragments. These same features were used to train the hybrid system. In the best case, the hybrid system was able to categorize observations correctly 93% of the time, which was markedly better than using neural networks alone. The average result for all pairwise comparisons of beds by the hybrid system is 76%, with the results for two beds that were not distinct removed. We conclude that it is possible to train the system to discriminate reasonably successfully among tephra layers with limited data. Because the system was not designed with any reliance on features specific to tephra layers, it may be possible to apply the system to the categorization of any stratigraphic units.
Integrated conditional global optimisation for discrete fracture network modelling
Authors: Nam H. Tran, Zhixi Chen, Sheik S. Rahman
Venue: Computers Geosciences (2006)

This paper presents a methodology to simulate discrete fracture networks for naturally fractured reservoirs by combining statistical and spatial analyses, object-based modelling and conditional global optimisation. The methodology examines and utilises both continuum and discrete fracture information, such as spatial distribution of fracture density, statistical and geostatistical distributions of fracture size and orientation. The output is a network of discrete fractures, with their corresponding details of location, size and orientation. The methodology is illustrated by a case study on the surface fault system in New York region. The results show that it is able to produce discrete fracture network that match closely to the target fault map, even in the case where data are limited. The results show that it is also able to improve results of several recent fracture models, such as integrated stochastic simulations as well as grid-based simulations.
Forward recursive scheme with improved marginal analysis heuristic for machine expansion scheduling in a new job shop
Authors: Ming-Guan Huang, Pao-Long Chang, Ying-Chyi Chou
Venue: Computers and Industrial Engineering (2006)

This study attempts to develop a method of machine expansion scheduling which determines how many machines should be purchased and allocated to every workstation during each period within the prescheduled ramp-up planning horizon in a new job shop to meet expected performance targets and minimize investment in machinery. This study suggests using the forward recursive scheme associated with marginal analysis heuristic, which itself is characterized by inherent simplicity and broad applicability, to solve the intricate problems of machine expansion scheduling. Furthermore, this study improves the marginal analysis heuristic, meaning that the algorithm incorporates an additional refining procedure to increase the possibility of achieving a solution close to the real optimum. Finally, this study used production data from an actual semiconductor manufacturer as an example to illustrate the proposed analytic algorithm.
Developing the earthquake markup language and database with UML and XML schema
Authors: Hassan A. Babaie, Abbed Babaei
Venue: Computers Geosciences (2005)

Seismic information is commonly shared across networks, and stored in heterogeneous databases that are maintained by different and autonomous groups. Because the metadata of the seismic information is generally not structured, information loss commonly occurs when data is transmitted from one source to another. Minimizing such information loss and enhancing information interchange over the Web require construction of specific markup languages for each part of seismology. The sub-disciplinary data syntax and vocabularies allow construction of corresponding relational databases to store and interchange the structured data. Developing modular data structures and databases for each sub-discipline allows composition of new languages through reuse, and enables future extension of the vocabulary and maintenance of the knowledge base. In this paper, we show how to design the conceptual and logical models for a small set of seismology that deals with earthquake (EarthquakeML). We discuss the application of the UML to the design and mapping of the object model into W3 XML Schema and database. The emphasis is on designing components that can be reused across discipline, and reduce redundancies by developing type libraries. We show how to map the XML schema into a relational data model; and implement the EarthquakeDB with the Microsoft Access DBMS.
Research on Worm Detection and Active Defense System
Authors: Wei-min Gao
Venue: Proceedings of the 2009 International Conference on Multimedia Information Networking and Security - Volume 02 (2009)

This paper aims at establishing a real-time detection and an active defense network worm attack safety system, by making a detailed analysis of worm detection and active defense technology, and researching its model. It also aims to restrain the worm spread effectively by adopting worm detection algorithm, so as to safeguard the Internet operation. A simulated experiment was made to prove that the Wdads system can subject itself to a good self-regulation and open structure, thus promising high detection rate and low misreport rate.
Scalable coding with side information for packet loss recovery
Authors: Christian Feldbauer, W. Bastiaan Kleijn
Venue: IEEE Transactions on Communications (2009)

This paper presents a packet loss recovery method that uses an incomplete secondary encoding based on scalar quantization as redundancy. The method is redundancy bit rate scalable and allows an adaptation to varying loss scenarios and a varying packeting strategy. The recovery is performed by minimum mean squared error estimation incorporating a statistical model for the quantizers to facilitate real-time adaptation. A bit allocation algorithm is proposed that extends 'reverse water filling' to the problem of scalar encoding dependent variables for a decoder with a final estimation stage and available side information. We apply the method to the encoding of line-spectral frequencies (LSFs), which are commonly used in speech coding, illustrating the good performance of the method.
A review of optimization techniques in metal cutting processes
Authors: Indrajit Mukherjee, Pradip Kumar Ray
Venue: Computers and Industrial Engineering (2006)

In today's rapidly changing scenario in manufacturing industries, applications of optimization techniques in metal cutting processes is essential for a manufacturing unit to respond effectively to severe competitiveness and increasing demand of quality product in the market. Optimization methods in metal cutting processes, considered to be a vital tool for continual improvement of output quality in products and processes include modelling of input-output and in-process parameters relationship and determination of optimal cutting conditions. However, determination of optimal cutting conditions through cost-effective mathematical models is a complex research endeavour, and over the years, the techniques of modelling and optimization have undergone substantial development and expansion. In this paper, the application potential of several modelling and optimization techniques in metal cutting processes, classified under several criteria, has been critically appraised, and a generic framework for parameter optimization in metal cutting processes is suggested for the benefits of selection of an appropriate approach.
A mobile tourist assistance and recommendation system based on complex networks
Authors: Alf-Christian Schering, Martin Dueffer, Andreas Finger, Ilvio Bruder
Venue: Proceeding of the 1st ACM international workshop on Complex networks meet information & knowledge management (2009)

In recent years, the tourism assistance community is gradually shifting its emphasis to digital, interactive systems. Our project aims for digital tourism assistance by combining mobile guiding and route recommendation based on community networks. We use complex networks to tackle problems, such as deriving optimal routes in conjunction with knowledge deduced from our social tourist network, and giving a respectable prediction of which meta- and multimedia data about touristic objects are needed while the tourist is on tour. This article describes ongoing work on the recommendation system and mobile semantic replication strategies.
Gravity modeling of 21/2-D sedimentary basins - a case of variable density contrast
Authors: V. Chakravarthi, N. Sundararajan
Venue: Computers Geosciences (2005)

An algorithm and associated codes are developed to determine the depths to bottom of a 2^1^/^2-D sedimentary basin in which the density contrast varies parabolically with depth. This algorithm estimates initial depths of a sedimentary basin automatically and modifies thereafter appropriately within the permissible limits in an iterative approach as described in the text. Efficacy of the method as well as the code is illustrated by interpreting a gravity anomaly of a synthetic model. Further, the applicability of the method is exemplified with the derived density-depth model of the Godavari sub-basin, India to interpret the gravity anomalies due to the basin. Interpretations based on parabolic density profile are more consistent with existing geological information rather than with those obtained with constant density profile. tant density profile.
Robust blind watermarking of point-sampled geometry
Authors: Parag Agarwal, Balakrishnan Prabhakaran
Venue: IEEE Transactions on Information Forensics and Security (2009)

Digital watermarking for copyright protection of 3-D meshes cannot be directly applied to point clouds, since we need to derive consistent connectivity information, which might change due to attacks, such as noise addition and cropping. Schemes for point clouds operate only on the geometric data and, hence, are generic and applicable to mesh-based representations of 3-D models. For building generic copyright schemes for 3-D models, this paper presents a robust blind watermarking mechanism for 3-D point-sampled geometry. The basic idea is to find a cluster tree from clusters of 3-D points. Using the cluster tree, watermarks can be embedded and extracted by deriving an order among points at global (intracluster) and local levels (intercluster). The multiple bit watermarks are encoded/decoded inside each cluster based on an extension of the cluster structure-based 3-D quantization index modulation. The encoding mechanism makes the technique robust against uniform affine transformations (rotation, scaling, and transformation), reordering, cropping, simplification, and noise addition attacks. The technique when applied to 3-D meshes also achieves robustness against retriangulation and progressive compression techniques. Customization of the bit-encoding scheme achieves high hiding capacity with embedding rates that are equal to 4 b/point, while maintaining the imperceptibility of the watermark with low distortions. The estimated time complexity is O(n log n), where n is the number of 3-D points.
Dynamic subchannel and slot allocation for OFDMA networks supporting mixed traffic: upper bound and a heuristic algorithm
Authors: Antonis Gotsis, Dimitris Komnakos, Philip Constantinou
Venue: IEEE Communications Letters (2009)

Optimizing resource allocation over only the frequency dimension in a single-cell OFDMA network is strictly suboptimal when heterogeneous non-real-time data users coexist. In this work we examine how to jointly exploit multi-user diversity on frequency and time domains towards maximizing cell throughput performance and satisfying individual rate and fairness QoS constraints. We develop a performance upper bound as well as an efficient heuristic algorithm for subcarrier and slot allocation which outperforms existing algorithms under polynomial complexity.