ppxscal/embedding_network huggingface dataset:
Blind Detection for Additive Embedding Using Underdetermined ICA
Authors: Hafiz Malik Ashfaq Khokhar, Rashid Ansari, Marco Salvemini
Venue: Proceedings of the Eighth IEEE International Symposium on Multimedia (2006)

This paper presents an efficient blind watermark detection scheme for additive embedding (AE) based on underdetermined independent component analysis (ICA) framework. The proposed detector assumes that the host signal and the watermark obey non-Gaussian distributions and watermark embedding follows AE model. The proposed blind watermark detector employs blind source separation (BSS) for underdetermined mixtures for watermark estimation. Simulation results are presented showing that the proposed detector performs significantly better than existing correlation based blind detectors operating without suppressing the host signal interference at the detector.
Optimal Relay Location for Resource-limited Energy-efficient Wireless Communication
Authors: Ionut Cardei, Mihaela Cardei, Lusheng Wang, Baogang Xu, Ding-Zhu Du
Venue: Journal of Global Optimization (2006)

In the design of wireless networks, techniques for improving energy efficiency and extending network lifetime have great importance, particularly for defense and civil/rescue applications where resupplying transmitters with new batteries is not feasible. In this paper we study a method for improving the lifetime of wireless networks by minimizing the length of the longest edge in the interconnecting tree by deploying additional relay nodes at specific locations. This optimization problem, known as the Bottleneck Steiner Tree Problem (BSTP), asks to find a Steiner tree for n terminals with at most k Steiner points such that the length of the longest edge in the tree is minimized. We present a ratio- $$\sqrt{3}+\epsilon$$ polynomial time approximation algorithm for BSTP, where $$\epsilon$$ is an arbitrary positive number.
Generic programming and high-performance libraries
Authors: Douglas Gregor, Jaakko Järvi, Mayuresh Kulkarni, Andrew Lumsdaine, David Musser, Sibylle Schupp
Venue: International Journal of Parallel Programming (2005)

Generic programming is an especially attractive paradigm for developing libraries for high-performance computing because it simultaneously emphasizes generality and efficiency. In the generic programming approach, interfaces are based on sets of specified requirements on types, rather than on any particular types, allowing algorithms to inter-operate with any data types meeting the necessary requirements. These sets of requirements, known as concepts, can specify syntactic as well as semantic requirements. Besides providing a powerful means of describing interfaces to maximize software reuse, concepts provide a uniform mechanism for more closely coupling libraries with compilers and for effecting domain-specific library-based compiler extensions. To realize this goal however, programming languages and their associated tools must support concepts as first-class constructs. In this paper we advocate better syntactic and semantic support to make concepts first-class and present results demonstrating the kinds of improvements that are possible with static checking, compiler optimization, and algorithm correctness proofs for generic libraries based on concepts.
Skew Estimation in Persian Documents: A Novel Approach
Authors: M. Y. Ashkan, D. S. Guru, P. Punitha
Venue: Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (2006)

In this paper, a robust method is proposed for estimating skew angle of Persian document images. The proposed algorithm applies morphological closing on blotched image to obtain image with text line blocks. Later, the text line blocks are subjected to thinning to obtain the skeleton and convert the text into line like structures used to estimate the skew angle of document image. The results of the experiments emphasizes that the proposed algorithm works well irrespective of the type of script even for a wide range of skew angle (with in ±90°).
Early Software Reliability Prediction with ANN Models
Authors: Q. P. Hu, M. Xie, S. H. Ng
Venue: Proceedings of the 12th Pacific Rim International Symposium on Dependable Computing (2006)

It is well-known that accurate reliability estimates can be obtained by using software reliability models only in the later phase of software testing. However, prediction in the early phase is important for costeffective and timely management. Also this requirement can be achieved with information from previous releases or similar projects. This basic idea has been implemented with nonhomogenerous Poisson process (NHPP) models by assuming the same testing/debugging environment for similar projects or successive releases. In this paper we study an approach to using past fault-related data with Artificial Neural Network (ANN) models to improve reliability predictions in the early testing phase. Numerical examples are shown with both actual and simulated datasets. Better performance of early prediction is observed compared with original ANN model with no such historical fault-related data incorporated. Also, the problem of optimal switching point from the proposed approach to original ANN model is studied, with three numerical examples.
Research of Secure Anycast
Authors: Zhiguo Zhou, Gaochao Xu, Jinxin He, Chunyan Deng
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

Anycast is a new communication mode in the standards of IPV6. A host can communicate with the "nearest" member in the destination group through anycast. Because the group management and the routing protocol in the anycast are not so mature, when communicating it is easy to be utilized by some attacks such as masquerading and denial of service, etc. Utilizing the correlative secure technology of Multicast, we propose a new group management method to secure anycast on the basis of others' research in this paper, which can ensure the security of anycast server and the secure communication between anycast client and server. This method has been proved to be effective.
Multiagent System for Reputation--based Web Services Selection
Authors: Hui Wang, Deguo Yang, Yuhui Zhao, Yuan Gao
Venue: Proceedings of the Sixth International Conference on Quality Software (2006)

QoS-based service selection mechanisms will play an essential role in service-oriented architectures, as e-Business applications want to use services that most accurately meet their requirements. However, current techniques provide no support for actually making rational selections. In this paper, we develop a multiagent framework for QoS-based Web services selection, through a combination of the respective merits of agent and service-oriented to enables dynamic and efficient delivery of composite Web services. We present a service-oriented distributed reputation assessment algorithm as a QoS support module in the framework. We give an example application and some preliminary results.
Statistical Test Compaction Using Binary Decision Trees
Authors: Sounil Biswas, Ronald D. (Shawn) Blanton
Venue: IEEE Design Test (2006)

Because of the significant cost of explicitly testing an integrated, heterogeneous device for all its specifications, there is a need for a test methodology that minimizes test cost while maintaining product quality and limiting yield loss. The authors are developing a decision-tree-based statistical-learning methodology to compact the complete specification-based test set of an integrated device by eliminating redundant tests. A test is deemed redundant if its output can be reliably predicted using other tests that are not eliminated. To ensure the required accuracy for commercial devices, the authors employ a number of modeling and data-massaging techniques to reduce prediction error. Test compaction results produced for a commercial MEMS accelerometer are promising in that they indicate it is possible to eliminate an expensive mechanical test.
A Two-Pass Improved Encoding Scheme for Fractal Image Compression
Authors: Kin-Wah Ching Eugene, Ghim-Hwee Ong
Venue: Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation (2006)

An improvement scheme, so named the Two-Pass Improved Encoding Scheme (TIES), for the application to image compression through the extension of the existing concept of Fractal Image Compression (FIC), which capitalizes on the self-similarity within a given image to be compressed, is proposed in this paper. We first briefly explore the existing image compression technology based on FIC, before proceeding to establish the concept behind the TIES algorithm. We then devise an effective encoding and decoding algorithm for the implementation of TIES through the consideration of the domain pool of an image, domain block transformation, scaling and intensity variation, range block approximation using linear combinations, and finally the use of an arithmetic compression algorithm to store the final data as close to source entropy as possible. We then conclude by explicitly comparing the performance of this implementation of the TIES algorithm against that of FIC under the same conditions, in which the TIES algorithm exhibits significantly better compression performance for large images (1024x1024px) compared to the FIC algorithm.
Diffeomorphic Matching of Diffusion Tensor Images
Authors: Yan Cao, Michael I. Miller, Susumu Mori, Raimond L. Winslow, Laurent Younes
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of tensor fields on the image volume, resulting in optimizing for geodesics on the space of diffeomorphisms connecting two diffusion tensor images. A coarse to fine multi-resolution and multikernel- width scheme is detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI brain and images.
Towards a Scalable Heterogeneous P2P Data and Knowledge Sharing Facility
Authors: S. M. Deen, K. Ponnamperuma, M. Takizawa
Venue: Proceedings of the 26th IEEE International ConferenceWorkshops on Distributed Computing Systems (2006)

Starting with research issues, the paper explores how a scalable P2P/DAKS facility can be supported with inter-peer schema heterogeneity, while preserving peer autonomy, such that a given peer can join multiple P2P coalitions (i.e. networks) of its interest. Peers need to provide coalition-specific ontologies of their schemas, which are dynamically integrated and managed by the system. The scalability is provided through the concepts of topic-tree, class, minder and domain, all of which can change dynamically without requiring any action from the peer users. An associated central issue covered is integration technique and integration compatibility of ontologies.
Modular BDI architecture
Authors: Peter Novák, Jürgen Dix
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

One of the main challenges in agent-oriented programming is the design of specialized programming languages for single agent development. They should provide transparent interfaces to existing mainstream programming languages for easy integration with external code and legacy software. The underlying architecture of such programming languages has to be robust enough to support various approaches to knowledge representation and agent reasoning models.In this paper we propose a modular BDI agent programming architecture, which is independent of the internal structure of its components and agent reasoning model. The connections between the components of such a BDI system are provided by interaction rules. Using this separation, we are able to draw a clear distinction between knowledge representation issues of a BDI agent system components and its dynamics.
Beyond streams and graphs: dynamic tensor analysis
Authors: Jimeng Sun, Dacheng Tao, Christos Faloutsos
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

How do we find patterns in author-keyword associations, evolving over time? Or in Data Cubes, with product-branch-customer sales information? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example.We propose to envision such higher order data as tensors,and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA.We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets.
Adapting Interaction Analysis to Support Evaluation and Regulation: A Case Study
Authors: Jose Antonio Marcos, Alejandra Martinez, Yannis Dimitriadis, Rocio Anguita
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Interaction analysis has become a basic function in the field of collaborative learning, as a means for supporting both regulation and evaluation of collaborative learning processes. In spite of the fact that these processes rely on the same basic functionalities, there is a lack of proposals or systems that integrate them. The most outstanding difference between the tools that support either of the approaches is that they are oriented to different types of users. Then, an interaction analysis tool able to adapt to different roles would be able to perform both functionalities. This paper presents an experience performed in an authentic learning scenario using interaction analysis for supporting regulation for students and evaluation for teachers adapting its functionality depending on analysis needs of teacher and learners that participate in the activity. A first evaluation carried out shows that the participants assess positively this experience and consider the information received easy to understand, reliable, useful and capable to modify their way of collaboration.
The reality of virtual reality: a focus on training
Authors: Robert G. Lowe
Venue: Proceedings of the 1996 international conference on Learning sciences (1996)

Northern Telecom (Nortel), a global telecommunications firm based in Dallas, TX, provides product training to over 20,000 students a year using conventional computer-based training. In 1995 they wanted to change existing methodology to include 1) on-demand training 2) product and skill specific training 3) have the program ready by a specific date and 4) be cost effective. They chose to use virtual reality software for delivering the cost-effective, measurable program. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Toward Efficient Peer-to-Peer Information Retrieval Based on Textual Entailment
Authors: Yasser Kotb
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

Peer-to-Peer networks are gaining increasing attention from both the scientific and the larger Internet user community. Peer-to-peer systems are very large computer networks, where peers collaborate to provide a common service. Providing large-scale Information retrieval, like searching the Internet, is an attractive application for P2P systems. Data discovery and retrieval is of great importance to computer users and the broad Internet community. In this paper, we propose a novel approach to retrieve not only textual documents that have specified keywords, but also to discover semantically equivalent or entailed documents from given keywords. This approach is based on the recent natural language processing approach called the Textual Entailment Approach.
Confidence-Based Active Learning
Authors: Mingkun Li Ishwar K. Sethi
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)

This paper proposes a new active learning approach, confidence-based active learning, for training a wide range of classifiers. This approach is based on identifying and annotating uncertain samples. The uncertainty value of each sample is measured by its conditional error. The approach takes advantage of current classifiers' probability preserving and ordering properties. It calibrates the output scores of classifiers to conditional error. Thus, it can estimate the uncertainty value for each input sample according to its output score from a classifier and select only samples with uncertainty value above a user-defined threshold. Even though we cannot guarantee the optimality of the proposed approach, we find it to provide good performance. Compared with existing methods, this approach is robust without additional computational effort. A new active learning method for support vector machines (SVMs) is implemented following this approach. A dynamic bin width allocation method is proposed to accurately estimate sample conditional error and this method adapts to the underlying probabilities. The effectiveness of the proposed approach is demonstrated using synthetic and real data sets and its performance is compared with the widely used least certain active learning method.
Fast Quality Driven Selection of Composite Web Services
Authors: Jae-Ho Jang, Dong-Hoon Shin, Kyong-Ho Lee
Venue: Proceedings of the European Conference on Web Services (2006)

The composite Web service selection is the process of building an executable composite Web service. Generally, the selection process involves decision making in terms of non-functional properties of Web services such as QoS requirements. Since the number of Web services is rapidly increasing and the QoS of the Web services environment changes dynamically, the fast selection is important. This paper presents a fast method for quality driven composite Web services selection based on a workflow partition strategy. The proposed method partitions an abstract workflow into two sub-workflows to decrease the number of candidate services that should be considered. A mixed integer linear programming is utilized for solving the service selection problem. Experimental results show that the partition strategy performed faster than the optimal strategy. Also, the qualities of the selected composite Web services are not significantly different from the optimal one.
A pluggable architectural model and a formally specified programming language independent API for an ontological knowledge base server
Authors: Alexander Paar, Jürgen Reuter, Jaron Schaeffer
Venue: Proceedings of the 2005 Australasian Ontology Workshop - Volume 58 (2005)

Recently, ontology engineering has become ever more important when it comes to conceptualize knowledge. However, writing software applications that operate on ontological knowledge still suffers from a lack of connectivity provided by available ontology management systems. Interfaces of ontology management systems are either based on error prone programming language agnostic remoting protocols or they are restricted to one particular programming language. We implemented an ontological Knowledge Base Server, which can expose the functionality of arbitrary off-the-shelf ontology management systems via arbitrary remoting protocols. Based on XML Schema Definition, we defined a full-fledged API for processing OWL ontologies. Client access code can be generated automatically for virtually any object oriented programming language. Using Description Logics terminology, the Knowledge Base Server API was formally specified, such that it could be used to validate implementations based on three different adapted ontology management systems.
On the expansion rate of Margulis expanders
Authors: Nathan Linial, Eran London
Venue: Journal of Combinatorial Theory Series B (2006)

In this note we determine exactly the expansion rate of an infinite 4-regular expander graph which is a variant of an expander due to Margulis. The vertex set of this graph consists of all points in the plane. The point (x, y) is adjacent to the points S(x, y), S-1 (x, y), T (x, y), T-1 (x, y) where S(x, y)=(x, x + y) and T (x, y) = (x + y, y). We show that the expansion rate of this 4-regular graph is 2. The main technical result asserts that for any compact planar set A of finite positive measure, |S(A) ∪ S-1(A) ∪ T(A) ∪ T-1(A) ∪ A|/|A| ≥ 2, where |B| is the Lebesgue measure of B.The proof is completely elementary and is based on symmetrization--a classical method in the area of isoperimetric problems. We also use symmetrization to prove a similar result for a directed version of the same graph.
When Action Turns into Words. Activation of Motor-Based Knowledge during Categorization of Manipulable Objects
Authors: Christian Gerlach, Ian Law, Olaf B. Paulson
Venue: Journal of Cognitive Neuroscience (2002)

Functional imaging studies have demonstrated that processing of man-made objects activate the left ventral premotor cortex, which is known to be concerned with motor function. This has led to the suggestion that the comprehension of man-made objects may rely on motor-based knowledge of object utilization (action knowledge). Here we show that the left ventral premotor cortex is activated during categorization of "both" fruit/vegetables and articles of clothing, relative to animals and nonmanipulable man-made objects. This observation suggests that action knowledge may not be important for the processing of man-made objects per se, but rather for the processing of manipulable objects in general, whether natural or man-made. These findings both support psycholinguistic theories suggesting that certain lexical categories may evolve from, and the act of categorization rely upon, motor-based knowledge of action equivalency, and have important implications for theories of category specificity. Thus, the finding that the processing of vegetables/fruit and articles of clothing give rise to similar activation is difficult to account for should knowledge representations in the brain be truly categorically organized. Instead, the data are compatible with the suggestion that categories differ in the weight they put on different types of knowledge.
Algorithm for Medical Image Patching Based on Mutual Information
Authors: Shaohai Hu, Likuan Wang, Shuwen Xu
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

This paper looks further at the method of medical image patching based on mutual information (MI). The methods of density estimation and the searching strategy of image registration have been compared. A new way of image preprocessing based on grads information has been used as well as a novel display method, which introduces directions of the edge curve to image interpolation. A good image patching result has been made with our scheme.