OpenRaiser/Intern-Atlas huggingface dataset:
The egalitarian sharing rule in provision of public projects
Venue: arXiv cs.GT 2005
Year: 2005

In this note we consider a society that partitions itself into disjoint jurisdictions, each choosing a location of its public project and a taxation scheme to finance it. The set of public project is multi-dimensional, and their costs could vary from jurisdiction to jurisdiction. We impose two principles, egalitarianism, that requires the equalization of the total cost for all agents in the same jurisdiction, and efficiency, that implies the minimization of the aggregate total cost within jurisdiction. We show that these two principles always yield a core-stable partition but a Nash stable partition may fail to exist.
Uplink Throughput in a Single-Macrocell/Single-Microcell CDMA System, with Application to Data Access Points
Venue: arXiv cs.IT 2005
Year: 2005

This paper studies a two-tier CDMA system in which the microcell base is converted into a data access point (DAP), i.e., a limited-range base station that provides high-speed access to one user at a time. The microcell (or DAP) user operates on the same frequency as the macrocell users and has the same chip rate. However, it adapts its spreading factor, and thus its data rate, in accordance with interference conditions. By contrast, the macrocell serves multiple simultaneous data users, each with the same fixed rate. The achieveable throughput for individual microcell users is examined and a simple, accurate approximation for its probability distribution is presented. Computations for average throughputs, both per-user and total, are also presented. The numerical results highlight the impact of a desensitivity parameter used in the base-selection process.
Exact and Approximation Algorithms for DNA Tag Set Design
Venue: arXiv cs.DS 2005
Year: 2005

In this paper we propose new solution methods for designing tag sets for use in universal DNA arrays. First, we give integer linear programming formulations for two previous formalizations of the tag set design problem, and show that these formulations can be solved to optimality for instance sizes of practical interest by using general purpose optimization packages. Second, we note the benefits of periodic tags, and establish an interesting connection between the tag design problem and the problem of packing the maximum number of vertex-disjoint directed cycles in a given graph. We show that combining a simple greedy cycle packing algorithm with a previously proposed alphabetic tree search strategy yields an increase of over 40% in the number of tags compared to previous methods.
Multi-Dimensional Hash Chains and Application to Micropayment Schemes
Venue: arXiv cs.CR 2005
Year: 2005

One-way hash chains have been used in many micropayment schemes due to their simplicity and efficiency. In this paper we introduce the notion of multi-dimensional hash chains, which is a new generalization of traditional one-way hash chains. We show that this construction has storage-computational complexity of O(logN) per chain element, which is comparable with the best result reported in recent literature. Based on multi-dimensional hash chains, we then propose two cash-like micropayment schemes, which have a number of advantages in terms of efficiency and security. We also point out some possible improvements to PayWord and similar schemes by using multi-dimensional hash chains
On the Complexity of Nonrecursive XQuery and Functional Query Languages on Complex Values
Venue: arXiv cs.DB 2005
Year: 2005

This paper studies the complexity of evaluating functional query languages for complex values such as monad algebra and the recursion-free fragment of XQuery. We show that monad algebra with equality restricted to atomic values is complete for the class TA[2^{O(n)}, O(n)] of problems solvable in linear exponential time with a linear number of alternations. The monotone fragment of monad algebra with atomic value equality but without negation is complete for nondeterministic exponential time. For monad algebra with deep equality, we establish TA[2^{O(n)}, O(n)] lower and exponential-space upper bounds. Then we study a fragment of XQuery, Core XQuery, that seems to incorporate all the features of a query language on complex values that are traditionally deemed essential. A close connection between monad algebra on lists and Core XQuery (with ``child'' as the only axis) is exhibited, and it is shown that these languages are expressively equivalent up to representation issues. We show that Core XQuery is just as hard as monad algebra w.r.t. combined complexity, and that it is in TC0 if the query is assumed fixed.
mod_oai: An Apache Module for Metadata Harvesting
Venue: arXiv cs.DL 2005
Year: 2005

We describe mod_oai, an Apache 2.0 module that implements the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). OAIPMH is the de facto standard for metadata exchange in digital libraries and allows repositories to expose their contents in a structured, application-neutral format with semantics optimized for accurate incremental harvesting. Current implementations of OAI-PMH are either separate applications that access an existing repository, or are built-in to repository software packages. mod_oai is different in that it optimizes harvesting web content by building OAI-PMH capability into the Apache server. We discuss the implications of adding harvesting capability to an Apache server and describe our initial experimental results accessing a departmental web site using both web crawling and OAIPMH harvesting techniques.
Geometric Models of Rolling-Shutter Cameras
Venue: arXiv cs.CV 2005
Year: 2005

Cameras with rolling shutters are becoming more common as low-power, low-cost CMOS sensors are being used more frequently in cameras. The rolling shutter means that not all scanlines are exposed over the same time interval. The effects of a rolling shutter are noticeable when either the camera or objects in the scene are moving and can lead to systematic biases in projection estimation. We develop a general projection equation for a rolling shutter camera and show how it is affected by different types of camera motion. In the case of fronto-parallel motion, we show how that camera can be modeled as an X-slit camera. We also develop approximate projection equations for a non-zero angular velocity about the optical axis and approximate the projection equation for a constant velocity screw motion. We demonstrate how the rolling shutter effects the projective geometry of the camera and in turn the structure-from-motion.
Monotonic and Nonmonotonic Preference Revision
Venue: arXiv cs.DB 2005
Year: 2005

We study here preference revision, considering both the monotonic case where the original preferences are preserved and the nonmonotonic case where the new preferences may override the original ones. We use a relational framework in which preferences are represented using binary relations (not necessarily finite). We identify several classes of revisions that preserve order axioms, for example the axioms of strict partial or weak orders. We consider applications of our results to preference querying in relational databases.
Probabilistic and Team PFIN-type Learning: General Properties
Venue: arXiv cs.LG 2005
Year: 2005

We consider the probability hierarchy for Popperian FINite learning and study the general properties of this hierarchy. We prove that the probability hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and p_2 and answers whether PFIN-type learning with the probability of success p_1 is equivalent to PFIN-type learning with the probability of success p_2. To prove our result, we analyze the topological structure of the probability hierarchy. We prove that it is well-ordered in descending ordering and order-equivalent to ordinal epsilon_0. This shows that the structure of the hierarchy is very complicated. Using similar methods, we also prove that, for PFIN-type learning, team learning and probabilistic learning are of the same power.
On the Effect of Fading on Ad hoc Networking
Venue: arXiv cs.NI 2005
Year: 2005

Most MANET (Mobile Ad hoc NETwork) research assumes idealized propagation models. Experimental results have shown significant divergence from simulation results due to the effect of signal fading in realistic wireless communication channels. In this paper, we characterize the impact of fading on protocol performance. We first study the effect of fading on MAC performance and show that its effect can be dominating. One of our important conclusions is that eliminating RTS/CTS packets results in more effective operation under fading. We also identify an unfairness problem that arises due to backoffs in the presence of fading. Moreover, fading results in several subtle interactions between the MAC and routing layers. We identify several of these problems and make observations about effective approaches for addressing them. For example, the criteria for determining the best path should not only consider the link status but also the link order. In addition, because routing protocols rely on MAC level transmission failure (when the retry limit is exceeded), route failure errors are often generated unnecessarily. Finally, because MAC level broadcasts are unreliable, they are especially vulnerable to fading. We analyze these effects and outline preliminary solutions to them.
Multiple Description Quantization via Gram-Schmidt Orthogonalization
Venue: arXiv cs.IT 2005
Year: 2005

The multiple description (MD) problem has received considerable attention as a model of information transmission over unreliable channels. A general framework for designing efficient multiple description quantization schemes is proposed in this paper. We provide a systematic treatment of the El Gamal-Cover (EGC) achievable MD rate-distortion region, and show that any point in the EGC region can be achieved via a successive quantization scheme along with quantization splitting. For the quadratic Gaussian case, the proposed scheme has an intrinsic connection with the Gram-Schmidt orthogonalization, which implies that the whole Gaussian MD rate-distortion region is achievable with a sequential dithered lattice-based quantization scheme as the dimension of the (optimal) lattice quantizers becomes large. Moreover, this scheme is shown to be universal for all i.i.d. smooth sources with performance no worse than that for an i.i.d. Gaussian source with the same variance and asymptotically optimal at high resolution. A class of low-complexity MD scalar quantizers in the proposed general framework also is constructed and is illustrated geometrically; the performance is analyzed in the high resolution regime, which exhibits a noticeable improvement over the existing MD scalar quantization schemes.
Reversible Fault-Tolerant Logic
Venue: arXiv cs.IT 2005
Year: 2005

It is now widely accepted that the CMOS technology implementing irreversible logic will hit a scaling limit beyond 2016, and that the increased power dissipation is a major limiting factor. Reversible computing can potentially require arbitrarily small amounts of energy. Recently several nano-scale devices which have the potential to scale, and which naturally perform reversible logic, have emerged. This paper addresses several fundamental issues that need to be addressed before any nano-scale reversible computing systems can be realized, including reliability and performance trade-offs and architecture optimization. Many nano-scale devices will be limited to only near neighbor interactions, requiring careful optimization of circuits. We provide efficient fault-tolerant (FT) circuits when restricted to both 2D and 1D. Finally, we compute bounds on the entropy (and hence, heat) generated by our FT circuits and provide quantitative estimates on how large can we make our circuits before we lose any advantage over irreversible computing.
Average Coset Weight Distribution of Combined LDPC Matrix Ensemble
Venue: arXiv cs.IT 2005
Year: 2005

In this paper, the average coset weight distribution (ACWD) of structured ensembles of LDPC (Low-density Parity-Check) matrix, which is called combined ensembles, is discussed. A combined ensemble is composed of a set of simpler ensembles such as a regular bipartite ensemble. Two classes of combined ensembles have prime importance; a stacked ensemble and a concatenated ensemble, which consists of set of stacked matrices and concatenated matrices, respectively. The ACWD formulas of these ensembles is shown in this paper. Such formulas are key tools to evaluate the ACWD of a complex combined ensemble. From the ACWD of an ensemble, we can obtain some detailed properties of a code (e.g., weight of coset leaders) which is not available from an average weight distribution. Moreover, it is shown that the analysis based on the ACWD is indispensable to evaluate the average weight distribution of some classes of combined ensembles.
Improving Spam Detection Based on Structural Similarity
Venue: arXiv cs.CR 2005
Year: 2005

We propose a new detection algorithm that uses structural relationships between senders and recipients of email as the basis for the identification of spam messages. Users and receivers are represented as vectors in their reciprocal spaces. A measure of similarity between vectors is constructed and used to group users into clusters. Knowledge of their classification as past senders/receivers of spam or legitimate mail, comming from an auxiliary detection algorithm, is then used to label these clusters probabilistically. This knowledge comes from an auxiliary algorithm. The measure of similarity between the sender and receiver sets of a new message to the center vector of clusters is then used to asses the possibility of that message being legitimate or spam. We show that the proposed algorithm is able to correct part of the false positives (legitimate messages classified as spam) using a testbed of one week smtp log.
Heterogeneous Relational Databases for a Grid-enabled Analysis Environment
Venue: arXiv cs.DC 2005
Year: 2005

Grid based systems require a database access mechanism that can provide seamless homogeneous access to the requested data through a virtual data access system, i.e. a system which can take care of tracking the data that is stored in geographically distributed heterogeneous databases. This system should provide an integrated view of the data that is stored in the different repositories by using a virtual data access mechanism, i.e. a mechanism which can hide the heterogeneity of the backend databases from the client applications. This paper focuses on accessing data stored in disparate relational databases through a web service interface, and exploits the features of a Data Warehouse and Data Marts. We present a middleware that enables applications to access data stored in geographically distributed relational databases without being aware of their physical locations and underlying schema. A web service interface is provided to enable applications to access this middleware in a language and platform independent way. A prototype implementation was created based on Clarens [4], Unity [7] and POOL [8]. This ability to access the data stored in the distributed relational databases transparently is likely to be a very powerful one for Grid users, especially the scientific community wishing to collate and analyze data distributed over the Grid.
Learning Polynomial Networks for Classification of Clinical Electroencephalograms
Venue: arXiv cs.AI 2005
Year: 2005

We describe a polynomial network technique developed for learning to classify clinical electroencephalograms (EEGs) presented by noisy features. Using an evolutionary strategy implemented within Group Method of Data Handling, we learn classification models which are comprehensively described by sets of short-term polynomials. The polynomial models were learnt to classify the EEGs recorded from Alzheimer and healthy patients and recognize the EEG artifacts. Comparing the performances of our technique and some machine learning methods we conclude that our technique can learn well-suited polynomial models which experts can find easy-to-understand.
Bounds on the Entropy of Patterns of I.I.D. Sequences
Venue: arXiv cs.IT 2005
Year: 2005

Bounds on the entropy of patterns of sequences generated by independently identically distributed (i.i.d.) sources are derived. A pattern is a sequence of indices that contains all consecutive integer indices in increasing order of first occurrence. If the alphabet of a source that generated a sequence is unknown, the inevitable cost of coding the unknown alphabet symbols can be exploited to create the pattern of the sequence. This pattern can in turn be compressed by itself. The bounds derived here are functions of the i.i.d. source entropy, alphabet size, and letter probabilities. It is shown that for large alphabets, the pattern entropy must decrease from the i.i.d. one. The decrease is in many cases more significant than the universal coding redundancy bounds derived in prior works. The pattern entropy is confined between two bounds that depend on the arrangement of the letter probabilities in the probability space. For very large alphabets whose size may be greater than the coded pattern length, all low probability letters are packed into one symbol. The pattern entropy is upper and lower bounded in terms of the i.i.d. entropy of the new packed alphabet. Correction terms, which are usually negligible, are provided for both upper and lower bounds.
A Scalable Stream-Oriented Framework for Cluster Applications
Venue: arXiv cs.DC 2005
Year: 2005

This paper presents a stream-oriented architecture for structuring cluster applications. Clusters that run applications based on this architecture can scale to tenths of thousands of nodes with significantly less performance loss or reliability problems. Our architecture exploits the stream nature of the data flow and reduces congestion through load balancing, hides latency behind data pushes and transparently handles node failures. In our ongoing work, we are developing an implementation for this architecture and we are able to run simple data mining applications on a cluster simulator.
Self-Organizing Multilayered Neural Networks of Optimal Complexity
Venue: arXiv cs.NE 2005
Year: 2005

The principles of self-organizing the neural networks of optimal complexity is considered under the unrepresentative learning set. The method of self-organizing the multi-layered neural networks is offered and used to train the logical neural networks which were applied to the medical diagnostics.
Proceedings of the Pacific Knowledge Acquisition Workshop 2004
Venue: arXiv cs.AI 2005
Year: 2005

Artificial intelligence (AI) research has evolved over the last few decades and knowledge acquisition research is at the core of AI research. PKAW-04 is one of three international knowledge acquisition workshops held in the Pacific-Rim, Canada and Europe over the last two decades. PKAW-04 has a strong emphasis on incremental knowledge acquisition, machine learning, neural nets and active mining. The proceedings contain 19 papers that were selected by the program committee among 24 submitted papers. All papers were peer reviewed by at least two reviewers. The papers in these proceedings cover the methods and tools as well as the applications related to develop expert systems or knowledge based systems.
Metalinguistic Information Extraction for Terminology
Venue: arXiv cs.CL 2005
Year: 2005

This paper describes and evaluates the Metalinguistic Operation Processor (MOP) system for automatic compilation of metalinguistic information from technical and scientific documents. This system is designed to extract non-standard terminological resources that we have called Metalinguistic Information Databases (or MIDs), in order to help update changing glossaries, knowledge bases and ontologies, as well as to reflect the metastable dynamics of special-domain knowledge.
An Improved Remote User Authentication Scheme Using Smart Cards
Venue: arXiv cs.CR 2005
Year: 2005

In 2000, Hwang and Li proposed a new remote user authentication scheme using smart cards. In the same year, Chan and Cheng pointed out that Hwang and Li’s scheme is not secure against the masquerade attack. Further, in 2003, Shen, Lin and Hwang pointed out a different type of attack on Hwang and Li’s scheme and presented a modified scheme to remove its security pitfalls. This paper presents an improved scheme which is secure against Chan-Cheng and all the extended attacks.
On the Unicity Distance of Stego Key
Venue: arXiv cs.CR 2005
Year: 2005

Steganography is about how to send secret message covertly. And the purpose of steganalysis is to not only detect the existence of the hidden message but also extract it. So far there have been many reliable detecting methods on various steganographic algorithms, while there are few approaches that can extract the hidden information. In this paper, the difficulty of extracting hidden information, which is essentially a kind of privacy, is analyzed with information-theoretic method in the terms of unicity distance of steganographic key (abbreviated stego key). A lower bound for the unicity distance is obtained, which shows the relations between key rate, message rate, hiding capacity and difficulty of extraction. Furthermore the extracting attack to steganography is viewed as a special kind of cryptanalysis, and an effective method on recovering the stego key of popular LSB replacing steganography in spatial images is presented by combining the detecting technique of steganalysis and correlation attack of cryptanalysis together. The analysis for this method and experimental results on steganographic software ``Hide and Seek 4.1" are both accordant with the information-theoretic conclusion.
Componentwise Least Squares Support Vector Machines
Venue: arXiv cs.LG 2005
Year: 2005

This chapter describes componentwise Least Squares Support Vector Machines (LS-SVMs) for the estimation of additive models consisting of a sum of nonlinear components. The primal-dual derivations characterizing LS-SVMs for the estimation of the additive model result in a single set of linear equations with size growing in the number of data-points. The derivation is elaborated for the classification as well as the regression case. Furthermore, different techniques are proposed to discover structure in the data by looking for sparse components in the model based on dedicated regularization schemes on the one hand and fusion of the componentwise LS-SVMs training with a validation criterion on the other hand. (keywords: LS-SVMs, additive models, regularization, structure detection)
On Optimality Condition of Complex Systems: Computational Evidence
Venue: arXiv cs.CC 2005
Year: 2005

A general condition determining the optimal performance of a complex system has not yet been found and the possibility of its existence is unknown. To contribute in this direction, an optimization algorithm as a complex system is presented. The performance of the algorithm for any problem is controlled as a convex function with a single optimum. To characterize the performance optimums, certain quantities of the algorithm and the problem are suggested and interpreted as their complexities. An optimality condition of the algorithm is computationally found: if the algorithm shows its best performance for a problem, then the complexity of the algorithm is in a linear relationship with the complexity of the problem. The optimality condition provides a new perspective to the subject by recognizing that the relationship between certain quantities of the complex system and the problem may determine the optimal performance.