ppxscal/embedding_network huggingface dataset:
Spurious Disambiguation Error Detection
Authors: Claudio Sacerdoti Coen, Stefano Zacchiroli
Venue: Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference (2007)

The disambiguation approach to the input of formulae enables the user to type correct formulae in a terse syntax close to the usual ambiguous mathematical notation. When it comes to incorrect formulae we want to present only errors related to the interpretation meant by the user, hiding errors related to other interpretations (spurious errors).We propose a heuristic to recognize spurious errors, which has been integrated with the disambiguation algorithm of [6].
A fuzzy-soft competitive learning algorithm for ophthalmological MRI segmentation
Authors: Miin-Shen Yang, Karen Chia-Ren Lin, Hsiu-Chih Liuc, Jiing-Feng Lirng
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

In this paper, we consider a fuzzy-soft competitive learning algorithm (FS-CLA) which is a sequential type of the fuzzy-soft learning vector quantization (FS-LVQ). The FS-CLA is a competitive learning with a fuzzy relaxation technique by using fuzzy membership functions as kernel type neighborhood interaction functions. We then apply the FS-CLA to magnetic resonance image (MRI) segmentation for a real case of ophthalmology recommended by a Neurologist with MR image data. The algorithm is used in segmenting the ophthalmological MRI data for reducing medical image noise effects with a learning mechanism. These segmentation results demonstrate that the proposed FS-CLA is useful for use in MRI segmentation as an aid for support diagnoses.
Comparison of Chang's with Sorenson's Attenuation Correction Method by Varying Linear Attenuation Coefficient Values in Tc-99m SPECT Imaging
Authors: Inayatullah Shah Sayed, Ahmed Zakaria, Norhafiza Nik
Venue: Medical Imaging and Informatics: 2nd International Conference, MIMI 2007, Beijing, China, August 14-16, 2007 Revised Selected Papers (2008)

Attenuation (scattering and absorption) of gamma photons in the patient's body is one of the major limitations among the others in single photon emission computed tomography (SPECT). It reduces quantitative accuracy of measured radioactivity concentration and causes hot rim artifacts in reconstructed images if not corrected for. A variety of approximate attenuation correction methods has been developed or proposed by various groups to date, but all methods have some limitations. In this paper two attenuation correction methods have been investigated and compared. Data are acquired with both the collimators either LEGP or LEHR by scanning R. A. Carlson cylindrical phantom over 360o with an acrylic block of holes simulating hot regions of various sizes at different locations with respect to the walls of the phantom, which was filled with water and Tc-99m solution was uniformly distributed. Results show that the Chang's attenuation correction method works better as compared to the Sorenson's method in terms of the linearity in measured counts in hot regions. However, Chang's method is sensitive with linear attenuation coefficient values and also gives higher standard deviation values particularly in smaller hot regions count density - with LEHR collimator data - compared to the Sorenson's method.
Moment+: Mining Closed Frequent Itemsets over Data Stream
Authors: Haifeng Li, Hong Chen
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Closed frequent itemsets(CFI) mining uses less memory to store the entire information of frequent itemsets thus is much suitable for mining stream. In this paper, we discuss recent CFI mining methods over stream and presents an improved algorithm Moment+ based on the existent one Moment. Moment+ focuses on the problem of mining CFI over data stream sliding window and proposes a new structure Extended Closed Enumeration Tree(ECET) to store the CFIs and nodes' BPN which is introduced to reduce the search space, with which new mining method is designed to mine more rapidly with little memory cost sacrifice. The experimental results show that this method is effective and efficient.
Performance Evaluation of a Gradual Differentiation Scheme for Telecommunication Networks
Authors: Tom Maertens, Joris Walraevens, Herwig Bruneel
Venue: Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications (2008)

Supporting different services with different Quality of Service (QoS) requirements is not an easy task in modern telecommunication networks. An efficient priority scheduling discipline is thus of great importance. The static, Head-Of-Line (HOL) discipline achieves differentiation between different types of traffic, but may have a too severe impact on the performance of low-priority traffic. In this paper, we propose a priority discipline with priority jumps: packets of the low-priority level can jump to the high-priority level in the course of time. We use probability generating functions to study the system analytically. Some interesting mathematical challenges thereby arise. The impact of priority jumps on the performance of a telecommunication system is finally evaluated by some numerical examples.
Fuzzy Neural Network Classification Design Using Support Vector Machine in Welding Defect
Authors: Xiao-Guang Zhang, Shi-Jin Ren, Xing-Gan Zhang, Fan Zhao
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

To cope up with the variability of defect shadows and the complexity between defect characters and classes in welding image and poor generalization of fuzzy neural network (FNN), a support vector machine (SVM)-based FNN classification algorithm for welding defect is presented. The algorithm firstly adopts supervisory fuzzy cluster to get the rules of input and output space and similarity probability is applied to calculate the importance of rules. Then the parameters and structure of FNN are determined through SVM. Finally, the FNN is trained to classify the welding defects. Simulation for recognizing defects in welding images shows the efficiency of the presented.
The Potential of Interactive Digital Storytelling for the Creation of Educational Computer Games
Authors: Sebastian A. Weiß, Wolfgang Müller
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

The usage of computer games for educational purposes is currently widely discussed. But, to what extend do computer games cope with the requirements for learning? In this paper we discuss the application of digital games to learning and the problems and demands resulting from this integration. The question on how to integrate learning successfully with elements of play and games seems to be unsolved. We propose to integrate Interactive Digital Storytelling (IDS) with game-based learning (GBL) as a concept for building educational computer games. Furthermore, we discuss the requirements for effective learning applications based on the IDS paradigm.
An Improved Fuzzy Neural Network for Ultrasonic Motors Control
Authors: Xu Xu, Yuxiao Zhang, Yanchun Liang, Xiaowei Yang, Zhifeng Hao
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

A newly developed non-symmetric sinusoidal membership function (NSSMF) is constructed. An improved fuzzy neural network controller using NSSMF is constructed to control the speed of ultrasonic motors. A dynamic algorithm with adaptive learning rate is used to train FNNC online. The global convergence of the FNNC systems could be guaranteed by adjusting the adaptive learning rate. The validity of the proposed scheme is examined by simulated experiments.
The inequalities for some types of q-integrals
Authors: Sladjana Marinković, Predrag Rajković, Miomir Stanković
Venue: Computers Mathematics with Applications (2008)

Using the restriction of the q-integral over [a,b] to a finite sum and q-integral of Riemann-type, we establish new integral inequalities of q-Chebyshev type, q-Gruss type, q-Hermite-Hadamard type and Cauchy-Buniakowsky type. Some inequalities which include the boundaries of functions are also indicated.
Natural Language Understanding by Combining Statistical Methods and Extended Context-Free Grammars
Authors: Stefan Schwärzler, Joachim Schenk, Frank Wallhoff, Günther Ruske
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

This paper introduces an novel framework for speech understanding using extended context-free grammars (ECFGs) by combining statistical methods and rule based knowledge. By only using 1st level labels a considerable lower expense of annotation effort can be achieved. In this paper we derive hierarchical non-deterministic automata from the ECFGs, which are transformed into transition networks (TNs) representing all kinds of labels. A sequence of recognized words is hierarchically decoded by using a Viterbi algorithm. In experiments the difference between a hand-labeled tree bank annotation and our approach is evaluated. The conducted experiments show the superiority of our proposed framework. Comparing to a hand-labeled baseline system ($\widehat{=} 100\%$) we achieve 95,4 % acceptance rate for complete sentences and 97.8 % for words. This induces an accuray rate of 95.1 % and error rate of 4.9 %, respectively F1-measure 95.6 % in a corpus of 1 300 sentences.
FACT-Graph: Trend Visualization by Frequency and Co-occurrence
Authors: Ryosuke Saga, Masahiro Terachi, Zhongqi Sheng, Hiroshi Tsuji
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

In order to visualize keyword trends embedded in documents, this paper proposes FACT-Graph (Frequency and Co-occurrence-based Trend Graph). First, we introduce four classes of keywords by TF (Term Frequency) and DF (Document Frequency). Then while some keywords stay in the same class between two periods, others stay in the difference classes. Paying attention to such class transition between periods, we make it a clue of trend analysis. Next, we identify relationship between keywords by their co-occurrence strength and their transition between two periods. Then, we propose FACT-Graph by combining class transition information and co-occurrence transition information. Finally, an application to newspaper article is also discussed.
Facilitating Collaboration in a Distributed Software Development Environment Using P2P Architecture
Authors: Maryam Purvis, Martin Purvis, Bastin Tony Savarimuthu
Venue: Agents and Peer-to-Peer Computing: 5th International Workshop, AP2PC 2006, Hakodate, Japan, May 9, 2006, Revised and Invited Papers (2006)

This paper describes efforts to facilitate collaborative work in a distributed environment by providing infrastructure that facilitates the understanding of inter-connected processes involved and how they interact. In this work we describe how our agent-based framework supports these. This distributed work environment makes use of both P2P and client-server architectures. Using an example of developing an open source software system, we explain how a collaborative work environment can be achieved. In particular, we address how the support for coordination, collaboration and communication are provided using our framework.
Writing Mathematics by Speech: A Case Study for Visually Impaired
Authors: Cristian Bernareggi, Valeria Brigatti
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

Speech input has proven to be useful for entering text in electronic documents, and seems to be a promising technique also for writing mathematical expressions. Up to now, there is no evidence about the potential advantages of speech input on blind persons who need to edit scientific documents. This paper introduces a technique to enable blind persons to input mathematics by speech. A system prototype has been developed, based on the LAMBDA mathematical editor and the Dragon NaturallySpeakingTM software. An early assessment has been undertaken with a group of blind users. The results of this evaluation will be discussed in this paper and will be exploited to enhance the system prototype.
Microprocessor-based neural network controller of a stepper motor for a manipulator arm
Authors: George K. Adam, Georgia Garani, Vilem Srovnal, Jiri Koziorek
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

The development and implementation of an FPGA neural network control system for motion control is a task that requires techniques and methods from several engineering fields. In this paper is presented the design of a microprocessor-based neural control system for the realization of the control of a stepper motor for a manipulator arm. The application circuit developed is a specific design of a low-cost embedded system. Simulation and analysis tests were carried out to verify the design of the prototype circuit board. The preliminary results obtained provide the basis for further future research and refinement of the controller.
Efficient Weight Learning for Markov Logic Networks
Authors: Daniel Lowd, Pedro Domingos
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powerful and increasingly popular representation for statistical relational learning. The state-of-the-art method for discriminative learning of MLN weights is the voted perceptron algorithm, which is essentially gradient descent with an MPE approximation to the expected sufficient statistics (true clause counts). Unfortunately, these can vary widely between clauses, causing the learning problem to be highly ill-conditioned, and making gradient descent very slow. In this paper, we explore several alternatives, from per-weight learning rates to second-order methods. In particular, we focus on two approaches that avoid computing the partition function: diagonal Newton and scaled conjugate gradient. In experiments on standard SRL datasets, we obtain order-of-magnitude speedups, or more accurate models given comparable learning times.
Characterizing Free-Regions of Sensor Nodes
Authors: Navin Rongratana, Laxmi Gewali, Henry Selvaraj
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

This paper considers the problem of relocating a sensor node for increasing the qiality of covarage. We present the notion of free-region of nodes in a sensor network. We investigate properties of free-regions and their characterizations in term of in-free region and out-free region. A centralized algorithm for constructing the free region of sensor node in presented. The algorithm runs in O(n^2) time, where n is the number of sensor nodes in the network.
Making monads first-class with template haskell
Authors: Pericles S. Kariotis, Adam M. Procter, William L. Harrison
Venue: Proceedings of the first ACM SIGPLAN symposium on Haskell (2008)

Monads as an organizing principle for programming and semantics are notoriously difficult to grasp, yet they are a central and powerful abstraction in Haskell. This paper introduces a domain-specific language, MonadLab, that simplifies the construction of monads, and describes its implementation in Template Haskell. MonadLab makes monad construction truly first class, meaning that arcane theoretical issues with respect to monad transformers are completely hidden from the programmer. The motivation behind the design of MonadLab is to make monadic programming in Haskell simpler while providing a tool for non-Haskell experts that will assist them in understanding this powerful abstraction.
YAGO: A Large Ontology from Wikipedia and WordNet
Authors: Fabian M. Suchanek, Gjergji Kasneci, Gerhard Weikum
Venue: Web Semantics: Science, Services and Agents on the World Wide Web (2008)

This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO's precision at 95%-as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO's data.
Tailoring a broadcast algorithm to particular network conditions
Authors: Carl Ellis, Hugo Miranda, François Taïani
Venue: Proceedings of the 6th workshop on Middleware for network eccentric and mobile applications (2008)

The broadcast of a message in Mobile Ad Hoc Networks requires its retransmission by multiple devices, consuming both bandwidth and power. The goal of broadcast algorithms is to select the most adequate nodes to retransmit so that the cost of the operation is reduced. Pampa is a broadcast algorithm that has been shown to perform well when the node distribution is uniform. This position paper identifies and discusses some cases where the performance of Pampa can be improved and presents the research directions that will be pursued to address them.
The emergence of overlapping scale-free genetic architecture in digital organisms
Authors: P. Gerlee, T. Lundh
Venue: Artificial Life (2008)

We have studied the evolution of genetic architecture in digital organisms and found that the gene overlap follows a scale-free distribution, which is commonly found in metabolic networks of many organisms. Our results show that the slope of the scale-free distribution depends on the mutation rate and that the gene development is driven by expansion of already existing genes, which is in direct correspondence to the preferential growth algorithm that gives rise to scale-free networks. To further validate our results we have constructed a simple model of gene development, which recapitulates the results from the evolutionary process and shows that the mutation rate affects the tendency of genes to cluster. In addition we could relate the slope of the scale-free distribution to the genetic complexity of the organisms and show that a high mutation rate gives rise to a more complex genetic architecture.
Reliable P2P File Sharing Service
Authors: Jung-Hwa Shin, Weon Shin, Kyung-Hyune Rhee
Venue: Agents and Peer-to-Peer Computing: 5th International Workshop, AP2PC 2006, Hakodate, Japan, May 9, 2006, Revised and Invited Papers (2006)

A P2P service is a popular for sharing various information through direct connection among two or more peer entities. This service which does not require a dedicated server can be used for finding and exchanging information freely. P2P file sharing systems have become popular as a new paradigm for information exchange. All users who use file sharing service can use shared files of each other freely by equal access privilege. Therefore, P2P file sharing service can suffer from free rider that only downloading without sharing on file. Also, some users can provide malicious files such as virus, worm. Recently, reputation information has been used to solve these problems. Hence, we propose the reliable P2P file sharing service model that can restrict a "free rider" and guarantee the reliability of shared files and users using reputation information.
Faster Algorithms for Incremental Topological Ordering
Authors: Bernhard Haeupler, Telikepalli Kavitha, Rogers Mathew, Siddhartha Sen, Robert E. Tarjan
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We present two online algorithms for maintaining a topological order of a directed acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm takes O(m 1/2) amortized time per arc and our second algorithm takes O(n 2.5/m) amortized time per arc, where n is the number of vertices and m is the total number of arcs. For sparse graphs, our O(m 1/2) bound improves the best previous bound by a factor of logn and is tight to within a constant factor for a natural class of algorithms that includes all the existing ones. Our main insight is that the two-way search method of previous algorithms does not require an ordered search, but can be more general, allowing us to avoid the use of heaps (priority queues). Instead, the deterministic version of our algorithm uses (approximate) median-finding; the randomized version of our algorithm uses uniform random sampling. For dense graphs, our O(n 2.5/m) bound improves the best previously published bound by a factor of n 1/4 and a recent bound obtained independently of our work by a factor of logn. Our main insight is that graph search is wasteful when the graph is dense and can be avoided by searching the topological order space instead. Our algorithms extend to the maintenance of strong components, in the same asymptotic time bounds.
From Interpretation to Compilation
Authors: Jan Martin Jansen, Pieter Koopman, Rinus Plasmeijer
Venue: Central European Functional Programming School: Second Summer School, CEFP 2007, Cluj-Napoca, Romania, June 23-30, 2007, Revised Selected Lectures (2008)

In this paper we sketch some experiments with the construction of a simple compiler for a high level intermediate lazy functional language, with C++ as a target language. Because the compiler is intended for educational and experimental use, simplicity and clearness of construction are considered to be more important than efficiency. Starting point for the construction is a simple interpreter. In a first step this interpreter is turned into a simple compiler in a straightforward manner. The performance of a number of compiled benchmarks is analysed in a comparison with the interpreter and the Clean and GHC compilers. This analysis leads to some suggestions for optimisations. Of these optimisations tail recursion optimisation and optimisation of numerical functions and numerical (sub)expressions in functions are implemented. It turns out that in many cases these optimisations suffice to obtain a competitive performance.
Delay Management Problem: Complexity Results and Robust Algorithms
Authors: Serafino Cicerone, Gianlorenzo D'Angelo, Gabriele Stefano, Daniele Frigioni, Alfredo Navarra
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

In this paper, we study the problem of planning a timetable for passenger trains considering that possible delays might occur due to unpredictable (but bounded) circumstances. Once arrival and departure events are scheduled, if the timetable cannot be respected since an external event has determined a delay to a train, the so called delay management problem occurs. Delays might be managed in several ways and the usual objective function considered for such purpose is the minimization of the overall waiting time caused to passengers.We analyze the interaction between timetable planning and delay management in terms of the recoverable robustness model, where a timetable is said to be robust if it is able to absorb small delays by possibly applying given recovery capabilities. The quality of a robust timetable is measured by the price of robustness that is the ratio between the cost of the robust timetable and that of a non-robust optimal timetable.We consider the problem of designing robust timetables subject to bounded delays. We show that finding an optimal solution for this problem is NP-hard. Hence, we propose robust algorithms and evaluate their prices of robustness. Moreover, we show that such algorithms are optimal with respect to particular assumptions.