ppxscal/embedding_network huggingface dataset:
Improving artificial neural networks' performance in seasonal time series forecasting
Authors: Coşkun Hamzaçebi
Venue: Information Sciences: an International Journal (2008)

In this study, an artificial neural network (ANN) structure is proposed for seasonal time series forecasting. The proposed structure considers the seasonal period in time series in order to determine the number of input and output neurons. The model was tested for four real-world time series. The results found by the proposed ANN were compared with the results of traditional statistical models and other ANN architectures. This comparison shows that the proposed model comes with lower prediction error than other methods. It is shown that the proposed model is especially convenient when the seasonality in time series is strong; however, if the seasonality is weak, different network structures may be more suitable.
Flexible Roles in a Holonic Multi-Agent System
Authors: Emmanuel Adam, Rene Mandiau
Venue: Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2007)

Holonic Multi-Agent organisations are particular pyramidal organisations where agents of a layer (having the same coordinator) are able to communicate and to negotiate directly between them. In order to benefit from control allowed by the hierarchical structure of a holonic organisation and from dynamic roles, we propose a holonic multi-agent organisation inspired from working mechanisms of the complex human organisations. We have implemented this architecture by offering a role management capacity to a hierarchical multi-agent platform.
Edge Detection Combined Entropy Threshold and Self-Organizing Map (SOM)
Authors: Kun Wang, Liqun Gao, Zhaoyu Pian, Li Guo, Jianhua Wu
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

An edge detection method by combining image entropy and Self -Organizing Map (SOM) is proposed in this paper. First, according to information theory image entropy is used to curve up the smooth region and the region of gray level abruptly changed. Then we transform the gray level image to ideal binary pattern of pixels. We define six classes' edge and six edge prototype vectors. These edge prototype vectors are fed into input layer of the Self-Organizing Map (SOM). Classifying the type of edge through this network, the edge image is obtained. At last, the speckle edges are discarded from the edge image. Experimental results show that it gained better edge image compared with Canny edge detection method.
Analysis of Basic Data Reordering Techniques
Authors: Tan Apaydin, Ali Şaman Tosun, Hakan Ferhatosmanoglu
Venue: Proceedings of the 20th international conference on Scientific and Statistical Database Management (2008)

Data reordering techniques are applied to improve the space and time efficiency of storage and query systems in various scientific and commercial applications. Run-length encoding is a prominent approach of compression in many areas, whose performance is significantly enhanced by achieving longer and fewer "runs" through data reordering. In this paper we theoretically study two reordering techniques, namely lexicographical order and Gray code order. We analyze these two methods in the context of bitmap indexes, which are known to have high query performances. We take into account the two commonly used bitmap encodings: equality and range. Our analysis indicates that, when we have all the possible data tuples, both ordering methods perform the same with equality encoding. However, Gray code achieves better compression with range encoding. Experimental results are provided to validate the theoretical analysis.
Stochastic analysis of real-time systems under preemptive priority-driven scheduling
Authors: José María López, José Luis Díaz, Joaquín Entrialgo, Daniel García
Venue: Real-Time Systems (2008)

Exact stochastic analysis of most real-time systems under preemptive priority-driven scheduling is unaffordable in current practice. Even assuming a periodic and independent task model, the exact calculation of the response time distribution of tasks is not possible except for simple task sets. Furthermore, in practice, tasks introduce complexities such as release jitter, blocking in shared resources, etc., which cannot be handled by the periodic independent task set model.In order to solve these problems, exact analysis must be abandoned for an approximated analysis. However, in the real-time field, approximations must not be optimistic, i.e. the deadline miss ratios predicted by the approximated analysis must be greater than or equal to the exact ones. In order to achieve this goal, the concept of pessimism needs to be mathematically defined in the stochastic context, and the pessimistic properties of the analysis carefully derived.This paper provides a mathematical framework for reasoning about stochastic pessimism, and obtaining mathematical properties of the analysis and its approximations. This framework allows us to prove the safety of several proposed approximations and extensions. We analyze and solve some practical problems in the implementation of the stochastic analysis, such as the problem of the finite precision arithmetic or the truncation of the probability functions. In addition, we extend the basic model in several ways, such as the inclusion of shared resources, release jitter or non-preemptive sections.
Curvature- and Model-Based Surface Hatching of Anatomical Structures Derived from Clinical Volume Datasets
Authors: Rocco Gasteiger, Christian Tietjen, Alexandra Baer, Bernhard Preim
Venue: Proceedings of the 9th international symposium on Smart Graphics (2008)

We present a texture-based method to hatch anatomical structures derived from clinical volume datasets. We consider intervention planning, where object and shape recognition are supported by adding hatching lines to the anatomical model. The major contribution of this paper is to enhance the curvature-based hatching of anatomical surfaces by incorporating model-based preferential directions of the underlying anatomical structures. For this purpose, we apply our method on vessels and elongated muscles. Additionally, the whole hatching process is performed without time-consuming user interaction for defining stroke direction and surface parameterization. Moreover, our approach fulfills requirements for interactive explorations like frame coherence and real time capability.
On the Use of Linear Cellular Automata for the Synthesis of Cryptographic Sequences
Authors: A. Fúster-Sabater, P. Caballero-Gil, O. Delgado
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

This work shows that a class of cryptographic sequences, the so-called interleaved sequences, can be generated by means of linear hybrid cellular automata. More precisely, linear multiplicative polynomial cellular automata generate all the components of this family of interleaved sequences. As an illustrative example, the linearization procedure of the self-shrinking generator is described. In this way, popular nonlinear sequence generators with cryptographic application are linearized in terms of simple cellular automata.
TOPTMH: Topology Predictor for Transmembrane α-Helices
Authors: Rezwan Ahmed, Huzefa Rangwala, George Karypis
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Alpha-helical transmembrane proteins mediate many key biological processes and represent 20%---30% of all genes in many organisms. Due to the difficulties in experimentally determining their high-resolution 3D structure, computational methods to predict the location and orientation of transmembrane helix segments using sequence information are essential. We present, TOPTMH a new transmembrane helix topology prediction method that combines support vector machines, hidden Markov models, and a widely-used rule-based scheme. The contribution of this work is the development of a prediction approach that first uses a binary SVM classifier to predict the helix residues and then it employs a pair of HMM models that incorporate the SVM predictions and hydropathy-based features to identify the entire transmembrane helix segments by capturing the structural characteristics of these proteins. TOPTMH outperforms state-of-the-art prediction methods and achieves the best performance on an independent static benchmark.
A Constructive Approach to Correctness, Exemplified by a Generator for Certified Java Card Applets
Authors: Alessandro Coglio, Cordell Green
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

We present a constructive approach to correctness and exemplify it by describing a generator for certified Java Card applets that we are building. A proof of full functional correctness is generated, along with the code, from the specification; the proof can be independently checked by a simple proof checker, so that the larger and more complex generator needs not be trusted. We argue that such an approach is a valuable alternative to post-hoc verification, in addressing the Program Verifier Grand Challenge.
Neural Network Based Multiagent System for Simulation of Investing Strategies
Authors: Darius Plikynas, Rimvydas Aleksiejūnas
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

Recent years of empirical research have collected enough evidences that for efficient markets the process of lower-wealth accumulation by capital investment is approximated by log-normal and high-wealth range by Pareto wealth distribution. This research aims to construct a simple neural network (NN) based multiagent system of heterogeneous agents' targeted to get on the efficiency frontier by combining investments to the real life index funds and nonrisky financial assets, diversifying the risk and maximizing the profits. Each agent is represented by the different stock trading strategy according to his portfolio, saving and risk aversion preferences. The goal is, following empirical evidences from the real investment markets, to find enough proofs that NN-based multiagent system, in principle, has the same fundamental properties of real investment markets described by the log-normal, Pareto wealth and Levy stock returns distributions and can be used further to simulate even more complex social phenomena.
Automatic Collection of Fuel Prices from a Network of Mobile Cameras
Authors: Y. F. Dong, S. Kanhere, C. T. Chou, N. Bulusu
Venue: Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems (2008)

It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
Distributions of Points in d Dimensions and Large k-Point Simplices
Authors: Hanno Lefmann
Venue: Discrete Computational Geometry (2008)

We consider a variant of Heilbronn&#x2019;s triangle problem by investigating for a fixed dimension d&#x2265;2 and for integers k&#x2265;2 with k&#x2264;d distributions of n points in the d-dimensional unit cube [0,1] d , such that the minimum volume of the simplices, which are determined by (k+1) of these n points is as large as possible. Denoting by &#x0394; k,d (n), the supremum of this minimum volume over all distributions of n points in [0,1]d , we show that c k,d &#x22c5;(log&#x2009;n)1/(d&#x2212;k+1)/n k/(d&#x2212;k+1)&#x2264;&#x0394; k,d (n)&#x2264;c k,d &#x2032;/n k/d for fixed 2&#x2264;k&#x2264;d, and, moreover, for odd integers k&#x2265;1, we show the upper bound &#x0394; k,d (n)&#x2264;c k,d &#x2033;/n k/d+(k&#x2212;1)/(2d(d&#x2212;1)), where c k,d ,c k,d &#x2032;,c k,d &#x2033;>0 are constants.
Stabbing Convex Polygons with a Segment or a Polygon
Authors: Pankaj K. Agarwal, Danny Z. Chen, Shashidhara K. Ganjugunte, Ewa Misiołek, Micha Sharir, Kai Tang
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

Let $\mathcal{O} = \{O_1, \ldots, O_m\}$ be a set of m convex polygons in ¿2 with a total of n vertices, and let B be another convex k-gon. A placement of B, any congruent copy of B (without reflection), is called free if B does not intersect the interior of any polygon in $\mathcal{O}$ at this placement. A placement z of B is called critical if B forms three "distinct" contacts with $\mathcal{O}$ at z. Let $\varphi(B, \mathcal{O})$ be the number of free critical placements. A set of placements of B is called a stabbing set of $\mathcal{O}$ if each polygon in $\mathcal{O}$ intersects at least one placement of B in this set.We develop efficient Monte Carlo algorithms that compute a stabbing set of size h = O(h *logm), with high probability, where h * is the size of the optimal stabbing set of $\mathcal{O}$. We also improve bounds on $\varphi(B, \mathcal{O})$ for the following three cases, namely, (i) B is a line segment and the obstacles in $\mathcal{O}$ are pairwise-disjoint, (ii) B is a line segment and the obstacles in $\mathcal{O}$ may intersect (iii) B is a convex k-gon and the obstacles in $\mathcal{O}$ are disjoint, and use these improved bounds to analyze the running time of our stabbing-set algorithm.
Counting Ordered Patterns in Words Generated by Morphisms
Authors: Sergey Kitaev, Toufik Mansour, Patrice Séébold
Venue: Language and Automata Theory and Applications: Second International Conference, LATA 2008, Tarragona, Spain, March 13-19, 2008. Revised Papers (2008)

We start a general study of counting the number of occurrences of ordered patterns in words generated by morphisms. We consider certain patterns with gaps (classical patterns) and that with no gaps (consecutive patterns). Occurrences of the patterns are known, in the literature, as rises, descents, (non-)inversions, squares and p-repetitions. We give recurrence formulas in the general case, then deducing exact formulas for particular families of morphisms. Many (classical or new) examples are given illustrating the techniques and showing their interest.
Type-Based Query Expansion for Sentence Retrieval
Authors: Keke Cai, Chun Chen, Jiajun Bu, Guang Qiu
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

In this paper, a novel sentence retrieval model with type-based expansion is proposed. In this retrieval model, sentences expected to be relevant should meet with the requirements both in query terms and query types. To obtain the information about query types, this paper proposes a solution based on classification, which utilizes the potential associations between terms and information types to obtain the optimized classification results. Inspired by the idea that relevant sentences always tend to occur nearby, this paper further re-ranks each sentence by considering the relevance of its adjacent sentences. The proposed retrieval model has been compared with other traditional retrieval models and experiment results indicate its significant improvements in retrieval effectiveness.
Tour into Virtual Environment in the Style of Pencil Drawing
Authors: Yang Zhao, Dang-En Xie, Dan Xu
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

Traditional virtual environment reconstructing methods have their own drawbacks, such as modeling complexity, higher time-consuming and much too realistic. Thus, they are difficult to be suitable for the real-time rendering. In this paper, we have described a system which can provide a simple pencil drawing scene model, the users can walk through in it freely, obtain enjoyable and real-time artistic experience. Firstly we design a new pencil texture generating method based on the pencil filter. This approach can conveniently generate the pencil drawing effect by convoluting the input image with the pencil filter. Secondly, we propose a modeling scheme for TIP based on the cubic panorama, which can not only overcome the disadvantage of fixed viewpoint in browsing panorama, but also can model easily and computes simply.
Scheduling Real-Time Multi-item Requests in On-Demand Broadcast
Authors: Jun Chen, Victor C. S. Lee, Joseph Kee-Yin Ng
Venue: Proceedings of the 2008 14th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2008)

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. Previous studies on time-critical on-demand data broadcast were under the assumption that each client requests only one data item at a time. With rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has considered on-demand broadcast environment with time-critical multi-item requests. In this paper, we investigate the scheduling problem arising in this new environment and observe that existing single item request based algorithms are unable to manage multi-item requests efficiently. Thus, an innovative algorithm that combines the strengths of data item scheduling and request scheduling is proposed. The performance results of our simulation show that the proposed algorithm is superior to other classical algorithms under a variety of circumstances. Our algorithm not only reduces deadline miss ratio of requests, but also saves broadcast channel bandwidth.
Semantic Technologies for Socially-Enhanced Context-Aware Mobile Learning
Authors: Melody Siadaty, Ty Mey Eap, Jelena Jovanovic, Dragan Gašević, Carlo Torniai, Marek Hatala
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

In this paper, we present an extended ontological framework, m-LOCO, in which potentials of mobile devices as content delivery media have been utilized in the design of a context-aware learning environment, where the learning content and/or the learning environment are matched to the needs and requirements of the learning context. Moreover, we suggest the affordance of a socially-enhanced self-regulated learning approach as the most efficient pedagogical roadmap for ubiquitous learning environments, with the goal of helping the learners to be more productive through fostering both their individual and social collaborative competencies and expertise.
Real-Time Emotion Recognition Using Echo State Networks
Authors: Stefan Scherer, Mohamed Oubbati, Friedhelm Schwenker, Günther Palm
Venue: Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems (2008)

The goal of this work is the exploration of real-time emotion recognition from speech. In this approach a novel type of recurrent neural networks called echo state networks (ESN) are utilized. Biologically motivated features representing modulations of the speech signal are used as input to the ESNs. The standard Berlin Database of Emotional Speech is used to evaluate the performance of the proposed approach. However, in this paper ongoing work is being presented and the final architecture has yet to be determined.
Topic Development Based Refinement of Audio-Segmented Television News
Authors: Alfredo Favenza, Mario Cataldi, Maria Luisa Sapino, Alberto Messina
Venue: Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems (2008)

With the advent of the cable based television model, there is an emerging requirement for random access capabilities, from a variety of media channels, such as smart terminals and Internet. Random access to the information within a newscast program requires appropriate segmentation of the news. We present text analysis based techniques on the transcript of the news, to refine the automatic audio-visual segmentation. We present the effectiveness of applying the text segmentation algorithm CUTS to the news segmentation domain. We propose two extensions to the algorithm, and show their impacts through an initial evaluation.
Contextualized Possibilistic Networks with Temporal Framework for Knowledge Base Reliability Improvement
Authors: Marco Grasso, Michèle Lavagna, Guido Sangiovanni
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

Possibilistic abductive reasoning is particularly suited for diagnostic problem solving affected by uncertainty. Being a Knowledge-Based approach, it requires a Knowledge Base consisting in a map of causal dependencies between failures (or anomalies) and their effects (symptoms). Possibilistic Causal Networks are an effective formalism for knowledge representation within this applicative field, but are affected by different issues. This paper is focused on the importance of a proper management of explicit contextual information and of the addition of a temporal framework to traditional Possibilistic Causal Networks for the improvement of diagnostic process performances. The necessary modifications to the knowledge representation formalism and to the learning approach are presented together with a brief description of an applicative test case for the concepts here discussed.
Anonymisation of Panel Enterprise Microdata --- Survey of a German Project
Authors: Maurice Brandt, Rainer Lenz, Martin Rosemann
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

The access of the scientific community to cross-section data in the field of business statistics in Germany has considerably improved over the last few years. The purpose of the project on "Business Panel data and de facto anonymisation" is to extend the data infrastructure in Germany for panel data on local units and on enterprises, so that business statistical data can be made available to empirical researchers for use on their own workstations. This paper gives an overview of the project, describes the data sets and the anonymisation methods which are considered to create scientific use files. The work to assess the analysis potential and anonymity of the data is outlined as well. Also, an example is given, applying anonymisation methods to achieve the best possible results regarding the well known trade off between data confidentiality and analysis potential.