ppxscal/embedding_network huggingface dataset:
Detection and Recognition of Scoreboard for Baseball Videos
Authors: Chaur-Heh Hsieh, Chin-Pan Huang, Mao-Hsiung Hung
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

This paper presents an effective and efficient detection and recognition of scoreboard caption method for baseball videos. The method first identifies the scoreboard type using template matching and then extracts the caption region of each type. Next it recognizes the extracted caption utilizing a novel digit recognition scheme which is constructed by a simple neural network classifier. It results in a much simpler method with significantly higher recognition rate over that of the universal OCR scheme. Experimental results demonstrate the effectiveness of the proposed method and indicate that it identifies twelve scoreboard types correctly and recognizes scoreboard caption over 98%.
A Reward-Value Based Constructive Method for the Autonomous Creation of Machine Controllers
Authors: Andreas Huemer, David Elizondo, Mario Gongora
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

A novel method for the creation of machine controllers autonomously is presented. The method is based on reward values which can represent the internal state of the machine or the rating of its task performance. The method consists of a biologically sound constructive neural network model with a minimum number of neurons and no connections initially. New connections and neurons are added when feedback is fed into the neural network using positive and negative reward values. This way the topology and the level of connectivity of the network are kept to a minimum. The method will be applied to a controller for an autonomous mobile robot.
Entwined Influences of Users' Behaviour and QoS: A Multi-model Approach
Authors: Julien Siebert, Vincent Chevrier, Laurent Ciarletta
Venue: Proceedings of the 2nd international conference on Autonomous Infrastructure, Management and Security: Resilient Networks and Services (2008)

In distributed, dynamic networks and applications, such as Peer-to-Peer (P2P), users' behaviour and quality of service/quality of experiment are known to influence each other. In worst cases, these mutual influences could lead the system to crash. We propose a novel approach to model relationships between users and QoS. It is based upon multi-agent systems in order to study the impact of situated behaviours on the global network and to integrate different levels of representation (users' behaviour, overlay protocols, network topology). This paper describes our approach to represent the different models required in such systems and a first implementation in an existing overlay simulator with the first results of experimentations.
Acquiring Expertise in Medical Radiology through Long-Term Interactions
Authors: Alexandre Direne, Marcos Sunyé, Marcos Castilho, Fabiano Silva, Luis Bona, Laura García, Donia Scott
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

This paper describes how cognitive and computational concepts can be applied to build interface and learner models for long-term tutorial interactions in medical radiology. Key human-to-human tutorial dialogue factors that typically occur in the different stages of skill acquisition are captured through an empirical study. The results of the study are detailed and linked to the design of RUI, an Intelligent Tutoring System for multiple domains of radiological expertise. A brief discussion and future research directions offer a comparative view of the method and tools.
Nondeterministic Finite Automata--Recent Results on the Descriptional and Computational Complexity
Authors: Markus Holzer, Martin Kutrib
Venue: Proceedings of the 13th international conference on Implementation and Applications of Automata (2008)

Nondeterministic finite automata (NFAs) were introduced in [67], where their equivalence to deterministic finite automata was shown. Over the last 50 years, a vast literature documenting the importance of finite automata as an enormously valuable concept has been developed. In the present paper, we tour a fragment of this literature. Mostly, we discuss recent developments relevant to NFAs related problems like, for example, (i) simulation of and by several types of finite automata, (ii) minimization and approximation, (iii) size estimation of minimal NFAs, and (iv) state complexity of language operations. We thus come across descriptional and computational complexity issues of nondeterministic finite automata. We do not prove these results but we merely draw attention to the big picture and some of the main ideas involved.
Tree Decomposition of Multiclass Problems
Authors: Ana C. Lorena, André C. P. L. F. de Carvalho
Venue: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks (2008)

Several popular Machine Learning techniques are originally designed for the solution of two-class problems. However, several classification problems have more than two classes. One approach to deal with multiclass problems using binary classifiers is to decompose the multiclass problem into multiple binary subproblems disposed in a binary tree. This approach requires a binary partition of the classes for each node of the tree, which defines the tree structure. This paper presents two algorithms to determine the tree structure taking into account information collected from the used dataset. This approach allows the tree structure to be determined automatically for any multiclass dataset.
Programming at Burroughs and Philco in the 1950s
Authors: Adele Mildred Koss
Venue: IEEE Annals of the History of Computing (2003)

In the 1950s, the dilemma for working women—then as now—was how to combine work with raising a family. Despite having fewer options available then, the author managed to smoothly blend family and work responsibilities. In this part of her memoirs as a programmer, she explains how she did so while working first for Burroughs and later for Philco.
Sensitivity Analysis for the Job Shop Problem with Uncertain Durations and Flexible Due Dates
Authors: Inés González-Rodríguez, Jorge Puente, Camino R. Vela
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

We consider the fuzzy job shop problem, a job shop scheduling problem with uncertain task durations and flexible due dates, with different objective functions and a GA as solving method. We propose a method to generate benchmark problems with variable uncertainty and analyse the performance of the objective functions in terms of the objective values and the sensitivity to variations in the uncertainty.
Computerized screening of children congenital heart diseases
Authors: Amir A. Sepehri, Joel Hancq, Thierry Dutoit, Arash Gharehbaghi, Armen Kocharian, A. Kiani
Venue: Computer Methods and Programs in Biomedicine (2008)

In this paper, we propose a method for automated screening of congenital heart diseases in children through heart sound analysis techniques. Our method relies on categorizing the pathological murmurs based on the heart sections initiating them. We show that these pathelogical murmur categories can be identified by examining the heart sound energy over specific frequency bands, which we call, Arash-Bands. To specify the Arash-Band for a category, we evaluate the energy of the heart sound over all possible frequency bands. The Arash-Band is the frequency band that provides the lowest error in clustering the instances of that category against the normal ones. The energy content of the Arash-Bands for different categories constitue a feature vector that is suitable for classification using a neural network. In order to train, and to evaluate the performance of the proposed method, we use a training data-bank, as well as a test data-bank, collectively consisting of ninety samples (normal and abnormal). Our results show that in more than 94% of cases, our method correctly identifies children with congenital heart diseases. This percentage improves to 100%, when we use the Jack-Knife validation method over all the 90 samples.
Engineering Issues for the Web 2.0
Authors: Yanchun Zhang, Florian Daniel, Santiago Melia, Katsumi Tanaka, Athman Bouguettaya, Daniela Nicklas
Venue: Proceedings of the 2008 international workshops on Web Information Systems Engineering (2008)

Web 2.0 is a technical term describing the trend in the use of World Wide Web technology and web design. The aims of Web 2.0 are to enhance creativity, information sharing and collaboration among users, rather than just retrieving information. For example, one of the most promising area emerged recently in Web 2.0 is the development and evolution of Web-based communities and hosted services, such as social networking sites, wikis, Blogs and folkonomies. They can build on the interactive facilities of "Web 1.0" to provide "Network as platform" computing, allowing users to run software or applications entirely through a browser. Users can own the data on a Web 2.0 site and exercise control over that data.
MAE-P3 -- A System to Gain Transparency of Production Structure as a Basis for Production Relocation Planning
Authors: Frederik Reichert, Andreas Kunz, Ralf Moryson
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

The design of global production networks receives increasing attention, due to changing conditions in the international competition. The basis for relocation planning within a network is the transparency of the production structure. The method MAE-P³ (MAE-P³: Machines And Equipment; Processes; Product flow; Planning) visualizes complex production structures in a two-dimensional matrix. It displays the sequence of the manufacturing processes of a production line and the sequence of the manufacturing processes of a product. The matrix allows comparing these sequences so that decisions about the relocation of a product within a production network can quickly be made and concepts for further production optimization can be deducted. Based on this method, a computer aided system was developed to systematically compare the sequence of the manufacturing processes of a product to several production lines within the network. The comparison is predicated on an algorithm from the science of bio-informatics – originally developed for the comparison of protein chains.
Effective Visualization of File System Access-Control
Authors: Alexander Heitzmann, Bernardo Palazzi, Charalampos Papamanthou, Roberto Tamassia
Venue: Proceedings of the 5th international workshop on Visualization for Computer Security (2008)

In this paper, we present a visual representation of access control permissions in a standard hierarchical file system. Our visualization of file permissions leverages treemaps, a popular graphical representation of hierarchical data. In particular, we present a visualization of access control for the NTFS file system that can help a non-expert user understand and manipulate file system permissions in a simple and effective way. While our examples are based on NTFS, our approach can be used for many other hierarchical file systems as well.
Dependent Weighted Geometric Averaging Operators and their Application to Decision Making
Authors: Yejun Xu
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

Chiclana introduced the ordered weighted geometric weighted averaging operator, in which the associated weights depend on the aggregated arguments. Based on the idea that the closer a preference argument to the mean, the more the weight, in this paper, we develop new dependent ordered weighted geometric averaging (DOWGA) operators and dependent weighted geometric averaging(DWGA) operators, which the associated weights only dependent on the aggregated arguments and can relieve the influence of unfair argument on the aggregated results by assigning low weights to those “false” and “biased” ones.
Using on-line multimedia with mobile device in the 3DIMW
Authors: Rong-Jyue Fang, Shih-Fann Chao, Yin-Shan Jong
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

A lot of experts predict the mobile learning is a new way of learning in the future. The primary reason was newly technology invents of information, and networks environment popularize in modern city. 3DIMW was an interactive platform for 3D models transform to 2D graphics learning, developed on 2004. This study was taken into consideration that benefit and weakness of mobile device, then further think to expand present resources useful performance with mobile device, and evaluate feasibility from mobile device access 3DIMW. Through 3DIMW on-line multimedia resources combine with mobile device access evaluate, we submit the conclusion. According the mobile device access test results, the "acceptable" was suitable word to describe present condition, in fact mobile device certainly extend the useful on presented resources, and we should focus on the serviceable parts.
Data Service Modeling in the AquaLogic Data Services Platform
Authors: Michael Carey, Panagiotis Reveliotis, Sachin Thatte, Till Westmann
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

An underexamined piece of the SOA puzzle is how data access and integration fit into the overall SOA architecture for an enterprise application. In this presentation, we will describe how the data service modeling concepts and methodology supported in AquaLogic Data Services Platform (ALDSP) 3.0, released in January of 2008, address this piece of the SOA puzzle. We will also describe some experiences (e.g., lessons learned from working with customers) that guided us to our current approach.
Behavior-Based Network Access Control: A Proof-of-Concept
Authors: Vanessa Frias-Martinez, Salvatore J. Stolfo, Angelos D. Keromytis
Venue: Proceedings of the 11th international conference on Information Security (2008)

Current NAC technologies implement a pre-connect phase where the status of a device is checked against a set of policies before being granted access to a network, and a post-connect phase that examines whether the device complies with the policies that correspond to its role in the network. In order to enhance current NAC technologies, we propose a new architecture based on behaviors rather than roles or identity, where the policies are automatically learned and updated over time by the members of the network in order to adapt to behavioral changes of the devices. Behavior profiles may be presented as identity cards that can change over time. By incorporating an Anomaly Detector (AD) to the NAC server or to each of the hosts, their behavior profile is modeled and used to determine the type of behaviors that should be accepted within the network. These models constitute behavior-based policies. In our enhanced NAC architecture, global decisions are made using a group voting process. Each host's behavior profile is used to compute a partial decision for or against the acceptance of a new profile or traffic. The aggregation of these partial votes amounts to the model-group decision. This voting process makes the architecture more resilient to attacks. Even after accepting a certain percentage of malicious devices, the enhanced NAC is able to compute an adequate decision. We provide proof-of-concept experiments of our architecture using web traffic from our department network. Our results show that the model-group decision approach based on behavior profiles has a 99% detection rate of anomalous traffic with a false positive rate of only 0.005%. Furthermore, the architecture achieves short latencies for both the pre- and post-connect phases.
Analysis of communication between RFID tags and their reader using statistical simulation model
Authors: C. Parikh, I. Zeid, S. Kamarthi, J. Benneyan
Venue: International Journal of Computer Applications in Technology (2008)

RFID collision occurs when two or more tags attempt to send a frame of data along the same communication channel at the same time. Collision typically results in false alarm. In this paper the authors simulate the communication flow between tags and their reader to investigate how acceptable reading time (defined as the reading time required to keep the rate of false alarms below 5%) is influenced by the size of tag population, data frame length, mean thinking time, and the statistical distribution of thinking time. The paper presents the results of the investigation and the effect of each parameter.
Wavelet-Based Compression and Segmentation of Hyperspectral Images in Surgery
Authors: Hamed Akbari, Yukio Kosugi, Kazuyuki Kojima, Naofumi Tanaka
Venue: Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality (2008)

Considering the anatomical variations and unpredictable nature of surgeries, visibility during surgery is very important especially to correctly diagnose problems. Hyperspectral imaging has developed as a compact imaging and spectroscopic tool that can be used for different applications including medical diagnostics. This paper presents the application of hyperspectral imaging as a visual supporting tool to detect different organs and tissues during surgeries. It will be useful for finding ectopic tissues and diagnosis of tissue abnormalities. The high-dimensional data were compressed using wavelet transform and classified using artificial neural networks. The performance of this method is evaluated for the detection of the spleen, colon, small intestine, urinary bladder, and peritoneum in a surgery on a pig.
PaPMo: packet accurate protocol monitoring system
Authors: Jae-Yong Yoo, Thomas Huehn, JongWon Kim
Venue: Proceedings of the third ACM international workshop on Wireless network testbeds, experimental evaluation and characterization (2008)

In wireless mesh networks, to analyze and debug the current and new protocols, packet-level accurate information of protocol status is crucially needed. Thus, this demonstration introduces PaPMo (Packet accurate Protocol Monitor) system, an active monitoring tool that captures snapshots of the protocol status as packets pass through the different layers. We show that PaPMo is able to correlate MAC-layer events, such as packet loss due to queue overflow, with TCP congestion control behavior with high fidelity.
Embedding a family of meshes into twisted cubes
Authors: Chia-Jui Lai, Chang-Hsiung Tsai
Venue: Information Processing Letters (2008)

The hypercube is one of the most popular interconnection networks since it has simple structure and is easy to implement. The twisted cube is an important variation of the hypercube. Let TQ"n denote the n-dimensional twisted cube. In this paper, we consider embedding a family of 2-dimensional meshes into a twisted cube. The main results obtained in this paper are: (1) For any odd integer n>=1, there exists a mesh of size 2x2^n^-^1 that can be embedded in the TQ"n with unit dilation and unit expansion. (2) For any odd integer n>=5, there exists a mesh of size 4x2^n^-^2 that can be embedded in the TQ"n with dilation 2 and unit expansion. (3) For any odd integer n>=5, a family of two disjoint meshes of size 4x2^n^-^3 can be embedded into the TQ"n with unit dilation and unit expansion. Results (1) and (3) are optimal in the sense that the dilations and expansions of the embeddings are unit values.
Partially Monotone Networks Applied to Breast Cancer Detection on Mammograms
Authors: Marina Velikova, Hennie Daniels, Maurice Samulski
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

In many prediction problems it is known that the response variable depends monotonically on most of the explanatory variables but not on all. Often such partially monotone problems cannot be accurately solved by unconstrained methods such as standard neural networks. In this paper we propose so-called MIN-MAX networks that are partially monotone by construction. We prove that this type of networks have the uniform approximation property, which is a generalization of the result by Sill on totally monotone networks. In a case study on breast cancer detection on mammograms we show that enforcing partial monotonicity constraints in MIN-MAX networks leads to models that not only comply with the domain knowledge but also outperform in terms of accuracy standard neural networks especially if the data set is relative small.
Validation of mutual information-based registration of CT and bone SPECT images in dual-isotope studies
Authors: Lisa Tang, Ghassan Hamarneh, Anna Celler
Venue: Computer Methods and Programs in Biomedicine (2008)

The registration of computed tomography (CT) and nuclear medicine (NM) images can substantially enhance patient diagnosis as it allows for the fusion of anatomical and functional information, as well as the attenuation correction of NM images. However, irrespective of the method used, registration accuracy depends heavily on the characteristics of the images that are registered and the degree of similarity between them. This poses a challenge for registering CT and NM images as they have very different characteristics and content. To address the particular problem of registering single photon emission computed tomography (SPECT) oncology studies with corresponding CT, we have proposed to perform a dual-isotope study with simultaneous injection of a tumor tracer and a bone imaging agent to obtain a tumor SPECT and a bone SPECT image that are inherently registered. As bone structures are generally visible in both CT and bone SPECT, performing registration of these images will be more easily attainable than registration of CT and tumor SPECT. By subsequently applying the spatial transformation determined from this registration to the tumor SPECT acquired from the same dual-isotope study, the optimal alignment between the CT and tumor SPECT images can be obtained. In this paper, we present the proof-of-concept of the proposed approach, the MI-based algorithm employed, and the techniques used to select the algorithm's parameters. Our objectives are to show the feasibility of CT and bone SPECT registration using this algorithm and to validate quantitatively the results generated using clinical data.
Computing Tight Time Windows for RCPSPWET with the Primal-Dual Method
Authors: András Kéri, Tamás Kis
Venue: Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (2007)

In this paper we combine OR and CP techniques to solve the Resource-Constrained Project Scheduling Problem with Earliness-Tardiness costs and general temporal constraints. Namely, we modify the Primal-Dual algorithm for solving the maximum-cost flow problem in a network to deduce tight time windows for activities with respect to a finite upper bound on the optimal objective function value. We compare our method to the only exact method in the literature. Our results show that time window computations and additional domain filtering techniques may improve the performance of tree-search based methods.
Parsimonious Kernel Fisher Discrimination
Authors: Kitsuchart Pasupa, Robert F. Harrison, Peter Willett
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The algorithm is simple, easily programmed and is shown to perform as well as or better than a number of leading machine learning algorithms on a substantial benchmark. It is then applied to a set of extreme small-sample-size problems in virtual screening where it is found to be less accurate than a currently leading approach but is still comparable in a number of cases.