ppxscal/embedding_network huggingface dataset:
A Telemedicine Network Using Secure Techniques and Intelligent User Access Control
Authors: Jader Wallauer, Aldo von Wangenheim, Rafael Andrade, Douglas Dyllon Jeronimo de Macedo
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

This paper reports the development and design of the State of Santa Catarina's telemedicine network in Brazil. The resources concentration, like hospitals and clinical staff, in Brazilian large cities have been a problem in public healthcare policies improvement. Telemedicine technology for large scale telediagnostic, processing of routine outpatient examinations, the electronic delivery of examinations results integrated with the decision process of wetter to provide further treatment for a patient, is one strategy to overcome the difficulties imposed by healthcare concentration. The State needs to reduce costs like patient transportation, improve the quality of healthcare service and the origins control of these examinations, motivated the creation of this medical knowledge network. As a result it promoted better patient care, making faster diagnosis, creating a patient information history, reducing the examinations redundancy, maximizing this way, the social welfare and the health technological park on the Santa Catarina State.
Acceptance test for Raytheon hurricane computer
Authors: F. J. Murray
Venue: Papers and discussions presented at the Dec. 8-10, 1953, eastern joint AIEE-IRE computer conference: information processing systems---reliability and requirements (1953)

The hurricane digital computer is a large-scale digital computer developed for the United States Navy by the Raytheon Manufacturing Company and installed at the Naval Air Missile Test Center at Point Mugu in the spring of 1953. It has many novel features including highly developed input and output equipment, magnetic-tape storage with an optical locating device, and a checking system which was designed not merely to detect errors but to prevent loss of any valid information available to the computer.
Uncertain Context Modeling of Dimensional Ontology Using Fuzzy Subset Theory
Authors: Ying Jiang, Hui Dong
Venue: Proceedings of the 2nd international conference on Scalable Uncertainty Management (2008)

Context-sensitive knowledge is widespread in Semantic Web, but traditional RDF triples lack references to situations, points in time, or generally contexts. In order to resolve this problem, Dimensional Ontology (DO) theory is put forward, which features dimensional relations, dimensional operators as well as reasoning mechanism for context-sensitive knowledge. The notion of context in DO is actuarially a vector of dimensions, which are crisp sets representing certain contextual aspects. We propose an approach of modeling uncertain contexts of DO through fuzzy subsets instead of crisp ones. In this way, DO provides the ability of representing fuzzy triples in uncertain contexts. Apart from describing uncertain context model of DO, we discuss how dimensional operators and reasoning mechanism can be applied to uncertain contexts to allow more complex manipulations.
A Statistical Language Modeling Approach to Online Deception Detection
Authors: Lina Zhou, Yongmei Shi
Venue: IEEE Transactions on Knowledge and Data Engineering (2008)

Online deception is disrupting our daily life, organizational process, and even national security. Existing approaches to online deception detection follow a traditional paradigm by using a set of cues as antecedents for deception detection, which may be hindered by ineffective cue identification. Motivated by the strength of statistical language models (SLMs) in capturing the dependency of words in text without explicit feature extraction, we developed SLMs to detect online deception. We also addressed the data sparsity problem in building SLMs in general and in deception detection in specific using smoothing and vocabulary pruning techniques. The developed SLMs were evaluated empirically with diverse datasets. The results showed that the proposed SLM approach to deception detection outperformed a state-of-the-art text categorization method as well as traditional feature-based methods.
Evolvable Face Recognition Based on Evolutionary Algorithm and Gabor Wavelet Networks
Authors: Chuansheng Wu, Yong Ding, Lishan Kang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Gabor Wavelet Networks (GWN) is a method for face recognition. Evolutionary Algorithms are proved to be efficient to deal with GWN optimization problems. Inver-over Evolutionary Algorithm (IOEA) mixed with GWN performs well in face recognition because of its considerable effects on complex functions optimizations, which is called IOEA-GWN put forward by us, and shows higher recognition rate than Simple Genetic Algorithm mixed with GWN (called SGA-GWN) in the same experiment conditions according to our practices.
Architecture and application of an autonomous robotic software engineering technology testbed (sett)
Authors: Barry Boehm
Venue:  (2007)

This research provides a new way to develop and apply a new form of software: software engineering technology testbeds designed to evaluate alternative software engineering technologies, and to accelerate their maturation and transition into project use. Software engineering technology testbeds include not only the specifications and code, but also the package of instrumentation, scenario drivers, seeded defects, experimentation guidelines, and comparative effort and defect data needed to facilitate technology evaluation experiments. The requirements and architecture to build a software engineering technology testbed has been developed and applied to evaluate a wide range of technologies. The technologies evaluated came from the fields of architecture, testing, state-model checking, and operational envelopes. The testbed evaluation showed (1) that certain technologies were complementary and cost-effective to apply to mission-critical systems; (2) that the testbed was cost-effective to use by researchers; (3) that collaboration in testbed use by researchers and the practitioners resulted in actions to accelerate technology maturity and transition into project use; and (4) that the software engineering technology testbed's requirements and architecture were suitable for evaluating technologies and to accelerate their maturation and transition into project use.
Unconstrained endpoint profiling (googling the internet)
Authors: Ionut Trestian, Supranamaya Ranjan, Aleksandar Kuzmanovi, Antonio Nucci
Venue: Proceedings of the ACM SIGCOMM 2008 conference on Data communication (2008)

Understanding Internet access trends at a global scale, i.e., what do people do on the Internet, is a challenging problem that is typically addressed by analyzing network traces. However, obtaining such traces presents its own set of challenges owing to either privacy concerns or to other operational difficulties. The key hypothesis of our work here is that most of the information needed to profile the Internet endpoints is already available around us - on the web. In this paper, we introduce a novel approach for profiling and classifying endpoints. We implement and deploy a Google-based profiling tool, which accurately characterizes endpoint behavior by collecting and strategically combining information freely available on the web. Our 'unconstrained endpoint profiling' approach shows remarkable advances in the following scenarios: (i) Even when no packet traces are available, it can accurately predict application and protocol usage trends at arbitrary networks; (ii) When network traces are available, it dramatically outperforms state-of-the-art classification tools; (iii) When sampled flow-level traces are available, it retains high classification capabilities when other schemes literally fall apart. Using this approach, we perform unconstrained endpoint profiling at a global scale: for clients in four different world regions (Asia, South and North America and Europe). We provide the first-of-its-kind endpoint analysis which reveals fascinating similarities and differences among these regions.
An Improved Extension of the D-S Evidence Theory to Fuzzy Sets
Authors: Y. Z. Miao, X. P. Ma, H. X. Zhang, J. W. Zhang, Z. X. Zhao
Venue: Proceedings of the 2008 The Third International Multi-Conference on Computing in the Global Information Technology (iccgi 2008) (2008)

To analyze fuzzy data in uncertain evidential reasoning, some researchers have recently extended the D-S evidence theory to fuzzy sets. But there are some insufficiencies in the definition of the fuzzy belief function and the combination rule on fuzzy sets of the D-S evidence theory. This paper describes a new definition of the similarity degree between two fuzzy sets and the improved extension combination rule of the evidence theory on fuzzy sets. It also presents the corresponding mathematical proof to validate the improved combination rule. Compared with other generalizing combination rules, the results of the numerical experiments show that the new combination rule in this paper can acquire more changing information to the change of fuzzy focal elements more effectively, and it overcomes the insufficiencies of other existing combination rules and enhances the robustness of fusion decision systems effectively.
A Meet-in-the-Middle Attack on 8-Round AES
Authors: Hüseyin Demirci, Ali Aydın Selçuk
Venue: Fast Software Encryption: 15th International Workshop, FSE 2008, Lausanne, Switzerland, February 10-13, 2008, Revised Selected Papers (2008)

We present a 5-round distinguisher for AES. We exploit this distinguisher to develop a meet-in-the-middle attack on 7 rounds of AES-192 and 8 rounds of AES-256. We also give a time-memory tradeoff generalization of the basic attack which gives a better balancing between different costs of the attack. As an additional note, we state a new square-like property of the AES algorithm.
Hierarchical self-organization in the finitary process soup
Authors: Olof Görnerup, James P. Crutchfield
Venue: Artificial Life (2008)

Current analyses of genomes from numerous species show that the diversity of their functional and behavioral characters is not proportional to the number of genes that encode the organism. We investigate the hypothesis that the diversity of organismal character is due to hierarchical organization. We do this with the recently introduced model of the finitary process soup, which allows for a detailed mathematical and quantitative analysis of the population dynamics of structural complexity. Here we show that global complexity in the finitary process soup is due to the emergence of successively higher levels of organization, that the hierarchical structure appears spontaneously, and that the process of structural innovation is facilitated by the discovery and maintenance of relatively noncomplex, but general, individuals in a population.
A conceptual model for negotiating in service-oriented environments
Authors: Jyhjong Lin
Venue: Information Processing Letters (2008)

In recent years, Web services have been developed as a fundamental technique for the new generation of B2B or EAI applications. As they have matured and a new vision of service-oriented computing has emerged, the software industry has shifted its attention from developing required software to delivering desired services. In order to benefit from such a service-oriented model of software, several critical issues must be addressed in a service-oriented environment such as differentiation of services by various attributes, dynamic selection and provision of services in a supply chain style, and commitment of services with prescribed rules. From the managerial perspective, these issues are concerned with a process of negotiating desired services in a service-oriented environment. In this paper, we propose an object-oriented model that specifies such a negotiation process by architectural constructs where these critical issues are adequately addressed. The model contains a use case diagram that depicts requirements for the negotiation process, an architecture diagram that describes collaborative components for satisfying these requirements, and a class/sequence diagram that specifies class objects in these components to perform all behaviors occurring within the negotiation process. For illustration, the model is applied to an exemplified negotiation process for book publishing.
Actively learning specific function properties with applications to statistical inference
Authors: Jeff Schneider
Venue:  (2007)

Active learning techniques have previously been shown to be extremely effective for learning a target function over an entire parameter space based on a limited set of observations. However, in many cases, only a specific property of the target function needs to be learned. For instance, when discovering the boundary of a region&mdash;such as the locations in which the wireless network strength is above some operable level,&mdash;we are interested in learning only the level-set of the target function. While techniques that learn the entire target function over the parameter space can be used to detection specific properties of the target function (e.g. level-sets), methods that learn only the required properties can be significantly more efficient, especially as the dimensionality of the parameter space increases. These active learning algorithms have a natural application in many statistical inference techniques. For example, given a set of data and a physical model of the data, which is a function of several variables, a scientist is often interested in determining the ranges of the variables which are statistically supported by the data. We show that many frequentist statistical inference techniques can be reduced to a level-set detection problem or similar search of a property of the target function, and hence benefit from active learning algorithms which target specific properties. Using these active learning algorithms significantly decreases the number of experiments required to accurately detect the boundaries of the desired 1&mdash;&alpha; confidence regions. Moreover, since computing the model of the data given the input parameters may be expensive (either computationally, or monetarily), such algorithms can facilitate analyses that were previously infeasible. We demonstrate the use of several statistical inference techniques combined with active learning algorithms on several cosmological data sets. The data sets vary in the dimensionality of the input parameters from two to eight. We show that naive algorithms, such as random sampling or grid based methods, are computationally infeasible for the higher dimensional data sets. However, our active learning techniques can efficiently detect the desired 1&mdash;&alpha; confidence regions. Moreover, the use of frequentist inference techniques allows us to easily perform additional inquiries, such as hypothetical restrictions on the parameters and joint analyses of all the cosmological data sets, with only a small number of additional experiments.
Plenary lecture VII: identification in sensor networks
Authors: Constantin Volosencu
Venue: Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information (2008)

In the last years sensor networks have proved their huge viability in the real world, even if their resources in terms of energy, memory, computational power and bandwidth are strictly limited. One of the important problems related to the usage of wireless sensor networks in harsh environments is the identification of the states of the physical variables in the field based on the measurements provided by the sensors. The sensor networks allow the usage of the multivariable estimation techniques in distributed systems. The paper presents a short survey of some identification techniques and some characteristics of the sensor networks. Some examples of applications of modeling of distributed systems in sensor networks and identification based on multivariable identification with auto-regression and neural networks are presented.
Knowledge Patterns
Authors: Peter Clark
Venue: Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns (2008)

At its heart, ontological engineering is a modeling endeavor. Creating a formal ontology requires not just creating a conceptual vocabulary but also specifying how terms in that vocabulary should be combined, and what conclusions should be drawn from a given set of assertions. As well as building specific ontologies, there is renewed interest in capturing useful modeling decisions at a more abstract level of reusable "patterns", namely representations which capture recurring structure within and across ontologies.While a variety of different styles of patterns have been proposed, we have been working with a particular style which we here call "knowledge patterns". Knowledge patterns are based on the observation that one often finds oneself repeating structurally similar patterns of axioms (e.g., about containers) when building a knowledge base. To make this shared structure explicit, and hence reusable, these patterns are explicitly represented as general theory schemata, and then used by transforming them (through symbol renaming) to create specific theories. In this way, the general theory can be reused in multiple ways, and the underlying modeling decisions about how the world seems to behave in accordance with those theories is made explicit.
Evaluation of Ranking Accuracy in Multi-Criteria Decisions
Authors: Edmundas Kazimieras Zavadskas, Algimantas Zakarevicius, Jurgita Antucheviciene
Venue: Informatica (2006)

The paper analyses the problem of ranking accuracy in multiple criteria decision-making (MCDM) methods. The methodology for measuring the accuracy of determining the relative significance of alternatives as a function of the criteria values is developed. An algorithm of the Technique for the Order Preference by Similarity to Ideal Solution (TOPSIS) that applies criteria values' transformation through a normalization of vectors and the linear transformation is considered. A computational experiment is presented, to compare the results of a multiple criteria analysis and the ranking accuracy in a particular situation.
Physicality and Cooperative Design
Authors: Dhaval Vyas, Dirk Heylen, Anton Nijholt
Venue: Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction (2008)

CSCW researchers have increasingly come to realize that the material work setting and its population of artefacts play a crucial part in coordination of distributed or co-located work. This paper uses the notion of physicality as a basis to understand cooperative work. Using examples from an ongoing fieldwork on cooperative design practices, it provides a conceptual understanding of physicality and shows that material settings and co-workers' working practices play an important role in understanding the physicality of cooperative design.
Finding Minimum Spanning/Distances Trees by Using River Formation Dynamics
Authors: Pablo Rabanal, Ismael Rodríguez, Fernando Rubio
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

River Formation Dynamics (RFD) is an heuristic method similar to Ant Colony Optimization (ACO). In fact, RFD can be seen as a gradient version of ACO, based on copying how water forms rivers by eroding the ground and depositing sediments. As water transforms the environment, altitudes of places are dynamically modified, and decreasing gradients are constructed. The gradients are followed by subsequent drops to create new gradients, reinforcing the best ones. By doing so, good solutions are given in the form of decreasing altitudes. We apply this method to solve two NP-complete problems, namely the problems of finding a minimum distances tree and finding a minimum spanning tree in a variable-cost graph. We show that the gradient orientation of RFD makes it specially suitable for solving these problems, and we compare our results with those given by ACO.
A Method and Its Implementation for Constructing Bäcklund Transformations to Nonlinear Evolution Equations
Authors: Zhibin Li, Yinping Liu, Haifeng Qian
Venue: Computer Mathematics: 8th Asian Symposium, ASCM 2007, Singapore, December 15-17, 2007. Revised and Invited Papers (2008)

An algorithmic method to construct a kind of auto Bäcklund transformations (BTs) is proposed. A Maple package AutoBT, which can entirely automatically generate auto BT is presented. AutoBT has been effectively applied to many nonlinear evolution equations with physical significance. Not only are previously known BT recovered but also in some cases new and more general form of BT are obtained.
Algorithm Improving the CELP Coder for Real-Time Communication
Authors: Sheng sheng Yu, Jie Yang, Jing li Zhou
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

Generally, the code excited linear predictive (CELP) coding is known as one of the best algorithms for bit rate between 4 kbps and 16 kbps. Recently, the algebraic CELP (ACELP) algorithm has been widely adopted in standard speech coders such as G.729 and Adaptive multi rate (AMR) and AMR Wide Band (AMR-WB).In order to improve the speech quality based on CELP algorithm, harmonic Pitch pre-emphasis is employed on the impulses and a new codebook search method is proposed. Pitch pre-emphasis is applied in the analysis-by-synthesis loop of the analyzer, so the harmonic structure is strengthened. The new codebook search method reduces the computations considerably at the expense of a slight degradation of speech quality. The experiment result shows that the speech quality of integrated speech coding algorithm offers almost the same quality as G.729 while takes less search space.
Automatic Subcortical Segmentation Using a Contextual Model
Authors: Jonathan H. Morra, Zhuowen Tu, Liana G. Apostolova, Amity E. Green, Arthur W. Toga, Paul M. Thompson
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Automatically segmenting subcortical structures in brain images has the potential to greatly accelerate drug trials and population studies of disease. Here we propose an automatic subcortical segmentation algorithm using the auto context model. Unlike many segmentation algorithms that separately compute a shape prior and an image appearance model, we develop a framework based on machine learning to learn a unified appearance and context model. We trained our algorithm to segment the hippocampus and tested it on 83 brain MRIs (of 35 Alzheimer's disease patients, 22 with mild cognitive impairment, and 26 normal healthy controls). Using standard distance and overlap metrics, the auto context model method significantly outperformed simpler learning-based algorithms (using AdaBoost alone) and the FreeSurfer system. In tests on a public domain dataset designed to validate segmentation [1] , our new algorithm also greatly improved upon a recently-proposed hybrid discriminative/generative approach [2], which was among the top three that performed comparably in a recent head-to-head competition.
A Custom Processor for a TDMA Solver in a CFD Application
Authors: Filipe Oliveira, C. Silva Santos, F. A. Castro, José C. Alves
Venue: Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications (2008)

This paper presents a custom processor designed to execute a time consuming function in a CFD application. The selected function implements the method TDMA (Tri-Diagonal Matrix Algorithm) for solving a tri-diagonal system of equations. The custom processor was implemented in a commercial PCI prototyping board based on Virtex4LX FPGAs and uses a dedicated memory cache system, address generators and a deep pipelined floating-point datapath. Running at 100MHz and assuming the input data already in the cache memories, the system reaches a throughput greater than 1.4GFLOPS.