ppxscal/embedding_network huggingface dataset:
Signature Enzyme Identification Using Amperometric Biosensor
Authors: Mihaela Hnatiuc, Mariarita Montereal
Venue: Proceedings of the 2009 Advanced Technologies for Enhanced Quality of Life (2009)

The amperometric biosensor has as fundament the enzyme-modified electrode. In this paper is present the k-means method to analyze the different parameter of input for identification the analyte concentration. The biosensor signal are acquired with Potentiostat - Autolab, PGStat10. The aim of this work is create an automatic system for data acquisition and processing. The analyte concentration identification depends of many parameters as the current corresponding to the neutral solution, the current peak of the saturated solution, dimensions of the electrodes, the time periods of saturation or neutralisation. These parameters are used as inputs classification system for a calibration current-time-concentration dependency. Amperometric biosensor has application in medicine, chemistry, physic, environment engineering.
Indeterminacy free frequency-domain blind separation of reverberant audio sources
Authors: Leandro Di Persia, Diego Milone, Masuzo Yanagida
Venue: IEEE Transactions on Audio, Speech, and Language Processing (2009)

Blind separation of convolutive mixtures is a very complicated task that has applications in many fields of speech and audio processing, such as hearing aids and man-machine interfaces. One of the proposed solutions is the frequency-domain independent component analysis. The main disadvantage of this method is the presence of permutation ambiguities among consecutive frequency bins. Moreover, this problem is worst when reverberation time increases. Presented in this paper is a new frequency-domain method, that uses a simplified mixing model, where the impulse responses from one source to each microphone are expressed as scaled and delayed versions of one of these impulse responses. This assumption, based on the similitude among waveforms of the impulse responses, is valid for a small spacing of the microphones. Under this model, separation is performed without any permutation or amplitude ambiguity among consecutive frequency bins. This new method is aimed mainly to obtain separation, with a small reduction of reverberation. Nevertheless, as the reverberation is included in the model, the new method is capable of performing separation for a wide range of reverberant conditions, with very high speed. The separation quality is evaluated using a perceptually designed objective measure. Also, an automatic speech recognition system is used to test the advantages of the algorithm in a real application. Very good results are obtained for both, artificial and real mixtures. The results are significantly better than those by other standard blind source separation algorithms.
A Medical Diagnosis Decision Support System Based on Bayesian Theorem and Web Services
Authors: Chung C. Chang, Hsueh-Ming Lu
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

This study employs the framework of web services in conjunction with Bayesian theorem to construct a web-services-based medical decision support system for diagnosis. The purpose is to help users (physicians) with issues pertinent to medical diagnosis decisions. Users through the system key in available prior probability and through computation based on Bayesian theorem obtain more accurate diagnosis. The process helps users enhance the quality and efficiency of medical decisions. On the other hand, features of web services enable this medical decision support system to offer more service platforms than conventional one. Users will have access to the system whether they use Windows, Macintosh, Linux or any other platforms that connect with the Internet via HTTP. More important is the fact that after the system is completed, all Internet service providers will be able to access the system as a software element freely and quickly. This way, the goal of this study - to provide a medical decision support tool and speedily integrate heterogeneous medical decision support systems can be effectively attained.
A General Framework of Feature Selection for Text Categorization
Authors: Hongfang Jing, Bin Wang, Yahui Yang, Yan Xu
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

Many feature selection methods have been proposed for text categorization. However, their performances are usually verified by experiments, so the results rely on the corpora used and may not be accurate. This paper proposes a novel feature selection framework called Distribution-Based Feature Selection (DBFS) based on distribution difference of features. This framework generalizes most of the state-of-the-art feature selection methods including OCFS, MI, ECE, IG, CHI and OR. The performances of many feature selection methods can be estimated by theoretical analysis using components of this framework. Besides, DBFS sheds light on the merits and drawbacks of many existing feature selection methods. In addition, this framework helps to select suitable feature selection methods for specific domains. Moreover, a weighted model based on DBFS is given so that suitable feature selection methods for unbalanced datasets can be derived. The experimental results show that they are more effective than CHI, IG and OCFS on both balanced and unbalanced datasets.
A Bayesian Algorithm for Reading 1D Barcodes
Authors: Ender Tekin, James Coughlan
Venue: Proceedings of the 2009 Canadian Conference on Computer and Robot Vision (2009)

The 1D barcode is a ubiquitous labeling technology, with symbologies such as UPC used to label approximately 99\% of all packaged goods in the US. It would be very convenient for consumers to be able to read these barcodes using portable cameras (e.g. mobile phones), but the limited quality and resolution of images taken by these cameras often make it difficult to read the barcodes accurately. We propose a Bayesian framework for reading 1D barcodes that models the shape and appearance of barcodes, allowing for geometric distortions and image noise, and exploiting the redundant information contained in the parity digit. An important feature of our framework is that it doesn't require that every barcode edge be detected in the image. Experiments on a publicly available dataset of barcode images explore the range of images that are readable, and comparisons with two commercial readers demonstrate the superior performance of our algorithm.
Reasoning on interval and point-based disjunctive metric constraints in temporal contexts
Authors: Federico Barber
Venue: Journal of Artificial Intelligence Research (2000)

We introduce a temporal model for reasoning on disjunctive metric constraints on intervals and time points in temporal contexts. This temporal model is composed of a labeled temporal algebra and its reasoning algorithms. The labeled temporal algebra defines labeled disjunctive metric point-based constraints, where each disjunct in each input disjunctive constraint is univocally associated to a label. Reasoning algorithms manage labeled constraints, associated label lists, and sets of mutually inconsistent disjuncts. These algorithms guarantee consistency and obtain a minimal network. Additionally, constraints can be organized in a hierarchy of alternative temporal contexts. Therefore, we can reason on context-dependent disjunctive metric constraints on intervals and points. Moreover, the model is able to represent non-binary constraints, such that logical dependencies on disjuncts in constraints can be handled. The computational cost of reasoning algorithms is exponential in accordance with the underlying problem complexity, although some improvements are proposed.
A Fast Approximation Algorithm for the k Partition-Distance Problem
Authors: Yen Hung Chen
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

Given a set of elements N, a partition consists on dividing the set of elements into two or more disjoint clusters that cover all elements. A cluster contains a non-empty subset of elements. The number of clusters of a partition is less than or equal to |N|. Different partitioning algorithms for the same application will produce different partitions from the same set of elements. To compute the distance and find the consensus partition (also called as consensus clustering) between two or more partitions are important and interesting problems that arise in many applications such as bioinformatics and data mining. However, different distance functions between two or more partitions will usually need to be computed by different algorithms. In this paper, we discuss the k partition-distance problem which can be applied in bioinformatics. Given a set of elements N with k partitions, the k partition-distance problem is to delete the minimum number of elements from each partition such that all remaining partitions become identical. However, this problem has been shown to be NP-complete when k > 2. We will present the first known approximation algorithm with performance ratio 2 to solve this problem in $O(k*\rho*|N|)$ time, where ρ is the maximum number of clusters of these k partitions. Then we perform our algorithm for simulation of random data and actual the set of organisms based on DNA markers. It will show that our approximation solution is at most twice the partition-distance of the optimal solution in practice.
A Semantic Based Framework for Supporting Negotiation in Service Oriented Architectures
Authors: Marco Comuzzi, Kyriakos Kritikos, Pierluigi Plebani
Venue: Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing (2009)

Negotiation is required before invoking a service in order to identify how the invocation must occur in terms of functional and non-functional criteria. This process is possible when all the involved parties agree on the same negotiation protocol (e.g., bilateral negotiations). Considering a Service Oriented Architecture (SOA), this negotiation protocol cannot be predefined, but it must be selected by considering the negotiation capabilities of the involved services.In this work, we propose a semantic-based framework for supporting the negotiation in SOA. Specifically, the framework allows to express the negotiation capabilities of service requesters and providers and proposes a mechanism for discovering the negotiation protocols that can be enacted when a negotiation is required.To improve the flexibility of the framework, the concept of delegation is introduced to deal with the situation in which a party, that is not able to support the negotiation protocol, wants to participate in a negotiation. In this case, the negotiation can be fully or partially delegated to one or more other parties that, instead, are able to support the negotiation protocol.
Research Summary: Termination of CHR
Authors: Paolo Pilozzi
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

Constraint Handling Rules (CHR) [7] is a high-level programming language, designed for the easy implementation of custom constraint solvers. CHR is a language-extension, built on top of Java, C and half a dozen Prolog-variants. The language is currently used in many projects [7].
Top-k Answers to Fuzzy XPath Queries
Authors: Bettina Fazzinga, Sergio Flesca, Andrea Pugliese
Venue: Proceedings of the 20th International Conference on Database and Expert Systems Applications (2009)

Data heterogeneity in XML repositories can be tackled by giving users the possibility to obtain approximate answers to their queries. In this setting, several approaches for XPath queries have been defined in the literature. In particular, fuzzy XPath queries have been recently introduced as a formalism to provide users with a clear understanding of the approximations that the query evaluation process introduces in the answers. However, in many cases, users are not a-priori aware of the maximum approximation degree they would allow in the answers; rather, they are interested in obtaining the first k answers ranked according to their approximation degrees. In this paper we investigate the problem of top-k fuzzy XPath querying, propose a query language and its associated semantics, and discuss query evaluation.
The Value Model of Customer Relationship Management Based on Rough Set and Fuzzy Theory
Authors: Xiguo Xie, Yongsheng Jin
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

The paper gives a new value model based on rough set and fuzzy theory to value the companies' customer relationship management. Because the number of customer relationship management' indices is too many and the calculation is very complex, this paper uses rough set to play down the complication. Because of the subjective effect of value people, the fuzzy theory is used to improve the objectivity. In the last, the paper used the of eight telecom companies' data to test the model. The experimental results show that the new model has better precision and it proved that the new model is a better method.
Vector Symmetry Reduction
Authors: Alastair F. Donaldson
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Symmetry reduction is an effective state-space reduction technique for model checking, and works by restricting search to equivalence class representatives with respect to a group of symmetries for a model. A major problem with symmetry reduction techniques is the time taken to compute the representative of a state, which can be prohibitive. In efficient implementations of symmetry reduction, a symmetry is applied to a state as a sequence of operations which swap component identities. We show that vector processing technology, common to modern computers, can be used to implement a vectorised swap operation, which can be incorporated into the representative computation algorithm to accelerate symmetry reduction. Via a worked example, we present details of this vector symmetry reduction method. We have implemented our techniques in the TopSpin symmetry reduction package for the Spin model checker, and present experimental results showing the speedups obtained via vectorisation for two case-studies running on a PowerPC vector processor.
Direct Perception Displays for Military Radar-Based Air Surveillance
Authors: Oliver Witt, Morten Grandt, Heinz Küttelwesch
Venue: Proceedings of the 8th International Conference on Engineering Psychology and Cognitive Ergonomics: Held as Part of HCI International 2009 (2009)

Air surveillance is among the time-critical and highly prioritized tasks of naval ships, in which the human operator will stay the decision maker in the future as well. User-oriented human-systems integration requires the provision of ergonomically optimized user interfaces. Based on functional system descriptions in the form of abstraction hierarchies, perceptive displays were developed for air surveillance that constitute an advancement with respect to so far principally alphanumerical displays supporting the operator with an improved situation awareness in his decision-making processes. It concerns, in detail, displays for the tactical situation picture, the explicit information about airborne contacts as well as the condition and the configuration of system state especially regarding the radar equipment.
A Study of Matrix Data on Reading and Writing Method in Simulation
Authors: Wang Lei, Lu Xianliang
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

In order to enhance the efficiency of software simulation system and shorten the running periods of system, the technique of Sybase Open Client Component is introduced here and a read-write method for matrix data is proposed, Write-Multi-Image method is given finally through experiments and analysis. Sybase Open Client interface technique was introduced firstly, and then the access-control structure of Sybase database, the formats of data described which was adopted in simulation was expatiated in sequence, finally the method of matrix data for reading and writing was researched and the writing method for matrix data was experimented and analyzed.
Study on Risk Evaluation of Bank Financing Products Based on Analytical Network Process
Authors: Zhongjun Wu, Jianyong Liu, Chengqun Fu, Ling Li
Venue: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering (case 2009) (2009)

The relative risk scale of bank financing products are extended nowadays, and lots of uncertain factors affects the evaluation process. In order to solve these problems, analytical network process (ANP) was utilized to estimate the risk possibility. Risk factors index system of bank financing products were built firstly. ANP considered all kinds of risk factors as well as influence among them. The feedback relation between every two factors were described and calculated. Finally all of the risk factors were sorted synthetically. The example shows that ANP is a suitable method.
Turning web text and search queries into factual knowledge: hierarchical class attribute extraction
Authors: Marius Pasca
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

A seed-based framework for textual information extraction allows for weakly supervised acquisition of open-domain class attributes over conceptual hierarchies, from a combination of Web documents and query logs. Automatically-extracted labeled classes, consisting of a label (e.g., painkillers) and an associated set of instances (e.g., vicodin, oxycontin), are linked under existing conceptual hierarchies (e.g., brain disorders and skin diseases are linked under the concepts BrainDisorder and SkinDisease respectively). Attributes extracted for the labeled classes are propagated upwards in the hierarchy, to determine the attributes of hierarchy concepts (e.g., Disease) from the attributes of their subconcepts (e.g., BrainDisorder and SkinDisease).
Passwords: If We're So Smart, Why Are We Still Using Them?
Authors: Cormac Herley, P. C. Oorschot, Andrew S. Patrick
Venue: Financial Cryptography and Data Security: 13th International Conference, FC 2009, Accra Beach, Barbados, February 23-26, 2009. Revised Selected Papers (2009)

While a lot has changed in Internet security in the last 10 years, a lot has stayed the same --- such as the use of alphanumeric passwords. Passwords remain the dominant means of authentication on the Internet, even in the face of significant problems related to password forgetting and theft. In fact, despite large numbers of proposed alternatives, we must remember more passwords than ever before. Why is this? Will alphanumeric passwords still be ubiquitous in 2019, or will adoption of alternative proposals be commonplace? What must happen in order to move beyond passwords? This note pursues these questions, following a panel discussion at Financial Cryptography and Data Security 2009.
Verifying Communication Protocols Using Live Sequence Chart Specifications
Authors: Rahul Kumar, Eric G. Mercer
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

The need for a formal verification process in System on Chip (SoC) design and Intellectual Property (IP) integration has been recognized and investigated significantly in the past. A major drawback is the lack of a suitable specification language against which definitive and efficient verification of inter-core communication can be performed to prove compliance of an IP block against the protocol specification. Previous research has yielded positive results of verifying systems against the graphical language of Live Sequence Charts (LSCs) but has identified key limitations of the process that arise from the lack of support for important constructs of LSCs such as Kleene stars, subcharts, and hierarchical charts. In this paper we further investigate the use of LSCs as a specification language and show how it can be formally translated to automata suitable for input to a model checker for automatic verification of the system under test. We present the translation for subcharts, Kleene stars, and hierarchical charts that are essential for protocol specification and have not been translated to automata before. Further, we successfully translate the BVCI protocol (point to point communication protocol) specification from LSC to an automaton and present a case study of verifying models using the resulting automaton.
A Model for University Counselor's Competence Evaluation Based on Grey Relational Analysis and Harmony Factor
Authors: Xing Gang, Ren RongRong
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

Considering the complexity of university counselor’s competence and some shortcomings of the current evaluation methods, with the text research, event interview, questionnaire, the multi-dimensional statistics analysis, this paper does research on the university counselor competence and designs an evaluation index system which includes 17 components and three dimensions, namely, cognitive ability, intercourse ability and professional quality. Go a step further, this paper set up a new model for university counselors’ competence evaluation based on grey relational analysis and harmony factor. At last, a case study is provided to verify the model. The research findings offer a workable approach in evaluating university counselor’s competence. It can serve as a basis in the selection, assessment and cultivation of university counselors in China.
A Supervised Method for Transliterated Person Name Identification
Authors: Guo Lei, Zhou Mei-ling, Yao Jian-Min, Zhu Qiao-Ming
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

Query translation mining is a key technique in cross-language information retrieval and machine translation knowledge acquisition. For better performance, the queries are classified into transliterated words and non-transliterated words, which can be further channeled to different mining processes. This paper is a pilot study on query classification for better translation mining performance, which is based on supervised classification and linguistic heuristics. The person name identification gets a precision of over 97%.
Google stemming mechanisms
Authors: Ahmet Uyar
Venue: Journal of Information Science (2009)

In this study we investigated the stemming mechanisms of Google. We used its web interface and submitted many queries via a program. Stemming is the process of correlating morphologically similar words with one another. Search engines use stemming to match documents having one form of a word with queries having another form of the same word. We investigated the stemming mechanism of Google for three classes of words: singulars/plurals, combined words, and verbs with many postfixes. Our results indicate that Google uses a document-based algorithm for stemming. It evaluates each document separately and makes a decision to index or not for the conflated forms of the words it has. It indexes documents only for word forms that are semantically strongly correlated. While it indexes documents for singulars and plurals frequently, it rarely indexes documents for word forms with the postfixes of -able or -tively.