Corpus based part-of-speech tagging

In natural language processing, a crucial subsystem in a wide range of applications is a part-of-speech (POS) tagger, which labels (or classifies) unannotated words of natural language with POS labels corresponding to categories such as noun, verb or adjective. Mainstream approaches are generally corpus-based: a POS tagger learns from a corpus of pre-annotated data how to correctly tag unlabeled data. Presented here is a brief state-of-the-art account on POS tagging. POS tagging approaches make use of labeled corpus to train computational trained models. Several typical models of three kings of tagging are introduced in this article: rule-based tagging, statistical approaches and evolution algorithms. The advantages and the pitfalls of each typical tagging are discussed and analyzed. Some rule-based and stochastic methods have been successfully achieved accuracies of 93–96 %, while that of some evolution algorithms are about 96–97 %.     

Stream fusion for multi-stream automatic speech recognition

Multi-stream automatic speech recognition (MS-ASR) has been confirmed to boost the recognition performance in noisy conditions. In this system, the generation and the fusion of the streams are the essential parts and need to be designed in such a way to reduce the effect of noise on the final decision. This paper shows how to improve the performance of the MS-ASR by targeting two questions; (1) How many streams are to be combined, and (2) how to combine them. First, we propose a novel approach based on stream reliability to select the number of streams to be fused. Second, a fusion method based on Parallel Hidden Markov Models is introduced. Applying the method on two datasets (TIMIT and RATS) with different noises, we show an improvement of MS-ASR.     

Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA

In this study, we wanted to discriminate between two groups of people. The database used in this study contains 20 patients with Parkinson’s disease and 20 healthy people. Three types of sustained vowels (/a/, /o/ and /u/) were recorded from each participant and then the analyses were done on these voice samples. Firstly, an initial feature vector extracted from time, frequency and cepstral domains. Then we used linear and nonlinear feature extraction techniques, principal component analysis (PCA), and nonlinear PCA. These techniques reduce the number of parameters and choose the most effective acoustic features used for classification. Support vector machine with its different kernel was used for classification. We obtained an accuracy up to 87.50 % for discrimination between PD patients and healthy people.     

Endogenous games with goals: side-payments among goal-directed agents

Boolean games have been developed as a paradigm for modelling societies of goal-directed agents. In boolean games agents exercise control over propositional variables and strive to achieve a goal formula whose realization might require the opponents’ cooperation. The presence of agents that are goal-directed makes it difficult for an external authority to be able to remove undesirable properties that are inconsistent with agents’ goals, as shown by recent contributions in the multi-agent literature. What this paper does is to analyse the problem of regulation of goal-direct agents from within the system, i.e., what happens when agents themselves are given the chance to negotiate the strategies to be played with one another. Concretely, we introduce endogenous games with goals, obtained coupling a general model of goal-directed agents (strategic games with goals) with a general model of pre-play negotiations (endogenous games) coming from game theory. Strategic games with goals are shown to have a direct correspondence with strategic games (Proposition 1) but, when side-payments are allowed in the pre-play phase, display a striking imbalance (Proposition 4). The effect of side-payments can be fully simulated by taxation mechanisms studied in the literature (Proposition 7), yet we show sufficient conditions under which outcomes can be rationally sustained without external intervention (Proposition 5). Also, integrating taxation mechanisms and side-payments, we are able to transform our starting models in such a way that outcomes that are theoretically sustainable thanks to a pre-play phase can be actually sustained even with limited resources (Proposition 8). Finally, we show how an external authority incentivising a group of agents can be studied as a special agent of an appropriately extended endogenous game with goals (Proposition 11).     

Business process compliance checking – applying and evaluating a generic pattern matching approach for conceptual models in the financial sector

Given the strong increase in regulatory requirements for business processes the management of business process compliance becomes a more and more regarded field in IS research. Several methods have been developed to support compliance checking of conceptual models. However, their focus on distinct modeling languages and mostly linear (i.e., predecessor-successor related) compliance rules may hinder widespread adoption and application in practice. Furthermore, hardly any of them has been evaluated in a real-world setting. We address this issue by applying a generic pattern matching approach for conceptual models to business process compliance checking in the financial sector. It consists of a model query language, a search algorithm and a corresponding modelling tool prototype. It is (1) applicable for all graph-based conceptual modeling languages and (2) for different kinds of compliance rules. Furthermore, based on an applicability check, we (3) evaluate the approach in a financial industry project setting against its relevance for decision support of audit and compliance management tasks.     

A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems

In game theoretical analysis of incentive mechanisms, all players are assumed to be rational. Since it is likely that mechanism participants in the real world may not be fully rational, such mechanisms may not work as effectively as in the idealized settings for which they were designed. Therefore, it is important to evaluate the robustness of incentive mechanisms against various types of agents with bounded rational behaviors. Such evaluations would provide us with the information needed to choose mechanisms with desired properties in real environments. In this article, we first propose a general robustness measure, inspired by research in evolutionary game theory, as the maximal percentage of invaders taking non-equilibrium strategies such that the agents sustain the desired equilibrium strategy. We then propose a simulation framework based on evolutionary dynamics to empirically evaluate the equilibrium robustness. The proposed simulation framework is validated by comparing the simulated results with the analytical predictions based on a modified simplex analysis approach. Finally, we implement the proposed simulation framework for evaluating the robustness of incentive mechanisms in reputation systems for electronic marketplaces. The results from the implementation show that the evaluated mechanisms have high robustness against a certain non-equilibrium strategy, but is vulnerable to another strategy, indicating the need for designing more robust incentive mechanisms for reputation management in e-marketplaces.     

Multi-label classification via learning a unified object-label graph with sparse representation

Automatic annotation is an essential technique for effectively handling and organizing Web objects (e.g., Web pages), which have experienced an unprecedented growth over the last few years. Automatic annotation is usually formulated as a multi-label classification problem. Unfortunately, labeled data are often time-consuming and expensive to obtain. Web data also accommodate much richer feature space. This calls for new semi-supervised approaches that are less demanding on labeled data to be effective in classification. In this paper, we propose a graph-based semi-supervised learning approach that leverages random walks and ? ₁ sparse reconstruction on a mixed object-label graph with both attribute and structure information for effective multi-label classification. The mixed graph contains an object-affinity subgraph, a label-correlation subgraph, and object-label edges with adaptive weight assignments indicating the assignment relationships. The object-affinity subgraph is constructed using ? ₁ sparse graph reconstruction with extracted structural meta-text, while the label-correlation subgraph captures pairwise correlations among labels via linear combination of their co-occurrence similarity and kernel-based similarity. A random walk with adaptive weight assignment is then performed on the constructed mixed graph to infer probabilistic assignment relationships between labels and objects. Extensive experiments on real Yahoo! Web datasets demonstrate the effectiveness of our approach.     

Using model transformation to refactor use case models based on antipatterns

Use Case modeling is a popular technique for documenting functional requirements of software systems. Refactoring is the process of enhancing the structure of a software artifact without changing its intended behavior. Refactoring, which was first introduced for source code, has been extended for use case models. Antipatterns are low quality solutions to commonly occurring design problems. The presence of antipatterns in a use case model is likely to propagate defects to other software artifacts. Therefore, detection and refactoring of antipatterns in use case models is crucial for ensuring the overall quality of a software system. Model transformation can greatly ease several software development activities including model refactoring. In this paper, a model transformation approach is proposed for improving the quality of use case models. Model transformations which can detect antipattern instances in a given use case model, and refactor them appropriately are defined and implemented. The practicability of the approach is demonstrated by applying it on a case study that pertains to biodiversity database system. The results show that model transformations can efficiently improve quality of use case models by saving time and effort.     

Understanding student participation in undergraduate course communities: A case study

Participation is the cornerstone of any community. Promoting, understanding and properly managing it allows not only keeping the community sustainable, but also providing personalized services to its members and managers. This article presents a case study in which student participation in a course community was motivated using two different extrinsic mechanisms, and mediated by a software platform. The results were compared with a baseline community of the same course, in which participation was not motivated by external means. The analysis of these results indicates that managing a partially virtual course community requires the introduction of monitoring services, community managers and extrinsic mechanisms to motivate participation. These findings allow community managers to improve their capability for promoting participation and keeping the community sustainable. The findings also raise several implications that should be considered in the design of software supporting this kind of community, when managing the participation of its members.     

Preference-based content replacement using recency-latency tradeoff

This work introduces and establishes a new model for cache management, where clients suggest preferences regarding their expectations for the time they are willing to wait, and the level of obsolescence they are willing to tolerate. The cache uses these preferences to decide upon entrance and exit of objects to and from its storage, and select the best copy of requested object among all available copies (fresh, cached, remote). We introduce three replacement policies, each evicts objects based on ongoing scores, considering users’ preferences combined with other objects’ properties such as size, obsolescence rate and popularity. Each replacement algorithm follows a different strategy: (a) an optimal solution that use dynamic programming approach to find the best objects to be kept (b) another optimal solution that use branch and bound approach to find the worst objects to be thrown out (c) an algorithm that use heuristic approach to efficiently select the objects to be evicted. Using these replacement algorithms the cache is able to keep the objects that are best suited for users preferences and dump the other objects. We compare our proposed algorithms to the Least-Recently-Used algorithm, and provide evidence to the advantages of our algorithms providing better service to cache’s users with less burden on network resources and reduced workloads on origin servers.