2555681
Judicial Support Systems: Ideas for a Privacy Ontology-Based Case Analyzer
Nowadays, ontology is applied as an integral part of many applications in several domains, especially in the world of law. The ontology based judicial support system is believed as a useful tool to support, for example, the legal argumentation assistant and legal decision taking in court. The privacy case analyzer is considered as one of the most interesting applications of ontology based privacy judicial support systems. The efficiency of privacy case analyzers depend on several factors such as how to tackle the problem of linking cases to legislations, how to imply the guidance of privacy principles, and how to improve the extraction of cases. This paper addresses those items and describes the research issues that will be investigated challenges of ontology based judicial support systems.
16344510
On Constructing, Grouping and Using Topical Ontology for Semantic Matching
An ontology topic is used to group concepts from different contexts (or even from different domain ontologies). This paper presents a pattern-driven modeling methodology for constructing and grouping topics in an ontology (PAD-ON methodology), which is used for matching similarities between competences in the human resource management (HRM) domain. The methodology is supported by a tool called PAD-ON. This paper demonstrates our recent achievement in the work from the EC Prolix project. The paper approach is applied to the training processes at British Telecom as the test bed.
17885523
On Constructing Semantic Decision Tables
Decision tables are a widely used knowledge management tool in the decision making process. Ambiguity and conceptual reasoning difficulties arise while designing large decision tables in a collaborative environment. We introduce the notion of Semantic Decision Table (SDT), which enhances a decision table with explicit decision semantics by annotating it properly with a domain ontology. In this paper, we focus on the SDT construction process. First, we map decision items to the ontology by building a rooted tree of decision binary facts and visualize it in a scalable manner. Formal ontological roles are used during this mapping process. Then, we commit the decision rules to the mapping results with a high level pseudo-natural language to ground their semantic. We illustrate with an SDT example from the domain of human resource management.
35426902
Towards Ontological Commitments with Omega -RIDL Markup Language
In the DOGMA (Developing Ontology-Grounded Methods and Applications) ontology engineering approach, ontology construction starts from an uninterpreted base of elementary fact types, called lexons, which are mined from linguistic descriptions. Applications that ontologically commit to such a lexon base are assigned a formal semantics by mapping the application symbols to paths in this lexon base. Besides specifying which concepts are used, we restrict how they may be used and queried with semantic constraints, or rules, based on the fact-based database modeling method NIAM/ORM. Such ontological commitments are specified in the Ω-RIDL1 language. In this paper we present the Ω -RIDL Markup Language and illustrate with a case from the field of Human Resources Management.
33017168
An ontology engineering methodology for DOGMA
Although ontologies occupy a central place in the Semantic Web and related research domains, there are currently not many fully fledged ontology engineering methodologies available. In this paper, we want to present an integrated methodology for ontology engineering from scratch, inspired by various scientific disciplines, in particular database semantics and natural language processing.
2826645
PROFILE COMPILER: Ontology-based, community-grounded, multilingual online services to support collaborative decision making
In todaypsilas industries, ontologies and the modern semantic technologies are offered as the potential solutions to support mutual understanding in the collaborative settings. Currently, organizations are unable to cope with collaborative decision making in a particular domain. Mastering domain knowledge and sharing decision rules is a complex yet important issue. In this paper, we first design a generic framework for community-grounded, collaborative decision making enabled, ontology based systems. This framework is constructed with three main engineering tracks: the linguistic engineering track, the ontology engineering track and the group decision engineering track. Each engineering track has its own methodology supported by a tool. Then, we use Semantic Decision Tables to elaborate the knowledge blocks resulting from these three engineering tracks. Semantic Decision Tables are mainly used (1) to elaborate concepts of the domain ontologies, (2) to gather semantically grounded decision rules within a decision group and (3) to provide tabular reports to the users. Finally, an online multilingual knowledge platform - the PROFILE COMPILER is developed as the result. It allows small or medium-sized enterprises (SMEs) to retrieve and personalize existing domain ontology, design and share their decision rules. We illustrate in the domain of Human Resource Management (HRM).
16895768
A Novel Stroke Extraction Model for Chinese Characters based on Steerable Filters
The technology of stroke extraction plays a key role in the off-line Chinese character recognition field. The accuracy and computing complexity of classifying depend on the output of stroke extraction. Based on the analysis of the existing methods, this paper proposed a novel stroke extraction model with steerable filters. The experiments indicate that this model can be used to extract strokes from both printed and handwritten Chinese characters. Meanwhile, it also can be applied to eliminate the redundant stroke connections impressively with considerable low computing complexity
29100810
An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques
The importance of requirements engineering (RE) has been raised numerous times in literatures. To choose suitable RE techniques for a particular project in a given situation is a challenging task, requiring substantial expertise and efforts. To help solving this problem, an expert system based approach is proposed. This expert system uses the knowledge from domain experts to model the causal factors of RE techniques. It can select suitable RE techniques for a software project. A web-based questionnaire is created in the first place to collect the expertise available in the community. The information collected by the questionnaire is analyzed and transformed into a new dataset for constructing a Bayesian Belief Network (BBN). The resulting BBN integrated with a GUI forms an expert system for RE techniques modeling and selection. Empirical study validates the transformed dataset and shows that the expert system outperforms other predictors in selecting suitable RE techniques in different RE phases.
22140835
The Algorithm of R Peak Detection in ECG Based on Empirical Mode Decomposition
A novel automatic R peak detection algorithm that is based on EMD (empirical mode decomposition) and adaptive threshold technique is presented. The R peaks are then identified by computing the first threshold and applying a set of adaptive thresholds that are not limited to a strict range. The proposed algorithm was able to detect R peak with high sensitivity when compared to other algorithms used in Physionet ECG database. Additionally, the new algorithm can successfully detect R peak for a wide variety of ECG shapes.
14166643
3D Model Simplification Method Based on ROI
Based on edge-collapse algorithm and ROI (region of interest), an effective 3D mesh simplification method is proposed. During the simplification procedure, this method could successfully preserve a high resolution for the 3D part that the user is interested in while the rest parts could meet the basic requirement with lower resolution. The experiments support the conclusion.
15679648
First-aid: surviving and preventing memory management bugs during production runs
Memory bugs in C/C++ programs severely affect system availability and security. This paper presents First-Aid, a lightweight runtime system that survives software failures caused by common memory management bugs and prevents future failures by the same bugs during production runs. Upon a failure, First-Aid diagnoses the bug type and identifies the memory objects that trigger the bug. To do so, it rolls back the programto previous checkpoints and uses two types of environmental changes that can prevent or expose memory bug manifestation during re-execution. Based on the diagnosis, First-Aid generates and applies runtime patches to avoid the memory bug and prevent its reoccurrence. Furthermore, First-Aid validates the consistent effects of the runtime patches and generates on-site diagnostic reports to assist developers in fixing the bugs.
 We have implemented First-Aid on Linux and evaluated it with seven applications that contain various types of memory bugs, including buffer overflow, uninitialized read, dangling pointer read/write, and double free. The results show that First-Aid can quickly diagnose the tested bugs and recover applications from failures (in 0.084 to 3.978 seconds). The results also show that the runtime patches generated by First-Aid can prevent future failures caused by the diagnosed bugs. Additionally, First-Aid provides detailed diagnostic information on both the root cause and the manifestation of the bugs. Furthermore, First-Aid incurs low overhead (0.4-11.6% with an average of 3.7%) during normal execution for the tested buggy applications, SPEC INT2000, and four allocation intensive programs.