Four Reasoning Models for C3 Metamodel

International audienceThe architecture is considered to be the driving aspect of the development process; it allows specifying which aspects and models in each level needed according to the software architecture design. Early Architecture Description Languages (ADLs), nearly exclusive, focus on structural abstraction hierarchy ignoring behavioural description hierarchy, conceptual hierarchy, and metamodeling hierarchy. In our approach these four hierarchies constitute views to appropriately “reason about” the architecture of a system described using our C3 metamodel. C3 is defined to be a minimal and complete architecture description language. In this paper we provide a set of mechanisms to deal with different levels of each type of hierarchy, also we introduce our proper structural definition for connector types used to instantiate any connexion elements deployed at the architectures and application levels

REPRESENTATION AND REASONING MODELS FOR C3 ARCHITECTURE DESCRIPTION LANGUAGE

International audienceComponent-based development is a proven approach to manage the complexity of software and its need for customization. At an architectural level, one describes the principal system components and their pathways of interaction. So, Architecture is considered to be the driving aspect of the development process; it allows specifying which aspects and models in each level needed according to the software architecture design. Early Architecture description languages (ADLs), nearly exclusive, focus on structural abstraction hierarchy ignoring behavioural description hierarchy, conceptual hierarchy, and metamodeling hierarchy. In this paper we focus on those four hierarchies which represent views to appropriately “reason about” software architectures described using our C3 metamodel which is a minimal and complete architecture description language. In this paper we provide a set of mechanisms to deal with different levels of each hierarchy, also we introduce our proper structural definition for connector's elements deployed in C3 Architectures

A comparative study of conversion aided methods for WordNet sentence textual similarity

In this paper, we present a comparison of three methods for taxonomic-based sentence semantic relatedness, aided with word parts of speech (PoS) conversion. We use WordNet ontology for determining word level semantic similarity while augmenting WordNet with two other lexicographical databases; namely Categorial Variation Database (CatVar) and Morphosemantic Database in assisting the word category conversion. Using a human annotated benchmark data set, all the three approaches achieved a high positive correlation reaching up to (r = 0.881647) with comparison to human ratings and two other baselines evaluated on the same benchmark data set

A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

In this paper, we propose a hybrid approach for sentence paraphrase identification. The proposal addresses the problem of evaluating sentence-to-sentence semantic similarity when the sentences contain a set of named-entities. The essence of the proposal is to distinguish the computation of the semantic similarity of named-entity tokens from the rest of the sentence text. More specifically, this is based on the integration of word semantic similarity derived from WordNet taxonomic relations, and named-entity semantic relatedness inferred from Wikipedia entity co-occurrences and underpinned by Normalized Google Distance. In addition, the WordNet similarity measure is enriched with word part-of-speech (PoS) conversion aided with a Categorial Variation database (CatVar), which enhances the lexico-semantics of words. We validated our hybrid approach using two different datasets; Microsoft Research Paraphrase Corpus (MSRPC) and TREC-9 Question Variants. In our empirical evaluation, we showed that our system outperforms baselines and most of the related state-of-the-art systems for paraphrase detection. We also conducted a misidentification analysis to disclose the primary sources of our system errors

Cancer prediction using graph-based gene selection and explainable classifier

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time

Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system’s outstanding performance in comparison to three different state-of-the-art systems

Requirement engineering of a Cooperative Information System using viewpoints

International audienceIn this paper we are interested in cooperative infor-mation systems (CIS) in inter-organizational environments. They are information systems on a large scale, which con-nect different organizations, often autonomous, sharing common goals, forming in this case inter-organizational system (IOS). In order to develop a CIS, we propose a Vp-CIs approach, which incorporates a notion of software en-gineering, which are the viewpoints from the needs analysis phase to describe their requirements and needs. This ap-proach defines a meta-model of viewpoint, which enable us to instantiate the viewpoints necessary to identify the needs and requirements of a CIS

Semi-Supervised learning for Face Anti-Spoofing using Apex frame

Conventional feature extraction techniques in the face anti-spoofing domain either analyze the entire video sequence or focus on a specific segment to improve model performance. However, identifying the optimal frames that provide the most valuable input for the face anti-spoofing remains a challenging task. In this paper, we address this challenge by employing Gaussian weighting to create apex frames for videos. Specifically, an apex frame is derived from a video by computing a weighted sum of its frames, where the weights are determined using a Gaussian distribution centered around the video's central frame. Furthermore, we explore various temporal lengths to produce multiple unlabeled apex frames using a Gaussian function, without the need for convolution. By doing so, we leverage the benefits of semi-supervised learning, which considers both labeled and unlabeled apex frames to effectively discriminate between live and spoof classes. Our key contribution emphasizes the apex frame's capacity to represent the most significant moments in the video, while unlabeled apex frames facilitate efficient semi-supervised learning, as they enable the model to learn from videos of varying temporal lengths. Experimental results using four face anti-spoofing databases: CASIA, REPLAY-ATTACK, OULU-NPU, and MSU-MFSD demonstrate the apex frame's efficacy in advancing face anti-spoofing techniques