Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.

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EEG Based Emotion Recognition: A Tutorial and Review

Improving quality of software product line by analysing inconsistencies in feature models using an ontological rule‐based approach

Feature model is amongst the widely used approach to manage variability and commonality in software product lines for enabling software reuse Modeling variability and developing variable software products from feature model is an arduous task Due to the growing complexity and size of feature models, defects such as false-optional and dead features can arise which lead to the development of low-quality erroneous software products We propose an ontological rule-based framework that improves software product line by identifying feature model defects due to false-optional and dead features with their causes This information further suggest corrections to the modelers for resolving defects The evaluation results show the applicability of proposed framework to handle false-optional and dead features, and indicate its accuracy and scalability for huge size feature models with 21, 050 features Thereby, improving the quality and reusability of software product line

Managing software product line using an ontological rule-based framework

Analyzing inconsistencies in software product lines using an ontological rule-based approach

A social recommendation system for academic collaboration in undergraduate research

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Latest tools for data mining and machine learning

Real-Life Applications of the Internet of Things

Software product line (SPL) is an emergent strategy for generating software products. The variability and commonality of SPL is illustrated by feature models (FMs). The quality of software products relies on the correctness of SPL. The overall benefits of software product line engineering (SPLE) are reduced by various kinds of defects such as dead features and false optional features in an FM. These defects can be inherited in the software products built from a defective product line model (PLM). In this paper, the problem of enhancing the quality of software products derived from SPLE is handled. An ontological based approach is proposed following first-order logic (FOL) rules to identify defects namely dead features and false optional features. The classification of cases for these defects in FMs that represent variability of SPL is defined. The presented approach has been explained with the help of an FM derived from the standard case in product line (PL) community. The initial empirical evaluation of the proposed approach analyses 35 FMs with different sizes. The results obtained exhibit that the proposed approach is accurate, effective, scalable up to 200 features and therefore improves SPL.

/pdf/improving-software-product-line-using-an-ontological-5f483wyv00.pdf

Improving software product line using an ontological approach

Software product line engineering improves software quality and diminishes development cost and time by efficiently developing software products. Its success lies in identifying the commonalities and variabilities of a set of software products which are generally modeled using feature models. The success of software product lines heavily relies upon the quality of feature models to derive high quality products. However, there are various defects that reduce profits of software product line. One of such defect is redundancy. While the majority of research work focuses on the identification of redundancies, their causes and corrections have been poorly explored. Causes and corrections must be as accurate and comprehensible as possible in order to support the developer in resolving the cause of a redundancy. This research work classified redundancies in the form of a typology. An ontological first-order logic rule based method is proposed to deal with redundancies. A two-step process is presented for mapping model to ontology based on predicate logic. First-order logic based rules are developed and applied to the generated ontology for identifying redundancies, their causes and corrections to resolve redundancies. The proposed method is illustrated using a case study from software product lines online tools repository. The results of experiments performed on 35 models with varied sizes of real world models as well as automatically-generated models from the Software Product Line Online Tools repository and models created via FeatureIDE tool conclude that the method is accurate, efficient and scalable with FM up to 30,000 features. Thus, enables deriving redundancy free end products from the product line and ultimately, improves its quality.

Megha Bhushan

Papers

Latest tools for data mining and machine learning

Real-Life Applications of the Internet of Things

Improving software product line using an ontological approach

Classifying and resolving software product line redundancies using an ontological first-order logic rule based method

Improving quality of software product line by analysing inconsistencies in feature models using an ontological rule‐based approach