Validation of software requirements is a primary phase in requirements engineering that ensures requirements match the target system with the intended needs of the acquirer. It aims to detect and correct errors that prevail in the specified requirements. Although there are tremendous requirements validation approaches, some software may fail because of limited or ineffective requirements validation techniques and unreliable requirements’ quality characteristics. In this study, a systematic literature review of requirements validation is performed. The study analyzes the most adopted validation techniques, reports requirements quality characteristics, and discovers significant challenges of validation techniques. The review identified 66 relevant primary studies analyzed to derive deep insights into the following aspects of requirements validation: trends of requirements validation methods, including their subtechnique strengths and weaknesses, requirements quality characteristics categories, and adopted tools and datasets in these techniques. We grouped validation techniques into categories: prototyping, inspection, knowledge-oriented, test-oriented, modeling and assessment, and formal models. The analysis reported 19 validation techniques, 27 tools, new requirements validation characteristics, and several challenges that prevailed through validation techniques. The trend of validation techniques is to those methods that apply machine learning techniques with knowledge from dictionaries and ontologies. Most challenges are about how to express the requirements and how to revert clients’ feedback. There is a strong relationship between validation techniques, software application domain, and requirements validation quality attributes. Thus, there is an immense need to unify the quality characteristics and domain-specific validation methods.

Advances in wearable technologies have the ability to revolutionize and improve people’s lives. The gains go beyond the personal sphere, encompassing business and, by extension, the global economy. The technologies are incorporated in electronic devices that collect data from consumers’ bodies and their immediate environment. Human activities recognition, which involves the use of various body sensors and modalities either separately or simultaneously, is one of the most important areas of wearable technology development. In real-life scenarios, the number of sensors deployed is dictated by practical and financial considerations. In the research for this article, we reviewed our earlier efforts and have accordingly reduced the number of required sensors, limiting ourselves to first-person vision data for activities recognition. Nonetheless, our results beat state of the art by more than 4% of F1 score.

In biological studies, species identification is considered one of the most important issues. Several methods have been suggested to identify species using the whole DNA sequences. In this study, we present new insights for species identification using only part of the DNA sequence. The Clustering k-Nearest Neighbor (K-C-NN) and Support Vector Machine (SVM) classifiers were used to test and evaluate the improved statistical features extracted from DNA sequences for four species (Aquifex aeolicus, Bacillus subtilis, Aeropyrum pernix and Buchnera sp). The results show that part of DNA sequences can be used to identify species.

Different Action recognition methods use Inertial Measurement Unit (IMU) and optical flow independently. This research aims to explore the usefulness of combining IMU and Optical flow for action recognition. We are investigating the effectiveness of using statistical features to build an expandable feature vector space.