Addressing STEM Geek Culture Through Peer Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.STEM is generally considered to be a male-dominated environment. The geek culture that often leads to social issues, and the gender imbalance that leads to fewer girls choosing a STEM subject, are becoming important topics of research. Peer learning has been widely used across the world to support retention and better grades with a more recent focus on adopting this approach to tackle issues around gender imbalance and perceived ‘laddish’ culture. Through peer learning, students are encouraged to work alongside their tutors, and to practice the critical soft skills that they will need as they move into the workplace. This paper explores the role of gender and geek culture, considering how students can break down the stereotypes while moving away from didactic approaches. The gender gap in STEM has narrowed, but women are still underrepresented. ‘Geek culture’ often creates a high-tech, androcentric environment. Policy makers have agreed that the geek culture needs to be researched and its impact identified. Social interactions and relations are the reflection of interpersonal values, and the peer norms may affect a students’ engagement and motivations in STEM subjects. The discussion will examine how peer learning can prepare students in Higher Education and offer insights into creating an environment in which students can become partners. Peer learning can represent a significant step in enabling students to become more engaged in their learning and is becoming an important element across institutions globally. There is a plethora of approaches to peer learning and it is encouraging to observe how students transform and mature by participating in the scheme. Evidence is accumulating that peer learning can enable students to become more confident and independent, enhancing not only their transition into Higher Education but also into industry. Peer learning can have a positive influence across the disciplines and supports students in achieving more than they might otherwise do. It can also examine, in an informal way, the gender issues, laddish and geek culture, and promote the sense of belongingness in STEM disciplines. This paper will inform readers about how peer learning can reconstruct the geek culture and transform it from self-centred to forming relationships and overcoming social issues. With regard to Higher Education specifically, we try to understand the different situational frames that are being generated by such cultures, how we can influence those stereotypes, and make them more acceptable and more inclusive

Biometric security: A novel ear recognition approach using a 3D morphable ear model

Biometrics is a critical component of cybersecurity that identifies persons by verifying their behavioral and physical traits. In biometric-based authentication, each individual can be correctly recognized based on their intrinsic behavioral or physical features, such as face, fingerprint, iris, and ears. This work proposes a novel approach for human identification using 3D ear images. Usually, in conventional methods, the probe image is registered with each gallery image using computational heavy registration algorithms, making it practically infeasible due to the time-consuming recognition process. Therefore, this work proposes a recognition pipeline that reduces the one-to-one registration between probe and gallery. First, a deep learning-based algorithm is used for ear detection in 3D side face images. Second, a statistical ear model known as a 3D morphable ear model (3DMEM), was constructed to use as a feature extractor from the detected ear images. Finally, a novel recognition algorithm named you morph once (YMO) is proposed for human recognition that reduces the computational time by eliminating one-to-one registration between probe and gallery, which only calculates the distance between the parameters stored in the gallery and the probe. The experimental results show the significance of the proposed method for a real-time application

Intelligent building systems: Security and facility professionals’ understanding of system threats,vulnerabilities and mitigation practice

Intelligent Buildings or Building Automation and Control Systems (BACS) are becoming common in buildings, driven by the commercial need for functionality, sharing of information, reduced costs and sustainable buildings. The facility manager often has BACS responsibility; however, their focus is generally not on BACS security. Nevertheless, if a BACS-manifested threat is realised, the impact to a building can be significant, through denial, loss or manipulation of the building and its services, resulting in loss of information or occupancy. Therefore, this study garnered a descriptive understanding of security and facility professionals’ knowledge of BACS, including vulnerabilities and mitigation practices. Results indicate that the majority of security and facility professionals hold a general awareness of BACS security issues, although they lacked a robust understanding to meet necessary protection. For instance, understanding of 23 BACS vulnerabilities were found to be equally critical with limited variance. Mitigation strategies were no better, with respondents indicating poor threat diagnosis. In contrast, cybersecurity and technical security professionals such as integrators or security engineering design professionals displayed a robust understanding of BACS vulnerabilities and resulting mitigation strategies. Findings support the need for greater awareness for both security management and facility professionals of BACS vulnerabilities and mitigation strategies

Cooperative co-evolution for feature selection in big data with random feature grouping

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because of some limitations, such as not considering feature interactions, dealing with only an even number of features, and decomposing the dataset statically. In this paper, a novel random feature grouping (RFG) has been introduced with its three variants to dynamically decompose Big Data datasets and to ensure the probability of grouping interacting features into the same subcomponent. RFG can be used in CC-based FS processes, hence called Cooperative Co-Evolutionary-Based Feature Selection with Random Feature Grouping (CCFSRFG). Experiment analysis was performed using six widely used ML classifiers on seven different datasets from the UCI ML repository and Princeton University Genomics repository with and without FS. The experimental results indicate that in most cases [i.e., with naïve Bayes (NB), support vector machine (SVM), k-Nearest Neighbor (k-NN), J48, and random forest (RF)] the proposed CCFSRFG-1 outperforms an existing solution (a CC-based FS, called CCEAFS) and CCFSRFG-2, and also when using all features in terms of accuracy, sensitivity, and specificity

A novel penalty-based wrapper objective function for feature selection in big data using cooperative co-evolution

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS

A survey of distributed certificate authorities in MANETs

A Certificate Authority (CA) provides the critical authentication and security services for Public Key Infrastructure (PKI) which are used for the Internet and wired networks. In MANETs (wireless and ad hoc) there is an inability to offer a centralized CA to provide these security services. Recent research has looked to facilitate the use of CAs within MANETs through the use of a Distributed Certificate Authority (DCA) for wireless and ad hoc networks. This paper presents a number of different types of DCA protocols and categorizes them into groups based on their factors and specifications. The paper concludes by proposing the best DCA security services in terms of performance and level of security

Correction to: Cooperative co‑evolution for feature selection in big data with random feature grouping (Journal of Big Data, (2020), 7, 1, (107), 10.1186/s40537-020-00381-y)

© 2020, The Author(s). Following publication of the original article [1], the author reported that the 2nd author affiliation was incorrect. It should only be “School of Science, Edith Cowan University, Joondalup, WA, Australia”. The affiliation is presented correctly in this correction article. The original article [1] has been corrected

An energy-efficient and secure data inference framework for internet of health things: A pilot study

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Privacy protection in electronic healthcare applications is an important consideration, due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks that are used within a healthcare setting have unique challenges and security requirements (integrity, authentication, privacy, and availability) that must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This poses certain restrictions on the practical implementation of these devices. In order to address these issues, this paper proposes a privacy-preserving two-tier data inference framework solution that conserves battery consumption by inferring the sensed data and reducing data size for transmission, while also protecting sensitive data from leakage to adversaries. The results from experimental evaluations on efficiency and privacy show the validity of the proposed scheme, as well as significant data savings without compromising data transmission accuracy, which contributes to energy efficiency of IoHT sensor devices