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Employee training and learning have transformed over the years. The movement from classroom training to the blended format represents the magnitude of this evolution. This has placed much attention on self-regulated learning. This study aimed to understand the individual and organizational mechanisms that sustain the formal learning process in organizations. It explored the goals the organizations and employees strive to achieve by investing in learning. Through this, the authors investigated how technology assistance makes learning more goal-oriented, despite the possibility of different goals for different stakeholders. They also examined how person-job fit can be achieved in employee training.

The study adopted a grounded theory-based inductive approach using a qualitative inquiry that used in-depth interviews of employees working in the Indian IT/ITES sector. This sector is knowledge-intensive and engages in constant skill development. A content analysis of the interview transcripts unraveled the most relevant themes from the participants' discussion.

Individual learners use dimensions of self-regulated learning to set and achieve goals such as better performance and career development. On the other hand, organizations use learning support mechanisms such as better access and flexibility to direct employee learning behavior to achieve organizational goals. Focusing on goal congruence leads to better achievement of results. Goal congruence also implies good person-organization fit.

This research established how aligning individual and organizational mechanisms can help achieve training goals that ultimately contribute to organizational performance. The study differentiated itself by investigating training goal setting and goal achievement at two levels – organizational and individual – using a qualitative approach. It also showed how goal congruence is vital in improving organizational performance and how technology-enabled training practices rely on self-regulated learning and help achieve goal congruence.

This study aims to propose novel edge device-tailored federated learning algorithms of local classifiers (stochastic gradient descent, support vector machines), namely, FL-lSGD and FL-lSVM. These algorithms are designed to address the challenge of large-scale ImageNet classification.

The authors’ FL-lSGD and FL-lSVM trains in a parallel and incremental manner to build an ensemble local classifier on Raspberry Pis without requiring data exchange. The algorithms load small data blocks of the local training subset stored on the Raspberry Pi sequentially to train the local classifiers. The data block is split into k partitions using the k-means algorithm, and models are trained in parallel on each data partition to enable local data classification.

Empirical test results on the ImageNet data set show that the authors’ FL-lSGD and FL-lSVM algorithms with 4 Raspberry Pis (Quad core Cortex-A72, ARM v8, 64-bit SoC @ 1.5GHz, 4GB RAM) are faster than the state-of-the-art LIBLINEAR algorithm run on a PC (Intel(R) Core i7-4790 CPU, 3.6 GHz, 4 cores, 32GB RAM).

Efficiently addressing the challenge of large-scale ImageNet classification, the authors’ novel federated learning algorithms of local classifiers have been tailored to work on the Raspberry Pi. These algorithms can handle 1,281,167 images and 1,000 classes effectively.

The paper discusses the use of co-design for staff professional learning within higher education. It suggests that three distinct approaches to professional learning can be characterised as help-yourself platforms/services, drive-by workshops and co-design workshops. It makes pragmatic suggestions for where co-design might be used and heuristics for its successful use, based upon the authors' collective experiences.

This practitioner paper presents a case-study of co-design in a university context. Staff from across disciplinary boundaries were brought together to co-design novel learning experiences for students for a non-traditional context.

Findings from a case study are used to highlight the strengths of a co-design approach, as understood through the lenses of networked learning and self-determination theory. It juxtaposes co-design for staff learning with other approaches and finds it to be valuable and underutilised.

The research discusses a single case study involving two workshops with a sample size of 112 participants. It is included as an example of co-design for professional learning in higher education.

Co-design for professional learning in higher education is poorly understood and presently underutilised. This paper addresses this gap by presenting an example of co-design for professional learning in higher education and theorising its significance.

This paper aims to study the relationship between the ball grid array (BGA) flip-chip underfilling process parameter and its void formation region.

A set of top-down scanning acoustic microscope images of BGA underfill is collected and void labelled. The labelled images are trained with a convolutional neural network model, and the performance is evaluated. The model is tested with new images, and the void area with its region is analysed with its dispensing parameter.

All findings were well-validated with reference to the past experimental results regarding dispensing parameters and their quantitative regional formation. As the BGA is non-uniform, 85% of the test samples have void(s) formed in the emptier region. Furthermore, the highest rating factor, valve dispensing pressure with a Gini index of 0.219 and U-type dispensing pattern set of parameters generally form a lower void percentage within the underfilling, although its consistency is difficult to maintain.

This study enabled manufacturers to forecast the void regional formation from its filling parameters and array pattern. The filling pressure, dispensing pattern and BGA relations could provide qualitative insights to understand the void formation region in a flip-chip, enabling the prompt to formulate countermeasures to optimise voiding in a specific area in the underfill.

The void regional formation in a flip-chip underfilling process can be explained quantitatively with indicative parameters such as valve pressure, dispensing pattern and BGA arrangement.

Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates multiple requests simultaneously making the system unavailable to users. Protection of internet services requires effective DoS attack detection to keep an eye on traffic passing across protected networks, freeing the protected internet servers from surveillance threats and ensuring they can focus on offering high-quality services with the fewest response times possible.

This paper aims to develop a hybrid optimization-based deep learning model to precisely detect DoS attacks.

The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.

The introduced detection approach effectively detects DoS attacks available on the internet.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

This conceptual article presents a schematic for use with extended cybernetic recursion in living systems and applies it to the issue of hyper vigilance as a demonstration of its utility.

The test-operate-test-exit (TOTE) schematic of Miller et al. (1960) is critically evaluated along with other schematics, including those of ordered cybernetics, and a new schematic is proposed, a recursive test-operate-test (rTOT), which emphasizes teleological purpose and hierarchical structure. The background psychophysiology of trauma is reviewed and then rTOT is applied to hyper vigilance, a cardinal component of post-traumatic stress disorder (PTSD).

Once the schematic was developed, it was applied to the behavior of hyper vigilance. Other applications are suggested.

As demonstrated, the rTOT schematic has potentially wide application because of its pragmatic and detailed structure.

The rTOT requires careful consideration of teleological purposes for its application and is simple enough, but also complex enough, for relevant utilization. Its compact nature and adjustable hierarchy scope are good mini-max complexity solutions for cybernetic, information modeling schematics.

The revealed teleological purpose of the trauma adaptation of hyper vigilance presents significant alternative formulation options for prevention and intervention.

While the rTOT schematic is derived from previous schematics, it is original in its emphasis on information processing, the teleological aspects of extended recursion and on the provision of a hierarchical structure for those recursions. It is considerably more compact than other schematics associated with the ordered cybernetics literature. The explication of the adaptation model for post-trauma consequences is significantly enhanced by the rTOT application.

The aim of this study is to report and reflect on the development of a tailored executive education programme rooted in state-of-the-art research and focused on enhancing and embedding project leadership competences in a multi-national project-based organisation.

Through a detailed description of an example of a leadership development programme, the paper reflects on the learning journey. Drawing on research in management education, leadership and project management competence development, the authors present the rationale, design, delivery and participant and organisational experiences.

Reflections on the learning journey identify five key points: a) the fundamental role of a programme champion, b) the importance of balancing rigour and relevance in executive education programme design, c) the importance of attending to the milieu of the clientele in terms of culture, demands and where they are in terms of their learning journey, d) the significant role that reflection can play in personal development and e) the competences necessary for project leadership.

This paper reflects on a single programme for a single organisation and is therefore at risk of being idiosyncratic and not generalisable. This is indeed a limitation. However, the authors are all experienced in delivering executive education – and therefore can use those experiences to view the reflections. Many of the considerations noted here resonate with other programmes (both in support and through experience of omission having detrimental effects).

The paper provides new insights into developing enduring and effective leadership competences in complex project management, i.e. one that has a lasting impact on the organisation and is recognised to provide benefits. The reflections on how the authors charted a course and embarked on a journey are deemed of relevance to academics and industry.

The paper provides, for both industry practitioners and academics, new insights into effective leadership development in complex project management, one that has a lasting impact on the individual and the organisation and is recognised to provide benefits.

This study aims to integrate social embeddedness with learning society philosophy to explore how grassroots associations of Indigenous women tourism entrepreneurs can leverage their social network to co-create value.

A critical feminist perspective considers the intersectional experiences of Indigenous women tourism entrepreneurs from the rural Manasalu region of Nepal. Twenty-one semi-structured interviews with local tourism stakeholders were carried out. Hermeneutics in tandem with Indigenous methods of analysis ensured consideration of Indigenous ontologies and social locations beyond being merely theoretically driven.

A paradox of Indigenous women’s empowerment emerged where several efforts for empowerment presented themselves as a double-edged sword. Individuals’ social capital and social support for the sustenance and stability of grassroots associations ensure collective and continuous learning through a value-creation framework.

Collective self-reflection and self-determination for knowledge creation and sharing amongst social ties shed new light on the role of an Indigenous standpoint on value creation.

To the best of the authors’ knowledge, this is the first study that captures how the intersectionality of Indigenous women entrepreneurs in grassroots associations use their social capital through contesting, leveraging and learning to transform their social network into a value network.

Podcasting is a new mobile technology application for language learning. Drawing upon the stimulus–organism–response model and the interest driven creator (IDC) theory from e-learning, this study aims to develop and test an integrative conceptual framework. This study investigates contextual and environmental stimuli effects (content richness [CR], self-directed learning [SDL] and situational interest [SI]) from a podcast English learning context on learners’ experience states (cognitive absorption [CA], pleasure [PL] and arousal [AR]) and their subsequent responses (continuance learning intention [CLI]).

Using 416 valid responses from five universities located in North Taiwan, data analysis is performed using a structural equation model.

The results show that most of the interest factor stimuli (CR, SDL and SI) have significant impacts on learners’ experiences (CA, PL and AR), which in turn affect their CLI.

The findings provide useful insights for English show podcasters and operators to invest in establishing learners’ interest factor and stimulating experiences to improve their CLI.

This paper contributes to a better understanding of students who use contextual factors of podcast English learning and how these factors influence their CLI via a framework of stimulus–organism–response and the IDC theory.