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The paper aims to enhance Arabic machine translation (MT) by proposing novel approaches: (1) a dimensionality reduction technique for word embeddings tailored for Arabic text, optimizing efficiency while retaining semantic information; (2) a comprehensive comparison of meta-embedding techniques to improve translation quality; and (3) a method leveraging self-attention and Gated CNNs to capture token dependencies, including temporal and hierarchical features within sentences, and interactions between different embedding types. These approaches collectively aim to enhance translation quality by combining different embedding schemes and leveraging advanced modeling techniques.

Recent works on MT in general and Arabic MT in particular often pick one type of word embedding model. In this paper, we present a novel approach to enhance Arabic MT by addressing three key aspects. Firstly, we propose a new dimensionality reduction technique for word embeddings, specifically tailored for Arabic text. This technique optimizes the efficiency of embeddings while retaining their semantic information. Secondly, we conduct an extensive comparison of different meta-embedding techniques, exploring the combination of static and contextual embeddings. Through this analysis, we identify the most effective approach to improve translation quality. Lastly, we introduce a novel method that leverages self-attention and Gated convolutional neural networks (CNNs) to capture token dependencies, including temporal and hierarchical features within sentences, as well as interactions between different types of embeddings. Our experimental results demonstrate the effectiveness of our proposed approach in significantly enhancing Arabic MT performance. It outperforms baseline models with a BLEU score increase of 2 points and achieves superior results compared to state-of-the-art approaches, with an average improvement of 4.6 points across all evaluation metrics.

The proposed approaches significantly enhance Arabic MT performance. The dimensionality reduction technique improves the efficiency of word embeddings while preserving semantic information. Comprehensive comparison identifies effective meta-embedding techniques, with the contextualized dynamic meta-embeddings (CDME) model showcasing competitive results. Integration of Gated CNNs with the transformer model surpasses baseline performance, leveraging both architectures' strengths. Overall, these findings demonstrate substantial improvements in translation quality, with a BLEU score increase of 2 points and an average improvement of 4.6 points across all evaluation metrics, outperforming state-of-the-art approaches.

The paper’s originality lies in its departure from simply fine-tuning the transformer model for a specific task. Instead, it introduces modifications to the internal architecture of the transformer, integrating Gated CNNs to enhance translation performance. This departure from traditional fine-tuning approaches demonstrates a novel perspective on model enhancement, offering unique insights into improving translation quality without solely relying on pre-existing architectures. The originality in dimensionality reduction lies in the tailored approach for Arabic text. While dimensionality reduction techniques are not new, the paper introduces a specific method optimized for Arabic word embeddings. By employing independent component analysis (ICA) and a post-processing method, the paper effectively reduces the dimensionality of word embeddings while preserving semantic information which has not been investigated before especially for MT task.

The purpose of this paper is to investigate the contributions of path-dependency and some contextual social capital drivers to housing tenure transitions in Britain. Different situations have continued to shape young adults’ housing tenure decisions. However, very little research has been done to investigate the impact of some social capital drivers, such as neighbourhood integration and strength of parental intimacy, on housing tenure decisions in Britain.

This paper is carried out by tracking a sample of young adults in the British Household Panel Survey from 1991 to 2015 until they make tenure transition. Multinomial fixed-effects logistic regression of time to tenure transition was useful for the models, incorporating established economic and demographic drivers and with the inclusion of contextual social capital variables.

The inclusion of the number of years of parental home ownership experience tends to improve on previous path-dependency indicators of tenure transition. With additional years of parental home ownership experience, British young adults are more likely to remain or return to parental housing. Also, individuals that exchange better with their neighbours are less likely to switch tenure. On the other hand, regularity of contact with parents showed a positive relationship with home ownership or parental housing transitions.

To the best of the author’s knowledge, no study has explored the impact of the duration of socialisation in parental housing and also the impact of some other social capital drivers, such as neighbourhood integration and strength of parental intimacy, on housing tenure decisions among young adults. Hence, it is believed that the findings will further assist policymakers in understanding the dimensions and drivers of tenure shifts.

The purpose of this paper is to evaluate e-business driven organisational transformation and its resource implications for a European automotive multinational corporation. The application of Business Process Change Model (BPCM) was used to investigate throughout the five developmental stages of transformation and the associated resource implications in business-to-business (B2B) and extranet applications.

Based on a case study design, B2B and extranet applications were selected. A triangulated research approach was used to capture the resource implications upon transformation, incorporating a participant observation and interviews.

The findings indicate that the resource implications change in a transformation process based on individual e-business applications as well as the developmental stages of BPCM. Throughout the transformation only resource investment remained present in both applications. The change in resource implications and iterations between the BPCM stages in the case of “process redefinition”, “continuous improvement” underline why organisational transformation in similar settings could indicate variances in organisations’ transformation results.

The paper offers an insight into the resource implications in an e-business enabled transformation and underpins the importance of using developmental stages to bridge the divide between planning and application. The resource impact has been evaluated within the automotive sector using BPCM, future empirical research is needed to test the BPCM in other industrial e-business enabled transformation settings.

Organisations should take a developmental approach to transformation that assesses resources implications. This also raises the need for using models such as the BPCM to fine-tune the transformation effort, through the inclusion of a more interactive iteration between BPCM stages.

In this paper, the authors present an analysis of an e-business related, organisational transformation project, including an assessment of its ultimate effectiveness. This is the first case study, which focuses on resource implications solely and utilises the BPCM framework to understand the evolution of transformation effort and its resource implications.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Practitioners should develop argumentation strategies based on contextual dependencies and the optimal strategy that aligns with the political cause.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

During discussions at the ASC 2013 Conference, the authors were stimulated to consider acting, learning and understanding in the context of organizational change, and in particular the relationship between organizational actors and external analysts. The purpose of this paper is to review from a cybernetic perspective how a socio-technical toolbox can help to facilitate organizational change, and to examine issues involved in use of such a toolbox by organizational actors supported by expert analysts.

The paper is conceptual and adopts a critical stance, i.e. to provide support for emancipation of individuals through ownership and control of their own analyses.

Drawing on work by e.g. Bateson, the authors consider organizations as dynamic and complex human activity systems, and how actors can be helped to develop a productive learning “spiral” of acting and reflecting by means of a proposed socio-technical toolbox. Acting and reflecting upon action can be seen to form a “double helix” of learning, leading to richer understandings of contextual dependencies. Engaged actors need support to surface their contextually dependent understandings, individual and collectively and engage in a “dance of change”.

Change is endemic in organizational life. When engaging with change activity that attempts to address complexity (as opposed to complicatedness), contextual experts need to be the key decision takers. This means a redistribution not only of responsibility and action but also decision-taking power.

The paper suggests augmentation of traditional socio-technical methods to address dynamic complexity.

Concepts of values-based leadership posit that school principals’ professional practice must be informed by values to ensure coherently purposeful activities. Contingency models stress the contextual dependency of professional practice and the need to match activities to local opportunities and constraints. The purpose of this paper is to reconcile both positions from an integrative perspective and to illustrate examples of “values-based contingency leadership” (Day et al., 2001).

Analyses draw on survey data from 56 German schools in order to relate professional values stated by the principals as well as organizational features of their schools to teacher ratings on leadership behaviour (n=910). Instead of scrutinizing singular variables in isolation, a typological approach serves to identify value profiles as well as organizational configurations. Analyses of variance are applied to examine the combined effects of both factors on leadership behaviour.

Interactional effects in the sample indicate that contextual influences are not homogenous across differing value profiles of principals who operate under equal conditions. Descriptive patterns of leadership behaviour within each organizational configuration reveal how principals accentuate leadership activities according to their value profile.

Due to the low statistical power of the small sample, findings are clearly exploratory in nature. However, replication and extension studies seem fruitful, as effect sizes of value-context interactions are consistent with theoretical assumptions and not artificially inflated by common-source variance.

This paper elaborates and exemplifies the moderating role of values in contextual influences on leadership behaviour. It also provides deeper insights into the content and structure of professional values advocated by school principals.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

Context‐awareness is an essential property of any pervasive system perceiving its environment. Such a system captures and processes context, i.e. the features describing the relevant aspects of environment state and user behaviour. However, development of these systems still requires solving a number of research and engineering challenges. The purpose of this paper is to propose perception framework, a RESTful middleware which simplifies and accelerates the development of pervasive systems. Perception framework allows constructing services' application logic using rules and context. Moreover, it collects sensor data and produces the context information that is required for the rules. The authors present the architecture, design, complete implementation, and prototype‐based verification of perception framework.

Development of context‐aware services is achieved with a novel architecture supporting building of the logic of web services using rules which directly manipulate the available elementary context represented with the Web Ontology Language (OWL) ontology. These rules are described using the Rule Interchange Format (RIF) with support for different rule languages. The implementation of this framework is aligned with RESTful principles, providing a lightweight and flexible solution for large‐scale context‐aware systems.

The fully implemented prototype verifies the feasibility of constructing the logic of context‐aware web services with the rules supported by perception framework.

The contributions of this paper include: the requirement specification for a generic context‐aware pervasive middleware; and the design and implementation of the framework (i.e. perception framework) supporting the development of context‐aware web services. The perception framework includes a generic rule‐based reasoner allowing developers to use several RIF‐compliant rule description languages.

The wider use of Internet of Things (IoT) makes it possible to create smart cities. The purpose of this paper is to identify key IoT challenges and understand the relationship between these challenges to support the development of smart cities.

Challenges were identified using literature review, and prioritised and elaborated by experts. The contextual interactions between the identified challenges and their importance were determined using Interpretive Structural Modelling (ISM). To interrelate the identified challenges and promote IoT in the context of smart cities, the dynamics of interactions of these challenges were analysed using an integrated Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC)-ISM approach. MICMAC is a structured approach to categorise variables according to their driving power and dependence.

Security and privacy, business models, data quality, scalability, complexity and governance were found to have strong driving power and so are key challenges to be addressed in sustainable cities projects. The main driving challenges are complexity and lack of IoT governance. IoT adoption and implementation should therefore focus on breaking down complexity in manageable parts, supported by a governance structure.

This research can help smart city developers in addressing challenges in a phase-wise approach by first ensuring solid foundations and thereafter developing other aspects.

A contribution originates from the integrated MICMAC-ISM approach. ISM is a technique used to identify contextual relationships among definite elements, whereas MICMAC facilitates the classification of challenges based on their driving and dependence power. The other contribution originates from creating an overview of challenges and theorising the contextual relationships and dependencies among the challenges.

The paper aims to analyse the contextual relationship and dependency amongst enablers for lean manufacturing implementation in Bulgarian small and medium-sized enterprises (SMEs).

In this study, the interpretive structural modelling (ISM) technique was used to develop a hierarchical structural model for enablers. Also, the interpretive ranking process (IRP) was used to analyse and rank enablers with reference to performance variables. For the ISM approach, a structural self- integration matrix was developed with the help of experts’ suggestions and opinions. Cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to analyse the relationship amongst enablers. A total of nine experts were chosen for collecting the primary data in which seven experts belong to the industry and two experts were academicians. The dominant relationship amongst the enablers was analysed through IRP modelling.

A total of 11 enablers were identified for the purpose of this study. The model shows that “leadership and commitment by management”, “human resource management”, “customer relation management”, “supplier relation management” and “information technology system” are the most significant enablers for lean implementation in Bulgarian SMEs as these are positioned at the bottom levels in ISM model. MICMAC analysis shows that five enablers fall in the independent factor, two enablers in linkage factor and four enablers in the dependant factor while there is no enabler in the autonomous factor. ISM and IRP models show that “continuous improvement” is an essential enabler for the successful implementation of lean in Bulgarian SMEs. This study also helps to explain the comparative analysis of ISM and IRP, which indicates that IRP is a more robust modelling approach than ISM, as it incorporates the relationship of enablers with performance variables.

ISM and IRP modelling approaches are based solely on expert opinions and responses. This limitation can be overcome with the help of empirical study.

This study supports the professionals/experts to prioritise and manage enablers at strategic and tactical levels while implementing lean manufacturing practices in Bulgarian SMEs. The models developed in the study will be helpful for practitioners to understand and analyse the interdependence of enablers for lean manufacturing implementation.

This study helps to identify and prioritise enablers that affect lean manufacturing adoption using ISM and IRP approaches. Literature shows that numerous authors have used the ISM approach but the use of IRP approach is limited. The models were developed in the study, totally dependent on data collected from the experts to ensure their real-life validity.