Patterns of Discovery

From a given directed weighted network of knowledge links between technology fields, the paper develops a multisector dynamic model of incremental innovation and R&D activity in these fields. The model is focused on the equilibrium share distribution of these variables, which is proved to be locally stable, with reference to a simple low dimensional case. Simulation methods suggest that local, and also global, stability extend to any model dimension. It is also shown how different network structures map to different asymptotic share distributions. Using the NBER patents and patent citation data files, the analytical framework is then used to analyse some general features of the pattern of knowledge creation and transfer in the period 1975-1999. From a descriptive viewpoint, the changes in the share distribution of innovation activity predicted by the model match reasonably well the actual changes in the perioddirected weighted network, knowledge spillovers, share distribution, incremental innovation and R&D dynamics, local stability, simulation, patents and patent citations

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

FAKE NEWS DETECTION ON THE WEB: A DEEP LEARNING BASED APPROACH

The acceptance and popularity of social media platforms for the dispersion and proliferation of news articles have led to the spread of questionable and untrusted information (in part) due to the ease by which misleading content can be created and shared among the communities. While prior research has attempted to automatically classify news articles and tweets as credible and non-credible. This work complements such research by proposing an approach that utilizes the amalgamation of Natural Language Processing (NLP), and Deep Learning techniques such as Long Short-Term Memory (LSTM). Moreover, in Information System’s paradigm, design science research methodology (DSRM) has become the major stream that focuses on building and evaluating an artifact to solve emerging problems. Hence, DSRM can accommodate deep learning-based models with the availability of adequate datasets. Two publicly available datasets that contain labeled news articles and tweets have been used to validate the proposed model’s effectiveness. This work presents two distinct experiments, and the results demonstrate that the proposed model works well for both long sequence news articles and short-sequence texts such as tweets. Finally, the findings suggest that the sentiments, tagging, linguistics, syntactic, and text embeddings are the features that have the potential to foster fake news detection through training the proposed model on various dimensionality to learn the contextual meaning of the news content

Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospects

Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning algorithms. However, when the inference dataset slightly differs from what the model has seen during one-time training, the model performance is greatly compromised. The situation requires restarting the training process using both the old and the new data which is computationally costly, does not align with the human learning process, and imposes storage constraints and privacy concerns. Alternatively, continual learning has emerged as a crucial approach for developing unified and sustainable deep models to deal with new classes, tasks, and the drifting nature of data in non-stationary environments for various application areas. Continual learning techniques enable models to adapt and accumulate knowledge over time, which is essential for maintaining performance on evolving datasets and novel tasks. This systematic review paper provides a comprehensive overview of the state-of-the-art in continual learning techniques applied to medical imaging analysis. We present an extensive survey of existing research, covering topics including catastrophic forgetting, data drifts, stability, and plasticity requirements. Further, an in-depth discussion of key components of a continual learning framework such as continual learning scenarios, techniques, evaluation schemes, and metrics is provided. Continual learning techniques encompass various categories, including rehearsal, regularization, architectural, and hybrid strategies. We assess the popularity and applicability of continual learning categories in various medical sub-fields like radiology and histopathology..

Tools of Trade of the Next Blue-Collar Job? Antecedents, Design Features, and Outcomes of Interactive Labeling Systems

Supervised machine learning is becoming increasingly popular - and so is the need for annotated training data. Such data often needs to be manually labeled by human workers, not unlikely to negatively impact the involved workforce. To alleviate this issue, a new information systems class has emerged - interactive labeling systems. However, this young, but rapidly growing field lacks guidance and structure regarding the design of such systems. Against this backdrop, this paper describes antecedents, design features, and outcomes of interactive labeling systems. We perform a systematic literature review, identifying 188 relevant articles. Our results are presented as a morphological box with 14 dimensions, which we evaluate using card sorting. By additionally offering this box as a web-based artifact, we provide actionable guidance for interactive labeling system development for scholars and practitioners. Lastly, we discuss imbalances in the article distribution of our morphological box and suggest future work directions

An aesthetics of touch: investigating the language of design relating to form

How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities

Towards a Service-Oriented Enterprise: The Design of a Cloud Business Integration Platform in a Medium-Sized Manufacturing Enterprise

This case study research followed the two-year transition of a medium-sized manufacturing firm towards a service-oriented enterprise. A service-oriented enterprise is an emerging architecture of the firm that leverages the paradigm of services computing to integrate the capabilities of the firm with the complementary competencies of business partners to offer customers with value-added products and services. Design science research in information systems was employed to pursue the primary design of a cloud business integration platform to enable the secondary design of multi-enterprise business processes to enable the dynamic and effective integration of business partner capabilities with those of the enterprise. The results from the study received industry acclaim for the designed solutions innovativeness and business results in the case study environment. The research makes contributions to the IT practitioner and scholarly knowledge base by providing insight into key constructs associated with service-oriented design and deployment of a cloud enterprise architecture and cloud intermediation model to achieve business results. The study demonstrated how an outside-in service-oriented architecture adoption pattern and cloud computing model enabled a medium-sized manufacturing enterprise to focus on a comprehensive approach to business partner integration and collaboration. The cloud integration platform has enabled a range of secondary designs that leveraged business services to orchestrate inter-enterprise business processes for choreography into service systems and networks for the purposes of value creation. The study results demonstrated enhanced levels of business process agility enabled by the cloud platform leading to secondary designs of transactional, differentiated, innovative, and improvisational business processes. The study provides a foundation for future scholarly research on the role of cloud integration platforms in enterprise computing and the increased importance of service-oriented secondary designs to exploit cloud platforms for sustained business performance

An exploratory study of the role and contribution of Absorptive Capacity levels in the commercialisation of knowledge in Knowledge Intensive SMEs

Absorptive Capacity (ACAP) is a construct introduced by Cohen and Leventhal in 1990 to describe the process by which an organisation recognises and absorbs new external knowledge to increase its current stock of knowledge, thereby giving it increased capability to create value for its customers, stakeholders and wider society. ACAP, as a construct, has gained widespread acceptance within academia and the construct has been further refined and developed over the last thirty years. However, the application and testing of the construct, is in practice, still in the early stages of development. The aim of this exploratory study was to investigate the role and contribution of varying levels of ACAP for the commercialisation of knowledge in indigenous firms in a small open economy. The Republic of Ireland was utilised in the study as an exemplar case. This study employs a multiple case study approach to explore the core research question cited above. These cases were selected on the geodemographic criteria of age, size, location and sector to provide a representative sample of the indigenous firms in the internationally traded sector in Ireland. A descriptive case study of each firm (n=19 cases) was produced from secondary and primary research. The data from each of the cases was coded and analysed using process and pattern coding and thematic analysis. A cross-case analysis was then conducted within the three cohorts of firms – Young (n=4), Adolescent (n=6) and Mature (n=9) – to identify variations in levels of ACAP between performing and non-performing firms within each cohort. Finally, a cross-cohort analysis was conducted to analyse how levels of ACAP differ across the stages of development of the firms in the study. It was found that ACAP, as a Dynamic Capability of the firm, underpins the innovation process in indigenous firms. Higher levels of ACAP were found in the more successful firms across all three stages of development, as defined in the study. The 5-Loop framework developed in the study from the extant literature, was able to identify varying levels of ACAP in firms using the diagnostic and evaluative instrument developed from this framework. This 5-Loop framework and instrument will be particularly beneficial to firm leadership and policymakers who wish to improve commercialisation results through improving key aspects of the firm’s innovation process

Interpersonal Influence and Pro-Societal Consumption: A Review of Diffusion, Conformity, Dissemination, Translation and Reflexivity

Understanding consumption behavior will facilitate the successful deployment of new pro-societal products and behaviors. Interpersonal influence is thought to play an important role in the successive adoption of pro-societal products and behaviors within groups across time, but processes of influence are poorly understood. This paper begins by presenting a typology for new products according functional, symbolic and pro-societal attributes. Five perspectives on interpersonal influence and pro-societal consumption behavior are identified and summarized. Contagion focuses on the effect of functional information flowing among people or groups. Conformity focuses on individual thresholds and motivations to mimic others. Dissemination is the intentional diffusion of information by a core group of motivated individuals. Translation is the tendency for various social groups to negotiate interpretations and valuations of the technology. Finally, reflexivity is a theoretical framework that explains the motivations of individuals seeking to establish lifestyle practices consistent with their self concept in an uncertain modern world. Strengths and weaknesses of each perspective are discussed