Responsiveness and agility in collaborative networks

This paper investigates the responsiveness of technology and network governance in the case of collaborative networks comprising multiple organizations and identifies factors affecting the structure and the sustainability of collaborative network governance models. More specifically, the study focuses on the effect of network configurations that combine integration and unbundling on the collaborative network’s agility. The inferences draw on the cross case analysis of four case studies, representing collaborative networks situated within different industry segments in one geopolitical location. Primary data were collected through semi-structured interviews with representatives from each collaborative network. The paper has identified effective communication among partners as an essential requirement for the success of any collaborative project. With regards to agility, a proposition may be advanced that for some type of collaborative services, a higher degree of vertical integration is a better approach compared to a higher degree of unbundling. However, the study findings also indicated that unbundling is a good approach in the case of collaborative services where a cloud based deployment and delivery approach is the priority, and the set of collaborators is diverse. Finally, the findings also showed that a participatory, and largely decentralized model of governance, is more effective in achieving responsiveness, in a collaborative network, than a centralized model

Towards Visual Syntactical Understanding

Syntax is usually studied in the realm of linguistics and refers to the arrangement of words in a sentence. Similarly, an image can be considered as a visual 'sentence', with the semantic parts of the image acting as 'words'. While visual syntactic understanding occurs naturally to humans, it is interesting to explore whether deep neural networks (DNNs) are equipped with such reasoning. To that end, we alter the syntax of natural images (e.g. swapping the eye and nose of a face), referred to as 'incorrect' images, to investigate the sensitivity of DNNs to such syntactic anomaly. Through our experiments, we discover an intriguing property of DNNs where we observe that state-of-the-art convolutional neural networks, as well as vision transformers, fail to discriminate between syntactically correct and incorrect images when trained on only correct ones. To counter this issue and enable visual syntactic understanding with DNNs, we propose a three-stage framework- (i) the 'words' (or the sub-features) in the image are detected, (ii) the detected words are sequentially masked and reconstructed using an autoencoder, (iii) the original and reconstructed parts are compared at each location to determine syntactic correctness. The reconstruction module is trained with BERT-like masked autoencoding for images, with the motivation to leverage language model inspired training to better capture the syntax. Note, our proposed approach is unsupervised in the sense that the incorrect images are only used during testing and the correct versus incorrect labels are never used for training. We perform experiments on CelebA, and AFHQ datasets and obtain classification accuracy of 92.10%, and 90.89%, respectively. Notably, the approach generalizes well to ImageNet samples which share common classes with CelebA and AFHQ without explicitly training on them

Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model

Transformers have shown dominant performance across a range of domains including language and vision. However, their computational cost grows quadratically with the sequence length, making their usage prohibitive for resource-constrained applications. To counter this, our approach is to divide the whole sequence into segments and apply attention to the individual segments. We propose a segmented recurrent transformer (SRformer) that combines segmented (local) attention with recurrent attention. The loss caused by reducing the attention window length is compensated by aggregating information across segments with recurrent attention. SRformer leverages Recurrent Accumulate-and-Fire (RAF) neurons' inherent memory to update the cumulative product of keys and values. The segmented attention and lightweight RAF neurons ensure the efficiency of the proposed transformer. Such an approach leads to models with sequential processing capability at a lower computation/memory cost. We apply the proposed method to T5 and BART transformers. The modified models are tested on summarization datasets including CNN-dailymail, XSUM, ArXiv, and MediaSUM. Notably, using segmented inputs of varied sizes, the proposed model achieves $6-22\%$ higher ROUGE1 scores than a segmented transformer and outperforms other recurrent transformer approaches. Furthermore, compared to full attention, the proposed model reduces the computational complexity of cross attention by around $40\%$.Comment: EMNLP 2023 Finding

A Study of Responsiveness and Agility for Networks of Collaborators

This research investigates the governance structures of collaborative networks to gain an insight into their functioning. The focus is on the factors that the collaborators view as important in their pursuit to achieve agility and responsiveness in their business models. Due to the exploratory nature of this research with the constraints of time, a qualitative research based on the multiple case study has been undertaken to find the answers to the research questions. Towards this objective, the research investigates four cases of collaborative networks that use ICT. The semi structured interviews constitute the primary source of data collection. The following are the main findings of this thesis, which are highlighted as follows: 1. It has concluded that effective communication is an essential requirement for the success of a CN project. 2. The thesis has validated a commonly held notion that skills and expertise in a specialised area is an essential ingredient for success in a network. 3. The thesis findings point to several factors that lead to achieving agility and responsiveness of business model for the participating organisations. Many of these factors are in conformity with earlier published work, thereby validating those. 4. The thesis has defined the factors responsible for achieving BM agility and an optimum level of responsiveness for a network of multiple (more than two) collaborators, from which the desired characteristics of an Agile and Responsive Business Model may be determined. Business model agility was also studied across the two different structures of networks. The first of these is the unbundled structure that relates to each of the collaborators working on their units of work. The other type is the vertically integrated, which deals with more tightly coupled organisations. The case studies were used to determine the impact of different modes of governance and network configuration on partner flexibility. As a result of the data analysis during the research, five distinct themes have emerged. Some of the themes are similar to those reported in earlier studies, thus validating them. Past studies on collaborative networks for the research areas, similar to this study are found to be scarce. Therefore this study has added to the body of knowledge for collaborative networks that aim to achieve responsiveness and agility of their business models. In the concluding chapter, the answers to the main research questions have been provided and the contribution of this thesis is discussed. Finally the limitations of the study, with the recommendations for further research areas that this study has opened up is discussed