Topic-based analysis for technology intelligence

University of Technology Sydney. Faculty of Engineering and Information Technology.Since the past several decades, scientific literature, patents and other semi-structured technology indicators have been generating and accumulating at a very rapid rate. Their growth provides a wealth of information regarding technology development in both the public and private domain. However, it has also caused increasingly severe information overload problems whereby researchers, analysts and decision makers are not able to read, summarize and understand massive technical documents and records manually. The concept and tools of technology intelligence aims to handle this issue. In the current technology intelligence research, one of the big challenges is that, the frameworks and applications of existing technology intelligence conducted semantic content analysis and temporal trend estimation separately, lacking a comprehensive perspective on trend analysis of the detailed content within an area. In addition, existing research of technology intelligence is mainly constructed on the fundamentals of semantic properties of the semi-structured technology indicators; however, single keywords and their ranking alone, are too general or ambiguous to represent complex concepts and their corresponding temporal patterns. Thirdly, systematic post-processing, forecasting and evaluation on both content analysis and trend identification outputs are still in great demand, for diverse and flexible technological decision support and opportunity discovery. This research aims to handle these three challenges in both theoretical and practical aspects. It first quantitatively defines and presents temporal characteristics and semantic properties of typical semi-structured technology indicators. Then this thesis proposes a framework of topic-based technology intelligence, with three main functionalities, including data-driven trend identification, topic discovery and comprehensive topic evaluation, to synthetically process and analyse technological publication count sequence, textual data and metadata of target technology indicators. To achieve the three functionalities, this research proposes an empirical technology trend analysis method to extract temporal trend turning points and trend segments, which help with producing a more reasonable time-based measure; a topic-based technological forecasting method to first discover and characterize the semantic knowledge underlying in massive textual data of technology indicators, meanwhile estimating the future trends of the discovered topics; a comprehensive topic evaluation method that links metadata and discovered topics, to provide integrated landscape and technological insight in depth. In order to demonstrate the proposed topic-based technology intelligence framework and all the related methods, this research presents case studies with both patents and scientific literature. Experimental results on Australian patents, United States patents and scientific papers from Web of Science database, showed that the proposed framework and methods are well-suited in dealing with semi-structured technology indicators analysis, and can provide valuable topic-based knowledge to facilitate further technological decision making or opportunity discovery with good performance

From Chinese Local State-Owned Enterprise to Global MNE: a Mixed Methods Investigation into pre- and post- Strategic Asset Seeking OFDI in sub-national CMNEs

Over the last several decades, Chinese local state owned enterprises (LSOEs) have become significant forces in Chinese outward foreign direct investment (OFDI). I first show, using a quantitative regression model, how LSOEs have a comparatively stronger strategic asset seeking (SAS) orientation. Operating within a diversified external political economy, and possessing unique status and features following decentralization, LSOEs face particular challenges, to which SAS oriented OFDI has arguably been one response. I then investigate the case of China’s Northern Heavy Industries (NHI) Group from Liaoning Province. The group operates in the Tunnel Boring Machinery (TBM) industry and has become one of the world’s most successful TBM players. It has done so through several large foreign strategic asset related acquisitions (one in France and one in the US). I draw from interviews and hand-collected primary information from the parent firm in China, and the acquired subsidiaries in France, the United States and Germany. I explore in particular pre and post SAS related FDI decision making and integration strategies and behaviours. I identify: (1) Local state ownership as an important factor determining pre-OFDI strategic decision making and post-OFDI integration; (2) The Chinese institutional environment as a potential comparative advantage for LSOEs in negotiating with foreign investment targets or partners; (3) the challenges and responses to post FDI SAS integration for local state-owned Chinese businesses. To date we know relatively little in detail about the ways in which local Chinese MNEs have managed to catch-up with developed market counterparts. This research therefore contributes to our understanding of theories like Mathews’ (2006) ‘LLL’ model, the ‘springboard’ perspective of Luo and Tung (2007), and Chinese OFDI determination theory by Buckley et al. (2007). It also sheds important new light on the institutional perspective, particularly the role of local government in spurring Chinese MNEs (CMNE) OFDI related catch-up

Semi-Supervised Learning via Swapped Prediction for Communication Signal Recognition

Deep neural networks have been widely used in communication signal recognition and achieved remarkable performance, but this superiority typically depends on using massive examples for supervised learning, whereas training a deep neural network on small datasets with few labels generally falls into overfitting, resulting in degenerated performance. To this end, we develop a semi-supervised learning (SSL) method that effectively utilizes a large collection of more readily available unlabeled signal data to improve generalization. The proposed method relies largely on a novel implementation of consistency-based regularization, termed Swapped Prediction, which leverages strong data augmentation to perturb an unlabeled sample and then encourage its corresponding model prediction to be close to its original, optimized with a scaled cross-entropy loss with swapped symmetry. Extensive experiments indicate that our proposed method can achieve a promising result for deep SSL of communication signal recognition

How Embeddedness Affects the Evolution of Collaboration: The Role of Knowledge Stock and Social Interactions

Science and technology are becoming increasingly collaborative. This paper aims to explore the factors and mechanisms that impact the dynamic changes of collaborative innovation networks. We consider both collaborative interactions of organizations and their knowledge element exchanges to reveal how social and knowledge network embeddedness affects the collaboration dynamics. Knowledge elements are extracted to present the core concepts of scientific and technical information, overcoming the limitations of using predefined categorizations such as IPC when representing the content. Based on multiple collaboration and knowledge networks, we then conduct a longitudinal analysis and apply a stochastic actor-oriented model (SAOM) to model network dynamics over different periods. The influence of network features and structures, individual node characteristics, and various dimensions of proximity on collaboration dynamics is tested and analyzed.Comment: 2 pages, 1 figure. Conference presentatio

Study on the Socioeconomic Factors Affecting Adoption of Agricultural Machinery

In the context of south Asia, There is enough support for suitable agricultural machinery for small farmers. These kind of agricultural machineries can improve the return of investment in land and labor, although large capital investments are still required that can impede ownership. The growing demand for machinery has resulted in comparatively more developed rental marketplaces for irrigation, tillage and other post-harvest procedures. Numerous small businesses are therefore accepting access to farm machinery that would otherwise be incredibly expensive to buy through service fee arrangements, although there is still room for expansion. In order to facilitate the advancement and investment of such machinery more effectively, it is necessary to better understand the associated factors with the purchase of agricultural machinery and the provision of services. Firstly, current paper reviews country’s policy structure which enabled the existence of such machinery markets. It then uses stratified random sample of 305 wheat producing households for the survey from six districts, identifying variables associated with the adoption of the most common smallholder agricultural machinery e irrigation pumps, threshers, and power tillers. Results of multinomial probit model show that education of farmer, member of farmer’ organization, livestock ownership, farm size and being part of non-farm work activities all were significantly positive in the adoption of farm machinery. Findings also suggest that institutions and policy making authorities not only need to focus on short projects to encourage adoption of machinery, also there must be a continuous attention to improve physical and civilian infrastructure & services, and ensuring the availability of credit to create an favorable  conditions where agricultural machinery is most likely to be used. Keywords: Agricultural machinery, Investment, Productivity, Efficiency. DOI: 10.7176/JESD/11-3-07 Publication date: February 29th 202

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning

Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-level black-box optimizers. However, this field is hindered by the lack of a unified benchmark. To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods. MetaBox offers a flexible algorithmic template that allows users to effortlessly implement their unique designs within the platform. Moreover, it provides a broad spectrum of over 300 problem instances, collected from synthetic to realistic scenarios, and an extensive library of 19 baseline methods, including both traditional black-box optimizers and recent MetaBBO-RL methods. Besides, MetaBox introduces three standardized performance metrics, enabling a more thorough assessment of the methods. In a bid to illustrate the utility of MetaBox for facilitating rigorous evaluation and in-depth analysis, we carry out a wide-ranging benchmarking study on existing MetaBBO-RL methods. Our MetaBox is open-source and accessible at: https://github.com/GMC-DRL/MetaBox.Comment: Accepted at NuerIPS 202

Short-term origin-destination demand prediction in urban rail transit systems: A channel-wise attentive split-convolutional neural network method

Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: (1) data availability: real-time OD flow is not available during the prediction; (2) data dimensionality: the dimension of the OD flow is much higher than the cardinality of transportation networks; (3) data sparsity: URT OD flow is spatiotemporally sparse. There is a great need to develop novel OD flow forecasting method that explicitly considers the unique characteristics of the URT system. To this end, a channel-wise attentive split-convolutional neural network (CAS-CNN) is proposed. The proposed model consists of many novel components such as the channel-wise attention mechanism and split CNN. In particular, an inflow/outflow-gated mechanism is innovatively introduced to address the data availability issue. We further originally propose a masked loss function to solve the data dimensionality and data sparsity issues. The model interpretability is also discussed in detail. The CAS-CNN model is tested on two large-scale real-world datasets from Beijing Subway, and it outperforms the rest of benchmarking methods. The proposed model contributes to the development of short-term OD flow prediction, and it also lays the foundations of real-time URT operation and management.Comment: This paper has been accepted by the Transportation Research Part C: Emerging Technologies as a regular pape

Neuroprotective Effects and Mechanism of β

Emerging evidence suggests that activated astrocytes play important roles in AD, and β-asarone, a major component of Acorus tatarinowii Schott, was shown to be a potential therapeutic candidate for AD. While our previous study found that β-asarone could improve the cognitive function of rats hippocampally injected with Aβ, the effects of β-asarone on astrocytes remain unclear, and this study aimed to investigate these effects. A rat model of Aβ1–42 (10 μg) was established, and the rats were intragastrically treated with β-asarone at doses of 10, 20, and 30 mg/kg or donepezil at a dose of 0.75 mg/kg. The sham and model groups were intragastrically injected with an equal volume of saline. Animals were sacrificed on the 28th day after administration of the drugs. In addition, a cellular model of Aβ1–42 (1.1 μM, 6 h) was established, and cells were treated with β-asarone at doses of 0, 2.06, 6.17, 18.5, 55.6, and 166.7 μg/mL. β-Asarone improved cognitive impairment, alleviated Aβ deposition and hippocampal damage, and inhibited GFAP, AQP4, IL-1β, and TNF-α expression. These results suggested that β-asarone could alleviate the symptoms of AD by protecting astrocytes, possibly by inhibiting TNF-α and IL-1β secretion and then downregulating AQP4 expression