Intelligent assistant to re-configure parameter-driven systems

Parameter-Driven Systems (PDS) are widely used in commerce for large-scale applications. Reusability is achieved with a PDS design by relocating implicit control structures in the software and the storage of explicit data in database files. This approach can accommodate various user requirements without tedious modification of the software. In order to specify appropriate parameters in a system, knowledge of both business activities and system behaviour are required. For large, complex software packages, this task becomes time consuming and requires specialist knowledge, yet the consistency and correctness still cannot be guaranteed. My research studied the types of knowledge required and agents involved in the PDS customisation. The work also identified the associated problems and constraints. A solution is proposed and implemented as an Intelligent Assistant prototype than a manual approach. Three areas of achievement have been highlighted: 1. The characteristics and problems of maintaining parameter instances in a PDS are defined. It is found that the verification is not complete with the technical/structural knowledge alone, but a context is necessary to provide semantic information and related business activities (thus the implemented parameters) so that mainline functions can relate with each other. 2. A knowledge-based modelling approach has been proposed and demonstrated via a practical implementation. A Specification Language was designed which can model various types of knowledge in a PDS and encapsulate relationships. The Knowledge-Based System (KBS) developed verifies parameters based on the interpreted model of a given context. 3. The performance of the Intelligent Assistant prototype was well received by the domain specialist from the participating organisation. The modelling and KBS approach developed in my research offers considerable promise in solving practical problems in the software industry

Warfarin Dose Estimation on High-dimensional and Incomplete Data

Warfarin is a widely used oral anticoagulant worldwide. However, due to the complex relationship between individual factors, it is challenging to estimate the optimal warfarin dose to give full play to its ideal efficacy. Currently, there are plenty of studies using machine learning or deep learning techniques to help with the optimal warfarin dose selection. But few of them can resolve missing values and high-dimensional data naturally, that are two main concerns when analyzing clinical real world data. In this work, we propose to regard each patient’s record as a set of observed individual factors, and represent them in an embedding space, that enables our method can learn from the incomplete date directly and avoid the negative impact from the high-dimensional feature set. Then, a novel neural network is proposed to combine the set of embedded vectors non-linearly, that are capable of capturing their correlations and locating the informative ones for prediction. After comparing with the baseline models on the open source data from International Warfarin Pharmacogenetics Consortium, the experimental results demonstrate that our proposed method outperform others by a significant margin. After further analyzing the model performance in different dosing subgroups, we can conclude that the proposed method has the high application value in clinical, especially for the patients in high-dose and medium-dose subgroups

Guidelines and an Example of Applying ELeRS - A Framework for Scoping E-Learning Research in Healthcare

Healthcare is a large, complex industry, involving many stakeholders, involving issues at individual, organizational, inter-organizational, and/or international levels. The ELeRS framework was recently formulated to help scope e-learning research in the healthcare industry. In this paper, we describe some practical guidelines to assist researchers use this framework. These guidelines assist researchers to either formulate an independent research study or a series of related studies, using the defined framework. A summary of the framework is first presented, followed by the guidelines, and then a concrete example of how it can be applied. Our experience shows ELeRS systematize the scoping of new research in e-learning. Some lessons learnt are discussed also

Complementary Organizational Mechanisms: A Case Study on Information Technology Business Value

The complex relationships between organizational practices have been the focus of information technology business value (ITBV) research in recent years. There appears to be a discernible trend toward a more nuanced view in which the effects of information technology (IT), various organizational practices and their relationships are systematically investigated. There is also emerging evidence of recent focus in organizational factors and an increasing shift towards “complementarities” in which organizational performance is linked to combining organizational practices in synergistic ways. The objective of this paper is to investigate important issues in ITBV research by examining complementarities of various resources within an organization. The goal of this paper is to establish a framework for analyzing different configurations of complementarities. The Configuration and Interestingness framework (CIF) proposed in this paper provides evidence on how important the understanding of those complex relationship structures amongst organizational practices is to maximizing business value. This framework serves as a preliminary attempt to reveal different possible classes of complementarities structures

Re-Temp: Relation-Aware Temporal Representation Learning for Temporal Knowledge Graph Completion

Temporal Knowledge Graph Completion (TKGC) under the extrapolation setting aims to predict the missing entity from a fact in the future, posing a challenge that aligns more closely with real-world prediction problems. Existing research mostly encodes entities and relations using sequential graph neural networks applied to recent snapshots. However, these approaches tend to overlook the ability to skip irrelevant snapshots according to entity-related relations in the query and disregard the importance of explicit temporal information. To address this, we propose our model, Re-Temp (Relation-Aware Temporal Representation Learning), which leverages explicit temporal embedding as input and incorporates skip information flow after each timestamp to skip unnecessary information for prediction. Additionally, we introduce a two-phase forward propagation method to prevent information leakage. Through the evaluation on six TKGC (extrapolation) datasets, we demonstrate that our model outperforms all eight recent state-of-the-art models by a significant margin.Comment: Findings of EMNLP 202