Graph neural network for merger and acquisition prediction

This paper investigates the application of graph neural networks (GNN) in Mergers and Acquisitions (M&A) prediction, which aims to quantify the relationship between companies, their founders, and investors. M&A is a critical management strategy to decide if the company is to grow or downsize, and M&A prediction has been a challenging research topic in the past few decades. However, the traditional methods of predicting M&A probability are only based on the company's fundamentals, such as revenue, profit, or news. Instead, GNN takes full advantage of those relationship data to expand feature dimension and improve the prediction result. Our M&A prediction solution integrates with the topic model for text analysis, advanced feature engineering, and several tricks to boost GNN. The approach achieves a high Area-Under-Curve score (AUC) 0.952, which is better than the previous record 0.888. The true positive rate is 83% with a low false positive rate 7.8%, which performance is better than the previous benchmark record 70.9%/10.6%

Algorithms: Law and Regulations

The legal status of AI and algorithms continues to be debated. Resume-sifting algorithms exhibit unethical, discriminatory, and illegal behavior; crime-sentencing algorithms are unable to justify their decisions; and autonomous vehicles' predictive analytics software will make life and death decisions

Data-driven urban management: Mapping the landscape

Big data analytics and artificial intelligence, paired with blockchain technology, the Internet of Things, and other emerging technologies, are poised to revolutionise urban management. With massive amounts of data collected from citizens, devices, and traditional sources such as routine and well-established censuses, urban areas across the world have – for the first time in history – the opportunity to monitor and manage their urban infrastructure in real-time. This simultaneously provides previously unimaginable opportunities to shape the future of cities, but also gives rise to new ethical challenges. This paper provides a transdisciplinary synthesis of the developments, opportunities, and challenges for urban management and planning under this ongoing ‘digital revolution’ to provide a reference point for the largely fragmented research efforts and policy practice in this area. We consider both top-down systems engineering approaches and the bottom-up emergent approaches to coordination of different systems and functions, their implications for the existing physical and institutional constraints on the built environment and various planning practices, as well as the social and ethical considerations associated with this transformation from non-digital urban management to data-driven urban management

Acceptability, Precision and Accuracy of 3D Photonic Scanning for Measurement of Body Shape in a Multi-Ethnic Sample of Children Aged 5-11 Years: The SLIC Study.

Information on body size and shape is used to interpret many aspects of physiology, including nutritional status, cardio-metabolic risk and lung function. Such data have traditionally been obtained through manual anthropometry, which becomes time-consuming when many measurements are required. 3D photonic scanning (3D-PS) of body surface topography represents an alternative digital technique, previously applied successfully in large studies of adults. The acceptability, precision and accuracy of 3D-PS in young children have not been assessed

Engaging with graduate attributes through encouraging accurate student self-assessment

Self-assessment can be conceptualised as the involvement of students in identifying assessment criteria and standards that they can apply to their work in order to make judgements about whether they have met these criteria (Boud, 1995). It is a process that promotes student learning rather than just grade allocation. However, self-assessment does not have obvious face validity for students; and many students find that making an objective assessment of their work difficult (Lindblom-ylanne, Pihlajamak & Kotkas, 2006). Previous business education research has also found that self-assessment does not closely reflect either peer or instructor assessments (Campbell, et al., 2001). The current study aimed to explore: (a) the relationship between self-assessment grading and teacher assessment; and (b) the effect of self-assessment in engaging students with graduate attributes, in order to explore the tenets of self-assessment This process of self-assessment was investigated through application of an online assessment system, Re View, to encourage more effective self-assessment in business education. Data collected from two groups (student and teacher) demonstrated that: (1) initial self-assessment results between the teaching academics and the students' self-assessment, were significantly different with students overestimating their ability on every criterion; (2) however, the variation diminished with time to the point that there was no significant difference between the two assessments; and (3) students' awareness of the graduate attributes for their degree program increased from the beginning to the end of the subject (Note 1)