Open Educational Resources | Pao Yue-kong Library, The Hong Kong Polytechnic University

MOOC

Future Trends of Fashion and Textiles

Everyone wishes to know the future!
Although we do not have the magic crystal ball to show us the future, we have interviewed more than forty (40+) international industrialists, academics, and business leaders. They are experts in their area and the drivers behind the industry. Our guests will share their views of the future and potential of the fashion industry with you.
During the interviews, we focused on the following questions:
What are the current challenges?
What are the new main directions in the coming five to ten (5 – 10) years?
What kind of new technologies will become popular?
Will there be any new business models (new ways of doing business)?
What role can a government play?
Will there be any ideology that may bring a big impact?
We fully understand that learners may face the obstacle of terminology and background. We will introduce the background of the fashion supply chain first. Then, we take the learners to attend the interviews week by week, sector by sector. Most importantly, the course content is bilingual in Chinese and English. Learners will enjoy our learning supports.
Suitable for students, consumers, anyone who is interested or working in the fashion business.

Subjects:: Fashion retailing and merchandising
Keywords:: Retail trade -- Management Clothing trade -- Management Business logistics
Resource Type:: MOOC

MOOC

Interdisciplinary Management of Cardiopulmonary Health and Disease - Pulmonary Focus

This course will equip you with the knowledge relevant to pulmonary diseases and broaden your understanding on the patient’s journey in healthcare from a holistic perspective – starting from symptoms emerge to the disease diagnosis and assessment, and finally, disease management in an interdisciplinary approach. In addition, as psychological intervention is also an important part in the all-round care of patients, this course also covers topics about the psychosocial needs of patients and caregivers.
What you'll learn:
The causes, signs and symptoms of common pulmonary diseases
Understand the psychosocial needs of patients and caregivers
Interpret the results of medical examinations for pulmonary diseases
Differentiate the roles of different healthcare professionals
Integrate the knowledge learnt from different disciplines in the management for patients with pulmonary diseases

Subjects:: Health Sciences
Keywords:: Lungs -- Diseases Lungs -- Diseases -- Patients Lungs -- Diseases -- Treatment
Resource Type:: MOOC

MOOC

Data Analytics in Accounting and Finance

This data analytics course takes an interdisciplinary approach to demonstrate the data analytics process in the context of accounting and finance. The growing volume of both structured and unstructured data has pushed forward a more data-driven form of decision-making in accounting and finance. In order to keep up with the Big Data era advancements, accountants and finance professionals need to have a data analyst mindset to excel in their jobs. This course will illustrate different concepts of accounting and finance with the application of data analytics. It will not only help the learners to develop their skills to ask the right questions but also teach them how to master the data and use different tools like Excel and Tableau to analyze the data. In the end, the learners will be able to interpret the results and make their decisions effectively.
This course will use a simple framework that helps the learners to develop an analytical mindset. This framework (QDAR) has four major components:
1. Ask the right Q uestions to address an issue in accounting or finance contexts.
2. Understand the different data types and how to retrieve and clean D ata.
3. Conduct different data A nalyses to answer the questions
4. Communicate the R esults to the decision-makers using graphs, visualizations and reports.
The whole course will cover different aspects of the framework in conjunction with different types of analyses. There will be additional datasets for the verified learners through which they can practice what they have learned during the course.

Subjects:: Accounting and Finance
Keywords:: Accounting -- Data processing Finance -- Data processing
Resource Type:: MOOC

MOOC

Introduction to air transportation and aviation management

Aviation is essential for global business as it supports worldwide travelling and cargo transportation. Before COVID-19, the economic benefits generated by the industry across countries showed a growing trend. This course is aimed to provide learners with a broad understanding of airport services in different phases in terms of design and engineering. Learners will receive basic knowledge of how an airport operates. Key topics include the future perspective of air traffic control, air traffic flow management, airport management, facility planning, airport terminal design for the post-pandemic world and ground services.

Subjects:: Aeronautical and Aviation Engineering and Logistics
Keywords:: Airports -- Management Aeronautics Commercial -- Management Airlines -- Management
Resource Type:: MOOC

MOOC

Easy Vlogging – Making Interactive Self-introduction Videos

“Easy Vlogging – making Interactive Self-introduction Videos” is an online course for people to learn, in self-paced format, how to create effective job interview videos for either academic pursuit or career needs. With the contents learned from this course, it helps learners apply for dream jobs easier and more successfully, producing self-introduction videos with good visual quality, superior self-expression and professional presence.
The purpose of this course is to offer students with:
• An overview of digital video production, including script writing and other preparation works;
• An introduction on various video production techniques, including the use of camera, lighting and video editing techniques; and
• An understanding on the role of digital self-introduction video for interview purposes.

Keywords:: Video recordings -- Production direction
Resource Type:: MOOC

Video

PolyU 85th and AMA 50th anniversary distinguished lecture : Algorithms for semidefinite programming

Convex Matrix Optimization (MOP) arises in a wide variety of applications. The last three decades have seen dramatic advances in the theory and practice of matrix optimization because of its extremely powerful modeling capability. In particular, semidefinite programming (SP) and its generalizations have been widely used to model problems in applications such as combinatorial and polynomial optimization, covariance matrix estimation, matrix completion and sensor network localization. The first part of the talk will describe the primal-dual interior-point methods (IPMs) implemented in SDPT3 for solving medium scale SP, followed by inexact IPMs (with linear systems solved by iterative solvers) for large scale SDP and discussions on their inherent limitations. The second part will present algorithmic advances for solving large scale SDP based on the proximal-point or augmented Lagrangian framework In particular, we describe the design and implementation of an augmented Lagrangian based method (called SDPNAL+) for solving SDP problems with large number of linear constraints. The last part of the talk will focus on recent advances on using a combination of local search methods and convex lifting to solve low-rank factorization models of SP problems.
Event date: 11/10/2022
Speaker: Prof. Kim-Chuan Toh (National University of Singapore)
Hosted by: Department of Applied Mathematics

Subjects:: Mathematics and Statistics
Keywords:: Convex programming Semidefinite programming
Resource Type:: Video

Video

PolyU 85th and AMA 50th anniversary distinguished lecture : DRSOM : a dimension-reduced second-order method for convex and nonconvex optimization

We introduce a Dimension-Reduced Second-Order Method (DRSOM) for convex and nonconvex (unconstrained) optimization. Under a trust-region-like framework, our method preserves the convergence of the second-order method while using only Hessian-vector products in two directions. Moreover; the computational overhead remains comparable to the first-order such as the gradient descent method. We show that the method has a local super-linear convergence and a global convergence rate of 0(∈-3/2) to satisfy the first-order and second-order conditions under a commonly used approximated Hessian assumption. We further show that this assumption can be removed if we perform one step of the Krylov subspace method at the end of the algorithm, which makes DRSOM the first first-order-type algorithm to achieve this complexity bound. The applicability and performance of DRSOM are exhibited by various computational experiments in logistic regression, L2-Lp minimization, sensor network localization, neural network training, and policy optimization in reinforcement learning. For neural networks, our preliminary implementation seems to gain computational advantages in terms of training accuracy and iteration complexity over state-of-the-art first-order methods including SGD and ADAM. For policy optimization, our experiments show that DRSOM compares favorably with popular policy gradient methods in terms of the effectiveness and robustness.
Event date: 19/09/2022
Speaker: Prof. Yinyu Ye (Stanford University)
Hosted by: Department of Applied Mathematics

Subjects:: Mathematics and Statistics
Keywords:: Convex programming Nonconvex programming Mathematical optimization
Resource Type:: Video

Video

Graduate School distinguished speaker series : Safety : the challenge of ML-enabled cyber-physical systems (CPS)

Machine learning can deliver unprecedented performance. Its application domain has expanded into safety-critical cyber-physical systems such as UAVs and self-driver cars. However, the safety assurance of vehicular control has two conditions: 1) an analytical model of system behaviors such as provable stability, and 2) the software safety certification process (e.g., DO 178C) requires that the software be simple enough so that software safety can be validated by a combination of model checking and near exhaustive testing. Although ML software, as is, does not meet these two safety requirements, the real-time physics model supervised ML architecture holds the promise to 1) meet the two safety requirements and 2) enable ML software to safely improve control performance and safely learn from its experience in real-time. This talk will review the structure of the proposed architecture and some methods to embed physics into ML-enabled CPS control.
Event Date: 12/05/2022
Speaker: Prof. Lui Sha (University of Illinois Urbana-Champaign)
Hosted by: Graduate School