This paper explores the most recent Fintech (financial technology) phenomenon from an ecosystem perspective. Differentiated from the earlier Fintech evolution led by traditional financial institutions, “cross-sector” Fintech that operates at the intersection of financial services and information technology disrupts existing business models of banks while creating novel ecosystem dynamics. This study explores the Fintech ecosystem composition to understand better business model innovation based on underlying ecosystem dynamics while focusing on the specific role of cross-sector actors. These actors have escaped scrutiny despite being mature and experienced and having strong resource bases. Adopting a comparative case study method by considering the China-based Alibaba Group and Tencent, the study’s findings indicate that novel business model developments based on strong technological expertise and scale-based resources by cross-sector Fintech render a functional perspective on fast-developing Fintech industry less practical. Apart from cross-sector Fintech, investors constitute a new dimension in the conceptualization of the Fintech ecosystem. Overall, the interconnectedness of the cross-sector Fintech beyond the Fintech sectors drives the fuzzy boundaries between ecosystems, established business models, terminology definitions, ecosystem actors’ roles and relationships, which appear to become more heterogeneous and changeable over time. The study contributes to the scant literature on Fintech ecosystems and their sustainable development.

It is not an easy task for organizers to observe the engagement level of a video meeting audience. This research was conducted to build an intelligent system to enhance the experience of video conversations such as virtual meetings and online classrooms using convolutional neural network (CNN)- and support vector machine (SVM)-based machine learning models to classify the emotional states and the attention level of the participants to a video conversation. This application visualizes their attention and emotion analytics in a meaningful manner. This proposed system provides an artificial intelligence (AI)-powered analytics system with optimized machine learning models to monitor the audience and prepare insightful reports on the basis of participants’ facial features throughout the video conversation. One of the main objectives of this research is to utilize the neural accelerator chip to enhance emotion and attention detection tasks. A custom CNN developed by Gyrfalcon Technology Inc (GTI) named GnetDet was used in this system to run the trained model on their GTI Lightspeeur 2803 neural accelerator chip.

The rapid expansion of video conferencing and remote works due to the COVID-19 pandemic has resulted in a massive volume of video data to be analyzed in order to understand the audience engagement. However, analyzing this data efficiently, particularly in real-time, poses a scalability challenge as online events can involve hundreds of people and last for hours. Existing solutions, especially open-sourced contributions, usually require dedicated and expensive hardware, and are designed as centralized cloud systems. Additionally, they may also require users to stream their video to remote servers, which raises privacy concerns. This paper introduces scalable and efficient computer vision algorithms for analyzing face orientation and eye blink in real-time on edge devices, including Android, iOS, and Raspberry Pi. An example solution is presented for proctoring online meetings, workplaces, and exams. It analyzes audiences on their own devices, thus addressing scalability and privacy issues, and runs at up to 30 fps on a Raspberry Pi. The proposed face orientation detection algorithm is extremely simple, efficient, and able to estimate the head pose in two degrees of freedom, horizontal and vertical. The proposed Eye Aspect Ratio (EAR) with simple adaptive threshold demonstrated a significant improvement in terms of false positives and overall accuracy compared to the existing constant threshold method. Additionally, the algorithms are implemented and open sourced as a toolkit with modular, cross-platform MediaPipe Calculators and Graphs so that users can easily create custom solutions for a variety of purposes and devices.

Vietnam, an emerging economy, leads the Southeast Asian region in terms of the growth rate of coconut production; yet, its sustainability is questionable because Vietnam lacks a credible strategy to reap the benefits of its export potential. While the literature is rich with studies on coconut-related export competitiveness and readiness, it seems that studies focused on a single set of factors, and very few studies take Vietnam as a case study for analysis. Studies we found use aggregate data for econometric analysis. On the contrary, this study attempts to address the literature’s gaps by proposing a comprehensive view of key factors identifying the export potential of bottled coconut water. It also adds to our comprehension of how large- and small-scale manufacturers prioritize factors differently. To answer the research questions, we collect qualitative and quantitative data for analysis. We use AHP and M-CRITIC-RP methods for ranking the factors. This study also incorporates the criteria weights into the gravity of the trade model to identify export potential to target markets. Our findings can draw researchers’ and policymakers’ attention to the importance of considering how firms and manufacturers prioritize key factors in export readiness, and thereby impact export potential and target markets.

The measurement of blood-oxygen saturation (SpO2), heart rate (HR), and body temperature are very critical in monitoring patients. Photoplethysmography (PPG) is an optical method that can be used to measure heart rate, blood-oxygen saturation, and many analytics about cardiovascular health of a patient by analyzing the waveform. With the COVID-19 pandemic, there is a high demand for a product that can remotely monitor such parameters of a COVID-19 patient. This paper proposes two major design architectures for the product with optimized system implementations by utilizing the ESP32 development environment and cloud computing. In one method, it discusses edge computing with the fast Fourier transform (FFT) and valley detection algorithms to extract features from the waveform before transferring data to the cloud, and the other method transfers raw sensor values to the cloud without any loss of information. This paper especially compares the performance of both system architectures with respect to bandwidth, sampling frequency, and loss of information.

The Sri Lankan Public Sector has often been criticized for being overstaffed, inefficient and a burden to the economy. This has partly been attributed to the deficiencies in employee talents management. This study attempts to find the direction for augmenting human resource in public sector in Sri Lanka through Talent Management (TM), by benchmarking against standards from Japan, Singapore, and Norway. This study adopts a qualitative research approach. One-on-one interviews were conducted with the Executive Officers of Public Service of Sri Lanka. Also, data on TM practices in Public Sector in Japan were gathered through a questionnaire. There was also an extensive review of relevant literature on the study concept from the Public Service of Japan, Singapore and Norway, which rank top in Government Efficiency Indicator. Using the Case study approach, the data obtained was analysed by comparative analysis to compare TM policies and activities of these countries. The study revealed several drawbacks concerning TM in Public Service of Sri Lanka. Analysing various TM models of countries having high standards, the benchmark study proposes an effective Talent Management Model for the Public Sector in Sri Lanka, including Talent Gap analysis, Talent Acquisition and Augmenting, development, assessment, measurement, retention, and transition.