Discovering AI-enabled convergences based on BERT and topic network

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-Associated research s collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective. © 2023 Korean Society for Internet Information. All rights reserved.

Dynamic spillovers between clean energy and non-ferrous metals markets in China: A network-based analysis during the COVID-19 pandemic

Due to the close production link between clean energy and non-ferrous metals, their price and market dynamics can easily affect one another through production costs. Furthermore, with the increased financialization of clean energy and non-ferrous metals markets, investment risk can easily spread between them. Therefore, this paper intends to explore the risk contagion between the two markets through the spillover index model and the minimum spanning tree (MST) method. Employing the data collected in China, this paper quantifies the magnitude of risk transfer by the volatility spillovers of eight clean energy stock markets as identified in The Energy Conservation and Environmental Protection Clean Industry Statistical Classification 2021 and the eight corresponding non-ferrous metals futures markets, while fully considering the heterogeneity between sub-markets. First, we find that risk is mainly transmitted from clean energy to non-ferrous metals. Second, this paper identifies not only the most influential market but also the shortest path of risk contagion based on the MST topology analysis. Last, the empirical results show that the COVID-19 has increased the scale of risk transmission between the two markets and their connectivity. During the COVID-19 period, the shortest path between the two markets shifted from "hydropower–gold” to "smart grid–zinc”, and the systematically influential markets correspondingly become smart grid and zinc. The results obtained in this paper might have practical implications for policymakers seeking to achieve effective risk management, which could also facilitate investors for diversification benefits. © 2023 Elsevier Ltd

Applying A Spatial Information Approach and Community-based Sourcing for Smart Container Allocation in the Post-Pandemic Era

The COVID-19 pandemic has globally changed ways of living. People have had to accept and adapt to a "new normal", with lockdowns, working from home, using masks, and ATK. As a result, plastic and infectious waste production have drastically increased, which requires prompt and proper ways of management. In this paper, we apply spatial Information Technology and a community-based sourcing method to establish a framework and a web application for visualizing the positions of all types of waste containers in Saensuk, a tourist town. The study aims to optimize the allocation coverage of infectious waste containers and pickup routes. The experiment shows that 19 current infection waste containers cover only 27.69% of all area. The recommended number is 15 infection waste containers in a specified area which represents 68.73% of coverage. © 2022 IEEE.

A Decision Making Approach for Street Lockdown to Cope with Covid-19 Cases by Using Shortest Path Selection Mechanism for Unplanned Colonies

Infectious disease syndrome like covid-19 falls under the Public health domain and needs to be addressed with timely decisions and rapid actions. For such diseases, the dispersal becomes exponential with frequent social gatherings, therefore the immediate strategy, to control the surging waves of covid-19, was to impose immediate lockdown of COVID-19 infected zones. In this paper, the concept of street networks has been incorporated with shortest path algorithm e.g. minimum spanning tree (MST) to define an approach to investigate the correlation between reported COVID-19 cases and relevant streets in order to adopt better lockdown strategy for unplanned colonies. Geo-spatial representation has been used for subsequent composition of patterns to identify the particular streets for locked down. Results show that MST provides better solution by evaluating explicit areas of concern for lockdown plans. © 2022 IEEE.

Optimizing the Interval-centric Distributed Computing Model for Temporal Graph Algorithms

Temporal graphs assign lifespans to their vertices, edges and attributes. Large temporal graphs are common for finding the shortest paths in transit networks and contact tracing for COVID-19. Graph programming ions like Interval-centric Computing Model (ICM) extend Google's Pregel model to intuitively compose and execute time-dependent graph algorithms in a distributed environment. However, the benefits of easier algorithmic design in ICM are offset by performance bottlenecks in its TimeWarp shuffle and messaging phases. Here, we design several optimizations to ICM to reduce these overheads. We propose local optimizations within a vertex execution by unrolling messages before TimeWarp (LU), and deferring messaging till all local computations complete (DS). We also temporally partition the interval graph into windows (WICM) to flatten the execution load. We offer a proof of equivalence between ICM and these techniques. Our detailed empirical evaluation for six real-world graphs with up to 133M vertices, 5.5B edges and 365 time-points, for six temporal traversal algorithms executing on a commodity cluster with 8 nodes, shows that LU, DS and WICM together significantly reduce the average algorithm runtime by ≈ 61% (≈ 15 mins) over ICM, and reduce message communication by ≈ 38%(≈ 3.2B) on average. © 2022 ACM.

An Enhanced Traffic Split Routing Heuristic for Layer 2 and Layer 1 Services

Virtual Private Networks (VPNs) have now taken an important place in computer and communication networks. A virtual private network is the extension of a private network that encompasses links through shared or public networks, such as the Internet. A VPN is a transmission network service for businesses with two or more remote locations. It offers a range of access speeds and options depending on the needs of each site. This service supports voice, data and video and is fully managed by the service provider, including routing equipment installed at the customer’s premises. According to its characteristics, VPN has widely deployed on ”COVID-19” offering extensive services to connect roaming employees to their corporate networks and have access to all the company information and applications. Hence, VPN focuses on two important issues such as security and Quality-of-Service. This latter has a direct relationship with network performance such as delay, bandwidth, throughput, and jitter. Traditionally, Internet Service Providers (ISPs) accommodate static point-to-point resource demand, named, Layer 1 VPN (L1VPN). The primary disadvantage of L1VPN is that the data plane connectivity does not guarantee control plane connectivity. Layer 2 VPN is designed to provide end-to-end layer 2 connection by transporting layer 2 frames between distributed sites. An L2VPN is suitable for supporting heterogeneous higher-level protocols. In this paper we propose an enhanced routing protocol based on Traffic Split Routing (TSR) and Shortest Path Routing (SPR) algorithms. Simulation results show that our proposed scheme outperforms the Shortest Path Routing (SPR) in term of network resources. Indeed, 72% of network links are used by the Enhanced Traffic Split Routing compared to Shortest Path Routing (SPR) which only used 44% of the network links © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved