Empowering Migrant Workers and Labor NGOs in China: Creating a Law Searching Tool through a Design Science Approach

Labor NGOs in China has been utilizing various social media tools, such as WeChat for networking workers and promotion. In the year 2017 WeChat Mini Program was released, allowing developers to create “sub-application” within WeChat. This research evolves from a laws-searching WeChat Mini Program development project, which will adopt a design science approach, follow the design science research methodology (DSRM) process model, and measure the effect of the WeChat Mini Program with the concepts from information systems success model and post-adoption behaviors. We propose that the law-searching WeChat Mini Program can sever labor NGOs in terms of legal service and promotion

Multi-Scale Attention for Audio Question Answering

Audio question answering (AQA), acting as a widely used proxy task to explore scene understanding, has got more attention. The AQA is challenging for it requires comprehensive temporal reasoning from different scales' events of an audio scene. However, existing methods mostly extend the structures of visual question answering task to audio ones in a simple pattern but may not perform well when perceiving a fine-grained audio scene. To this end, we present a Multi-scale Window Attention Fusion Model (MWAFM) consisting of an asynchronous hybrid attention module and a multi-scale window attention module. The former is designed to aggregate unimodal and cross-modal temporal contexts, while the latter captures sound events of varying lengths and their temporal dependencies for a more comprehensive understanding. Extensive experiments are conducted to demonstrate that the proposed MWAFM can effectively explore temporal information to facilitate AQA in the fine-grained scene.Code: https://github.com/GeWu-Lab/MWAFMComment: Accepted by InterSpeech 202

Modelling the Economic Impacts of Compound Hazards through the Production Supply Chain in the Post-pandemic World.

Climate change and fast urbanization are increasing the likelihood of compound hazards - events where multiple drivers and/or hazards interact with multiplicatively destructive environmental and socio-economic consequences. This includes increases in the frequency of not only concurrent natural extremes (heatwaves and droughts, storm surges and extreme rainfalls, etc.), but also collisions between natural and manmade disasters (air pollution, infectious disease transmission, trade wars, etc.) particularly in the post-pandemic world. Entanglement of different hazardous factors increases the complexity of impact accounting and risk management and requires an integrated solution to tackle the vulnerabilities of human societies towards compound risks. However, most of the research in disaster analysis investigates one hazard at a time. Only a few emerging perspectives have noticed or warned the potential of compound hazards, but they are still far from capacity building for the compound resilience to future crises. This PhD thesis presents a full set of methodology to systematically assess the economic impacts from single to compound hazards. The concept of ‘disaster footprint’ is used here to capture the direct and indirect impacts rippling through the economic supply chain during a single or compound disaster event. A four-stage research framework is proposed. It starts from the direct disaster footprint assessment, which links physical characteristics of hazards with property damage or health impairment by simulating hazard-specific exposure-damage functions. The direct footprint is then fed into an input-output-based (IO-based) hybrid economic model to calculate the indirect disaster footprint that propagates through intersectoral and interregional connections to wider economic systems. The improved IO-based disaster footprint model is built here for single hazard analysis, with innovations regarding inventory adjustment and cross-regional substitutability. Third, within the same disaster footprint framework, the economic interplays between diverse types of hazards are synthesized into the impact assessment, and thereby a Compound Hazard Economic Footprint Assessment (CHEFA) model is developed for compound events. Finally, favourable response and recovery plans, which are aimed to mitigate the total disaster footprint, are suggested by comparing the modelling results under wide ranging scenarios and identifying crucial influencing factors through sensitivity analysis. A major contribution of this thesis is that it takes the first step in the field of disaster analysis to integrate multiple hazardous factors within a macro-economic impact assessment framework that accounts for both direct and indirect disaster footprint into sectoral and regional details. The proposed modelling framework is first applied to three types of hazards (i.e., heat stress, air pollution and climate extremes) on the provincial and national scales in China to demonstrate its flexibility for a wide range of disaster risks. The total economic costs of heat stress, air pollution and climate extreme events in China have increased from US$207.9 billion (1.79$317.1 billion (2.16% of GDP) in 2020. Despite the decreasing economic costs of air pollution and climate extreme events, the economic costs from heat-related health impacts have continued the concerning growing trend. Among the three types of hazards, the economic costs of heat stress were the biggest and accounted for over 70% of the total costs. Heat stress affects the economy mainly by reducing labour productivity. For each unit of direct costs, heat stress was also inclined to cause more indirect supply chain costs than air pollution and climate extremes. Most of the heat-induced direct costs occurred outdoors in the agriculture and construction sectors, while most of the heat-induced indirect costs happened indoors in the manufacture and service sectors. At the regional level, hotspot provinces with prominent economic risks from these hazards have been identified for China. Southern provinces were more economically vulnerable to heat stress than northern provinces, while northern provinces tended to suffer larger economic costs from air pollution than southern provinces. By contrast, the economic impacts of climate extreme events were more spatially distributed in China than the other two types of hazards. Location-specific economic impacts of climate change require location-specific responses, including enhancing inter-departmental cooperation, strengthening climate emergency preparedness, supporting scientific research, raising public awareness, and promoting climate change mitigation and adaptation. Economic implications of climate change are also evaluated with a focus on future flood risks in six developing countries (i.e., Brazil, China, India, Egypt, Ethiopia and Ghana) around the end of 21st century (2086-2115). A physical model cascade of climate-hydrological-flood models is linked with the disaster footprint economic model through a set of country and sector specific depth-damage functions. The total (direct and indirect) economic losses of fluvial flooding are projected for each country, with or without socio-economic development, under a range of warming levels from <1.5°C to 4°C. As a share of national GDP, Egypt suffers the largest flood-induced losses under both climate change (CC) and climate change plus socio-economic development (CC+SE) experiments, reaching 2.3% and 3.0% of GDP under 4°C warming. Climate change acts as a driving factor that increases the flood losses in all countries, but the effect of socio-economic development differs among the countries and warming levels. For Ethiopia and China, future flood losses as a proportion of GDP under different warmings decline from the baseline levels when socio-economic development is modelled, suggesting a more resilient economic growth that helps reduce future flood risks. However, for Brazil, Ghana, and India, while losses as a proportion of GDP initially decline at lower warming levels, increases are seen from 2.5°C or 3°C warming onwards, suggesting a tipping point where increasing flood risk outweighs any relative benefits of socio-economic development. These results highlight the importance of including socio-economic development when estimating future flood losses, essential to provide a more comprehensive picture of potential losses that will be important for decision makers. With the development of the CHEFA model, the economic interaction between concurrent hazardous factors comes into analysis. A hypothetical perfect storm consisting of floods, pandemic control, and trade restrictions (as a proxy for deglobalization) is assumed to test the applicability and robustness of the model. The model also considers simultaneously cross-regional substitution and production specialization, which can influence the resilience of the economy to multiple shocks. Scenarios are first designed to investigate economic impacts when a flood and a pandemic lockdown collide and how these are affected by the timing, duration, intensity/strictness of each event. The results reveal that a global pandemic control aggravates the flood impacts by hampering the post-flood capital reconstruction, but a flood exacerbates the pandemic impacts only when the flood damage is large enough to exceed the stimulus effect of the flood-related reconstruction. Generally, an immediate, stricter but shorter pandemic control policy would help to reduce the economic costs inflicted by a perfect storm. The study then examines how export restrictions and retaliatory countermeasures during the pandemic and floods influence the economic consequences and recovery, especially when there is specialization of production of key sectors. It finds that the trade restriction of a region to ‘protect’ its product that can be substituted by the same product made elsewhere, while hampering the global recovery, may alleviate the region’s own loss during the compound disasters if the increasing domestic demand exceeds the negative impacts of falling exports. By comparison, the trade restriction on a non-substitutable product has greater negative impacts on the global recovery, which ultimately propagates backward to the region through the supply chain and exacerbates its own loss. The results also indicate that the potential retaliation from another region and sector would further deteriorate the global recovery and make everyone lose, with the region which initiates the trade war losing even more when the retaliatory restriction is also imposed on a non-substitutable product. Therefore, regional or global cooperation is needed to address the spillover effects of such compound events, especially in the context of the risks from deglobalization. The CHEFA model has been then successfully applied to a real compound event of the 2021 extreme floods and a COVID-19 wave in Zhengzhou, the capital city of Henan province in China. The event was rare in history and has caused enormous economic consequences (direct damage worthy of 66,603 million yuan and indirect losses worthy of 44,340 million yuan) to the city, reaching a total of 10.28% of its GDP during the previous year. The negative impacts also spilled over to the whole nation through the production supply chain, making the total economic losses amount to 131,714 million yuan (0.13% of China’s GDP in the previous year). The local lockdown to control the spread of COVID-19 has increased the indirect losses by 77% and the indirect/direct loss ratio from 0.55 to 0.98. While a majority (29%) of direct losses happened in Zhengzhou’s real estate industry, the indirect losses were more distributed in Zhengzhou’s non-metallic mineral products (13%), food and tobacco (10%), and transportation services (10%). Zhengzhou’s non-metallic mineral sector is also a critical sector with strong propagation effects. The reduction in its production has triggered a supply chain loss of 10,537 million yuan in terms of trades with other sectors and regions, which nearly doubled its value-added loss. In regions outside Zhengzhou, the agriculture, mining, petroleum and coking, chemical products, accommodation and restaurants, and financial services were the sectors significantly affected by this compound event. Among them, the agriculture in Henan (outside Zhengzhou) suffered the greatest indirect (or value-added) loss at 2,760 million yuan. The study also finds that the post-disaster economic resilience is most sensitive to factors such as road recovery rate, reconstruction efficiency and consumption subsidies, and the COVID-19 control tends to reduce the marginal economic benefits of flood emergency efforts. As low-likelihood compound extreme events become more frequent with global warming, concerted actions are in urgent need to address the intricate dilemma between disaster relief, disease control and economic growth at both individual and institutional levels. Overall, this PhD study develops an integrated assessment framework for the direct and indirect economic impacts from single to compound hazardous events. Within this framework, consistent and comparable loss metrics are elicited for different types of hazards, either single or compound ones, advancing the understanding of their economic risk transmission channels through the production supply chain. Knowing the economic complexity intrinsic to the disaster mixes will foster a sustainable risk management strategy that balances different emergency needs at the minimal economic costs, and guide investment to risk preparedness against the growing threats under climate change. In addition, collaborative efforts are required from the local to global levels to enhance the economic resilience towards future crises in complex situations. This is crucial to achieve the mitigation and adaptation targets in the Paris Agreement and Sendai Framework for Disaster Risk Reduction

The SECI model and external sources of knowledge: a field study on the distribution of search routines

The SECI model is one of most popular and systemic models of knowledge creation. However, its main application is in a set of routines (socialization, externalization, combination and internalization) that emphasizes internal sources of knowledge. This thesis explores how and when organizations locate external sources of knowledge. It uses routines as the main unit of analysis for understanding how each stage of the SECI model is impacted by different sources of knowledge. It argues that external sources of knowledge can be discovered and represented by a search routine. A search routine helps us understand, theoretically, why only certain rules (tacit knowledge) and products (explicit knowledge) are reinforced amongst organizational members. This thesis shows how routines are modified by these search routines when organizations take on the risk of acquiring external sources of knowledge, which involves technology unrelated to their own. The SECI model is explored in two stages: the first stage of the methodology explores the search routine outcomes of SECI using 20 small technology companies; and the second stage replicates those findings onto eight large consulting firms. The field study provided insight into the external sources of knowledge in the SECI model by classifying when its search routines arise under different competitive pressures. Findings suggest that organizations began with internal sources of knowledge, but search routines determined which external sources of knowledge should be distributed for SECI: socialization externalization, combination and internalization. The thesis explains what conditions make some search routines more prevalent than others when locating internal and external sources of knowledge. The author recommends that each stage of the SECI model still be explored, but under a different competitive environment. Building on these classifications of search routines would add to the explanation of why the search for knowledge beyond the organization is more necessary for some industries than others

The Influence of Social Comparison and Peer Group Size on Risky Decision-Making

This study explores the influence of different social reference points and different comparison group sizes on risky decision-making. Participants were presented with a scenario describing an exam, and presented with the opportunity of making a risky decision in the context of different information provided about the performance of their peers. We found that behavior was influenced, not only by comparison with peers, but also by the size of the comparison group. Specifically, the larger the reference group, the more polarized the behavior it prompted. In situations describing social loss, participants were led to make riskier decisions after comparing themselves against larger groups, while in situations describing social gain, they become more risk averse. These results indicate that decision making is influenced both by social comparison and the number of people making up the social reference group

The effects of cognitive style and emotional trade-off difficulty on information processing in decision-making

This study investigated the effects of cognitive style and emotional trade‐off difficulty (ETOD) on information processing in decision‐making. Eighty undergraduates (73.75% female, M = 21.90), grouped according to their cognitive style (field‐dependent or field‐independent), conducted an Information Display Board (IDB) task, through which search time, search depth and search pattern were measured. Participants' emotional states were assessed both before and after the IDB task. The results showed that participants experienced significantly more negative emotion under high ETOD compared to those under low ETOD. While both cognitive style and ETOD had significant effects on search time and search depth, only ETOD significantly influenced search pattern; individuals in both cognitive style groups tended to use attribute‐based processing under high ETOD and to use alternative‐based processing under low ETOD. There was also a significant interaction between cognitive style and ETOD for search time and search depth. We propose that these results are best accounted for by the coping behaviour framework under high ETOD, and by the negative emotion hypothesis under low ETOD

Watermarking Classification Dataset for Copyright Protection

Substantial research works have shown that deep models, e.g., pre-trained models, on the large corpus can learn universal language representations, which are beneficial for downstream NLP tasks. However, these powerful models are also vulnerable to various privacy attacks, while much sensitive information exists in the training dataset. The attacker can easily steal sensitive information from public models, e.g., individuals' email addresses and phone numbers. In an attempt to address these issues, particularly the unauthorized use of private data, we introduce a novel watermarking technique via a backdoor-based membership inference approach named TextMarker, which can safeguard diverse forms of private information embedded in the training text data. Specifically, TextMarker only requires data owners to mark a small number of samples for data copyright protection under the black-box access assumption to the target model. Through extensive evaluation, we demonstrate the effectiveness of TextMarker on various real-world datasets, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference with negligible effect on model utility. We also discuss potential countermeasures and show that TextMarker is stealthy enough to bypass them