Search results for: Yifan Lu

Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

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Authors: Yifan Dong, Xincheng Pu

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Rural settlements originate from the accumulation of residential building elements, and their agglomeration forms the settlement pattern and defines the relationship between the settlement and the inside and outside. The settlement boundary is an important part of the settlement pattern. Compared with the simplification of the urban settlement boundary, the settlement of the country is more complex, fuzzy and uncertain, and then presents a rich and diverse boundary morphological phenomenon. In this paper, China traditional rural settlements plane boundary as the research object, using fractal theory and fractal dimension method, quantitative analysis of planar shape boundary settlement, and expounds the research for the architectural design, ancient architecture protection and renewal and development and the significance of the protection of settlements.

Keywords: rural settlement, border, fractal, quantification

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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Nowadays, dialogue systems increasingly become the way for humans to access many computer systems. So, humans can interact with computers in natural language. A dialogue system consists of three parts: understanding what humans say in natural language, managing dialogue, and generating responses in natural language. In this paper, we survey deep learning based methods for dialogue management, response generation and dialogue evaluation. Specifically, these methods are based on neural network, long short-term memory network, deep reinforcement learning, pre-training and generative adversarial network. We compare these methods and point out the further research directions.

Keywords: dialogue management, response generation, deep learning, evaluation

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Authors: Su-Ping Liu, Yue Tian, Yifan Shen

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This study investigates the effect of corporate governance on earnings management when firms have reported a long stream of earnings increases (hereafter referred to as earnings beaters). We expect that good quality of corporate governance decreases the probability of income-increasing earnings management. We employ transparent tools to capture firms’ opportunistic management behavior, specifically, the repurchase of stock. In addition, we use corporate governance proxies to measure the degree of corporate governance, including board size, board independence, CEO duality, and the frequency of meeting. The results hold after the controlling of variables that suggested in prior literature. We expect that the simple technique, that is, firms’ degree of corporate governance, to be used as an inexpensive first step in detecting earnings management.

Keywords: corporate governance, earnings management, earnings patterns, stock repurchase

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Authors: Mosab Alrashed, Wei Xu, Stephen Abineri, Yifan Zhao, Jörn Mehnen

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Thermographic inspection is a relatively new technique for Non-Destructive Testing (NDT) which has been gathering increasing interest due to its relatively low cost hardware and extremely fast data acquisition properties. This technique is especially promising in the area of rapid automated damage detection and quantification. In collaboration with a major industry partner from the aerospace sector advanced thermography-based NDT software for impact damaged composites is introduced. The software is based on correlation analysis of time-temperature profiles in combination with an image enhancement process. The prototype software is aiming to a) better visualise the damages in a relatively easy-to-use way and b) automatically and quantitatively measure the properties of the degradation. Knowing that degradation properties play an important role in the identification of degradation types, tests and results on specimens which were artificially damaged have been performed and analyzed.

Keywords: NDT, correlation analysis, image processing, damage, inspection

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Authors: Yifan Fan, Xudong Luo, Pingping Lin

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An essential task in the field of artificial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes finite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.

Keywords: deep learning, generative, knowledge, response generation, retrieval

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Wu Yufeng, Gu Yifan, Wang Hengguang, Gongyu, Zuo Tieyong

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Indium is one the scarce resources which can be only used less than 30 years, and more than 70% of the indium is used for the production of the LCD. The benefit of recycling Indium from waste LCD is large. Take the LCD-TV for example, the yield of which was close to 90 million units in 2010. If it was available to recycle the indium effectively, the yield of the secondary-indium could reach up to 110 metric ton, which accounted for one third of the primary indium production in China. And compared with the dispersion and long process extraction of the primary indium resources, secondary indium concentrates in the waste LCD, the exploitation has great economic and environmental benefits. However, the potential benefits were indefinite, resulting in China’s government did not pay enough attention to the indium recycling industry. In our study, an estimation model was constructed to analyze the potential of the indium in the waste LCD. The different types of LCD were detected to find out the content of indium. Then, the potential of the indium in the waste LCD was estimated in China. Furthermore, the pollution emissions of the product process of the primary and secondary indium was analyzed respectively to calculate the economic and environmental benefits of the indium recycling from the waste LCD in China.

Keywords: indium recycling, waste liquid crystal displays, benefits, China

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Authors: Yifan Zheng, Jinmin Zhu, Jiji Li, Gulling Li, Huahong Shi

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Secondary microplastics originate from the fragmentation of large plastics, and weathering is supposed to be the main cause of fragmentation. In this study, we investigated burrows and burrowing invertebrates on Styrofoam floats from the mariculture areas of China’s coastal waters. Various burrows were found on the submerged surface of Styrofoam floats and could be divided into ‘I’, ‘S’, ‘J’, and ‘Y’ types based on the burrow entrance number and passage curvature. Different invertebrate species, including 5 isopods, 8 clamworms, and 12 crabs, were found inside the burrows. Micro-foams were found in the bodies of these burrowers, with an average abundance of 4.2 ± 0.3 (isopod), 6.9 ± 2.0 (clamworm), and 3.0 ± 0.5 (crab) micro-foams per individual. In the laboratory, we observed the boring process of crabs in abandoned floats. Field and laboratory evidence suggested that these invertebrates bored various burrows. The total volume of crab burrows on a 3-year-used float was estimated to be 2.6 × 10³ cm³, producing 4.1 × 10⁸ microplastics. This study highlights the critical role of bioerosion in destroying man-made substrates and prompting microplastic pollution.

Keywords: burrowing invertebrate, mariculture area, styrofoam float, fragmentation, microplastics

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Authors: Yang Song, Yifan Guo, Edward F. Gehringer

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Fifty years of research has found great potential for peer assessment as a pedagogical approach. With peer assessment, not only do students receive more copious assessments; they also learn to become assessors. In recent decades, more educational peer assessments have been facilitated by online systems. Those online systems are designed differently to suit different class settings and student groups, but they basically fall into two categories: rating-based and ranking-based. The rating-based systems ask assessors to rate the artifacts one by one following some review rubrics. The ranking-based systems allow assessors to review a set of artifacts and give a rank for each of them. Though there are different systems and a large number of users of each category, there is no comprehensive comparison on which design leads to higher reliability. In this paper, we designed algorithms to evaluate assessors' reliabilities based on their rating/ranking against the global ranks of the artifacts they have reviewed. These algorithms are suitable for data from both rating-based and ranking-based peer assessment systems. The experiments were done based on more than 15,000 peer assessments from multiple peer assessment systems. We found that the assessors in ranking-based peer assessments are at least 10% more reliable than the assessors in rating-based peer assessments. Further analysis also demonstrated that the assessors in ranking-based assessments tend to assess the more differentiable artifacts correctly, but there is no such pattern for rating-based assessors.

Keywords: peer assessment, peer rating, peer ranking, reliability

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Authors: Yifan Jia

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The Metis is a group of indigenous peoples in Canada. Having experienced centuries of injustices, beginning with colonialism dating back to the 16th century, culminating with military defeats in the 1800s and the establishment of residential schools, and continuing with structural injustices in the 21st century, Metis people have long been, and continue to be marginalized and made invisible in the Canadian society. In particular, Metis women born between 1997 and 2012 face intersectional discrimination based on not only race, but also a multitude of identity factors, including gender, age, geographical location, health, sexual orientation, and lateral violence from First Nations peoples. This paper uncovers the multilayered oppression against young Metis women through a literature review and uses several theories to analyze the invisibility of this discrimination in society, including color-blind racism, collective shame, lack of understanding of intersectionality, and Mauvaise foi (bad faith). To address the invisible, intersectional discrimination against young Metis women, several suggestions and possibilities could be considered. These include amending the education system, fostering group affiliation, bringing structural changes to federal policies and funding systems, and cooperating with other indigenous nations such as First Nations and Inuit.

Keywords: discrimination, Metis Women, indigenous rights, intersectionality

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Authors: Zihan Geng, Yifan Hou

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Background: Loneliness is the feeling of being isolated, which is becoming increasingly common among adolescents. A cross-sectional study was performed to determine the association between loneliness and different demographics. Methods: To identify the presence of loneliness, the UCLA Loneliness Scale (Version 3) was employed. The "Questionnaire Star" in Chinese version, as the online survey on the official website, was used to distribute the self-rating questionnaires to the students in Beijing from Grade 7 to Grade 12. The questionnaire includes sociodemographic items and the UCLA Loneliness Scale. Results: Almost all of the participants exhibited “caseness” for loneliness, as defined by UCLA. Out of 266 questionnaires, 2.6% (7 in 266) students fulfilled the presence criteria for a low degree of loneliness. 29.7% (79 in 266) of adolescents met the criteria for a moderate degree of loneliness. Moreover, 62.8% (167 in 266) and 4.9% (13 in 266) of students fulfilled the presence criteria for a moderately high and high degree of loneliness, respectively. In the Pearson χ2 test, there were significant associations between loneliness and some demographic factors, including grade (P<0.001), the number of adults in the family (P=0.001), the evaluation of appearance (P=0.034), the evaluation of self-satisfaction (P<0.001), the love in family (P<0.001), academic performance (P=0.001) and emotional support from friends (P<0.001). In the multivariate logistic analysis, the number of adults (2 vs.≤1, OR=0.319, P=0.015), time spent on social media (≥4h vs. ≤1h, OR=4.862, P=0.029), emotional support of friends (more satisfied vs. dissatisfied, OR=0.363, P=0.027) were associated with loneliness. Conclusions: Our results suggest the relationship between loneliness and some sociodemographic factors, which raise the possibility to reduce the loneliness among adolescents. Therefore, the companionship of family, the encouragement from friends and regulating the time spent on social media may decrease the loneliness in adolescents.

Keywords: loneliness, adolescents, demographic factors, UCLA loneliness scale

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Authors: Yifan Gao

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Owing to the high breakdown field, high saturation drift velocity, 2DEG with high density and mobility and so on, AlGaN/GaN HEMTs have been widely used in high-frequency and high-power applications. To acquire a higher power often means higher breakdown voltage and higher drain current. Surface leakage current is usually the key issue affecting the breakdown voltage and power performance. In this work, we have performed in-situ N₂/NH₃ pretreatment before the passivation to suppress the surface leakage and achieve device performance enhancement. The AlGaN/GaN HEMT used in this work was grown on a 3-in. SiC substrate, whose epitaxial structure consists of a 3.5-nm GaN cap layer, a 25-nm Al₀.₂₅GaN barrier layer, a 1-nm AlN layer, a 400-nm i-GaN layer and a buffer layer. In order to analyze the mechanism for the N-based pretreatment, the details are measured by XPS analysis. It is found that the intensity of Ga-O bonds is decreasing and the intensity of Ga-N bonds is increasing, which means with the supplement of N, the dangling bonds on the surface are indeed reduced with the forming of Ga-N bonds, reducing the surface states. The surface states have a great influence on the leakage current, and improved surface states represent a better off-state of the device. After the N-based pretreatment, the breakdown voltage of the device with Lₛ𝒹=6 μm increased from 93V to 170V, which increased by 82.8%. Moreover, for HEMTs with Lₛ𝒹 of 6-μm, we can obtain a peak output power (Pout) of 12.79W/mm, power added efficiency (PAE) of 49.84% and a linear gain of 20.2 dB at 60V under 3.6GHz. Comparing the result with the reference 6-μm device, Pout is increased by 16.5%. Meanwhile, PAE and the linear gain also have a slight increase. The experimental results indicate that using N₂/NH₃ pretreatment before passivation is an attractive approach to achieving power performance enhancement.

Keywords: AlGaN/GaN HEMT, N-based pretreatment, output power, passivation

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Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

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Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Riina I. Bray, Yifan Wang, Nikolas Argiropoulos, Stephanie Robins, John Molot, Kelly Tragash, Lynn M. Marshall, Margaret E. Sears, Marie-Andrée Pigeon, Michel Gaudet, Pierre Auger, Emily Bélanger, Rohini Peris

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Multiple chemical sensitivity (MCS) is a chronic medical condition characterized by intolerances to chemical substances. Since the arrival of the COVID-19 pandemic and associated health measures, people experiencing MCS (PEMCS) are at a heightened risk of environmental exposures associated with cleaners, disinfectants, and sanitizers. Little attention has been paid to the well-being of PEMCS in the context of the COVID-19 pandemic. Objective: This study assesses the lived experiences of Canadian adults with MCS in relation to their living environment, access to healthcare, and levels of perceived social support before and during the pandemic. Methods: A total of 119 PEMCS completed an online questionnaire. McNemar Chi-Squared and Wilcoxon Signed Rank tests were used to evaluate if there were statistically significant changes in participants’ perception of their living environment, access to healthcare, and levels of social support before and after March 11, 2020. Results: Both positive and negative outcomes were noted. Participants reported an increase in exposure to disinfectants/sanitizers that entered their living environment (p<.001). There was a reported decrease in access to a family doctor during the pandemic (p<0.001). Although PEMCS experienced increased social isolation (p<0.001), they also reported an increase in understanding from family (p<0.029) and a decrease in stigma for wearing personal protective equipment (p<0.001). Conclusion: PEMCS reported experiencing: increased exposure to disinfectants or sanitizers, a loss of social support, and barriers in accessing healthcare during the pandemic. However, COVID-19 provided an opportunity to normalize the living conditions of PEMCS, such as wearing masks and social isolation. These findings can guide decision-makers on the importance of implementing nontoxic alternatives for cleaning and disinfection, as well as improving accommodation measures for PEMCS.

Keywords: covid-19, multiple chemical sensitivity, MCS, quality of life, social isolation, physical environment, healthcare

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Authors: Zichao Zhang, Yifan Zhao, Samuel Court

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The carbon emission problem of the traditional construction industry has long been a pressing issue. With the growing emphasis on environmental protection and advancement of science and technology, the organic integration of digital technology and emission reduction has gradually become a mainstream solution. Among various sophisticated digital technologies, digital twins, which involve creating virtual replicas of physical systems or objects, have gained enormous attention in recent years as tools to improve productivity, optimize management and reduce carbon emissions. However, the relatively high implementation costs including finances, time, and manpower associated with digital twins have limited their widespread adoption. As a result, most of the current applications are primarily concentrated within a few industries. In addition, the creation of digital twins relies on a large amount of data and requires designers to possess exceptional skills in information collection, organization, and analysis. Unfortunately, these capabilities are often lacking in the traditional construction industry. Furthermore, as a relatively new concept, digital twins have different expressions and usage methods across different industries. This lack of standardized practices poses a challenge in creating a high-quality digital twin framework for construction. This paper firstly reviews the current academic studies and industrial practices focused on reducing greenhouse gas emissions in the construction industry using digital twins. Additionally, it identifies the challenges that may be encountered during the design and implementation of a digital twin framework specific to this industry and proposes potential directions for future research. This study shows that digital twins possess substantial potential and significance in enhancing the working environment within the traditional construction industry, particularly in their ability to support decision-making processes. It proves that digital twins can improve the work efficiency and energy utilization of related machinery while helping this industry save energy and reduce emissions. This work will help scholars in this field to better understand the relationship between digital twins and energy conservation and emission reduction, and it also serves as a conceptual reference for practitioners to implement related technologies.

Keywords: digital twins, emission reduction, construction industry, energy saving, life cycle, sustainability

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Authors: Yufeng Wu, Yifan Gu, Bin Li, Wei Wang

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Resource recycling process contains a subsystem with interactions of three dimensions including coupling allocation of primary and secondary resources, responsibility coordination of stakeholders in forward and reverse supply chains, and trans-boundary transfer of hidden resource and environmental responsibilities between regions. Overlap or lack of responsibilities is easy to appear at the intersection of the three management dimensions. It is urgent to make an overall design of the policy system for recycling resources. From theoretical perspective, this paper analyzes the unique external differences of resource and environment in various dimensions and explores the reason why the effects of trans-dimensional policies are strongly correlated. Taking the example of the copper resources contained in the waste electrical and electronic equipment, this paper constructs reduction effect accounting model of resources recycling and set four trans-dimensional policy scenarios including resources tax and environmental tax reform of the raw and secondary resources, application of extended producer responsibility system, promotion of clean development mechanism, and strict entry barriers of imported wastes. In these ways, the paper simulates the impact effect of resources recycling process on resource deduction and emission reduction of waste water and gas, and constructs trans-dimensional policy mix scenario through integrating dominant strategy. The results show that combined application of various dimensional policies can achieve incentive compatibility and the trans-dimensional policy mix scenario can reach a better effect. Compared with baseline scenario, this scenario will increase 91.06% copper resources reduction effect and improve emission reduction of waste water and gas by eight times from 2010 to 2030. This paper further analyzes the development orientation of policies in various dimension. In resource dimension, the combined application of compulsory, market and authentication methods should be promoted to improve the use ratio of secondary resources. In supply chain dimension, resource value, residual functional value and potential information value contained in waste products should be fully excavated to construct a circular business system. In regional dimension, it should give full play to the comparative advantages of manufacturing power to improve China’s voice in resource recycling in the world.

Keywords: resource recycling, trans-dimension, policy design, incentive compatibility, life cycle

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Authors: Yifan Lu, Ying Cui, Ying Zhu, Edward S. Lu, Rebecca Zeng, Rohan Bajaj, Raviv Katz, Rongrong Le, Jay C. Wang, John B. Miller

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Purpose: Retinal artery occlusion (RAO) is an ophthalmic emergency that can lead to poor visual outcome and is associated with an increased risk of cerebral stroke and cardiovascular events. Fluorescein angiography (FA) is the traditional diagnostic tool for RAO; however, wide-field swept-source optical coherence tomography angiography (WF SS-OCTA), as a nascent imaging technology, is able to provide quick and non-invasive angiographic information with a wide field of view. In this study, we looked for associations between OCT-A vascular metrics and visual acuity in patients with prior diagnosis of RAO. Methods: Patients with diagnoses of central retinal artery occlusion (CRAO) or branched retinal artery occlusion (BRAO) were included. A 6mm x 6mm Angio and a 15mm x 15mm AngioPlex Montage OCT-A image were obtained for both eyes in each patient using the Zeiss Plex Elite 9000 WF SS-OCTA device. Each 6mm x 6mm image was divided into nine Early Treatment Diabetic Retinopathy Study (ETDRS) subfields. The average measurement of the central foveal subfield, inner ring, and outer ring was calculated for each parameter. Non-perfusion area (NPA) was manually measured using 15mm x 15mm Montage images. A linear regression model was utilized to identify a correlation between the imaging metrics and visual acuity. A P-value less than 0.05 was considered to be statistically significant. Results: Twenty-five subjects were included in the study. For RAO eyes, there was a statistically significant negative correlation between vision and retinal thickness as well as superficial capillary plexus vessel density (SCP VD). A negative correlation was found between vision and deep capillary plexus vessel density (DCP VD) without statistical significance. There was a positive correlation between vision and choroidal thickness as well as choroidal volume without statistical significance. No statistically significant correlation was found between vision and the above metrics in contralateral eyes. For NPA measurements, no significant correlation was found between vision and NPA. Conclusions: This is the first study to our best knowledge to investigate the utility of WF SS-OCTA in RAO and to demonstrate correlations between various retinal vascular imaging metrics and visual outcomes. Further investigations should explore the associations between these imaging findings and cardiovascular risk as RAO patients are at elevated risk for symptomatic stroke. The results of this study provide a basis to understand the structural changes involved in visual outcomes in RAO. Furthermore, they may help guide management of RAO and prevention of cerebral stroke and cardiovascular accidents in patients with RAO.

Keywords: OCTA, swept-source OCT, retinal artery occlusion, Zeiss Plex Elite

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