Eye-tracking technology has become a powerful tool for biomedical-related applications due to its simplicity of operation and low requirements on patient language skills. This study aims to use the machine-learning models and deep-learning networks to identify key features of eye movements in Alzheimer's Disease (AD) under specific visual tasks, thereby facilitating computer-aided diagnosis of AD. Firstly, a three-dimensional (3D) visuospatial memory task is designed to provide participants with visual stimuli while their eye-movement data are recorded and used to build an (...) eye-tracking dataset. Then, we propose a novel deep-learning-based model for identifying patients with Alzheimer's Disease (PwAD) and healthy controls (HCs) based on the collected eye-movement data. The proposed model utilizes a nested autoencoder network to extract the eye-movement features from the generated fixation heatmaps and a weight adaptive network layer for the feature fusion, which can preserve as much useful information as possible for the final binary classification. To fully verify the performance of the proposed model, we also design two types of models based on traditional machine-learning and typical deep-learning for comparison. Furthermore, we have also done ablation experiments to verify the effectiveness of each module of the proposed network. Finally, these models are evaluated by four-fold cross-validation on the built eye-tracking dataset. The proposed model shows 85% average accuracy in AD recognition, outperforming machine-learning methods and other typical deep-learning networks. (shrink)

It is widely acknowledged that language rights are basic human rights. Given the critical role they play in society, multiple constitutions and statutes have been drafted and implemented to ensure...

The race among global firms to launch its respective products and services into the market sooner than the competitors puts pressure to equip its employees with job-related skills at the pace of business. Today’s global and dynamic business requires employees to develop highly complex cognitive skills such as decision-making, problem-solving, troubleshooting to perform their jobs proficiently. Traditional training models used by some organizations lead to a very slow speed at which employees gain an acceptable level of proficiency in the targeted (...) job skills. Also, these models have long and regimented instructional development cycle. Thus, traditional models are inherently counter-productive to the business and do not enable employees and organizations for today’s business needs. Therefore, business organizations need to explore new training models and strategies that could reduce the time an employee takes to reach target proficiency in complex skills without compromising the effectiveness or outcome. A comprehensive review of the literature shows a very limited amount of academic or practitioner research on this topic. This doctorate research study aims to find various training strategies that have proven successful in organizations for accelerating proficiency of employees in complex job skills. The researcher collected data primarily through 74 in-depth interviews with 86 training experts with known work experience of reducing time-to-proficiency in various settings. A total of 105 project cases have been collected across 42 industries to date. A grounded theory approach with constant comparison method is used to guide the theoretical saturation, analyze the data and to develop a theoretical model of training strategies. This unique study revealed a range of training strategies from non-training solutions to classroom instructions to workplace training to accelerate proficiency of employees in acquiring complex skills. This paper will specifically report the preliminary findings and conceptual model of major classroom-based instructional strategies only that has been found to hold the potential to shorten time-to-proficiency of employees at the workplace. This paper will also discuss the implications of the findings for the practitioners. Results in this study confirmed that classroom-based instructional strategies need to be designed and delivered the way the work is being performed in reality by the learners. Research findings suggest a pattern of five classroom-based instructional strategies that are more successful in reducing time-to-proficiency - 1) Contextualized learning 2) Performance analysis of star performer 3) Active processing and emotional involvement 4) Access to self-guided learning resources 5) Social interconnectivity and informal learning. (shrink)

Previous studies of the relationship between hope and life satisfaction left the underlying mechanism of how hope predicts life satisfaction unexplored to scholars. This study thus investigates the two potential mediators in the relationship between hope and life satisfaction among a sample of Chinese shadow education institution tutors who may be under immense professional development pressure from a cross-sectional approach. The main body of the study consists of an online survey in which 221 SEI tutors reported their hope, positive coping, (...) perceived social support, and life satisfaction. The survey results were analyzed using mediation and moderation analysis via SPSS 23.0. The results indicated that positive coping improved the relationship between hope and life satisfaction, supporting the hypothesis regarding the serial mediating effect of positive coping and perceived social support. In other words, tutors with a high level of hope tend to adopt positive coping strategies, thus will receive more social support and improve life satisfaction. Our findings revealed the independent and accumulative mediating effects of positive coping and perceived social support on the relationship between hope and life satisfaction, and had implications for the psychological intervention of SEI tutors who are currently facing enormous industry pressure. (shrink)

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we (...) illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality. (shrink)

Customers are skeptical about shopping online because e-commerce environments are typically considered impersonal. To assure product quality and to enhance customer proclivity in such environments, post-sale services may be considered to alleviate customers’ skepticism. Therefore, this study’s objective is to investigate the role of an online retailer’s post-sale services on customers’ attitudinal and behavioral aspects. Structural equation modeling is applied to data collected through an online survey answered by 409 online customers of jd.com. Research findings show that product return, exchange, (...) and maintenance services are strongly predictive of online customer satisfaction, and satisfaction significantly impacts customer trust. Both customer satisfaction and trust, as indicators of relationship quality, further mediate the links between product return, exchange, and maintenance services and online customer repurchase intention. In addition, differences between male and female customers were found in various aspects of online retailers’ product return, exchange, and maintenance services. This is the first empirical study that not only examines the influence of all three dimensions of online retailers’ post-sale services on customers’ online shopping perceptions and decisions, but also considers differences between male and female customers. Finally, this research provides theoretical and managerial implications based on conceptual and empirical evidence. (shrink)

Developing World Bioethics, Volume 22, Issue 3, Page 170-178, September 2022.

本书对自组织理论进行综合性研究,并探讨了关于自然界自组织演化的自然图景和自然观,关于自然科学自组织演化的科学图景和科学观。.

EDITOR’S ABSTRACTIn this article, Huang stresses the important role played by the Chinese cultural context in the historical process of translation of Western concepts. Huang exemplifies this point through an analysis of Yan Fu’s translation of “individualism.”.

Electroencephalography is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network. However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were selected for the (...) extraction and classification of preprocessed data, namely, common spatial patterns- CNN and wavelet transform- CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN. (shrink)

Entity linking involves mapping ambiguous mentions in documents to the correct entities in a given knowledge base. Most existing methods failed to link when a mention appears multiple times in a document, since the conflict of its contexts in different locations may lead to difficult linking. Sentence representation, which has been studied based on deep learning approaches recently, can be used to resolve the above issue. In this paper, an effective entity linking model is proposed to capture the semantic meaning (...) of the sentences and reduce the noise introduced by different contexts of the same mention in a document. This model first uses the symmetry of the Siamese network to learn the sentence similarity. Then, the attention mechanism is added to improve the interaction between input sentences. To show the effectiveness of our sentence representation model combined with attention mechanism, named ELSR, extensive experiments are conducted on two public datasets. Results illustrate that our model outperforms the baselines and achieves the superior performance. (shrink)

Given the colexification of perception and cognition, Georgakopoulos et al. (2022. Universal and macro-areal patterns in the lexicon: A case-study in the perception-cognition domain. Linguistic Typology 26(2). 439–487) claim that uncontrolled experience rather than controlled activity has a direct linkage to cognition. To test whether this is a universal tendency, this study conducts a contrastive behavioral profile analysis of two basic vision verbs in Mandarin: kàn, a controlled activity verb, and jiàn, an uncontrolled experience verb. The results show that (i) (...) both kàn and jiàn colexify visual activity and cognitive senses; (ii) kàn is able to express a wider range of cognitive meanings, i.e., know, understand, and think, whereas jiàn only extends into know; and (iii) visual and cognitive meanings have more proximal relationships in the colexification pattern of kàn than that of jiàn. The study confirms the universality of the colexification between vision and cognition while providing counterexamples to the above-mentioned large data-based findings. Accordingly, a dichotomy of controlled activity versus uncontrolled experience is proposed for the colexification of vision and cognition in cognitive typology. (shrink)

In 2022, the renowned historian Katja Guenther published “The Mirror and the Mind: A History of Self-Recognition in the Human Sciences.” Throughout the entirety of the book, Guenther meticulously t...

Marketers are increasingly relying on promotional practices (variously labeled as stealth marketing, hybrid messages, covert advertising) based on the diffusion of product information by third parties that appear to be independent of advertisers. In this paper, we examine to what extent the media treat their advertisers favorably, providing these advertisers’ products extra visibility in supposedly neutral editorial content. Empirically, we model the determinant of media coverage of Italian fashion products in an extended dataset of consumer magazines in Italy, France, Germany, (...) UK, and the USA. Research findings show that advertising is an important determinant of product placements in editorial content, and this result is consistent across countries and publishers. Our results imply that resource-rich advertisers engaging in compensatory advertising (that is, advertising investments that compensate poor product quality) might bias media coverage in their favor. Consumers will consequently be exposed twice to favorable messages about those products, in both advertisements and media content, resulting in higher purchases and reduced consumer welfare. (shrink)