Information Input Research Articles

Research on the Multimodal Learning Path of thought and Politics in Medical Education Curriculum under the Enabling of Artificial Intelligence Technology

The innovation of artificial intelligence technology has promoted the revolution of multi-industries, and the combination of medical education courses and artificial intelligence technology has been deepened. mixed reality and other technologies can help the input and output of multimodal information in the process of teaching and build a multimodal learning path. This paper makes an in-depth analysis of the current situation of higher medical education in colleges and universities, the application of artificial intelligence technology in medical teaching activities, and the related research of multimodal teaching interaction model theory. this paper discusses the multimodal learning path of thought and politics of medical education curriculum under the enabling of artificial intelligence technology, hoping to provide useful guidance and reference for related research.

A reliability assessment methodology of system-in-package based virtual qualification

System-in-Package (SiP) is a cutting-edge packaging technique that is flourishing in the semiconductor sector. Physical testing, which can be time-consuming and lead to design modification iterations delaying product release or delivery, has traditionally been used to assess the reliability of SiPs. Simulation methods that mimic physical tests can be used to shorten product development cycles, increase reliability, and lower development costs. This paper introduces a SiP reliability assessment methodology based on virtual qualification. A life prediction methodology of SiP based on physics-of-failure (PoF) is studied in conjunction with multi-source fusion life information of typical structures. Based on this, a comprehensive reliability assessment methodology of SiP is formed. The information input includes material types, structure size, and environmental load conditions. The finite element analysis (FEA) is used to analyze the typical structures. The life distribution of SiP components is obtained through the theory of the cumulative damage and competitive failure model. Through multi-source information fusion, the SiP comprehensive reliability assessment methodology is determined. Finally, a case study is done to determine the accuracy of the approach.

Direct evidence for proactive suppression of salient-but-irrelevant emotional information inputs.

It has long been debated whether emotional information inherently captures attention. The mainstream view suggests that the attentional processing of emotional information is automatic and difficult to be controlled. Here, we provide direct evidence that salient-but-irrelevant emotional information inputs can be proactively suppressed. First, we demonstrated that both negative and positive emotional distractors (fearful and happy faces) induced attentional capture effects (i.e., more attention allocated to emotional distractors than neutral distractors) in the singleton-detection mode (Experiment 1), but attentional suppression effects (i.e., less attention allocated to emotional distractors than neutral distractors) in the feature-search mode that strengthened task motivation (Experiment 2). The suppression effects in the feature-search mode disappeared when emotional information was disrupted through face inversion, showing that the suppression effects were driven by emotional information rather than low-level visual factors (Experiment 3). Furthermore, the suppression effects also disappeared when the identity of emotional faces became unpredictable (Experiment 4), suggesting that the suppression was highly dependent on the predictability of emotional distractors. Importantly, we reproduced the suppression effects using eye-tracking methods and found that there was no attentional capture by emotional distractors before the appearance of the attentional suppression effects (Experiment 5). These findings suggest that irrelevant emotional stimuli that have the potential to cause distraction can be proactively suppressed by the attention system. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Influence of algorithmization and interface for the preparation of management decisions

In modern conditions, managerial decision-making is carried out using automated systems under the general name “Decision Support Systems” (DSS). When creating them, it is important to consider two key points. The first is the algorithmic component, which reflects the logic of the system as a whole and its individual parts. The second is the application interface through which the user interacts with it. The interface is a graphical interpretation of the algorithms that are implemented within the system. Therefore, it is very important to design and create such a relationship between the algorithm and the interface so that the user is as comfortable as possible using the DSS to solve current tasks (information input, its processing, presentation and analysis for decision making). Thus, there is a directly proportional relationship between the interface and the algorithm. Moreover, despite the fact that there are many studies on these aspects, both theoretical and practical, there are still questions to which one should pay attention to in terms of application. The purpose of this study is to formulate practical recommendations to prevent the entry of incorrect information into the DSS database and to present the results in a form convenient for its analysis. The main tasks of the work are to show by means of examples which errors can contribute to the entry of unreliable information into the database, as well as how best to present information on the monitor screen in accordance with the psychophysiological characteristics of a person in order to reduce the time for its analysis and decision-making.

Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence

Aerosols play a crucial role in the climate system due to direct and indirect effects, such as scattering and absorbing radiant energy. They also have adverse effects on visibility and human health. Humans are exposed to fine PM2.5, which has adverse health impacts related to cardiovascular and respiratory-related diseases. Long-term trends in PM concentrations are influenced by emissions and meteorological variations, while meteorological factors primarily drive short-term variations. Factors such as vegetation cover, relative humidity, temperature, and wind speed impact the divergence in the PM2.5 concentrations on the surface. Machine learning proved to be a good predictor of air quality. This study focuses on predicting PM2.5 with these parameters as input for spatial and temporal information. The work analyzes the in situ observations for PM2.5 over Singapore for seven years (2014–2021) at five locations, and these datasets are used for spatial prediction of PM2.5. The study aims to provide a novel framework based on temporal-based prediction using Random Forest (RF), Gradient Boosting (GB) regression, and Tree-based Pipeline Optimization Tool (TP) Auto ML works based on meta-heuristic via genetic algorithm. TP produced reasonable Global Performance Index values; 7.4 was the highest GPI value in August 2016, and the lowest was −0.6 in June 2019. This indicates the positive performance of the TP model; even the negative values are less than other models, denoting less pessimistic predictions. The outcomes are explained with the eXplainable Artificial Intelligence (XAI) techniques which help to investigate the fidelity of feature importance of the machine learning models to extract information regarding the rhythmic shift of the PM2.5 pattern.

NEW TRENDS IN THE FORMATION OF EXCURSIONS IN UKRAINE

The conventional methods of organizing excursions are being reshaped by factors such as technology advancements, changing traveler preferences, and innovative approaches to showcasing cultural and historical sites. By staying informed about emerging trends, regions can adapt their offerings to capitalize on new opportunities, potentially boosting economic growth and job creation. The article aims to highlight the changing preferences, interests, and demands of tourists, as well as advancements in technology and tourism management that are shaping the way excursions are developed, promoted, and experienced in the country. Modern specialists in excursion activities need to master the basics of psychology, which will contribute to the highly qualified organization of the excursion process, its optimal activation, the introduction of interesting forms of excursions and interactive methods of their conduct, and the maximum delivery of thematic information to each category of excursionists. Such forms of excursions as quest tours, in turn, help tourists to better memorize information and apply it in the future. the role of modern interactive tours is significant. Due to the convergence of entertainment and scientific and cognitive components, they give the younger generation in the usual form of activity (game, creative task, participation in theatricalization, etc.) the opportunity to improve their intellectual level through the emotional input of new information, the development of observation, the ability to perceive the beauty of the world around them; the ability to emotionally and practically contact the world around them, to receive convincing, vivid, visual answers to questions. At this time, not only natural curiosity is satisfied, but also the child's natural need for movement, a variety of activities, and games. The dynamic nature of the tourism industry necessitates a thorough investigation of the new trends in excursion formation in Ukraine. By comprehending these trends, stakeholders can enhance tourist experiences, drive economic growth, and safeguard cultural heritage. The relevance of this study extends beyond the realm of tourism, influencing various facets of societal and economic development in the country.

E-commerce Live Streaming Marketing and Consumer Psychology: The E.B.K Model and the JD-fresh & Douyu Marketing Case

2016 has been known as the year of Chinas online live streaming, the live streaming market entered a period of explosive growth. Online live streaming presents enormous marketing opportunities and releases consumer potential. This study constructs a consumer psychology-behavior model for online marketing based on the E.B.K. (Engel) model, which includes four stages: information input, information processing, decision-making, and external influence. The study focuses on the interaction between consumer psychology and online marketing strategies in the context of live streaming, and analyzes the mutual dynamics between consumer psychology and online marketing strategies, using the specific case of the JD-Fresh & Douyu 6.18 marketing campaign in 2016. On the one hand, online marketing strategies influence consumer psychology through virtual interactions, which in turn affects consumer behavior. On the other hand, consumer evaluations and feedback after purchase further affect the adjustment and improvement of the online live streaming marketing environment. This case study demonstrates the feasibility of the model and provides new ideas for brand marketing and promotion strategies in future online live streaming.

Evaluation of personal, socioeconomic and psychological characteristics of FFS and non-FFS groundnut growers

The personal, socioeconomic and psychological characteristics play a vital role in planning any capacity building programmes to farmers. A study was conducted during 2012-13 to know the personal, socioeconomic and psychological characteristics of Farmer Field School (FFS) and non-FFS farmers of groundnut in Chittoor district of Andhra Pradesh. There was no significant difference in farming experience and socioeconomic status was observed between FFS and non-FFS farmers of groundnut. Significant differences in extension participation, mass media exposure, innovativeness, achievement motivation, management orientation, economic orientation, information input behavior, information process behaviour and information output behaviour were observed between FFS and non-FFS farmers of groundnut.

Cognitive Process and Skill Training of Time-Limited Sight Translation

In sight translation, a written message is converted into a spoken message, or a written text must be read aloud in another language. Sight translation has long been considered a hybrid of translation and interpreting and a preparatory training method for simultaneous interpreting. However, because the information input in sight translation is in the form of text rather than fleeting speech, not enough attention is paid to the time factor of sight translation. This paper addresses the definition, cognitive process, and training of the skills of time-limited sight translation. The aim is to highlight the importance of time constraint in sight translation to strengthen the role of sight translation in language skill acquisition, including supplementing cognitive knowledge and improving bilingual conversion ability.

Digital worker feedback infrastructures: The digitalisation of worker rights monitoring in global value chains

AbstractIn this paper, we present empirical research on what we call ‘digital worker feedback infrastructures’ (DWFI); these are communication systems based on digital technologies that allow for creating so-called ‘feedback data’ via different forms of information input of workers in global value chains (GVC). The paper provides an overview of over 50 current DWFIs in GVCs and asks about the main differences between management-oriented and worker-centred digital feedback infrastructures in their usage of worker data. In the first part, we trace the emergence of DWFIs at the intersection of different trends: the continuous non-improvement of working conditions through auditing, the permanent politicisation, and contestation of this fact through labour and activist networks as well as the development of new digital technologies. In the second section, we elaborate the main features of DWFIs and analyse potential shortcomings in the context of the ‘ethical’ audit and monitoring regime for GVCs. Third, we use our dataset to present an overview of the heterogeneity of DWFIs. We pay particular attention to examples of civil society developed tools as we suggest that they provide a glimpse of the potential of worker feedback technologies from below, which could contribute to better monitoring of worker rights and facilitate a more democratic coordination of workplaces and GVCs.

인컨텍스트 러닝을 통한 ChatGPT의 시적 표현 해석 가능성 연구

In 2023, as artificial intelligence(AI) moves beyond the realm of imitation and into the realm of creation, Korean literature is also entering the era of “creative AI,” with Slitscope’s SIA releasing a book of poetry, “시를 쓰는 이유(Why I Write Poetry).” However, the time and cost required to reach such a level is quite burdensome for the general public to attempt.
 Therefore, this study focuses on in-context learning of conversational A.I. that has already been trained through deep learning to explore the possibility of learning about A.I.’s allegory. In particular, by using ChatGPT as a research object, we can gain research advantages in terms of accessibility and functionality over previous studies on the model.
 In order to study the learning potential of ChatGPT in an in-context learning environment, this study consists of a structure of (1) pre-learning verification, (2) output request based on pre-learning, (3) related information input, and (4) verification re-execution of (2) after learning, and examines the possibility of conversational AI learning about poetic alliteration.
 The poetry texts that are input as learning information in the above process are allegorical poetry texts extracted from Yoon Gon-gang’s 『動物詩集』(Hansung Book Co., 1938) because it was judged to have value as the first Korean poetry collection with a single animal theme, which was approached and interpreted from the perspective of satirical and allegorical poetry at the time and even now.
 This study determines the learning potential of literary rhetoric for ChatGPT through the education and output process based on Yoon Gon-kang’s poems, and considers its utilization in various aspects such as education, creation, and education in the future. This will be an exploration of possibilities to solidify the position of literature in the era of artificial intelligence that will become a reality in the future.

Amplitude-assisted tagging of longitudinally polarised bosons using wide neural networks

Extracting longitudinal modes of weak bosons in LHC processes is essential to understand the electroweak-symmetry-breaking mechanism. To that end, we propose a general method, based on wide neural networks, to properly model longitudinal-boson signals and hence enable the event-by-event tagging of longitudinal bosons. It combines experimentally accessible kinematic information and genuine theoretical inputs provided by amplitudes in perturbation theory. As an application we consider the production of a Z boson in association with a jet at the LHC, both at leading order and in the presence of parton-shower effects. The devised neural networks are able to extract reliably the longitudinal contribution to the unpolarised process. The proposed method is very general and can be systematically extended to other processes and problems.

Mapping Seagrass Biodiversity Indicators of Pari Island using Multiple WorldView-2 Bands Derivatives

Comprehensive information on seagrass biodiversity indicators, such as species composition, percentage cover, and biomass carbon stock, remains limited across various regions globally. Mapping these indicators using remote sensing images requires extracting maximum information from the input images to achieve effective results. This study aims to map seagrass distribution, percent cover (PC), and aboveground carbon stock (AGC) as biodiversity indicators in the optically shallow waters surrounding Pari Island. We integrate WorldView-2 (WV2) derivatives, field seagrass data, and RF classification and regression algorithms to accomplish this objective. The WV2 image derivatives encompass surface reflectance bands, band ratios, mean and variance co-occurrence texture bands, and principle component bands. These inputs are used individually and collectively for mapping, employing a random forest algorithm trained with field seagrass data. Our results demonstrate that the most accurate benthic habitat map achieves an overall accuracy (OA) of 65.2%, with a user's accuracy of 65.2% and a producer's accuracy of 72.8% for the seagrass-dominated class. Seagrass PC mapping yields a root mean square error (RMSE) of 17.1%, with an average PC of 47.4 ± 9.9%. Seagrass AGC mapping achieves an RMSE of 5.0 g C m-2, with an average AGC range of 6.2 – 29.1 g C m-2, estimating the study area's aboveground biomass carbon stock at 27.9 tons C. Combined inputs produce the most accurate results for all biodiversity indicators, emphasizing the importance of utilizing combined bands from SR band derivatives to maximize information input for training mapping algorithms, instead of using derivative bands individually or as replacements for the initial SR bands.
 Keywords : Seagrass; Biodiversity; Mapping; WorldView-2; Pari Island
 
 Copyright (c) 2023 Geosfera Indonesia and Department of Geography Education, University of Jember
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Orthographic influence in the distributional learning of non-native speech sounds

This study investigated the role of orthographic information in the acquisition of non-native speech sounds by monolingual English listeners. Two potentially important orthographic variables were explored: Orthographic compatibility (whether the orthographic information supports or contradicts the distributional information) and orthographic familiarity (whether the native and target languages share the same orthography). Ten groups of learners were trained on either a unimodal or bimodal distribution of two length continua. Out of the 10 groups, eight groups were also exposed to orthographic cues that varied in their compatibility with the distributional information (compatible vs. incompatible) and familiarity with the orthography of learners’ native language (Roman vs. Arabic). Following training, all participants performed an AX discrimination task to test their discrimination of the length contrast. The results revealed that, in general, the availability of either familiar or unfamiliar orthographic input which signaled the existence of a single length category significantly lowered learners’ discrimination of the length contrast regardless of the auditory distribution. Further, the exposure to orthographic input that supported a two-category length distinction enhanced the discrimination of the length contrast irrespective of the distribution. However, the most significant improvement occurred when both distributional information and familiar orthographic input were compatible. Overall, these findings indicate that orthographic input, regardless of its level of compatibility or familiarity, may influence the acquisition of non-native speech sounds.

Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction.

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial-temporal graph convolutional networks' bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.

Advancing Health Equity in Neurologic Disorders and Stroke: Stakeholder Insights Into Health Disparities, Research Gaps, and Potential Interventions.

The purpose of this study was to analyze the National Institute of Neurological Disorders and Stroke (NINDS) Request for Information (RFI) input from the public-including health care providers, researchers, patients, patient advocates, caregivers, advocacy organizations, professional societies, and private and academic stakeholders with an interest in health disparities (HDs) in neurologic disease. RFI questions were structured to solicit input on what stakeholders believe are neurologic disease HD research priorities, drivers of health inequity, and potential interventions. Furthermore, these stakeholder insights were examined within the context of contemporary scientific literature and research frameworks on health equity and health disparities. The NINDS published a RFI from March 31 to July 15, 2020. The RFI analysis presented here is part of a larger strategic planning process aimed to guide future NINDS efforts in neurologic disorder health equity (HE) research and training. The public commented on facilitators of HDs, populations that experience HDs (HDPs), potential interventions, and research opportunities related to HDs in neurologic disease and/or care in the United States across the lifespan. Responses were analyzed using qualitative methodology. Frequently suggested interventions were thematically clustered using the interpretive phenomenological analysis methodology and are presented in this article to provide a stakeholder-identified roadmap for advancing HE. Respondents identified socioecological factors as driving HDs in 89% of determinants reported. Stakeholder-reported HD determinants and subsequent interventions could be classified into the following conceptual categories: HDP neurospecialty care access, innovative HDP engagement and research inclusion strategies, and development of a well-trained clinician-scientist HD workforce. Clustering of the feedback from patient and patient-adjacent respondents (i.e., caretakers and patient advocates) highlighted the prevalence of patient-provider interpersonal factors and limited resources driving access-to-care barriers among their sentiments. Respondent sentiments suggest prioritization of social determinants of health (SDOH) research, shifting away from the common target of biological and behavioral themes addressed in the existing body of HE research provided by the stakeholder. Overall, respondents suggest focusing research prioritization on access to care, engagement across the HE research and care landscape, and HE workforce development.

The Relationship Between Perceived Organizational Support And Work Engagement In The Inpatient Room Of RS X Lamongan

To provide good health services, health workers in a hospital must have good work engagement. Work engagement is defined as a positive, satisfying, work-related state of mind characterized by passion, dedication, and absorption. High-perceived organizational support, employees are more satisfied with their jobs, feel closer to the organization, are more driven to see the goals of the organization as their own, and are more loyal and committed to the organization. To determine the relationship between perceived organizational support and work engagement in nurses and midwives in the Inpatient Installation of X Hospital Lamongan. Research Method: The research design is quantitative observational with cross-sectional analytics. The statistical test uses the Spearman correlation test. The results of the study: there is a relationship between perceived organizational support and work engagement among employees at the Inpatient Installation of X Hospital Lamongan indicated by P <0.05 (0.001 <0.05). In the correlation coefficient, the result is 0.728 which shows that this relationship is strong. Suggestion: the results of this study can be used as information material and input for X Hospital Lamongan.

Sistem Informasi Geografis Berbasis Web Pada Industri Pariwisata

Geographic Information System (GIS) is a type of software that can be used for input, storage, manipulation, display, and output of geographic information and its attributes (Prahasta). Geographic Information Systems have a very important role in combining data, organizing data, conducting data analysis which can ultimately produce data and can be used as a reference for decision making on issues related to geography.

Physiologically-Informed Gaussian Processes for Interpretable Modelling of Psycho-Physiological States.

The widespread popularity of Machine Learning (ML) models in healthcare solutions has increased the demand for their interpretability and accountability. In this paper, we propose the Physiologically-Informed Gaussian Process (PhGP) classification model, an interpretable machine learning model founded on the Bayesian nature of Gaussian Processes (GPs). Specifically, we inject problem-specific domain knowledge of inherent physiological mechanisms underlying the psycho-physiological states as a prior distribution over the GP latent space. Thus, to estimate the hyper-parameters in PhGP, we rely on the information from raw physiological signals as well as the designed prior function encoding the physiologically-inspired modelling assumptions. Alongside this new model, we present novel interpretability metrics that highlight the most informative input regions that contribute to the GP prediction. We evaluate the ability of PhGP to provide an accurate and interpretable classification on three different datasets, including electrodermal activity (EDA) signals collected during emotional, painful, and stressful tasks. Our results demonstrate that, for all three tasks, recognition performance is improved by using the PhGP model compared to competitive methods. Moreover, PhGP is able to provide physiological sound interpretations over its predictions.