scholarly journals Challenges in the Automatic Analysis of Students’ Diagnostic Reasoning

2019 ◽  
Vol 33 ◽  
pp. 6974-6981
Author(s):  
Claudia Schulz ◽  
Christian M. Meyer ◽  
Iryna Gurevych
Keyword(s):  
Large Scale ◽  
Diagnostic Reasoning ◽  
Evaluation Framework ◽  
Future Research ◽  
Automatic Identification ◽  
Correct Identification ◽  
Performance Metric ◽  
Evidence Evaluation ◽  
Corpus Creation ◽  
Epistemic Activity

Diagnostic reasoning is a key component of many professions. To improve students’ diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reasoning selfexplanations of students from two domains annotated with epistemic activities. Based on insights from the corpus creation and the task’s characteristics, we discuss three challenges for the automatic identification of epistemic activities using AI methods: the correct identification of epistemic activity spans, the reliable distinction of similar epistemic activities, and the detection of overlapping epistemic activities. We propose a separate performance metric for each challenge and thus provide an evaluation framework for future research. Indeed, our evaluation of various state-of-the-art recurrent neural network architectures reveals that current techniques fail to address some of these challenges.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

2017 ◽  
Vol 5 (1) ◽  
pp. 70-82
Author(s):  
Soumi Paul ◽  
Paola Peretti ◽  
Saroj Kumar Datta
Keyword(s):  
Social Media ◽  
New Product Development ◽  
Value Creation ◽  
Large Scale ◽  
Customer Orientation ◽  
Customer Relationships ◽  
Future Research ◽  
Management Competencies ◽  
Business Decisions ◽  
Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented. 

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

Ways that nurse practitioner students self-explain during diagnostic reasoning

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Leah Burt ◽  
Susan Corbridge ◽  
Colleen Corte ◽  
Laurie Quinn ◽  
Lorna Finnegan ◽  
...  
Keyword(s):  
Problem Solving ◽  
Health Care Providers ◽  
Nurse Practitioner ◽  
Descriptive Analysis ◽  
Diagnostic Errors ◽  
Diagnostic Reasoning ◽  
Knowledge Structures ◽  
Future Research ◽  
Biological Knowledge ◽  
Care Providers

Abstract Objectives An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? Methods Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. Results Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing. Conclusions Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

scholarly journals ‘CTRL’: an online, Dynamic Consent and participant engagement platform working towards solving the complexities of consent in genomic research

2021 ◽  
Author(s):  
Matilda A. Haas ◽  
Harriet Teare ◽  
Megan Prictor ◽  
Gabi Ceregra ◽  
Miranda E. Vidgen ◽  
...  
Keyword(s):  
Large Scale ◽  
Genomic Medicine ◽  
International Standards ◽  
Security And Privacy ◽  
Genomic Research ◽  
Return Of Results ◽  
Future Research ◽  
Design And Development ◽  
Participant Engagement ◽  
Dynamic Consent

AbstractThe complexities of the informed consent process for participating in research in genomic medicine are well-documented. Inspired by the potential for Dynamic Consent to increase participant choice and autonomy in decision-making, as well as the opportunities for ongoing participant engagement it affords, we wanted to trial Dynamic Consent and to do so developed our own web-based application (web app) called CTRL (control). This paper documents the design and development of CTRL, for use in the Australian Genomics study: a health services research project building evidence to inform the integration of genomic medicine into mainstream healthcare. Australian Genomics brought together a multi-disciplinary team to develop CTRL. The design and development process considered user experience; security and privacy; the application of international standards in data sharing; IT, operational and ethical issues. The CTRL tool is now being offered to participants in the study, who can use CTRL to keep personal and contact details up to date; make consent choices (including indicate preferences for return of results and future research use of biological samples, genomic and health data); follow their progress through the study; complete surveys, contact the researchers and access study news and information. While there are remaining challenges to implementing Dynamic Consent in genomic research, this study demonstrates the feasibility of building such a tool, and its ongoing use will provide evidence about the value of Dynamic Consent in large-scale genomic research programs.

scholarly journals Cyberstalking Victimization Model Using Criminological Theory: A Systematic Literature Review, Taxonomies, Applications, Tools, and Validations

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1670
Author(s):  
Waheeb Abu-Ulbeh ◽  
Maryam Altalhi ◽  
Laith Abualigah ◽  
Abdulwahab Ali Almazroi ◽  
Putra Sumari ◽  
...  
Keyword(s):  
Data Analysis ◽  
Structural Equation ◽  
Large Scale ◽  
Review Paper ◽  
Essential Element ◽  
Routine Activities ◽  
Criminological Theory ◽  
Equation Modeling ◽  
Future Research ◽  
Proposed Model

Cyberstalking is a growing anti-social problem being transformed on a large scale and in various forms. Cyberstalking detection has become increasingly popular in recent years and has technically been investigated by many researchers. However, cyberstalking victimization, an essential part of cyberstalking, has empirically received less attention from the paper community. This paper attempts to address this gap and develop a model to understand and estimate the prevalence of cyberstalking victimization. The model of this paper is produced using routine activities and lifestyle exposure theories and includes eight hypotheses. The data of this paper is collected from the 757 respondents in Jordanian universities. This review paper utilizes a quantitative approach and uses structural equation modeling for data analysis. The results revealed a modest prevalence range is more dependent on the cyberstalking type. The results also indicated that proximity to motivated offenders, suitable targets, and digital guardians significantly influences cyberstalking victimization. The outcome from moderation hypothesis testing demonstrated that age and residence have a significant effect on cyberstalking victimization. The proposed model is an essential element for assessing cyberstalking victimization among societies, which provides a valuable understanding of the prevalence of cyberstalking victimization. This can assist the researchers and practitioners for future research in the context of cyberstalking victimization.

scholarly journals Large-Scale Object Monitoring in Internet-of-Things: Energy-Efficient Perspectives

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 461
Author(s):  
Yongbin Yim ◽  
Euisin Lee ◽  
Seungmin Oh
Keyword(s):  
Energy Efficient ◽  
Large Scale ◽  
Future Research ◽  
Technological Evolution ◽  
Node Density ◽  
Mobile Objects ◽  
Research Challenges ◽  
Energy Efficient Communication ◽  
Efficient Communication ◽  
Architectural Principles

Recently, the demand for monitoring a certain object covering large and dynamic scopes such as wildfires, glaciers, and radioactive contaminations, called large-scale fluid objects (LFOs), is coming to the fore due to disasters and catastrophes that lately happened. This article provides an analytic comparison of such LFOs and typical individual mobile objects (IMOs), namely animals, humans, vehicles, etc., to figure out inherent characteristics of LFOs. Since energy-efficient monitoring of IMOs has been intensively researched so far, but such inherent properties of LFOs hinder the direct adaptation of legacy technologies for IMOs, this article surveys technological evolution and advances of LFOs along with ones of IMOs. Based on the communication cost perspective correlated to energy efficiency, three technological phases, namely concentration, integration, and abbreviation, are defined in this article. By reviewing various methods and strategies employed by existing works with the three phases, this article concludes that LFO monitoring should achieve not only decoupling from node density and network structure but also trading off quantitative reduction against qualitative loss as architectural principles of energy-efficient communication to break through inherent properties of LFOs. Future research challenges related to this topic are also discussed.

scholarly journals Enhanced Wastewater Treatment by Immobilized Enzymes

2021 ◽  
Author(s):  
Jakub Zdarta ◽  
Katarzyna Jankowska ◽  
Karolina Bachosz ◽  
Oliwia Degórska ◽  
Karolina Kaźmierczak ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Process Optimization ◽  
Organic Pollutants ◽  
Large Scale ◽  
Removal Rate ◽  
Immobilized Enzymes ◽  
Future Research ◽  
Process Conditions ◽  
Removal Rates ◽  
Catalytic Cycles

Abstract Purpose of Review In the presented review, we have summarized recent achievements on the use of immobilized oxidoreductases for biodegradation of hazardous organic pollutants including mainly dyes, pharmaceuticals, phenols, and bisphenols. In order to facilitate process optimization and achievement of high removal rates, effect of various process conditions on biodegradation has been highlighted and discussed. Recent Findings Current reports clearly show that immobilized oxidoreductases are capable of efficient conversion of organic pollutants, usually reaching over 90% of removal rate. Further, immobilized enzymes showed great recyclability potential, allowing their reuse in numerous of catalytic cycles. Summary Collected data clearly indicates immobilized oxidoreductases as an efficient biocatalytic tools for removal of hazardous phenolic compounds, making them a promising option for future water purification. Data shows, however, that both immobilization and biodegradation conditions affect conversion efficiency; therefore, process optimization is required to achieve high removal rates. Nevertheless, we have demonstrated future trends and highlighted several issues that have to be solved in the near-future research, to facilitate large-scale application of the immobilized oxidoreductases in wastewater treatment.

scholarly journals Constrained Urban Airspace Design for Large-Scale Drone-Based Delivery Traffic

Aerospace ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 38
Author(s):  
Malik Doole ◽  
Joost Ellerbroek ◽  
Victor L. Knoop ◽  
Jacco M. Hoekstra
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Large Scale ◽  
Cost Savings ◽  
Future Research ◽  
Fast Time ◽  
Potential Cost ◽  
Horizontal Structure ◽  
Traffic Demand ◽  
Demand Patterns

Large-scale adoption of drone-based delivery in urban areas promise societal benefits with respect to emissions and on-ground traffic congestion, as well as potential cost savings for drone-based logistic companies. However, for this to materialise, the ability of accommodating high volumes of drone traffic in an urban airspace is one of the biggest challenges. For unconstrained airspace, it has been shown that traffic alignment and segmentation can be used to mitigate conflict probability. The current study investigates the application of these principles to a highly constrained airspace. We propose two urban airspace concepts, applying road-based analogies of two-way and one-way streets by imposing horizontal structure. Both of the airspace concepts employ heading-altitude rules to vertically segment cruising traffic according to their travel direction. These airspace configurations also feature transition altitudes to accommodate turning flights that need to decrease the flight speed in order to make safe turns at intersections. While using fast-time simulation experiments, the performance of these airspace concepts is compared and evaluated for multiple traffic demand densities in terms of safety, stability, and efficiency. The results reveal that an effective way to structure drone traffic in a constrained urban area is to have vertically segmented altitude layers with respect to travel direction as well as horizontal constraints imposed to the flow of traffic. The study also makes recommendations for areas of future research, which are aimed at supporting dynamic traffic demand patterns.

scholarly journals A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5458
Author(s):  
Sangjun Kim ◽  
Kyung-Joon Park
Keyword(s):  
Machine Learning ◽  
Physical System ◽  
Large Scale ◽  
Computing System ◽  
Future Research ◽  
Physical Security ◽  
Computing Systems ◽  
Physical Systems ◽  
Security Research ◽  
Simultaneous Control

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

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