Expert Assignment Research Articles

Using the Fuzzy Version of the Pearl’s Algorithm for Environmental Risk Assessment Tasks

In risk assessment, numerous subfactors influence the probabilities of the main factors. These main factors reflect adverse outcomes, which are essential in risk assessment. A Bayesian network can model the entire set of subfactors and their interconnections. To assess the probabilities of all possible states of the main factors (adverse consequences), complete information about the probabilities of all relevant subfactor states in the network nodes must be utilized. This is a typical task of probabilistic inference. The algorithm proposed by J. Pearl is widely used for point estimates of relevant probabilities. However, in many practical problems, including environmental risk assessment, it is not possible to assign crisp probabilities for relevant events due to the lack of sufficient statistical data. In such situations, expert assignment of probabilities is widely used. Uncertainty in expert assessments can be successfully modeled using triangular fuzzy numbers. That is why this article proposes a fuzzy version of this algorithm, which can solve the problem of probabilistic inference on a Bayesian network when the initial probability values are given as triangular fuzzy numbers.

Assessment of international MOGAD diagnostic criteria in patients with overlapping MOG-associated disease and multiple sclerosis phenotypes.

The clinical spectrum and diagnosis of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has evolved in the setting of an optimized anti-MOG-IgG cell-based assay and expert consensus. The McDonald criteria for MS have been revised multiple times to improve the accuracy and specificity of diagnosis on a framework based on clinical presentation, MRI findings, and CSF results. While the uses of MS and MOGAD diagnostic criteria are helpful for typical cases, such utility for patients with overlapping clinical, laboratorial, and imaging features is unknown, posing diagnostic and management uncertainties. To report a multicenter cohort of patients with overlapping phenotypic features of MOGAD and MS and evaluate the application of new MOGAD diagnostic criteria. A collaborative retrospective cohort study was performed to identify patients with both positive serum anti-MOG-IgG and fulfillment of the MS revised 2017 McDonald criteria. Clinical and radiographic features of patients fulfilling inclusion criteria were reviewed longitudinally, including relapses, repeated MRI, and MOG-IgG testing in detail to allow the panel of expert opinion to assign to each case. The International MOGAD Panel proposed criteria were applied at onset and last follow-up to each case and compared to the expert author diagnosis assignment based on presentation, clinical and imaging features, and response to treatment. Ten of 225 (4%) MOG-IgG seropositive cases met study inclusion criteria [seven of 10 were female; age at initial event: eight adults (mean age 26.8years), two adolescents (mean age 14.5years)]. AQP4-IgG was negative for all. Apart from serum titers of MOG-IgG, distinguishing clinical and radiographic features [i.e., clinical severity of the initial demyelinating event, radiographic features (optic nerve/spine/brain), and presence/absence of lesion normalization on serial scans] led to consensus of three separate classifications differing by degrees of shared features of MOGAD and MS. Patients were classified by expert panel into (1) Classic MOGAD even with MS-like, well-defined brain lesions, when severe events and most T2 lesions normalized (n = 5; MOG-IgG titers 1:100, 1:20, 1:160, 1:40, 1:200); (2) Classic RRMS included cases thought to have likely false positive or clinically irrelevant MOG-IgG, due to mild clinical events and no radiographic normalization of well-defined MS-like lesions (n = 3; MOG titers 1:20, 1:100, 1:40); (3) MOGAD and MS overlapping phenotype was defined by those with a combination of mild and severe clinical events, partial T2 lesion normalization, both well- and ill-defined lesions (n = 2; MOG titers 1:20, 1:100). The application of the International MOGAD Panel criteria categorized five patients (50%) in agreement with expert assignment. One additional patient was classified in agreement to assignment when MOGAD criteria were applied after serial MOG-IgG titers testing. While the International MOGAD Panel diagnostic criteria have helped with accuracy for the diagnosis of this condition, in a group of patients seropositive for MOG-IgG with overlapping clinical and imaging features of RRMS criteria review may lead to increased accuracy. Serial serologies, repeated imaging, close attention to clinical course, and response to therapy are possible variables to consider for further refinement of MOGAD diagnostic criteria.

Practical Exploration of Cultural Self-consciousness Enhancement of Civic Education for College Students in the Context of Deep Learning

Abstract Cultural self-awareness reflects a nation’s recognition of its own culture and other related values, which relies on a deep cultural foundation and is of great significance to national unity. This paper firstly analyzes the content and characteristics of cultural self-awareness, discusses the promotion role of college civic education on the cultivation and enhancement of college students’ cultural self-awareness, introduces the four-layer, four-ring model of educating people to innovate the form of civic education teaching and establishes a system of indicators for evaluating the effect of cultural self-awareness in civic education on the cultivation of people. Secondly, the subjective and objective weights of the evaluation indexes were solved by the AHP method and CRITIC method, respectively. The subjective and objective weights were combined by linear weighting method. Then, a fuzzy comprehensive evaluation model was established to comprehensively evaluate the effect of culturally conscious parenting in Civic and Political Education. Finally, five colleges and universities in a province were used as examples, and the research data was obtained through questionnaires and expert assignments to analyze the effect of culturally conscious parenting in each college and university. The results of the study show that in the effect of culturally conscious parenting on college students’ civic education, the weight of strengthening the innovation of cultural teaching content reaches 0.0726, while the weight value of promoting network culture innovation is only 0.0463, and the comprehensive evaluation score for the effect of cultural conscious parenting of civic education in college A is 9.884 points. The effect of cultural self-awareness in college students’ Civic and Political Education needs to rely on excellent culture, adopt richer teaching contents and forms, and encourage college students to internalize cultural knowledge and promote their cultural self-awareness.

Application of Big Data Technology in Intelligent Decision-Making of Public Policy

For the analysis of the application effect of public policies in life, this paper uses big data technology to conduct in-depth understanding and analysis. Firstly, use big data analysis technology to build a more comprehensive, scientific and reasonable evaluation index system; Secondly, based on the concept of "whole process equilibrium", all primary indicators are given equal weight, and the improved group G2 model is constructed based on the expert assignment interval to solve the weight of each secondary indicator; Finally, according to the evaluation results, the application of big data technology in public policy aided decision-making analysis is realized

A Hybrid Genetic Algorithm Based on Imitation Learning for the Airport Gate Assignment Problem.

Airport gates are the main places for aircraft to receive ground services. With the increased number of flights, limited gate resources near to the terminal make the gate assignment work more complex. Traditional solution methods based on mathematical programming models and iterative algorithms are usually used to solve these static situations, lacking learning and real-time decision-making abilities. In this paper, a two-stage hybrid algorithm based on imitation learning and genetic algorithm (IL-GA) is proposed to solve the gate assignment problem. First of all, the problem is defined from a mathematical model to a Markov decision process (MDP), with the goal of maximizing the number of flights assigned to contact gates and the total gate preferences. In the first stage of the algorithm, a deep policy network is created to obtain the gate selection probability of each flight. This policy network is trained by imitating and learning the assignment trajectory data of human experts, and this process is offline. In the second stage of the algorithm, the policy network is used to generate a good initial population for the genetic algorithm to calculate the optimal solution for an online instance. The experimental results show that the genetic algorithm combined with imitation learning can greatly shorten the iterations and improve the population convergence speed. The flight rate allocated to the contact gates is 14.9% higher than the manual allocation result and 4% higher than the traditional genetic algorithm. Learning the expert assignment data also makes the allocation scheme more consistent with the preference of the airport, which is helpful for the practical application of the algorithm.

Learning Skill Training Schedules from Domain Experts for a Multi-Patient Multi-Robot Rehabilitation Gym.

A robotic gym with multiple rehabilitation robots allows multiple patients to exercise simultaneously under the supervision of a single therapist. The multi-patient training outcome can potentially be improved by dynamically assigning patients to robots based on monitored patient data. In this paper, we present an approach to learn dynamic patient-robot assignment from a domain expert via supervised learning. The dynamic assignment algorithm uses a neural network model to predict assignment priorities between patients. This neural network was trained using a synthetic dataset created in a simulated rehabilitation gym to imitate a domain expert's assignment behavior. The approach is evaluated in three simulated scenarios with different complexities and different expert behaviors meant to achieve different training objectives. Evaluation results show that our assignment algorithm imitates the expert's behavior with mean accuracies ranging from 75.4% to 84.5% across scenarios and significantly outperforms three baseline assignment methods with respect to mean skill gain. Our approach solves simplified patient training scheduling problems without complete knowledge about the patient skill acquisition dynamics and leverages human knowledge to learn automated assignment policies.

Expertise as a Control and Supervisory Action: Gaps in Legislation

Subject of study. Provisions of Federal Law No. 248-FZ of 31.07.2020 "On State Control (Supervision) and Municipal Control in the Russian Federation" concerning the procedure for implementing the powers of an inspector to engage an expert or expert organization to conduct an examination when carrying out a control (supervisory) measure. The purpose of the study. Justification of the need to supplement and specify normative legal acts, in terms of establishing requirements to the execution of the inspector's decision to conduct an examination, the content of the expert assignment, the timing and method of sending it to the expert, the procedure for notifying the supervised person about the examination, the content and execution of the expert report. The method and methodology of the study. The formal-logical, formal-legal, sociological methods of research, the method of system analysis and the method of legal interpretation were used during the study. Novelty of research, conclusions. The article defines the place and importance of the institute of expertise in the sphere of control and supervisory activity. Expertise acts as an independent control (supervisory) action, providing research on issues, the resolution of which requires special knowledge in various fields of science, technology, arts or crafts. Organization of expertise in the sphere of control and supervisory activity is one of the important powers of an official of a control and supervisory body (inspector), the implementation of which allows getting a professional assessment of compliance with mandatory requirements by a supervised person. In the course of a systematic analysis the basic provisions of expert activities in the field of state control and supervision were formulated, gaps in legislation were identified in terms of regulation of the procedure for implementing the powers of an inspector to engage an expert or expert organization to conduct an examination during the implementation of control (supervisory) activities. It is established that the normative provisions of expert activities in the field of state control and supervision require improvement. The author of the article substantiates possible ways to eliminate the identified normative gaps.

THE NECESSITY OF REGULATION OF THE DEFINITION “DETAILED DESCRIPTION OF THE STUDIES IN THE EXPERT’S CONCLUSION”

The article deals with the problem of interpretation of the normatively fixed definition “detailed description of the studies in the expert’s conclusion”, which (that is, conclusion) is a source of evidence in the procedural law of Ukraine. There are presented the views of forensic scientists, the international standards adopted in Ukraine are analyzed, the ILAC G19: 08/2014 “Modules in a Forensic Science Process” guidelines, the draft of fifth part of the ISO 21043 standard “Reporting” regarding the content of this definition. It is stated that “the detailed description of the studies in the expert’s conclusion” is based on the dialectical-materialistic method, methods of formal logic, general scientific methods, methods of maternal sciences and special methods, that is, methodologies of conducting forensic examinations. A parallel is drawn with the latter as with normative documents that should regulate the process of description in the research part of the conclusion. Based on the obtained results of the analysis, it was concluded that in the forensic examination methodology, as a detailed program for solving an expert assignment, in the section that regulates the procedure for formalizing the research conducted by an expert conclusion, the procedure for a detailed description of the research should be determined, which allows for interpretation the results obtained (assessment of the results of the studies carried out and the formulation of conclusions) by others who did not conduct an examination, by qualified specialists having the appropriate competence to confirm the reliability of the results and formulated answers to the questions posed, including by reproducing and/or repeating the process of producing the examination. In order to solve the problem raised, it is necessary to develop the state standard of Ukraine - DSTU XXXX: 202__ “Forensic expert activity. Forensic examination methodologies. Requirements”, in which to determine, inter alia, the content of the definition “detailed description of the studies in the expert’s conclusion”.

Legal aspects of forensic medico-social expertise assignment

The topic presented in the title of the article has been the matter of numerous studies. In spite of this, it remains relevant these days. This is because in practice there arise controversies related to the procedural issues of forensic medico-social expertise assignment and conducting. The authors have attempted to conduct a comprehensive study of the legal issues of the assignment and production of forensic socio-medical expertise, both in civil cases related to appeal against decisions of medico-social assessment services, and in criminal cases, in particular, with regard to fraud in receiving payments. The article analyzes the practice of appealing against decisions of medical and social assessment services. This practice reveals the lack of uniformity in the assignment of forensic medical and social expertise. Based on previous studies in this area, as well as the provisions of regulatory legal acts, the authors distinguish medico-social expertise from other types of medical and forensic examinations. Due to the limited space of the paper, not all relevant aspects on this issue are considered. The paper concentrates on the issues of determining the institution and persons who have special knowledge sufficient to produce medico-social expertise. The authors have developed recommendations on the assignment of forensic medico-social expertise, emphasizing that the production of medico-social expertise requires special knowledge and should be carried out by specialists who have received special training in the specialty of medical and social expertise. In this connection, the production of forensic medico-social expertise cannot be entrusted to forensic medical establishments and business organizations. The authors emphasize that such an expertise should be entrusted to highly qualified employees with specialized training in medical and social expertise and working for medical and social expertise services.

Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method

This study presents a fuzzy logical decision-making algorithm based on block theory to effectively determine discontinuous rock slope reliability under various wedge and planar slip scenarios. The algorithm was developed to provide rapid response operations without the need for extensive quantitative stability evaluations based on the rock slope sustainability ratio. The fuzzy key-block analysis method utilises a weighted rational decision (multi-criteria decision-making) function to prepare the ‘degree of reliability (degree of stability-instability contingency)’ for slopes as implemented through the Mathematica software package. The central and analyst core of the proposed algorithm is provided as based on discontinuity network geometrical uncertainties and hierarchical decision-making. This algorithm uses block theory principles to proceed to rock block classification, movable blocks and key-block identifications under ambiguous terms which investigates the sustainability ratio with accurate, quick and appropriate decisions especially for novice engineers in the context of discontinuous rock slope stability analysis. The method with very high precision and speed has particular matches with the existing procedures and has the potential to be utilised as a continuous decision-making system for discrete parameters and to minimise the need to apply common practises. In order to justify the algorithm, a number of discontinuous rock mass slopes were considered as examples. In addition, the SWedge, RocPlane softwares and expert assignments (25-member specialist team) were utilised for verification of the applied algorithm which led to a conclusion that the algorithm was successful in providing rational decision-making.

A decision-making support system for Enterprise Architecture Modelling

Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods have been proposed to deal with drawbacks of manual modelling, such as error-proneness, time-consumption, slow and poor re-adaptation, and cost. However, automatic modelling is not effective for the most abstract concepts in EA like strategy or motivational aspects. Thus, companies are demanding hybrid approaches that combines automatic with manual modelling. In this context there are no clear relationships between the input artefacts (and mining techniques) and the target EA viewpoints to be automatically modelled, as well as relationships between the experts' roles and the viewpoints to which they might contribute in manual modelling. Consequently, companies cannot make informed decisions regarding expert assignments in EA modelling projects, nor can they choose appropriate mining techniques and their respective input artefacts. This research proposes a decision support system whose core is a genetic algorithm. The proposal first establishes (based on a previous literature review) the mentioned missing relationships and EA model specifications. Such information is then employed using a genetic algorithm to decide about automatic, manual or hybrid modelling by selecting the most appropriate input artefacts, mining techniques and experts. The genetic algorithm has been optimized so that the system aids EA architects to maximize the accurateness and completeness of EA models while cost (derived from expert assignments and unnecessary automatic generations) are kept under control.

Scaling Human Effort in Idea Screening and Content Evaluation

Brands and advertisers often tap into the crowd to generate ideas for new products and ad creatives by hosting ideation contests. Content evaluators then winnow thousands of submitted ideas before a separate stakeholder, such as a manager or client, decides on a small subset to pursue. We demonstrate the information value of data generated by content evaluators in past contests and propose a proof-of-concept machine learning approach to efficiently surface the best submissions in new contests with less human effort. The approach combines ratings by different evaluators based on their correlation with the past stakeholder choices, controlling for submission characteristics and textual content features. Using field data from a crowdsourcing platform, we demonstrate that the approach improves performance by identifying nonlinear transformations and efficiently reweighting evaluator ratings. Implementing the proposed approach can affect the optimal assignment of internal experts to ideation contests. Two evaluators whose votes were a priori equally correlated with sponsor choices may provide substantially different incremental information to improve the model-based idea ranking. We provide additional support for our findings using simulations based on a product design survey.

Trends and Challenges in Measuring Performance of Reviewer Paper Assignment

Abstract The remarkable increase in the number of authors and publications is observed in almost all fields in last decade. Inherently publication engages process of peer review and is a vital segment in research and development progression. The Reviewer Assignment Problem (RAP) is one of the most important and apparent process that involves the process of assigning a reviewer to paper. RAP has motivated many researchers in the recent decade as the conference organizers and journal editors are demanding for automatic assignment with fair and accurate reviews. Fair and accurate reviews mostly depend on accurate expert assignment to the paper. RAP entails two core phases: matching submitted manuscript and experts, and expert assignment to paper for review. Matching manuscript and expert involves building both paper and expert profiles correctly and then matching them accurately. Similarly assigning an expert to paper accurately involves solving assignment problem by satisfying constraints like a conflict of interest, fulfilling coverage requirements and balancing the load of the expert reviewer. Many researchers have contributed to the topic by proposing solutions for RAP. Each proposed methodology necessitates using an appropriate performance metric for proving its effectiveness. In the case of RAP, we can measure the performance of the whole process; one can measure the performance of the core phases separately as both phases engross different processes and different metric. Testing performance and reducing errors in phases will lead to overall improved results. Both subjective (also known as qualitative) and objective (also known as quantitative) measures have been practiced by the researchers. In this paper, we present trends in using performance measures, their applicability and suitability for RAP. It is observed that ‘Unanimity’ is the most appropriate measure for the overall review process. Use of performance measures- matching score, authority, coverage, and diversity are the most suitable for measuring the performance of phase one- matching paper and reviewer. It is noticed that load balance, fairness and accuracy are suitable for measuring the performance of phase two that assigns a reviewer to paper by satisfying constraints. The study derives the conclusion that hybrid approach with the proper combination of these measures is the best for measuring the performance of process of assigning reviewer to the paper.

ME R-CNN: Multi-Expert R-CNN for Object Detection.

We introduce Multi-Expert Region-based Convolutional Neural Network (ME R-CNN) which is equipped with multiple experts (ME) where each expert is learned to process a certain type of regions of interest (RoIs). This architecture better captures the appearance variations of the RoIs caused by different shapes, poses, and viewing angles. In order to direct each RoI to the appropriate expert, we devise a novel "learnable" network, which we call, expert assignment network (EAN). EAN automatically learns the optimal RoI-expert relationship even without any supervision of expert assignment. As the major components of ME R-CNN, ME and EAN, are mutually affecting each other while tied to a shared network, neither an alternating nor a naive end-to-end optimization is likely to fail. To address this problem, we introduce a practical training strategy which is tailored to optimize ME, EAN, and the shared network in an end-to-end fashion. We show that both of the architectures provide considerable performance increase over the baselines on PASCAL VOC 07, 12, and MS COCO datasets.

Research on optimization of index system design and its inspection method

Purpose To construct a scientific and reasonable indicator system, it is necessary to design a set of standardized indicator primary selection and optimization inspection process. The purpose of this paper is to provide theoretical guidance and reference standards for the indicator system design process, laying a solid foundation for the application of the indicator system, by systematically exploring the expert evaluation method to optimize the index system to enhance its credibility and reliability, to improve its resolution and accuracy and reduce its objectivity and randomness. Design/methodology/approach The paper is based on system theory and statistics, and it designs the main line of “relevant theoretical analysis – identification of indicators – expert assignment and quality inspection” to achieve the design and optimization of the indicator system. First, the theoretical basis analysis, relevant factor analysis and physical process description are used to clarify the comprehensive evaluation problem and the correlation mechanism. Second, the system structure analysis, hierarchical decomposition and indicator set identification are used to complete the initial establishment of the indicator system. Third, based on expert assignment method, such as Delphi assignments, statistical analysis, t-test and non-parametric test are used to complete the expert assignment quality diagnosis of a single index, the reliability and validity test is used to perform single-index assignment correction and consistency test is used for KENDALL coordination coefficient and F-test multi-indicator expert assignment quality diagnosis. Findings Compared with the traditional index system construction method, the optimization process used in the study standardizes the process of index establishment, reduces subjectivity and randomness, and enhances objectivity and scientificity. Originality/value The innovation point and value of the paper are embodied in three aspects. First, the system design process of the combined indicator system, the multi-dimensional index screening and system optimization are carried out to ensure that the index system is scientific, reasonable and comprehensive. Second, the experts’ background is comprehensively evaluated. The objectivity and reliability of experts’ assignment are analyzed and improved on the basis of traditional methods. Third, aim at the quality of expert assignment, conduct t-test, non-parametric test of single index, and multi-optimal test of coordination and importance of multiple indicators, enhance experts the practicality of assignment and ensures the quality of expert assignment.

Assignment of Alcoholic Beverages in the Munich Composite International Diagnostic Interview (M-CIDI): an Online Survey Among German Students and Non-students

The objective of this study is to investigate whether the materials presented during alcohol consumption assessment sufficiently aid interviewees in categorizing the beverages they consumed during their lifetime. In the cross-sectional “AF-CIDI” online survey, N = 162 adult drinkers (61% female, 40% non-students) aged 27 ± 8.2 years assigned beverages names to one of ten categories of the Munich Composite International Diagnostic Interview (M-CIDI) supplementary list. Eighty percent of these 4465 laymen beverage assignments were correctly classified in accordance with expert assignments. Assignment correctness was associated with confidence, and, to a smaller degree, exposure, prevalence, utility of examples, and age group. Alcohol by volume (ABV) estimates based on subjective classifications differed 2.7 points on average from CIDI-based ABVs, which in turn differed 3.6 points on average from real ABVs measured with Fourier transform infrared spectroscopy. Beverages with low lifetime prevalences (≤ 25%) were frequently misclassified. This understudied response bias might be part of the well-known underreporting phenomenon.

On the validation of variable fidelity multi-physics simulations

The difficulties encountered in applying current normative approaches for validation to computational models of complex multi-physics engineering systems are identified and are associated with the untestable, and sometimes unprincipled, nature of these models. The behaviour of a structural panel on the surface of a hypersonic flight vehicle when subject to complex interactions between aerothermal, aeroelastic and material responses is employed as a key exemplar. A wide range of positions in the philosophy of science, that are applicable to validation, are discussed within the context of a schematic matrix, which allows models to be categorised according to whether they are testable and principled. In the absence of test data against which to assess the accuracy of predictions, it is proposed that a model's credibility should be established based on its epistemic values, theoretical ancestry and the credentials of the modelling techniques. This shift from an objectivist to a relativist approach requires the assignment of experts who acknowledge their biases while engaging intellectually and ethically with the model, the community of knowledge and stakeholders, in a hermeneutical approach.

FORMS FOR CALCULATION OF UNIVERSAL COEFFICIENTS WHEN ADOPTING MULTIPLE CRITICAL DECISIONS

The problem of multi-criteria choice is a key element in making complex decisions. A number of methods have been proposed that suggest that decisions made with their use are the most rational. Their main element is the linear convolution of particular criteria, and the difference is in those or other heuristic or expert methods for specifying the numerical coefficients of the relative importance of the criteria. Previously, the author developed an approach that allows the use of pre-calculated universal tables of numerical coefficients of importance of particular criteria when forming a linear convolution. It significantly reduces both the laboriousness of the decision-making process and the inevitable subjectivity that arises during the heuristic selection or expert assignment of coefficients of importance. At the same time, the Monte-Carlo method was used for the calculation, which, in event of with a large number of criteria, created significant computational difficulties due to the lack of accuracy of the random number generator and the avalanche-like increase in the amount of computation. In this article, we managed to derive exact formulas for calculating universal importance coefficients. They are based on the so-called numerological approach, summarizing the patterns that emerged in the analysis of a number of tables of universal coefficients calculated by the statistical method. The formulas obtained allowed, in particular, the use of universal coefficients of importance of criteria in problems with any number of criteria, even without special software, which will contribute to the expansion of the scale of application of scientifically-based methods for making decisions.

Expert recommendation for trouble ticket routing

A trouble ticket is an important information carrier in system maintenance, which records problem symptoms, the resolving process, and resolutions. A critical challenge for the ticket management system is how to quickly assign a proper expert to deal with trouble tickets and fix problems. Thousands of tickets bouncing among multiple experts before being fixed will consume limited system maintenance resources and may also violate the service level agreement (SLA). Thus, for an incoming ticket, an expert should be recommended as quickly as possible in order to reduce the processing delay.In this paper, to address the challenge in the expert assignment, we exploit an expert collaboration network model by combining expertise profiles and social profiles learned from problem descriptions and resolution sequences of the historical resolved tickets, and develop several two-stage expert recommendation algorithms to determine a resolver to solve the problem. To evaluate the effectiveness of expert recommendation algorithms, we conduct extensive experiments on a real ticket data set. The experimental results show that the proposed algorithms can effectively shorten the mean number of steps to resolve (MSTR) with a high recommendation precision, especially for the long routing sequences generated from manual assignments. The proposed model and algorithms have the potential of being used in a ticket routing recommendation engine to greatly reduce human intervention in the routing process.

Non‐Graded Peer Teaching in the Gross Anatomy Lab: Student Participation and its Efficacy in Improving Performance on Written Examinations

Gross anatomy dissection laboratory is a clinically relevant, hands‐on learning opportunity for first year medical students, where active, peer‐based learning is able to flourish. To maximize the effectiveness of this process, for each weekly Gross Anatomy lab, each student in a lab group was assigned an “individual expert assignment” (IEA) on a specific topic that they were to prepare before lab and then present to their group during the lab session. In this study, we examined student participation in this non‐graded activity and whether preparing and presenting the IEA affected student performance on questions related to the IEA topic on written, summative examinations.In order to determine participation in this non‐graded activity, at the beginning of each Gross Anatomy lab session, each student was asked discretely to rate, on a scale of 0 – 2, in 0.5 increments, how well they had prepared their IEA. Similarly, at the end of lab, each student was asked to rate how well they presented their topic to their group. These self‐reported responses were tabulated for each lab session and assessed in relation to the timing of major exams in other courses. In order to determine the efficacy of this activity, written, end‐of‐unit exam questions were linked to the pertinent IEA, and scores on these questions were compared between students who were assigned that IEA, versus students not assigned that IEA. This analysis was further refined by correlating performance on relevant exam questions with the self‐reported level of preparation and presentation of the IEA. This study was determined to be exempt by the PNWU IRB (#2017‐019).Although students received no grade for participation in this activity, on lab days where there were no major exams in other classes, the mean (± SD) student self‐reported preparation and presentation scores were 1.40 ± 0.10 and 1.36 ± 0.06 respectively. For labs occurring on days where students had examinations in other courses, these scores dropped significantly to 0.85 ± 0.39 and 0.92 ± 0.28 respectively (p<0.05).For each specific IEA, questions on the written, end‐of‐unit exam were identified that were related to the IEA. There was no significant difference in performance on these questions between students assigned the IEA and those not assigned that specific IEA (mean of 81.6% versus 79.2%, respectively). However, the performance of students who self‐reported being well‐prepared for their IEA (85.5% ± 15.2%) or doing a good job of presenting the topic to their lab partners (85.4% ± 17.0%) was significantly better on the related written exam questions compared to the performance of students who were not assigned that specific IEA (79.2% ± 11.9%) (p<0.05). Participation in this activity may have had academic benefits for the dissection group as a whole, since groups in the upper quartile of the mean self‐reported preparation and presentation scores per student tended to have higher overall examination scores (80.9% ± 11.5%) than did groups in the lower quartile (75.9% ± 14.1%) (p=0.053).In conclusion, even though these IEAs in the Gross Anatomy lab were not graded, the majority of students typically completed this task, except on laboratory days where there were exam in other courses. These findings also suggest that the active learning that accompanies preparation and presentation of a specific topic during Gross Anatomy lab may positively impact performance on related questions during written, end‐of‐unit examinations.Support or Funding InformationNoneThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.