Learning a Generalizable Model of Team Conflict from Multiparty Dialogues

2021 ◽  
Vol 15 (04) ◽  
pp. 441-460
Author(s):  
Ayesha Enayet ◽  
Gita Sukthankar
Keyword(s):  
Task Conflict ◽  
Communication Styles ◽  
Semantic Features ◽  
Conversational Agents ◽  
Good Communication ◽  
Team Conflict ◽  
Dialogue Acts ◽  
Multiple Domains ◽  
Machine Learning Models ◽  
Better Than

Good communication is indubitably the foundation of effective teamwork. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomena in which humans synchronize their linguistic choices. Conversely, teams may experience conflict due to either personal incompatibility or differing viewpoints. We tackle the problem of predicting team conflict from embeddings learned from multiparty dialogues such that teams with similar post-task conflict scores lie close to one another in vector space. Embeddings were extracted from three types of features: (1) dialogue acts, (2) sentiment polarity, and (3) syntactic entrainment. Machine learning models often suffer domain shift; one advantage of encoding the semantic features is their adaptability across multiple domains. To provide intuition on the generalizability of different embeddings to other goal-oriented teamwork dialogues, we test the effectiveness of learned models trained on the Teams corpus on two other datasets. Unlike syntactic entrainment, both dialogue act and sentiment embeddings are effective for identifying team conflict. Our results show that dialogue act-based embeddings have the potential to generalize better than sentiment and entrainment-based embeddings. These findings have potential ramifications for the development of conversational agents that facilitate teaming.

Identification of Anti-cancer Peptides Based on Multi-classifier System

2020 ◽  
Vol 22 (10) ◽  
pp. 694-704 ◽  
Author(s):  
Wanben Zhong ◽  
Bineng Zhong ◽  
Hongbo Zhang ◽  
Ziyi Chen ◽  
Yan Chen
Keyword(s):  
Machine Learning ◽  
Side Effect ◽  
Learning Models ◽  
Normal Cells ◽  
Classifier System ◽  
Prediction Rate ◽  
Anti Cancer ◽  
Feature Information ◽  
Machine Learning Models ◽  
Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

scholarly journals Things Are Not Always What They Seem: The Origins and Evolution of Intragroup Conflict

2020 ◽  
pp. 000183922096518 ◽  
Author(s):  
Priti Pradhan Shah ◽  
Randall S. Peterson ◽  
Stephen L. Jones ◽  
Amanda J. Ferguson
Keyword(s):  
Social Networks ◽  
Qualitative Study ◽  
Team Performance ◽  
Task Conflict ◽  
Intragroup Conflict ◽  
Team Conflict ◽  
Student Teams ◽  
Evolutionary Trajectories ◽  
Or Team ◽  
Over Time

Teams scholars have historically conceptualized and measured intragroup conflict at the team level. But emerging evidence suggests that perceptions of intragroup conflict are often not uniform, shared, or static. These findings suggest important questions about the microfoundations of intragroup conflict: Where does conflict within teams originate? And how does it evolve over time? We address these and other questions in three abductive studies. We consider four origination points—an individual, dyad, subgroup, or team—and three evolutionary trajectories—conflict continuity, contagion, and concentration. Study 1, a qualitative study of narrative accounts, and Study 2, a longitudinal social networks study of student teams, reveal that fewer than 30 percent of teams experience team-level conflict. Instead, conflict more commonly originates and persists at individual, dyadic, or subgroup levels. Study 2 further demonstrates that traditional psychometric intragroup conflict scales mask the existence of these various origins and trajectories of conflict. Study 3, a field study of manufacturing teams, reveals that individual and dyadic task conflict origins positively predict team performance, whereas traditional intragroup task conflict measures negatively predict team performance. The results raise serious concerns about current methods and theory in the team conflict literature and suggest that researchers must go beyond team-level conceptualizations of conflict.

Talking their language

1999 ◽  
Vol 5 (2) ◽  
pp. 78-81 ◽  
Author(s):  
Jocelyn Ryder‐Smith
Keyword(s):  
Team Members ◽  
Good Communication ◽  
Team Conflict ◽  
Team Working

Good communication is crucial for effective team working. A failure to understand and value personal differences in style and approach often blocks good communication and leads to unnecessary team conflict. The article outlines key elements of a framework for understanding different personalities and priorities in a way which enables team members to recognise profound difference and its value and to “talk each other’s language” to unblock sticking points and conflict. The article recognises we all use all the ways of working but have preferences among them. It describes first those who prefer to work with logic and practicality; second, those primarily interested in relationships and practicalities; third, those focusing first on logical options, and fourth, people who care most about vision and values for people. Understanding and working with these differences enables better communication and better decisions.

scholarly journals Three machine learning models for the 2019 Solubility Challenge

ADMET & DMPK ◽  
2020 ◽  
Author(s):  
John Mitchell
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gold Standard ◽  
Challenge Test ◽  
Wisdom Of Crowds ◽  
Learning Models ◽  
The Third ◽  
Aqueous Solubilities ◽  
Machine Learning Models ◽  
Better Than

<p class="ADMETabstracttext">We describe three machine learning models submitted to the 2019 Solubility Challenge. All are founded on tree-like classifiers, with one model being based on Random Forest and another on the related Extra Trees algorithm. The third model is a consensus predictor combining the former two with a Bagging classifier. We call this consensus classifier Vox Machinarum, and here discuss how it benefits from the Wisdom of Crowds. On the first 2019 Solubility Challenge test set of 100 low-variance intrinsic aqueous solubilities, Extra Trees is our best classifier. One the other, a high-variance set of 32 molecules, we find that Vox Machinarum and Random Forest both perform a little better than Extra Trees, and almost equally to one another. We also compare the gold standard solubilities from the 2019 Solubility Challenge with a set of literature-based solubilities for most of the same compounds.</p>

scholarly journals Forecasting admissions in psychiatric hospitals before and during Covid-19

2021 ◽  
Author(s):  
Jan Wolff ◽  
Ansgar Klimke ◽  
Michael Marschollek ◽  
Tim Kacprowski
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Hospital Admissions ◽  
Model Performance ◽  
Psychiatric Hospitals ◽  
Time Series Models ◽  
Learning Models ◽  
One Step ◽  
Machine Learning Models ◽  
Better Than

Introduction The COVID-19 pandemic has strong effects on most health care systems and individual services providers. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. Methods We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. Our models were trained and validated with data from the first two years and tested in prospectively sliding time-windows in the last two years. Results A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naive forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44), which adjusted more quickly to the shock effects of the COVID-19 pandemic. Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Conclusion Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naive forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, different forecast horizons could be used simultaneously to allow both early planning and quick adjustments to external effects.

scholarly journals Marital Adjustment and Communication Styles Among Married Adults in Ilorin, Kwara State, Nigeria

2022 ◽  
Vol 14 (1) ◽  
pp. 162-179
Author(s):  
Falilat Anike Okesina
Keyword(s):  
Significant Relationship ◽  
Marital Adjustment ◽  
Survey Design ◽  
Educational Status ◽  
Study Data ◽  
Communication Styles ◽  
Good Communication ◽  
Research Questions ◽  
High Level ◽  
Descriptive Survey

This study examined the marital adjustment and communication styles among married adults in Ilorin, Kwara State, Nigeria. A descriptive survey design was adopted. The population consists of married adults in Ilorin, Kwara State. Two research questions were raised and four null hypotheses were postulated in the study. Data were collected using a questionnaire tagged ‘Relationship between Marital Adjustment and Communication Styles Questionnaire’ (RMACSQ). Data analysis was done using Pearson’s Product Moment Correlation (PPMC). The result obtained revealed that there was a high level of marital adjustment among married adults in Ilorin, Kwara State. Married adults in Ilorin Kwara State adopt good communication styles in marriage. There was no significant relationship between marital adjustment and communication styles of married adults in Kwara State based on age, gender and educational status. There was a significant relationship between marital adjustment and communication styles among married adults in Kwara State on the basis of years of marriage. Based on the findings of this study, it was recommended that seminars and conferences should be organized for married adults in other to enlighten them on the challenges of marital relationships. This would enable them to adjust and communicate better in their new home.

scholarly journals Machine Learning for Localizing Epileptogenic-Zone in the Temporal Lobe: Quantifying the Value of Multimodal Clinical-Semiology and Imaging Concordance

2021 ◽  
Vol 3 ◽  
Author(s):  
Ali Alim-Marvasti ◽  
Fernando Pérez-García ◽  
Karan Dahele ◽  
Gloria Romagnoli ◽  
Beate Diehl ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Lobe ◽  
Support Vector ◽  
Seizure Freedom ◽  
Epileptogenic Zone ◽  
Learning Models ◽  
Seizure Semiology ◽  
Multimodal Features ◽  
Machine Learning Models ◽  
Better Than

Background: Epilepsy affects 50 million people worldwide and a third are refractory to medication. If a discrete cerebral focus or network can be identified, neurosurgical resection can be curative. Most excisions are in the temporal-lobe, and are more likely to result in seizure-freedom than extra-temporal resections. However, less than half of patients undergoing surgery become entirely seizure-free. Localizing the epileptogenic-zone and individualized outcome predictions are difficult, requiring detailed evaluations at specialist centers.Methods: We used bespoke natural language processing to text-mine 3,800 electronic health records, from 309 epilepsy surgery patients, evaluated over a decade, of whom 126 remained entirely seizure-free. We investigated the diagnostic performances of machine learning models using set-of-semiology (SoS) with and without hippocampal sclerosis (HS) on MRI as features, using STARD criteria.Findings: Support Vector Classifiers (SVC) and Gradient Boosted (GB) decision trees were the best performing algorithms for temporal-lobe epileptogenic zone localization (cross-validated Matthews correlation coefficient (MCC) SVC 0.73 ± 0.25, balanced accuracy 0.81 ± 0.14, AUC 0.95 ± 0.05). Models that only used seizure semiology were not always better than internal benchmarks. The combination of multimodal features, however, enhanced performance metrics including MCC and normalized mutual information (NMI) compared to either alone (p &lt; 0.0001). This combination of semiology and HS on MRI increased both cross-validated MCC and NMI by over 25% (NMI, SVC SoS: 0.35 ± 0.28 vs. SVC SoS+HS: 0.61 ± 0.27).Interpretation: Machine learning models using only the set of seizure semiology (SoS) cannot unequivocally perform better than benchmarks in temporal epileptogenic-zone localization. However, the combination of SoS with an imaging feature (HS) enhance epileptogenic lobe localization. We quantified this added NMI value to be 25% in absolute terms. Despite good performance in localization, no model was able to predict seizure-freedom better than benchmarks. The methods used are widely applicable, and the performance enhancements by combining other clinical, imaging and neurophysiological features could be similarly quantified. Multicenter studies are required to confirm generalizability.Funding: Wellcome/EPSRC Center for Interventional and Surgical Sciences (WEISS) (203145Z/16/Z).

How to Integrate Emotions in Dialogues With Pedagogic Conversational Agents to Teach Programming to Children

2020 ◽  
pp. 66-91
Author(s):  
Elizabeth Katalina Morales-Urrutia ◽  
Jose Miguel Ocaña ◽  
Diana Pérez-Marín
Keyword(s):  
Primary Education ◽  
Interactive Systems ◽  
Conversational Agents ◽  
Teacher Student ◽  
Multiple Domains

Pedagogic conversational agents are interactive systems that allow students to dialogue with them about a certain domain to learn. PCAs have been used in multiple domains from pre-primary education to university, in roles such as teacher, student, or companion. In this chapter, Alcody, a PCA to teach programming to children, is enhanced with a new proposal to manage emotions in the dialogue with students. The goal is that when children are learning to program, Alcody can help them with the emotions associated to the learning. Six emotions have been integrated into Alcody: happiness, anger, sadness, fear, surprise, and disgust. A description of how a PCA to teach programming can modify its face and verbal expressions according to the emotion detected in the student. This is given for any other researcher that would like to incorporate emotions in dialogues between PCAs and students.

MO353MACHINE LEARNING-BASED PREDICTION OF ACUTE KIDNEY INJURY AFTER NEPHRECTOMY IN PATIENTS WITH RENAL CELL CARCINOMA

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Sejoong Kim ◽  
Yeonhee Lee ◽  
Seung Seok Han
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Kidney Injury ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Learning Models ◽  
Scoring Model ◽  
Postoperative Aki ◽  
Machine Learning Models ◽  
Better Than

Abstract Background and Aims The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. Method A total of 4,104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. Machine learning models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Results Postoperative AKI developed in 1,167 patients (28.4%). All the machine learning models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the machine learning models than the LR-based scoring model over all the ranges of threshold probabilities. The LightGBM and random forest models, but not others, were well calibrated. Conclusion The application of machine learning algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

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