Confidence Measure Research Articles

Modeling Stereo-Confidence out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems. Grounded in the foundational concepts of disparity definition and the disparity plane sweep, the proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. Based on this idea, the proposed stereo-confidence method can be summarized in three folds. 1) Using the disparity plane sweep, multiple disparity maps can be obtained and treated as a 3-D volume (predicted disparity volume), like the cost volume is constructed. 2) One of these disparity maps serves as an anchor, allowing us to define a desirable (or ideal) disparity profile at every spatial point. 3) By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement. Extensive experimental results using various stereo-matching networks and datasets demonstrate that the proposed stereo-confidence method not only shows competitive performance on its own but also consistent performance improvements when it is used as an input modality for learning-based stereo-confidence methods.

Robust chromatin state annotation.

With the goal of mapping genomic activity, international projects have recently measured epigenetic activity in hundreds of cell and tissue types. Chromatin state annotations produced by segmentation and genome annotation (SAGA) methods have emerged as the predominant way to summarize these epigenomic data sets in order to annotate the genome. These chromatin state annotations are essential for many genomic tasks, including identifying active regulatory elements and interpreting disease-associated genetic variation. However, despite the widespread applications of SAGA methods, no principled approach exists to evaluate the statistical significance of chromatin state assignments. Here, we propose the first method for assigning calibrated confidence scores to chromatin state annotations. Toward this goal, we performed a comprehensive evaluation of the reproducibility of the two most widely used existing SAGA methods, ChromHMM and Segway. We found that their predictions are frequently irreproducible. For example, when applying the same SAGA method on two sets of experimental replicates, 27%-69% of predicted enhancers fail to replicate. This suggests that a substantial fraction of predicted elements in existing chromatin state annotations cannot be relied upon. To remedy this problem, we introduce SAGAconf, a method for assigning a measure of confidence (r-value) to chromatin state annotations. SAGAconf works with any SAGA method and assigns an r-value to each genomic bin of a chromatin state annotation that represents the probability that the label of this bin will be reproduced in a replicated experiment. Thus, SAGAconf allows a researcher to select only the reliable predictions from a chromatin annotation for use in downstream analyses.

Increasing pediatric radiation oncology capacity in sub-saharan Africa using technology: a pilot of a pediatric radiation oncology virtual training course

BackgroundThe shortage of skilled healthcare professionals in pediatric oncology and the limited access to training programs remain significant challenges in Nigeria and sub-Saharan Africa. The the Pediatric Radiation Oncology (Virtual) Course, ‘PedROC’ project aims to contribute to improving pediatric cancer outcomes in Nigeria by increasing the capacity of radiation oncology professionals. To address the gap in access to pediatric radiation oncology professional development, the PedROC project was created, harnessing technology to improve radiation oncology training via a curriculum delivered through web-conferencing. This study aimed to evaluate the effectiveness of the PedROC pilot in enhancing the capacity, confidence, and skill of radiation oncologists in decision-making, prescribing, and treatment planning of radiotherapy for children diagnosed with cancer.MethodsA multidisciplinary faculty of specialists in radiation oncology, pediatric oncology, oncology nursing, radiation therapy technology, and medical physics collaborated to identify the key learning needs in pediatric radiation oncology in the country. The team collaborated to develop a comprehensive curriculum covering the most common pediatric cancers in sub-Saharan Africa for the training program. The training course was conducted over two days, delivering twenty-four half-hour sessions for a total of 12 h, from July 31 to August 01, 2021.ResultsAnalysis of pre and post - training surveys showed a significant increase in self-reported confidence measures across all domains among radiation oncologists. The program successfully improved participants’ knowledge and confidence levels in managing common pediatric cancers using radiotherapy, particularly addressing radiotherapy-specific issues such as appropriate dose, target volume delineation, treatment planning, dose constraints, and plan evaluation.ConclusionThe PedROC pilot showed the efficacy of this model in enhancing the capacity and confidence of radiation oncology professionals involved in the treatment of pediatric cancer. The findings indicate that technology holds significant potential to increase pediatric radiation oncology capacity in Africa, ensuring improved access to proper treatment and ultimately improving pediatric cancer outcomes.

Development and Evaluation of a Reliable Medication Management Self-Assessment Checklist.

An ability to effectively self-manage medications is the result of several factors influencing a person's decision to take medications. The need for new approaches to medication self-management are evident in the persistent trends of ineffective medication use and unfortunate consequences, referred to as drug-related morbidity and mortality. Fortunately, pioneering initiatives have emerged to reshape our approach for developing a rational organizational paradigm so that patients can confidently self-manage medications. Favorable outcomes of studies pertaining to the delivery of comprehensive medication therapy management services within the practice of pharmaceutical care prompts the question, 'Can patients and family members apply a consistent and systematic 4-step pharmacotherapy assessment process to better organize their decision-making and confidence in medication self-management?' To answer this question an Effective Medication Self-Management Toolkit based on this 4-step process, and a Medication Management Self-efficacy Checklist, were developed and evaluated for feasibility, acceptability, and internal consistency reliability. The first evaluation established the preliminary acceptability and feasibility of the toolkit using a convenience sample of 39 residents of independent living facilities in focus group sessions. All participants indicated they perceive that the 4-step process can help individuals successfully self-manage medications. At the conclusion of the focus group sessions, all 39 participants completed the 7-item post-session checklist. This paper presents the second evaluation to establish the internal consistency reliability of the toolkit's Medication Management Self-efficacy Checklist using Cronbach's alpha. There was good internal consistency of the self-efficacy checklist with a Cronbach's alpha value of 0.82. This investigation of a novel approach for applying the 4-step pharmacotherapy assessment process by patients suggests that the medication self-efficacy checklist provides a reliable and useful measure of a patient's confidence in self-managing medications.

The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence

AbstractThe digital divide remains an ongoing societal concern, with digital exclusion shown to have a significantly detrimental impact on people’s quality of life. Artificial intelligence (AI), the latest wave of digitalisation, is being integrated into the fabric of society at an accelerated rate, the speed of which has prompted ethical concerns. Without addressing the digital divide, the AI revolution risks exacerbating the existing consequences of digital exclusion and limiting the potential for all people to reap the benefits provided by AI. To understand the factors that might contribute to experiences of AI, and how these might be related to digital exclusion, we surveyed a diverse online community sample (N = 303). We created a novel measure of digital confidence capturing individual levels of awareness, familiarity, and sense of competence with digital technology. Results indicated that measures of digital confidence were predicted by structural, behavioural, and psychological differences, such that women, older people, those on lower salaries, people with less digital access, and those with lower digital well-being, reported significantly less digital confidence. Furthermore, digital confidence significantly moderated the relationship between people’s experiences with everyday AI technologies and their general attitudes towards AI. This understanding of the spill-over effects of digital exclusion onto experiences of AI is fundamental to the articulation and delivery of inclusive AI.

Ballots and Padlocks: Building Digital Trust and Security in Democracy through Information Governance Strategies and Blockchain Technologies

This research explores the integration of Information Governance (IG) strategies and Blockchain Technologies (BT) in enhancing digital trust and security within democratic processes. Amid concerns about the integrity and vulnerability of electoral systems in the digital era, this study examines how these technologies can collectively safeguard democracy. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM), bootstrapping analysis for mediation effects, and the Fornell-Larcker Criterion for discriminant validity, the analysis was conducted on data from 934 participants involved in the electoral process. Key findings demonstrate that IG strategies significantly impact digital trust, indicating the importance of robust data management, legal compliance, and privacy measures for public confidence in electoral systems. Blockchain Technologies positively affect the security of democratic processes due to their decentralized and immutable characteristics. Furthermore, digital trust is identified as a critical mediator between IG strategies, BT, and the security of democratic processes, highlighting the importance of trust in the effectiveness of these technologies. Based on the insights gained, three actionable recommendations are proposed: Electoral authorities should adopt comprehensive IG frameworks to enhance data integrity and transparency; Pilot blockchain projects should be expanded to refine and understand the broader implementation implications for election security; Efforts should be increased to foster digital literacy and trust among the electorate, emphasizing the role of these technologies in securing electoral integrity.

Fuzzy association rule mining for personalized the Chinese language teaching from higher education

Chinese Language learning grows ever more essential to develop the students’ personalities and values as the curriculum, thereby improving teaching strategies based on students’ learning preferences are more crucial. Students’ participation in learning the Chinese Language is generally minimal and typically operates in a passive learning mode. The development of the Chinese Language instruction in these higher educational settings will be impacted by the absence of an organized strategy for teaching the Chinese Language. An algorithm is called the Fuzzy Pattern-driven Personalized Teaching (FPPT) has been proposed to identify the association between the students learning patterns and interests in the Chinese Language in the higher education for providing the personalized teaching to solve these challenges. Fuzzy sets are incorporated into FP-Growth for personalized the Chinese Language learning to improve the suggestions by considering the ambiguous preferences and the proficiency levels. The fuzzy pattern is unrevealed by implementing the Frequent Pattern (FP) growth algorithm to find patterns in the students learning activity and preferences so that personalized the teaching methods can be developed to meet the needs of each student and maximize their motivation for the language learning. Using the support and the confidence measures, these identified Fuzzy association relationships of student learning interest results in personalized the Chinese Language teaching in the higher education. The experimental results showed that the proposed FPPT system significantly improved each student’s learning outcome, communication effectiveness, learning motivation, and the Language proficiency level.

Uncertainty quantification for acoustical problems

Acoustical parameter estimation is a routine task in many domains and is typically done using signal processing methods. The performance of existing estimation methods is affected due to external uncertainty and yet the methods provide no measure of confidence in the outputs. Hence it is crucial to quantify uncertainty in the estimates before real-world deployment. Conformal prediction is a simple method to obtain statistically valid prediction intervals from an estimation model. In this work, conformal prediction is used for obtaining statistically valid uncertainty intervals for various acoustical parameter estimation tasks. We consider the tasks of DOA estimation and localization of one or more sources in an acoustical environment. The performance is validated on plane wave data with different sources of uncertainty including ambient noise, interference, and sensor location uncertainty, using statistical metrics. Results demonstrate that conformal prediction is a suitable and easy-to-use technique to generate statistically valid uncertainty quantification for acoustical estimation tasks.

Skills on wheels: initial pre-post findings from a pilot study of a pediatric wheelchair skills training program

Skills on Wheels, a 5-week pediatric wheelchair skills training program implemented over 2 years, was developed to address confidence, social participation, and mobility for wheelchair-using children. This study tests the hypothesis that pediatric wheelchair skills training will increase wheelchair skill ability, confidence, and participation of wheelchair-using children. Individualized instruction was delivered by occupational (N = 50) and physical (N = 12) therapy practitioners and doctoral students. The primary program intervention was adapted for pediatric wheelchair users from the adult Wheelchair Skills Training Program, developed by Dalhousie University, consisting of a total of 33 wheelchair skills. An adapted version of the Wheelchair Confidence measure (Wheel-Con-M-P), the Participation and Environment Measure for Children and Youth (PEM-CY), and Wheelchair Skills Test (WST) were used to measure participants confidence and ability to complete wheelchair skills, endurance, and participation and satisfaction in community, school, and home. The study resulted in: increases in confidence in ability to move wheelchair over threshold (+1.00, p < 0.05), ability to carry lunchbox/bookbag (+0.65, p < 0.05), ability to move in wheelchair when worried or scared (+1.3, p < 0.05), ability to ask for help, and ability to say “no” if they don’t need help (+0.58, p < 0.05) were indicated. The WST indicated increased ability (p < 0.05) on several skills. The Fear of Falling Measure found an overall decrease in fear of falling (−2.37, p < 0.01). The Skills on Wheels program demonstrated many successes in this initial 2-year study and data suggest a positive trend for iterations to come.

Machine learning enhanced evaluation of semiconductor quantum dots

A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.

An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values.

When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequently, it is crucial to report a measure of confidence and quantify the uncertainty in the model's predictions during test time. Here, we adopt the conformal prediction technique to evaluate the confidence of a prediction for each member of the core set of the CASF 2016 benchmark. The conformal prediction technique requires a diverse ensemble of predictors for uncertainty estimation. To this end, we introduce ENS-Score as an ensemble predictor, which includes 30 models with different protein-ligand representation approaches and achieves Pearson's correlation of 0.842 on the core set of the CASF 2016 benchmark. Also, we comprehensively investigate the residual error of each data point to assess the normality behavior of the distribution of the residual errors and their correlation to the structural features of the ligands, such as hydrophobic interactions and halogen bonding. In the end, we provide a local host web application to facilitate the usage of ENS-Score. All codes to repeat results are provided at https://github.com/miladrayka/ENS_Score.

The leading types of the coaches and its reflections on the creative organizational confidence among the advanced Iraqi league players in football for the season 2018-2019

The current research aims to know the leading types of the coaches and its reflections on the creative organizational confidence among the advanced Iraqi league players in football for the season 2018-2019 and to what extent the correlation relationship between them. The research is depended on the descriptive method by the method of correlation relations on 400 players sample that being divided into three sample based on the procedural research( Reconnaissance, psychometric and main experience) . A group has been determined along with its definition and its procedures , and that being linked to a set of phrases indicating variable of the research , with 31 phrases for the leading types measurement and 28 phrases for the organizational confidence measurement. Data has been processed through statistical means ( SPSS) in a way that correspond to the required goals. The results have indicated as to respond to the sample, that there is a spiritual indication to have the research variances applied and correlation relationship between them. The most important conclusions lie in the ability of the sample to determine the organizational confidence level among them and leading types of the coaches , as well as the actual desire among coaches to acquire the confidence of the players to be integrated under the equity and justice . Most important recommendations to have the club administration depend on the leading types of coaches and organizational confidence of players to show level of performance, to have its concepts applied constantly and to bolster feeling of responsibility to generate a complete persuasion to efficiently work to acquire willingness and allegiance to all parties

Recursive noisy label learning paradigm based on confidence measurement for semi-supervised depth completion

Recursive noisy label learning paradigm based on confidence measurement for semi-supervised depth completion

Maritime decision-makers and cyber security: deck officers’ perception of cyber risks towards IT and OT systems

Through a quantitative study of deck officers’ cyber risk perceptions towards information (IT) and operational (OT) systems, this paper contributes to substantiate the importance of considering human behaviour within maritime cyber security. Using survey data from 293 deck officers working on offshore vessels, statistical analyses were conducted to measure and predict the participants cyber risk perceptions towards IT and OT systems. Performing a Wilcoxon signed-rank test revealed a significant discrepancy in the levels of cyber risk perception between the system categories. Hierarchical regression analyses were conducted to develop statistical models, considering multiple independent variables, including perceived benefit, cyber security training, experience with cyber-attacks, and trust towards various stakeholders. Key findings revealed distinct results for IT and OT systems, and the regression models varied in both predictive power and significance of the independent variables. Perceived benefit positively predicts deck officers cyber risk perception for both IT and OT systems, while trust, which included measures of social trust and confidence, was not found to be significant. Cyber security training and experience with cyber-attacks only influence deck officers’ perception of cyber risks related to operational technology. Practical implications of this work provide actionable recommendations for the maritime industry, including tailored risk communication tools, training programs, reporting systems, and holistic policies.

Partial Sequence Labeling With Structured Gaussian Processes.

Existing partial sequence labeling models mainly focus on a max-margin framework that fails to provide an uncertainty estimation of the prediction. Furthermore, the unique ground-truth disambiguation strategy employed by these models may include wrong label information for parameter learning. In this article, we propose structured Gaussian processes for partial sequence labeling (SGPPSL), which encodes uncertainty in the prediction and does not need extra effort for model selection and hyperparameter learning. The model employs factor-as-piece approximation that divides the linear-chain graph structure into the set of pieces, which preserves the basic Markov random field structure and effectively avoids handling a large number of candidate output sequences generated by partially annotated data. Then, confidence measure is introduced in the model to address different contributions of candidate labels, which enables the ground-truth label information to be utilized in parameter learning. Based on the derived lower bound of the variational lower bound of the proposed model, variational parameters and confidence measures are estimated in the framework of alternating optimization. Moreover, a weighted Viterbi algorithm is proposed to incorporate confidence measures to sequence prediction, which considers label ambiguity arose from multiple annotations in the training data and thus helps improve the performance. SGPPSL is evaluated on several sequence labeling tasks and the experimental results show the effectiveness of the proposed model.

Sport-Specific AMCaMP: New Modular Tools for Measuring Adolescent Self-Confidence In Sport-Specific Movement.

Despite increasing interest in psychological factors that affect the impact of self-efficacy on readiness to return to play, few clinical tools are available to assess self-confidence in performing sports-specific movement patterns in the pediatric/adolescent population. The purpose of this study was to test the psychometric properties of sports-specific modules to supplement a general measure of movement self-efficacy, the Adolescent Measure of Confidence and Movement Performance (AMCaMP). Quasi-experimental cross-sectional validation. After preliminary testing for readability and ease of administration, one of 12 sport-specific modules pertinent to the individual's sport (baseball, softball, basketball, football, gymnastics, cheerleading, soccer, ballet, swimming, lacrosse, tennis, and cross country) were administered to 14,744 patients, 11-18 years of age, drawn from 12 pediatric sports physical therapy facilities in a single health care system. Respondents completed the assigned sport-specific self-report questionnaire at initial visit and conclusion of the episode of care. Based on sample sizes, Bartlett's Test of Sphericity, and Kaiser-Myer-Olkin measures, nine modules (baseball, softball, basketball, football, gymnastics, cheerleading, soccer, ballet, and swimming) were deemed suitable for factor analysis. Each module sample was divided into test validation samples. Exploratory factor analysis revealed an underlying structure ranging from one to three factors depending upon the module. Subsequent confirmatory factor analyses fully supported the hypothesized factor structures for each module except swimming. Additional analyses to determine coefficient alpha (range=0.8-0.976), Standard Error of Measurement (range=1.12-2.33), and Minimum Detectable Change (range=3.1-6.47) confirmed the reliability of each of these modules. AMCAMP sport-specific modules are reliable and valid self-report tools to capture self-confidence in performing sport-specific movements to supplement AMCAMP's evaluation of self-efficacy in performing the general movements of everyday life. The results of this study support using these modules as part of the overall clinical evaluation of psychological readiness to return to sport. Level 3b.

MVIndEmo: a dataset for micro video public-induced emotion prediction on social media

Distinct from the realm of perceived emotion research, induced emotion pertains to the emotional responses engendered within content consumers. This facet has garnered considerable attention and finds extensive application in the analysis of public social media. However, the advent of micro videos presents unique challenges when attempting to discern the induced emotional patterns exhibited by content consumers, owing to their free-style representation and other factors. Consequently, we have put forth two novel tasks concerning the recognition of public-induced emotion on micro videos: emotion polarity and emotion classification. Additionally, we have introduced a accessible dataset specifically tailored for the analysis of public-induced emotion on micro videos. The data corpus has been meticulously collected from Tiktok, a burgeoning social media platform renowned for its trendsetting content. To construct the dataset, we have selected eight captivating topics that elicit vibrant social discussions. In devising our label generation strategy, we have employed an automated approach characterized by the fusion of multiple expert models. This strategy incorporates a confidence measure method that relies on three distinct models for effectively aggregating user comments. To accommodate adaptable benchmark configurations, we provide both binary classification labels and probability distribution labels. The dataset encompasses a vast collection of 7,153 labeled micro videos. We have undertaken an extensive statistical analysis of the dataset to provide a comprehensive overview composition. It is our earnest aspiration that this dataset will serve as a catalyst for pioneering research avenues in the analysis of emotional patterns and the understanding of multi-modal information.

War in Europe, but Still Low Tension in the High North? An Analysis of Norwegian Mitigation Strategies

Russia’s invasion of Ukraine has created the most precarious security situation in Europe, including the High North, since the Second World War. This article studies how Norway manages High North security dilemmas in the context of this ongoing war. Based on security dilemma theory, we direct our attention to a set of mitigation strategies and discuss the effectiveness of these. We build our arguments on Robert Jervis’ article “Cooperation under the Security Dilemma” in World Politics (1978), and his understanding that a security dilemma occurs when an increase in one state’s security leads to other states fearing for their own security, thus creating tension or conflict escalation. To limit such dilemmas, Norway has pursued a policy mix of both deterrence and reassurance measures. Our contribution to the research debate is the term mitigation-strategies, derived from security dilemma theory. First, we discuss people-to-people cooperation and analyse how this is a trust-building measure. Secondly, we explore how Norway approaches confidence and security building measures in the High North. Finally, we discuss the implications of letting the Arctic Council become an arena for security- and defence political coordination. Building upon insights from security dilemma theory, we demonstrate how Norway contributes to maintaining lower levels of tension in the High North.

Retracted: Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure

Retracted: Music Segmentation Algorithm Based on Self-Adaptive Update of Confidence Measure

Social identity and learning: Adult literacy program in India

The paper examines the effect of social identity on adult learning within a hierarchical social setting— an important yet often understudied issue for effective adult education. We leverage the random matching of students and teachers from a randomized controlled experiment in India, where illiterate adult female learners aged 18–45 were randomly assigned to a literacy program. We find a positive and significant impact of matching an upper caste teacher with a lower caste adult student on literacy scores. We also find suggestive evidence of an increase in students' confidence measures when matched with an upper caste teacher, indicating a plausible impact mechanism. Our findings highlight the need for future research on social identity and its influence on adult learning, particularly in countries with existing deep-rooted hierarchical social constructs.