Truth Values Research Articles

Deriving Ground Truth Labels for Regression Problems Using Annotator Precision

When training machine learning models with practical applications, a quality ground truth dataset is critical. Unlike in classification problems, there is currently no effective method for determining a single ground truth value or landmark from a set of annotations in regression problems. We propose a novel method for deriving ground truth labels in regression problems that considers the performance and precision of individual annotators when identifying each label separately. In contrast to the commonly accepted method of computing the global mean, our method does not assume each annotator to be equally capable of completing the specified task, but rather ensures that higher-performing annotators have a greater contribution to the final result. The ground truth selection method described within this paper provides a means of improving the quality of input data for machine learning model development by removing lower-quality labels. In this study, we objectively demonstrate the improved performance by applying the method to a simulated dataset where a canonical ground truth position can be known, as well as to a sample of data collected from crowd-sourced labels.

Implementation of a deep convolution neural network model for identifying and classifying Pleuropulmonary Blastoma on DNA sequences

Pleuro Pulmonary Blastoma is a lung cancer affecting children's lung tissues. The development of the DICER1 gene in the patients leads to PPB. This disease causes trouble breathing along with lung infections. It can only be found by analyzing the DNA sequence of the PPB tissues. The DNA sequence of the tissues affected by cancer is extracted from the infections. The DNA sequences reveal the hereditary cause of the PPB, which can help in detecting the abnormalities of their family members. DNA sequences are complex, and it is impossible to rely on manual clinical methods to give faster and more accurate predictions. Several research works have suggested machine learning models which process the DNA sequences to detect PPB. However, machine learning algorithms cannot understand the complex DNA patterns of cancer tissues. This paper designs and implements a deep learning algorithm, a novel Deep Convolution Neural Network, to overcome such difficulties. The whole DNA sequence can be processed by more layers involved in the DCNN. The proposed DCNN learns the DNA sequence data part by part and compares it with the ground truth values, identifying odd data and abnormal sequence patterns to predict the presence of PPB. The DCNN is implemented in Python software with the DICER1 gene dataset for predicting the PPB. From the result of the experiment, the efficacy of the proposed DCNN model is verified and compared with other traditional models to evaluate the performance. The proposed CNN obtained 98.67% of accuracy for CT image classification and 96% of accuracy for DICER1-DNA data analysis.

β-fuzzy equivalence relations, β-fuzzy partitions and the rough set model

This paper selects a residuated lattice L as the truth value table and selects a fixed element β∈L as the degree criterion, we introduce concepts of β-fuzzy equivalence relations and β-fuzzy partitions. The β-fuzzy partition is a natural generalization of fuzzy β-coverings. It is shown that they are one-to-one corresponding if and only if β=1. In order to check the reasonableness of β-fuzzy partitions, we define a pair of fuzzy upper/lower rough approximation operators and show that each of them has two equivalent descriptions, which is not possessed by fuzzy β-coverings.

Perspectives: replication is more than meets the eye

Drawing on recent research and debates in social sciences, this paper situates replication in an advertising research context. We clarify the role of replication in the field and outline the challenges inherent in replication studies in advertising research. We further elaborate on how researchers should engage in replication research to increase the truth value of advertising research while overcoming the obstacles to replication research. Finally, we discuss how advertising scholars, reviewers, and editors can facilitate replication research to reduce the share of false-positive results and accumulate knowledge in the discipline. We see replication as critical in advertising research, given the high variability of experimental factors and the applied nature of the field. Therefore, a better understanding of replications and the challenges of advertising research should inspire scholars to engage in more replication attempts and reviewers and editors to consider it for publication.

Host group degeneracy in gravitational lensing time delay determination of H 0

Abstract Massive elliptical galaxies, that serve as lenses in gravitational lensing time delay measurements of the Hubble parameter H 0, often reside in a host group. We consider degeneracies in the modeling of the group halo. When the group effect on imaging can be summarized by its flexion (the next order term beyond shear in the tidal expansion), the posterior likelihood map can develop disjoint local minima, associated with an approximate discrete symmetry of a dominant flexion term. Monte-Carlo Markov Chain (MCMC) algorithms that are not designed to explore a rich posterior landscape can miss some of the minima, introducing systematic bias. We study mock data and demonstrate that the bias in H 0 can exceed 10%, and pulls the inference value of H 0 above its truth value, for a reason that can be traced to the structure of a mismodeled flexion term. MCMC algorithms that are designed to cope with a rich posterior landscape can uncover the structure. If the group is X-ray bright enough, X-ray data may also help to resolve the degeneracy, by pinpointing the group's center of mass. Finally, we show that some implementations in the literature used an inaccurate kinematical prior, mis-modeling the group velocity dispersion by as much as 20%.

Remote sensing target detection algorithm based on perceptual extension and anchor frame best-fit matching

Aiming at the small imaging, complex background and crowded distribution of remote sensing image targets, a remote sensing image target detection algorithm (HQ-S2ANet) based on perceptual extension and anchor frame optimal matching is proposed by using the rotating target detection method S2ANet as a baseline network. Firstly, a cooperative attention(SEA) module is built to capture the relationship among the feature pixels when extending the model perception area to realize the relationship modeling between the target and the global. Secondly, the feature pyramid (FPN) feature fusion process is improved to form a perceptual extension feature pyramid module (HQFPN), which guarantees the low-level detail position information in the down sampling process when extending the perception area to enhance the model information capturing capability. Finally, a high-quality anchor frame is used to detect the target by using the high quality anchor frame as the baseline network. The high-quality anchor frame matching method (MaxIoUAssigner_HQ) is used to control the anchor frame truth value assignment by using a constant factor to ensure the recall rate while preventing the generation of low-quality anchor frame matching. The experimental results show that, under the DOTA dataset, the average accuracy(mAP) of HQ-S2ANet is improved by 3.1%, the parameters number increased by only 2.61M and the average recall(recall) is improved by 1.6% compared with the S2ANet algorithm, and the present algorithm effectively enhances the detection capability of the remote sensing image target.

Causal Semantics for Implicative Verbs

Abstract Implicative verbs (e.g., manage, dare) are characterized by complement inferences (Karttunen, 1971). English manage entails its complement; the entailment reverses polarity with matrix negation, and is accompanied by a projective inference to the complement’s non-triviality (Coleman, 1975; Karttunen & Peters, 1979). I use data from Finnish and English to argue that the implicative inferential profile is derived from backgrounded relations of causal necessity and causal sufficiency (defined over the structure of a formal causal model; Pearl 2000; Schulz 2011) which link the lexical content of an implicative verb to the realization of its complement. The proposal builds on Baglini & Francez’s (2016) causal analysis of manage, but significantly revises the earlier proposal to offer a treatment which accounts not only for English manage, but extends to the lexical semantics of the full implicative class, including ‘polarity-reversing’ verbs like fail, lexically specific verbs like dare, and their Finnish counterparts. Unlike earlier analyses, the proposed causal semantics also provides a natural explanation of the commonalities between two-way entailing verbs like manage and a related class of weaker ‘one-way’ implicatives such as Finnish jaksaa (‘have the strength’), which entail complement truth values under only one matrix polarity, but generate strong pragmatic implicatures in the two-way implicative pattern under the non-entailing polarity.

Sugeno Integral Based on Overlap Function and Its Application to Fuzzy Quantifiers and Multi-Attribute Decision-Making

The overlap function is an important class of aggregation function that is closely related to the continuous triangular norm. It has important applications in information fusion, image processing, information classification, intelligent decision-making, etc. The usual multi-attribute decision-making (MADM) is to select the decision object that performs well on all attributes (indicators), which is quite demanding. The MADM based on fuzzy quantifiers is to select the decision object that performs well on a certain proportion or quantification (such as most, many, more than half, etc.) of attributes. Therefore, it is necessary to study how to express and calculate fuzzy quantifiers such as most, many, etc. In this paper, the Sugeno integral based on the overlap function (called the O-Sugeno integral) is used as a new information fusion tool, and some related properties are studied. Then, the truth value of a linguistic quantified proposition can be estimated by using the O-Sugeno integral, and the O-Sugeno integral semantics of fuzzy quantifiers is proposed. Finally, the MADM method based on the O-Sugeno integral semantics of fuzzy quantifiers is proposed and the feasibility of our method is verified by several illustrative examples such as the logistics park location problem.

Using machine learning for image-based analysis of sweetpotato root sensory attributes

The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders.In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as high-throughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained R2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained R2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable R2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers.

Searching for protein variants with desired properties using deep generative models

BackgroundProtein engineering aims to improve the functional properties of existing proteins to meet people’s needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features contained in amino acid sequences. However, the existing generative models need to be improved when capturing the relationship between amino acid sites on longer sequences. At the same time, the distribution of protein sequences in the homologous family has a specific positional relationship in the latent space. We want to use this relationship to search for new variants directly from the vicinity of better-performing varieties.ResultsTo improve the representation learning ability of the model for longer sequences and the similarity between the generated sequences and the original sequences, we propose a temporal variational autoencoder (T-VAE) model. T-VAE consists of an encoder and a decoder. The encoder expands the receptive field of neurons in the network structure by dilated causal convolution, thereby improving the encoding representation ability of longer sequences. The decoder decodes the sampled data into variants closely resembling the original sequence.ConclusionCompared to other models, the person correlation coefficient between the predicted values of protein fitness obtained by T-VAE and the truth values was higher, and the mean absolute deviation was lower. In addition, the T-VAE model has a better representation learning ability for longer sequences when comparing the encoding of protein sequences of different lengths. These results show that our model has more advantages in representation learning for longer sequences. To verify the model’s generative effect, we also calculate the sequence identity between the generated data and the input data. The sequence identity obtained by T-VAE improved by 12.9% compared to the baseline model.

ETIKA PROFESI HAKIM

Discourses about professional ethics of judges started out from the reality of law enforcement officials (especially judges) that ignore the values of morality. Although judges have already had the professional code of ethics as moral standards it has not yet brought positive impact on negative image in public view. One way to enforce the rule of law is to urge ethics, professionalism, and discipline. Professional ethics of judges contain moral values underlying professional personality, namely freedom, fairness and honesty. Professional ethics of judges and law are a unity that is inherently contained ethical values of Islam which is the foundation of understanding the Qur’an, so basically the code of professional conduct of judges is in line with the values of the Islamic ethical system. Ethics Islamic law is built on four basic values, namely the values of truth, justice, free will and responsibility

Analysis of Ethnic Elements in Xinjiang Music Works Based on PPM Model under the Perspective of “One Belt, One Road

Abstract Under the background of digitalization of big data and information, the national “One Belt and One Road” initiative has produced a new huge effect on the ancient Silk Road between Xinjiang and Eurasia, how to revitalize the traditional music culture of ethnic minorities and how to make the traditional music culture resources of ethnic minorities be effectively protected and developed in the process of protection and inheritance of intangible cultural heritage in the world? Based on the analysis of ethnic elements in Xinjiang music works based on the PPM model under the perspective of “One Belt, One Road”, this paper first introduces the concept of PPM, the Bayesian structure algorithm, constructs the PPM model, and analyzes the data using by the structural equation modeling method, and explores the evaluation of PPM model under different sample sizes and different indirect The performance of four methods, Bayesian structural equation modeling method, structural equation modeling method, classical regression method and Bayesian regression method, was evaluated for the analysis of ethnic elements in Xinjiang music works under different sample sizes and different indirect effect truth values. The results show that the standard errors under the four models become larger as the regression coefficient b between Y and X increases, but in the case of (a=0, b=0.59), the standard error of the structural equation modeling method for the analysis of ethnic elements in Xinjiang music works is 0.048, which is the smallest compared with the standard errors of other algorithms. The structural equation algorithm of the PPM model is very applicable when dealing with practical problems. It can not only be used in theoretical research but also has a great influence on realistic guidance, which has an important theoretical significance and practical value for the survival and development of the traditional music culture of ethnic minorities in China.

Homogeneous Sensor Fusion Optimization for Low-Cost Inertial Sensors.

The article deals with sensor fusion and real-time calibration in a homogeneous inertial sensor array. The proposed method allows for both estimating the sensors' calibration constants (i.e., gain and bias) in real-time and automatically suppressing degraded sensors while keeping the overall precision of the estimation. The weight of the sensor is adaptively adjusted according to the RMSE concerning the weighted average of all sensors. The estimated angular velocity was compared with a reference (ground truth) value obtained using a tactical-grade fiber-optic gyroscope. We have experimented with low-cost MEMS gyroscopes, but the proposed method can be applied to basically any sensor array.

Free speech versus defence of the nation? The media as sources of national insecurity in Ukraine

ABSTRACT The media can cause concern in the context of national security: they are described as potential tools and targets of attack that can be “weaponised”, and as a space where “information war” is waged. Governments may try to block media that are deemed a security threat, but the rationale for taking such an action deserves careful consideration, given the tension between media restrictions and the democratic principle of free speech. This article scrutinises the security rationale for restrictions imposed by Ukraine on Russian and “pro-Russian” media from 2014. When justifying restrictions, Ukrainian officials highlighted the threat of media content both distorting perceptions of reality and weakening the foundations of Ukrainian nationhood. We, therefore, analyse survey data to investigate whether the use of the banned media was associated with variation in Ukrainian citizens’ perceptions of truth and national values. We find that the use of the banned media was linked to mistaken beliefs about the veracity of news headlines, both true and false; it was also associated with lower support for democracy in Ukraine (a key national constitutional value). This evidence from the Ukrainian case informs our discussion about the media’s impact on national (in)security and rationales for media restrictions in democratic contexts more broadly.

Truth Discovery Technology for Mobile Crowd Sensing in Water Quality Monitoring

The water quality of urban inland rivers is an important index of urban environmental health, which can reflect a city’s development level and its social and economic development. The water quality of these rivers strongly impacts the health and quality of life of the residents of urban and surrounding areas. Therefore, it is necessary to accurately assess the quality of water in urban inland rivers, which can also aid environmental protection departments in providing river governance. Generally, the water quality status of a city’s inland rivers is assessed and released by environmental monitoring stations in various regions that deploy the corresponding water quality detection equipment at certain major locations of the river. However, these detection devices can only detect water quality at fixed locations, and often, the water quality of an urban inland river changes owing to the impact of the surrounding environment and residents it serves. Therefore, the water quality around a detection point does not always reflect the water quality of the entire river section. To better express the water quality status of a city’s inland river, we propose a method based on a mobile crowd-sensing system that obtains the water quality data of the river during an entire period of time and then fuzes these sensing data to obtain the best truth-value estimate of the water quality of the river. We can use this water quality truth value to conduct an objective evaluation of the water quality of a city’s inland rivers. The water quality parameters obtained by the method can better represent the water quality status of the river, and the data are more accurate compared to the data collected and released by an environmental monitoring station. Through simulation and comparative analysis, we found that the water quality data obtained by the proposed method were more accurate, indicating that our method has more practical value than the detection device method.

VALIDITY NOW

ABSTRACTThis review essay offers an extended analysis of Martin Jay's Genesis and Validity: The Theory and Practice of Intellectual History, highlighting Jay's emphasis on the need for intellectual historians to address the question of the present‐day validity of ideas. In this volume of essays on twentieth‐century philosophy and historiography, Jay contends that the perennial tension between historicism and truth value is integral to intellectual history; however, it is establishing the latter that is the most crucial, and perhaps most difficult, part of our practice. In thirteen separate but related pieces, Jay explores subjects such as the common ground shared by Quentin Skinner and Hayden White, the “styles” of thought represented by Walter Benjamin and Isaiah Berlin, and recent French theories of the historical sublime and the “advent” of ideas. This review essay discusses these pieces and compares Jay's essay on historical truthfulness to ancient rhetorical discussions of the many forms of lying about history. Its final section deals with the ways in which insights from the history of science might help us to connect genesis and validity by examining the practices of idea‐ or knowledge‐making, and it argues that validity is also, and perhaps most importantly, something we need to embrace in our roles as teachers. What makes ideas valid is the next generation of thinkers, and to make wise and well‐informed life choices, they need to have a full range of older and newer ideas about the human condition from which to choose.

Law and logic: A deductive criterion for determination of truth value of evidence in civil and administrative procedures – Part I

The study examines the possibility of applying a deductive logical criterion for determination of the truth value of evidence in civil and administrative procedures. Part I points out types of evidence and facts with ex lege definite truth value according to current legislation in Bulgaria. It reviews the opportunity evidence without such ex lege definite truth value to be subjected to verification using the proposed deductive criterion. It is also argued that in the criterion’s application as premises evidence and facts with ex lege definite truth value should be involved in order to guarantee the truth value of the conclusions. The study provides a detailed review of the propositional logic apparatus involved. Part II reviews the deductive method applied – the method of natural deduction. The application of the deductive criterion in determining the truth value of evidence in an example from legal practice is also shown. At the end the method of natural deduction is applied to a particular kind of proof – based only on circumstantial evidence with ex lege definite truth value. The study concludes with summary of the advantages and limitations of the application of the deductive criterion, some new areas of application of deductive logic in civil procedure are reviewed and some open research issues are pointed.

Igbo values and women

This article discusses some of the core values among the Igbos. This is done partly as a way of showing the way in which these values play out both in gender relations in and highlighting the way women are viewed. In this sense, our attempt here should be understood as an investigation. The values that we examine are those of truthfulness or truth, respect or respectfulness and industry or industriousness or hard work. The aim is to help make the case that in Igboland women are neither marginalised nor treated as inferior to men. In presenting these ideas, we draw on some proverbs (to show the values of truth and respect) and some characters and actors in Chinua Achebe’s Things Fall Apart (to demonstrate the values of respect and industry) for both men and women.

Using Machine Learning Classifiers in SAT Branching [Extended Abstract

The Boolean Satisfiability Problem (SAT) can be framed as a binary classification task. Recently, numerous machine and deep learning techniques have been successfully deployed to predict whether a CNF has a solution. However, these approaches do not provide a variables assignment when the instance is satisfiable and have not been used as part of SAT solvers. In this work, we investigate the possibility of using a machine-learning SAT/UNSAT classifier to assign a truth value to a variable. A heuristic solver can be created by iteratively assigning one variable to the value that leads to higher predicted satisfiability. We test our approach with and without probing features and compare it to a heuristic assignment based on the variable's purity. We consider as objective the maximisation of the number of literals fixed before making the CNF unsatisfiable. The preliminary results show that this iterative procedure can consistently fix variables without compromising the formula's satisfiability, finding a complete assignment in almost all test instances.

Quantitative Optical Coherence Elastography: A Novel Intensity-Based Inversion Method Versus Strain-Based Reconstructions

Many diseases like cancer or atherosclerosis bear micro-scale tissue stiffness changes, which can be visualised by optical coherence elastography (OCE). OCE is a promising research field, where a growing number of publications present qualitative displacement maps. Quantitative OCE results have also been presented, but still lack high precision and good reproducibility, which are important for clinical applications. In this work, we compare three reconstruction methods for the Young's modulus in intensity-based quasi-static compressional OCE: uniaxial analysis, strain map based reconstruction facilitating a particle tracking improved optical flow (EOFM), and a novel image-based inverse reconstruction method (IIM). The quality of the proposed reconstruction methods is investigated by comparing their performance on twelve silicone elastomer phantoms with inclusions of varying size and stiffness. While the uniaxial reconstruction is strongly affected by lateral motion, EOFM is capable of deriving precise strain maps from consecutive OCT images during compression. However, for a valid Young's modulus reconstruction additional stress map information is required. IIM performs best, precisely reconstructing Young's modulus of inclusion and background, closely corresponding to separately determined ground truth values. The method offers a significant improvement of the relative error by a factor of 3.5 compared to uniaxial and strain-based analysis.