Interpretation Techniques Research Articles

Interpretable machine learning for psychological research: Opportunities and pitfalls.

In recent years, machine learning methods have become increasingly popular prediction methods in psychology. At the same time, psychological researchers are typically not only interested in making predictions about the dependent variable, but also in learning which predictor variables are relevant, how they influence the dependent variable, and which predictors interact with each other. However, most machine learning methods are not directly interpretable. Interpretation techniques that support researchers in describing how the machine learning technique came to its prediction may be a means to this end. We present a variety of interpretation techniques and illustrate the opportunities they provide for interpreting the results of two widely used black box machine learning methods that serve as our examples: random forests and neural networks. At the same time, we illustrate potential pitfalls and risks of misinterpretation that may occur in certain data settings. We show in which way correlated predictors impact interpretations with regard to the relevance or shape of predictor effects and in which situations interaction effects may or may not be detected. We use simulated didactic examples throughout the article, as well as an empirical data set for illustrating an approach to objectify the interpretation of visualizations. We conclude that, when critically reflected, interpretable machine learning techniques may provide useful tools when describing complex psychological relationships. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Study of the Techniques of Consecutive Interpretation Based on a Speech Named How to Realistically Decarbonize the Oil and Gas Industry

At present, all countries are facing the threat of epidemic, and the process of globalization is forced to stop. But it's all temporary. Even if the epidemic situation is normalized in the future, the exchange and development of countries and globalization will be the general trend of history. In such an era, translators are irreplaceable by any technology. As a type of translation, consecutive interpretation plays an important role in foreign exchange. Therefore, this paper takes the speech how to realistically declare the oil and gas industry as an example to simulate consecutive interpretation, so as to explore the skills of consecutive interpretation.

Improving a neural network model by explanation-guided training for glioma classification based on MRI data

In recent years, artificial intelligence systems have come to the forefront. These systems, mostly based on deep learning, achieve excellent results in areas such as image processing, natural language processing and speech recognition. Despite the statistically high accuracy of deep learning models, their output is often based on ”black box” decisions. Thus, interpretability methods (Reyes et al. in Radiol Artif Intell 2(3):e190043, 2020) have become a popular way to gain insight into the decision-making process of deep learning models (Miller in Artif Intell 267:1–38, 2019). Explanation of deep learning models is desirable in the medical domain since experts have to justify their judgments to the patients. In this work, we proposed a method for explanation-guided training that uses a layer-wise relevance propagation technique to force the model to focus only on the relevant part of the image. We experimentally verified our method on a convolutional neural network model for low-grade and high-grade glioma classification problems. Our experiments produced promising results in the way where we use interpretation techniques in the training process.

Berobat Kampung: Religion and Traditional Knowledge of Seeking Recovery in Borneo

This article explores the variants of tradition in seeking recovery within a group of religious community in the upstream Kapuas in Borneo island. It is presented using indigenous psychology, narrative analysis and psycho-anthropology. The data were collected using the techniques of in-depth interviews, psycho-anthropological observations, narrative interpretation and documentation. This work finds that the use of Quranic verses and dhikr as well as religious symbols are are very prominent. The people of the upstream Kapuas river interpret and leave a legacy for traditional healing, beginning with an "agreement", reciting a prayer, enhancing, testing to form and hardening. Passing occurs in a closed, difficult and secret process. The preservation of traditional healing is due to very important facts; that it is low cost, being located far from modern health facilities and short time between risk of death and health resources. In addition, the traditional healers are always ready, providing fast treatment, guaranteed recovery from certain types of diseases. This article suggests that the Sufistic-style traditional healing is profound within the tradition.

A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases.

During a pandemic, medical specialists have substantial challenges in discovering and validating new disease risk factors and designing effective treatment strategies. Traditionally, this approach entails several clinical studies and trials that might last several years, during which strict preventive measures are enforced to manage the outbreak and limit the death toll. Advanced data analytics technologies, on the other hand, could be utilized to monitor and expedite the procedure. This research integrates evolutionary search algorithms, Bayesian belief networks, and innovative interpretation techniques to provide a comprehensive exploratory-descriptive-explanatory machine learning methodology to assist clinical decision-makers in responding promptly to pandemic scenarios. The proposed approach is illustrated through a case study in which the survival of COVID-19 patients is determined using inpatient and emergency department (ED) encounters from a real-world electronic health record database. Following an exploratory phase in which genetic algorithms are used to identify a set of the most critical chronic risk factors and their validation using descriptive tools based on the concept of Bayesian Belief Nets, the framework develops and trains a probabilistic graphical model to explain and predict patient survival (with an AUC of 0.92). Finally, a publicly available online, probabilistic decision support inference simulator was constructed to facilitate what-if analysis and aid general users and healthcare professionals in interpreting model findings. The results widely corroborate intensive and expensive clinical trial research assessments.

Pro-innovation discourses in Geography and History classes

Currently, educational innovation is an unavoidable premise for teachers, school headteachers and families. This contribution aims to provide better understanding of the configuration of pro-innovation discourses from a lexicometric perspective. To this end, we applied various textual statistical strategies to the analysis of a corpus made up of twelve verbal texts from four discursive fields: the education policy, the school, the Social Sciences Department and the Geography and History class in which, finally, pro-innovation discourses become practices. The results obtained, which focus on the discursive associations of innovation (collocations, combinations, concordances and co-occurrences), indicate that the meaning of educational innovation is taken for granted, that it has only positive connotations and that it functions as an argument to justify educational reform. In addition, it is noted that innovation is especially linked to new technologies and business vocabulary. We conclude this paper with a defence of the usefulness of the techniques used, but also with a warning of the need to complement the analysis carried out with other linguistic and interpretative techniques.

Pengaruh Brand Equity dan Brand Image terhadap Consumer Satisfaction Dalam Melakukan Repurchase Intention Produk Elektronik di Tokopedia


 Tokopedia is one of the first e-commerce in Indonesia to use a marketplace system. Many choose Tokopedia as a means to find electronic product needs ranging from electronic household appliances, electronic sports equipment, medical devices, smartphones, laptop and computer equipment, electronic accessories and many more. Brand quality and quantity are highly considered by consumers when looking for products on Tokopedia to get a satisfaction in generating repurchase intention. The purpose of this study was to determine the effect of brand equity and brand image on repurchase intention of electronic products in Tokopedia with consumer satisfaction as a intervening variable. This type of research is Quantitative Research. The technique used in this study is Non-Probability Sampling with a purposive sampling approach, namely Tokopedia consumers who have purchased electronic products three times. The sample in this study was 100 respondents. Data collection was carried out using a Questionnaire. The technique of analysis and interpretation of data uses Multiple Regression Analysis, according to the model developed in this study, and operates through the SPSS program. The results showed that brand equity (X1) and brand image (X2) had a positive effect on consumer satisfaction (Z). Then consumer satisfaction (Z) influences repurchase intention (y). Brand equity, brand image, and consumer satisfaction have a positive and significant effect on repurchase intention. Consumer satisfaction is the variable that has the highest influence on repurchase intention.
 Keywords: E-commerce, Brand Equity, Brand Image, Consumer Satisfaction, Repurchase Intention

Landslide susceptibility modeling by interpretable neural network

Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute to slope stability. Artificial neural networks (ANN) have been shown to improve prediction accuracy but are largely uninterpretable. Here we introduce an additive ANN optimization framework to assess landslide susceptibility, as well as dataset division and outcome interpretation techniques. We refer to our approach, which features full interpretability, high accuracy, high generalizability and low model complexity, as superposable neural network (SNN) optimization. We validate our approach by training models on landslide inventories from three different easternmost Himalaya regions. Our SNN outperformed physically-based and statistical models and achieved similar performance to state-of-the-art deep neural networks. The SNN models found the product of slope and precipitation and hillslope aspect to be important primary contributors to high landslide susceptibility, which highlights the importance of strong slope-climate couplings, along with microclimates, on landslide occurrences.

Is it possible to use the Langmuir Isotherm in electrochemical studies? A critical view on the subject

This article undertakes a critical evaluation of the literature regarding the use of the Langmuir isotherm, specifically in the context of inhibitor research. The Langmuir isotherm is frequently employed to derive thermodynamic and kinetic parameters, which are then used to gauge inhibitor performance. However, the validity of the Langmuir isotherm has been challenged, particularly by Walczak et al. (2019), whose work has served as a basis for others to reject its use as inadequate. Imposing limitations on the Langmuir isotherm would involve restrictions on heterogeneous kinetics, impedance interpretation, and other transient techniques. These aspects will be subjected to a more in-depth analysis in this discussion paper.

RILEM TC 266-MRP: round-robin rheological tests on high performance mortar and concrete with adapted rheology—rheometers, mixtures and procedures

Recent developments in understanding the rheology of mortar and concrete as well as applying this understanding in the practice of construction necessitate an accurate assessment of materials’ rheological properties. It is well known that different rheometers for mortar and concrete deliver different results, as this was shown over 15 years ago in two measuring campaigns comparing concrete rheometers. Considering newly developed rheometers, including those to evaluate interface rheology and structural build-up at rest, as well as additional measurement procedures and data interpretation techniques, a new comparison campaign was carried out in 2018 at the Université d’Artois, in Bethune, France. This new campaign focused on measuring workability characteristics, flow curves, static yield stress values, interface properties and tribological data. A total of 14 different devices capable of measuring one or more of the above-mentioned characteristics were employed. These devices included four ICAR rheometers, the Viskomat XL, the eBT-V, the RheoCAD (two geometries), the 4SCC rheometer (two geometries), the plate test, the sliding pipe rheometer, a tribometer and an interface tool for the ICAR rheometer. This paper describes the mixture design and rationale of the five investigated concrete and three investigated mortar mixtures, design and analysis of the experiments, and comparison of test results. The findings confirmed some of the conclusions from two previous testing campaigns and expanded the findings to more modern concrete mixtures and more diversified sets of rheological devices. The investigated rheometers yielded different absolute values for material parameters, but they all were able to similarly distinguish between mixtures qualitatively. For static yield stress and interface rheology measurements, similar conclusions were obtained as for flow curves.

Delineation of source magnetization and subsurface discontinuity of Ririwai Ring Complex along Kano, North Western Nigeria

High Resolution Aeromagnetic (HRAM) data of Ririwai (sheet 126) which lies between latitude 10° 30' to 11° 00' N and longitude 8° 30' E to 9° 00' E, was used for this research. The study area, Ririwai is situated along Kano, in the Northwestern part of Nigeria, both qualitative and quantitative interpretation techniques were employed in this study. This procedure was carefully undertaken by employing enhancement filters; vertical derivative, upward continuation, analytical signal amplitude processing, and quantitative approaches such as Euler 3D deconvolution and an apparent susceptibility filter to help further give solutions for depth estimates and magnetization. The Euler deconvolution was able to show that the solution plotted clustered around the region where the geological structures are located with average depths range of <139.8 to >904.5 m, with very few solutions having depths less than 200 m, the most prominent structure particularly the Ririwai ring complex have a depth range of 182.9 to 301.5 m. The magnetic susceptibility reading obtained shows that the area is composed of rocks high in magnetic minerals such as magnetite along the western end of the area, it is also flanked by medium to high magnetic values around the central parts with only regions of low magnetic values predominantly along the Ririwai complex and its adjoining outcrops towards the eastern half of the area under study.

Manajemen Pembelajaran Pendidikan Al-Islam di SMA Muhammadiyah

This study aims to determine the learning management of Al-Islam education at SMA Muhammadiyah 2 Surabaya by focusing on 4 sub-topics of discussion which include planning, organizing, implementing, and evaluating. This study uses a qualitative research method with a case study approach. Data collection techniques in this study are observation, interviews, and documentation. The techniques of analysis and interpretation of data in this study are data reduction, data presentation, and data verification. Data sources in this study include primary data sources where the data is obtained from informants when making observations at the research location. While secondary data sources are obtained from articles, theses, and journals that support primary data sources. The results of this study indicate that the learning management of Al-Islam education at SMA Muhammadiyah 2 Surabaya has been carried out following the learning mechanism and is conducted properly based on the learning objectives that have been set. To achieve all aspects Of learning management al Islam education is very dependent on the commitment and capability of educators towards education management itself.

Teacher Creativity in Aqidah Akhlak Learning After the Covid-19 Pandemic at Madrasah Ibtidaiyah

This study aims to determine the creativity of teachers in learning moral aqidah after the Covid-19 pandemic at MI Ma'arif Pamotan. This research is a descriptive qualitative research. In this study, the informants were mi principal Ma'arif Pamotan, moral aqidah teachers and female students. The location of this study was conducted at MI Ma'arif Pamotan. Data collection techniques in the form of interviews, observations, documentation. Data analysis and interpretation techniques use analysis models from Miles and Huberman, namely data reduction, data presentation, and drawing conclusions. The results of this study show that the teacher's creativity in learning aqidah akhlak consists of a form of teacher creativity which is manifested in the management of kela, learning methods in the form of Problem Based Learning, jigsaw-type cooperative methods and the resulting work, namely props in the form of sholih children's trees and intelligent puppets.

Non-linear shrinking of linear model errors

BackgroundArtificial neural networks (ANNs) can be a powerful tool for spectroscopic data analysis. Their ability to detect and model complex relations in the data may lead to outstanding predictive capabilities, but the predictions themselves are difficult to interpret due to the lack of understanding of the black box ANN models. ANNs and linear methods can be combined by first fitting a linear model to the data followed by a non-linear fitting of the linear model residuals using an ANN. This paper explores the use of residual modelling in high-dimensional data using modern neural network architectures. ResultsBy combining linear- and ANN modelling, we demonstrate that it is possible to achieve both good model performance while retaining interpretations from the linear part of the model. The proposed residual modelling approach is evaluated on four high-dimensional datasets, representing two regression and two classification problems. Additionally, a demonstration of possible interpretation techniques are included for all datasets. The study concludes that if the modelling problem contains sufficiently complex data (i.e., non-linearities), the residual modelling can in fact improve the performance of a linear model and achieve similar performance as pure ANN models while retaining valuable interpretations for a large proportion of the variance accounted for. Significance and noveltyThe paper presents a residual modelling scheme using modern neural network architectures. Furthermore, two novel extensions of residual modelling for classification tasks are proposed. The study is seen as a step towards explainable AI, with the aim of making data modelling using artificial neural networks more transparent.

Mapping Structurally Controlled Alterations Sparked by Hydrothermal Activity in the Fatira–Abu Zawal Area, Eastern Desert, Egypt

AbstractThe Eastern Desert of Egypt suffered a protracted period of deformation triggered by cratonization of the new juvenile crust known as the Arabian Nubian Shield (ANS), which has been proposed for potential gold discoveries associated with the corresponding tectonic event. The Fatira area, on the border of Egypt's Northern and Central Eastern Deserts, is covered with metavolcanic rocks twisted by a dextral relocation of the Fatira Shear Zone (FSZ) relative to the Barud magmatic body. The recent study evaluated many deformed post‐orogenic granitic intrusions and felsite dikes associated with promising mineralization localities, notably orogenic gold deposits. The combination of various field observations and remote sensing data, followed by the analysis of aeromagnetic enhanced maps, allowed the differentiation of distinct lithologies, structural features, and hydrothermal alterations in the study area. Additionally, the integrated results obtained from the different interpretation techniques are utilized to identify and confirm the previously supposed mineralized localities in the Fatira and Abu Zawal areas and predict other matched localities. The final ASTER, Sentinel 2 hydrothermal alteration, and orientation entropy heat maps demonstrate the association between these mineralized regions and major structures related to the FSZ late stage of deformation rather than other structures studied throughout the area of interest.

Stochastic hyperbola fitting, probabilistic inversion, reverse-time migration and clustering: A novel interpretation toolbox for in-situ planetary radar

Ground-penetrating radar (GPR) is becoming a mainstream tool in planetary exploration, and one of the few in-situ planetary geophysical methods. There are currently three missions (Perseverance, Tianwen-1, Chang’E-4) with GPR-equipped rovers, and two future missions (Chang’E-7, ExoMars) that will include GPR in their scientific payload. The large number of GPR data, combined with the novel setup of the measurements, creates the need for new data processing and interpretation techniques to address the unique challenges of in-situ planetary radar. The current paper proposes an interpretation pipeline that starts with a novel stochastic hyperbola fitting that estimates the probability kernel density of the bulk permittivity at different depths. Subsequently, the bulk permittivity distribution is transformed via a novel probabilistic inversion to a 1-dimensional (1D) permittivity profile. The inverted 1D permittivity profile is then used as an input to a bespoke reverse-time migration (RTM) using the finite-difference time-domain (FDTD) method. RTM using FDTD does not assume a clinical homogeneous half-space; instead, it accounts for the expected layered structure of the investigated medium. Lastly, the migrated radargram is clustered in order to identify subsurface targets and distinguish them from the background medium. Each of the processing steps has never been reported in planetary radar; and together act as a complete processing toolbox tuned for planetary science. The suggested interpretation pipeline is validated numerically in a 1D case study with a complex layered structure and multiple subsurface targets. The proposed processing scheme is then applied to the GPR data from the Chang’E-4 mission at the Von Kármán crater, revealing a previously unseen layered structure and a complex distribution of rocks/boulders.

BIBLIOMETRIC REVIEW OF THE FOREST AS A TOOL FOR ENVIRONMENTAL EDUCATION

The contact with the forest started in childhood enables the construction of values with nature. Bibliometric studies can help in the search for works on the forest theme. Thus, the objective of this work was to analyze the general panorama of scientific research on environmental education activities related to the forest, through bibliometric characterization in WoS and Scopus databases. The works were filtered from search codes that covered the theme. All articles were checked, and those that carried out environmental education activities and used the forest as a base were considered valid. The filtering resulted in 102 articles, of these, 62 articles it was possible to carry out a methodological analysis. Articles from 37 countries were found, with a greater number for the USA and 274 different authors, but the majority (93.4%) published only one manuscript. In the methodological analysis, most include non-formal teaching in specific environmental education activities. The forest in general is used for works focused on environmental perception and awareness, and the most used environmental interpretation techniques are field practices, in guided and self-guided interpretive trails. More than 50% of the articles do not justify the applied method and several works show methodological flaws that make it difficult to replicate the practices in other studies. It is concluded in the bibliometric analysis that there are a variety of authors talking about the subject, but a reference specialist author was not identified. The methodological flaws found prove the challenges of obtaining scientific advances in environmental education from the forest.

Eight recommendations to improve the reporting of qualitative social research in urban nature studies

Researchers studying urban nature should engage with qualitative social methods (QSM) because cities are full of people and what they think and feel matters. QSM are associated with a social science research approach that focuses on understanding how people think and behave by collaborating with participants in the generation of knowledge and including these participants - who may otherwise be excluded - in the research process. QSM are based on descriptive, exploratory, and explanatory research procedures that usually involve participant-centred, constructive, subjective, negotiated, and interpretative research techniques. QSM can provide insights about how urban nature influences humans, and, in turn, how humans influence urban nature. The use of QSM is becoming more common in urban nature research. However, there are recurring shortcomings in reporting. Many researchers do not report enough methodological details for a reader to understand what was done, how it was done, and why, as well as the context of the findings. This article is written to encourage QSM researchers to improve their reporting. The article is based on a list of common issues in urban nature research based on QSM emanating from the author’ s experiences using QSM, reviewing QSM studies, and analysing the empirical literature. Eight recommendations for improving QSM reporting are made, including: 1) articulating the “why” of the qualitative approach; 2) describing the data analysis procedures and techniques in more detail; 3) reporting the coding framework; 4) reporting results as insights extracted from data instead of describing the data; 5) describing participant recruitment and data collection strategies; 6) describing the participants; 7) including the questions of the research; and 8) collaborating with QSM researchers. Researchers working at the intersection of humans and nature in cities frequently cross disciplinary and methodological boundaries. Crossing these boundaries is essential for enriching urban nature research. When reporting the findings of research activities that involve QSM, researchers should consider these eight recommendations and describe what they did, how they did it, and why the did it, with more clarity and intention.

Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.

Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system. We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation. The computation time was 0.44s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions. Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.

Motivating Santri to Choose Islamic Boarding Schools as a Means of Shaping Islamic Character

This study aims to determine, understand, and describe the motivation of students to choose Islamic boarding schools as a means of forming Islamic character and the most dominant motivational factors for students in Sungon Hamlet. This type of research is qualitative using a naturalistic approach. Sources of data obtained are primary and secondary data sources. Data collection techniques were carried out by means of observation, interviews and documentation. While the technique of data analysis and interpretation by means of data reduction, data presentation and data verification. The results of this study reveal that students choose Islamic boarding schools as a means of forming Islamic character because they get encouragement from their parents. In addition, there is self-motivation. Because every parent wants a child who has noble character, is good at the Koran, avoids negative actions and is able to know the teachings of Islam widely. Factors that motivate students to choose Islamic boarding schools as a means of character building are dominated by external factors. The factors that encourage students to choose Islamic boarding schools are factors from their parents and factors that grow from within themselves. So, it can be concluded that the factor that encourages students to continue their education in Islamic boarding schools and aims to build their character is that the majority of the encouragement comes from their parents.