Natural Categories Research Articles

XAI & I: Self-explanatory AI facilitating mutual understanding between AI and human experts

Traditionally, explainable artificial intelligence seeks to provide explanation and interpretability of high-performing black-box models such as deep neural networks. Interpretation of such models remains difficult, because of their high complexity. An alternative method is to instead force a deep-neural network to use human-intelligible features as the basis for its decisions. We tested this approach using the natural category domain of rock types. We compared the performance of a black-box implementation of transfer-learning using Resnet50 to that of a network first trained to predict expert-identified features and then forced to use these features to categorise rock images. The performance of this feature-constrained network was virtually identical to that of the unconstrained network. Further, a partially constrained network forced to condense down to a small number of features that was not trained with expert features did not result in these abstracted features being intelligible; nevertheless, an affine transformation of these features could be found that aligned well with expert-intelligible features. These findings show that making an AI intrinsically intelligible need not be at the cost of performance.

ნაბეღლავის მიწისქვეშა მინერალური წყლების ჰიდროქიმიური დახასიათება

Georgia is rich in groundwater deposits, which renew over time and are characterized by the best indicators of water quality and a stable regime. Groundwater is one of the main natural productive forces of Georgia, which plays an important role in the economic development and export industry. During 2015–2020, chemical composition of the Nabeghlavi mineral waters has been studied by the means of the modern unified methods. Almost all data from previous chemical analyzes have also been retrieved and systematized/collated. Using mathematical statistical analysis, the maximum, minimum, and mean arithmetic values of the major and specific components of water and the empirical deviation from the arithmetic mean have been calculated. Though comparison of the recent and historic data on chemical composition, based on the results of statistical analysis of the major ions, the natural fluctuation limits of the waters and stability of waters from all exploitation drill holes have been shown. According to the chemical composition and total mineralization there are three groups of waters in fresh and low mineralized category. It is shown that chemical composition of water from all exploitation drill holes meets requirements for the natural mineral water category, both the normative document of Georgia and directive of the European Commission.

The VIA virtue model: Half-Baked or brilliant?

ABSTRACT I have been studying the VIA virtue modelfor a number of years, partly because it complements other recent literature on virtue in important ways, and partly because the model was founded in what I consider a brilliant intuition: that common attributes underlying the ‘natural categories’ of positive personality, for which the 24 VIA character strengths attempt to be a comprehensive listing, should point us to the more abstract culturally shared values of the well-lived life. The potential utility of this intuition for research on virtues as concepts relevant to person-within-culture has been underestimated. This intuition provides a framework for exploring at least one longstanding issue in discussions of virtue, which is the development of a practically useful and objectively defensible list of core virtues. For these reasons, I think the VIA virtue model deserves as much respect as Martin Seligman’s more celebrated contributions.

Sustainable Materials for Liquid Repellent Coatings

A modern environmental safety approach requires the implementation of green or sustainable strategies, such as banning or significantly lowering the presence of harmful substances on the market or in the industrial environment. To date, the majority of highly performing solutions are still based on fluorine chemistry, even with a growing effort to lower its impact. Economic costs, but also persistence, long-term degradation, and transformation in the environment can raise issues about medium- and long-term effects on human health and wildlife. Coatings with high water and oil repellence are used worldwide in daily life and in industrial and research fields, such as self-cleansing, anti-icing, and anti-biofouling. The combination of a particular geometry or surface structure and low-energy materials results in unique properties related to a range of materials in natural or synthetic categories aiming to build, when possible, a fluorine-free world. This work revises recent and key literature to propose valid alternatives to fluoro compounds in terms of water and oil repellence, as well as stability and resistance to physico-chemical agents. In this paper, natural compounds like fatty acids and waxes are addressed together with more synthetic systems like silicon-based solutions, and polymeric and inorganic nanostructured coatings. Most of the revised papers deal with topics fulfilling environmental requirements but are mainly restricted to highly repellent water and aqueous systems. Nevertheless, new and sustainable strategies for providing suitable, highly oleophobic surfaces to lower fluorine presence have been reported from a small but growing body of literature.

Machine Learning and Medication Adherence: Scoping Review.

This is the first scoping review to focus broadly on the topics of machine learning and medication adherence. This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence. PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems. Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

Distinct timescales for the neuronal encoding of vocal signals in a high-order auditory area

The ability of the auditory system to selectively recognize natural sound categories while maintaining a certain degree of tolerance towards variations within these categories, which may have functional roles, is thought to be crucial for vocal communication. To date, it is still largely unknown how the balance between tolerance and sensitivity to variations in acoustic signals is coded at a neuronal level. Here, we investigate whether neurons in a high-order auditory area in zebra finches, a songbird species, are sensitive to natural variations in vocal signals by recording their responses to repeated exposures to identical and variant sound sequences. We used the songs of male birds which tend to be highly repetitive with only subtle variations between renditions. When playing these songs to both anesthetized and awake birds, we found that variations between songs did not affect the neuron firing rate but the temporal reliability of responses. This suggests that auditory processing operates on a range of distinct timescales, namely a short one to detect variations in vocal signals, and longer ones that allow the birds to tolerate variations in vocal signal structure and to encode the global context.

The Attentional Blink when viewing Natural vs. Urban Scenes

The current study aimed to identify how the allocation of attention when viewing natural vs. urban scenes impacts the attentional blink. Previous research has found that natural scenes produce a broader allocation of attention, allowing attention to spread across an image and potentially use less cognitive resources. Urban scenes, on the other hand, produce a narrowed allocation of attention, potentially allowing more efficient encoding of relevant information and inhibition of irrelevant information. Participants in Experiment 1 viewed a rapid serial visual presentation (RSVP) of 16 images from either a natural or urban scene category, where two target scenes were in color and the rest of the scenes were in greyscale. An attentional blink was evident by reduced accuracy for reporting a second target that occurred two or three scenes after the first, given that the first target was accurately reported. The attentional blink was reduced for urban scenes compared to natural scenes, suggesting that the difference in scene type contributes to more efficient allocation of attention for urban scenes. To test whether this difference in the size of the attentional blink is accompanied by a difference in spread of attention between scene types, Experiment 2 used a similar RSVP task to Experiment 1 but added a peripheral target detection task. A black dot was placed around the corner of some of the images and participants reported at the end of each trial if a dot was present or not. Dots were better detected for natural scene trials, suggesting that participants have a broader spread of attention for natural scenes. The current results suggest that urban scenes lead to a narrowed attention allocation that allowed participants to more efficiently prioritize and identify targets compared to a broad spread of attention when viewing natural scenes.

SHREC’21: Quantifying shape complexity

This paper presents the results of SHREC’21 track: Quantifying Shape Complexity. Our goal is to investigate how good the submitted shape complexity measures are (i.e. with respect to ground truth) and investigate the relationships between these complexity measures (i.e. with respect to correlations). The dataset consists of three collections: 1800 perturbed cube and sphere models classified into 4 categories, 50 shapes inspired from the fields of architecture and design classified into 2 categories, and the data from the Princeton Segmentation Benchmark, which consists of 19 natural object categories. We evaluate the performances of the methods by computing Kendall rank correlation coefficients both between the orders produced by each complexity measure and the ground truth and between the pair of orders produced by each pair of complexity measures. Our work, being a quantitative and reproducible analysis with justified ground truths, presents an improved means and methodology for the evaluation of shape complexity.

A comparison of organic-certified versus non-certified natural foods: Perceptions and motives and their influence on purchase behaviors

The present study investigates how consumers perceive organic-certified versus non-certified natural foods relative to each other, compares motives for purchasing them, and explores how these influence purchase behaviors toward the combined natural foods category. Analysis of a USA shopper survey finds there are still pronounced differences in perceptions, trust, and willingness to pay. Some 57% of respondents agree at a level of 4 or 5 (out of 5) that there are differences between organic-certified and non-certified natural foods. Mean levels of trust for natural and certified organic labels are 2.67 and 3.50 respectively. Consumers are willing to pay price premiums of 25% and 37% for non-certified and organic-certified respectively. Results show shoppers have shared and different motives for purchasing. No added growth hormones and No artificial additives or preservatives are the two reasons that appear in the top five for purchasing organic-certified and non-certified foods. Other top five reasons for organic-certified are agriculture oriented: No chemical pesticides, herbicides, or fertilizers, No antibiotics, and USDA organic certification. Other top five reasons for non-certified are processing oriented: No artificial flavors, colors, or sweeteners, Minimal processing, and No high fructose corn syrup. Some of these differences influence intended purchase behaviors. Trust in organic-certified and non-certified labels affects willingness to pay. Four of the organic-certified attributes have a relationship with one or more of purchase frequency, number of items per trip, weekly spending, and willingness to pay.

In the eye of the beholder: Is color classification consistent among human observers?

Colorful displays have evolved in multiple plant and animal species as signals to mutualists, antagonists, competitors, mates, and other potential receivers. Studies of color have long relied on subjective classifications of color by human observers. However, humans have a limited ability to perceive color compared to other animals, and human biological, cultural, and environmental variables can influence color perception. Here, we test the consistency of human color classification using fruit color as a model system. We used reflectance data of 67 tropical fruits and surveyed 786 participants to assess the degree to which (a) participants of different cultural and linguistic backgrounds agree on color classification of fruits; and (b) human classification to a discrete set of commonly used colors (e.g., red, blue, green) corresponds to natural clusters based on light reflectance measures processed through visual systems of other animals. We find that individual humans tend to agree on the colors they attribute to fruits across language groups. However, these colors do not correspond to clearly discernible clusters in di‐ or tetrachromatic visual systems. These results indicate that subjective color categorizations tend to be consistent among observers and can be used for large synthetic studies, but also that they do not fully reflect natural categories that are relevant to animal observers.

Spontaneous recovery of adaptation aftereffects of natural facial categories

Adaptation to a natural face attribute such as a happy face can bias the perception of a subsequent face in this dimension such as a neutral face. Such face adaptation aftereffects have been widely found in many natural facial categories. However, how temporally tuned mechanisms could control the temporal dynamics of natural face adaptation aftereffects remains unknown. To address the question, we used a deadaptation paradigm to examine whether the spontaneous recovery of natural facial aftereffects would emerge in four natural facial categories including variable categories (emotional expressions in Experiment 1 and eye gaze in Experiment 2) and invariable categories (facial gender in Experiment 3 and facial identity in Experiment 4). In the deadaptation paradigm, participants adapted to a face with an extreme attribute (such as a 100% angry face in Experiment 1) for a relatively long duration, and then deadapted to a face with an opposite extreme attribute (such as a 100% happy face in Experiment 1) for a relatively short duration. The time courses of face adaptation aftereffects were measured using a top-up manner. Deadaptation only masked the effects of initial longer-lasting adaptation, and the spontaneous recovery of adaptation aftereffects was observed at the post-test stage for all four natural facial categories. These results likely indicate that the temporal dynamics of adaptation aftereffects of natural facial categories may be controlled by multiple temporally tuned mechanisms.

Social categories in the making: construction or recruitment?

Real kinds, both natural and social categories, are characterized by rich inductive potential. They have relatively stable sets of conceptually independent projectable properties. Somewhat surprisingly, even some purely social categories (e.g., ethnicity, gender, political orientation) show such multiple projectability. The article explores the origin of the inductive richness of social categories and concepts. I argue that existing philosophical accounts provide only a partial explanation, and mechanisms of boundary formation and stabilization must be brought into view for a more comprehensive account of inductively rich social categories.

Introducing the structural bases of typicality effects in deep learning

In this paper, we hypothesize that the effects of the degree of typicality in natural semantic categories can be generated based on the structure of artificial categories learned with deep learning models. Motivated by the human approach to representing natural semantic categories and based on the Prototype Theory foundations, we propose a novel Computational Prototype Model (CPM) to represent the internal structure of semantic categories. Unlike other prototype learning approaches, our mathematical framework proposes a first approach to provide deep neural networks with the ability to model abstract semantic concepts such as category central semantic meaning, typicality degree of an object's image, and family resemblance relationship. We proposed several methodologies based on the typicality's concept to evaluate our CPM-model in image semantic processing tasks such as image classification, a global semantic description, and transfer learning. Our experiments on different image datasets, such as ImageNet and Coco, showed that our approach might be an admissible proposition in the effort to endow machines with greater power of abstraction for the semantic representation of objects' categories.

Fiber Selection for Reinforced Additive Manufacturing.

The purpose of this review is to survey, categorize, and compare the mechanical and thermal characteristics of fibers in order to assist designers with the selection of fibers for inclusion as reinforcing materials in the additive manufacturing process. The vast “family of fibers” is described with a Venn diagram to highlight natural, synthetic, organic, ceramic, and mineral categories. This review explores the history and practical uses of particular fiber types and explains fiber production methods in general terms. The focus is on short-cut fibers including staple fibers, chopped strands, and whiskers added to polymeric matrix resins to influence the bulk properties of the resulting printed materials. This review discusses common measurements for specific strength and tenacity in the textile and construction industries, including denier and tex, and discusses the proposed “yuri” measurement unit. Individual fibers are selected from subcategories and compared in terms of their mechanical and thermal properties, i.e., density, tensile strength, tensile stiffness, flexural rigidity, moisture regain, decomposition temperature, thermal expansion, and thermal conductivity. This review concludes with an example of the successful 3D printing of a large boat at the University of Maine and describes considerations for the selection of specific individual fibers used in the additive manufacturing process.

UJI DAYA HAMBAT PEWARNA ALAMI KULIT BATANG JAMBLANG (Syzygium cumini) TERHADAP BAKTERI Escherichia coli, Staphylococcus aureus DAN JAMUR Aspergilus niger

ABSTRACTJamblang (Syzygium cumini) belongs to the family of the guava tribe which has benefits as a food ingredient as well as herbal medicines. Jamblang bark produces tanners (tannins) to color the nets. The purpose of this study was to determine the inhibition of natural dyes of jamblang (Syzygium cumini) bark against Escherichia coli, Staphylococcus aureus, and Aspergillus niger fungi. The method used in this study wis an experimental method, which consists of 3 treatments of natual dyes of the bark cuminis where 40%, 60%, and 80% consentrations and 3 replicates. Data of the inhibitory activity where tested using paper disk method and was analyzed statistically using ANOVA test with SPSS 16.0. statistic aplication. The natural dyes from each concentration showed a inhibitoryactivity against E. coli, S. aureus, and A. niger. The effective concentration of inhibitory activity in E. Coli was 60%, natural dyes category zonas 4.0 mm or weak inhibitory activity The effective concentration of inhibitory activity in S. aureus was 40%, natural dyes category zonas was 7.0 mm or moderateinhibitory activity. The effective concentration of inhibitory activity in A. Niger was 80% natural dyes category zonas was 4.0 mm or weak inhibitory activity.Kaywords :Antibacterial test,Jamblang (Syzygiumcumini) bark, natural coloring Escherichia coli, Staphylococcus aureus, Aspergillus niger.

De‐Naturalizing the Novice: A Critique of the Theory of Language Socialization

ABSTRACTThis article critiques the concept “novice” in the language socialization paradigm. Although rarely theorized, the concept “novice” has framed what is seen as an event of language socialization: it must include at least one person who has not yet acquired some socially valuable characteristic or skill. Conversely, we propose that novicehood is not a natural category: agents who appear as “novices” only do so relative to ideological worlds in which they are made to appear relatively incapable. We then go on to consider three consequences of this reformulation: agents and objects of socialization do not have to be human, socialization does not necessarily lead “upward” towards expertise, and the linguistic mediation of these processes does not always summon agents towards maturity. This critique leads us to propose a theory that both includes a broader range of socializable agents and connects socialization to questions of power and exclusion. [language socialization, novice, ideology, life course, race]

How to Manage Disasters while Considering Responsibility to Protect Theory?

Disasters are an integral part of human life, which have been grown increasingly in recent years. Disasters are divided into human-made and natural categories [1]. In this study, two cases are considered. One is deliberate human-made disasters in such conditions that human crimes or vicious crimes occur in countries, and the government is the criminal factor or is not able to control the crimes. The other case is natural disasters in which people are hurt and suffer from death and diseases, and although the host country is not able to be responsive which it refuses to accept international grants.

Changes in the Intelligence Levels and Structure in Russia: An ANOVA Method Based on Discretization and Grouping of Factors

In the present paper, we investigate how the general intelligence quotient (IQ) and its subtests changed for students from Russian University from 1991 to 2013. This study of the effect of such factors as gender, department, and year on the IQ response is carried out using the ANOVA model. Given the unevenness of the initial sample by years and departments, and consequently, heterogeneity of variances when divided by the original natural categories, we decided to aggregate the values of explanatory variables to build an adequate model. The paper proposes and investigates an algorithm for joint discretization and grouping, which uses the procedure of partial screening of solutions. It is an intermediate option between the greedy algorithm and exhaustive search. As a goodness function (an optimality criterion), we investigate 26 intermediate options between the AIC and BIC criteria. The BIC turned out to be the most informative and the most acceptable criterion for interpretation, which penalizes the complexity of the model, due to some decrease in accuracy. The resulting partition of the explanatory variables values into categories is used to interpret the modeling results and to arrive at the final conclusions of the data analysis. As a result, it is revealed that the observed features of the IQ dynamics are caused by changes in the education system and the socio-economic status of the family that occurred in Russia during the period of restructuring the society and intensive development of information technologies.

Concepts and essential features of environmental land use

Among the most important problems of socio-economic development of society, special importance belongs to the effective use of land and resources, on which depends the economic, social, environmental security of the state and living standards. Negative trends observed in the process of land use require new organizational and legal approaches to regulating land use and creating economic incentives for rational use and protection of land in Ukraine. In this article the concept and functioning of "land use" as a natural, legal and economic category is considered. The essence and formation of an integrated system of environmental land use, which includes: part of a single land fund (land, land and rights to them), provided by the state or acquired or leased by a separate owner or user, the boundaries of which are determined on the ground; land object of law, object of economic, ecological, town-planning, agricultural and other land relations, for which the land user is issued a document certifying the right to land with certain boundaries, area, composition of lands, property objects and, if necessary, with geodetic coordinates of boundary signs defined in nature and the process of human (society) use of the integrated potential of the natural territory are revealed. The concept and essence of "land-territorial resource" are studied.

On planetary systems as ordered sequences

ABSTRACT A planetary system consists of a host star and one or more planets, arranged into a particular configuration. Here, we consider what information belongs to the configuration, or ordering, of 4286 Kepler planets in their 3277 planetary systems. First, we train a neural network model to predict the radius and period of a planet based on the properties of its host star and the radii and period of its neighbours. The mean absolute error (MAE) of the predictions of the trained model is a factor of 2.1 better than the MAE of the predictions of a naive model that draws randomly from dynamically allowable periods and radii. Secondly, we adapt a model used for unsupervised part-of-speech tagging in computational linguistics to investigate whether planets or planetary systems fall into natural categories with physically interpretable ‘grammatical rules.’ The model identifies two robust groups of planetary systems: (1) compact multiplanet systems and (2) systems around giant stars (log g ≲ 4.0), although the latter group is strongly sculpted by the selection bias of the transit method. These results reinforce the idea that planetary systems are not random sequences – instead, as a population, they contain predictable patterns that can provide insight into the formation and evolution of planetary systems.