Set Of Categories Research Articles

Urban residential water and electricity consumption behavior: A systematic literature review

Consumer behavior is an understudied determinant of residential water and electricity demand and, when studied, capturing, measuring, and quantifying attitudes and behaviors is often problematic. This research explores behavioral determinants of the demand for water and electricity, contributing a set of categories and items that may be useful for understanding knowledge, attitudes, and practices related to consumption and constructing valid scales. These results may help researchers capture the complexity of consumer behavior regarding water and electricity and decision-makers in designing tailored demand management strategies.

Use of SafeGraph visitation patterns for the identification of essential services during COVID-19

Abstract. Mobility was severely impacted during the COVID-19 pandemic due to travel restrictions, curfews, and closed locations, such as businesses, schools, administrative offices, and other points of interest (POIs). This study uses SafeGraph visitation data to identify which POI categories experienced a significant drop in visitation counts and distance travelled from home between March/April 2019 (pre-pandemic) and March/April 2020 (during the pandemic) in Florida and California. The analysis identified a small set of POI categories for which visitation counts and travel distance did not change significantly. They can be characterized as essential services which are needed for a community’s everyday operations. This study provides insight into the effect of COVID-19 on people’s travel behavior to different types of trip destinations during the pandemic.

Category-specific learning of color, orientation, and position regularities guide visual search.

In six experiments, we examined how object categories structure the learning of environmental regularities to guide visual search. Participants searched for pictures of exemplars from a set of real-world categories in a repeated search task modeled on the contextual cuing literature. Each trial began with a category label cue, followed by a search array of natural object photographs, with one target object matching the category label. Participants completed a series of search blocks, each containing one search trial per category. Individual categories were assigned either to the Repeated condition or to the Novel condition. For Repeated categories, a perceptual feature value of target objects remained constant across each search for that category: color (Experiments 1 and 3), orientation (Experiment 2), and position (Experiment 4). For Novel categories, the relevant feature value varied randomly for each search for that category. We observed a categorical cuing effect, with faster improvement in reaction time across blocks for Repeated compared with Novel categories. This effect reflected both the episodic retrieval of the immediately preceding search episode in that category and cumulative learning across multiple searches within a category. The cuing effect was observed from the very first repetition, a point in the experiment where the learning effect was not plausibly strategic. Finally, participants could reliably retrieve and report the repeated values in memory tests administered either at the end of the experiment or when the effect first emerged (Experiments 5 and 6), demonstrating that nonstrategic guidance of attention can be driven by explicitly available memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Performance comparison of different convolutional neural networks for vegetable and fruit recognition

Image classification, a significant research problem in the computer vision community, aims to assign different types of images to a certain category in a fixed category set according to different information features reflected in images. With the continuous improvement of living standards, people's daily demand for accurate identification of food categories (such as vegetable, fruit, etc.) is growing. Early vegetable and fruit recognition mostly relied on manual features and machine learning algorithms, with low recognition accuracy and weak generalization ability. Thanks to the rapid evolution of the convolutional neural network, vegetable and fruit recognition based on depth learning has made breakthroughs in accuracy and speed. In this paper, three representative convolutional neural networks are introduced around vegetable and fruit recognition, and their performance differences are quantitatively compared on different data sets to explore the boundaries of their applications. In addition, we summarized the research problems in vegetable and fruit recognition and discussed its future development direction.

Conceptual complexes in multimodal advertising

Several scholars have dealt with the workings of metaphor and metonymy in multimodal advertisements (see Forceville, 2009; Uriós-Aparisi, 2009; or Pérez-Sobrino, 2017, among others). The present study investigates conceptual complexity to broaden the set of analytical categories to be used in multimodal analysis by making use of some of the latest developments on conceptual complexes, or principled combinations of cognitive models (e.g., frames, metaphors, metonymies), as discussed in Ruiz de Mendoza (2017, 2021) to a multimodal context. Work on conceptual complexity in Cognitive Linguistics has taken two main directions. One is provided by Blending Theory, which focuses on accounting for the emergence of new structure not present in the contributing conceptual constructs after selected integration. Another direction studies patterns of conceptual interaction with a view to finding regularities that can be formulated as high-level generalizations. We will adopt this second direction. The main aim of the study is to test these analytical categories and principles of knowledge organization in terms of their communicative impact within a multimodal environment. A subsidiary aim is to further develop the theoretical apparatus underlying this initial work. Analysing a corpus of 62 multimodal advertisements, we found that: (i) the nature of a frame determines its function, i.e., matrix frames are receiving frames, which ‘situationalize’ conceptual structure, whereas donor frames play a focal role; (ii) sometimes there is no frame integration but rather internal development within a given frame, which is possible thanks to the incorporation of an external element that is not integrated, but simply facilitates the development of the frame; (iii) there are also cases in which there is frame composition instead of integration; (iv) metonymy proves to be a licensing factor previous to integration, and (v) high-level non-metaphorical correlations can act as cues for the activation of metaphorical frameworks.

KTSDroid: A Framework for Android Malware Categorization Using the Kernel Task Structure

The penetration of malicious applications in the Android market has enhanced the significance of designing malware mitigation systems for Android. Malware detection systems are being developed by examining applications using static and dynamic analysis techniques. The use of code obfuscation has highlighted the importance of dynamic analysis as many static analysis schemes can be evaded by code obfuscation strategies. In order to record the true working of the application, a volatile memory-based solution for application analysis is presented in this study. Time-based memory dumps are collected after interactions with an application. Process-specific artifacts of the application under analysis are extracted by examining the kernel task structure of memory. The features in the kernel task structure belong to nine broad categories based on their semantics. An important contribution of the study is the analysis of the kernel task structure for determining the set of effective categories and features for Android malware categorization. Three of the most important categories and fourteen valuable features are reported. The proposed system categorizes the applications into five classes: adware, banking Trojans, riskware, SMS Trojans, and benign. The proposed system is able to categorize applications with an average F1-score of 0.984, which is the highest score reported so far for multiclass Android malware categorization with a minimum number of kernel task structure-based features.

Private security discretion: Economic rules, social norms and situational judgement in the night-time economy

This article seeks to transform private security discretion from a subject discussed in passing to a substantive area of research. It first draws upon the police discretion literature to construct a socio-economic model of private security discretion. It then uses this model to explore one specific articulation of discretion: the moral discretion of door supervisors as they deal with serious incidents in the night-time economy. It uncovers three distinctive rule–norm–discretion configurations: ‘enabling’ (where economic rules and moral standards line up to facilitate a straightforward mode of moral discretion); ‘constraining’ (where economic rules override countervailing moral standards to prevent a desired mode of moral discretion); and ‘complex’ (where moral standards take precedence over prohibitive economic rules to generate a circuitous mode of moral discretion). Through this analysis, the article offers an original set of categories for studying private security discretion while simultaneously deepening our socio-economic understanding of the market for security.

Grammatical category versus comparative concept in missionary grammars of Tamil (16th-18th centuries): the description of the relative clause

ABSTRACT Linguists today presuppose that for both description/analysis and comparison of languages, there is a substantial set of universal crosslinguistic categories. In the same way, in the grammaticisation of non-European languages, missionaries took the grammatical categories elaborated for Latin as a set of universal crosslinguistic categories. One could assume that they fell into the trap of transferring time-tested terminology from traditional Latin grammar adapting it to the description of a language with rather different structural properties without really capturing its ‘genius’. Even if this assumption may certainly be true to some extent, it does not present the whole picture and it does not do justice to the efforts missionaries made in describing new languages. Therefore, focusing on how missionaries understood and described the Tamil relative clause and relativiser marker, this paper aims to discuss the tension arising in grammatical descriptions between language-particular categories and crosslinguistic conceptual transfer. The paper thus demonstrates how missionaries did not limit their accounts to the transfer of a priori grammatical categories, tailored for Latin and applied to Tamil, but rather how they refined their descriptions through a crosslinguistic conceptual transfer that may be considered the first step towards comparative concepts.

Core Challenges of Social Robot Navigation: A Survey

Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in crowded environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in robot planning, behavior design, and evaluation methodologies. Within these categories, we review past work and offer directions for future research. Our work builds upon and extends earlier survey efforts by (a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and (b) offering constructive feedback and ideas that could inspire research in the field over the coming decade.

VacancySBERT: the approach for representation of titles and skillsfor semantic similarity search in the recruitment domain

The paper focuses on deep learning semantic search algorithms applied in the HR domain. The aim of the article is developinga novel approach to training a Siamese network to link the skills mentioned in the job ad with the title.It has been shown that the title normalization process can be based either on classification or similarity comparison approaches. While classification algorithms strive to classify a sample into predefined set of categories, similarity search algorithms take a more flexible approach, since they are designed to find samples that are similar to a given query sample, without requiring pre-defined classes and labels. In this article semantic similarity search to find candidates for title normalization has been used. A pre-trained language model has been adapted while teaching it to match titles and skills based on co-occurrence information. For the purpose of this research fifty billion title-descriptions pairs had been collected for training the model and thirty three thousand title-description-normalized title triplets, where normalized job title was picked up manually by job ad creator for testing purposes. As baselines FastText, BERT, SentenceBert and JobBert have been used. As a metric of the accuracy of the designed algorithm is Recall in top one, five and ten model’s suggestions. It has been shown that the novel training objective lets it achieve significant improvement in comparison to other generic and specific text encoders. Two settings with treating titles as standalone strings, and with included skills as additional features during inference have been used and the results have been compared in this article. Improvements by 10% and 21.5% have been achieved using VacancySBERT and VacancySBERT (with skills) respectively. The benchmark has been developed as open-source to foster further research in the area.

A novel multisensory quality index of a food product: An analysis of a sausage properties

A novel multisensory quality index of a food product is proposed as the negative common logarithm of the probability of a joint event, when the product quality properties under control are characterized simultaneously by the specified set of categories of the assessor/expert responses. This index is related to the entropy (a measure of uncertainty) of the probability distribution of the responses. R-code for calculation of the index was developed and validated. As a case study, the index was evaluated for a sausage, produced according to the same standard by two different manufacturers in a two-year period. Each batch of sausage was tested at the manufactures' laboratories for its chemical composition and for five sensory properties: appearance, consistency, color, smell and taste. The sensory properties were examined by five experts employed by each manufacturer. A total of 650 expert responses to the quality properties of 26 sausage batches of one manufacturer, and 1350 responses to the properties of 54 batches of the such sausage of the second manufacturer were studied. Since sausage sensory data are influenced by the product's chemical composition, the hypotheses of homogeneity of the product composition (measured values of mass fractions of protein, fat, moisture and salt) between the two manufacturers were tested and rejected. Then, for each manufacturer separately, the hypothesis of homogeneity of the responses by the different experts on the same quality property was tested and not rejected. A correlation analysis of the responses to different properties was performed for the corresponding datasets. The data of the first manufacturer were assumed to be uncorrelated, while correlation between all pairs of quality properties of the second manufacturer was statistically significant. The influence of the correlation on the quality index values was assessed, and the sausage quality of the two manufacturers was compared.

Transcriptome analysis reveals insight into the protective effect of N-acetylcysteine against cadmium toxicity in Ganoderma lucidum (Polyporales: Polyporaceae)

Ganoderma lucidum is widely cultivated and used as traditional medicine in China and other Asian countries. As a member of macrofungi, Ganoderma lucidum is also prone to bioaccumulation of cadmium and other heavy metals in a polluted environment, which affects the growth and production of Ganoderma lucidum, as well as human health. N-Acetyl-L-cysteine (NAC) is considered a general antioxidant and free radical scavenger that is involved in the regulation of various stress responses in plants and animals. However, whether NAC could regulate cadmium stress responses in macrofungi, particularly edible fungi, is still unknown. In this work, we found that the exogenous NAC could alleviate Cd-induced growth inhibition and reduce the cadmium accumulation in Ganoderma lucidum. The application of the NAC cloud also inhibit cadmium-induced H2O2 production in the mycelia. By using transcriptome analysis, 2920 and 1046 differentially expressed unigenes were identified in "Cd100 vs CK" and "NAC_Cd100 vs Cd100," respectively. These differential unigenes were classified into a set of functional categories and pathways, which indicated that various biological pathways may play critical roles in the protective effect of NAC against Cd‑induced toxicity in Ganoderma lucidum. Furthermore, it suggested that the ATP-binding cassette transporter, ZIP transporter, heat shock protein, glutathione transferases, and Cytochrome P450 genes contributed to the increased tolerance to cadmium stress after NAC application in Ganoderma lucidum. These results provide new insight into the physiological and molecular response of Ganoderma lucidum to cadmium stress and the protective role of NAC against cadmium toxicity.

Evangelical or Mainline? Doctrinal Similarity and Difference in Asian American Christianity: Sketching a Social-Practical Theory of Christian Doctrine

This article takes Asian American Christianity to be an analytically productive religion for advancing a theory of Christian doctrine. This is in large part due to the trans-Pacific character of Asian American Christians who, by virtue of their racialization, make explicit the different social circumstances—from Anglo-European Christians—as well as shared ends in which Christian doctrinal commitments operate. Asian American Christians problematize the conventional wisdom assumed in the academic and public discourses concerning Christianity in the US. One of the primary set of categories in the discourses about Christianity in the US is the theological difference between evangelical and mainline Protestants. Moreover, these theological and doctrinal categories are taken to describe and define these two social groups of Christians. By centering empirical studies of Asian American Christian faith and practice, this article claims that doctrinal similarity and doctrinal difference, such as that between evangelical and mainline Protestants, do not simply explain social group similarity or difference as assumed by conventional wisdom. Instead, these Asian American case studies point to the need for a new theory of Christian doctrine that can explain the normative significance of doctrinal similarity and difference in terms of the uses of doctrine.

Rethinking symbolic and visual context in Referring Expression Generation.

Situational context is crucial for linguistic reference to visible objects, since the same description can refer unambiguously to an object in one context but be ambiguous or misleading in others. This also applies to Referring Expression Generation (REG), where the production of identifying descriptions is always dependent on a given context. Research in REG has long represented visual domains through symbolic information about objects and their properties, to determine identifying sets of target features during content determination. In recent years, research in visual REG has turned to neural modeling and recasted the REG task as an inherently multimodal problem, looking at more natural settings such as generating descriptions for objects in photographs. Characterizing the precise ways in which context influences generation is challenging in both paradigms, as context is notoriously lacking precise definitions and categorization. In multimodal settings, however, these problems are further exacerbated by the increased complexity and low-level representation of perceptual inputs. The main goal of this article is to provide a systematic review of the types and functions of visual context across various approaches to REG so far and to argue for integrating and extending different perspectives on visual context that currently co-exist in research on REG. By analyzing the ways in which symbolic REG integrates context in rule-based approaches, we derive a set of categories of contextual integration, including the distinction between positive and negative semantic forces exerted by context during reference generation. Using this as a framework, we show that so far existing work in visual REG has considered only some of the ways in which visual context can facilitate end-to-end reference generation. Connecting with preceding research in related areas, as possible directions for future research, we highlight some additional ways in which contextual integration can be incorporated into REG and other multimodal generation tasks.

A forecasting method of multi-category product sales: analysis and application

To solve the problems of high prediction costs and difficult practices in multi-category product classification in the retail industry, optimize the inventory, and improve resilience, this work introduces a forecasting method for multi-category product sales. The forecasting method divides the data into a category set and a numerical set, uses the stacking strategy, and combines it with catboost, decision tree, and extreme gradient boosting. During the feature engineering process, the ratio and classification features are added to the category feature set; the sales at t are used for training to obtain the prediction at (t + 1); and the features used in the prediction at time (t + 1) are generated by the prediction results at t. The update processes of the two sets are combined to form a joint feature update mechanism, and multiple features of k categories are jointly updated. Using this method, data of all categories of retail stores can be linked so that historical data of different categories of goods can provide support for the prediction of each category of goods and solve the problem of insufficient product data and features. The method is verified on the retail sales data obtained from the Kaggle platform, and the mean absolute error and weighted mean absolute percentage error are adopted to analyze the performance of the model. The results reveal that the forecasting method can provide a useful reference to decision-makers.

Hochschild homology, and a persistent approach via connectivity digraphs

We introduce a persistent Hochschild homology framework for directed graphs. Hochschild homology groups of (path algebras of) directed graphs vanish in degree i≥2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i\\ge 2$$\\end{document}. To extend them to higher degrees, we introduce the notion of connectivity digraphs, and analyse two main examples; the first, arising from Atkin’s q-connectivity, and the second, here called n-path digraphs, generalising the classical notion of line graph. Based on a categorical setting for persistent homology, we propose a stable pipeline for computing persistent Hochschild homology groups. This pipeline is also amenable to other homology theories; for this reason, we complement our work with a survey on homology theories of directed graphs.

Separable Cowreaths Over Clifford Algebras

The fundamental notion of separability for commutative algebras was interpreted in categorical setting where also the stronger notion of heavily separability was introduced. These notions were extended to (co)algebras in monoidal categories, in particular to cowreaths. In this paper, we consider the cowreath left( Aotimes H_{4}^{op}, H_{4}, psi right) , where H_{4} is the Sweedler 4-dimensional Hopf algebra over a field k and A=Cl(alpha , beta , gamma ) is the Clifford algebra generated by two elements G, X with relations G^{2}=alpha , X^{2}=beta and XG+GX=gamma , (alpha , beta , gamma in k ) which becomes naturally an H_{4}-comodule algebra. We show that, when textrm{char}left( k right) ne 2, this cowreath is always separable and h-separable as well.

Building an Equitable Future Through Data Disaggregation.

Building an Equitable Future Through Data Disaggregation.

Analysis on technical structure and competitiveness of ICT manufacturing industries

In order to analyze the technical structure and international comparative advantage of the information and communication technology (ICT) manufacturing industry, a complete set of ICT manufacturing product categories has been constructed by matching National Economical Industry Classification (GB/T4754-2017) with Harmonized System (HS) Codes, based on the relevant definitions in International Standard Industrial Classification (ISIC). The proposed definition overcomes inherent defects such as inaccurate scopes, lagging data and rough categories, which are characterized by commonly utilized product-level based classification approaches. Within the given framework, this paper has designed the technology content related indicators from the perspective of production distribution, and divided ICT product categories into high-end, medium-end and low-end manufacturing classifications according to respective global shares. Then, we have calculated international market shares (IMS), revealed comparative advantages (RCA), and market penetration rates (MPR) of ICT manufacturing exports for major economies from 2010 to 2021. Finally, development characterizations of ICT manufacturing industries for China's Mainland are analyzed, and several practical suggestions are provided.

Gradual Sets: An Approach to Fuzzy Sets

In the fuzzy theory of sets and groups, the use of α-levels is a standard to translate problems from the fuzzy to the crisp framework. Using strong α-levels, it is possible to establish a one to one correspondence which makes possible doubly, a gradual and a functorial treatment of the fuzzy theory. The main result of this paper is to identify the class of fuzzy sets, respectively, fuzzy groups, with subcategories of the functorial categories Set (0, 1], resp., Gr (0, 1]. In this line, the algebraic potential of this theory will be reached, in forthcoming papers.