Implicit Classification Research Articles

An enhanced crack segmentation method using implicit classification and inference rules in steel bridge

An enhanced crack segmentation method using implicit classification and inference rules in steel bridge

A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling

This paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based on an implicit decision variable classification approach (DCC/IDVCA) is developed to search for robust order schedules. To significantly reduce the computational resources required for solving the high-dimensional robust order scheduling problem, we propose decomposing the original decision variables through implicit classification methods. First, a novel estimation method is introduced to evaluate the weighted contribution of variables to robustness. This method utilizes historical information, including the variation of the overall mean effective fitness and the frequency of variables being classified into highly robustness-related subcomponents in previous cycles, for evaluating their weighted contribution to robustness. Then, based on the corresponding weighted robustness contributions, the original variables are classified into highly and weakly robustness-related variables. Finally, these two types of variables are decomposed into highly and weakly robustness-related subgroups within a dynamic cooperative coevolution framework and optimized separately. In the experimental section, the proposed algorithm is applied to two practical order scheduling problems in discrete manufacturing industry. The experimental results demonstrate that the proposed algorithm achieves competitive outcomes compared to state-of-the-art high-dimensional robust multi-objective optimization algorithms.

Combining Contrastive Learning with Auto-Encoder for Out-of-Distribution Detection

Reliability and robustness are fundamental requisites for the successful integration of deep-learning models into real-world applications. Deployed models must exhibit an awareness of their limitations, necessitating the ability to discern out-of-distribution (OOD) data and prompt human intervention, a critical competency. While several frameworks for OOD detection have been introduced and achieved remarkable results, most state-of-the-art (SOTA) models rely on supervised learning with annotated data for their training. However, acquiring labeled data can be a demanding, time-consuming or, in some cases, an infeasible task. Consequently, unsupervised learning has gained substantial traction and has made noteworthy advancements. It empowers models to undergo training solely on unlabeled data while still achieving comparable or even superior performance compared to supervised alternatives. Among the array of unsupervised methods, contrastive learning has asserted its effectiveness in feature extraction for a variety of downstream tasks. Conversely, auto-encoders are extensively employed to acquire indispensable representations that faithfully reconstruct input data. In this study, we introduce a novel approach that amalgamates contrastive learning with auto-encoders for OOD detection using unlabeled data. Contrastive learning diligently tightens the grouping of in-distribution data while meticulously segregating OOD data, and the auto-encoder augments the feature space with increased refinement. Within this framework, data undergo implicit classification into in-distribution and OOD categories with a notable degree of precision. Our experimental findings manifest that this method surpasses most of the existing detectors reliant on unlabeled data or even labeled data. By incorporating an auto-encoder into an unsupervised learning framework and training it on the CIFAR-100 dataset, our model enhances the detection rate of unsupervised learning methods by an average of 5.8%. Moreover, it outperforms the supervised-based OOD detector by an average margin of 11%.

Development of a Machine-Learning Classification Model for an Electrochemical Nitrogen Oxides Sensor in Gasoline Powertrains

<div>Future automotive emission regulations are becoming increasingly dependent on off-cycle (acquired on road and referred to as “real-world”) driving and testing. This was driven in part by the often-observed fact that laboratory emission drive cycles (developed to evaluate a vehicle’s emissions on a chassis dynamometer) may not fully capture the nature of real-world driving. As a result, portable emission measurement systems were developed that could be fit in the trunk of a vehicle, but were relatively large, expensive, and complex to operate. It would be advantageous to have low-cost and simple to operate on-board sensors that could be used in a gasoline powertrain to monitor important criteria emission species, such as NO<sub>x</sub>. The electrochemical NO<sub>x</sub> sensor is often used for emissions control systems in diesel powertrains and a proven technology for application to the relatively harsh environment of automotive exhaust. However, electrochemical NO<sub>x</sub> sensors are nearly equally sensitive to both NO<sub>x</sub> and NH<sub>3</sub>, setting up an implicit classification problem that must be solved before they can accurately measure NO<sub>x</sub>. In this work, we develop a machine-learning model to classify the output of a NO<sub>x</sub> sensor in a gasoline powertrain. A model generalization study is conducted, and the model is found to be ~96% accurate and able to predict NO<sub>x</sub> mass emitted over a drive cycle within ~9% of a perfectly classified NO<sub>x</sub> sensor.</div>

An adaptive anti-noise gear fault diagnosis method based on attention residual prototypical network under limited samples

Deep learning networks are widely used to realize the intelligent diagnosis of gear faults. However, the problem of the insufficient number of typical fault samples and strong noise often occur in practical applications. Thence, this paper proposes an adaptive anti-noise gear fault diagnosis method based on attention residual prototypical network (ARPN) under the limited sample. In order to maximize the characterization of implicit classification information under fewer samples, frequency slice wavelet transform is applied to convert the vibration signal into a time–frequency image. Then, the Bayesian optimization algorithm is introduced to automatically adjust hyperparameters to meet different application conditions. And the feature embedding stage combined with the improved Non-Local-Pooling-Attention module is constructed to capture the effective feature information better under strong noise conditions. Finally, the internal principle of the proposed model is analyzed based on the visualization process. Meanwhile, the initial application of an interpretable deep learning network in the classification of gear health status has been realized. The ARPN is verified on the Connecticut standard gear data set and the data set collected actually by the laboratory. The results show that the diagnostic accuracy of the ARPN is higher and has stronger recognition ability under different loads.

Implicit Aspect-Based Opinion Mining and Analysis of Airline Industry Based on User-Generated Reviews

Mining opinions from reviews has been a field of ever-growing research. These include mining opinions on document level, sentence level and even aspect level. While explicitly mentioned aspects from user-generated texts have been widely researched, very little work has been done in gathering opinions on aspects that are implied and not explicitly mentioned. Previous work to identify implicit aspects and opinion was limited to syntactic-based classifiers or other machine learning methods trained on restaurant dataset. In this paper, the present is a novel study for extracting and analysing implicit aspects and opinions from airline reviews in English. Through this study, an airline domain-specific aspect-based annotated corpus, and a novel two-way technique that first augments pre-trained word embeddings for sequential with stochastic gradient descent optimized conditional random fields (CRF) and second using machine and ensemble learning algorithms to classify the implied aspects is devised and developed. This two-way technique resolves double-implicit problem, most encountered by previous work in implicit aspect and opinion text mining. Experiments with a hold-out test set on the first level i.e., entity extraction by optimized CRF yield a result of ROC-AUC score of 96% and F1 score of 94% outperforming few baseline systems. Further experiments with a range of machine and ensemble learning classifier algorithms to classify implied aspects and opinions for each entity yields a result of ROC-AUC score ranging from 71 to 94.8% for all implied entities. This two-level technique for implicit aspect extraction and classification outperforms many baseline systems in this domain.

Les enjeux de la collaboration entre médecins généralistes et psychiatres

La collaboration des médecins généralistes et des psychiatres dans la prise en charge de la dépression chronique est considérée comme nécessaire, mais qualifiée de suboptimale dans la littérature. Cette étude qualitative vise à mieux cerner les facteurs qui influent sur la décision de référer un patient dépressif chronique au psychiatre. Pour ce faire, nous avons mené trois focus groups avec des généralistes suisses romands qui ont compté un total de vingt-deux participants. Les focus groups ont été enregistrés et retranscrits, puis codés par trois membres de l’équipe pluridisciplinaire de recherche, à l’aide du logiciel MaxQDA. Nous montrons qu’il existe un processus de classement des patients par les généralistes, entre ceux qui constituent des « bons cas » pour le psychiatre et ceux qui expriment leur souffrance uniquement par le corps, et qui ne seront traités qu’au cabinet du généraliste. Un tel classement peut, dès lors, produire des inégalités d’accès à la psychothérapie. Différentes raisons mènent les généralistes à référer leur patient. Nous constatons que celles-ci ne concernent que rarement la reconnaissance d’une expertise spécifique du psychiatre dans la prise en charge de la dépression chronique. Il apparaît aussi que les généralistes se perçoivent comme des spécialistes de la relation , qu’ils considèrent comme centrale dans la prise en charge de la dépression chronique. Par ailleurs, certains d’entre eux entretiennent des représentations négatives des psychiatres. Ces éléments évoquent l’existence d’enjeux autour des frontières professionnelles, ce qui peut jouer en défaveur d’une collaboration. En conclusion, une clarification des spécificités de chacun – par la formation et des rencontres interprofessionnelles – permettrait de diminuer les représentations négatives des généralistes sur les psychiatres et de favoriser la collaboration, facilitant ainsi l’acte de référer des patients dépressifs chroniques au psychiatre.

“Maybe it’s … skin colour?” How race-ethnicity and gender function in consumers’ formation of classification styles of cultural content

ABSTRACT Race-ethnicity and gender, while rarely explicitly considered for cultural consumption choices, are characteristics that can function implicitly in the classification of cultural content like films, literature or music. Embedded in classification styles – recurrent classificatory patterns in the habitual ways people choose, weigh and combine classifications at their disposal – such attributes are important for consumption practices. Based on visual Q methodology and 27 interviews with American and Dutch rock music consumers, we examine how consumers attend to, weigh and lump classifications, and to what extent gender and race-ethnicity drive classification processes in rock music – a genre historically dominated by white men. We identify four classification styles that consumers employ, in which race-ethnicity and gender function as classificatory tools. The analysis reveals that the implicit classification of “good” rock music as white and male, even though this is rejected discursively, is key in keeping whiteness and masculinity in place.

Cultural Structures of Knowledge from Wikipedia Networks of First Links

Knowledge is useless without structure. While the classification of knowledge has been an enduring philosophical enterprise, it recently found applications in computer science, notably for artificial intelligence. The availability of large databases allowed for complex ontologies to be built automatically, for example by extracting structured content from Wikipedia. However, this approach is subject to manual categorization decisions made by online editors. Here we show that an implicit classification hierarchy emerges spontaneously on Wikipedia. We study the network of first links between articles, and find that it centers on a core cycle involving concepts of fundamental classifying importance. We argue that this structure is rooted in cultural history. For European languages, articles like Philosophy and Science are central, whereas Human and Earth dominate for East Asian languages. This reflects the differences between ancient Greek thought and Chinese tradition. Our results reveal the powerful influence of culture on the intrinsic architecture of complex data sets.

Using children’s search patterns to predict the quality of their creative problem solving

PurposeThe purpose of this paper is to propose a computational model that implicitly predict the children’s creative quality of solutions by analyzing the query pattern on a problem-solving-based lesson.Design/methodology/approachA search task related to the competencies acquired in the classroom was applied to automatically measure children’ creativity. A blind review process of the creative quality was developed of 255 primary school students’ solutions.FindingsWhile there are many creativity training programs that have proven effective, many of these programs require measuring creativity previously which involves time-consuming tasks conducted by experienced reviewers, i.e. far from primary school classroom dynamics. The authors have developed a model that predicts the creative quality of the given solution using the search queries pattern as input. This model has been used to predict the creative quality of 255 primary school students’ solutions with 80 percent sensitivity.Research limitations/implicationsAlthough the research was conducted with just one search task, participants come from two different countries. Therefore, the authors hope that this model provides detection of non-creative solutions to enable prompt intervention and improve the creative quality of solutions.Originality/valueThis is the first implicit classification model of query pattern in order to predict the children’ creative quality of solutions. This model is based on a conceptual relation between the concept association of creative thinking and query chain model of information search.

Cluster-type models in the logistics industry

The aim of the present article is to detect the basic principles of secure electronic document circulation (EDCS) systems for multi-location structure with multiple non-uniform connections. As far as the object of research is concerned, transport-logistical clusters are used. It is heterogeneous system with implicit classification. In the article were examined the basic stages of designing, methods and models of information security in electronic document circulation systems, operating in distributed network and interacting with other informational systems. The article contains analysis of regulatory and legal base in information security field of Russian Federation. The model of cluster information flows has been created for research purposes, based on hierarchical interaction model of transport-logistical cluster components. We describe transport-logistical cluster subjects, categorized information and supposed information assets. Based on potential threats and system vulnerabilities, mechanisms of EDCS information security have been described. EDCS data exchange process with another system, containing personal data has been described. As a result, a comprehensive set of measures was formed as well as information security tools based on requirements for data protection of personal data, and automated control systemsand transport-logistical cluster information systems at critical objects.

Improving Human-Machine Cooperative Classification Via Cognitive Theories of Similarity.

Acquiring perceptual expertise is slow and effortful. However, untrained novices can accurately make difficult classification decisions (e.g., skin-lesion diagnosis) by reformulating the task as similarity judgment. Given a query image and a set of reference images, individuals are asked to select the best matching reference. When references are suitably chosen, the procedure yields an implicit classification of the query image. To optimize reference selection, we develop and evaluate a predictive model of similarity-based choice. The model builds on existing psychological literature and accommodates stochastic, dynamic shifts of attention among visual feature dimensions. We perform a series of human experiments with two stimulus types (rectangles, faces) and nine classification tasks to validate the model and to demonstrate the model's potential to boost performance. Our system achieves high accuracy for participants who are naive as to the classification task, even when the classification task switches from trial to trial.

A Comparative Study of Feature Extraction Methods in Images Classification

Feature extraction is an important step in image classification. It allows to represent the content of images as perfectly as possible. However, in this paper, we present a comparison protocol of several feature extraction techniques under different classifiers. We evaluate the performance of feature extraction techniques in the context of image classification and we use both binary and multiclass classifications. The analyses of performance are conducted in term of: classification accuracy rate, recall, precision, f-measure and other evaluation measures. The aim of this research is to show the relevant feature extraction technique that improves the classification accuracy rate and provides the most implicit classification data. We analyze the models obtained by each feature extraction method under each classifier.

Integrative analysis of histone ChIP-seq and transcription data using Bayesian mixture models

Histone modifications are a key epigenetic mechanism to activate or repress the transcription of genes. Datasets of matched transcription data and histone modification data obtained by ChIP-seq exist, but methods for integrative analysis of both data types are still rare. Here, we present a novel bioinformatics approach to detect genes that show different transcript abundances between two conditions putatively caused by alterations in histone modification. We introduce a correlation measure for integrative analysis of ChIP-seq and gene transcription data measured by RNA sequencing or microarrays and demonstrate that a proper normalization of ChIP-seq data is crucial. We suggest applying Bayesian mixture models of different types of distributions to further study the distribution of the correlation measure. The implicit classification of the mixture models is used to detect genes with differences between two conditions in both gene transcription and histone modification. The method is applied to different datasets, and its superiority to a naive separate analysis of both data types is demonstrated. R/Bioconductor package epigenomix. h.klein@uni-muenster.de Supplementary information: Supplementary data are available at Bioinformatics online.

Predictive value and reward in implicit classification learning

Learning efficacy depends on its emotional context. The contents learned and the feedback received during training tinges this context. The objective here was to investigate the influence of content and feedback on the efficacy of implicit learning and to explore using functional imaging how these factors are processed in the brain. Twenty-one participants completed 150 trials of a probabilistic classification task (predicting sun or rain based on combinations of playing cards). Smileys or frowneys were presented as feedback. In 10 of these subjects, the task was performed during functional magnetic resonance imaging. Card combinations predicting sun were remembered better than those predicting rain. Similarly, positive feedback fortified learning more than negative feedback. The presentation of smileys recruited bilateral nucleus accumbens, sensorimotor cortex, and posterior cingulum more than negative feedback did. The higher the predictive value of a card combination, the more activation was found in the lateral cerebellum. Both context and feedback influence implicit classification learning. Similar to motor skill acquisition, positive feedback during classification learning is processed in part within the sensorimotor cortex, potentially reflecting the activation of a dopaminergic projection to motor cortex (Hosp et al., 2011). Activation of the lateral cerebellum during learning of combinations with high predictive value may reflect the formation of an internal model.

Implicit classification reveals priority of processing physical properties in novices but processing of higher cognitive properties in experts

Implicit classification reveals priority of processing physical properties in novices but processing of higher cognitive properties in experts

The relevance of classification theory to textual analysis

This article demonstrates the applicability of classification theory to various textual-analytic approaches such as grounded theory, content analysis, discourse analysis, and conversation analysis/membership categorization analysis. This applicability is based on three factors: extant and elicited texts can be broken down into categories that are essentially classification systems created and defined by the researcher; extant texts are themselves explicit or implicit classification systems; and classificatory frameworks can be applied to extant and elicited texts “in order to clarify their contribution to processes of meaning-making” (Fairclough, N. (2003). Analysing discourse: Textual analysis for social research. London: Routledge, p. 11). The recommendation is made that classification theory should be incorporated in the teaching of textual-analytic approaches in university-level research-methods courses, especially in the field of library and information science (LIS).

Enriched namespace to support content-aware authorization policies

In the near future, data on the order of hundreds of Petabytes will be spread in multiple storage systems worldwide dispersed in, potentially, billions of replicated data items. Users, typically, are agnostic about the location of their data and they want to get access by either specifying logical names or using some lookup mechanism.A global namespace is a logical layer that allows the view of data resources independently from the physical location. Usually, the naming scheme is designed to be easily interpreted by humans and it is organized into a purely user-defined directory hierarchy. Within this model, a data resource is uniquely addressed by file name and path. Nevertheless, this hierarchical structures of logical namespace lacks adequate flexibility to manage sophisticated organization of data. In particular the implicit classification of the data item derived from the path is not enough meaningful to classify data objects when different orthogonal dimensions are considered.In this paper we expose an enriched namespace able to support a new type of data access authorization policy based on tags. The tags are organized in well-defined hierarchies providing a simple representation of the domain ontology. Only authorized users can label data resources with different tags taken from the domain tag hierarchies. In this way an overlay of classical hierarchical structure of logical namespace with faceted hierarchical tags provides a semantics classification of data entities. Authorization policies defined in respect of tags are content-aware.

Patients with Parkinson's disease learn to control complex systems—an indication for intact implicit cognitive skill learning

Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

A model of aesthetic appreciation and aesthetic judgments

Although aesthetic experiences are frequent in modern life, there is as of yet no scientifically comprehensive theory that explains what psychologically constitutes such experiences. These experiences are particularly interesting because of their hedonic properties and the possibility to provide self-rewarding cognitive operations. We shall explain why modern art's large number of individualized styles, innovativeness and conceptuality offer positive aesthetic experiences. Moreover, the challenge of art is mainly driven by a need for understanding. Cognitive challenges of both abstract art and other conceptual, complex and multidimensional stimuli require an extension of previous approaches to empirical aesthetics. We present an information-processing stage model of aesthetic processing. According to the model, aesthetic experiences involve five stages: perception, explicit classification, implicit classification, cognitive mastering and evaluation. The model differentiates between aesthetic emotion and aesthetic judgments as two types of output.