Classification Rules Research Articles

An Atlas of Grass Carp IgM+ B Cells in Homeostasis and Bacterial Infection Helps to Reveal the Unique Heterogeneity of B Cells in Early Vertebrates.

Teleost B cells are primitive lymphocytes with both innate and adaptive immune functions. However, the heterogeneity and differentiation trajectory of teleost B cells remain largely unknown. In this study, the landscape of grass carp IgM+ (gcIgM+) B cells was revealed by single-cell RNA sequencing. The results showed that gcIgM+ B cells mainly comprise six populations: (im)mature B cells, innate B cells, proliferating B cells, plasma cells, CD22+ cells, and CD34+ cells, among which innate B cells and proliferating B cells were uncommon B cell subsets with, to our knowledge, new characteristics. Remarkably, three functional IgMs were discovered in grass carp, and a significant percentage of gcIgM+ B cells, especially plasma cells, expressed multiple Igμ genes (Igμ1, Igμ2, and/or Igμ3). More importantly, through single-cell sorting combined with Sanger sequencing, we found that distinct VHDJH recombination patterns of Igμ genes were present in single IgM+ B cells, indicating that individual teleost B cells might produce multiple Abs by coexpressing rearranged IgM subclass genes. Moreover, the percentage of IgM1highIgM2highIgM3high plasma cells increased significantly after bacterial infection, suggesting that individual plasma cells might tend to produce multiple IgMs to resist the infection in teleost fish. In summary, to our knowledge, this study not only helps to uncover the unique heterogeneity of B cells in early vertebrates but also provided significant new evidence supporting the recently proposed "one cell-multiple Abs" paradigm, challenging the classical rule of "one cell-one Ab."

Neighborhood Rough Fuzzy Penetration Control Method with Variable Precision Based on GMAW

Considering the nonlinear, time-varying, and multivariate coupling nature of the welding process, achieving excellent control of the welding process can be challenging. In addition, welding experience varies from person to person, making it difficult to establish a uniform standard. In this work, rough set theory is introduced and applied to arc welding process modeling and quality control to achieve effective online control of weld penetration during welding. A variable precision neighborhood rough-fuzzy method is proposed to enhance the efficiency and adaptability of rough set theory for information processing in the welding process. By designing welding experiments with different gaps and currents, descriptors such as the tail area coefficient and the length-width ratio of the melt pool have been proposed to characterize the melt pool. Rough set theory has been used to extract decision classification rules for welding percolation state information, and clustering analysis, fuzzy logic, and min-max fuzzy methods have been introduced for knowledge modeling. The proposed variable precision neighborhood rough-fuzzy control model is verified via three sets of experiments, and the results show that the model has excellent stability and effectiveness.

The ReadFree tool for the identification of poor readers: a validation study based on a machine learning approach in monolingual and minority-language children

In this study, we validated the “ReadFree tool”, a computerised battery of 12 visual and auditory tasks developed to identify poor readers also in minority-language children (MLC). We tested the task-specific discriminant power on 142 Italian-monolingual participants (8–13 years old) divided into monolingual poor readers (N = 37) and good readers (N = 105) according to standardised Italian reading tests. The performances at the discriminant tasks of the “ReadFree tool” were entered into a classification and regression tree (CART) model to identify monolingual poor and good readers. The set of classification rules extracted from the CART model were applied to the MLC’s performance and the ensuing classification was compared to the one based on standardised Italian reading tests. According to the CART model, auditory go-no/go (regular), RAN and Entrainment100bpm were the most discriminant tasks. When compared with the clinical classification, the CART model accuracy was 86% for the monolinguals and 76% for the MLC. Executive functions and timing skills turned out to have a relevant role in reading. Results of the CART model on MLC support the idea that ad hoc standardised tasks that go beyond reading are needed.

Optimal test procedures for multiple hypotheses controlling the familywise expected loss.

We consider the problem of testing multiple null hypotheses, where a decision to reject or retain must be made for each one and embedding incorrect decisions into a real-life context may inflict different losses. We argue that traditional methods controlling the Type I error rate may be too restrictive in this situation and that the standard familywise error rate may not be appropriate. Using a decision-theoretic approach, we define suitable loss functions for a given decision rule, where incorrect decisions can be treated unequally by assigning different loss values. Taking expectation with respect to the sampling distribution of the data allows us to control the familywise expected loss instead of the conventional familywise error rate. Different loss functions can be adopted, and we search for decision rules that satisfy certain optimality criteria within a broad class of decision rules for which the expected loss is bounded by a fixed threshold under any parameter configuration. We illustrate the methods with the problem of establishing efficacy of a new medicinal treatment in non-overlapping subgroups ofpatients.

Searching for explanations of black-box classifiers in the space of semantic queries

Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.

Coupled Lattice Boltzmann and Discrete Element Simulations of Ship-Ice Interactions

Evaluating ice loads acting on ships is essential for the safety of ships navigating in ice-covered seas. In this study, we develop a CFD method to handle ship, ice, and fluid interaction. The lattice Boltzmann method, capable of large-scale calculations, is applied to the simulation of free-surface fluids. The ice motion is computed by solving the equations of motion of a rigid body, and the discrete element method models the ice-ice and ice-ship contact interactions. A momentum exchange scheme couples the lattice Boltzmann method and particle-based rigid body simulation. We introduce tree-based adaptive mesh refinement and multiple GPU computing to improve grid resolution and computational time. The proposed method is applied to model scale simulations of ship navigation in a brash ice channel. Simulations were performed for various conditions with different ice concentrations and ship velocities, and we observed that ice resistance increased with the ice concentration and the ship velocity increased. The ice motions and resistances obtained from our simulations are reasonable compared to model equations of Finnish-Swedish ice class rules (FSICR) and numerical analyses of a previous study.

The in-group egalitarian Owen values

This paper proposes a new class of allocation rules, which is referred to as the in-group egalitarian Owen values, that integrates two seemingly conflicting principles—marginalism and egalitarianism—in the framework of cooperative games with coalition structures. This class of allocation rules facilitates the use of different principles of allocation in different layers of a social structure such as allocation across coalitions and within a coalition. Therefore, each coalition can employ its specific distributive principle for intra-coalition distribution. Our main results provide axiomatic foundations for the class of allocation rules, in which monotonicity properties highlight the importance of the proposed solutions. We also show that various monotonicity properties generate various allocation rules, where the Owen value and the in-group egalitarian division value are special cases. These results extend the egalitarian Shapley values to the games with coalition structures and offer a rationale for monotonic allocation rules in the presence of social structures.

ANALISIS STRATEGI GURU DALAM PELAKSANAAN PENGELOLAAN KELAS OLEH GURU KELAS V SD SWASTA ASSISI MEDAN

The purpose of this research is to find out the teacher's strategy in implementing management class V in SD Private Assisi Medan. Type study descriptive qualitative. Technique collection data observation and interview. Results study that is strategy teacher in implementation management class Among other is teacher carry out organizing class with form organization class, list picket and class rules that have a goal so that the class is more organized and students have direction in the responsible answer on class. In arranging the place to sit down, the teacher places students according to the physical, psychological, and model conditions of learning that are applied in the classroom. Tools lessons are neatly arranged, the space class is maintained with good, clean, and comfortable, lighting enough, ventilation function, as well as Wall class gives color interesting for students.

Mapping the vertical forest structure in a large subtropical region using airborne LiDAR data

Vertical forest structure (VFS) refers to the vertical stratification or layering of forest communities in space, which is a fundamental characteristic of a plant community. It plays a vital role in forest vitality and facilitates various ecological activities and processes. The mapping of VFS is of significant value for both ecological and forestry purposes. In this paper, we presented a novel approach for the automated mapping of VFS in a large subtropical region based on discrete airborne LiDAR data. Firstly, the LiDAR point clouds of a stand (plot or grid cell) were segmented into 100 height bins from the top to the ground, and a height-frequency histogram was obtained by calculating the proportion of the number of returns in the bins to the total number of returns, which objectively represented the vertical distribution of canopy material. Secondly, a univariate ten-order polynomial was used to fit the height-frequency histogram, enabling the generation of a continuous vertical canopy profile (pseudo-waveform) of the stand. Thirdly, a comprehensive set of vertical structure parameters was defined and extracted based on the pseudo-waveforms, which effectively characterized the vertical profile layer and the canopy layer. Fourthly, to construct a comprehensive framework, 43 model profiles were summarized from the field plots, taking into account the number of effective peaks in the pseudo-waveforms and other vertical structure parameters. Finally, 43 classification rules were developed and 18 judgment criteria were established using the vertical structure parameters of the mode profiles. They classified vertical forest structures into 24 classes with explicit spatial definitions. The classification of 1147 field plots resulted in an overall accuracy of 94.7% and a kappa coefficient of 0.937. The VFS mapping over a large area demonstrated an effective execution rate of 99.8% for both rules and criteria. The proposed approach exhibits high accuracy and excellent generalization ability across different forest types, species, and study sites, highlighting its ecological and forestry significance. It represents a significant advance in the automated classification and mapping of VFS in large subtropical regions using airborne LiDAR data. However, the proposed approach needs to be validated in other vegetation zones to assess its generalizability and extend its applicability.

Classroom management practices: Indonesian secondary schools teachers’ perceptions

The present study aims to investigate the Indonesian secondary school teachers' perspectives of practicing their classroom management to create a conducive classroom environment for learning. This research method is qualitative study, which is able to explore the perspectives of participants regarding their understanding and experience in managing classrooms to create conducive learning. The participants in this study were two teachers who taught in the urban and in rural area. After negotiating with the headmaster, the participants were recruited under a purposive sampling approach and gave their consent to participate in this study. In this study, data were collected through one-to-one interviews and analyzed using thematic analysis. Employing Garwood and Harris (2020) five parameters, two themes were resulted in each parameter. The participants were the teachers recognizing students' names and paying attention to their attitudes and intelligence. They also tried to have a good appearance, performance, and attitude as well as to make games. Regarding a consequence of breaking rules, the teacher in urban area applied academic punishments such as memorizing vocabularies; while, the one in rural area preferred to provide social punishments such as taking out the trash from classrooms. Furthermore, the teachers did not involve students in managing class rules, but made them individually or privately. Finally, both teachers informed class procedures in their teachings. The findings of this study implied that classroom management should be understood as well as practiced properly and appropriately as one of attempts the teacher could make to maintain and create a conducive learning atmosphere.

On the efficient implementation of classification rule learning

AbstractRule learning methods have a long history of active research in the machine learning community. They are not only a common choice in applications that demand human-interpretable classification models but have also been shown to achieve state-of-the-art performance when used in ensemble methods. Unfortunately, only little information can be found in the literature about the various implementation details that are crucial for the efficient induction of rule-based models. This work provides a detailed discussion of algorithmic concepts and approximations that enable applying rule learning techniques to large amounts of data. To demonstrate the advantages and limitations of these individual concepts in a series of experiments, we rely on BOOMER—a flexible and publicly available implementation for the efficient induction of gradient boosted single- or multi-label classification rules.

Kinship Terminology as a (Still) Significant Anthropological Analytical Category

This paper explains the concept of kinship terminology as an analytical category in anthropology and highlights its importance. Special attention is given to the differences in approach and methodology employed in anthropology compared to comparative philology and historical linguistics when it comes to this type of research. In addition to discussing the typology of kinship terminology systems, the paper addresses current knowledge regarding two longstanding questions: 1. the relationship between kinship terminology and non-linguistic elements (principles of social organisation and kinship behaviour), and 2. historical changes and transformations of kinship terminological systems.
 From the perspective of contemporary anthropology, when interpreting the prevalence of different types of kinship terminology worldwide, it is necessary to consider and distinguish between two principles of grouping: the ‘principle of propinquity’ and the ‘principle of form.’ The principle of propinquity can be applied when dealing with societies that are linguistically, geographically, and historically connected. When terminologies with the same formal characteristics, including identical rules of kinship classification, exist in geographically, linguistically, and historically distinct and distant societies, the principle of form is applied, which focuses more on logical rather than historical connections between systems.
 While earlier studies focused on finding logically powerful and empirically efficient analytical means of classification, in recent decades the emphasis has increasingly shifted towards broader social, historical, and cognitive issues and processes. Contemporary approaches to kinship terminology aim to identify and explain variations rather than categorise such systems into one general category or another. Because what matters is not which category fits better, but rather which features can be combined in different ways to form a concrete system in the real world.

Classification of Ancient Glass Based on Logistic Regression and K-means Clustering Algorithm

Ancient glass is easy to weather, after a large number of internal and external elements exchange, its composition ratio will change, thus affecting the correct judgment of the type of glass. In order to classify the types of glass, this paper first preprocessed the data, analyzed the classification rules of high-potassium glass and lead-barium glass by using logistic regression, and got a classification model that could identify the types of glass according to the content of chemical components. Next, K-means clustering algorithm is adopted to divide lead, barium and high potassium into A and B respectively according to the content of silica. After that, the rationality test was carried out on the clustering results. The test index was the belongingness of the sample to its category, and the clustering results passed the rationality test. Finally, the sensitivity analysis of the clustering results was carried out.

Social contagion and the survival of diverse investment styles

We examine the contagion of investment ideas in a multiperiod setting in which investors are more likely to transmit their ideas to other investors after experiencing higher payoffs in one of two investment styles with different return distributions. We show that heterogeneous investment styles are able to coexist in the long run, implying a greater diversity than predicted by traditional theory. We characterize the survival and popularity of styles in relation to the distribution of security returns. In addition, we demonstrate that psychological effects such as conformist preference can lead to oscillations and bubbles in the choice of style. These results remain robust under a wide class of replication rules and endogenous returns. They offer empirically testable predictions, and provide new insights into the persistence of the wide range of investment strategies used by individual investors, hedge funds, and other professional portfolio managers.

Estimating error rate of classification into several normal populations under equal mean restriction

Classification of observation into several univariate normal populations is considered when the population means are unknown but equal. Plug-in Bayes classification rules based on different estimators of the common mean are proposed for k populations. When the variances are ordered, the rule based on the Graybill–Deal estimator is compared with another rule. We prove the consistency property of the classification rules. Confidence intervals of conditional error rate are derived for two and three populations. Under the assumption of ordered variances, Bayes estimator of the ratio of variances is derived to use as a plug-in estimator for classification. We derive estimators of the parameters of mixture densities associated with two normal populations with a common mean and propose classification rules for mixture distribution. An extensive simulation is performed to compare different rules and interval estimators of the conditional error rates.

CAVIDS: Real time intrusion detection system for connected autonomous vehicles using logical analysis of data

In recent times, Connected Autonomous Vehicles (CAVs) have been gaining popularity and have already begun to revolutionize the transport industry, mainly using real-time monitoring, analysis and exchange of data through In-Vehicle Networks (IVN). IVN can also interact with the outside world and roadside infrastructures. With all these advantages, certain weaknesses also creep in as the communication in IVN lacks proper security measures like encryption and authentication. These lack of security measures in IVN makes CAVs vulnerable to cyber-attacks which may have fatal consequences. The de facto standard for communication in IVN is known as Controller Area Network or CAN, and it also lacks said security features to protect CAN from outside threats like cyber attacks, sensor tempering etc. Real-time intrusion detection with minimal computational resources is the need of the hour to prevent disruptive consequences and fatal crashes. In this paper, we propose an intrusion detection method based on Logical Analysis of Data (LAD) which facilitates real-time intrusion detection in CAVs using minimal computational resources. LAD is a two-class classification technique which uses the concept of a partially defined Boolean function (pdBf) to extract different rules for classification from a historical dataset consisting of CAN messages only. Usually, the normal behaviour of any CAV is specified using the extracted rules, and any unusual behaviour that does not conform with the extracted rules is treated as an anomaly. In this paper, we have extracted rules for anomaly and treated all behaviours that do not conform with these extracted rules as normal. The proposed method outperforms many state-of-the-art approaches, which include other rule-based methods, machine learning and deep learning methods both in terms of F1 score and detection time. For CarChallenge 2020 dataset, the proposed method requires lesser than 47.5 μs for detection using a Raspberry Pi device, which is far less than the average time difference between two consecutive CAN messages and thus can be considered as real-time intrusion detection. Real-time intrusion detection is important in preventing any untoward incidents in CAVs.

Towards standardized reporting and failure classification of safety equipment: Semi-automated classification of failure data for safety equipment in the operating phase

Safety instrumented systems (SISs) are installed on process plants to protect against undesired events like e.g., gas leakage and overpressure. A SIS has reliability requirements that are determined during design, and conformance to these requirements should be verified during operation. It is therefore important that all SIS failures are recorded and classified according to their impact on the SIS reliability. Failures of SIS equipment classified as dangerous undetected are of particular interest because they are dormant (undetected) and will prevent the execution of the safety function (dangerous). Analysis of the failure mode and detection method is essential when deciding if a failure is dangerous and undetected. Such information is often provided as unstructured text in notifications registered into the maintenance management system. Therefore, the work of classifying failures requires considerable manual effort in reading and analyzing the texts. Approaches within natural language processing, like technical language processing, have the potential to be deployed more actively for this purpose. However, successful adoption relies on groundwork where classification rules are derived from international standards and commonly agreed industry practice. This paper presents a semi-automated process that incorporates classification rules and gives examples that indicate some of the capabilities of technical language processing for failure classification. The paper also elaborates on how the work relates to Industry 4.0 in creating digital representations to monitor the performance of safety instrumented systems. This work has been carried out as part of the APOS project (Automated process for follow-up of safety instrumented systems). The APOS project has developed knowledge and specifications that simplify and automate the design and operation of safety equipment and investigated how the failure classification process can be made more efficient.

Impact of trading hours extensions on foreign exchange volatility: intraday evidence from the Moscow exchange

Using transaction-level tick-by-tick data of same- and next-day settlement of the Russian Ruble versus the US Dollar exchange rate (RUB/USD) traded on the Moscow Exchange Market during the period 2005–2013, we analyze the impact of trading hours extensions on volatility. During the sample period, the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate. To analyze the effect of the implementations, various measures of historical and realized volatility are calculated for 5- and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions. Besides historical volatility measures, we also examine volume and spread. We apply an autoregressive moving average-autoregressive conditional heteroscedasticity (ARMA-GARCH) model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading. The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes. Volatility changes mostly occur after the opening of the market. The length of the extension has a significant positive effect on realized volatility. The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement, whereas this is not observed for next-day settlement. Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets, they may also lead to higher volatility in the market. Furthermore, this distortion is more significant at opening and midday. A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.

A Numerical Prediction of the Resistance of Bulk Carriers in Brash Ice Channels

Ship resistance increases significantly when navigating a brash ice channel. In this study, the numerical method is applied to predict the full-scale ship resistance of bulk carriers in brash ice channels. The viscous flow computational fluid dynamics (CFD) solver was coupled with the discrete element method (DEM) to establish the brash ice model. The Euler multiphase flow’s volume of fluid (VOF) model was applied to simulate the interaction between the ship and water. The ship–brash ice interaction was simulated. Predictions of ships’ total resistance based on the numerical method and the Finnish Swedish ice class rules (FSICR) method were compared with the experimental results carried out in Hamburg Ship Model Basin (HSVA) ice tank. The numerical resistance shows a good agreement with the HSVA experiment reports and a better performance than the FSICR method. The present study shows that the numerical method could provide reasonable and practical ice resistance predictions for engineering applications.

Knowledge Representation for Legal Document Summarization

This paper presents a novel approach for legal document summarization. Proposed approach is based on Ripple-Down Rules (RDR). It is an incremental knowledge acquisition method. RDR allows us to quickly build an extendable knowledge base using classification rules. The classification rules are written using a set of features. Summary is generated using the identified rhetorical roles in the document. Experiments demonstrate that the RDR based Legal Document summarization approach outperforms the supervised and unsupervised machine learning models.