Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine classifier to control a robot arm in order to help a person to find an object in the (...) environment. The main focus here is to search for the best features that describe efficiently the electroencephalogram data during such imagined gestures by comparing two feature extraction methods, namely the continuous wavelet transform and the empirical modal decomposition, combined with the principal component analysis that were fed through a linear and radial basis function kernel SVM classifier. The experimental results showed high performance achieving an average accuracy across all the subjects of 92.75% with an RBF kernel SVM classifier using CWT and PCA compared to 80.25% accuracy obtained with EMD and PCA. The proposed system has been implemented and tested using data collected from five male subjects and it enabled the control of the robot arm in the right and the left direction. (shrink)

Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it (...) is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy. (shrink)

. Night driving is one of the major factors which affects traffic safety. Although detecting oncoming vehicles at night time is a challenging task, it may improve traffic safety. If the oncoming vehicle is recognised in good time, this will motivate drivers to keep their eyes on the road. The purpose of this paper is to present an approach to detect vehicles at night based on the employment of a single onboard camera. This system is based on detecting vehicle headlights (...) by recognising their shapes via an SVM classifier which was trained for this purpose. A pairing algorithm was designed to pair vehicle headlights to ensure that the two headlights belong to the same vehicle. A multi-object tracking algorithm was invoked to track the vehicle throughout the time the vehicle is in the scene. The system was trained with 503 single objects and tested using 144 587 single objects which were extracted from 1410 frames collected from 15 videos and 27 moving vehicles. It was found that the accuracy of recognition was 97.9% and the vehicle recognition rate was 96.3% which indicates clearly the high robustness attained by this system. (shrink)

. This paper illustrates the applications of various ensemble methods for enhanced classification accuracy. The case in point is the Pima Indian Diabetic Dataset. The computational model comprises of two stages. In the first stage, k-means clustering is employed to identify and eliminate wrongly classified instances. In the second stage, a fine tuning in the classification was effected. To do this, ensemble methods such as AdaBoost, bagging, dagging, stacking, decorate, rotation forest, random subspace, MultiBoost and grading were invoked along with (...) five chosen base classifiers, namely support vector machine, radial basis function network, decision tree J48, naïve Bayes and Bayesian network. The k-fold cross validation technique is adopted. Computational experiments with the proposed method showed an improvement of 16.14% to 22.49% in the classification accuracy compared to literature survey. Among the ensemble methods tried, MultiBoost ensemble with SVM classifier and grading ensemble with naïve Bayes showed the best performance followed by MultiBoost, stacking and grading ensemble with Bayesian classifier, rotation forest ensemble with RBF and grading and rotation forest ensemble with J48. This investigation conclusively proves the significance of cascading k-means clustering with ensemble methods in the enhanced accuracy in categorization of diabetic dataset. (shrink)

The objective of this work is to propose ten efficient scaling techniques for the Wisconsin Diagnosis Breast Cancer dataset using the support vector machine. These scaling techniques are efficient for the linear programming approach. SVM with proposed scaling techniques was applied on the WDBC dataset. The scaling techniques are, namely, arithmetic mean, de Buchet for three cases p = 1,2, and ∞, equilibration, geometric mean, IBM MPSX, and Lp-norm for three cases p = 1,2, and ∞. The experimental results show (...) that the equilibration scaling technique overcomes the benchmark normalization scaling technique used in many commercial solvers. Finally, the experimental results also show the effectiveness of the grid search technique which gets the optimal parameters for the SVM classifier. (shrink)

Legislation usually lacks a systematic organization which makes the management and the access to norms a hard problem to face. A more analytic semantic unit of reference (provision) for legislative texts was identified. A model of provisions (provisions types and their arguments) allows to describe the semantics of rules in legislative texts. It can be used to develop advanced semantic-based applications and services on legislation. In this paper an automatic bottom-up strategy to qualify existing legislative texts in terms of provision (...) types is described. (shrink)

The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National (...) Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases. (shrink)

We propose an empirical mode decomposition (EMD-) based method to extract features from the multichannel recordings of local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs) with time scales dependent on the data. Second, we adopt unsupervised K-means clustering to group (...) the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP) approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI). (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (...) and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)

Judgments concerning animals have arisen across a variety of established practice areas. There is, however, no publicly available repository of judgments concerning the emerging practice area of animal protection law. This has hindered the identification of individual animal protection law judgments and comprehension of the scale of animal protection law made by courts. Thus, we detail the creation of an initial animal protection law repository using natural language processing and machine learning techniques. This involved domain expert classification of 500 judgments (...) according to whether or not they were concerned with animal protection law. 400 of these judgments were used to train various models, each of which was used to predict the classification of the remaining 100 judgments. The predictions of each model were superior to a baseline measure intended to mimic current searching practice, with the best performing model being a support vector machine (SVM) approach that classified judgments according to term frequency—inverse document frequency (TF-IDF) values. Investigation of this model consisted of considering its most influential features and conducting an error analysis of all incorrectly predicted judgments. This showed the features indicative of animal protection law judgments to include terms such as ‘welfare’, ‘hunt’ and ‘cull’, and that incorrectly predicted judgments were often deemed marginal decisions by the domain expert. The TF-IDF SVM was then used to classify non-labelled judgments, resulting in an initial animal protection law repository. Inspection of this repository suggested that there were 175 animal protection judgments between January 2000 and December 2020 from the Privy Council, House of Lords, Supreme Court and upper England and Wales courts. (shrink)

The most prominent form of human communication and interaction is speech. It plays an indispensable role for expressing emotions, motivating, guiding, and cheering. An ill-intentioned speech can mislead people, societies, and even a nation. A misguided speech can trigger social controversy and can result in violent activities. Every day, there are a lot of speeches being delivered around the world, which are quite impractical to inspect manually. In order to prevent any vicious action resulting from any misguided speech, the development (...) of an automatic system that can efficiently detect suspicious speech has become imperative. In this study, we have presented a framework for acquisition of speech along with the location of the speaker, converting the speeches into texts and, finally, we have proposed a system based on long short-term memory which is a variant of recurrent neural network to classify speeches into suspicious and nonsuspicious. We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model. A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system. The experimental results show that our proposed deep learning-based model provides the highest accuracy compared to other algorithms. (shrink)

The number of scientific publications is growing exponentially. Research articles cite other work for various reasons and, therefore, have been studied extensively to associate documents. It is argued that not all references carry the same level of importance. It is essential to understand the reason for citation, called citation intent or function. Text information can contribute well if new natural language processing techniques are applied to capture the context of text data. In this paper, we have used contextualized word embedding (...) to find the numerical representation of text features. We further investigated the performance of various machine-learning techniques on the numerical representation of text. The performance of each of the classifiers was evaluated on two state-of-the-art datasets containing the text features. In the case of the unbalanced dataset, we observed that the linear Support Vector Machine achieved 86% accuracy for the “background” class, where the training was extensive. For the rest of the classes, including “motivation,” “extension,” and “future,” the machine was trained on less than 100 records; therefore, the accuracy was only 57 to 64%. In the case of a balanced dataset, each of the classes has the same accuracy as trained on the same size of training data. Overall, SVM performed best on both of the datasets, followed by the stochastic gradient descent classifier; therefore, SVM can produce good results as text classification on top of contextual word embedding. (shrink)

One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as (...) a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains. (shrink)

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation. Here, we proposed a DA method for the motor imagery EEG signal called brain-area-recombination. For the BAR, each sample was first separated into two ones by left/right brain channels, and the artificial samples were generated by (...) recombining the half-samples. We then designed two schemas corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification. To make a comparative investigation, we selected two common DA methods, and the BAR method beat them. Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM, note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI. (shrink)

Most clinical neurofeedback studies based on functional magnetic resonance imaging use the patient's own neural activity as feedback. The objective of this study was to create a subject-independent brain state classifier as part of a real-time fMRI neurofeedback system that can guide patients with depression in achieving a healthy brain state, and then to examine subsequent clinical changes. In a first step, a brain classifier based on a support vector machine was trained from the neural information of happy (...) autobiographical imagery and motor imagery blocks received from a healthy female participant during an MRI session. In the second step, 7 right-handed female patients with mild or moderate depressive symptoms were trained to match their own neural activity with the neural activity corresponding to the “happiness emotional brain state” of the healthy participant. The training was carried out using the rt-fMRI NF system guided by the brain-state classifier we had created. Thus, the informative voxels previously obtained in the first step, using SVM classification and Effect Mapping, were used to classify the Blood-Oxygen-Level Dependent activity of the patients and converted into real-time visual feedback during the neurofeedback training runs. Improvements in the classifier accuracy toward the end of the training were observed in all the patients [Session 4–1 Median = 6.563%; Range = 4.10–27.34; Wilcoxon Test, 2-tailed p = 0.031]. Clinical improvement also was observed in a blind standardized clinical evaluation [HDRS CE2-1 Median = 7; Range 2 to 15; Wilcoxon Test, 2-tailed p = 0.016], and in self-report assessments [BDI-II CE2-1 Median = 8; Range 1–15; Wilcoxon Test, 2-tailed p = 0.031]. In addition, the clinical improvement was still present 10 days after the intervention [BDI-II CE3-2_Median = 0; Range −1 to 2; Wilcoxon Test, 2-tailed p = 0.50/ HDRS CE3-2 Median = 0; Range −1 to 2; Wilcoxon Test, 2-tailed p = 0.625]. Although the number of participants needs to be increased and a control group included to confirm these findings, the results suggest a novel option for neural modulation and clinical alleviation in depression using noninvasive stimulation technologies. (shrink)

The study investigates the possibility of applying texture analysis (TA) for testing Duchenne Muscular Dystrophy (DMD) therapies. The work is based on the Golden Retriever Muscular Dystrophy (GRMD) canine model, in which 3 phases of canine growth and/or dystrophy development are identified: the first phase (0–4 months of age), the second phase (from over 4 to 6 months), and the third phase (from over 6 months to death). Two differentiation problems are posed: (i) the first phase vs. the second phase (...) and (ii) the second phase vs. the third phase. Textural features are derived from T2-weighted Magnetic Resonance Imaging (MRI) images. In total, 37 features provided by 8 different TA methods (statistical, filter-based, and model-based) have been tested. The work focuses on finding such textural features that evolve along with the dog’s growth. These features are indicated by means of statistical analyses and eliminated from further investigation, as they may disturb the correct assessment of response to treatment in dystrophy. The relative importance of each remaining feature is then assessed with the use of the Monte Carlo (MC) procedure. Furthermore, feature selection based on the MC procedure is employed to find the optimal subset of age-independent features. Finally, three classifiers are used for evaluating different sets of textural features: Adaptive Boosting (AB), back-propagation Neural Network (NN), and nonlinear Support Vector Machines (SVM). The best subsets of age-independent features ensure 80.0% and 78.5% of correctly identified phases of dystrophy progression, for the first (i) and second (ii) differentiation problem respectively. (shrink)

This abstract presents a novel brain-computer interface (BCI) framework to control a prosthetic leg, for the rehabilitation of patients suffering from locomotive disorders, using functional near-infrared spectroscopy (fNIRS). fNIRS signals corresponding to walking intention and rest are used to initiate and stop the gait cycle and a nonlinear proportional derivative computed torque controller (PD-CTC) with gravity compensation is used to control torques of hip and knee joints for minimization of position error. The brain signals of walking intention and rest tasks (...) are acquired from primary motor cortex in the left hemisphere for nine subjects. After acquiring brain signals, in order to remove motion artifacts and physiological noises, the performance of six different filters i.e. Kalman, Wiener, Gaussian, hemodynamic response filter (hrf), Band-pass and finite impulse response, is evaluated. Afterwards, six different features are extracted from oxygenated hemoglobin signals and their different combinations were used for classification. The classification performance of five different classifiers i.e. artificial neural network, quadratic discriminant analysis, linear discriminant analysis (LDA), Naïve Bayes and support vector machine (SVM) is used. The classification accuracies obtained from SVM using hrf filter are significantly higher (p < 0.005) than the other combinations of classifier and filters. These accuracies are 77.5%, 72.5%, 68.3%, 74.2%, 73.3%, 80.8%, 65%, 76.7%, and 86.7% for all nine subjects. The control commands generated using classifier initiate and stop the gait cycle of the prosthetic leg whose knee and hip torques are controlled using PD-CTC to minimize position error. The proposed scheme can be effectively used for neurofeedback training and rehabilitation of lower limb amputation of paralyzed patients. (shrink)

High-dimensional data analysis has become the most challenging task nowadays. Dimensionality reduction plays an important role here. It focuses on data features, which have proved their impact on accuracy, execution time, and space requirement. In this study, a dimensionality reduction method is proposed based on the convolution of input features. The experiments are carried out on minimal preprocessed nine benchmark datasets. Results show that the proposed method gives an average 38% feature reduction in the original dimensions. The algorithm accuracy is (...) tested using the decision tree, support vector machine, and K-nearest neighbor classifiers and evaluated with the existing principal component analysis algorithm. The average increase in accuracy is 8.06 for DT, 5.80 for SVM, and 18.80 for the KNN algorithm. The most significant characteristic feature of the proposed model is that it reduces attributes, leading to less computation time without loss in classifier accuracy. (shrink)

Detecting Intrusions and anomalies is becoming much more challenging with new attacks popping out over a period of time. Achieving better accuracies by applying benchmark classifier algorithms used for identifying intrusions and anomalies have several hidden data mining challenges. Although neglected by many research findings, one of the most important and biggest challenges is the similarity or membership computation. Another challenge that cannot be simply neglected is the number of features that attributes to dimensionality. This research aims to come (...) up with a new membership function to carry similarity computation that can be helpful for addressing feature dimensionality issues. In principle, this work is aimed at introducing a novel membership function that can help to achieve better classification accuracies and eventually lead to better intrusion and anomaly detection. Experiments are performed on KDD dataset with 41 attributes and also KDD dataset with 19 attributes. Recent approaches CANN and CLAPP have showed new approaches for intrusion detection. The proposed classifier is named as UTTAMA. UTTAMA performed better to both CANN and CLAPP approaches w.r.t overall classifier accuracy. Another promising outcome achieved using UTTAMA is the U2R and R2L attack accuracies. The importance of proposed approach is that the accuracy achieved using proposed approach outperforms CLAPP, CANN, SVM, KNN and other existing classifiers. (shrink)

We employ the support vector machine classifier, over different types of kernels, to investigate whether observable variables of individuals and their household information are able to describe their consumption decision of film at theaters in Brazil. Using a very big dataset of 340,000 individuals living in metropolitan areas of a whole large developing economy, we performed a Knowledge Discovery in Databases to classify the film consumers, which results in 80% instances correctly classified. To reduce the degrees of freedom for (...) SVM and to learn the more important determinants of film consumption, we apply the Linear Discriminant Analysis that allows us to identify the key determinants of this consumption. The main individual characteristics are age, education, income, and preferences for cultural goods. Regarding the main geographic characteristics, these are the timing of sample, population concentration, and supply of movie theaters. The results point to an ineffective policy for the sector at the time investigated. (shrink)

The temperament of the body is an essential constituent for health conservancy and diagnosis of several diseases. Hence, general body temperament and uterine dystemperament with abnormal vaginal discharge need evaluation. In addition, we also applied a computational intelligence technique for enhancing scientific validity to classify the warm-cold and wet-dry temperaments. This trial included a total of 66 participants with a vaginal discharge of reproductive age. Data included demographic characteristics of the participants, symptoms associated with vaginal discharge, women’s general temperament, and (...) symptoms of uterine dystemperament. Correlation between general body temperament and age, abnormal vaginal discharge, and its associated symptoms was also performed. Additionally, we used the Support Vector Machine-Radial Basis Function model to classify the warm-cold and wet-dry temperaments. Warm general temperament was highly prevalent, followed by moderate on the warm-cold temperament scale. In wet-dry temperament, moderate general body temperament was more prevalent. In warm-cold and wet-dry scores, 78.78% and 74.24% had warm and wet uterine dystemperament, respectively. The age and symptoms were correlated with general temperament. A strong positive correlation was found between warm general temperament and warm dystemperament of the uterus. In addition, our SVM-RBF CV-5 classification model achieved the highest accuracy. Our results showed that vaginal discharge is more common in warm general temperament and warm-wet dystemperament of the uterus. The same has been proven by computational intelligence. Nevertheless, vaginal discharge can also happen in normal and other temperaments. (shrink)

Through the analysis of emotional tendency in online public opinion, governments and enterprises can stabilize people’s emotion more effectively and maintain social stability. The problem studied in this paper is how to analyze the emotional tendency of online public opinion efficiently, and finally, this paper chooses deep learning algorithm to perform fast analysis of emotional tendency of online public opinion. This paper briefly introduced the structure of the basic model used for emotional tendency analysis of online public opinion and the (...) convolutional neural network model used for text emotion classification. Then, the CNN model was improved by long short-term memory. A simulation experiment was carried out on MATLAB for the improved text emotion classification model to verify the influence of activation function type on the improved model and the performance difference between the improved model and support vector machine and traditional CNN models. The results showed that the improved classification model that adopted the sigmoid activation function had higher accuracy and was less affected by language than the relu and tanh activation functions; the improved classification model had the highest accuracy, recall rate, and F-value in classifying emotional tendency of web texts, followed by the traditional CNN model and the SVM model. (shrink)

Classifying Madness (Springer, 2005) concerns philosophical problems with the Diagnostic and Statistical Manual of Mental Disorders, more commonly known as the D.S.M. The D.S.M. is published by the American Psychiatric Association and aims to list and describe all mental disorders. The first half of Classifying Madness asks whether the project of constructing a classification of mental disorders that reflects natural distinctions makes sense. Chapters examine the nature of mental illness, and also consider whether mental disorders fall into natural kinds. The (...) second half of the book addresses epistemic worries. Even supposing a natural classification system to be possible in principle, there may be reasons to be suspicious of the categories included in the D.S.M. I examine the extent to which the D.S.M. depends on psychiatric theory, and look at how it has been shaped by social and financial factors. I aim to be critical of the D.S.M. without being antagonistic towards it. Ultimately, however, I am forced to conclude that although the D.S.M. is of immense practical importance, it is unlikely to come to reflect the natural structure of mental disorders. (shrink)

Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a (...) set of classification concepts that capture stereotypical fact patterns that effect the strength of a legal claim, here trade secret misappropriation. Using these classifications, the program can evaluate and explain predictions about a problem’s outcome given a database of previously classified cases. This paper provides an extended example illustrating both functions, prediction by IBP and text classification by SMILE, and reports empirical evaluations of each. While IBP’s results are quite strong, and SMILE’s much weaker, SMILE + IBP still has some success predicting and explaining the outcomes of case scenarios input as texts. It marks the first time to our knowledge that a program can reason automatically about legal case texts. (shrink)

In the political discourse regarding gender identity, the concept of biological sex has been weaponised by gender critical commentators to oppose gender affirmation for trans people. Recently, these commentators have appealed to an essentialist model of sex based on anisogamy, or relative gamete size, to argue that one’s sex is an immutable characteristic. I argue that the gender critical argument is unsound. The diverse purposes of sex classification and the complex variability of people’s sexual characteristics show that an essentialist model (...) is untenable. I then consider how a more adequate theoretical framework from the philosophy of biology can accommodate this complexity and capture how sex is classified in relevant contexts. Further implications of the framework are explored which concern the vagueness, polysemy, and mutability of sex. These undercut the gender critical argument and show that the appeal to biological sex fails to undermine gender affirmation for trans people. (shrink)

In this volume, leading philosophers of psychiatry examine psychiatric classification systems, including the Diagnostic and Statistical Manual of Mental Disorders, asking whether current systems are sufficient for effective diagnosis, treatment, and research. Doing so, they take up the question of whether mental disorders are natural kinds, grounded in something in the outside world. Psychiatric categories based on natural kinds should group phenomena in such a way that they are subject to the same type of causal explanations and respond similarly to (...) the same type of causal interventions. When these categories do not evince such groupings, there is reason to revise existing classifications. The contributors all question current psychiatric classifications systems and the assumptions on which they are based. They differ, however, as to why and to what extent the categories are inadequate and how to address the problem. Topics discussed include taxometric methods for identifying natural kinds, the error and bias inherent in DSM categories, and the complexities involved in classifying such specific mental disorders as "oppositional defiance disorder" and pathological gambling. -/- Contributors George Graham, Nick Haslam, Allan Horwitz, Harold Kincaid, Dominic Murphy, Jeffrey Poland, Nancy Nyquist Potter, Don Ross, Dan Stein, Jacqueline Sullivan, Serife Tekin, Peter Zachar. (shrink)

The aim of this paper is to propose a systematic classification of emotions which can also characterize their nature. The first challenge we address is the submission of clear criteria for a theory of emotions that determine which mental phenomena are emotions and which are not. We suggest that emotions as a subclass of mental states are determined by their functional roles. The second and main challenge is the presentation of a classification and theory of emotions that can account for (...) all existing varieties. We argue that we must classify emotions according to four developmental stages: 1. pre-emotions as unfocussed expressive emotion states, 2. basic emotions, 3. primary cognitive emotions, and 4. secondary cognitive emotions. We suggest four types of basic emotions (fear, anger, joy and sadness) which are systematically differentiated into a diversity of more complex emotions during emotional development. The classification distinguishes between basic and non-basic emotions and our multi-factorial account considers cognitive, experiential, physiological and behavioral parameters as relevant for constituting an emotion. However, each emotion type is constituted by a typical pattern according to which some features may be more significant than others. Emotions differ strongly where these patterns of features are concerned, while their essential functional roles are the same. We argue that emotions form a unified ontological category that is coherent and can be well defined by their characteristic functional roles. Our account of emotions is supported by data from developmental psychology, neurobiology, evolutionary biology and sociology. (shrink)

Though whistleblowing is quickly becoming an accepted means of addressing wrongdoing, whistleblower protection laws and the relevant case law are either awkwardly silent, unclear or mutually inconsistent concerning public disclosures of classified government information. I remedy this problem by first arguing that such disclosures constitute a pro tanto wrong as they violate (1) promissory obligations, (2) role obligations and (3) the obligation to respect the democratic allocation of power. However, they may be justified if (1) the information disclosed concerns grave (...) government wrongdoing, (2) alternative channels of disclosure are first exhausted and (3) steps are taken to minimize harm. (shrink)

This article examines the distinction between active matter and active materials, and it offers foundational remarks toward a system of classification for active materials. Active matter is typically identified as matter that exhibits two characteristic features: self-propelling parts, and coherent dynamical activity among the parts. These features are exhibited across a wide range of organic and inorganic materials, and they are jointly sufficient for classifying matter as active. Recently, the term “active materials” has entered scientific use as a complement, supplement, (...) and extension of “active matter.” At the same time, new work in the philosophy of science has considered the problem of how to classify the products of synthetic and laboratory processes, and the extent to which the aims of classifying natural kinds compare and contrasts with the aims of classifying these synthetic kinds. In this article, I apply those considerations to the problems of classifying and characterizing active materials. In doing so, I also argue for a conception of active materials’ coherent dynamical activity as multiscale, rather than emergent, and I discuss how the special non-equilibrium status of active materials factors in to classificatory concerns. (shrink)

By a classifying topos for a first-order theory , we mean a topos such that, for any topos models of in correspond exactly to open geometric morphisms → . We show that not every first-order theory has a classifying topos in this sense, but we characterize those which do by an appropriate ‘smallness condition’, and we show that every Grothendieck topos arises as the classifying topos of such a theory. We also show that every first-order theory has a conservative extension (...) to one which possesses a classifying topos, and we obtain a Heyting-valued completeness theorem for infinitary first-order logic. (shrink)

Computably enumerable equivalence relations received a lot of attention in the literature. The standard tool to classify ceers is provided by the computable reducibility \. This gives rise to a rich degree structure. In this paper, we lift the study of c-degrees to the \ case. In doing so, we rely on the Ershov hierarchy. For any notation a for a non-zero computable ordinal, we prove several algebraic properties of the degree structure induced by \ on the \ equivalence relations. (...) A special focus of our work is on the existence of infima and suprema of c-degrees. (shrink)

This paper argues that we should replace the common classification of theories of welfare into the categories of hedonism, desire theories, and objective list theories. The tripartite classification is objectionable because it is unduly narrow and it is confusing: it excludes theories of welfare that are worthy of discussion, and it obscures important distinctions. In its place, the paper proposes two independent classifications corresponding to a distinction emphasised by Roger Crisp: a four-category classification of enumerative theories (about which items constitute (...) welfare), and a four-category classification of explanatory theories (about why these items constitute welfare). (shrink)

How should one understand comparisons in which neither of two alternatives is at least as good as the other? Much recent literature on comparability problems focuses on what the appropriate explanation of the phenomenon is. Is it due to vagueness or the possibility of non-conventional comparative relations such as parity? This paper argues that the discussions on how to best explain comparability problems has reached an impasse at which it is hard to make any progress. To advance the discussion we (...) suggest a new classification of comparability problems that focuses on the problems they cause for practical reasoning. (shrink)

Basic Formal Ontology was created in 2002 as an upper-level ontology to support the creation of consistent lower-level ontologies, initially in the subdomains of biomedical research, now also in other areas, including defense and security. BFO is currently undergoing revisions in preparation for the release of BFO version 2.0. We summarize some of the proposed revisions in what follows, focusing on BFO’s treatment of material entities, and specifically of the category object.

Multi-Task Learning tries to improve the learning process of different tasks by solving them simultaneously. A popular Multi-Task Learning formulation for SVM is to combine common and task-specific parts. Other approaches rely on using a Graph Laplacian regularizer. Here we propose a combination of these two approaches that can be applied to L1, L2 and LS-SVMs. We also propose an algorithm to iteratively learn the graph adjacency matrix used in the Laplacian regularization. We test our proposal with synthetic and real (...) problems, both in regression and classification settings. When the task structure is present, we show that our model is able to detect it, which leads to better results, and we also show it to be competitive even when this structure is not present. (shrink)

We begin by describing recent developments in the burgeoning discipline of applied ontology, focusing especially on the ways ontologies are providing a means for the consistent representation of scientific data. We then introduce Basic Formal Ontology (BFO), a top-level ontology that is serving as domain-neutral framework for the development of lower level ontologies in many specialist disciplines, above all in biology and medicine. BFO is a bicategorial ontology, embracing both three-dimensionalist (continuant) and four-dimensionalist (occurrent) perspectives within a single framework. We (...) examine how BFO-conformant domain ontologies can deal with the consistent representation of scientific data deriving from the measurement of processes of different types, and we outline on this basis the first steps of an approach to the classification of such processes within the BFO framework. (shrink)

Given two (positive) equivalence relations ∼ 1 , ∼ 2 on the set ω of natural numbers, we say that ∼ 1 is m-reducible to ∼ 2 if there exists a total recursive function h such that for every x, y ∈ ω, we have $x \sim_1 y \operatorname{iff} hx \sim_2 hy$ . We prove that the equivalence relation induced in ω by a positive precomplete numeration is complete with respect to this reducibility (and, moreover, a "uniformity property" holds). This (...) result allows us to state a classification theorem for positive equivalence relations (Theorem 2). We show that there exist nonisomorphic positive equivalence relations which are complete with respect to the above reducibility; in particular, we discuss the provable equivalence of a strong enough theory: this relation is complete with respect to reducibility but it does not correspond to a precomplete numeration. From this fact we deduce that an equivalence relation on ω can be strongly represented by a formula (see Definition 8) iff it is positive. At last, we interpret the situation from a topological point of view. Among other things, we generalize a result of Visser by showing that the topological space corresponding to a partition in e.i. sets is irreducible and we prove that the set of equivalence classes of true sentences is dense in the Lindenbaum algebra of the theory. (shrink)

Classify and Label is a philosophical treatment of classification in the social sciences and everyday life, focusing on its moral, social, and political implications. This book stands at the intersection of philosophy of the social sciences, feminist philosophy, philosophy of sex, and social and political philosophy.

We show that the Lascar group $\operatorname {Gal}_L$ of a first-order theory T is naturally isomorphic to the fundamental group $\pi _1|)$ of the classifying space of the category of models of T and elementary embeddings. We use this identification to compute the Lascar groups of several example theories via homotopy-theoretic methods, and in fact completely characterize the homotopy type of $|\mathrm {Mod}|$ for these theories T. It turns out that in each of these cases, $|\operatorname {Mod}|$ is aspherical, i.e., (...) its higher homotopy groups vanish. This raises the question of which homotopy types are of the form $|\mathrm {Mod}|$ in general. As a preliminary step towards answering this question, we show that every homotopy type is of the form $|\mathcal {C}|$ where $\mathcal {C}$ is an Abstract Elementary Class with amalgamation for $\kappa $ -small objects, where $\kappa $ may be taken arbitrarily large. This result is improved in another paper. (shrink)

The representation and classification of the structure of natural arguments has been one of the most important aspects of Aristotelian and medieval dialectical and rhetorical theories. This traditional approach is represented nowadays in models of argumentation schemes. The purpose of this article is to show how arguments are characterized by a complex combination of two levels of abstraction, namely, semantic relations and types of reasoning, and to provide an effective and comprehensive classification system for this matrix of semantic and quasilogical (...) connections. To this purpose, we propose a dichotomous criterion of classification, transcending both levels of abstraction and representing not what an argument is but how it is understood and interpreted. The schemes are grouped according to an end-means criterion, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

The representation and classification of the structure of natural arguments has been one of the most important aspects of Aristotelian and medieval dialectical and rhetorical theories. This traditional approach is represented nowadays in models of argumentation schemes. The purpose of this article is to show how arguments are characterized by a complex combination of two levels of abstraction, namely, semantic relations and types of reasoning, and to provide an effective and comprehensive classification system for this matrix of semantic and quasilogical (...) connections. To this purpose, we propose a dichotomous criterion of classification, transcending both levels of abstraction and representing not what an argument is but how it is understood and interpreted. The schemes are grouped according to an end-means criterion, which is strictly bound to the ontological structure of the conclusion and the premises. On this view, a scheme can be selected according to the intended or reconstructed purpose of an argument and the possible strategies that can be used to achieve it. (shrink)

This chapter shows how necessary it is for any science, including a science of evidence, to be able to classify phenomena of interest. It presents an evidence classification scheme that is ‘substance blind’, meaning that the classes of individual items of evidence identified are recurrent and apply regardless of the substance or content of the evidence. There are also substance-blind combinations of evidence that are also recurrent. The chapter shows how substance-blindness occurs as a matter of course involving concepts encountered (...) throughout science, logic, and mathematics. (shrink)

Analogies come in several forms that serve distinct functions. Inductive analogy is a common type of analogical argument, but critical thinking texts sometimes treat all analogies as inductive. Such an analysis ignores figurative analogies, which may elucidate but do not argue; and also neglects a priori arguments by analogy, a type of analogical argument prominent in law and ethics. A priori arguments by analogy are distinctive, but--contrary to the claims of Govier and Sunstein-they are best understood as deductive, rather than (...) a special form of non deductive reasoning. (shrink)