The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide (...) a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts. (shrink)

We are frequently exposed to hand written digits 0-9 in today’s modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain-computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of fMRI (functional (...) Magnetic Resonance imaging) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25-30. Results in different brain lobes (frontal, occipital, parietal and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor and language processing), but the most relevant one to the task was language processing network according to the voxel selection. (shrink)

Spam electronic mails (emails) refer to harmful and unwanted commercial emails sent to corporate bodies or individuals to cause harm. Even though such mails are often used for advertising services and products, they sometimes contain links to malware or phishing hosting websites through which private information can be stolen. This study shows how the adaptive intelligent learning approach, based on the visual anti-spam model for multi-natural language, can be used to detect abnormal situations effectively. The application of this approach is (...) for spam filtering. With adaptive intelligent learning, high performance is achieved alongside a low false detection rate. There are three main phases through which the approach functions intelligently to ascertain if an email is legitimate based on the knowledge that has been gathered previously during the course of training. The proposed approach includes two models to identify the phishing emails. The first model has proposed to identify the type of the language. New trainable model based on Naive Bayes classifier has also been proposed. The proposed model is trained on three types of languages (Arabic, English and Chinese) and the trained model has used to identify the language type and use the label for the next model. The second model has been built by using two classes (phishing and normal email for each language) as a training data. The second trained model (Naive Bayes classifier) has been applied to identify the phishing emails as a final decision for the proposed approach. The proposed strategy is implemented using the Java environments and JADE agent platform. The testing of the performance of the AIA learning model involved the use of a dataset that is made up of 2,000 emails, and the results proved the efficiency of the model in accurately detecting and filtering a wide range of spam emails. The results of our study suggest that the Naive Bayes classifier performed ideally when tested on a database that has the biggest estimate (having a general accuracy of 98.4%, false positive rate of 0.08%, and false negative rate of 2.90%). This indicates that our Naive Bayes classifier algorithm will work viably on the off chance, connected to a real-world database, which is more common but not the largest. (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)

Parkinson’s Disease can be treated with the use of microelectrode recording and stimulation. This paper presents a data stream classifier that analyses raw data from micro-electrodes and decides whether the measurements were taken from the subthalamic nucleus (STN) or not. The novelty of the proposed approach is based on the fact that distances based on raw data are used. Two distances are investigated in this paper, i.e. Normalized Compression Distance (NCD) and Lempel-Ziv Jaccard Distance (LZJD). No new features needed to (...) be extracted due to the fact that in the case of high-dimensional data the process is extremely time-consuming. The k-nearest neighbour (k-NN) was chosen as the classifier due to its simplicity, which is essential in data stream classification. Results obtained from classifiers based on k-NN: k-NN, k-NN were compared with Probabilistic Approximate Window (k-NN with PAW); k-NN with Probabilistic Approximate Window and Adaptive Windowing (k-NN with PAW and ADWIN); and Self Adjusting Memory k-NN (SAM k-NN), which use the proposed distances, with the performance of the same classifiers but using standard Euclidean distance. Prequential accuracy was chosen as the performance measure. The results of the experiments performed with the described approach are in most cases better, i.e. the performance measures for kNN classifiers that use NCD and LZJD distances are better by up to 8.5 per cent and 14 per cent, respectively. Moreover, the proposed approach performs better when compared with other stream classification algorithms, i.e. Hoeffding Tree, Naive Bayes, and Leveraging Bagging. In the discussed case, an improvement of classification rate of up to 17.9 per cent when using Lempel-Ziv Jaccard Distance instead of the Euclidean was noted. (shrink)

Knowledge discovery in databases has been studied intensively recent years. In KDD, inductive classifier learning methods which were developed in statistics and machine learning have been used to extract classification rules from databases. Although in KDD we have to deal with large databases in many cases, many of the previous classifier learning methods are not suitable for large databases. They were designed under assumption that any data in databases is accessible on demand and they usually need to access a datum (...) several times in a process of learning. So, they require a huge memory space or a large I/O cost to access storage devices. In this paper, we propose a classifier learning method, we call CIDRE, in which data summaries are constructed and classifiers are learned from the summaries. This learning method is realized by using a clustering method, we call MCF-tree, which is an extension of CF-tree proposed by Zhang et al. In the method, we can specify the size of memory space occupied by data summaries, and databases are swept only once to construct the summaries. In addition, new instances can be inserted into the summaries incrementally. Thus, the method possesses important properties which are desirable to deal with large databases. We also show empirical results, which indicate that our method performs very well in comparison to C4.5 and naive Bayes, and the extension from CF-tree to MCF-tree is indispensable to achieve high classification accuracy. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree, K nearest (...) neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

Malware is a sophisticated, malicious, and sometimes unidentifiable application on the network. The classifying network traffic method using machine learning shows to perform well in detecting malware. In the literature, it is reported that this good performance can depend on a reduced set of network features. This study presents an empirical evaluation of two statistical methods of reduction and selection of features in an Android network traffic dataset using six supervised algorithms: Naïve Bayes, support vector machine, multilayer perceptron (...) neural network, decision tree, random forest, and K-nearest neighbors. The principal component analysis and logistic regression methods with p value were applied to select the most representative features related to the time properties of flows and features of bidirectional packets. The selected features were used to train the algorithms using binary and multiclass classification. For performance evaluation and comparison metrics, precision, recall, F-measure, accuracy, and area under the curve were used. The empirical results show that random forest obtains an average accuracy of 96% and an AUC-ROC of 0.98 in binary classification. For the case of multiclass classification, again random forest achieves an average accuracy of 87% and an AUC-ROC over 95%, exhibiting better performance than the other machine learning algorithms. In both experiments, the 13 most representative features of a mixed set of flow time properties and bidirectional network packets selected by LR were used. In the case of the other five classifiers, their results in terms of precision, recall, and accuracy, are competitive with those obtained in related works, which used a greater number of input features. Therefore, it is empirically evidenced that the proposed method for the selection of features, based on statistical techniques of reduction and extraction of attributes, allows improving the identification performance of malware traffic, discriminating it from the benign traffic of Android applications. (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)

To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical (...) information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral. (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)

In this research work, a user-friendly decision support framework is developed to analyze the behavior of Pakistani students in academics. The purpose of this article is to analyze the performance of the Pakistani students using an intelligent decision support system (DSS) based on the three-level machine learning (ML) technique. The neural network used a three-level classifier approach for the prediction of Pakistani student achievement. A self-recorded dataset of 1,011 respondents of graduate students of English and Physics courses are used. The (...) ten interviews along with ten questions were conducted to determine the perception of the individual student. The chi-squared ( χ ) \left(\chi ) test was applied to test statistical significancy of the questionnaire. The statistical calculations and computation of data were performed by using the statistical package of IBMM SPSS version 21.0. The seven different algorithms were tested to improve the data classification. The Java-based environment was used for the development of numerous prediction classifiers. C4.5 algorithm shows the finest accuracy, whereas Naïve Bayes (NB) algorithm shows the least. The results depict that the classifier’s efficiency was improved by using a three-level proposed scheme from 83.2% to 88.8%. This prediction has shown remarkable results when compared with the individual level classifier technique of ML. This improvement in the accuracy of DSSs is used to identify more efficiently the gray areas in the education stratum of Pakistan. This will pave a path for making policies in the higher education system of Pakistan. The presented framework can be deployed on different platforms under numerous operating systems. (shrink)

Online social networks are highly dynamic and sparse. One of the main problems in analyzing these networks is the problem of predicting the existence of links between users on these networks: the link prediction problem. Many studies have been conducted to predict links using a variety of techniques like the decision tree and the logistic regression approaches. In this work, we will illustrate the use of the rough set theory in predicting links over the Facebook social network based on homophilic (...) features. Other supervised learning algorithms are also employed in our experiments and compared with the rough set classifier, such as naive Bayes, J48 decision tree, support vector machine, logistic regression, and multilayer perceptron neural network. Moreover, we studied the influence of the “common groups” and “common page likes” homophilic features on predicting friendship between users of Facebook, and also studied the effect of using the Jaccard coefficient in measuring the similarity between users’ homophilic attributes compared with using the overlap coefficient. We conducted our experiments on two different datasets obtained from the Facebook online social network, where users in each dataset live within the same geographical region. The results showed that the rough set classifier significantly outperformed the other classifiers in all experiments. The results also demonstrated that the common groups and the common page likes features have a significant influence on predicting the friendship between users of Facebook. Finally, the results revealed that using the overlap coefficient homophilic features provided better results than that of the Jaccard coefficient features. (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)

There are a lot of research studies that look at "fake news" from an Arabic online source, but they don't look at what makes those fake news spread. The threat grows, and at some point, it gets out of hand. That's why this paper is trying to figure out how to predict the features that make Arabic online fake news spread. It's using Naive Bayes, Logistic Regression, and Random forest of Machine Learning to do this. Online news stories that (...) were made up were used. They are found by using Term Frequency-Inverse Document Frequency. The best partition for testing and validating the prediction was chosen at random and used in the analysis. So, all three machine learning classifications for predicting fake news in Arabic online were done. The results of the experiment show that Random Forest Classifier outperformed the other two algorithms. It had the best TF-IDF with an accuracy of 86 percent. Naive Bayes had an accuracy rate of 84%, and Logistic Regression had an accuracy rate of 85%, so they all did well. As such, the model shows that the features in TF-IDF are the most essential point about the content of an online Arabic fake news. (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)

Humans excel in categorization. Yet from a computational standpoint, learning a novel probabilistic classification task involves severe computational challenges. The present paper investigates one way to address these challenges: assuming class-conditional independence of features. This feature independence assumption simplifies the inference problem, allows for informed inferences about novel feature combinations, and performs robustly across different statistical environments. We designed a new Bayesian classification learning model that incorporates varying degrees of prior belief in class-conditional independence, learns whether or not independence holds, (...) and adapts its behavior accordingly. Theoretical results from two simulation studies demonstrate that classification behavior can appear to start simple, yet adapt effectively to unexpected task structures. Two experiments—designed using optimal experimental design principles—were conducted with human learners. Classification decisions of the majority of participants were best accounted for by a version of the model with very high initial prior belief in class-conditional independence, before adapting to the true environmental structure. Class-conditional independence may be a strong and useful default assumption in category learning tasks. (shrink)

This article outlines a theory of naive probability. According to the theory, individuals who are unfamiliar with the probability calculus can infer the probabilities of events in an extensional way: They construct mental models of what is true in the various possibilities. Each model represents an equiprobable alternative unless individuals have beliefs to the contrary, in which case some models will have higher probabilities than others. The probability of an event depends on the proportion of models in which it occurs. (...) The theory predicts several phenomena of reasoning about absolute probabilities, including typical biases. It correctly predicts certain cognitive illusions in inferences about relative probabilities. It accommodates reasoning based on numerical premises, and it explains how naive reasoners can infer posterior probabilities without relying on Bayes's theorem. Finally, it dispels some common misconceptions of probabilistic reasoning. (shrink)

There has long been interest in why languages are shaped the way they are, and in the relationship between sign language and gesture. In sign languages, entity classifiers are handshapes that encode how objects move, how they are located relative to one another, and how multiple objects of the same type are distributed in space. Previous studies have shown that hearing adults who are asked to use only manual gestures to describe how objects move in space will use gestures (...) that bear some similarities to classifiers. We investigated how accurately hearing adults, who had been learning British Sign Language for 1–3 years, produce and comprehend classifiers in locative and distributive constructions. In a production task, learners of BSL knew that they could use their hands to represent objects, but they had difficulty choosing the same, conventionalized, handshapes as native signers. They were, however, highly accurate at encoding location and orientation information. Learners therefore show the same pattern found in sign-naïve gesturers. In contrast, handshape, orientation, and location were comprehended with equal accuracy, and testing a group of sign-naïve adults showed that they too were able to understand classifiers with higher than chance accuracy. We conclude that adult learners of BSL bring their visuo-spatial knowledge and gestural abilities to the tasks of understanding and producing constructions that contain entity classifiers. We speculate that investigating the time course of adult sign language acquisition might shed light on how gesture became conventionalized during the genesis of sign languages. (shrink)

Background and objectivesThe study of brain functional connectivity alterations in children with Attention-Deficit/Hyperactivity Disorder has been the subject of considerable investigation, but the biological mechanisms underlying these changes remain poorly understood. Here, we aim to investigate the brain alterations in patients with ADHD and Typical Development children and accurately classify ADHD children from TD controls using the graph-theoretical measures obtained from resting-state fMRI.Materials and methodsWe investigated the performances of rs-fMRI data for classifying drug-naive children with ADHD from TD controls. Fifty (...) six drug-naive ADHD children and 56 age matched TD controls were included in this study. The graph measures extracted from rs-fMRI functional connectivity were used as features. Extracted network-based features were fed to the RFE feature selection algorithm to select the most discriminating subset of features. We trained and tested Support Vector Machine, Random Forest, and Gradient Boosting using Peking center data from ADHD-200 database to classify ADHD and TD children using discriminative features. In addition to the machine learning approach, the statistical analysis was conducted on graph measures to discover the differences in the brain network of patients with ADHD.ResultsAn accuracy of 78.2% was achieved for classifying drug-naive children with ADHD from TD controls employing the optimal features and the GB classifier. We also performed a hub node analysis and found that the number of hubs in TD controls and ADHD children were 8 and 5, respectively, indicating that children with ADHD have disturbance of critical communication regions in their brain network. The findings of this study provide insight into the neurophysiological mechanisms underlying ADHD.ConclusionPattern recognition and graph measures of the brain networks, based on the rs-fMRI data, can efficiently assist in the classification of ADHD children from TD controls. (shrink)

1 Boolean retrieval 1 2 The term vocabulary and postings lists 19 3 Dictionaries and tolerant retrieval 49 4 Index construction 67 5 Index compression 85 6 Scoring, term weighting and the vector space model 109 7 Computing scores in a complete search system 135 8 Evaluation in information retrieval 151 9 Relevance feedback and query expansion 177 10 XML retrieval 195 11 Probabilistic information retrieval 219 12 Language models for information retrieval 237 13 Text classification and Naive Bayes (...) 253 14 Vector space classification 289 15 Support vector machines and machine learning on documents 319 16 Flat clustering 349 17 Hierarchical clustering 377 18 Matrix decompositions and latent semantic indexing 403 19 Web search basics 421 20 Web crawling and indexes 443 21 Link analysis 461.. (shrink)

I consider the potential of eight text-scaling methods for the analysis of jurisprudential change. I use a small corpus of well-documented German Federal Constitutional Court opinions on European integration to compare the machine-generated scores to scholarly accounts of the case law and legal expert ratings. Naive Bayes, Word2Vec, Correspondence Analysis and Latent Semantic Analysis appear to perform well. Less convincing are the performance of Wordscores, ML Affinity and lexicon-based sentiment analysis. While both the high-dimensionality of judicial texts and the (...) validation of computer-based jurisprudential estimates pose major methodological challenges, I conclude that automated text-scaling methods hold out great promise for legal research. (shrink)

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number of (...) irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research. (shrink)

IntroductionAccurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance.ObjectiveThe present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies.MethodsThe data, consisting of 16 seasons of world-class women’s all-around speed (...) skating competition results, used in the present study came from the International Skating Union. According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method.ResultsThe results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC. Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals.ConclusionCompared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals. (shrink)

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning and deep learning models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized (...) artificial neural network, have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score. (shrink)

The crime is difficult to predict; it is random and possibly can occur anywhere at any time, which is a challenging issue for any society. The study proposes a crime prediction model by analyzing and comparing three known prediction classification algorithms: Naive Bayes, Random Forest, and Gradient Boosting Decision Tree. The model analyzes the top ten crimes to make predictions about different categories, which account for 97% of the incidents. These two significant crime classes, that is, violent and nonviolent, (...) are created by merging multiple smaller classes of crimes. Exploratory data analysis is performed to identify the patterns and understand the trends of crimes using a crime dataset. The accuracies of Naive Bayes, Random Forest, and Gradient Boosting Decision Tree techniques are 65.82%, 63.43%, and 98.5%, respectively, and the proposed model is further evaluated for precision and recall matrices. The results show that the Gradient Boosting Decision Tree prediction model is better than the other two techniques for predicting crime, based on historical data from a city. The analysis and prediction model can help the security agencies utilize the resources efficiently, anticipate the crime at a specific time, and serve society well. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the (...) analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

In this work, we focus on the problem of selecting low-level heuristics in a hyperheuristic approach with offline learning, for the solution of instances of different problem domains. The objective is to improve the performance of the offline hyperheuristic approach, identifying equivalence classes in a set of instances of different problems and selecting the best performing heuristics in each of them. A methodology is proposed as the first step of a set of instances of all problems, and the generic characteristics (...) of each instance and the performance of the heuristics in each one of them are considered to define the vectors of characteristics and make a grouping of classes. Metalearning with statistical tests is used to select the heuristics for each class. Finally, we used the Naive Bayes to test the set instances with k-fold cross-validation, and we compared all results statistically with the best-known values. In this research, the methodology was tested by applying it to the problems of capacitated vehicle routing and graph coloring. The experimental results show that the proposed methodology can improve the performance of the offline hyperheuristic approach, correctly identifying the classes of instances and applying the appropriate heuristics in each case. This is based on the statistical comparison of the results obtained with those of the state of the art of each instance. (shrink)

Smart lighting systems utilize advanced data, control, and communication technologies and allow users to control lights in new ways. However, achieving user comfort, which should be the focus of smart lighting research, is challenging. One cause is the passive infrared sensor that inaccurately detects human presence to control artificial lighting. We propose a novel classification-integrated moving average model method to solve the problem. The moving average increases the Pearson correlation coefficient of motion sensor features to human presence. The classification model (...) is for a smart lighting intelligent control based on these features. Several classification models are proposed and compared, namely, k-nearest neighbor, support vector machine, decision tree, näive Bayes, and ensemble voting. We build an Internet of things system to collect movement data. It consists of a PIR sensor, a NodeMCU microcontroller, a Raspberry Pi-based platform, a relay, and LED lighting. With a sampling rate of 10 seconds and a collection period of 7 days, the system achieved 56852 data records. In the PC test, movement data from the PIR sensor has a correlation coefficient of 0.36 to attendance, while the MA correlation to attendance can reach 0.56. In an exhaustive search of an optimum classification model, KNN has the best and the most robust performance, with an accuracy of 99.8%. It is more accurate than direct light control decisions based on motion sensors, which are 67.6%. Our proposed method can increase the correlation value of movement features on attendance. At the same time, an accurate and robust KNN classification model is applicable for human presence-based smart lighting control. (shrink)

Embryonic stem cells (ESCs) are now classified into two types of pluripotency: “naïve” and “primed” based upon their differing characteristics. Conventional human ESCs have much more in common with mouse epiblast stem cells and are now deemed to be primed. Naïve human ESCs that resemble mouse ESCs have recently been generated from their primed counterpart by cellular reprogramming. Isolation of naïve hESCs from human embryos has proven to be difficult. Is the inability to capture naïve hESCs (...) the result of suboptimal derivation conditions or because they are so transient they cannot be “captured” in vitro? Prevailing evidence surrounding this issue are inconclusive and require additional human embryo research. However, negative public opinion regarding human embryo research, may make this an uphill battle. The solution may come from cellular reprogramming. (shrink)

Realism concerning a given subject-matter is characterised as a semantic doctrine with metaphysical consequences, namely as the adoption, for the relevant class of statements, of a truth-conditional theory of meaning resting upon the classical two-valued semantics. it is argued that any departure from classical semantics may, though will not necessarily, be seen as in conflict with some variety of realism. a sharp distinction is drawn between the rejection of realism and the acceptance of a reductionist thesis; though intimately related, neither (...) entails the other. realism is to be classified as "naive", "semi-naive" or "sophisticated": the first of these involves an all but unintelligible epistemological component. (shrink)

The challenge of explaining the emotions has engaged the attention of the best minds in philosophy and science throughout history. Part of the fascination has been that the emotions resist classification. As adequate account therefore requires receptivity to knowledge from a variety of sources. The philosopher must inform himself of the relevant empirical investigation to arrive at a definition, and the scientist cannot afford to be naive about the assumptions built into his conceptual apparatus. The contributors to this volume have (...) approached the problem of characterizing and classifying emotions from the perspectives of neurophysiology, psychology, and social psychology as well as that of philosophical psychology. They discuss the difficulties that arise in classifying the emotions, assessing their appropriateness and rationality, and determining their function in motivating moral action. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Trauma, Addiction, and Temporal Bulimia in Madame BovaryElissa Marder (bio)Lisez, et ne rêvez pas. Plongez-vous dans de longues études. Il n’y a de continuellement bon que l’habitude d’un travail entêté. Il s’en dégage un opium qui engourdit l’âme [Read and do not dream. The only thing that is continually good is the habit of stubborn work. It emits an opium that numbs the soul].—Gustave Flaubert to Louise ColetMadame Bovary (...) I daresay is about bad drugs.—Avital Ronell, Crack WarsIf Flaubert’s Madame Bovary remains so timely, it is because its heroine, Emma, suffers from the quintessential malady of modernity, the inability to incorporate time into experience. Emma’s missed encounter with her own life, her inability to “get a life,” as we say now in America, renders her our contemporary in the strangest sense of the word. She is our contemporary not because we live in the same time but because her failure to live in time has come to define our own. Paradoxically, Emma Bovary has been so well preserved (she is, in some sense, “more alive” now than ever) because she incarnates and inaugurates a modernity that can be defined by the erosion of the possibility of living in time. In Flaubert’s minute and meticulous descriptions of the particular temporal disorders that afflict Emma (as we shall see, she can neither bear witness to an event nor remember one, she can neither live in the present nor project a future, she is incessantly subject to bouts of involuntary forgetting even as she is preoccupied by obsessive rites of recollection, she attempts simultaneously to conjure up time and to stop it), we can read the prophetic traces of a depiction of the temporal structure of the many forms of trauma and addiction that have come to define contemporary American culture.But before turning to Flaubert’s prescient and powerful analysis of how Emma Bovary suffers from a “temporal disorder,” let us take a brief look at how the notions of trauma and addiction have been described in a contemporary analysis of modern culture. In his recent book On Flirtation, the British child psychoanalyst Adam Phillips redescribes ordinary neurosis as a traumatic response to an inability to live in time. He writes:People come to psychoanalysis when there is something they cannot forget, something they cannot stop telling themselves, often by their actions, about their lives. And these dismaying repetitions—this unconscious limiting or coercion of the repertoire of life stories—create the illusion of time having stopped (or [End Page 49] rather, people believe—behave as if they have stopped time). In our repetitions we seem to be staying away from the future, keeping it at bay.[153]Phillips goes on to explain that “[f]or Freud these repetitions are the consequence of a failure to remember.... Whatever cannot be transformed, psychically processed, reiterates itself. A trauma is whatever there is in a person’s experience that resists useful redescription. Traumas, like beliefs, are ways of stopping time” [153–54]. Phillips’s argument rings with a seductive simplicity: neurosis is defined as a temporal disorder, and the contemporary psychoanalytic “cure” does nothing less than promise time. It presumably offers the subject the option of accepting the contingencies of living in time over the pleasure and pain that attend the attempt to stop it.Nonetheless, Phillips’s claim, that contemporary neurosis can be expressed as an attempt to stop time, should perhaps make us pause. Stopping time, in the time of Balzac, Gautier, Baudelaire, and Flaubert, was once conceived of as part of the privileged domain reserved for art and the artist. We might have naively assumed that in the realm of the “real world,” for “real people” time remains an implacable fact of life. The task of falling out of time, after all, would seem to necessitate either an accident of enormous magnitude (a trauma in the traditional sense) or the reliance upon an external substance—hashish, opium, alcohol, or cocaine—one of those mind-altering substances that so preoccupied the nineteenth-century advocates of paradis artificiels. So what is surprising and particularly suggestive... (shrink)

Taste, perhaps the most intimate of the five senses, has traditionally been considered beneath the concern of philosophy, too bound to the body, too personal and idiosyncratic. Yet, in addition to providing physical pleasure, eating and drinking bear symbolic and aesthetic value in human experience, and they continually inspire writers and artists. In Making Sense of Taste, Carolyn Korsmeyer explains how taste came to occupy so low a place in the hierarchy of senses and why it is deserving of greater (...) philosophical respect and attention. Korsmeyer begins with the Greek thinkers who classified taste as an inferior, bodily sense; she then traces the parallels between notions of aesthetic and gustatory taste that were explored in the formation of modern aesthetic theories. She presents scientific views of how taste actually works and identifies multiple components of taste experiences. Turning to taste's objects—food and drink—she looks at the different meanings they convey in art and literature as well as in ordinary human life and proposes an approach to the aesthetic value of taste that recognizes the representational and expressive roles of food. Korsmeyer's consideration of art encompasses works that employ food in contexts sacred and profane, that seek to whet the appetite and to keep it at bay; her selection of literary vignettes ranges from narratives of macabre devouring to stories of communities forged by shared eating. (shrink)

In the last published round of his debate with Walter Block on economic methodology,1 Bryan Caplan introduces Bayes’ Rule as ‘a cure for methodological schizofrenia’. Block had raised the question ‘Why do economists react so violently to empirical evidence against the conventional view of the minimum wage’s effect?’ and answered it with the suggestion that economists do so because they are covert praxeologists. This means that they base most of their economic arguments on conclusions derived from their a priori (...) understanding of human action, although, as methodologists, they prefer to maintain that their arguments are merely appropriately qualified generalisations of empirical observations. Against this, Caplan maintained that neoclassical economists are Bayesians with some strong prior beliefs, which lead them to ascribe low probability to any statement that goes against the strongly held consensus. Presumably, there is such a strongly held consensus with respect to the minimum wage effect. Caplan concluded that ‘[t]he Bayesian position stakes out a compelling middle ground between atheoretical positivism and praxeology. On the one hand, the Bayesian view emphasizes that few propositions are known with certainty, and that we should adjust our probabilities as new information comes in. On the other hand, the Bayesian view recognizes that the rational view is not an average of past empirical findings, much less a naïve faith in the last prominent study.’ Caplan’s references to Bayes should be considered carefully before we accept that Bayesianism makes for a middle ground—let alone a compelling one— between positivism and praxeology. The image of a middle ground may be soothing, but it is no more than a metaphor. Whether it makes sense in this context, is an altogether different matter. (shrink)

This paper develops an appearance view of perception (focusing on vision). When we see an object, we see it by having it appear some way to us. We see the object, not the appearance; but we see the object via the appearance. The appearance is subjectively conditioned: aspects of it depend on attributes of the subject. We mentally have the appearance and can reflect on it as an appearance. But in the primary instance, of veridical perception, it is the object (...) that we focus on and experience. I contrast this view with naive realism (sometimes called disjunctivism) (Brewer, Campbell, Noe) and with intentionalist or representationalist views, which I call the physical content view (Dretske, Tye, Crane, Hill, Burge). I agree with these views in rejecting sense-data intermediaries and in affirming direct perception, but I argue that direct perception occurs via appearances, making mine a critical direct realism. The other views attempt to explain the spatial content of perception (shape, size, distance) entirely through the physical properties of objects and their physical relation to the perceiver (viewing position). I argue instead that there are ineliminably subjective aspects of normal spatial perception, which are systematically manifested as a lack of full phenomenal constancy. This systematic underconstancy can’t be accounted for at all by a consistent naive realism; for the physical content view, it requires that most of our ordinary perception is classified as illusory, violating the spirit of that view. Accepting phenomenal underconstancy as pervasive, I suggest that our ability to perceive the mind-independent properties of objects depends on developed conceptual abilities. In the final section, I compare my appearance view with the adverbialism of Chisholm and the critical direct realism of Roy Wood Sellars. (shrink)

Navigating the ontology of color used to be a simple affair. There was the naive view that colors really are in objects the way they appear, and the view that they are secondary qualities to cause certain experiences in us. Today, there are myriad well-developed views but no satisfactory taxonomy of philosophical theories on color. In this article, I first examine the two newest taxonomies on offer and argue that they are inadequate. In particular, I look at Brogaard’s taxonomy and (...) then Cohen’s. One of the reasons I am displeased with Brogaard and Cohen’s taxonomies is that I find it implausible that dispositions are relational properties. I provide an argument against this way of classifying dispositions. Having learned from the vices and virtues of Brogaard and Cohens’ taxonomies, I provide what I believe is a much-enhanced way of taxonomizing philosophical views on color. My taxonomy rules out certain views, clarifies others, and shows that there is an unnoticed view worthy of consideration. (shrink)

According to an ordinary view, we distinguish, classify, and appreciate food and beverages according to their taste. However, scientists seem to disagree with this naive view. They maintain that we don't really perceive the lemony taste of a cake or the delicate smoky taste of a single-malt whiskey, because what we ascribe to taste is in reality mostly perceived by smell. As opposed to this scientific consensus regarding taste, I will defend a naive view of taste and deny that olfaction (...) is involved in what we naively call taste. Like the uninformed layman, I will maintain that when I eat a strawberry, what I really perceive is its taste, not its smell or flavor. (shrink)

Quantum mechanics and probability theory share one peculiarity. Both have well established mathematical formalisms, yet both are subject to controversy about the meaning and interpretation of their basic concepts. Since probability plays a fundamental role in QM, the conceptual problems of one theory can affect the other. We first classify the interpretations of probability into three major classes: inferential probability, ensemble probability, and propensity. Class is the basis of inductive logic; deals with the frequencies of events in repeatable experiments; describes (...) a form of causality that is weaker than determinism. An important, but neglected, paper by P. Humphreys demonstrated that propensity must differ mathematically, as well as conceptually, from probability, but he did not develop a theory of propensity. Such a theory is developed in this paper. Propensity theory shares many, but not all, of the axioms of probability theory. As a consequence, propensity supports the Law of Large Numbers from probability theory, but does not support Bayes theorem. Although there are particular problems within QM to which any of the classes of probability may be applied, it is argued that the intrinsic quantum probabilities are most naturally interpreted as quantum propensities. This does not alter the familiar statistical interpretation of QM. But the interpretation of quantum states as representing knowledge is untenable. Examples show that a density matrix fails to represent knowledge. (shrink)

There is now substantial agreement about the representational component of a normative theory of causal reasoning: Causal Bayes Nets. There is less agreement about a normative theory of causal discovery from data, either computationally or cognitively, and almost no work investigating how teaching the Causal Bayes Nets representational apparatus might help individuals faced with a causal learning task. Psychologists working to describe how naïve participants represent and learn causal structure from data have focused primarily on learning from (...) single trials under a variety of conditions. In contrast, one component of the normative theory focuses on learning from a sample drawn from a population under some experimental or observational study regime. Through a virtual Causality Lab that embodies the normative theory of causal reasoning and which allows us to record student behavior, we have begun to systematically explore how best to teach the normative theory. In this paper we explain the overall project and report on pilot studies which suggest that students can quickly be taught to (appear to) be quite rational. (shrink)

Ribā/interest is one of the prominent regulations in Islam regarding commercial life. The commercial lives of Muslims and laws related to commerce were regulated in accordance with the prohibition of ribā, and by this a society that avoided ribā with sensitivity was created in practice. This sensitivity about ribā manifested in the principle that the suspicion of ribā is evaluated as ribā, and it is ruled as haram like riba. In fiqh sources, besides issues regarding ribā, issues involving suspicion of (...) ribā were also mentioned, and some rulings were justified with the presence of suspicion of riba. However, the logic of using the concept of ribā suspicion in fiqh sources may vary from case to case: the reasoning behind the use of riba suspicion in one case may not fit into another case, and thus a completely different reasoning of ribā suspicion may appear in another case. To clarify the framework of the concept of ribā suspicion, it would be noteworthy to study these each of the uses in the classical fiqh sources. The purpose of this article is to examine the use of the concept of ribā suspicion based on the cases in the Ḥanafī sources. Methodologically, first, the cases in which the concept of ribā suspicion is used are determined. Secondly, the reasoning made for each example is examined by considering the contextual framework of the case. Lastly, these uses are classified and presented to the reader with examples. The principle of evaluating “the suspicion of ribā like ribā” in Islamic law is indeed related to the rule of considering “any suspicion as certainty when it comes to harams or in matters where precaution is substantial”. Since ribā is a haram that Muslims should meticulously avoid, and therefore requires caution, its suspicion is subject to the same rulings as ribā itself. In the Hanafi fiqh books, the concept of suspicion of ribā is mostly referred in the following cases: - Not knowing whether there is equality between the subject matters in barter transactions where equality is stipulated. For example, when exchanging two batches of the same kind of ribawi goods, though quantities are unknown. - It is not known whether tariq al-itibār has taken place in changes that may be permissible through tariq al-itibār. For example, in the exchange of a silver-embroidered sword with silver, if the amount of silver in the ornamented part is not known, the suspicion of ribā arises. - The emergence of a situation similar to ribā because of a transaction that is normally permissible: While there are two legitimate sales in Bay' al-inah, the conclusion of being the same as an interest bearing loan is considered as ribā suspicion. - When the case has two different sides; from one side the act is ribā, but from other side it is not. For example, in the case of salam contract of ribawi goods, if goods of different quality are brought during delivery and the quality difference is requested from the customer; suspicion of ribā arises. Apart from these uses in the Hanafi fiqh books, it is seen that the concept of ribā suspicion is employed to prove an opinion disputed between schools of Islamic law. For example, to argue that ribā rules are also valid for goods sold by piece (adadiyyāt) or by length measures (zirāiyyāt), the Hanafis say that one of the two causes (illah) of ribā is found in the exchange of these goods by type, and the suspicion of ribā arises with just a single cause (illah). On the other hand, the Shafiis use the concept of suspicion of ribā to argue that determining the subjects (ta’yin) in the exchange of ribavi goods other than gold and silver is not sufficient. Another use of the term emerges because of the authors' preference for a cautious language. Some authors acted cautiously on some cases that were normally ribā and were described by other authors in this manner, and used the term "ribā suspicion" instead of ribā; probably as they both require the same ruling (harām). Another wrongly used (in our opinion) is the use of “suspicion of ribā” in some cases that are actually considered only as disliked acts (makruh). (shrink)

Philosophers of perception have a notoriously difficult time trying to account for hallucinatory experiences. One surprisingly quite popular move, and one that cross-cuts the representationalism/relationalism divide, is to say that hallucinations involve an awareness of uninstantiated properties. In this paper, I provide a new argument against this view. Not only are its proponents forced to classify many hallucinations as veridical, such experiences turn out to be necessarily veridical. In addition, I show that representationalists who endorse the uninstantiated property view must (...) reject the common fundamental kind claim and adopt disjunctivism, and naïve realists/relationalists must radically modify their disjunctivism: The distinction between ‘veridical’ and ‘hallucinatory’ will no longer track a metaphysical distinction between the relevant experiences. (shrink)

Efficient thinking is the foundation of efficient operation. The correct definition of concepts, especially the basic ones for a given field, in order to reach the truth, is a condition for the development of science and its social utility. The Petrażycki’s research methodology of law is a thoroughly modern method, as it enables effective examination of the accuracy of contemporary legal theories created after Petrażycki’s input. A model contemporary theory susceptible to an examination through the research methodology of law by (...) Petrażycki is the normative theory of legal rules and non-legal standards by Dworkin. For this purpose some falsifications will be subject, i.e. selected ad hoc among many others, two important theories of normative law theory Dworkin. The first one is the thesis classifying legal norms into two groups of norms, namely legal rules and non-legal standards. The second one is a thesis about the existence of who are capable of discovering and issuing lawful and, at the same time, fair court decisions, which are also the only ones for resolving particular court disputes. Unfortunately, owing to the seemingly cognitive research methodology of Petrażycki, both Dworkin’s deformed division of legal norms as well as Dworkin’s Hercules judges - cannot stand the test of authenticity. Due to the Petrażycki’s methodology, the legal-normative theory of Dworkin does not lose an innovative outlook on the existence of social norms, which are being discovered by judges in the jurisprudence, indifferently to the doubts over their proper classification. Moreover, Dworkin’s theory is placed between naive theories, regardless of whether they are considered realistically naive theories or nihilistically naive theories A few random reflections on the well-known work of Dworkin with the help of Petrażycki’s methodology serve to provide a new perspective on the contemporary legal normativity. (shrink)

We summarize the evidence and arguments usually employed against the use of the profit maximization assumption in microeconomic theory, and then pass directly to the methodological arguments. Two arguments are considered. The first summarizes positions which have been taken by various defenders of the "people who think a theory should be given up just because it is false are naive and confused" view. To rebut this view, we develop a scheme for classifying theoretical assumptions and show that the specious plausibility (...) of the argument in question derives from ignoring the distinctions we have drawn. The second argument attempts to show that a better theory is more likely to be attained by continuing the development of the orthodox theory. We show that this view is mistaken and then discuss steps which have been taken to develop a better theory. (shrink)