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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

Over the years, Skolem’s Paradox has generated a fairly steady stream of philosophical discussion; nonetheless, the overwhelming consensus among philosophers and logicians is that the paradox doesn’t constitute a mathematical problem (i.e., it doesn’t constitute a real contradiction). Further, there’s general agreement as to why the paradox doesn’t constitute a mathematical problem. By looking at the way firstorder structures interpret quantifiers—and, in particular, by looking at how this interpretation changes as we move from structure to structure—we can give a technically (...) adequate “solution” to Skolem’s Paradox. So, whatever the philosophical upshot of Skolem’s Paradox may be, the mathematical side of Skolem’s Paradox seems to be relatively straightforward. (shrink)

Lately in the past couple of years, there are an increasing in the normal rate of playing computer games or video games compared to the E-learning content that are introduced for the safety of our children, and the impact of the video game addictiveness that ranges from (Musculoskeletal issues, Vision problems and Obesity). Furthermore, this paper introduce an intelligent tutoring system for both parent and their children for enhancement the experience of gaming and tell us about the health problems and (...) how we can solve them, with an easy user interface that way can our children be happy and excited about the information and their health. (shrink)

Plagiarism detection is the process of finding similarities on electronic based documents. Recently, this process is highly required because of the large number of available documents on the internet and the ability to copy and paste the text of relevant documents with simply Control+C and Control+V commands. The proposed solution is to investigate and develop an easy, fast, and multi-language support plagiarism detector with the easy of one click to detect the document plagiarism. This process will be done with the (...) support of intelligent system that can learn, change and adapt to the input document and make a cross-fast search for the content on the local repository and the online repository and link the content of the file with the matching content everywhere found. Furthermore, the supported document type that we will use is word, text and in some cases, the pdf files –where is the text can be extracting from them- and this made possible by using the DLL file from Word application that Microsoft provided on OS. The using of DLL will let us to not constrain on how to get the text from files; and will help us to apply the file on our Delphi project and walk throw our methodology and read the file word by word to grantee the best working scenarios for the calculation. In the result, this process will help in the uprising the documents quality and enhance the writer experience related to his work and will save the copyrights for the official writer of the documents by providing a new alternative tool for plagiarism detection problem for easy and fast use to the concerned Institutions for free. (shrink)

It is not my claim that the ‘L¨ owenheim-Skolem paradox’ is an antinomy in formal logic; but I shall argue that it is an antinomy, or something close to it, in philosophy of language. Moreover, I shall argue that the resolution of the antinomy—the only resolution that I myself can see as making sense—has profound implications for the great metaphysical dispute about realism which has always been the central dispute in the philosophy of language.

This paper concerns Tarski’s use of the term “model” in his 1936 paper “On the Concept of Logical Consequence.” Against several of Tarski’s recent defenders, I argue that Tarski employed a non-standard conception of models in that paper. Against Tarski’s detractors, I argue that this non-standard conception is more philosophically plausible than it may appear. Finally, I make a few comments concerning the traditionally puzzling case of Tarski’s ω-rule example.

I present two generalizations of Putnam's model-theoretic argument against realism. The first replaces Putnam's model theory with some new, and substantially simpler, model theory, while the second replaces Putnam's model theory with some more accessible results from astronomy. By design, both of these new arguments fail. But the similarities between these new arguments and Putnam's original arguments illuminate the latter's overall structure, and the flaws in these new arguments highlight the corresponding flaws in Putnam's arguments.

Skolem's Paradox involves a seeming conflict between two theorems from classical logic. The Löwenheim Skolem theorem says that if a first order theory has infinite models, then it has models whose domains are only countable. Cantor's theorem says that some sets are uncountable. Skolem's Paradox arises when we notice that the basic principles of Cantorian set theory—i.e., the very principles used to prove Cantor's theorem on the existence of uncountable sets—can themselves be formulated as a collection of first order sentences. (...) How can the very principles which prove the existence of uncountable sets be satisfied by a model which is itself only countable? How can a countable model satisfy the first order sentence which says that there are uncountably many mathematical objects—e.g., uncountably many real numbers? (shrink)

In his new paper, “Eligibility and Inscrutability,” J. R. G. Williams presents a surprising new challenge to David Lewis’ theory of interpretation. Although Williams frames this challenge primarily as a response to Lewis’ criticisms of Putnam’s model-theoretic argument, the challenge itself goes to the heart of Lewis’ own account of interpretation. Further, and leaving Lewis’ project aside for a moment, Williams’ argument highlights some important—and some fairly general—points concerning the relationship between model theory and semantic determinacy.

In order to explore whether and how Jewish teachings influence the attitudes of strictly-orthodox Jews to clinical trials, 10 strictly-orthodox Jews were purposively selected and interviewed, using a semi-structured schedule. Relevant literature was searched for similar studies and for publications covering relevant Jewish teachings. Thematic analysis was used to analyse transcribed interviews and explore relationships between attitudes and Jewish teachings identified in the review. Participants’ attitudes were influenced in a variety of ways: by Jewish teachings on the over-riding importance of (...) preserving life—the need to avoid risks affecting life and health, while taking risks to preserve life—and the religious obligation to help others, as well as by previous experience. Attitudes mirrored those in the general population, enabling many participants to reach conclusions that did not differ materially from those of the general population or research ethics committees. (shrink)

This paper concerns Tarski's use of the term "model" in his 1936 paper "On the Concept of Logical Consequence." Against several of Tarski's recent defenders, I argue that Tarski employed a non-standard conception of models in that paper. Against Tarski's detractors, I argue that this non-standard conception is more philosophically plausible than it may appear. Finally, I make a few comments concerning the traditionally puzzling case of Tarski's $\omega$-rule example.

The Lowenheim-Skolem theorems say that if a first-order theory has infinite models, then it has models which are only countably infinite. Cantor's theorem says that some sets are uncountable. Together, these theorems induce a puzzle known as Skolem's Paradox: the very axioms of set theory which prove the existence of uncountable sets can be satisfied by a merely countable model. ;This dissertation examines Skolem's Paradox from three perspectives. After a brief introduction, chapters two and three examine several formulations of Skolem's (...) Paradox in order to disentangle the roles which set theory, model theory, and philosophy play in these formulations. In these chapters, I accomplish three things. First, I clear up some of the mathematical ambiguities which have all too often infected discussions of Skolem's Paradox. Second, I isolate a key assumption upon which Skolem's Paradox rests, and I show why this assumption has to be false. Finally, I argue that there is no single explanation as to how a countable model can satisfy the axioms of set theory ;In chapter four, I turn to a second puzzle. Why, even though philosophers have known since the early 1920's that Skolem's Paradox has a relatively simple technical solution, have they continued to find this paradox so troubling? I argue that philosophers' attitudes towards Skolem's Paradox have been shaped by the acceptance of certain, fairly specific, claims in the philosophy of language. I then tackle these philosophical claims head on. In some cases, I argue that the claims depend on an incoherent account of mathematical language. In other cases, I argue that the claims are so powerful that they render Skolem's Paradox trivial. In either case, though, examination of the philosophical underpinnings of Skolem's Paradox renders that paradox decidedly unparadoxical. ;Finally, in chapter five, I turn away from "generic" formulations of Skolem's Paradox to examine Hilary Putnam's "model-theoretic argument against realism." I show that Putnam's argument involves mistakes of both the mathematical and the philosophical variety, and that these two types of mistake are closely related. Along the way, I clear up some of the mutual charges of question begging which have characterized discussions between Putnam and his critics. (shrink)

Largely due to the difficulty of observing behavior, empirical business ethics research relies heavily on the scenario methodology. While not disputing the usefulness of the technique, this paper highlights the importance of a careful assessment of the fit between the context of the situation described in the scenario and the knowledge and experience of the respondents. Based on a study of online auctions, we provide evidence that even respondents who have direct knowledge of the situation portrayed in the scenario may (...) develop significantly different assessments of the level of unethical behavior. Further, those assessments may be conditioned in different ways by the same moderating variables. We conclude that care should be exercised when recruiting respondents to choose only those who can be expected to understand the scenario in its true context and that separate analyses should be conducted for groups of respondents who have different perspectives within that context. (shrink)

Largely due to the difficulty of observing behavior, empirical business ethics research relies heavily on the scenario methodology. While not disputing the usefulness of the technique, this paper highlights the importance of a careful assessment of the fit between the context of the situation described in the scenario and the knowledge and experience of the respondents. Based on a study of online auctions, we provide evidence that even respondents who have direct knowledge of the situation portrayed in the scenario may (...) develop significantly different assessments of the level of unethical behavior. Further, those assessments may be conditioned in different ways by the same moderating variables. We conclude that care should be exercised when recruiting respondents to choose only those who can be expected to understand the scenario in its true context and that separate analyses should be conducted for groups of respondents who have different perspectives within that context. (shrink)

PurposeAs interest in technology ethics is increasing, so is the interest in bringing schools of ethics from non-Western philosophical traditions to the field, particularly when it comes to information and communication technology. In light of this development and recent publications that result from it, this paper aims to present responds critically to recent work on Confucian virtue ethics (CVE) and technology.Design/methodology/approachFour critiques are presented as theoretical challenges to CVE in technology, claiming that current literature insufficiently addresses: overall applicability, collective ethics (...) issues, epistemic overconfidence within technology corporations and amplification of epistemic overconfidence by the implementation of CVE. These challenges make use of general CVE literature and work on technology critique, political philosophy, epistemology and business ethics.FindingsImplementing CVE in technology may yield some benefits, but these may be outweighed by other outcomes, include strengthening hierarchies, widening inequities, increasing, rather than limiting, predictive activity, personal data collection, misinformation, privacy violations and challenges to the democratic process.Originality/valueThough not directly advocating against CVE, the paper reveals hitherto unidentified and serious issues that should be addressed before CVE are used to inform ethics guidelines or regulatory policies. It also serves as a foundation for further inquiry into how Eastern philosophy more broadly can inform technology ethics in the West. (shrink)

In 1999, Jeffrey Ketland published a paper which posed a series of technical problems for deflationary theories of truth. Ketland argued that deflationism is incompatible with standard mathematical formalizations of truth, and he claimed that alternate deflationary formalizations are unable to explain some central uses of the truth predicate in mathematics. He also used Beth’s definability theorem to argue that, contrary to deflationists’ claims, the T-schema cannot provide an ‘implicit definition’ of truth. In this article, I want to challenge this (...) final argument. Whatever other faults deflationism may have, the T-schema does provide an implicit definition of the truth predicate. Or so, at any rate, I shall argue. (shrink)

In this dissertation we study two-cardinal phenomena—both of the admitting cardinals variety and of the Chang’s Conjecture variety—under the assumption that all our models have stable theories. All our results involve two, relatively widely accepted generalizations of the traditional definitions in this area. First, we allow the relevant subsets of our models to be picked out by (perhaps infinitary) partial types; second we consider δ-cardinal problems as well as two-cardinal problems.