Future prediction with automatically extracted morphosemantic patterns

In the following paper, we investigated the usefulness of future reference sentence patterns in the prediction of the unfolding of future events. To obtain such patterns we first collected sentences that have any reference to the future from newspapers and Web news. Based on this collection, we developed a novel method for automatic extraction of frequent patterns from such sentences. The extracted patterns, consisting of multilayer semantic information and morphological information, were implemented in the formation of a general model of linguistically expressed future. To fully assess the performance of the proposed method we performed a number of evaluation experiments. In the first experiment, we evaluated the automatic extraction of future reference sentence patterns with the proposed extraction algorithm. In the second set of experiments, we estimated the effectiveness of those patterns and applied them to automatically classify sentences into future referring and other. The final model was then tested for performance in retrieving a new set of future reference sentences from a large news corpus. The obtained results confirmed that the proposed method outperformed state-of-the-art method in fully automatic retrieval of future reference sentences. Lastly, we applied the method in practice to confirm its usefulness in two tasks. The first is to support human readers in the everyday prediction of unfolding future events. In the second task, we developed a fully automatic prototype method for future prediction and tested its performance using the tasks included in the official Future Prediction Competence Test. The results indicate that the prototype system outperforms natural human foreseeing capability.     

Digital dream analysis: A revised method

This article demonstrates the use of a digital word search method designed to provide greater accuracy, objectivity, and speed in the study of dreams. A revised template of 40 word search categories, built into the website of the Sleep and Dream Database (SDDb), is applied to four “classic” sets of dreams: The male and female “Norm” dreams of Hall and Van de Castle (1966), the “Engine Man” dreams discussed by Hobson (1988), and the “Barb Sanders Baseline 250” dreams examined by Domhoff (2003). A word search analysis of these original dream reports shows that a digital approach can accurately identify many of the same distinctive patterns of content found by previous investigators using much more laborious and time-consuming methods. The results of this study emphasize the compatibility of word search technologies with traditional approaches to dream content analysis.     

Perceived risk vs actual driving performance during distracted driving: A comparative analysis of phone use and other secondary distractions

The present study attempts to explore the association of drivers’ risk perception towards phone usage as well as other everyday distractions (operating a music player and eating during driving), and their driving performance observed during these distracted conditions. For this purpose, driving simulator experiments were conducted with 90 participants to collect their driving performance data and a questionnaire was conducted to obtain their basic details along with their risk perceptions. Firstly, the driving performance was divided into clusters using hierarchical clustering and the clustered subgroups were compared for crash and non-crash cases to identify the groups having significant performance degradation. Based on this comparison, the driving performance subgroups were then divided into the following crash risk probabilities: High risk, Moderate risk and Low risk. Further, the associations of perceived risk with these performance subgroups and other potential factors were analyzed using association rules mining technique. Most of the drivers (72.06%) reported texting as an extremely risky task. But, surprisingly none of them considered conversation as an extremely risky task. However, in case of conversation, it was found that even though the professional drivers reported the task to be not at all risky, the observed crash risk was high for them (S = 5.21%, C = 67.86%), indicating an underestimation of the associated risk by the drivers. Similarly, the results revealed that for music player and eating tasks, drivers reported the distracting tasks to be less risky, but, in some instances, their driving performance was associated with higher chances of crash occurrence. Many interesting associations of risk perception and driving performance with respect to demographic and driving characteristics were also obtained. The findings can be useful while designing the awareness programs related to distracted driving with an aim to reduce such practices.     

EEG decoding of semantic category reveals distributed representations for single concepts

Achieving a clearer picture of categorial distinctions in the brain is essential for our understanding of the conceptual lexicon, but much more fine-grained investigations are required in order for this evidence to contribute to lexical research. Here we present a collection of advanced data-mining techniques that allows the category of individual concepts to be decoded from single trials of EEG data. Neural activity was recorded while participants silently named images of mammals and tools, and category could be detected in single trials with an accuracy well above chance, both when considering data from single participants, and when group-training across participants. By aggregating across all trials, single concepts could be correctly assigned to their category with an accuracy of 98%. The pattern of classifications made by the algorithm confirmed that the neural patterns identified are due to conceptual category, and not any of a series of processing-related confounds. The time intervals, frequency bands and scalp locations that proved most informative for prediction permit physiological interpretation: the widespread activation shortly after appearance of the stimulus (from 100 ms) is consistent both with accounts of multi-pass processing, and distributed representations of categories. These methods provide an alternative to fMRI for fine-grained, large-scale investigations of the conceptual lexicon.     

Priming semantic concepts affects the dynamics of aesthetic appreciation

Aesthetic appreciation (AA) plays an important role for purchase decisions, for the appreciation of art and even for the selection of potential mates. It is known that AA is highly reliable in single assessments, but over longer periods of time dynamic changes of AA may occur. We measured AA as a construct derived from the literature through attractiveness, arousal, interestingness, valence, boredom and innovativeness. By means of the semantic network theory we investigated how the priming of AA-relevant semantic concepts impacts the dynamics of AA of unfamiliar product designs (car interiors) that are known to be susceptible to triggering such effects. When participants were primed for innovativeness, strong dynamics were observed, especially when the priming involved additional AA-relevant dimensions. This underlines the relevance of priming of specific semantic networks not only for the cognitive processing of visual material in terms of selective perception or specific representation, but also for the affective-cognitive processing in terms of the dynamics of aesthetic processing.     

Twitter sentiment analysis with a deep neural network: An enhanced approach using user behavioral information

Sentiment analysis on social media such as Twitter has become a very important and challenging task. Due to the characteristics of such data—tweet length, spelling errors, abbreviations, and special characters—the sentiment analysis task in such an environment requires a non-traditional approach. Moreover, social media sentiment analysis is a fundamental problem with many interesting applications. Most current social media sentiment classification methods judge the sentiment polarity primarily according to textual content and neglect other information on these platforms. In this paper, we propose a neural network model that also incorporates user behavioral information within a given document (tweet). The neural network used in this paper is a Convolutional Neural Network (CNN). The system is evaluated on two datasets provided by the SemEval-2016 Workshop. The proposed model outperforms current baseline models (including Naive Bayes and Support Vector Machines), which shows that going beyond the content of a document (tweet) is beneficial in sentiment classification, because it provides the classifier with a deep understanding of the task.     

A novel machine learning approach for early detection of hepatocellular carcinoma patients

Liver cancer is quite common type of cancer among individuals worldwide. Hepatocellular carcinoma (HCC) is the malignancy of liver cancer. It has high impact on individual’s life and investigating it early can decline the number of annual deaths. This study proposes a new machine learning approach to detect HCC using 165 patients. Ten well-known machine learning algorithms are employed. In the preprocessing step, the normalization approach is used. The genetic algorithm coupled with stratified 5-fold cross-validation method is applied twice, first for parameter optimization and then for feature selection. In this work, support vector machine (SVM) (type C-SVC) with new 2level genetic optimizer (genetic training) and feature selection yielded the highest accuracy and F₁-Score of 0.8849 and 0.8762 respectively. Our proposed model can be used to test the performance with huge database and aid the clinicians.     

Religious Identity,Expression, and Civility in Social Media: Results of Data Mining Latter‐Day Saint Twitter Accounts

This study explores religious self‐identification, religious expression, and civility among projected Latter‐Day Saint Twitter accounts (201,107 accounts and 1,542,229 tweets). Novel methods of data collection and analysis were utilized to test hypotheses related to religious identity and civility against social media data at a large scale. Results indicated that (1) projected LDS Twitter accounts tended to represent authentic (rather than anonymous or pseudonymous) identities; (2) local minority versus majority status did not influence users’ willingness to religiously self‐identify; (3) isolation stigma did not occur when users religiously self‐identified; (4) participants exhibited much lower degrees of incivility than was anticipated from previous studies; and (5) religious self‐identification was connected to improved civility. Results should be of interest to scholars of religion for better understanding participation patterns and religious identity among Latter‐Day Saints and for exploring how these results may transfer to other groups of religious people.     

Computing Ledgers and the Political Ontology of the Blockchain

This paper investigates ontological dimensions of the blockchain by asking what kind of socio‐technical object bitcoin is. It discusses both blockchain's political qualities and the political forms enabled by its emergence. It first observes recent approaches to the ontology of money and the political qualities of the ledgers used by the current fractional reserve banking model. It then directs the same questions at blockchain technology. The paper discusses an ontology proposed by Ole Bjerg ( 2016 ) and argues in favour of a mixed‐ontology approach to blockchains. It then questions the political qualities of the distributed ledger as a digital object and highlights the apparent absence of authority figures in the model. Finally, it argues that the political ontology of the blockchain can be framed as the displacement of authority from institutional actors into instrumental control of trust, in a dynamically distributed environment.