Predicting Patients' Satisfaction With Doctors in Online Medical Communities

Online medical communities have revolutionized the way patients obtain medical-related information and services. Investigating what factors might influence patients’ satisfaction with doctors and predicting their satisfaction can help patients narrow down their choices and increase their loyalty towards online medical communities. Considering the imbalanced feature of dataset collected from Good Doctor, we integrated XGBoost and SMOTE algorithm to examine what factors and these factors can be used to predict patient satisfaction. SMOTE algorithm addresses the imbalanced issue by oversampling imbalanced classification datasets. And XGBoost algorithm is an ensemble of decision trees algorithm where new trees fix errors of existing trees. The experimental results demonstrate that SMOTE and XGBoost algorithm can achieve better performance. We further analyzed the role of features played in satisfaction prediction from two levels: individual feature level and feature combination level.

Eye Size Affects Cuteness in Different Facial Expressions and Ages

Researchers have suggested that infants exhibiting baby schema are considered cute. These similar studies have mainly focused on changes in overall baby schema facial features. However, whether a change in only eye size affects the perception of cuteness across different facial expressions and ages has not been explicitly evaluated until now. In the present study, a paired comparison method and 7-point scale were used to investigate the effects of eye size on perceived cuteness across facial expressions (positive, neutral, and negative) and ages (adults and infants). The results show that stimuli with large eyes were perceived to be cuter than both unmanipulated eyes and small eyes across all facial expressions and age groups. This suggests not only that the effect of baby schema on cuteness is based on changes in a set of features but also that eye size as an individual feature can affect the perception of cuteness.

FUNCTIONING OF MANIPULATIVE ARGUMENTS IN JUDICIAL SPEECH (BASED ON THE MATERIAL OF THE PROSECUTOR’S SPEECHES IN THE DEBATE)

Введение. Рассмотрены софизмы в судебной речи одного из томских прокуроров. Значимость исследования определяется сопряжением проблем эффективности современной судебной речи, обозначением ее места в структуре судебного процесса с лингвоперсонологическими категориями. Цель – оценить уместность использования софизмов в речевой практике современного томского прокурора. Материал и методы. Материалом явились устные записи выступлений томского прокурора, адвоката и подсудимого, сделанные в ходе одного судебного процесса. Объем материала составляет около 11,5 тыс. словоупотреблений: речь прокурора в прениях – 7 287 словоупотреблений, реплика прокурора в прениях – 2 053 словоупотребления, речь адвоката в прениях – 925 словоупотреблений, речь подсудимого в прениях – 1 244 словоупотребления. Объект анализа – речевая деятельность языковой личности – томского прокурора (женщина, 38 лет). Метод – наблюдение, научное описание, дискурсивный анализ. Результаты и обсуждение. Описываются различные виды софизмов, отмеченные в речах прокурора: подмена тезиса, произвольные доводы, доводы к человеку и т. д. Использование софизмов является частью традиций современной судебной практики, начало которой положено еще в ХIХ в. Отмечается, что в случае использования манипулятивных доводов наиболее ярко проявляется черта речевого портрета говорящего: субъективная оценочность и эмоциональность. Заключение. Таким образом, выбранная стратегия, предполагающая наличие софизмов, доказала свою ситуативную эффективность, поскольку, вероятно, стала одним из факторов, повлиявших на решение суда, поддержавшего обвинение. Однако рекомендовано исключить из публичных судебных речей доводы к личностным качествам как основанию оценки. Introduction. The article considers the role of sophisms in the judicial speech of one of the Tomsk prosecutors. The significance of the research is determined by the combination of the problems of the effectiveness of modern judicial speech, the designation of its place in the structure of the judicial process with linguistic and phonological categories. The purpose is to assess the relevance of the use of sophisms in the speech practice of the modern Tomsk prosecutor. The object of the analysis is the speech activity of a linguistic personality-the prosecutor of Tomsk, a woman, 38 years old. Material and methods. The following methods were used: observation, scientific description, discursive analysis. The material was oral recordings of speeches of the Tomsk prosecutor, lawyer and the defendant, made during one trial. The volume of the material is about 11.5 words. Results and discussion. The article describes various types of sophisms noted in the speeches of the prosecutor: substitution of the thesis, false arguments, arbitrary arguments, arguments against a person, etc. It is found that rhetorical arguments prevail over logical arguments in the speech in the debate. Actually, manipulative techniques are concentrated in that part of the judicial controversy, which is called a replica. To confirm this postulate, the main theses were highlighted, which were proved and evaluated during the debate. Variants of speculative use of evaluative rhetorical techniques prevail. Less common are “false analogy” and “female logic”. There are few cases of distortion of the thesis: “narrowing-expansion of the thesis”, “substitution of the thesis”. At this stage, it is difficult to assess whether the commitment to manipulative techniques is an individual feature of the speaker, or it is typical for a modern judicial process. The use of sophisms is part of the traditions of modern judicial practice, which began in the nineteenth century. It is noted that in the case of using manipulative arguments, the feature of the speaker’s speech portrait is most clearly manifested: subjective assessment and emotionality. Conclusion. We can say that the chosen strategy, assuming the presence of sophisms, proved its situational effectiveness, since it probably became one of the factors that influenced the decision of the court that supported the prosecution. Each speaker, as a professional language personality, has his own arsenal of effective and ineffective ways of organizing a discussion, it is necessary to replenish his communicative arsenal with the most effective management strategies that influenced the court’s decision.

Pengalaman Mahasiswa pada Sistem Pembelajaran Daring (E-Leaning) di Masa Pandemi dengan Analisis Thematic

Persepsi mahasiswa dari pengalaman yang mereka rasakan selama mengikuti proses pembelajaran daring selama lockdown pandemi Covid-19 tentunya penting, dalam upaya perbaikan proses pembelajaran jarak jauh. Pembelajaran jarak jauh dapat dikategorikan sebagai persoalan system informasi karena melibatkan teknologi, lingkungan, organisasi, serta individu. Pada penelitian ini dilakukan penggalian secara mendalam terkait pengalaman mahasiswa, untuk mengetahui permasalahan dan apa yang dirasakan mahasiswa, baik terhadap proses maupun teknologi dalam pembelajaran jarak jauh. Partisipan wawancara semi struktur melibatkan 10 mahasiswa Jurusan Teknologi Informasi Politeknik Negeri Malang. Jumlah responden dalam penelitian ini ditentukan dengan saturasi data. Proses wawancara semi struktur direkam secara langsung dengan menggunakan aplikasi video conference Google Meet yang telah mendapatkan izin dari seluruh responden. Setiap sesi wawancara membutuhkan waktu 45 – 60 menit untuk setiap responden. Dari hasil pengumpulan data melalui pendekatan wawancara semi struktur yang telah dianalisis dengan metode analisis tematik coding menggunakan bantuan aplikasi Atlast.ti didapatkan tujuh tema utama yaitu: (Infrastruktur (insfrastruktur TI dan budaya pembelajaran), individual feature, interaction, teaching process, akses proses pembelajaran, assessment, content) dan 24 sub tema yang dapat mendeskripsikan pengalaman mahasiswa terhadap sistem pembelajaran daring selama pandemi Covid-19. Saran diberikan berdasarkan hasil temuan tersebut.

The Influence of Key Facial Features on Recognition of Emotion in Cartoon Faces

Cartoon faces are widely used in social media, animation production, and social robots because of their attractive ability to convey different emotional information. Despite their popular applications, the mechanisms of recognizing emotional expressions in cartoon faces are still unclear. Therefore, three experiments were conducted in this study to systematically explore a recognition process for emotional cartoon expressions (happy, sad, and neutral) and to examine the influence of key facial features (mouth, eyes, and eyebrows) on emotion recognition. Across the experiments, three presentation conditions were employed: (1) a full face; (2) individual feature only (with two other features concealed); and (3) one feature concealed with two other features presented. The cartoon face images used in this study were converted from a set of real faces acted by Chinese posers, and the observers were Chinese. The results show that happy cartoon expressions were recognized more accurately than neutral and sad expressions, which was consistent with the happiness recognition advantage revealed in real face studies. Compared with real facial expressions, sad cartoon expressions were perceived as sadder, and happy cartoon expressions were perceived as less happy, regardless of whether full-face or single facial features were viewed. For cartoon faces, the mouth was demonstrated to be a feature that is sufficient and necessary for the recognition of happiness, and the eyebrows were sufficient and necessary for the recognition of sadness. This study helps to clarify the perception mechanism underlying emotion recognition in cartoon faces and sheds some light on directions for future research on intelligent human-computer interactions.

Permutation-based identification of important biomarkers for complex diseases via machine learning models

Study of human disease remains challenging due to convoluted disease etiologies and complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine learning-based methods have been developed and widely used to alleviate some analytic challenges in complex human disease studies. While enjoying the modeling flexibility and robustness, these model frameworks suffer from non-transparency and difficulty in interpreting each individual feature due to their sophisticated algorithms. However, identifying important biomarkers is a critical pursuit towards assisting researchers to establish novel hypotheses regarding prevention, diagnosis and treatment of complex human diseases. Herein, we propose a Permutation-based Feature Importance Test (PermFIT) for estimating and testing the feature importance, and for assisting interpretation of individual feature in complex frameworks, including deep neural networks, random forests, and support vector machines. PermFIT (available at https://github.com/SkadiEye/deepTL) is implemented in a computationally efficient manner, without model refitting. We conduct extensive numerical studies under various scenarios, and show that PermFIT not only yields valid statistical inference, but also improves the prediction accuracy of machine learning models. With the application to the Cancer Genome Atlas kidney tumor data and the HITChip atlas data, PermFIT demonstrates its practical usage in identifying important biomarkers and boosting model prediction performance.

Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

Analysis of the territorial vocalization ritual of the common pheasant Phasianus colchicus

The aim of the study was to analyze the ritual of territorial vocalization of pheasant cocks. The research aim was to determine whether the duration of the ritual of the territorial vocalization of pheasant cocks depends on the season of the year or whether it is an individual feature. The work was performed based on the analysis of film recordings of 7 male pheasants recorded in the Bystrzyca Valley in Lublin (Poland). Several-minute recordings were made from April 19 to June 27, 2018 in the morning (around 6: 00-8: 00). The characteristic for the species ritual vocalization were divided into four phases. The study concluded that the average duration of individual phases of ritual vocalization in individual birds varies and is an individual value. The number flaps of the wings before and after the vocalization varies depending on the cock and is bird-specific, and depends to a small extent on the month of observation.