A lexical approach for identifying behavioural action sequences

Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called “BASS” to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.

Pelatihan Bahasa Rusia Untuk Meningkatkan Kinerja TNI AD Dalam Bidang Helikopter Seri MI

TNI AD memiliki helikopter seri MI yang diproduksi di Rusia. Helikopter ini digunakan untuk kepentingan pengiriman logistik, pengantaran dan penjemputan anggota TNI AD yang bertugas di Indonesia bagian Timur, transportasi bagi orang-orang yang mengalami kecelakaan baik kecelakaan kerja maupun yang terkena musibah. Helikopter ini merupakan helikopter tercanggih di kelasnya karena bisa mengangkut pasien-pasien dalam posisi berbaring sebanyak 12 orang. Helikopter ini sangat membantu TNI AD dalam melaksanakan tugas-tugas yang berada di daerah-daerah terpencil khususnya daerah-daerah yang sulit dijangkau alat transportasi lain, seperti Papua. Semua keterangan yang terdapat baik di dalam helikopter maupun buku-buku manual ditulis dalam Bahasa Rusia. Oleh karena itu anggota TNI AD yang bekerja di bidang helikopter ini wajib mempelajari bahasa Rusia. Base camp helikopter ini terdapat di Semarang dan di Jakarta. Dengan demikian kesatuan yang menangani helikopter ini selalu mengirim para anggotanya untuk mempelajari bahasa Rusia. Pelatihan diadakan di pusat pendidikan yang bertempat di Cimahi. Peserta pelatihan sebanyak 12 orang yang terdiri dari 2 orang pilot 2 orang co-pilot, 6 orang teknisi, dan 2 orang dari bagian pemeriksa suku cadang. Metode yang digunakan adalah metode deskriptif dengan pendekatan Grammar translation method, oral approach, lexical approach. Hasil yang diperoleh dari kegiatan ini adalah sebanyak 8 orang mampu berkomunikasi, menulis, serta membaca dan paham bahasa Rusia, 2 orang hanya mampu berkomunikasi secara terbata-bata dan membaca memahami teks memerlukan waktu yang lebih lama, dan 2 orang lainnya tidak berhasil sama sekali.

The Empirical Study of Lexical Approach in College English Classroom Teaching and its Effects on Art Major’s Writing

Lexical approach has been believed to be an ideal way for language teaching and learning, but few empirical studies are concerned with the effects that lexical approach has on art major students’ English writing. This study aims to explore the effects the lexical has on writing through an experiment in which the lexical approach was applied in an English class for art majors. All of them attended the pre-test before the experiment and the post-test after the experiment. Through analyzing the data of composition scores and the number of lexical chunks used in compositions by paired sample t tests, this study finds that the lexical approach has positive effect on art majors’ writing proficiency and it is quite feasible and effective to apply the lexical approach to college English teaching, especially in English writing teaching.   

The Role of Lexical Chunks in Writing and Its Implications for College English Teaching

Lexical chunks are considered as composites of form and function and they are the ideal units of language learning and teaching. This study aims to review the role of lexical chunks in writing and discuss its pedagogical implications for college English teaching. Through reviewing the related research on lexical chunks, it is found that most of the research supports that lexical chunks play vital role in improving fluency, accuracy and idiomaticity of English writing. In view of these findings, this study suggests that it is necessary and feasible in Chinese universities to apply the lexical approach to English teaching, divert students’ attention to larger segments of texts and cultivate students’ awareness of lexical chunks. However, this study is just a bibliographic review of the research on lexical chunks and doesn’t conduct any experiment on the application of lexical approach in classroom and its effects on English writing.

Sentiment Analysis of Healthcare Reviews Using Context-Based Feature Weight Embedding Technique

Healthcare reviews play a major role in providing feedback to consumers as well as medical care information to users. Historically, the sentiment analysis of clinical documents will help patients in analyzing the medicines and identifying the relevant medicines. Existing methods of word embeddings use only the context of words; hence, they ignore the sentiment of texts. Medical review analysis is important due to several reasons. Patients will know the results of using medicines since such information is not easily obtained from any other source. Historical results of predictive analysis say that among people aged 55-80, the death rate from 2005 to 2015 in the US was at the top for the deadliest disease, which increased exponentially. Traditional machine learning techniques use a lexical approach for feature extraction. In this paper, baseline algorithms are checked with the proposed work of the recurrent network, and results show that the method outperforms baseline methods by a significant improvement in terms of precision, recall, f-score, and accuracy.

A Lexical Approach to Assessing Stress: Development and Proof-of-Concept

Objective We describe a methodology that provides a nonobtrusive means of detecting stress and related deficits through the assessment of spontaneous verbal output in ongoing communications. Background In high-demand environments, operational personnel are exposed to an array of environmental, task, and interpersonal stressors that can negatively impact performance as well as jeopardize safety and well-being. In these settings, the requirement exists to assess cognitive and emotional state “at a distance” and without interfering with ongoing performance. Method We describe a lexical approach to assessing stress effects from ongoing or spontaneous verbal output. This approach is examined in a spaceflight analog setting. Results We assess stress effects in terms of five core dimensions and develop lexical indicators of these core stress dimensions and relevant sub-facets. We establish the proof-of-concept of this approach by presenting representative data from a spaceflight analog. Conclusion This approach provides an unobtrusive means to evaluate ongoing task communications at the individual and team level in order to assess cognitive/emotional states such as workload, negative affect, attentional focus, anxiety, and team orientation. Application There are many high-demand settings in which it is valuable to monitor the potential negative effects of stress on operational personnel. These environments include spaceflight, the military, aviation, law enforcement, and medicine.