Understanding Query Combination Behavior in Exploratory Searches

In exploratory search, users sometimes combine two or more issued queries into new queries. We present such a kind of search behavior as query combination behavior. We find that the queries after combination usually can better meet users’ information needs. We also observe that users combine queries for different motivations, which leads to different types of query combination behaviors. Previous work on understanding user exploratory search behaviors has focused on how people reformulate queries, but not on how and why they combine queries. Being able to answer these questions is important for exploring how users search and learn during information retrieval processes and further developing support to assist searchers. In this paper, we first describe a two-layer hierarchical structure for understanding the space of query combination behavior types. We manually classify query combination behavior sessions from AOL and Sogou search engines and explain the relationship from combining queries to success. We then characterize some key aspects of this behavior and propose a classifier that can automatically classify types of query combination behavior using behavioral features. Finally, we summarize our findings and show how search engines can better assist searchers.

Predicting Security Vulnerable Developers Based On Their Techno-Behavioral Characteristics

Assigning developers for highly secured software projects requires identifying developers’ tendency to contribute towards vulnerable software codes called developer-centric security vulnerability to mitigate issues on human resource management, financial and project timelines. There are problems in assessing the previous codebases in evaluating the developer-centric security vulnerability level of each developer. Thus, this paper suggests a method to evaluate this through the techno-behavioral features of their previous projects. Consequently, we present results of an exploratory study of the developer-centric security vulnerability level prediction using a dataset of 1827 developers by logically selecting 13 techno-behavioral features. Our results depict that there is a correlation between techno-behavioral features and developer-centric security vulnerability with 89.46% accuracy. This model enables to predict developer-centric security vulnerability level of any developer if the required techno-behavioral features are available avoiding the analysis of his/her previous codebases.

Take a deep breath… The evolution of the respiratory system of symphytognathoid spiders (Araneae, Araneoidea)

Spiders are unique in having a dual respiratory system with book lungs and tracheae, and most araneomorph spiders breathe simultaneously via book lungs and tracheae, or tracheae alone. The respiratory organs of spiders are diverse but relatively conserved within families. The small araneoid spiders of the symphytognathoid clade exhibit a remarkably high diversity of respiratory organs and arrangements, unparalleled by any other group of ecribellate orb weavers. In the present study, we explore and review the diversity of symphytognathoid respiratory organs. Using a phylogenetic comparative approach, we reconstruct the evolution of the respiratory system of symphytognathoids based on the most comprehensive phylogenetic frameworks to date. There are no less than 22 different respiratory system configurations in symphytognathoids. The phylogenetic reconstructions suggest that the anterior tracheal system evolved from fully developed book lungs and, conversely, reduced book lungs have originated independently at least twice from its homologous tracheal conformation. Our hypothesis suggests that structurally similar book lungs might have originated through different processes of tracheal transformation in different families. In symphytognathoids, the posterior tracheal system has either evolved into a highly branched and complex system or it is completely lost. No evident morphological or behavioral features satisfactorily explains the exceptional variation of the symphytognathoid respiratory organs.

COGNITIVE FOUNDATION OF ZOONYMS IN KUBAN SUB-DIALECTS: SYNCHRONOUS AND DIACHRONOUS MOTIVEMES

This article, based on cognitive dialectology principles, presents the results of research of motivational models for animal naming in Kuban region sub-dialects. It establishes synchronous and diachronous motivational characteristics of dialect zoonyms, which have direction properties and are generally similar to the ones observed in Russian sub-dialects and Common Slavic context. It identifies various motivemes different by their degree of productivity, which include age, color, habitat, sounds made by an animal, its behavioral features, means of getting food, human actions it is exposed to, etc. The article reviews personified folk names of animals and zoonym euphemisms. It demonstrates that the folk etymology can be projected not only in a synchronous plane, but in a diachronous one as well, thus showing the metalinguistic consciousness of linguistically creative sub-dialect carriers who possess some background knowledge. The article demonstrates the ethnocultural features that reflect the unique world view of Kuban sub-dialect carriers, which manifests in peculiar implementation of motivemes in folk naming of animals.

MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Recent neuroscience studies demonstrate that a deeper understanding of brain function requires a deeper understanding of behavior. Detailed behavioral measurements are now often collected using video cameras, resulting in an increased need for computer vision algorithms that extract useful information from video data. Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut) with unsupervised dimensionality reduction methods to produce interpretable, low-dimensional representations of behavioral videos that extract more information than pose estimates alone. We demonstrate this tool by extracting interpretable behavioral features from videos of three different head-fixed mouse preparations, as well as a freely moving mouse in an open field arena, and show how these interpretable features can facilitate downstream behavioral and neural analyses. We also show how the behavioral features produced by our model improve the precision and interpretation of these downstream analyses compared to using the outputs of either fully supervised or fully unsupervised methods alone.

Exposure to robotic virtual agent affects adoption of intentional stance

Understanding how and when humans attribute intentionality to artificial agents is a key issue in contemporary human and technological sciences. This paper addresses the question of whether adopting intentional stance can be modulated by exposure to a 3D animated robot character, and whether this depends on the human-likeness of the character's behavior. We report three experiments investigating how appearance and behavioral features of a virtual character affect humans’ attribution of intentionality toward artificial social agents. The results show that adoption of intentional stance can be modulated depending on participants' expectations about the agent. This study brings attention to specific features of virtual agents and insights for further work in the field of virtual interaction.