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"Computer assisted research"
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Automated identification of media bias in news articles: an interdisciplinary literature review
Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and scalable methods are available, but few approaches systematically analyze media bias. The models used to analyze media bias in computer science tend to be simpler compared to models established in the social sciences, and do not necessarily address the most pressing substantial questions, despite technically superior approaches. Computer science research on media bias thus stands to profit from a closer integration of models for the study of media bias developed in the social sciences with automated methods from computer science. This article first establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework, which can be targeted by approaches from computer science. Next, we investigate different forms of media bias and review how each form is analyzed in the social sciences. For each form, we then discuss methods from computer science suitable to (semi-)automate the corresponding analysis. Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.
Journal Article
Contemporary computer-assisted language learning
This is a state-of-the-art reference volume on contemporary computer-assisted language learning, including chapters on research and methodology by leading international figures in the field.
Book
Visual simultaneous localization and mapping: a survey
Visual SLAM (simultaneous localization and mapping) refers to the problem of using images, as the only source of external information, in order to establish the position of a robot, a vehicle, or a moving camera in an environment, and at the same time, construct a representation of the explored zone. SLAM is an essential task for the autonomy of a robot. Nowadays, the problem of SLAM is considered solved when range sensors such as lasers or sonar are used to built 2D maps of small static environments. However SLAM for dynamic, complex and large scale environments, using vision as the sole external sensor, is an active area of research. The computer vision techniques employed in visual SLAM, such as detection, description and matching of salient features, image recognition and retrieval, among others, are still susceptible of improvement. The objective of this article is to provide new researchers in the field of visual SLAM a brief and comprehensible review of the state-of-the-art.[PUBLICATION ABSTRACT]
Journal Article
Computer corpora and open source software for language learning : emerging research and opportunities
\"This book explores the use of free open source software (NoSketch Engine) for learning the Croatian, English, and German languages in Croatian primary and secondary schools\"-- Provided by publisher.
Book
Overview of behavior recognition based on deep learning
Human behavior recognition has always been a hot spot for research in computer vision. With the wide application of behavior recognition in virtual reality and short video in recent years and the rapid development of deep learning algorithms, behavior recognition algorithms based on deep learning have emerged. Compared with traditional methods, behavior recognition algorithms based on deep learning have the advantages of strong robustness and high accuracy. This paper systemizes and introduces behavior recognition algorithms based on deep learning proposed in recent years, then focuses on a series of behavior recognition algorithms based on image and bone data; deeply analyzes their theories and performance, and finally, puts forward further prospects.
Journal Article
Comparing the Similarity of Responses Received from Studies in Amazon’s Mechanical Turk to Studies Conducted Online and with Direct Recruitment
Computer and internet based questionnaires have become a standard tool in Human-Computer Interaction research and other related fields, such as psychology and sociology. Amazon's Mechanical Turk (AMT) service is a new method of recruiting participants and conducting certain types of experiments. This study compares whether participants recruited through AMT give different responses than participants recruited through an online forum or recruited directly on a university campus. Moreover, we compare whether a study conducted within AMT results in different responses compared to a study for which participants are recruited through AMT but which is conducted using an external online questionnaire service. The results of this study show that there is a statistical difference between results obtained from participants recruited through AMT compared to the results from the participant recruited on campus or through online forums. We do, however, argue that this difference is so small that it has no practical consequence. There was no significant difference between running the study within AMT compared to running it with an online questionnaire service. There was no significant difference between results obtained directly from within AMT compared to results obtained in the campus and online forum condition. This may suggest that AMT is a viable and economical option for recruiting participants and for conducting studies as setting up and running a study with AMT generally requires less effort and time compared to other frequently used methods. We discuss our findings as well as limitations of using AMT for empirical studies.
Journal Article
Social media in second and foreign language teaching and learning: Blogs, wikis, and social networking
This review surveys and synthesizes the findings of 87 focal pieces, published primarily since 2009 and mid 2018, on the formal and informal use of social media—blogs, wikis, and social networking—for second and foreign language teaching and learning (L2TL), including studies on the use of educational sites like Livemocha and Busuu and vernacular sites like Facebook and Twitter. The article frames the review in the development of social media and the history of social computer-assisted language learning (CALL) research. Synthesis identifies common findings, including that social media can afford the development of intercultural, sociopragmatic, and audience awareness, language learner and user identities, and particular literacies. Presentation of the focal pieces and common findings is intertwined with discussion of problematic issues, and each section concludes with a summary and implications for future research and practice.
Journal Article
Can autism be catered with artificial intelligence-assisted intervention technology? A comprehensive survey
This article presents an extensive literature review of technology based intervention methodologies for individuals facing autism spectrum disorder (ASD). Reviewed methodologies include: contemporary computer aided systems, computer vision assisted technologies and virtual reality (VR) or artificial intelligence (AI)-assisted interventions. The research over the past decade has provided enough demonstrations that individuals with ASD have a strong interest in technology based interventions, which are useful in both, clinical settings as well as at home and classrooms. Despite showing great promise, research in developing an advanced technology based intervention that is clinically quantitative for ASD is minimal. Moreover, the clinicians are generally not convinced about the potential of the technology based interventions due to non-empirical nature of published results. A major reason behind this lack of acceptability is that a vast majority of studies on distinct intervention methodologies do not follow any specific standard or research design. We conclude from our findings that there remains a gap between the research community of computer science, psychology and neuroscience to develop an AI assisted intervention technology for individuals suffering from ASD. Following the development of a standardized AI based intervention technology, a database needs to be developed, to devise effective AI algorithms.
Journal Article
Intelligent Cinematography: a review of AI research for cinematographic production
This paper offers the first comprehensive review of artificial intelligence (AI) research in the context of real camera content acquisition for entertainment purposes and is aimed at both researchers and cinematographers. Addressing the lack of review papers in the field of intelligent cinematography (IC) and the breadth of related computer vision research, we present a holistic view of the IC landscape while providing technical insight, important for experts across disciplines. We provide technical background on generative AI, object detection, automated camera calibration and 3-D content acquisition, with references to assist non-technical readers. The application sections categorize work in terms of four production types: General Production, Virtual Production, Live Production and Aerial Production. Within each application section, we (1) sub-classify work according to research topic and (2) describe the trends and challenges relevant to each type of production. In the final chapter, we address the greater scope of IC research and summarize the significant potential of this area to influence the creative industries sector. We suggest that work relating to virtual production has the greatest potential to impact other mediums of production, driven by the growing interest in LED volumes/stages for in-camera virtual effects (ICVFX) and automated 3-D capture for virtual modeling of real world scenes and actors. We also address ethical and legal concerns regarding the use of creative AI that impact on artists, actors, technologists and the general public.
Journal Article