scholarly journals The Communicative Effectiveness of Education Videos: Towards an Empirically-Motivated Multimodal Account

2018 ◽  
Vol 2 (3) ◽  
pp. 59 ◽  
Author(s):  
John Bateman ◽  
Florian Schmidt-Borcherding
Keyword(s):  
Empirical Study ◽  
Theoretical Foundation ◽  
State Of The Art ◽  
Empirical Studies ◽  
Multimodal Communication ◽  
Multimodal Learning ◽  
Fine Grained ◽  
Multimodal Interactions ◽  
Cognitive Research ◽  
Basic Concepts

Educational content of many kinds and from many disciplines are increasingly presented in the form of short videos made broadly accessible via platforms such as YouTube. We argue that understanding how such communicative forms function effectively (or not) demands a more thorough theoretical foundation in the principles of multimodal communication that is also capable of engaging with, and driving, empirical studies. We introduce the basic concepts adopted and discuss an empirical study showing how functional measures derived from the theory of multimodality we employ and results from a recipient-based study that we conducted align. We situate these results with respect to the state of the art in cognitive research in multimodal learning and argue that the more complex multimodal interactions and artifacts become, the more a fine-grained view of multimodal communication of the kind we propose will be essential for engaging with such media, both theoretically and empirically.

Behavioral Genetics: Concepts for Research and Practice in Language Development and Disorders

1995 ◽  
Vol 38 (5) ◽  
pp. 1126-1142 ◽  
Author(s):  
Jeffrey W. Gilger
Keyword(s):  
Language Development ◽  
Behavioral Genetics ◽  
State Of The Art ◽  
Genetic Research ◽  
Great Promise ◽  
Behavioral Genetic ◽  
Fine Grained ◽  
Future Goals ◽  
Current State ◽  
Research Designs

This paper is an introduction to behavioral genetics for researchers and practioners in language development and disorders. The specific aims are to illustrate some essential concepts and to show how behavioral genetic research can be applied to the language sciences. Past genetic research on language-related traits has tended to focus on simple etiology (i.e., the heritability or familiality of language skills). The current state of the art, however, suggests that great promise lies in addressing more complex questions through behavioral genetic paradigms. In terms of future goals it is suggested that: (a) more behavioral genetic work of all types should be done—including replications and expansions of preliminary studies already in print; (b) work should focus on fine-grained, theory-based phenotypes with research designs that can address complex questions in language development; and (c) work in this area should utilize a variety of samples and methods (e.g., twin and family samples, heritability and segregation analyses, linkage and association tests, etc.).

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

scholarly journals BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yikui Zhai ◽  
He Cao ◽  
Wenbo Deng ◽  
Junying Gan ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Classification Accuracy ◽  
Learning Strategy ◽  
State Of The Art ◽  
Activation Function ◽  
Training Data ◽  
Fine Grained ◽  
Pattern Recognition Problem ◽  
Face Features ◽  
Facial Beauty

Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

My Brother’s Reaper: Examining Officially Reported Siblicide Incidents in the United States, 2000–2007

2014 ◽  
Vol 29 (3) ◽  
pp. 523-540 ◽  
Author(s):  
Jeffrey A. Walsh ◽  
Jessie L. Krienert
Keyword(s):  
United States ◽  
Theoretical Foundation ◽  
Empirical Studies ◽  
The United States ◽  
Male And Female ◽  
Sibling Violence ◽  
Extreme Form ◽  
Older Brothers ◽  
Intrafamilial Violence ◽  
Frequent Offenders

With higher rates than any other form of intrafamilial violence, Hoffman and Edwards (2004) note, sibling violence “constitutes a pandemic form of victimization of children, with the symptoms often going unrecognized and the effect ignored” (p. 187). Approximately 80% of children reside with at least one sibling (Kreider, 2008), and in its most extreme form sibling violence manifests as siblicide. Siblicide is poorly understood with fewer than 20 empirical studies identified in the extant literature since 1980 (see Eriksen & Jensen, 2006). The present work employs 8 years of Supplemental Homicide Report (SHR) data, 2000–2007, with siblicide victims and offenders age 21 years and younger, to construct contemporary victim and offender profiles examining incident characteristics. Findings highlight the sex-based nature of the offense with unique victimization patterns across victims and offenders. Older brothers using a firearm are the most frequent offenders against both male and female siblings. Strain as a theoretical foundation of siblicide is offered as an avenue for future inquiry.

Findings from an empirical study of fine-grained human social contacts

2009 ◽  
Author(s):  
Yi Wang ◽  
Bhaskar Krishnamachari ◽  
Thomas W. Valente
Keyword(s):  
Empirical Study ◽  
Social Contacts ◽  
Fine Grained

scholarly journals Beyond Mindfulness

Open Theology ◽  
2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas G. Plante
Keyword(s):  
Research Evidence ◽  
State Of The Art ◽  
Empirical Studies ◽  
Religion And Spirituality ◽  
Spiritual Values ◽  
Quality Research ◽  
The Past ◽  
Enormous Amount ◽  
Consulting Psychology ◽  
Areas Of Interest

AbstractSince the publication of Bergin’s classic 1980 paper “Psychotherapy and Religious Values” in the Journal of Clinical and Consulting Psychology, an enormous amount of quality research has been conducted on the integration of religious and spiritual values and perspectives into the psychotherapy endeavor. Numerous empirical studies, chapters, books, blogs, and specialty organizations have emerged in the past 35 years that have helped researchers and clinicians alike come to appreciate the value of religion and spirituality in the psychotherapeutic process. While so much has been accomplished in this area of integration, so much more needs to occur in order for the psychotherapeutic world to benefit from the wisdom of the great religious and spiritual traditions and values. While state-of-the-art quality research has and continues to demonstrate how religious and spiritual practices and values can be used effectively to enhance the benefits of behavioral and psychological interventions, too often the field either gets overly focused on particular and perhaps trendy areas of interest (e.g., mindfulness) or fails to appreciate and incorporate the research evidence supporting (or not supporting) the use of certain religiously or spiritually informed assessments and interventions. The purpose of this article is to reflect on where the field integrating religion, spirituality and psychotherapy has evolved through the present and where it still needs to go in the future. In doing so I hope to reflect on the call for integration that Bergin highlights in his classic 1980 paper.

Reliability Analysis of Great Lakes Marine Diesels: State of the Art and Current Modeling

1990 ◽  
Vol 27 (04) ◽  
pp. 237-249
Author(s):  
Anastassios N. Perakis ◽  
Bahadir Inozu
Keyword(s):  
Great Lakes ◽  
Diesel Engines ◽  
Failure Modes ◽  
State Of The Art ◽  
Relevant Literature ◽  
Fault Tree Analysis ◽  
Reliability Engineering ◽  
Marine Industry ◽  
Marine Diesel ◽  
Basic Concepts

Some essential steps for the application of reliability, availability, and maintainability (RAM) techniques to marine diesel engines are presented. The paper begins with a summary of the basic concepts of reliability engineering, followed by a survey of the relevant literature on RAM applications to the marine industry and to marine diesel engines in particular. Next, the results of an informal survey of the reliability, maintenance, and replacement practices of Great Lakes operators are presented. Finally, the first two steps for a RAM application, failure modes and effects analysis and fault tree analysis, are introduced and applied for a prototype Colt-Pielstick marine diesel engine.

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