scholarly journals Discriminating Cognitive Disequilibrium and Flow in Problem Solving: A Semi-Supervised Approach Using Involuntary Dynamic Behavioral Signals

2020 ◽  
Vol 34 (01) ◽  
pp. 420-427
Author(s):  
Mononito Goswami ◽  
Lujie Chen ◽  
Artur Dubrawski
Keyword(s):  
Problem Solving ◽  
Supervised Learning ◽  
Detection Methods ◽  
Social Environments ◽  
Affective States ◽  
Body Movements ◽  
Complexity Measures ◽  
Low Dimensional ◽  
Short Time ◽  
Cognitive Disequilibrium

Problem solving is one of the most important 21st century skills. However, effectively coaching young students in problem solving is challenging because teachers must continuously monitor their cognitive and affective states, and make real-time pedagogical interventions to maximize their learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affect-sensitive intelligent tutoring, many researchers have investigated automated cognitive and affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world dataset reveal a significant advantage of these representations in discriminating cognitive disequilibrium and flow, as compared to traditional complexity measures from dynamical systems literature, and demonstrate their potential in transferring learned models to previously unseen subjects.

scholarly journals Modeling Involuntary Dynamic Behaviors to Support Intelligent Tutoring (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13799-13800
Author(s):  
Mononito Goswami ◽  
Lujie Chen ◽  
Chufan Gao ◽  
Artur Dubrawski
Keyword(s):  
Problem Solving ◽  
Supervised Learning ◽  
Intelligent Tutoring ◽  
Detection Methods ◽  
Social Environments ◽  
Affective States ◽  
Body Movements ◽  
Learning Framework ◽  
Low Dimensional ◽  
Short Time

Problem solving is one of the most important 21st century skills. However, effectively coaching young students in problem solving is challenging because teachers must continuously monitor their cognitive and affective states and make real-time pedagogical interventions to maximize students' learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affect-sensitive intelligent tutoring, many researchers have investigated automated cognitive and affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world dataset reveal a significant utility of these representations in discriminating cognitive disequilibrium and flow and demonstrate their potential in transferring learned models to previously unseen subjects.

Adversarial training for few-shot text classification

2021 ◽  
Vol 14 (2) ◽  
pp. 201-214
Author(s):  
Danilo Croce ◽  
Giuseppe Castellucci ◽  
Roberto Basili
Keyword(s):  
Supervised Learning ◽  
Language Processing ◽  
Reproducing Kernel ◽  
Generative Adversarial Networks ◽  
Training Material ◽  
Semantic Classification ◽  
Universal Sentence ◽  
Kernel Hilbert Spaces ◽  
Supervised Methods ◽  
Low Dimensional

In recent years, Deep Learning methods have become very popular in classification tasks for Natural Language Processing (NLP); this is mainly due to their ability to reach high performances by relying on very simple input representations, i.e., raw tokens. One of the drawbacks of deep architectures is the large amount of annotated data required for an effective training. Usually, in Machine Learning this problem is mitigated by the usage of semi-supervised methods or, more recently, by using Transfer Learning, in the context of deep architectures. One recent promising method to enable semi-supervised learning in deep architectures has been formalized within Semi-Supervised Generative Adversarial Networks (SS-GANs) in the context of Computer Vision. In this paper, we adopt the SS-GAN framework to enable semi-supervised learning in the context of NLP. We demonstrate how an SS-GAN can boost the performances of simple architectures when operating in expressive low-dimensional embeddings; these are derived by combining the unsupervised approximation of linguistic Reproducing Kernel Hilbert Spaces and the so-called Universal Sentence Encoders. We experimentally evaluate the proposed approach over a semantic classification task, i.e., Question Classification, by considering different sizes of training material and different numbers of target classes. By applying such adversarial schema to a simple Multi-Layer Perceptron, a classifier trained over a subset derived from 1% of the original training material achieves 92% of accuracy. Moreover, when considering a complex classification schema, e.g., involving 50 classes, the proposed method outperforms state-of-the-art alternatives such as BERT.

Developmental stages in the Digenea

Parasitology ◽  
1964 ◽  
Vol 54 (2) ◽  
pp. 295-312 ◽  
Author(s):  
Elon E. Byrd ◽  
William P. Maples
Keyword(s):  
Body Wall ◽  
Developmental Stages ◽  
Snail Host ◽  
The Body ◽  
Body Movements ◽  
Hatching Enzyme ◽  
Apical Papilla ◽  
Short Time ◽  
Membranous Body ◽  
Egg Capsule

The naturally oviposited egg of Dasymetra conferta is fully embryonated and it hatches only after it is ingested by the snail host, Physa spp.Hatching appears to be in response to some stimulus supplied by the living snail. The stimulus causes the larva to exercise a characteristic series of body movements and to liberate a granular sustance (hatching enzyme) from the larger pair of its cephalic glands. This enzyme reacts with the vitelline fluid to create pressure within the egg capsule, and with the cementum of the operculum, so that it may be lifted away. The larva's escape from the shell, therefore, is due to a combination of pressure and body movements.The hatched larva has a membranous body wall, supporting six epidermal plates, an apical papilla, two penetration glands and a central matrix (the presumptive brood mass).It lives for about an hour within the snail and during this time there is a reorganization of the central matrix which terminates in the formation of an 8-nucleated syncytial brood mass.The miracidial ‘case’, consisting of the body wall and the epidermal plates, ultimately ruptures to liberate the brood mass. Once the brood mass is free it penetrates through the gut wall in an incredibly short time.

Multicultural Literature as a Context for Problem Solving: Children and Parents Learning Together

2002 ◽  
Vol 8 (8) ◽  
pp. 448-454
Author(s):  
Marilyn E. Strutchens
Keyword(s):  
Problem Solving ◽  
Mathematics Classroom ◽  
Multicultural Literature ◽  
School Mathematics ◽  
Social Environments ◽  
Children And Parents ◽  
Learning School ◽  
Different Cultures ◽  
Mathematics Community ◽  
Learning Together

In recent years, the mathematics community has given more attention to the role that mathematics plays in our cultural society and the contributions of different cultures to mathematics (Bishop 1988; D'Ambrosio 1985; NCTM 1989; Frankenstein 1990; Joseph 1993). Teachers are encouraged to include culture in a variety of ways in the mathematics classroom. Students can be encouraged to use mathematics as a tool to examine their cultural and social environments, traditions, and artifacts. In addition, mathematics learned by students outside the classroom can be used as a bridge to learning school mathematics.

scholarly journals The role of self–other distinction in understanding others' mental and emotional states: neurocognitive mechanisms in children and adults

2016 ◽  
Vol 371 (1686) ◽  
pp. 20150074 ◽  
Author(s):  
Nikolaus Steinbeis
Keyword(s):  
Social Interactions ◽  
Fundamental Problem ◽  
Building Blocks ◽  
Neural Circuitry ◽  
Developmental Changes ◽  
Emotional States ◽  
Social Environments ◽  
Affective States ◽  
Affective Domains

Social interactions come with the fundamental problem of trying to understand others' mental and affective states while under the overpowering influence of one's own concurrent thoughts and feelings. The ability to distinguish between simultaneous representations of others' current experiences as well as our own is crucial to navigate our complex social environments successfully. The developmental building blocks of this ability and how this is given rise to by functional and structural brain development remains poorly understood. In this review, I outline some of the key findings on the role of self–other distinction in understanding others' mental as well as emotional states in children and adults. I will begin by clarifying the crucial role for self–other distinction in avoiding egocentric attributions of one's own cognitive as well as affective states to others in adults and outline the underlying neural circuitry in overcoming such egocentricity. This will provide the basis for a discussion of the emergence of self–other distinction in early childhood as well as developmental changes therein throughout childhood and into adulthood. I will demonstrate that self–other distinction of cognitive and emotional states is already dissociable early in development. Concomitantly, I will show that processes of self–other distinction in cognitive and affective domains rely on adjacent but distinct neural circuitry each with unique connectivity profiles, presumably related to the nature of the distinction that needs to be made.

SELF-FOCUSED ATTENTION: COMMONALITIES ACROSS PSYCHOPATHOLOGIES AND PREDICTORS

2001 ◽  
Vol 29 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Janet Woodruff-Borden ◽  
Andrew J. Brothers ◽  
Sally C. Lister
Keyword(s):  
Problem Solving ◽  
Panic Disorder ◽  
Negative Relationship ◽  
Primary Diagnosis ◽  
Focused Attention ◽  
Affective States ◽  
Significant Relationships ◽  
Causal Direction ◽  
Clinical Syndromes ◽  
The Relationship

Self-focused attention, also thought of a self-absorption, has been linked to a variety of affective states and clinical syndromes, including depression, panic disorder, social anxiety, schizophrenia, and alcoholism. Ingram (1990b) has suggested that self-focus may be a “nonspecific process” that is common across psychopathologies. Studies with nonclinical samples have supported this contention, and the current study assessed whether self-focus was common across various clinically diagnosed groups. A second issue, given this commonality, was to examine the factors across diagnostic conditions to which self-focus was related. One hundred and thirty-eight outpatients were included, and were divided into three groups based on primary diagnosis: “depression”, “panic”, and “other anxiety”. They were assessed with the ADIS-R/IV and completed measures assessing self-focus, affective states, global psychopathology, and problem-solving. Self-focus was common across groups, with minor valence variations. Severity of primary diagnosis predicted total self-focus, with level of depression and trait anxiety predicting negative self-focus. Correlational analyses suggested that self-focused attention is related to general measures of psychopathology and severity, and negatively related to problem-solving. The pattern with negative self-focus was even more pronounced, with significant relationships to all measures of psychopathology, clinician-rated severity, and a negative relationship with problem-solving. Results are discussed in terms of differences between “normal” and problematic self-focus, the causal direction in the relationship between self-focus and negative affect, and the link between self-focus and problem-solving.

Learning Factorial Codes by Predictability Minimization

1992 ◽  
Vol 4 (6) ◽  
pp. 863-879 ◽  
Author(s):  
Jürgen Schmidhuber
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
General Principle ◽  
Novelty Detection ◽  
The Other ◽  
Detection Methods ◽  
Local Algorithm ◽  
The Novel ◽  
Abstract Concepts ◽  
Internal Representations

I propose a novel general principle for unsupervised learning of distributed nonredundant internal representations of input patterns. The principle is based on two opposing forces. For each representational unit there is an adaptive predictor, which tries to predict the unit from the remaining units. In turn, each unit tries to react to the environment such that it minimizes its predictability. This encourages each unit to filter "abstract concepts" out of the environmental input such that these concepts are statistically independent of those on which the other units focus. I discuss various simple yet potentially powerful implementations of the principle that aim at finding binary factorial codes (Barlow et al. 1989), i.e., codes where the probability of the occurrence of a particular input is simply the product of the probabilities of the corresponding code symbols. Such codes are potentially relevant for (1) segmentation tasks, (2) speeding up supervised learning, and (3) novelty detection. Methods for finding factorial codes automatically implement Occam's razor for finding codes using a minimal number of units. Unlike previous methods the novel principle has a potential for removing not only linear but also nonlinear output redundancy. Illustrative experiments show that algorithms based on the principle of predictability minimization are practically feasible. The final part of this paper describes an entirely local algorithm that has a potential for learning unique representations of extended input sequences.

scholarly journals Recent advances of fluorescent biosensors based on cyclic signal amplification technology in biomedical detection

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hongke Qu ◽  
Chunmei Fan ◽  
Mingjian Chen ◽  
Xiangyan Zhang ◽  
Qijia Yan ◽  
...  
Keyword(s):  
Signal Amplification ◽  
Low Cost ◽  
Rolling Circle Amplification ◽  
High Sensitivity ◽  
High Specificity ◽  
Detection Methods ◽  
Research Progress ◽  
Rolling Circle ◽  
Displacement Reactions ◽  
Short Time

AbstractThe cyclic signal amplification technology has been widely applied for the ultrasensitive detection of many important biomolecules, such as nucleic acids, proteins, enzymes, adenosine triphosphate (ATP), metal ions, exosome, etc. Due to their low content in the complex biological samples, traditional detection methods are insufficient to satisfy the requirements for monitoring those biomolecules. Therefore, effective and sensitive biosensors based on cyclic signal amplification technology are of great significance for the quick and simple diagnosis and treatment of diseases. Fluorescent biosensor based on cyclic signal amplification technology has become a research hotspot due to its simple operation, low cost, short time, high sensitivity and high specificity. This paper introduces several cyclic amplification methods, such as rolling circle amplification (RCA), strand displacement reactions (SDR) and enzyme-assisted amplification (EAA), and summarizes the research progress of using this technology in the detection of different biomolecules in recent years, in order to provide help for the research of more efficient and sensitive detection methods. Graphical Abstract

Boxwood Blight: Threat to Ornamentals

2019 ◽  
Vol 57 (1) ◽  
pp. 189-209 ◽  
Author(s):  
Margery L. Daughtrey
Keyword(s):  
Genetic Resistance ◽  
The United States ◽  
Invasive Disease ◽  
Detection Methods ◽  
Foliar Blight ◽  
The United Kingdom ◽  
Western Asia ◽  
Cultivar Susceptibility ◽  
Short Time ◽  
Ornamental Species

Boxwood blight, caused by Calonectria pseudonaviculata and Calonectria henricotiae, has had devastating effects in gardens since its first appearance in the United Kingdom in 1994. The disease affects two other plants in the Buxaceae: sweet box ( Sarcococca spp.) and pachysandra ( Pachysandra spp.). C. pseudonaviculata was likely introduced to Europe by nursery trade from East Asia on an ornamental species and then to western Asia and North America. Thus far, C. henricotiae has been seen only in Europe. Boxwood, valued at $126 million wholesale per year in the United States alone, is now besieged by an aggressive foliar blight active over a broad temperature range when there are long periods of leaf wetness. Research on inoculum, means of dissemination, cultivar susceptibility, environmental influences, fungicides, sanitizers, and detection methods has vastly improved knowledge of this new invasive disease in a short time. Boxwood with genetic resistance to the disease is critically needed.

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