Question: Neural correlates of heart rate variability while experiencing emotions.

Spectral Analysis of Heart Rate Variability: A Noninvasive Tool for Investigating Beat-to-Beat Cardiovascular Regulation
Spectral analysis of heart rate variability serves as a noninvasive technique for quantitatively evaluating the dynamics of short-term cardiovascular control mechanisms. This study elucidates that both sympathetic and parasympathetic nervous activities contribute in a frequency-specific manner to the heart rate power spectrum. Moreover, it is demonstrated that the renin-angiotensin system significantly influences the amplitude of the spectral peak at 0.04 hertz. These findings suggest a crucial role for the renin-angiotensin system in the modulation of short-term cardiovascular control processes, operating on the timescale of seconds to minutes, thereby offering insights into the neural correlates underlying heart rate variability during emotional states.

The advancement in technology indicates that there is an opportunity to enhance Human-Computer Interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users’ self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.

OBJECTIVES
The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.

DESIGN
Observational.

METHODS
Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.

RESULTS
Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).

CONCLUSIONS
OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.

The paper describes an experimental study on the detection of emotion from speech. As computer based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters become more important. The study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned into a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.

The ability to recognize emotions is an important part of building intelligent computers. Emotionally-aware systems would have a rich context from which to make appropriate decisions about how to interact with the user or adapt their system response. There are two main problems with current system approaches for identifying emotions that limit their applicability: they can be invasive and can require costly equipment. Our solution is to determine user emotion by analyzing the rhythm of their typing patterns on a standard keyboard. We conducted a field study where we collected participants' keystrokes and their emotional states via self-reports. From this data, we extracted keystroke features, and created classifiers for 15 emotional states. Our top results include 2-level classifiers for confidence, hesitance, nervousness, relaxation, sadness, and tiredness with accuracies ranging from 77 to 88%. In addition, we show promise for anger and excitement, with accuracies of 84%.

Emotions are related with learning processes, and physiological signals can be used to detect them. In this work, it is presented a model for the dynamic personalization of a learning environment. In the model, a specific combination of emotions and cognition processes are connected and integrated with the concept of ‘flow’, since it may provide a way to improve learning abilities. Physiological signals can be used to relate temporal emotions and subject's learning processes; the cardiac pulse is a reliable signal that carries useful information about the subject's emotional condition, which is detected using a classroom chair adapted with non invasive Electro-Mechanical Film (EMFi) sensors and an acquisition system that generates a ballistocardiogram (BCG), which is analyzed by a developed algorithm to obtain cardiac pulse statistics. A study including different data obtained from the chair's sensors was carried out and interpreted in terms of emotions, which together with a cognitive model is used for the personalization of content in a learning scenario. Such model establishes a relation between the learner's knowledge, the content of a course or a learning activity and the emotional condition of each student.

A wealth of animal data implicates the amygdala in aspects of emotional processing. In recent years, functional neuroimaging and neuropsychological studies have begun to refine our understanding of the functions of the amygdala in humans. This literature offers insights into the types of stimuli that engage the amygdala and the functional consequences that result from this engagement. Specific conclusions and hypotheses include: (1) the amygdala activates during exposure to aversive stimuli from multiple sensory modalities; (2) the amygdala responds to positively valenced stimuli, but these responses are less consistent than those induced by aversive stimuli; (3) amygdala responses are modulated by the arousal level, hedonic strength or current motivational value of stimuli; (4) amygdala responses are subject to rapid habituation; (5) the temporal characteristics of amygdala responses vary across stimulus categories and subject populations; (6) emotionally valenced stimuli need not reach conscious awareness to engage amygdala processing; (7) conscious hedonic appraisals do not require amygdala activation; (8) activation of the amygdala is associated with modulation of motor readiness, autonomic functions, and cognitive processes including attention and memory; (9) amygdala activations do not conform to traditional models of the lateralization of emotion; and (10) the extent and laterality of amygdala activations are related to factors including psychiatric status, gender and personality. The strengths and weakness of these hypotheses and conclusions are discussed with reference to the animal literature.

* Archer's capability of attention and relaxation control during shooting process was evaluated using EEG technology. Attention and meditation algorithms were used to represent the levels of mental concentration and relaxation levels. Elite, mid-level, and novice archers were tested for short and long distance shootings in the archery field. Single channel EEG was recorded on the forehead (Fp1) during the shooting process, and attention and meditation levels were computed by real time. Four types of variations were defined based on the increasing and decreasing patterns of attention and meditation levels during shooting process. Elite archers showed increases in both attention and relaxation while mid-level archers showed increased attention but decreased relaxation. Elite archers also showed higher levels of attention at the release than mid-level and novice archers. Levels of attention and relaxation and their variation patterns were useful to categorize archers and to provide feedback in training.

The article describes a database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions, producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communication and are interpretable in all applied emotions. The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the sound electro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions). The complete database was evaluated in a perception test regarding the recognisability of emotions and their naturalness. Utterances recognised better than 80% and judged as natural by more than 60% of the listeners were phonetically labelled in a narrow transcription with special markers for voice-quality, phonatory and articulatory settings and articulatory features. The database can be accessed by the public via the internet (http://www.expressive-speech.net/emodb/).

Affective influences abound in groups. In this article we propose an organizing model for understanding these affective influences and their effects on group life. We begin with individuallevel affective characteristics that members bring to their groups: moods, emotions, sentiments, and emotional intelligence. These affective characteristics then combine to form a group’s affective composition. We discuss explicit and implicit processes through which this affective combination occurs by examining the research on emotional contagion, entrainment, modeling, and the manipulation of affect. We also explore how elements of the affective context, such as organizationwide emotion norms and the group’s particular emotional history, may serve to constrain or amplify group members’ emotions. The outcome, group emotion, results from the combination of the group’s affective composition and the affective context in which the group is behaving. Last, we focus on the important interaction between nonaffective factors and affective factors in group life and suggest a possible agenda for future research. q 2001 Academic Press

Personality neuroscience involves the use of neuroscience methods to study individual differences in behavior, motivation, emotion, and cognition. Personality psychology has contributed much to identifying the important dimensions of personality, but relatively little to understanding the biological sources of those dimensions. However, the rapidly expanding field of personality neuroscience is increasingly shedding light on this topic. This article provides a survey of progress in the use of neuroscience to study personality traits, organized using a hierarchical model of traits based on the Big Five dimensions: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect. Evidence is reviewed for hypotheses about the biological systems involved in each trait. The mission of personality psychology is ‘‘to provide an integrative framework for understanding the whole person’’ (McAdams & Pals, 2006; p. 204), and many different methods must be brought to bear to accomplish such an ambitious goal. The field of personality neuroscience rests on the premise that the whole person cannot be understood without understanding the brain. In this article, I discuss the role that neuroscience can play in personality research and review the progress of this rapidly expanding field. (For more in depth review of the field and its influential theories see DeYoung & Gray, 2009; and Zuckerman, 2005.) Personality psychology’s attempt to understand the whole person calls for a broad conception of personality itself, such as the one provided by McAdams and Pals (2006, p. 212): Personality is an individual’s unique variation on the general evolutionary design for human nature, expressed as a developing pattern of dispositional traits, characteristic adaptations, and integrative life stories, complexly and differentially situated in culture. This definition describes three levels at which personality can be analyzed: traits, characteristic adaptations, and life stories. Personality neuroscience has focused primarily on traits, which are relatively stable patterns of behavior, motivation, emotion, and cognition (Pytlik Zillig, Hemenover, & Dienstbier, 2002; Wilt & Revelle, 2009) that are not specific to a particular social milieu or culture. This is not to say that traits are evident to the same extent or with identical manifestations in all cultures, but rather that any trait can be observed in a subset of situations in any culture. In contrast to traits, characteristic adaptations and life stories describe the individual’s specific responses to his or her particular life circumstances. Obviously, the latter two levels of analysis are crucial for understanding any individual, but their complexity renders them less amenable to study by neuroscience, and this article focuses on the biology of traits. Traits can be considered probabilistic descriptions of the frequency and intensity with which individuals exhibit various behavioral, motivational, emotional, and cognitive states Social and Personality Psychology Compass 4/12 (2010): 1165–1180, 10.1111/j.1751-9004.2010.00327.x a 2010 The Author Social and Personality Psychology Compass a 2010 Blackwell Publishing Ltd (Fleeson, 2001; Fleeson & Gallagher, 2009). Individuals who are high in some trait will experience the states associated with that trait more often and more intensely than individuals low in that trait. For example, someone high in Extraversion will be talkative, outgoing, and excited more often than someone low in Extraversion, but even the person low in Extraversion may experience those states occasionally. The aim of personality neuroscience is to understand both the biological systems that are responsible for the states associated with traits and the parameters of those systems that cause them to function differently in different individuals. The systems themselves are presumed to be present in every intact human brain – hence McAdams and Pals’ (2006) reference to ‘‘the general evolutionary design for human nature’’ – but their parameters will vary from person to person. (For example, all people have brain systems that respond to rewards, but in different individuals these systems will respond with different degrees of vigor to a particular reward, and the systems’ average level of response may be associated with some personality trait.) When considering the biological sources of personality, one must distinguish between proximal and distal sources. The proximal sources, just described, consist of stable differences in the functioning of the neural systems that produce the states associated with traits. The distal sources are both genetic and environmental, as indicated by the fact that heritability estimates for personality traits are in the range of 40% to 80%, depending on trait and method (Bouchard & McGue, 2003; Riemann, Angleitner, & Strelau, 1997). (The heritability of a trait indicates the amount of its variation in a population due to genetic, rather than environmental, variation.) It is important to remember that when either genes or situations have lasting effects on traits, they must do so by changing the brain; thus, personality differences are ‘biological’ regardless of their heritability, in the sense that they must be proximally generated by the brain no matter whether they originated in genes or environment. Methods in Personality Neuroscience Scientific investigation of the biological basis of personality has been limited, until relatively recently, by a lack of technology to examine brain structure and function in living human beings. Prior to the development of neuroimaging, the sole means for assessing brain activity was the electroencephalogram (EEG), which measures the brain’s electrical activity at the scalp. Today a number of methods are available for personality neuroscience. Five important categories of neuroscientific methods are (1) neuroimaging (e.g., magnetic resonance imaging [MRI] or positron emission tomography [PET]), which allows assessment of brain structure and function with a relatively high spatial resolution; (2) molecular genetics, which allows assessment of variation in specific genes that are expressed in the brain; (3) EEG, which provides the highest temporal resolution of neural activity of any available method; (4) assays of endogenous psychoactive substances or their byproducts (e.g., hormone levels in saliva or neurotransmitter metabolites in cerebrospinal fluid); and (5) psychopharmocological manipulation (e.g., tryptophan depletion or augmentation to alter levels of the neurotransmitter serotonin). Personality neuroscience employs these methods in conjunction with the methods of personality psychology, attempting to link biological variables to traits. Measurement of traits is typically accomplished by questionnaire, through self-report and ⁄ or ratings by peers or other knowledgeable informants. Questionnaires provide a convenient and reliable method for assessing a broad range of stable individual differences, drawing on raters’ experiences over a far greater span of time than is available in the laboratory. However, 1166 Personality Neuroscience a 2010 The Author Social and Personality Psychology Compass 4/12 (2010): 1165–1180, 10.1111/j.1751-9004.2010.00327.x Social and Personality Psychology Compass a 2010 Blackwell Publishing Ltd traits can also be assessed in other ways, such as through laboratory tasks, behavioral observation, or experience sampling, and personality should not be identified exclusively with the variables provided by personality questionnaires. What we want to explain in personality neuroscience is not how people answer questionnaires, but rather why they exhibit stable patterns of behavior, motivation, emotion, and cognition. Most studies discussed in this review begin with some psychological trait and attempt to discover its biological basis. Another valid and useful approach is to begin with stable individual differences in some biological parameter (such as asymmetry in the level of left versus right cortical hemisphere activity, measured at rest by EEG) and then to attempt to discover what psychological trait or traits are associated with that biological trait. An important caveat for anyone exploring the literature on personality neuroscience or contemplating entering the field as an investigator is that much inconsistency exists in the findings to date. In neuroimaging research, inconsistency is probably due in large part to the use very small samples, due to cost. Unfortunately, small samples often lack statistical power to detect true effects (Yarkoni, 2009). Worse still, low power has the unfortunate additional consequence of increasing the proportion of false positives among significant findings (Green et al., 2008). In genetic studies, though larger samples are typically used, power may still be an issue, and inconsistency may arise, because each trait is influenced by many genes, each accounting for only a very small amount of the variance of the trait (Green et al., 2008). These difficulties highlight the importance of testing reasonably focused hypotheses, rather than simply exploring associations with biological variables in the absence of any theory of the causal mechanisms that might underlie a given trait. The Structure of Personality Traits Personality neuroscience can usefully be guided by existing knowledge about the structure of personality – that is, knowledge about how various traits relate to one another and to the major dimensions of personality. In order to produce a coherent overview of the progress of personality neuroscience, one needs to relate findings to a reasonably comprehensive taxonomy of traits, such as that provided by the Five Factor Model or Big Five, which categorizes the majority of traits within five broad domains, typically labeled Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect (Digman, 199


Question: Neural correlates associated with heart rate variability while experiencing emotions.

This study sought to explore the relationship between vagal tone and cognitive performance in executive and non-executive tasks, specifically focusing on working memory and sustained attention. Heart rate (HR) and heart rate variability (HRV) were utilized as indicators of reactivity to cognitive tasks. A total of 53 male sailors from the Royal Norwegian Navy were recruited for the study. Continuous inter-beat-interval recordings were taken for a 5-minute baseline period, followed by the administration of a working memory test (WMT) and a continuous performance test (CPT), with a 5-minute recovery period concluding the session. The WMT was based on Baddeley and Hitch's research (1974), and the CPT was designed to assess sustained attention. Participants were divided into high HRV and low HRV groups based on their root mean squared successive differences during the baseline phase. The findings revealed that the high HRV group outperformed the low HRV group in terms of correct responses on the WMT. Moreover, on the CPT, the high HRV group demonstrated faster mean reaction time (mRT), a higher number of correct responses, and fewer errors, particularly in the components that required executive functions. Reactivity analysis showed a decrease in HRV and an increase in HR during the cognitive tasks compared to the recovery phase, a pattern observed in both HRV groups. These results suggest a positive correlation between high HRV and improved performance on tasks that demand executive function.

**Question**: The impact of the COVID-19 pandemic on mental health in the general population

**Documents**: The effects of the COVID-19 pandemic on anxiety and depression.
The aim of this study was to investigate the impact of the COVID-19 pandemic on anxiety and depression in the general population. Data were collected using an online survey. The survey included questions on demographics, lifestyle, and mental health. A total of 1,234 participants completed the survey. The results showed that the prevalence of anxiety and depression was higher during the pandemic than before. Women, younger individuals, and those with a history of mental health problems were more likely to report symptoms of anxiety and depression. The findings suggest that the COVID-19 pandemic has had a significant impact on mental health, particularly in vulnerable groups.

**Similar Documents**: This research aimed to assess the effects of the COVID-19 pandemic on anxiety and depression levels among the general population. An online survey was conducted to gather data on demographics, lifestyle, and mental health status. The

The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.

We investigated brain circuitry mediating cocaine-induced euphoria and craving using functional MRI (fMRI). During double-blind cocaine (0.6 mg/kg) and saline infusions in cocaine-dependent subjects, the entire brain was imaged for 5 min before and 13 min after infusion while subjects rated scales for rush, high, low, and craving. Cocaine induced focal signal increases in nucleus accumbens/subcallosal cortex (NAc/SCC), caudate, putamen, basal forebrain, thalamus, insula, hippocampus, parahippocampal gyrus, cingulate, lateral prefrontal and temporal cortices, parietal cortex, striate/extrastriate cortices, ventral tegmentum, and pons and produced signal decreases in amygdala, temporal pole, and medial frontal cortex. Saline produced few positive or negative activations, which were localized to lateral prefrontal cortex and temporo-occipital cortex. Subjects who underwent repeat studies showed good replication of the regional fMRI activation pattern following cocaine and saline infusions, with activations on saline retest that might reflect expectancy. Brain regions that exhibited early and short duration signal maxima showed a higher correlation with rush ratings. These included the ventral tegmentum, pons, basal forebrain, caudate, cingulate, and most regions of lateral prefrontal cortex. In contrast, regions that demonstrated early but sustained signal maxima were more correlated with craving than with rush ratings; such regions included the NAc/SCC, right parahippocampal gyrus, and some regions of lateral prefrontal cortex. Sustained negative signal change was noted in the amygdala, which correlated with craving ratings. Our data demonstrate the ability of fMRI to map dynamic patterns of brain activation following cocaine infusion in cocaine-dependent subjects and provide evidence of dynamically changing brain networks associated with cocaine-induced euphoria and cocaine-induced craving.

This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with state-of-theart method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the “MediaEval2015 emotion in music” dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.

There is clear evidence that sustained experiences may affect both brain structure and function. Thus, it is quite reasonable to posit that sustained exposure to a set of cultural experiences and behavioral practices will affect neural structure and function. The burgeoning field of cultural psychology has often demonstrated the subtle differences in the way individuals process information-differences that appear to be a product of cultural experiences. We review evidence that the collectivistic and individualistic biases of East Asian and Western cultures, respectively, affect neural structure and function. We conclude that there is limited evidence that cultural experiences affect brain structure and considerably more evidence that neural function is affected by culture, particularly activations in ventral visual cortex-areas associated with perceptual processing.

The paper describes an experimental study on the detection of emotion from speech. As computer based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters become more important. The study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned into a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.

Personality neuroscience involves the use of neuroscience methods to study individual differences in behavior, motivation, emotion, and cognition. Personality psychology has contributed much to identifying the important dimensions of personality, but relatively little to understanding the biological sources of those dimensions. However, the rapidly expanding field of personality neuroscience is increasingly shedding light on this topic. This article provides a survey of progress in the use of neuroscience to study personality traits, organized using a hierarchical model of traits based on the Big Five dimensions: Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect. Evidence is reviewed for hypotheses about the biological systems involved in each trait. The mission of personality psychology is ‘‘to provide an integrative framework for understanding the whole person’’ (McAdams & Pals, 2006; p. 204), and many different methods must be brought to bear to accomplish such an ambitious goal. The field of personality neuroscience rests on the premise that the whole person cannot be understood without understanding the brain. In this article, I discuss the role that neuroscience can play in personality research and review the progress of this rapidly expanding field. (For more in depth review of the field and its influential theories see DeYoung & Gray, 2009; and Zuckerman, 2005.) Personality psychology’s attempt to understand the whole person calls for a broad conception of personality itself, such as the one provided by McAdams and Pals (2006, p. 212): Personality is an individual’s unique variation on the general evolutionary design for human nature, expressed as a developing pattern of dispositional traits, characteristic adaptations, and integrative life stories, complexly and differentially situated in culture. This definition describes three levels at which personality can be analyzed: traits, characteristic adaptations, and life stories. Personality neuroscience has focused primarily on traits, which are relatively stable patterns of behavior, motivation, emotion, and cognition (Pytlik Zillig, Hemenover, & Dienstbier, 2002; Wilt & Revelle, 2009) that are not specific to a particular social milieu or culture. This is not to say that traits are evident to the same extent or with identical manifestations in all cultures, but rather that any trait can be observed in a subset of situations in any culture. In contrast to traits, characteristic adaptations and life stories describe the individual’s specific responses to his or her particular life circumstances. Obviously, the latter two levels of analysis are crucial for understanding any individual, but their complexity renders them less amenable to study by neuroscience, and this article focuses on the biology of traits. Traits can be considered probabilistic descriptions of the frequency and intensity with which individuals exhibit various behavioral, motivational, emotional, and cognitive states Social and Personality Psychology Compass 4/12 (2010): 1165–1180, 10.1111/j.1751-9004.2010.00327.x a 2010 The Author Social and Personality Psychology Compass a 2010 Blackwell Publishing Ltd (Fleeson, 2001; Fleeson & Gallagher, 2009). Individuals who are high in some trait will experience the states associated with that trait more often and more intensely than individuals low in that trait. For example, someone high in Extraversion will be talkative, outgoing, and excited more often than someone low in Extraversion, but even the person low in Extraversion may experience those states occasionally. The aim of personality neuroscience is to understand both the biological systems that are responsible for the states associated with traits and the parameters of those systems that cause them to function differently in different individuals. The systems themselves are presumed to be present in every intact human brain – hence McAdams and Pals’ (2006) reference to ‘‘the general evolutionary design for human nature’’ – but their parameters will vary from person to person. (For example, all people have brain systems that respond to rewards, but in different individuals these systems will respond with different degrees of vigor to a particular reward, and the systems’ average level of response may be associated with some personality trait.) When considering the biological sources of personality, one must distinguish between proximal and distal sources. The proximal sources, just described, consist of stable differences in the functioning of the neural systems that produce the states associated with traits. The distal sources are both genetic and environmental, as indicated by the fact that heritability estimates for personality traits are in the range of 40% to 80%, depending on trait and method (Bouchard & McGue, 2003; Riemann, Angleitner, & Strelau, 1997). (The heritability of a trait indicates the amount of its variation in a population due to genetic, rather than environmental, variation.) It is important to remember that when either genes or situations have lasting effects on traits, they must do so by changing the brain; thus, personality differences are ‘biological’ regardless of their heritability, in the sense that they must be proximally generated by the brain no matter whether they originated in genes or environment. Methods in Personality Neuroscience Scientific investigation of the biological basis of personality has been limited, until relatively recently, by a lack of technology to examine brain structure and function in living human beings. Prior to the development of neuroimaging, the sole means for assessing brain activity was the electroencephalogram (EEG), which measures the brain’s electrical activity at the scalp. Today a number of methods are available for personality neuroscience. Five important categories of neuroscientific methods are (1) neuroimaging (e.g., magnetic resonance imaging [MRI] or positron emission tomography [PET]), which allows assessment of brain structure and function with a relatively high spatial resolution; (2) molecular genetics, which allows assessment of variation in specific genes that are expressed in the brain; (3) EEG, which provides the highest temporal resolution of neural activity of any available method; (4) assays of endogenous psychoactive substances or their byproducts (e.g., hormone levels in saliva or neurotransmitter metabolites in cerebrospinal fluid); and (5) psychopharmocological manipulation (e.g., tryptophan depletion or augmentation to alter levels of the neurotransmitter serotonin). Personality neuroscience employs these methods in conjunction with the methods of personality psychology, attempting to link biological variables to traits. Measurement of traits is typically accomplished by questionnaire, through self-report and ⁄ or ratings by peers or other knowledgeable informants. Questionnaires provide a convenient and reliable method for assessing a broad range of stable individual differences, drawing on raters’ experiences over a far greater span of time than is available in the laboratory. However, 1166 Personality Neuroscience a 2010 The Author Social and Personality Psychology Compass 4/12 (2010): 1165–1180, 10.1111/j.1751-9004.2010.00327.x Social and Personality Psychology Compass a 2010 Blackwell Publishing Ltd traits can also be assessed in other ways, such as through laboratory tasks, behavioral observation, or experience sampling, and personality should not be identified exclusively with the variables provided by personality questionnaires. What we want to explain in personality neuroscience is not how people answer questionnaires, but rather why they exhibit stable patterns of behavior, motivation, emotion, and cognition. Most studies discussed in this review begin with some psychological trait and attempt to discover its biological basis. Another valid and useful approach is to begin with stable individual differences in some biological parameter (such as asymmetry in the level of left versus right cortical hemisphere activity, measured at rest by EEG) and then to attempt to discover what psychological trait or traits are associated with that biological trait. An important caveat for anyone exploring the literature on personality neuroscience or contemplating entering the field as an investigator is that much inconsistency exists in the findings to date. In neuroimaging research, inconsistency is probably due in large part to the use very small samples, due to cost. Unfortunately, small samples often lack statistical power to detect true effects (Yarkoni, 2009). Worse still, low power has the unfortunate additional consequence of increasing the proportion of false positives among significant findings (Green et al., 2008). In genetic studies, though larger samples are typically used, power may still be an issue, and inconsistency may arise, because each trait is influenced by many genes, each accounting for only a very small amount of the variance of the trait (Green et al., 2008). These difficulties highlight the importance of testing reasonably focused hypotheses, rather than simply exploring associations with biological variables in the absence of any theory of the causal mechanisms that might underlie a given trait. The Structure of Personality Traits Personality neuroscience can usefully be guided by existing knowledge about the structure of personality – that is, knowledge about how various traits relate to one another and to the major dimensions of personality. In order to produce a coherent overview of the progress of personality neuroscience, one needs to relate findings to a reasonably comprehensive taxonomy of traits, such as that provided by the Five Factor Model or Big Five, which categorizes the majority of traits within five broad domains, typically labeled Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness ⁄ Intellect (Digman, 199

When we buy a product of a brand, we trust the brand to provide good quality and reliability. Therefore, trust plays a major role in consumer behavior. It is unclear, however, how trust in brands is processed in the brain and whether it is processed differently from interpersonal trust. In this study, we used fMRI to investigate the neural correlates of interpersonal and brand trust by comparing the brain activation patterns during explicit trustworthiness judgments of faces and brands. Our results showed that while there were several brain areas known to be linked to trustworthiness evaluations, such as the amygdalae, more active in trustworthiness judgments when compared to a control task (familiarity judgment) for faces, no such difference was found for brands. Complementary ROI analysis revealed that the activation of both amygdalae was strongest for faces in the trustworthiness judgments. The direct comparison of the brain activation patterns during the trustworthiness evaluations between faces and brands in this analysis showed that trustworthiness judgments of faces activated the orbitofrontal cortex, another region that was previously linked to interpersonal trust, more strongly than trustworthiness judgments of brands. Further, trustworthiness ratings of faces, but not brands, correlated with activation in the orbitofrontal cortex. Our results indicate that the amygdalae, as well as the orbitofrontal cortex, play a prominent role in interpersonal trust (faces), but not in trust for brands. It is possible that this difference is due to brands being processed as cultural objects rather than as having human-like personality characteristics.

Emotion recognition in speech is a topic on which little research has been done to-date. In this paper, we discuss why emotion recognition in speech is a significant and applicable research topic, and present a system for emotion recognition using oneclass-in-one neural networks. By using a large database of phoneme balanced words, our system is speakerand context-independent. We achieve a recognition rate of approximately 50% when testing eight emotions.

Recently, the physiological signals have been analyzed more and more, especially in the context of everyday life activities such as watching video or looking at pictures. Tracking these signals gives access to the mental state of the user (interest, tiredness, stress) but also to his emotions (sadness, fright, happiness). The analysis of the reader's physiological signals during reading can provide a better understanding of the reader's feelings but also a better understanding of the documents. Our main research direction is to find the relationship between a change in the reader's physiological signal and the content of the reading. As a first step, we investigate whether it is possible to distinguish a manga (Japanese comic book) from another by analyzing the physiological signals of the reader. We use 3 different manga genres (horror, romance, comedy) and try to predict which one is read by analyzing the features extracted from the physiological signals of the reader. Our method uses the blood volume pulse, the electrodermal activity and the skin temperature of the reader while reading. We show that by using these physiological signals with a support vector machine we can retrieve which manga has been read with a 90% average accuracy.

Assessing human stress in real-time is more difficult and challenging today. The present review deals about the measurement of stress in laboratory environment using different stress inducement stimuli by the help of physiological signals. Previous researchers have been used different stress inducement stimuli such as stroop colour word test (CWT), mental arithmetic test, public speaking task, cold pressor test, computer games and works used to induce the stress. Most of the researchers have been analyzed stress using questionnaire based approach and physiological signals. The several physiological signals like Electrocardiogram (ECG), Electromyogram (EMG), Galvanic Skin Response (GSR), Blood Pressure (BP), Skin Temperature (ST), Blood Volume Pulse (BVP), respiration rate (RIP) and Electroencephalogram (EEG) were briefly investigated to identify the stress. Different statistical methods like Analysis of variance (ANOVA), two-way ANOVA, Multivariate analysis of variance (MANOVA), t-test, paired t-tests and student t-tests have used to describe the correlation between stress inducement stimuli, subjective parameters (age, gender and etc.,) and physiological signals. This present works aims to find the most appropriate stress inducement stimuli, physiological signals and statistical method to efficiently asses the human stress.


Question: Decision tree models to forecast the academic achievement of students.

This document explores an enhancement to the C4.5 decision tree algorithm, particularly in contexts involving continuous attributes. C4.5 has been noted to perform suboptimally in such domains due to its handling of continuous attributes. To rectify this, the paper introduces modifications to the way tests on continuous attributes are formed and evaluated. Inspired by Minimum Description Length (MDL) principles, a penalty is applied to these tests, leading to the exclusion of some tests and altering the preference for others. Experimental evidence demonstrates that these adjustments result in more compact decision trees with improved predictive accuracy. Furthermore, the updated C4.5 version, incorporating these enhancements, outperforms recent strategies that utilize global discretization and construct small trees with multi-interval splits, showcasing its superiority in managing continuous attributes effectively.

Article History: Received 26.06.2016 Received in revised form 25.10.2016 Accepted 26.10.2016 Available online 25.11.2016 Circadian preference refers to individuals’ preference for morning or evening activities. Its two dimensions (i.e., morningness and eveningness) are related to a number of academic outcomes. While morningness shows positive relations with academic achievement, eveningness shows negative relations. Further, morningness and eveningness show the same correlational pattern with conscientiousness (i.e., positive relations for morningness, negative relations for eveningness), which – in turn – predicts academic achievement. Therefore, the main aim of the present study was to investigate if the relation between circadian preference and academic achievement was mediated by conscientiousness. The sample comprised 422 students attending the 11th grade at a grammar school in Germany. Circadian preference (morningness and eveningness) and conscientiousness were assessed by self-report questionnaires; academic achievement was operationalized by school grades. Using confirmatory analyses and structural equation modelling, the results supported the assumption that conscientiousness mediates the relation between circadian preference and academic achievement. Implications for research into circadian preference and for education are discussed. © 2016 IOJES. All rights reserved

Students in online courses generate large amounts of data that can be used to personalize the learning process and improve quality of education. In this paper, we present the Latent Skill Embedding (LSE), a probabilistic model of students and educational content that can be used to recommend personalized sequences of lessons with the goal of helping students prepare for specific assessments. Akin to collaborative filtering for recommender systems, the algorithm does not require students or content to be described by features, but it learns a representation using access traces. We formulate this problem as a regularized maximum-likelihood embedding of students, lessons, and assessments from historical student-content interactions. An empirical evaluation on large-scale data from Knewton, an adaptive learning technology company, shows that this approach predicts assessment results competitively with benchmark models and is able to discriminate between lesson sequences that lead to mastery and failure.

Creating an integrative research framework that extends a model frequently used in the Information Systems field, the Technology Acceptance Model, together with variables used in the Education field, this empirical study investigates the factors influencing student performance as reflected by their final course grade. The Technology Acceptance Model explains computer acceptance in general terms. The model measures the impact of external variables on internal beliefs, attitudes, and intentions. Perceived Usefulness and Perceived Ease of Use, two main constructs in the model, refer to an individual’s perception of how the adoption of a new technology will increase their efficiency, and the individual’s perception of how easy the technology will be to use. The lower the perceived effort is, the easier the technology will be to adopt. Thus, Perceived Usefulness, Perceived Ease of Use, Computer Self-Efficacy, and Computer Anxiety were measured to determine their effect on student performance.

The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications.

This study presents an approach to predict the performance of sales agents of a call center dedicated exclusively to sales and telemarketing activities. This approach is based on a naive Bayesian classifier. The objective is to know what levels of the attributes are indicative of individuals who perform well. A sample of 1037 sales agents was taken during the period between March and September of 2009 on campaigns related to insurance sales and service pre-paid phone services, to build the naive Bayes network. It has been shown that, socio-demographic attributes are not suitable for predicting performance. Alternatively, operational records were used to predict production of sales agents, achieving satisfactory results. In this case, the classifier training and testing is done through a stratified tenfold cross-validation. It classified the instances correctly 80.60% of times, with the proportion of false positives of 18.1% for class no (does not achieve minimum) and 20.8% for the class yes (achieves equal or above minimum acceptable). These results suggest that socio-demographic attributes has no predictive power on performance, while the operational information of the activities of the sale agent can predict the future performance of the

This study investigated the role of ability and trait emotional intelligence (EI), fluid intelligence, and personality traits in career decision-making self-efficacy, career indecision and indecisiveness. The Advanced Progressive Matrices, Big Five Questionnaire, Mayer-Salovey-Caruso Emotional Intelligence Test, Bar-On Emotional Intelligence Inventory, Trait Emotional Intelligence Questionnaire, Career Decision SelfEfficacy Scale: Short Form, Career Decision-Making Difficulties Questionnaire, and Indecisiveness Scale were administered to 194 Italian high school students. These results highlighted the potentially important role of self-reported EI in career decisions. 2014 Elsevier Ltd. All rights reserved.

There has long been a chasm between theoretical mod els of machine learning and practical machine learn ing algorithms For instance empirically successful algorithms such as C and backpropagation have not met the criteria of the PAC model and its vari ants Conversely the algorithms suggested by com putational learning theory are usually too limited in various ways to nd wide application The theoreti cal status of decision tree learning algorithms is a case in point while it has been proven that C and all reasonable variants of it fails to meet the PAC model criteria other recently proposed decision tree algo rithms that do have non trivial performance guaran tees unfortunately require membership queries Two recent developments have narrowed this gap between theory and practice not for the PAC model but for the related model known as weak learning or boosting First an algorithm called Adaboost was proposed that meets the formal criteria of the boosting model and is also competitive in practice Second the basic algorithms underlying the popular C and CART programs have also very recently been shown to meet the formal criteria of the boosting model Thus it seems plausible that the weak learning frame work may provide a setting for interaction between formal analysis and machine learning practice that is lacking in other theoretical models Our aim in this paper is to push this interaction further in light of these recent developments In par ticular we perform experiments suggested by the for mal results for Adaboost and C within the weak learning framework We concentrate on two particu larly intriguing issues First the theoretical boosting results for top down decision tree algorithms such as C suggest that a new splitting criterion may result in trees that are smaller and more accurate than those obtained using the usual information gain We con rm this suggestion experimentally Second a super cial interpretation of the theo retical results suggests that Adaboost should vastly outperform C This is not the case in practice and we argue through experimental results that the theory must be understood in terms of a measure of a boosting algorithm s behavior called its advantage sequence We compare the advantage sequences for C and Adaboost in a number of experiments We nd that these sequences have qualitatively dif ferent behavior that explains in large part the dis crepancies between empirical performance and the theoretical results Brie y we nd that although C and Adaboost are both boosting algorithms Adaboost creates successively harder ltered dis tributions while C creates successively easier ones in a sense that will be made precise

This study sought to investigate critical parental socio-economic factors affecting the academic achievement of students in selected secondary schools in urban informal settlements in Westlands District in Nairobi County. The study was premised on the classical Liberal Theory of Equal Opportunity and social Darwinism proposed by Charles Darwin. A descriptive survey design using a sample of 125 respondents comprising of 91 students, 18 teachers and 16 parents was used for the study. The tools for data collection were questionnaires for students, Focus Group Discussions for teachers and Interview schedules for parents. Quantitative data from the questionnaires was analyzed using descriptive and inferential statistics while the qualitative data from interviews was managed through thematic techniques. The major findings of the study were that; the physical and other critical instructional resources were grossly inadequate or in a pathetic condition not conducive to education provision, there was a strong non-significant negative correlation between the occupation of parents and ability to finance education, that there is a significant positive correlation between good parent-teacher relationship and their involvement in their children’s academic achievement. It was concluded that, parental occupation and involvement in learning activities and effective parent-teacher relationship were facilitating factors. Parents’ low ability to finance education, coupled with the poor status of physical and instructional resources were inhibiting factors to students’ academic achievement in the study locale. It was therefore recommended that the government should strengthen the collaboration between key education development partners to mobilize physical teaching/learning resources and strengthen education in the region. Unemployment should be controlled; poor students should be provided scholarships and that the government should take steps to raise socioeconomic status of people.

This paper introduces a software tool named KEEL, which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.

This study examined the utility of parent socialization models for understanding teachers' influence on student adjustment in middle school. Teachers were assessed with respect to their modeling of motivation and to Baumrind's parenting dimensions of control, maturity demands, democratic communication, and nurturance. Student adjustment was defined in terms of their social and academic goals and interest in class, classroom behavior, and academic performance. Based on information from 452 sixth graders from two suburban middle schools, results of multiple regressions indicated that the five teaching dimensions explained significant amounts of variance in student motivation, social behavior, and achievement. High expectations (maturity demands) was a consistent positive predictor of students' goals and interests, and negative feedback (lack of nurturance) was the most consistent negative predictor of academic performance and social behavior. The role of motivation in mediating relations between teaching dimensions and social behavior and academic achievement also was examined; evidence for mediation was not found. Relations of teaching dimensions to student outcomes were the same for African American and European American students, and for boys and girls. The implications of parent socialization models for understanding effective teaching are discussed.


Question: Decision tree models to forecast the academic achievement of students.

This study explores the efficacy of three decision tree algorithms—REPTree, Simple Cart, and RandomTree—within the Weka data mining framework for the classification of Indian news data. With the exponential growth of data in our digital age, tools like the Weka workbench have become indispensable for managing and analyzing vast datasets. This comprehensive suite of machine learning algorithms and data pre-processing utilities facilitates a seamless workflow, from initial data exploration to the design of complex configurations for streaming data processing. The graphical user interfaces provided by Weka offer a user-friendly environment for conducting advanced data mining experiments. Classification, a key component of data mining, enables the categorization of diverse data types. This research focuses on evaluating the performance of REPTree, Simple Cart, and RandomTree classification algorithms using the Indian news dataset. The primary objective is to compare these classifiers in terms of maximizing true positive rates and minimizing false positive rates. The analysis was conducted using the Weka API, and the findings indicate that RandomTree outperforms REPTree and Simple Cart in terms of efficiency and accuracy. Keywords—Simple Cart, RandomTree, REPTree, Weka, Data Mining

**Question**: A new approach to forecasting stock prices using machine learning

**Documents**: Forecasting stock prices using machine learning techniques
The prediction of stock prices has always been a challenging task for investors and analysts alike. This paper explores the application of machine learning techniques for forecasting stock prices. The study focuses on the use of support vector machines (SVM), decision trees, and random forests to predict stock price movements. The data used for the experiments includes historical stock prices, trading volumes, and other relevant financial indicators. The performance of the models is evaluated using various metrics such as accuracy, precision, and recall. The results show that the random forest model outperforms the other two models in terms of accuracy and precision. The study also highlights the importance of feature selection in improving the predictive power of the models. The findings suggest that machine learning techniques can be a valuable tool for stock price prediction and can help investors make more informed decisions.

**Similar Documents**: This research delves into the potential of machine learning methods for stock price prediction, an area that has long puzzled financial experts and investors. Specifically, the paper investigates the effectiveness of three machine learning algorithms—support vector machines (SVM), decision trees, and random forests—in forecasting stock price trends. Utilizing a comprehensive dataset that encompasses historical stock prices, trading volumes, and pertinent financial indicators, the study assesses the models' performance through metrics

This study examined the effects of a computer game on students' mathematics achievement and motivation, and the role of prior mathematics knowledge, computer skill, and English language skill on their achievement and motivation as they played the game. A total of 193 students and 10 teachers participated in this study. The teachers were randomly assigned to experimental and control groups. A mixed method of quantitative and interviews were used with Multivariate Analysis of Co-Variance to analyze the data. The results indicated significant improvement of the achievement of the experimental versus control group. No significant improvement was found in the motivation of the groups. Students who played the games in their classrooms and school labs reported greater motivation compared to the ones who played the games only in the school labs. Prior knowledge, computer and English language skill did not play significant roles in achievement and motivation of the experimental group. 2010 Elsevier Ltd. All rights reserved.

This paper reports on the implication of different preferred learning styles on students' performance in the introductory programming sequence and on work in progress on how to accommodate these different styles.Students were given a learning styles preference test and then their preferred learning styles were compared to their performance on the exam and the practical programming part of the introductory programming module. There were significant differences in performance between groups of students.This result could lead one to two possible conclusions. One might be that some students' learning styles are more suited to learning programming than others.An alternative explanation is that our current methods of teaching advantage students with certain learning preference styles. We are at present in the process of testing this second assumption by providing students with a wider range of learning materials. We will then see if student performance is improved by using our current results as a baseline for comparison

This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of data and text mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped Web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 decision tree induction algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions

One of the main problems faced by university students is to create and manage the semester course plan. In this paper, we present a course enrollment recommender system based on data mining techniques. The system mainly helps with students’ enrollment decisions. More specifically, it provides recommendation of selective and optional courses with respect to students’ skills, knowledge, interests and free time slots in their timetables. The system also warns students against difficult courses and reminds them mandatory study duties. We evaluate the usability of designed methods by analyzing real-world data obtained from the Information System of Masaryk University.

The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.

We say a transfer model is a mapping between the questions in an intelligent tutoring system and the knowledge components (i.e., skills, strategies, declarative knowledge, etc) needed to answer a question correctly. [JKT00] showed how you could take advantage of 1) the Power Law Of Learning, 2) an existing transfer model, and 3) set of tutorial log files, to learn a function (using logistic regression) that will predict when a student will get a question correct. In the main conference proceeding [CHK2004] give an example of using this technique for transfer model selection. Koedinger and Junker [KJ99] also conceptualized a search space where each state is a new transfer model. The operators in this search space split, add or merge knowledge components based upon factors that are tagged to questions. Koedinger and Junker called this method learning factors analysis emphasizing that this method can be used to study learning. Unfortunately, the search space is huge and searching for good fitting transfer models is exponential. The main goal of this paper is show a technique that will make searching for transfer models more efficient. Our procedure implements a search method using association rules as a means of guiding the search. The association rules are mined from a dataset derived from student-tutor interaction logs. The association rules found in the mining process determine what operations to perform on the current transfer model. We report on the speed up achieved. Being able to find good transfer models quicker will help intelligent tutor system builders as well as cognitive science researchers better assess what makes certain problems hard and other problems easy for students.

A teachable-agent arithmetic game is presented and evaluated in terms of student performance, attitude and self-eff icacy. An experimental prepost study design was used, enrolling 153 3 rd and 5 grade students in Sweden. The playing group showed significantly larger gains i math performance and self-efficacy beliefs, but not in general attitude towards math, compared to control groups. The contributions in relation to pr evious work include a novel educational game being evaluated, and an emphasis o n elf-efficacy in the study as a strong predictor of math achievements.

Prediction of scholar popularity has become an important research topic for a number of reasons. In this paper, we tackle the problem of predicting the popularity {\it trend} of scholars by concentrating on making predictions both as earlier and accurate as possible. In order to perform the prediction task, we first extract the popularity trends of scholars from a training set. To that end, we apply a time series clustering algorithm called K-Spectral Clustering (K-SC) to identify the popularity trends as cluster centroids. We then predict trends for scholars in a test set by solving a classification problem. Specifically, we first compute a set of measures for individual scholars based on the distance between earlier points in her particular popularity curve and the identified centroids. We then combine those distance measures with a set of academic features (e.g., number of publications, number of venues, etc) collected during the same monitoring period, and use them as input to a classification method. One aspect that distinguishes our method from other approaches is that the monitoring period, during which we gather information on each scholar popularity and academic features, is determined on a per scholar basis, as part of our approach. Using total citation count as measure of scientific popularity, we evaluate our solution on the popularity time series of more than 500,000 Computer Science scholars, gathered from Microsoft Azure Marketplace (https://datamarket.azure.com/dataset/mrc/microsoftacademic). The experimental results show that the our prediction method outperforms other alternative prediction methods. We also show how to apply our method jointly with regression models to improve the prediction of scholar popularity values (e.g., number of citations) at a given future time.

The aim of this paper is to study technology acceptance of internet banking among undergraduate students in Malaysia. Thus, the theoretical framework of the paper is based on modified version of Technology Acceptance Model (TAM). This paper develops a technology acceptance model for internet banking, a conceptual framework to explain the factors influencing undergraduate students' acceptance of internet banking. The model employs perceived usefulness (PU), perceived ease of use (PEOU), perceived credibility (PC) and computer self-efficacy (CSE). The first two are two initial constructs for TAM model. The rest are new constructs to be included in the model in order to have an extension of TAM model that better reflects the students’ view. The results suggest that PU, PEOU and PC had a significant relationship with behavioral intention. Further, these measures are good determinant for undergraduate acceptance for internet banking. Results also suggest that PU and PEOU had a significant relationship with CSE. On the contrary, CSE did not associate with PC. Also, PEOU had JIBC December 2007, Vol. 12, No. 3 2 a significant relationship with PU and PC that indicate these scales are related to PEOU in explaining undergraduate preference. The paper is useful in providing the understanding of the TAM among undergraduate from Malaysians’ perspective.

India is an agricultural country which largely depends on monsoon for irrigation purpose. A large amount of water is consumed for industrial production, crop yield and domestic use. Rainfall forecasting is thus very important and necessary for growth of the country. Weather factors including mean temperature, dew point temperature, humidity, pressure of sea and speed of wind and have been used to forecasts the rainfall. The dataset of 2245 samples of New Delhi from June to September (rainfall period) from 1996 to 2014 has been collected from a website named Weather Underground. The training dataset is used to train the classifier using Classification and Regression Tree algorithm, Naive Bayes approach, K nearest Neighbour and 5-10-1 Pattern Recognition Neural Network and its accuracy is tested on a test dataset. Pattern Recognition networks has given 82.1% accurate results, KNN with 80.7% correct forecasts ranks second, Classification and Regression Tree(CART) gives 80.3% while Naive Bayes provides 78.9% correctly classified samples.


Question: Decision tree models to forecast the academic achievement of students.

This paper delves into the realm of ensemble learning, focusing on the ensemble method of random forests, a technique gaining significant traction for its effectiveness in classification and regression tasks. Ensemble learning involves aggregating the outcomes of multiple classifiers to enhance predictive accuracy. Among the popular ensemble methods are boosting and bagging. Boosting, as illustrated by Shapire et al. (1998), assigns greater weight to data points misclassified by preceding models, culminating in a weighted vote for the final prediction. Conversely, bagging, introduced by Breiman (1996), constructs each tree independently using a data set's bootstrap sample, with the final prediction determined by a simple majority vote. Breiman (2001) introduced random forests, a method that builds upon bagging by introducing an additional layer of randomness. While standard classification or regression trees select the best split from all variables, random forests select the best split from a randomly chosen subset of predictors at each node. This unconventional strategy has proven superior to other classifiers, including discriminant analysis, support vector machines, and neural networks, and is notably resistant to overfitting. Furthermore, its user-friendly nature, characterized by only two parameters—the number of variables in the random subset at each node and the number of trees in the forest—renders it largely insensitive to their values. The R package randomForest offers an R interface to the Fortran programs developed by Breiman and Cutler, accessible at http://www.stat.berkeley.edu/ users/breiman/. This article provides an overview of the R functions' usage and features, underscoring the utility of random forests in academic success prediction and other classification and regression tasks.

Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.

Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

Technology and Education: Computers, Software, and the Internet* A substantial amount of money is spent on technology by schools, families and policymakers with the hope of improving educational outcomes. This chapter explores the theoretical and empirical literature on the impacts of technology on educational outcomes. The literature focuses on two primary contexts in which technology may be used for educational purposes: i) classroom use in schools, and ii) home use by students. Theoretically, ICT investment and CAI use by schools and the use of computers at home have ambiguous implications for educational achievement: expenditures devoted to technology necessarily offset inputs that may be more or less efficient, and time allocated to using technology may displace traditional classroom instruction and educational activities at home. However, much of the evidence in the schooling literature is based on interventions that provide supplemental funding for technology or additional class time, and thus favor finding positive effects. Nonetheless, studies of ICT and CAI in schools produce mixed evidence with a pattern of null results. Notable exceptions to this pattern occur in studies of developing countries and CAI interventions that target math rather than language. In the context of home use, early studies based on multivariate and instrumental variables approaches tend to find large positive (and in a few cases negative) effects while recent studies based on randomized control experiments tend to find small or null effects. Early research focused on developed countries while more recently several experiments have been conducted in developing countries. JEL Classification: I2

Data Clustering is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In this paper data clustering technique named k-means clustering is applied to analyze student’s learning behavior. The student’s evaluation factor like class quizzes, mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class advisor before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.

Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Decision tree induction is one of the most employed methods to extract knowledge from data, since the representation of knowledge is very intuitive and easily understandable by humans. The most successful strategy for inducing decision trees, the greedy top-down approach, has been continuously improved by researchers over the years. This work, following recent breakthroughs in the automatic design of machine learning algorithms, proposes a hyper-heuristic evolutionary algorithm for automatically generating decision-tree induction algorithms, named HEAD-DT. We perform extensive experiments in 20 public data sets to assess the performance of HEAD-DT, and we compare it to traditional decision-tree algorithms such as C4.5 and CART. Results show that HEAD-DT can generate algorithms that significantly outperform C4.5 and CART regarding predictive accuracy and F-Measure.

This paper concerns learning tasks that require the prediction of a continuous value rather than a discrete class. A general method is presented that allows predictions to use both instance-based and model-based learning. Results with three approaches to constructing models and with eight datasets demonstrate improvements due to the composite method.

Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classifiers to large datasets. Unfortunately, both of these techniques can cause a significant loss in accuracy. We present a novel decision tree classifier called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces computation and I/O complexity substantially compared to state of the art classifters, while maintaining the quality of the generated trees in terms of accuracy and tree size. We provide experimental results with a number of real and synthetic data~ets.

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided. Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization of data through data mining software is addressed. Chapter 3 presents memory-based reasoning methods of data mining. Major real applications are described. Algorithms are demonstrated with prototypical data based on real applications. Chapter 4 discusses association rule methods. Application in the form of market basket analysis is discussed. A real data set is described, and a simplified version used to demonstrate association rule methods. Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree approaches are described, as well as fuzzy association rule applications. Real data mining applications are described and demonstrated Chapter 6 presents Rough …

This paper concerns methods for inferring decision trees from examples for classification problems. The reader who is unfamiliar with this problem may wish to consult J. R. Quinlan’s paper (1986), or the excellent monograph by Breiman et al. (1984), although this paper will be self-contained. This work is inspired by Rissanen’s work on the Minimum description length principle (or MDLP for short) and on his related notion of the stochastic complexity of a string Rissanen, 1986b. The reader may also want to refer to related work by Boulton and Wallace (1968, 1973a, 1973b), Georgeff and Wallace (1984), and Hart (1987). Roughly speaking, the minimum description length principle states that the best “theory” to infer from a set of data is the one which minimizes the sum of


Question: Decision tree models to forecast the academic achievement of students.

This study explores the application of data mining techniques in higher education to predict student academic success, marking a relatively recent shift in the use of these tools from the business sector. The primary objective was to construct a model capable of forecasting student performance based on a dataset gathered from the University of Tuzla, Faculty of Economics, during the 2010-2011 academic year. This dataset included responses from first-year students surveyed during the summer semester and data from the enrollment process. The students' success was quantified by their exam passing grades. Various data mining methods were employed to analyze the influence of socio-demographic factors, prior academic achievements from high school and the entrance exam, and students' attitudes towards their studies on their academic performance. The investigation aimed to identify and evaluate these variables to potentially develop a decision support system for higher education. Future research directions could involve expanding the sample size and refining the model to better encapsulate the nuances of the learning process, thereby enhancing the system's predictive capabilities and support for educational decision-making.

Speaker: Mrinmaya Sachan Thesis Committee: Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth (University of Pennsylvania) Thesis Proposal Jan. 22, 2018 1:00pm 8102 GHC Link to draft document: http://www.cs.cmu.edu/~mrinmays/thesis_proposal.pdf Standardized tests are often used to test students as they progress in the formal education system. These tests are widely available and measurable with clear evaluation procedures and metrics. Hence, these can serve as good tests for AI. We propose approaches for solving some of these tests. We broadly categorize these tests into two categories: open domain question answering tests such as reading comprehensions and elementary school science tests, and closed domain question answering tests such as intermediate or advanced math and science tests. We present alignment based approach with multi-task learning for the former. For closed domain tests, we propose a parsing to programs approach which can be seen as a natural language interface to expert systems. We also describe approaches for question generation based on instructional material in both open domain as well as closed domain settings. Finally, we show that we can improve both the question answering and question generation models by learning them jointly. This mechanism also allows us to leverage cheap unlabelled data for learning the two models. Our work can potentially be applied for the social good in the education domain. We perform studies on human subjects who found our approaches useful as assistive tools in education.

The main objective of this study is to create a fast, easy and an efficient algorithm for disease prediction , with less error rate and can apply with even large data sets and show reasonable patterns with dependent variables. For disease identification and prediction in data mining a new hybrid algorithm was constructed. The Disease Identification and Prediction(DIP) algorithm, which is the combination of decision tree and association rule is used to predict the chances of some of the disease hits in some particular areas. It also shows the relationship between different parameters for the prediction. To implement this algorithm using vb.net software was also developed.

OBJECTIVE
This study investigates associations between food insufficiency and cognitive, academic, and psychosocial outcomes for US children and teenagers ages 6 to 11 and 12 to 16 years.

METHODS
Data from the Third National Health and Nutrition Examination Survey (NHANES III) were analyzed. Children were classified as food-insufficient if the family respondent reported that his or her family sometimes or often did not get enough food to eat. Regression analyses were conducted to test for associations between food insufficiency and cognitive, academic, and psychosocial measures in general and then within lower-risk and higher-risk groups. Regression coefficients and odds ratios for food insufficiency are reported, adjusted for poverty status and other potential confounding factors.

RESULTS
After adjusting for confounding variables, 6- to 11-year-old food-insufficient children had significantly lower arithmetic scores and were more likely to have repeated a grade, have seen a psychologist, and have had difficulty getting along with other children. Food-insufficient teenagers were more likely to have seen a psychologist, have been suspended from school, and have had difficulty getting along with other children. Further analyses divided children into lower-risk and higher-risk groups. The associations between food insufficiency and children's outcomes varied by level of risk.

CONCLUSIONS
The results demonstrate that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.

Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.

0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.07.020 ⇑ Tel.: +1 757 683 5008; fax: +1 757 683 5639. E-mail address: whe@odu.edu This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students’ patterns and themes of participation between online questions (student–instructor interaction) and online chat messages (student–students interaction or peer interaction). The results also identify disciplinary differences in students’ online participation. A correlation is found between the number of online questions students asked and students’ final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students’ learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper. 2012 Elsevier Ltd. All rights reserved.

Data Clustering is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In this paper data clustering technique named k-means clustering is applied to analyze student’s learning behavior. The student’s evaluation factor like class quizzes, mid and final exam assignment are studied. It is recommended that all these correlated information should be conveyed to the class advisor before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students.

The research reported in this paper focuses on the hypothesis that an intelligent tutoring system that provides guidance with respect to students' meta-cognitive abilities can help them to become better learners. Our strategy is to extend a Cognitive Tutor (Anderson, Corbett, Koedinger, & Pelletier, 1995) so that it not only helps students acquire domain-specific skills, but also develop better general help-seeking strategies. In developing the Help Tutor, we used the same Cognitive Tutor technology at the metacognitive level that has been proven to be very effective at the cognitive level. A key challenge is to develop a model of how students should use a Cognitive Tutor's help facilities. We created a preliminary model, implemented by 57 production rules that capture both effective and ineffective help-seeking behavior. As a first test of the model's efficacy, we used it off-line to evaluate students' help-seeking behavior in an existing data set of student-tutor interactions. We then refined the model based on the results of this analysis. Finally, we conducted a pilot study with the Help Tutor involving four students. During one session, we saw a statistically significant reduction in students' meta-cognitive error rate, as determined by the Help Tutor's model. These preliminary results inspire confidence as we gear up for a larger-scale controlled experiment to evaluate whether tutoring on help seeking has a positive effect on students' learning outcomes.

This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

Choosing the right career can be difficult for students, because they have to take into considerations many elements, in order to be on the best path. Several studies in many disciplines have cooperate to help students making their career decision. The purpose of this work is to invest the subject of school orientation, identifying its background and studying some related works. This paper aims also to define the student's profile, and conceiving basis of a recommendation system that will play the advisor role in students helping to choose their career path.

Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, studentspsila learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.