13-00-ACA-Mood.pdf
•
0 likes•3 views
This document provides an overview of mood recognition in audio content analysis. It discusses how mood recognition aims to identify the mood or emotion of a song by extracting features and classifying using methods like regression. Key challenges include defining and quantifying moods/emotions, developing ground truth data, and determining appropriate mood models. Common approaches include classifying into mood clusters or mapping to the arousal-valence circumplex model of affect using regression techniques. Results can vary significantly based on the data but classification rates around 50% and regression errors of 0.1-0.4 are typical.
Report
Share
Report
Share
Download to read offline
Recommended
Issas 2011
На этом семинаре я расскажу о прошедшей в августе на gene've'ской земле международной школе по аффективным наукам. Первый вопрос, конечно -- что такое аффективные науки. Это область когнитологии напрямую связанная с эмоциями. Также внимание будет уделено устройству школы и тому как работники Жene've'ского университета проводили это мероприятие в течении 10 дней. Также мой рассказ будет включать в себя следующие темы.
Три главные теории эмоций и откуда они пришли.
Что считать моделированием эмоций.
Чем эмоции отличаются от рассудка.
Принятые модели эмоций.
Какие выводы о работе эмоций можно сделать изучая мозг.
Как исследователи обозначают эмоции.
Над какими корпусами сейчас работают в этой области.
Чего не хватает.
Arise study on emotional intelligence
1) The document discusses various definitions and models of emotional intelligence, including Mayer and Salovey's ability model and Bar-On's trait model.
2) The Mayer and Salovey ability-based model (MSCEIT) measures emotional intelligence as a set of skills measured through tasks and problems. It assesses four branches - perceiving, using, understanding, and managing emotions.
3) In contrast, the Bar-On model (EQ-i) is a self-report measure that assesses emotionally and socially intelligent behavior as an array of traits that influence how well one can cope with daily demands.
Quantifying Affect
The document discusses several methods for quantifying affect, or feelings and emotions, including typical measuring devices used in museums, their problems, and three promising alternative models: PAD, PANAS, and the Semantic Differential. PAD measures pleasure, arousal, and dominance on bipolar scales. PANAS measures positive and negative affect. The Semantic Differential uses bipolar adjective pairs to measure attitudes on evaluation, potency, and activity dimensions.
David papini escape emotional intelligence traps
What happens to emotional IQ in a daily practice to pursue freedom? Answer is in the way we use language and body.
In the session attendees will learn how to connect emotional intelligence theory with clean linguistic and cognitive practices. They will experiment simple techniques to leverage emotions in any goal-oriented setting, be it their work, their teamwork or their relationships. They will learn also to convert very common misconceptions about emotions in powerful, mindset changing and practical behaviors. The tools that we’ll use in the session are language and body. We will learn that language can be effective or not in emotional intelligence, depending on how we use it (and we’ll see the four main uses of language) and also that speech and body are not alternative means of getting things done and goals achieved.
Autonomy support and Academic Emotions
This document reports on a study that examined the relationship between perceived autonomy support and academic emotions in foreign language classes. The study surveyed 547 students across 31 French classes. It found that greater perceived autonomy support was associated with more positive emotions like joy and less negative emotions like boredom and anger at the individual level. At the class level, greater perceived autonomy support was associated with less reported boredom. The results provide support for autonomy support influencing students' academic emotions in foreign language learning.
Mood music
This lesson teaches students about emotions, moods, and how music can affect them. Students discuss triggers for emotions and responses. They conduct an experiment by rating their moods before and after different music to see the effects. Students analyze the results and create personal playlists intended to manage emotions and improve moods. The goal is for students to learn to recognize their emotions and choose healthy responses.
Mental models - Final Presentation
These are the final slides used for the presentation of “Mental Models” at University of Central Lancashire.
King1 Ppt Ch01 3
The document provides an overview of chapter 1 of a psychology textbook. It defines psychology as the scientific study of behavior and mental processes. It discusses the historical foundations of psychology from Western philosophy to early theorists like Wundt who established the first psychology lab. It also summarizes the seven main contemporary approaches to psychology and areas of specialization within the field. Finally, it discusses the connections between psychology and health and wellness.
Recommended
Issas 2011
На этом семинаре я расскажу о прошедшей в августе на gene've'ской земле международной школе по аффективным наукам. Первый вопрос, конечно -- что такое аффективные науки. Это область когнитологии напрямую связанная с эмоциями. Также внимание будет уделено устройству школы и тому как работники Жene've'ского университета проводили это мероприятие в течении 10 дней. Также мой рассказ будет включать в себя следующие темы.
Три главные теории эмоций и откуда они пришли.
Что считать моделированием эмоций.
Чем эмоции отличаются от рассудка.
Принятые модели эмоций.
Какие выводы о работе эмоций можно сделать изучая мозг.
Как исследователи обозначают эмоции.
Над какими корпусами сейчас работают в этой области.
Чего не хватает.
Arise study on emotional intelligence
1) The document discusses various definitions and models of emotional intelligence, including Mayer and Salovey's ability model and Bar-On's trait model.
2) The Mayer and Salovey ability-based model (MSCEIT) measures emotional intelligence as a set of skills measured through tasks and problems. It assesses four branches - perceiving, using, understanding, and managing emotions.
3) In contrast, the Bar-On model (EQ-i) is a self-report measure that assesses emotionally and socially intelligent behavior as an array of traits that influence how well one can cope with daily demands.
Quantifying Affect
The document discusses several methods for quantifying affect, or feelings and emotions, including typical measuring devices used in museums, their problems, and three promising alternative models: PAD, PANAS, and the Semantic Differential. PAD measures pleasure, arousal, and dominance on bipolar scales. PANAS measures positive and negative affect. The Semantic Differential uses bipolar adjective pairs to measure attitudes on evaluation, potency, and activity dimensions.
David papini escape emotional intelligence traps
What happens to emotional IQ in a daily practice to pursue freedom? Answer is in the way we use language and body.
In the session attendees will learn how to connect emotional intelligence theory with clean linguistic and cognitive practices. They will experiment simple techniques to leverage emotions in any goal-oriented setting, be it their work, their teamwork or their relationships. They will learn also to convert very common misconceptions about emotions in powerful, mindset changing and practical behaviors. The tools that we’ll use in the session are language and body. We will learn that language can be effective or not in emotional intelligence, depending on how we use it (and we’ll see the four main uses of language) and also that speech and body are not alternative means of getting things done and goals achieved.
Autonomy support and Academic Emotions
This document reports on a study that examined the relationship between perceived autonomy support and academic emotions in foreign language classes. The study surveyed 547 students across 31 French classes. It found that greater perceived autonomy support was associated with more positive emotions like joy and less negative emotions like boredom and anger at the individual level. At the class level, greater perceived autonomy support was associated with less reported boredom. The results provide support for autonomy support influencing students' academic emotions in foreign language learning.
Mood music
This lesson teaches students about emotions, moods, and how music can affect them. Students discuss triggers for emotions and responses. They conduct an experiment by rating their moods before and after different music to see the effects. Students analyze the results and create personal playlists intended to manage emotions and improve moods. The goal is for students to learn to recognize their emotions and choose healthy responses.
Mental models - Final Presentation
These are the final slides used for the presentation of “Mental Models” at University of Central Lancashire.
King1 Ppt Ch01 3
The document provides an overview of chapter 1 of a psychology textbook. It defines psychology as the scientific study of behavior and mental processes. It discusses the historical foundations of psychology from Western philosophy to early theorists like Wundt who established the first psychology lab. It also summarizes the seven main contemporary approaches to psychology and areas of specialization within the field. Finally, it discusses the connections between psychology and health and wellness.
Eq presentation
The document discusses definitions of intelligence and emotional intelligence. It describes Gardner's theory of multiple intelligences which includes interpersonal and intrapersonal intelligence. The Mayer-Salovey model is an ability-based measure of emotional intelligence that assesses skills like identifying emotions, using emotions to facilitate thought, understanding emotions, and managing emotions. The MSCEIT is provided as an example that measures emotional intelligence through tasks and problems rather than self-reports.
Eq presentation
The document discusses definitions of intelligence and emotional intelligence. It describes Gardner's theory of multiple intelligences which includes interpersonal and intrapersonal intelligence. The Mayer-Salovey model is an ability-based measure of emotional intelligence that assesses four branches: identifying emotions, using emotions to facilitate thought, understanding emotions, and managing emotions. The MSCEIT is described as a performance test that measures emotional intelligence abilities.
Eq presentation
The document discusses different models of emotional intelligence (EI), including ability-based models like the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) and self-report models like the Bar-On EQ-i. The MSCEIT measures EI as an ability through tasks, while the EQ-i measures self-perceived EI through self-reports. Research shows the two measures are not highly correlated, suggesting they capture different aspects of EI. Both aim to predict outcomes better than IQ alone by incorporating emotional and social factors.
I am fine with the actual analysis of the data on SPSS, however I am.pdf
I am fine with the actual analysis of the data on SPSS, however I am struggling to decide which
test to use on each of my research questions.
The study I am looking at is looking at the consequences of different patterns of brain damage
on different cognitive processes. Patients are divided into three groups: patients with unilateral
left hemisphere (LH) lesions, patients with unilateral right hemisphere (RH) lesions and patients
with bilateral lesions. There is also a control group. Each group has fifteen participants.
As soon as possible after the injury occurred, or at the beginning of the study for control
participants, all participants completed three cognitive tasks. The stimuli were faces, words or
objects. For each task the participants were shown twenty stimuli. For faces they had to say if it
was a famous face or not. For words they had to say whether it was a word or a non-word. For
objects, they had to say whether it was a man made or a naturally occurring object. Performance
was measured as percentage correct (high scores = accurate). The tests were repeated six months
later. I have three main areas that I want to look into:
For performance at time point one, does accuracy for processing different types of stimuli differ
across the four participant groups?
I was thinking a mixed anova could be used to analyse this question, although someone did
suggest a t-test.
Looking only at accuracy for processing faces, do any of the participant groups show a
significant change from time point one to time point two?
I was thinking of using a repeated measures anova to analyse this research question
For patients with left hemisphere lesions only, does the change in accuracy from time point one
to time point two differ across the different types of stimuli?
I was thinking of using a repeated measure anova for this research question.
I would really appreciate any help on identifying the type of test to use as I can not find any one
else to help.
Solution
Despite predictions following from cognitive theories of anxiety, evidence for
memory biases in social anxiety has been mixed. This study extends previous research by using
stimuli relevant to participants’ concerns and allowing time for post-event processing.
Participants high (n = 42) or low (n = 39) in social anxiety symptoms gave speeches and received
standardized feedback on their and a confederate’s performance. Participants then took
recognition and recall tests for the feedback immediately after it was given and after a two-day
delay. Results showed no recall biases. However, the hypothesized recognition biases were
found: the high social anxiety group remembered the confederate’s feedback more positively
than their own, remembered their negative feedback as worse than the low group, and diminished
positive feedback over time. Moreover, post-event processing mediated the relationship between
social anxiety and memory for negative feedback. Results suggest that biased recognition of
soci.
Ingtelligence notes for review
This document discusses definitions of intelligence from various researchers and experts. It provides definitions from 1952 researchers, Alfred Binet, Carolus Slovinec, Howard Gardner, Cyril Burt, and D. Samuel Nuessle. The definitions focus on abilities like reasoning, problem-solving, thinking abstractly, learning quickly, and applying knowledge. The document also briefly discusses the history and development of IQ tests, including their creation by Alfred Binet and later modifications at Stanford University.
Helpful Information for writing the mental status examMENTAL .docx
Helpful Information for writing the mental status exam:
MENTAL STATUS EXAM
The Mental Status Exam is the basis for understanding the client's presentation and beginning to conceptualize their current functioning into a diagnosis. At first, this might seem overwhelming and time consuming, but it is not difficult to do. It can generally be done in a few minutes as the vast majority of this information is obtained through your careful observations of the client during the intake interview. This is why developing your observation skills is important.
APPEARANCE
·
Presenting Appearance, including sex, chronological and apparent age, ethnicity, apparent height and weight (average, stocky, healthy, petite), any physical deformities (hearing impaired, injured and bandaged right hand)
·
Basic Grooming and Hygiene, dress and whether it was appropriate attire for the weather, for a doctor’s interview, accessories like glasses or a cane
·
Gait and Motor Coordination (awkward, staggering, shuffling, rigid, trembling with intentional movement or at rest), posture (slouched, erect), work speed, any noteworthy mannerisms or gestures
MANNER & APPROACH
·
Interpersonal Characteristics and Approach to Evaluation (oppositional/resistant, submissive, defensive, open and friendly, candid and cooperative, shows subdued mistrust and hostility, excessive shyness)
·
Behavioral Approach (distant, indifferent, unconcerned, evasive, negative, irritable, depressive, anxious, sullen, angry, assaultive, exhibitionistic, seductive, frightened, alert, agitated, lethargic, needed minor/considerable reinforcement and soothing)
·
Speech (normal rate and volume, pressured, slow, accent, enunciation quality, loud, quiet, impoverished)
·
Eye Contact (makes, avoids, seems hesitant to make eye contact)
·
Expressive Language (no problems expressing self, circumstantial and tangential responses, anomia, difficulties finding words, misuse of words in a low-vocabulary-skills way, misuse of words in a bizarre-thinking-processes way, echolalia or perseveration, mumbling)
· Note if English is not the primary language here and comment on their command of the language
·
Receptive Language (normal, able to comprehend questions, difficulty understanding questions)
·
Recall and Memory (could explain recent and past events in their personal history, recalls three words (e.g., Cadillac, zebra, and purple) immediately after two rehearsals, and then again five minutes later (five minutes is how long it takes for information to move from short-term to long term memory). If they cannot, you can prompt them (e.g., “Was the first one a kind of tree, color, or car? A car, OK was it a Camaro, Continental, or Cadillac?”)
ORIENTATION, ALERTNESS, & THOUGHT PROCESSES
·
Orie.
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Overview of alternative approaches to modeling emotions in user models and cognitive-affective agent architectures.
Emotional Profiling of Locations based on Social Media
Presentation held in the ITQM 2015 Congress on how to use geo-localized Social Media to create emotional profiles of locations
Influencing & Conforming (Chapter 6)This chapter deals with on
Influencing & Conforming (Chapter 6)
This chapter deals with one of the most classic areas of social psychology, that of Social Influence. This refers to the efforts by people to change attitudes, beliefs, or behaviors of other people. Some classic topics within this area include:
· Conformity
· Obedience
· Power
· Leadership
Do note that some of these studies have several criticisms aimed against the methodology, procedures and conclusions. It is important to note that replications are necessary and no simplistic conclusions can be drawn from any one study. For purposes of this class, note that these are “classics” in the field of social psychology so know the basic methodology and the specific findings; in general, they serve to highlight the importance of external or situational factors on our behavior.
Social Influence & Conformity
· Spontaneous, adaptive, imitation
· Informational social influence (accuracy, social comparison and private acceptance)
· Normative social influence (to be liked, social norms and public compliance)
· Majority influence (Sherif’s “autokinetic” study and Asch’s “line” study)
· Minority influence – don’t need to know in detail
· Situational determinants – group size/social impact; unanimity (don’t worry about importance of task)
Obedience, Power, & Leadership
· Milgram’s original obedience study – what are some important situational factors from variations (there are some personality factors as well – don’t need to know for this section)
· Zimbardo’s prison study
· Types of power (just know basic)
· Types of leaders (just know basic)
· Don’t worry about leadership and person-situation interaction
Person, Gender, and Cultural Differences in Conformity (consider some of the important concepts and findings for each one)
What is psychological reactance? (it helps to think of some examples)
Well, that’s it for one of the most powerful topics within social psychology. In this chapter, you have just covered some classic concepts within the field and been exposed to some classic studies in all of psychology. Hope you enjoyed it.
We move on next to considering what attracts us to others in a chapter covering “Liking and Loving.”
Chapter 5
https://opentextbc.ca/socialpsychology/part/perceiving-others/
chapter 6
https://opentextbc.ca/socialpsychology/part/influencing-and-conforming/
Chapter 7
https://opentextbc.ca/socialpsychology/part/liking-and-loving/
chapter 11
https://opentextbc.ca/socialpsychology/part/stereotypes-prejudice-and-discrimination/
Stereotypes, Prejudice, & Discrimination (Chapter 11)
We end our second section of class with the classic topics of stereotyping, prejudice, and discrimination. Do keep in mind that social psychologists differentiate these three concepts for ease of studying but they certainly do overlap and have complex relationships to each other:
· Prejudice = affective component = unjustifiable negative emotions/feelings toward members of a social group
· Stereotype = ...
PSYCHOMETRY
Projective techniques are indirect personality tests that analyze a person's responses to ambiguous stimuli to understand their unconscious motivations, attitudes, and feelings. Common projective tests discussed in the document include the Rorschach inkblot test, Thematic Apperception Test (TAT), word association test, and sentence completion test. The document provides details on the administration and interpretation of these tests and explains that projective techniques are useful for understanding both conscious and unconscious aspects of a person's psychology.
Sentence completion test
Sentence completion tests consist of item stems that people complete to reflect their personality. How they complete the sentences can provide clues about underlying attitudes, affects, and concerns. Sentence completion tests have been constructed for personality assessment, clinical applications, and measuring constructs like egocentricity, moral attitudes, and depression. They can assess psychological characteristics and contribute to assessing personality as well as intellectual functioning.
Normality Cwl
The document discusses various topics related to assessing mental health, including:
1. The three main types of psychological assessment are clinical interviews, behavioral observations, and psychological testing.
2. Clinical interviews involve structured questioning to understand a person's mental health history and current state. Behavioral observations provide information on how a person acts in natural environments. Psychological testing uses standardized measures to evaluate areas like personality and intelligence.
3. Classification systems like the DSM-IV-TR provide standardized criteria for diagnosing mental illnesses based on patterns of symptoms. A diagnosis involves evaluating a person across five axes of information.
Emotion analysis of songs based on lyrical
In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.
Emotion recognition
This document discusses emotion recognition and provides definitions and theories related to basic emotions and dimensional emotions. It begins with definitions of basic emotions, including Ekman's six basic emotions of happy, sad, anger, surprise, fear and disgust. It then discusses dimensional emotion models, including Russell's circumplex model which uses the two dimensions of valence and arousal. The document also covers social emotions and related theories, bioreactions to different emotions, and complex topics like emotional intelligence.
Emotions TOKUHAMA Part 2 1-30-15 Positive Psychology Jan 15.pptx
This document discusses the concept of emotional intelligence. It begins by defining both emotions and intelligence individually, citing various definitions from sources. It then discusses models of emotional intelligence proposed by researchers, including models by Salovey and Mayer, Goleman, and Bar-On. It also briefly discusses tests that have been used to measure emotional intelligence, such as the MEIS, EQ-i, and Marshmallow Test. Finally, it compares and contrasts emotional intelligence with related concepts such as multiple intelligences and social cognition.
A General Architecture for an Emotion-aware Content-based Recommender System
A General Architecture for an Emotion-aware Content-based Recommender System
Fedelucio Narducci, Marco De Gemmis, Pasquale Lops
3rd Empire Workshop
RecSys 2015, Vienna, Austria, 16-20 September 2015
projective test.pptx
There are a number of important issues that researchers should be mindful of when undertaking investigations, with regards to the selection and subsequent treatment of their participants:
Confidentiality. ...
Informed consent. ...
Deception. ...
Debriefing. ...
Right to withdraw. ...
Protection of participants. ...
Working with animals.
projective test in Psychology
projective test, in psychology, examination that commonly employs ambiguous stimuli, notably inkblots (Rorschach Test) and enigmatic pictures
Assignment 9 Answer the following questions. 1. Review the lis.docx
Assignment 9
Answer the following questions.
1. Review the list of Freud’s defense mechanisms discussed in your textbook. Choose three and describe instances when you observed yourself or someone else utilizing them. Explain how the use of defense mechanisms can be both healthy and unhealthy.
2. Your textbook highlights several different types of psychological tests. Identify three types of tests and explain what they are used for. Do you think these types of tests would be useful for human resources departments to use to determine personnel decisions, such as hiring, promotion and termination? Explain your answer.
3. Define stress, including the role of cognitive appraisal and coping strategies.
350 words per question and use the outline paper attached for the format.
Reflection Paper
INTRODUCTION
One of the main goals in this course is for you to develop personally through knowledge of psychology. This assignment will give you the opportunity to apply psychological principles to your own life, to your relationships, and to your life in society.
For this assignment you will write a reflection paper detailing the personal development you feel you have achieved.
Your paper should adhere to the following guidelines:
Content: See paper description below.
Length: Approximately 1200 to 1800 words in length (about 5 to 7 pages long)
Typography: Use a size 12 Times New Roman or Arial font, double-spaced
Citation style: Include APA style references to the textbook and other course materials. Please note: While APA style doesn’t usually allow the writer to refer to her/himself in the first person (i.e., using “I” or “me” to refer to yourself), this paper will require the use of first person language.
Due date and grade weighting: The paper is due no later than the final day of the term and will account for 10% of your final grade.
PAPER DESCRIPTION
During this term you have learned about, discussed, and written about the following topics:
Research methods
Neuroscience and behavior
States of consciousness
Learning and memory functions
Thinking, language, and intelligence
Motivation and emotion
Human development
Health psychology: understanding stress and coping
Personality
As you review the topics in this list, reflect on how a better understanding of these topics in psychology has impacted you in four areas:
Personal: How has taking this course allowed you to better understand yourself? What insights have you gained? In what ways has this course helped you to understand your thoughts, feelings or behaviors? In what areas of your life do you still struggle and need help with?
Social: How has taking this course allowed you to better understand your relationship(s) with someone else? Think about a significant relationship, and discuss how topics studied in this course have allowed you to gain a better understanding of that relationship.
Organizational and Societal: How has taking this cour ...
Psych 101 Chapter One
This chapter provides an overview of the field of psychology. It defines psychology as the scientific study of behavior and mental processes. The chapter discusses the historical foundations of psychology in Western philosophy, biology, and early schools of thought. It also summarizes seven contemporary approaches to psychology and areas of specialization within the field. The chapter concludes by exploring careers in psychology and examining the mind-body connection in health and wellness.
0A-02-ACA-Fundamentals-Convolution.pdf
This document provides an introduction to audio content analysis and convolution. It discusses linear time-invariant (LTI) systems and how convolution describes the output of an LTI system based on the input and impulse response. Examples of simple low-pass filters like moving average and single-pole filters are provided to illustrate convolution. The objectives are to understand LTI systems, convolution operations, and implement basic filters.
12-02-ACA-Genre.pdf
This document provides an overview of musical genre classification. It discusses how genre classification is one of the early research topics in music information retrieval and involves extracting audio features from songs and using those features to classify the songs into genres. The document describes common features used like spectral, pitch, rhythm, and intensity features. It also outlines the typical processing steps of feature extraction, normalization, selection and classification. Examples of challenges with genre classification like ill-defined genres and overfitting are also mentioned.
More Related Content
Similar to 13-00-ACA-Mood.pdf
Eq presentation
The document discusses definitions of intelligence and emotional intelligence. It describes Gardner's theory of multiple intelligences which includes interpersonal and intrapersonal intelligence. The Mayer-Salovey model is an ability-based measure of emotional intelligence that assesses skills like identifying emotions, using emotions to facilitate thought, understanding emotions, and managing emotions. The MSCEIT is provided as an example that measures emotional intelligence through tasks and problems rather than self-reports.
Eq presentation
The document discusses definitions of intelligence and emotional intelligence. It describes Gardner's theory of multiple intelligences which includes interpersonal and intrapersonal intelligence. The Mayer-Salovey model is an ability-based measure of emotional intelligence that assesses four branches: identifying emotions, using emotions to facilitate thought, understanding emotions, and managing emotions. The MSCEIT is described as a performance test that measures emotional intelligence abilities.
Eq presentation
The document discusses different models of emotional intelligence (EI), including ability-based models like the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) and self-report models like the Bar-On EQ-i. The MSCEIT measures EI as an ability through tasks, while the EQ-i measures self-perceived EI through self-reports. Research shows the two measures are not highly correlated, suggesting they capture different aspects of EI. Both aim to predict outcomes better than IQ alone by incorporating emotional and social factors.
I am fine with the actual analysis of the data on SPSS, however I am.pdf
I am fine with the actual analysis of the data on SPSS, however I am struggling to decide which
test to use on each of my research questions.
The study I am looking at is looking at the consequences of different patterns of brain damage
on different cognitive processes. Patients are divided into three groups: patients with unilateral
left hemisphere (LH) lesions, patients with unilateral right hemisphere (RH) lesions and patients
with bilateral lesions. There is also a control group. Each group has fifteen participants.
As soon as possible after the injury occurred, or at the beginning of the study for control
participants, all participants completed three cognitive tasks. The stimuli were faces, words or
objects. For each task the participants were shown twenty stimuli. For faces they had to say if it
was a famous face or not. For words they had to say whether it was a word or a non-word. For
objects, they had to say whether it was a man made or a naturally occurring object. Performance
was measured as percentage correct (high scores = accurate). The tests were repeated six months
later. I have three main areas that I want to look into:
For performance at time point one, does accuracy for processing different types of stimuli differ
across the four participant groups?
I was thinking a mixed anova could be used to analyse this question, although someone did
suggest a t-test.
Looking only at accuracy for processing faces, do any of the participant groups show a
significant change from time point one to time point two?
I was thinking of using a repeated measures anova to analyse this research question
For patients with left hemisphere lesions only, does the change in accuracy from time point one
to time point two differ across the different types of stimuli?
I was thinking of using a repeated measure anova for this research question.
I would really appreciate any help on identifying the type of test to use as I can not find any one
else to help.
Solution
Despite predictions following from cognitive theories of anxiety, evidence for
memory biases in social anxiety has been mixed. This study extends previous research by using
stimuli relevant to participants’ concerns and allowing time for post-event processing.
Participants high (n = 42) or low (n = 39) in social anxiety symptoms gave speeches and received
standardized feedback on their and a confederate’s performance. Participants then took
recognition and recall tests for the feedback immediately after it was given and after a two-day
delay. Results showed no recall biases. However, the hypothesized recognition biases were
found: the high social anxiety group remembered the confederate’s feedback more positively
than their own, remembered their negative feedback as worse than the low group, and diminished
positive feedback over time. Moreover, post-event processing mediated the relationship between
social anxiety and memory for negative feedback. Results suggest that biased recognition of
soci.
Ingtelligence notes for review
This document discusses definitions of intelligence from various researchers and experts. It provides definitions from 1952 researchers, Alfred Binet, Carolus Slovinec, Howard Gardner, Cyril Burt, and D. Samuel Nuessle. The definitions focus on abilities like reasoning, problem-solving, thinking abstractly, learning quickly, and applying knowledge. The document also briefly discusses the history and development of IQ tests, including their creation by Alfred Binet and later modifications at Stanford University.
Helpful Information for writing the mental status examMENTAL .docx
Helpful Information for writing the mental status exam:
MENTAL STATUS EXAM
The Mental Status Exam is the basis for understanding the client's presentation and beginning to conceptualize their current functioning into a diagnosis. At first, this might seem overwhelming and time consuming, but it is not difficult to do. It can generally be done in a few minutes as the vast majority of this information is obtained through your careful observations of the client during the intake interview. This is why developing your observation skills is important.
APPEARANCE
·
Presenting Appearance, including sex, chronological and apparent age, ethnicity, apparent height and weight (average, stocky, healthy, petite), any physical deformities (hearing impaired, injured and bandaged right hand)
·
Basic Grooming and Hygiene, dress and whether it was appropriate attire for the weather, for a doctor’s interview, accessories like glasses or a cane
·
Gait and Motor Coordination (awkward, staggering, shuffling, rigid, trembling with intentional movement or at rest), posture (slouched, erect), work speed, any noteworthy mannerisms or gestures
MANNER & APPROACH
·
Interpersonal Characteristics and Approach to Evaluation (oppositional/resistant, submissive, defensive, open and friendly, candid and cooperative, shows subdued mistrust and hostility, excessive shyness)
·
Behavioral Approach (distant, indifferent, unconcerned, evasive, negative, irritable, depressive, anxious, sullen, angry, assaultive, exhibitionistic, seductive, frightened, alert, agitated, lethargic, needed minor/considerable reinforcement and soothing)
·
Speech (normal rate and volume, pressured, slow, accent, enunciation quality, loud, quiet, impoverished)
·
Eye Contact (makes, avoids, seems hesitant to make eye contact)
·
Expressive Language (no problems expressing self, circumstantial and tangential responses, anomia, difficulties finding words, misuse of words in a low-vocabulary-skills way, misuse of words in a bizarre-thinking-processes way, echolalia or perseveration, mumbling)
· Note if English is not the primary language here and comment on their command of the language
·
Receptive Language (normal, able to comprehend questions, difficulty understanding questions)
·
Recall and Memory (could explain recent and past events in their personal history, recalls three words (e.g., Cadillac, zebra, and purple) immediately after two rehearsals, and then again five minutes later (five minutes is how long it takes for information to move from short-term to long term memory). If they cannot, you can prompt them (e.g., “Was the first one a kind of tree, color, or car? A car, OK was it a Camaro, Continental, or Cadillac?”)
ORIENTATION, ALERTNESS, & THOUGHT PROCESSES
·
Orie.
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Overview of alternative approaches to modeling emotions in user models and cognitive-affective agent architectures.
Emotional Profiling of Locations based on Social Media
Presentation held in the ITQM 2015 Congress on how to use geo-localized Social Media to create emotional profiles of locations
Influencing & Conforming (Chapter 6)This chapter deals with on
Influencing & Conforming (Chapter 6)
This chapter deals with one of the most classic areas of social psychology, that of Social Influence. This refers to the efforts by people to change attitudes, beliefs, or behaviors of other people. Some classic topics within this area include:
· Conformity
· Obedience
· Power
· Leadership
Do note that some of these studies have several criticisms aimed against the methodology, procedures and conclusions. It is important to note that replications are necessary and no simplistic conclusions can be drawn from any one study. For purposes of this class, note that these are “classics” in the field of social psychology so know the basic methodology and the specific findings; in general, they serve to highlight the importance of external or situational factors on our behavior.
Social Influence & Conformity
· Spontaneous, adaptive, imitation
· Informational social influence (accuracy, social comparison and private acceptance)
· Normative social influence (to be liked, social norms and public compliance)
· Majority influence (Sherif’s “autokinetic” study and Asch’s “line” study)
· Minority influence – don’t need to know in detail
· Situational determinants – group size/social impact; unanimity (don’t worry about importance of task)
Obedience, Power, & Leadership
· Milgram’s original obedience study – what are some important situational factors from variations (there are some personality factors as well – don’t need to know for this section)
· Zimbardo’s prison study
· Types of power (just know basic)
· Types of leaders (just know basic)
· Don’t worry about leadership and person-situation interaction
Person, Gender, and Cultural Differences in Conformity (consider some of the important concepts and findings for each one)
What is psychological reactance? (it helps to think of some examples)
Well, that’s it for one of the most powerful topics within social psychology. In this chapter, you have just covered some classic concepts within the field and been exposed to some classic studies in all of psychology. Hope you enjoyed it.
We move on next to considering what attracts us to others in a chapter covering “Liking and Loving.”
Chapter 5
https://opentextbc.ca/socialpsychology/part/perceiving-others/
chapter 6
https://opentextbc.ca/socialpsychology/part/influencing-and-conforming/
Chapter 7
https://opentextbc.ca/socialpsychology/part/liking-and-loving/
chapter 11
https://opentextbc.ca/socialpsychology/part/stereotypes-prejudice-and-discrimination/
Stereotypes, Prejudice, & Discrimination (Chapter 11)
We end our second section of class with the classic topics of stereotyping, prejudice, and discrimination. Do keep in mind that social psychologists differentiate these three concepts for ease of studying but they certainly do overlap and have complex relationships to each other:
· Prejudice = affective component = unjustifiable negative emotions/feelings toward members of a social group
· Stereotype = ...
PSYCHOMETRY
Projective techniques are indirect personality tests that analyze a person's responses to ambiguous stimuli to understand their unconscious motivations, attitudes, and feelings. Common projective tests discussed in the document include the Rorschach inkblot test, Thematic Apperception Test (TAT), word association test, and sentence completion test. The document provides details on the administration and interpretation of these tests and explains that projective techniques are useful for understanding both conscious and unconscious aspects of a person's psychology.
Sentence completion test
Sentence completion tests consist of item stems that people complete to reflect their personality. How they complete the sentences can provide clues about underlying attitudes, affects, and concerns. Sentence completion tests have been constructed for personality assessment, clinical applications, and measuring constructs like egocentricity, moral attitudes, and depression. They can assess psychological characteristics and contribute to assessing personality as well as intellectual functioning.
Normality Cwl
The document discusses various topics related to assessing mental health, including:
1. The three main types of psychological assessment are clinical interviews, behavioral observations, and psychological testing.
2. Clinical interviews involve structured questioning to understand a person's mental health history and current state. Behavioral observations provide information on how a person acts in natural environments. Psychological testing uses standardized measures to evaluate areas like personality and intelligence.
3. Classification systems like the DSM-IV-TR provide standardized criteria for diagnosing mental illnesses based on patterns of symptoms. A diagnosis involves evaluating a person across five axes of information.
Emotion analysis of songs based on lyrical
In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.
Emotion recognition
This document discusses emotion recognition and provides definitions and theories related to basic emotions and dimensional emotions. It begins with definitions of basic emotions, including Ekman's six basic emotions of happy, sad, anger, surprise, fear and disgust. It then discusses dimensional emotion models, including Russell's circumplex model which uses the two dimensions of valence and arousal. The document also covers social emotions and related theories, bioreactions to different emotions, and complex topics like emotional intelligence.
Emotions TOKUHAMA Part 2 1-30-15 Positive Psychology Jan 15.pptx
This document discusses the concept of emotional intelligence. It begins by defining both emotions and intelligence individually, citing various definitions from sources. It then discusses models of emotional intelligence proposed by researchers, including models by Salovey and Mayer, Goleman, and Bar-On. It also briefly discusses tests that have been used to measure emotional intelligence, such as the MEIS, EQ-i, and Marshmallow Test. Finally, it compares and contrasts emotional intelligence with related concepts such as multiple intelligences and social cognition.
A General Architecture for an Emotion-aware Content-based Recommender System
A General Architecture for an Emotion-aware Content-based Recommender System
Fedelucio Narducci, Marco De Gemmis, Pasquale Lops
3rd Empire Workshop
RecSys 2015, Vienna, Austria, 16-20 September 2015
projective test.pptx
There are a number of important issues that researchers should be mindful of when undertaking investigations, with regards to the selection and subsequent treatment of their participants:
Confidentiality. ...
Informed consent. ...
Deception. ...
Debriefing. ...
Right to withdraw. ...
Protection of participants. ...
Working with animals.
projective test in Psychology
projective test, in psychology, examination that commonly employs ambiguous stimuli, notably inkblots (Rorschach Test) and enigmatic pictures
Assignment 9 Answer the following questions. 1. Review the lis.docx
Assignment 9
Answer the following questions.
1. Review the list of Freud’s defense mechanisms discussed in your textbook. Choose three and describe instances when you observed yourself or someone else utilizing them. Explain how the use of defense mechanisms can be both healthy and unhealthy.
2. Your textbook highlights several different types of psychological tests. Identify three types of tests and explain what they are used for. Do you think these types of tests would be useful for human resources departments to use to determine personnel decisions, such as hiring, promotion and termination? Explain your answer.
3. Define stress, including the role of cognitive appraisal and coping strategies.
350 words per question and use the outline paper attached for the format.
Reflection Paper
INTRODUCTION
One of the main goals in this course is for you to develop personally through knowledge of psychology. This assignment will give you the opportunity to apply psychological principles to your own life, to your relationships, and to your life in society.
For this assignment you will write a reflection paper detailing the personal development you feel you have achieved.
Your paper should adhere to the following guidelines:
Content: See paper description below.
Length: Approximately 1200 to 1800 words in length (about 5 to 7 pages long)
Typography: Use a size 12 Times New Roman or Arial font, double-spaced
Citation style: Include APA style references to the textbook and other course materials. Please note: While APA style doesn’t usually allow the writer to refer to her/himself in the first person (i.e., using “I” or “me” to refer to yourself), this paper will require the use of first person language.
Due date and grade weighting: The paper is due no later than the final day of the term and will account for 10% of your final grade.
PAPER DESCRIPTION
During this term you have learned about, discussed, and written about the following topics:
Research methods
Neuroscience and behavior
States of consciousness
Learning and memory functions
Thinking, language, and intelligence
Motivation and emotion
Human development
Health psychology: understanding stress and coping
Personality
As you review the topics in this list, reflect on how a better understanding of these topics in psychology has impacted you in four areas:
Personal: How has taking this course allowed you to better understand yourself? What insights have you gained? In what ways has this course helped you to understand your thoughts, feelings or behaviors? In what areas of your life do you still struggle and need help with?
Social: How has taking this course allowed you to better understand your relationship(s) with someone else? Think about a significant relationship, and discuss how topics studied in this course have allowed you to gain a better understanding of that relationship.
Organizational and Societal: How has taking this cour ...
Psych 101 Chapter One
This chapter provides an overview of the field of psychology. It defines psychology as the scientific study of behavior and mental processes. The chapter discusses the historical foundations of psychology in Western philosophy, biology, and early schools of thought. It also summarizes seven contemporary approaches to psychology and areas of specialization within the field. The chapter concludes by exploring careers in psychology and examining the mind-body connection in health and wellness.
Similar to 13-00-ACA-Mood.pdf (20)
I am fine with the actual analysis of the data on SPSS, however I am.pdf
I am fine with the actual analysis of the data on SPSS, however I am.pdf
Helpful Information for writing the mental status examMENTAL .docx
Helpful Information for writing the mental status examMENTAL .docx
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Emotional Profiling of Locations based on Social Media
Emotional Profiling of Locations based on Social Media
Influencing & Conforming (Chapter 6)This chapter deals with on
Influencing & Conforming (Chapter 6)This chapter deals with on
Emotions TOKUHAMA Part 2 1-30-15 Positive Psychology Jan 15.pptx
Emotions TOKUHAMA Part 2 1-30-15 Positive Psychology Jan 15.pptx
A General Architecture for an Emotion-aware Content-based Recommender System
A General Architecture for an Emotion-aware Content-based Recommender System
Assignment 9 Answer the following questions. 1. Review the lis.docx
Assignment 9 Answer the following questions. 1. Review the lis.docx
More from AlexanderLerch4
0A-02-ACA-Fundamentals-Convolution.pdf
This document provides an introduction to audio content analysis and convolution. It discusses linear time-invariant (LTI) systems and how convolution describes the output of an LTI system based on the input and impulse response. Examples of simple low-pass filters like moving average and single-pole filters are provided to illustrate convolution. The objectives are to understand LTI systems, convolution operations, and implement basic filters.
12-02-ACA-Genre.pdf
This document provides an overview of musical genre classification. It discusses how genre classification is one of the early research topics in music information retrieval and involves extracting audio features from songs and using those features to classify the songs into genres. The document describes common features used like spectral, pitch, rhythm, and intensity features. It also outlines the typical processing steps of feature extraction, normalization, selection and classification. Examples of challenges with genre classification like ill-defined genres and overfitting are also mentioned.
10-02-ACA-Alignment.pdf
This document summarizes an introduction to audio content analysis module on audio-to-audio and audio-to-score alignment. It discusses using audio feature extraction and dynamic time warping to align two audio sequences or an audio sequence to a musical score. Key steps include extracting features from the audio, computing a distance matrix using measures like Euclidean distance, and finding the optimal alignment path. Evaluation of alignment accuracy can be challenging due to differences in the domains of audio and scores.
09-03-ACA-Temporal-Onsets.pdf
This document discusses onset detection in audio signals. It describes onset detection as a two-step process: 1) generating a novelty function to measure signal changes over time, and 2) peak picking to identify onset locations in the novelty function. Common novelty functions are based on time-domain envelopes, pitch contours, or STFT differences. Peak picking typically uses thresholding to find local maxima above a likelihood threshold. The goal of evaluation is to compare predicted onset times to ground truth labels.
09-07-ACA-Temporal-Structure.pdf
This document discusses music structure detection. It introduces self-similarity matrices which show pairwise similarities between segments of a song and can be used to detect structural changes based on novelty, homogeneity, or repetitions. Ground truth annotations of music structure are challenging due to ambiguity and different hierarchical levels. Typical accuracy of automatic structure detection ranges from 50-70% compared to human annotations.
09-01-ACA-Temporal-Intro.pdf
This document introduces key concepts for analyzing musical rhythm, including onset, tempo, meter, bar, and rhythm. It discusses how humans perceive and group temporal musical events, and describes typical onset durations for different instruments. Accuracy for detecting onsets ranges from 2-300ms, depending on factors like tempo and instrumentation. Musical works represent these temporal concepts through notation of tempo, time signature, bars, and note values.
07-06-ACA-Tonal-Chord.pdf
This document discusses chord detection in music audio. It introduces musical chords and their properties, describes the process of extracting pitch chroma and comparing it to chord templates, and explains how Hidden Markov Models (HMMs) can be used to model chord progressions over time using transition probabilities between states representing different chords. The Viterbi algorithm is used to find the most likely chord sequence given the observations.
15-00-ACA-Music-Performance-Analysis.pdf
This document provides an overview of music performance analysis, including defining music performance, common parameters analyzed like tempo and dynamics, goals of performance analysis like understanding the performer and listener experience, challenges like disentangling variables and limited reliable datasets, and opportunities to address challenges through cross-disciplinary work.
12-01-ACA-Similarity.pdf
This document discusses music similarity analysis and clustering. It describes how music similarity is multidimensional and subjective. Common approaches for measuring music similarity involve extracting audio features and calculating distances between songs in the feature space. Visualization techniques like self-organizing maps can be used to project the high-dimensional feature spaces into 2D for visualization. Evaluation is challenging as there is no clear ground truth, but can involve using genre labels or listening surveys.
09-04-ACA-Temporal-BeatHisto.pdf
The document discusses the beat histogram, which is a representation of the periodicities in an audio signal. It can be computed from a novelty function or series of onset times. Features are then extracted from the beat histogram like the mean, max peak value, and standard deviation to characterize the rhythmic properties. However, the beat histogram lacks phase information so it provides a limited description of rhythm.
09-05-ACA-Temporal-Tempo.pdf
This document summarizes different approaches to tempo detection and beat tracking from audio signals, including oscillator, filterbank, and template-based approaches. The oscillator approach models beat tracking as an adaptive oscillator that predicts beat locations. The filterbank approach uses a bank of filters spaced at different intervals to detect the tempo. The template-based approach correlates templates of pulses at different tempos with the audio signal. Challenges discussed include detecting multiple hierarchical tempo levels and handling sudden tempo changes.
11-00-ACA-Fingerprinting.pdf
This document provides an overview of audio fingerprinting. It discusses the goals of representing an audio recording with a compact fingerprint and matching fingerprints to identify recordings. It describes fingerprinting applications in broadcast monitoring and value-added services. The document outlines fingerprint requirements and compares fingerprinting to watermarking. It then details the processing steps in Philips' audio fingerprinting system, including fingerprint extraction, database storage, and identification.
08-00-ACA-Intensity.pdf
This document provides an overview of an audio content analysis module about intensity and loudness. It discusses human perception of loudness and how it relates to physical measurements of intensity and magnitude. It then describes several common low-level features used to represent intensity in audio signals, including root mean square (RMS), peak envelope, weighted RMS, and models of loudness like the Zwicker model. The goal is to discuss intensity and loudness from both a perceptual and technical feature extraction perspective.
03-05-ACA-Input-Features.pdf
This document provides an overview of instantaneous audio features that can be extracted from audio signals. It discusses that features are low-level descriptors of audio that are not necessarily perceptually meaningful on their own. Common instantaneous features described include spectral centroid, spectral spread, spectral rolloff, and spectral flux, which characterize the spectral shape and are useful for timbre perception. Mel-frequency cepstral coefficients (MFCCs) are also covered as they represent the short-term power spectrum of a sound. The document aims to describe the computation and meaning of various instantaneous audio features.
07-03-03-ACA-Tonal-Monof0.pdf
This document discusses approaches for detecting the fundamental frequency (f0) of monophonic audio signals. It describes time-domain methods like zero-crossing rate analysis and autocorrelation, as well as frequency-domain techniques like harmonic product spectrum, harmonic sum spectrum, and spectral maximum likelihood. It also covers auditory-motivated approaches and notes that while older, tweaked versions of these methods remain state-of-the-art for monophonic f0 detection.
05-00-ACA-Data-Intro.pdf
This document discusses data requirements, data splits, cross validation, and data augmentation for machine learning. It covers dividing data into training, validation, and test sets; using cross validation to evaluate models; and techniques for augmenting data like degrading quality, adding effects, and segmentation when data is insufficient. The overall goal is to have representative, clean data that is sufficient for the task and to maximize its use through validation and augmentation.
07-03-05-ACA-Tonal-F0Eval.pdf
This document discusses evaluating pitch tracking systems by comparing predicted pitch to ground truth pitch. It describes challenges in annotating pitch and time, and defines different pitch tracking tasks from individual notes to polyphonic mixtures. Metrics for score-based and F0-based ground truths are provided to measure accuracy and deviation between predicted and ground truth pitch. Results should be aggregated at the frame/note and file levels.
03-03-01-ACA-Input-TF-Fourier.pdf
This document provides an introduction and overview of time-frequency representations using the Fourier transform. It defines the continuous Fourier transform and discusses its key properties including invertibility, superposition, convolution/multiplication relationships, Parseval's theorem, and effects of time/frequency shifts and scaling. It also covers the Fourier transform of sampled signals, the short-time Fourier transform (STFT), and the discrete Fourier transform (DFT).
03-06-ACA-Input-FeatureLearning.pdf
This document discusses feature learning techniques for audio content analysis. It introduces feature learning as an alternative to traditional hand-crafted features that can automatically learn features from data. Two main approaches are dictionary learning, which learns a dictionary of templates to represent audio signals, and deep learning using neural networks to learn high-level representations through multiple layers. Learned features may contain more useful information than hand-crafted ones but require large amounts of data and computation.
07-03-04-ACA-Tonal-Polyf0.pdf
This document provides an overview of techniques for fundamental frequency detection in polyphonic signals, including iterative subtraction methods and non-negative matrix factorization (NMF). Iterative subtraction methods work by repeatedly detecting the most salient frequency, removing its contribution, and iterating until all frequencies are identified. NMF decomposes a signal into additive combinations of spectral templates to separate voices. The document discusses algorithms like Cheveigné, Meddis, and Klapuri that apply these techniques, and compares their approaches, advantages, and limitations.
More from AlexanderLerch4 (20)
Recently uploaded
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
20 Comprehensive Checklist of Designing and Developing a Website
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Recently uploaded (20)
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
20 Comprehensive Checklist of Designing and Developing a Website
20 Comprehensive Checklist of Designing and Developing a Website
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
13-00-ACA-Mood.pdf
- 1. Introduction to Audio Content Analysis module 13: mood recognition alexander lerch
- 2. overview intro mood models regression results summary introduction overview corresponding textbook section chapter 13 lecture content • introduction to emotion and mood • models for mood • linear regression learning objectives • describe Russel’s arousal-valence plane • discuss commonalities and differences between mood recognition and genre classification • implement linear regression in Matlab module 13: mood recognition 1 / 9
- 3. overview intro mood models regression results summary introduction overview corresponding textbook section chapter 13 lecture content • introduction to emotion and mood • models for mood • linear regression learning objectives • describe Russel’s arousal-valence plane • discuss commonalities and differences between mood recognition and genre classification • implement linear regression in Matlab module 13: mood recognition 1 / 9
- 4. overview intro mood models regression results summary mood recognition introduction objective:identify mood/emotion of a song terminology: • Music Mood Recognition and Music Emotion Recognition usually used synonymously processing steps (similar to genre and similarity tasks) • extract features • classify (possibly regression) module 13: mood recognition 2 / 9
- 5. overview intro mood models regression results summary mood recognition introduction objective:identify mood/emotion of a song terminology: • Music Mood Recognition and Music Emotion Recognition usually used synonymously processing steps (similar to genre and similarity tasks) • extract features • classify (possibly regression) module 13: mood recognition 2 / 9
- 6. overview intro mood models regression results summary mood recognition introduction objective:identify mood/emotion of a song terminology: • Music Mood Recognition and Music Emotion Recognition usually used synonymously processing steps (similar to genre and similarity tasks) • extract features • classify (possibly regression) module 13: mood recognition 2 / 9
- 7. overview intro mood models regression results summary mood recognition challenges What is the difference between mood and emotion module 13: mood recognition 3 / 9
- 8. overview intro mood models regression results summary mood recognition challenges What is the difference between mood and emotion many definitions out there but general consensus on emotion: • temporary, evanescent • (directly) related to external stimuli mood: • longer term, stable • diffuse affect state module 13: mood recognition 3 / 9
- 9. overview intro mood models regression results summary mood recognition challenges ground truth data • verbalization of emotions/moods usually misleading • not easily quantifiable/categorizable • changing over time? research questions • are basic emotions (happiness, anger, fear, . . . ) representative for music perception? • should aesthetic emotions be distinguished from other emotions (guilt, shame, disgust, ...)? • aroused vs. transported/evoked vs. conveyed moods? module 13: mood recognition 4 / 9
- 10. overview intro mood models regression results summary mood recognition challenges ground truth data • verbalization of emotions/moods usually misleading • not easily quantifiable/categorizable • changing over time? research questions • are basic emotions (happiness, anger, fear, . . . ) representative for music perception? • should aesthetic emotions be distinguished from other emotions (guilt, shame, disgust, ...)? • aroused vs. transported/evoked vs. conveyed moods? module 13: mood recognition 4 / 9
- 11. overview intro mood models regression results summary mood recognition challenges ground truth data • verbalization of emotions/moods usually misleading • not easily quantifiable/categorizable • changing over time? research questions • are basic emotions (happiness, anger, fear, . . . ) representative for music perception? • should aesthetic emotions be distinguished from other emotions (guilt, shame, disgust, ...)? • aroused vs. transported/evoked vs. conveyed moods? module 13: mood recognition 4 / 9
- 12. overview intro mood models regression results summary mood recognition models classification into label clusters1 Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Rowdy Amiable/Good Natured Literate Witty Volatile Rousing Sweet Wistful Humorous Fiery Confident Fun Bittersweet Whimsical Visceral Boisterous Rollicking Autumnal Wry Aggressive Passionate Cheerful Brooding Campy Tense/Anxious Poignant Quirky Intense Silly mood model, circumplex model 1 X. Hu and J. S. Downie, “Exploring Mood Metadata: Relationships with Genre, Artist and Usage Metadata,” in Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Vienna, 2007. module 13: mood recognition 5 / 9
- 13. overview intro mood models regression results summary mood recognition models classification into label clusters mood model, circumplex model1 Pleasure Excitement Arousal Distress Misery Depression Sleepiness Contentment 1 J. A. Russel, “A Circumplex Model of Affect,” Journal of Personality and Social Psychology, vol. 39, no. 6, pp. 1161–1178, 1980, issn: 1939-1315(Electronic);0022-3514(Print). doi: 10.1037/h0077714. module 13: mood recognition 5 / 9
- 14. overview intro mood models regression results summary mood recognition mood model: regression modeling mapping • (N-dimensional) observation (feature) to 2-dimensional coordinate (valence/arousal) training • find model to minimize error between data points and “prediction” module 13: mood recognition 6 / 9
- 15. overview intro mood models regression results summary regression regression linear regression: fit a linear function to a series of points (xj , yj ) yn = m · xn + b other regression approaches: SVR, DNNs, etc. module 13: mood recognition 7 / 9 matlab source: plotLinearRegression.m
- 16. overview intro mood models regression results summary mood recognition range of results highly dependent on data 5 mood clusters: 40–60% classification rate mood model: 0.1–0.4 absolute prediction error (unit circle) module 13: mood recognition 8 / 9
- 17. overview intro mood models regression results summary mood recognition range of results highly dependent on data 5 mood clusters: 40–60% classification rate mood model: 0.1–0.4 absolute prediction error (unit circle) module 13: mood recognition 8 / 9
- 18. overview intro mood models regression results summary summary lecture content emotion and mood • emotion: temporary, related to external stimuli • mood: long term, diffuse affective state features 1 baseline features are identical to genre and similarity tasks inference 1 often done as regression (as opposed to classification) module 13: mood recognition 9 / 9