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The NEUROGES® analysis system is an objective and reliable interdisciplinary research tool for analysis of body movement and gesture. The system has been field-tested on more than 500 individuals from cultures of five continents, including healthy adults and children as well as individuals with mental illness or brain damage, and further, on non-human primates. Following up on the first book on theoretical background of the NEUROGES® system, which was published in 2013, this book contains the complete coding manual for the application of the system in research including an interactive video learning tool with video examples of all NEUROGES® hand movement and gesture types, training videos, and a template file for the Multimedia Annotator ELAN.

Behavior analysis using deep learning has emerged as a powerful tool for understanding human actions, intentions, and patterns. By leveraging advanced neural networks, researchers can analyze vast amounts of data from videos, images, and sensors to derive meaningful insights. This paper explores the methodologies, applications, and challenges of behavior analysis using deep learning techniques. It examines various architectures such as convolutional neural networks CNNs , recurrent neural networks RNNs , and transformer based models in analyzing behavior for applications in healthcare, security, marketing, and smart cities. This research is in line with the ongoing efforts to use cutting edge ICTs for providing advanced smart city applications, and its aim is to improve modeling human behavior for different smart city applications. Mansi Soni | Arjun Parihar "Deep Learning Techniques for Behavior Analysis: Methods, Applications, and Insights using CNN model" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-6 , December 2024, URL: https://www.ijtsrd.com/papers/ijtsrd72709.pdf Paper Url: https://www.ijtsrd.com/engineering/computer-engineering/72709/deep-learning-techniques-for-behavior-analysis-methods-applications-and-insights-using-cnn-model/mansi-soni

Defining the museum as a learning environment, this book analyzes devices that can be used to make the experience more meaningful for visitors in existing and new exhibits. Although the typical museum audience is heterogeneous and voluntary with no particular instructional objectives on which to base museum exploration, instructional technology can be used to arrange media and activities in order to facilitate specific learning outcomes. Elements of two experimental programs, which were initiated in the Milwaukee Public Museum, are described. Audiocassettes and portable visitor response devices can serve as adjuncts to already existing exhibits to give them interactive properties. For example, the visitor may carry and play the cassette as he examines an exhibit, and he can punch answers on a punchboard in response to audio-script questions. The devices can be wired so that the cassette stops playing until the correct response has been punched. Similar nonportable machines can be attractively programed for pre- and posttesting or self-testing. Other techniques for self-testing could involve latent image response cards, punchboards, and mechanical response devices. Statistics are included for several studies that use various techniques over different periods of time. (AV)

Environmental learning during COVID-19 experienced a change to elearning. The use of learning models must be compatible; one model that can be chosen was the ILMIZI model. In addition to learning the concept of COVID-19, students must also be able to implement it in the form of Pro-Environmental Behavior (PEB) COVID-19. The purpose of this study was to analyze descriptively about PEB-COVID-19 and also analyze the potential of ILMIZI to be applied in e-learning. The research conducted using a descriptive method. The sample was 148 Junior High School students located in Bekasi City and Bandung city. The data was collected online using Google form. The results showed that the PEB-COVID-19 student score was already in the very high category. While the results of the analysis show that the ILMIZI model allows it to be used in elearning. This study concluded that the score of PEB-COVID-19 students was still in the very low category and the use of the ILMIZI model was one form of learning innovation one form of learning innovation.

The present study suggests that initial learning rates of young children with autism receiving early, intensive, home-based behavioral intervention are moderately correlated with outcome variables after four years of treatment. 20 children with autism who had Childhood Autism Rating Scale scores between 37.5 and 58 and Vineland Adaptive Behavior Scales scores between 38 and 63 at the beginning of treatment were re-evaluated after 4 years of treatment through the Rutgers Autism Program. School placement follow-up data were also available after 4 years. Treatment data reflecting rate of initial acquisition of skills was significantly correlated with school placement, severity of autism symptomatology, and adaptive behavior profiles four years into treatment, with those children having faster early skill acquisition showing greater gains in adaptive functioning, fewer or less severe symptoms of autism, and less restrictive educational placements after 4 years. The data are consistent with previous research showing the impact of intensive ABA intervention and variability in outcomes associated with such intervention, and also lend support to other published findings that early learning rates are correlated with outcome.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 15 titles deal with the following topics: remedial reading teacher attitudes, how parent training in behavior modification and transactional analysis affects remedial reading instruction, the effect of role change on the reading achievement of high school remedial reading students, a comparison of two reading readiness programs, how context affects word recognition and comprehension, the role of Head Start programs in identifying learning disabilities, how select program variables relate to reading achievement, how task analysis and diagnostic remedial assessment affect behavior during reading instruction, the evaluation of a paraprofessional tutorial strategy, the language experience approach compared to a basal approach in remedial reading instruction, how units of drill and practice affect decoding performances, the use of oral language and cultural heritage in beginning reading instruction, evaluating a diagnostic/prescriptive approach to reading, specific factors related to the reading improvement of students in a reading laboratory program, and how reading material organization affects reading achievement in an individualized reading program. (RL)

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 15 titles deal with the following topics: remedial reading teacher attitudes, how parent training in behavior modification and transactional analysis affects remedial reading instruction, the effect of role change on the reading achievement of high school remedial reading students, a comparison of two reading readiness programs, how context affects word recognition and comprehension, the role of Head Start programs in identifying learning disabilities, how select program variables relate to reading achievement, how task analysis and diagnostic remedial assessment affect behavior during reading instruction, the evaluation of a paraprofessional tutorial strategy, the language experience approach compared to a basal approach in remedial reading instruction, how units of drill and practice affect decoding performances, the use of oral language and cultural heritage in beginning reading instruction, evaluating a diagnostic/prescriptive approach to reading, specific factors related to the reading improvement of students in a reading laboratory program, and how reading material organization affects reading achievement in an individualized reading program. (RL)

The recent surge in Bitcoin price performance has attracted significant attention from both the market and academic researchers. This paper constitutes the first principled attempt to determine market risk own-funds requirements for Bitcoin. To this end, we examine price microstructure of the USD per bitcoin, and compare to other financial variables, as a proxy toward classifying Bitcoin into the appropriate risk-class. Using the outcomes of this analysis, we classify and quantify the entailed risk from a market risk minimum capital requirements perspective. To perform the prescribed analysis, we introduce a novel methodological paradigm, which adopts bleeding-edge concepts from the field of Data Science and Machine Learning.

I hope this textbook will help any college students in need who don't have the money to buy this text book which is used for Psychology of Learning. 

Michael Commons, president of the Behavior Systems Corporation in New York City discusses techniques of behavior research with Elsa Knight Thompson.

This study examined factors that predict a general education teacher's efficacy beliefs for instructing students with learning and behavior problems and whether a teacher's perceived efficacy has a stronger direct effect on reported success than other variables. One hundred twenty-eight second grade teachers completed a survey instrument designed to examine the following variables: preservice and inservice preparation, administrative support, class size, socioeconomic status, collegiality, and teacher efficacy. Path analysis techniques were used to test the initial theoretical model. Reduced models were retested and compared to previous models to develop a final model. As hypothesized, teacher's efficacy beliefs had the strongest direct effect on reported success. Collegiality with special education teachers and quality inservice in special education also directly affected teachers' reports of success, but to a lesser degree. However, general education teachers who experienced better collegial relationships with general education peers and students with higher socioeconomic status were less likely to report success in instructing students with learning and behavior problems. Also, quality of preservice preparation had a strong direct effect on teachers' efficacy beliefs as did collegiality with special education teachers. Finally, quality of special education inservice and principal support for mainstreaming students with disabilities positively affected collegiality with special education teachers. (Contains 23 references.) (DB)

This study examined factors that predict a general education teacher's efficacy beliefs for instructing students with learning and behavior problems and whether a teacher's perceived efficacy has a stronger direct effect on reported success than other variables. One hundred twenty-eight second grade teachers completed a survey instrument designed to examine the following variables: preservice and inservice preparation, administrative support, class size, socioeconomic status, collegiality, and teacher efficacy. Path analysis techniques were used to test the initial theoretical model. Reduced models were retested and compared to previous models to develop a final model. As hypothesized, teacher's efficacy beliefs had the strongest direct effect on reported success. Collegiality with special education teachers and quality inservice in special education also directly affected teachers' reports of success, but to a lesser degree. However, general education teachers who experienced better collegial relationships with general education peers and students with higher socioeconomic status were less likely to report success in instructing students with learning and behavior problems. Also, quality of preservice preparation had a strong direct effect on teachers' efficacy beliefs as did collegiality with special education teachers. Finally, quality of special education inservice and principal support for mainstreaming students with disabilities positively affected collegiality with special education teachers. (Contains 23 references.) (DB)

This research work uses machine learning (ML) approaches to classify on board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swerving, and sideslipping). The RF classifier achieved the highest accuracy, which reinforces the strength of the chosen models. The inclusion of comparisons with other techniques supports arguments about the model's performance. The related works section connects the references to the central topic by highlighting prior approaches and research studies related to OBD II and driver behaviour analysis. The goals of this study are improving the accuracy of driving behaviour classification, with implications for traffic safety, driver education, and insurance sectors. 

Self-regulated learning (SRL) is often assumed as an important factor in the description of learning in higher education, but students tend to lack necessary abilities and misjudge the efficacy of effective learning strategies. To compensate for individual differences in prerequisites, specific support for students is needed from the beginning of their studies. Therefore, we assess self-reported SRL abilities to detect students at risk and foster their learning. For the latter, a tutorial system, called Co-Tutor, is offered in the lecture for educational studies at the University of Mannheim in the first semester. Co-Tutor provides support by providing training tasks and feedback tailored to the lecture’s content and the following exam. Currently, this combined assessment and training is utilized in the fourth year of this annual lecture. Results of the first three years (2019-21) demonstrated the predictive validity of the aggregated learning index of the tutorial system but not for self-reported SRL with respect to success in the following exam. The elaboration index explained additional variance of the exam’s grade even if GPA and self-reported SRL were controlled but the opposite is not true for the self-reported SRL as an overall measure. But learner’s SRL self-reports have also shown to differ on an individual level. This leads to the question, if different latent subgroups exist in our sample relating to the SRL questionnaire and the use behavior of Co-Tutor, which can be used to detect students at risk at an early stage. For this a normal mixture modeling approach is used to find different latent profiles of SRL self-reports and set them in connection to objective learning data and learning outcomes. Through first exploratory analysis a three-profile solution is found over all the cohorts (2019 - 2022). This three-profile solution further shows descriptive differences regarding the use behavior in the tutorial system in the cohort 2019 and could therefore potentially guide the way for an early detection of students with difficulties that can be aimed by tailored interventions. The profiles found in the profile analysis have been named low SRL, overall high SRL, and specific high SRL profile. Over the past two years the first found differences in the external criteria could not be replicated, which could be explained with differences in the type of lecture visited due to pandemic restrictions. Therefore the pattern found for the last cohort in a presence lecture (2019) is predicted to replicate for the year 2022.

The aim of the study is to evaluate the effects of EGCG on behavioral parameters, motor coordination, learning, memory, the activity of the enzyme dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) and the concentration of DYRK1A protein in Ts65Dn mice, as an experimental model of Down.

The aim of the study is to evaluate the effects of EGCG on behavioral parameters, motor coordination, learning, memory, the activity of the enzyme dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) and the concentration of DYRK1A protein in Ts65Dn mice, as an experimental model of Down.

The aim of the study is to evaluate the effects of EGCG on behavioral parameters, motor coordination, learning, memory, the activity of the enzyme dual specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) and the concentration of DYRK1A protein in Ts65Dn mice, as an experimental model of Down.

This paper reports the findings of a comparative analysis of online learner behavioral interactions, time-on-task, attendance, and performance at different points throughout a semester (beginning, during, and end) based on two online courses: one course offering authentic discussion-based learning activities and the other course offering authentic design/development-based learning activities. Web log data were collected to determine the number of learner behavioral interactions with the Moodle learning management system (LMS), the number of behavioral interactions with peers, the time-on-task for weekly tasks, and the recorded attendance. Student performance on weekly tasks was also collected from the course data. Behavioral interactions with the Moodle LMS included resource viewing activities and uploading/downloading file activities. Behavioral interactions with peers included discussion postings, discussion responses, and discussion viewing activities. A series of Mann-Whitney tests were conducted to compare the two types of behavioral interactions between the two courses. Additionally, each student's behavioral interactions were visually presented to show the pattern of their interactions. The results indicated that, at the beginning of the semester, students who were involved in authentic design/development-based learning activities showed a significantly higher number of behavioral interactions with the Moodle LMS than did students involved in authentic discussion-based learning activities. However, in the middle of the semester, students engaged in authentic discussion-based learning activities showed a significantly higher number of behavioral interactions with peers than did students involved in authentic design/development-based learning activities. Additionally, students who were given authentic design/development-based learning activities received higher performance scores both during the semester and at the end of the semester and they showed overall higher performance scores than students who were given authentic discussion-based learning activities. No differences were found between the two groups with respect to time-on-task or attendance.

This pre-registration contains the plan for carrying out my master thesis, which is based on my research proposal.