The thesis processes data from the research of the company H-edu, which developed a course to help students understand the issue of fractions in the difficult times of Covid-19. Using a large number of materials, an interactive environment, videos and professional guidance, the company helped those interested in understanding the basic principles for perceiving and counting fractions. The thesis compares the results before entering the course and after its completion, which presents the details associated with the course and allows for an evaluation of the course design. The data also helps to identify where students make the most mistakes. Furthermore the thesis contains information on how to identify more demanding tasks or why students might need more time during calculations. At the same time, it explains the importance of gradation of tasks associated with higher success of respondents. The thesis also deals with the structure of questions that affect the representation of incorrect answers. For the most frequently recorded incorrect answers the thesis explains its cause and the procedure by which the respondents came to the wrong result. Recurring errors highlight the need for thorough explanation and practice of the substance. Moreover, the thesis contains a detailed analysis of the...

The development of multiple theoretical models and measures of impulsivity has led to inconsistent use of this term and disagreement regarding the most salient predictors of alcohol-related outcomes. The present study examined whether self-report and behavioral measures of impulsivity measure the same construct and how eight conceptually distinct facets of impulsivity relate to alcohol-related outcomes. Participants completed measures and tasks to assess alcohol use, alcohol problems, trait impulsivity, and behavioral impulsivity. The UPPS-P and behavioral measures of impulsivity were largely uncorrelated with each other. Negative urgency and alcohol use emerged as direct predictors of alcohol-related problems. Lack of premeditation demonstrated an indirect effect on alcohol-related problems. Results support previous research suggesting behavioral and self-report measures of impulsivity do not assess the same construct. Further, results suggest that negative urgency may be the most predictive of alcohol-related problems when accounting for self-report and behavioral components of impulsivity.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

This thesis investigates the analysis and optimization of safety and performance-critical computational tasks for autonomous robots, operating in unknown and unstructured environments with complex objectives under strict computational and power constraints. Our primary contribution is a novel safety-performance (SP) metric that emphasizes on safety while rewarding enhanced performance of real-time computational tasks, expanding the notion of nominal safety in the autonomous vehicle domain. We adopt the Stochastic Heterogeneous Parallel Directed Acyclic Graph (SHP-DAG) model to capture the uncertain nature of robotic applications and their required computations, modeling execution times using probability distributions instead of deterministic worst-case execution time (WCET). We argue that computational tasks enabling robotic autonomy, such as localization and mapping, path planning, task allocation, depth estimation, and optical flow, must be scheduled and optimized to guarantee timely and correct behavior while allowing for runtime reconfiguration of scheduling parameters. To attain computational awareness in autonomous robots, we conduct a data-driven study of these computational tasks from the resource management perspective, profiling and analyzing their timing, power, and memory performance across three embedded computing platforms. Our SP metric allows us to apply the schedulers First-In-First-Out (FIFO) and Completely Fair Scheduler (CFS) of the Linux kernel on complex robotic computational tasks and compare the SP metric with baseline metrics, such as average and worst-case makespan. Extensive experimental results on NVIDIA Jetson AGX Xavier hardware demonstrate the effectiveness of the proposed SP metric in managing computational tasks while balancing safety and performance in robotic systems. Our findings reveal a correlation between task performance and a robot's operational environment, which justifies the concept of computation-aware robots and highlights the importance of our work as a crucial step towards this goal. Finally, we also integrate a custom scheduler with the FIFO priorities with our SHP-DAG and show the efficacy of our framework in comparison to default fair scheduler. / Doctor of Philosophy / This paper explores how to improve the safety and performance of autonomous robots operating in unpredictable and complex environments. These robots need to carry out various tasks such as mapping, path planning, and depth estimation, while managing limited computing power and energy resources. To achieve this, we introduce a new safety-performance (SP) metric that prioritizes safety while rewarding better task performance. We use a cutting-edge model that captures the uncertainty of robotic tasks and their required computing resources. By doing so, we can better schedule and optimize these tasks to ensure timely and correct behavior while allowing for adjustments to scheduling parameters during operation. Our study investigates the performance of key computing tasks on various embedded computing platforms. By comparing our SP metric with traditional measures, we can demonstrate the effectiveness of our approach in managing these tasks while balancing safety and performance in robotic systems. We also do system integration of a real-time scheduler with robotic tasks, which shows the efficacy of our framework. Our findings show a connection between a robot's environment and its computing performance, highlighting the importance of our work as a critical step towards creating smarter and safer autonomous robots that can better adapt to their surroundings.

<p>Though modern deep learning based approaches have achieved remarkable progress in computer vision community such as image classification using a static image dataset, it suf- fers from catastrophic forgetting when learning new classes incrementally in a phase-by-phase fashion, in which only data for new classes are provided at each learning phase. In this work we focus on continual learning with the objective of learning new tasks from sequentially available data without forgetting the learned knowledge. We study this problem from three perspectives including (1) continual learning in online scenario where each data is used only once for training (2) continual learning in unsupervised scenario where no class label is pro- vided and (3) continual learning in real world applications. Specifically, for problem (1), we proposed a variant of knowledge distillation loss together with a two-step learning technique to efficiently maintain the learned knowledge and a novel candidates selection algorithm to reduce the prediction bias towards new classes. For problem (2), we introduced a new framework for unsupervised continual learning by using pseudo labels obtained from cluster assignments and an efficient out-of-distribution detector is designed to identify whether each new data belongs to new or learned classes in unsupervised scenario. For problem (3), we proposed a novel training regime targeted on food images using balanced training batch and a more efficient exemplar selection algorithm. Besides, we further proposed an exemplar-free continual learning approach to address the memory issue and privacy concerns caused by storing part of old data as exemplars.</p> <p>In addition to the work related to continual learning, we study the image-based dietary assessment with the objective of determining what someone eats and how much energy is consumed during the course of a day by using food or eating scene images. Specifically, we proposed a multi-task framework for simultaneously classification and portion size estima- tion by future fusion and soft-parameter sharing between backbone networks. Besides, we introduce RGB-Distribution image by concatenating the RGB image with the energy distri- bution map as the fourth channel, which is then used for end-to-end multi-food recognition and portion size estimation.</p>