The document provides information on training courses offered by Velocity Advisory Group. It summarizes 14 different training courses focused on areas like team effectiveness, strategy, leadership, culture, and performance. The courses provide insights and tools to help participants excel in today's workplace and achieve organizational success through executing strategy, developing leaders, and building culture.
Exercise repetition detection for resistance training based on smartphonesWookjae Maeng
油
Regular exercise is one of the most important factors in maintaining a good state of health. In the past, different systems have been proposed to assist people when exercising. While most of those systems focus only on cardio exercises such as running and cycling, we exploit smartphones to support leisure activities with a focus on resistance training. We describe how off-the-shelf smartphones without additional external sensors can be leveraged to capture resistance training data and to give reliable training feedback. We introduce a dynamic time warping-based algorithm to detect individual resistance training repetitions from the smartphones acceleration stream. We evaluate the algorithm in terms of the number of correctly recognized repetitions. Additionally, for providing feedback about the quality of repetitions, we use the duration of an individual repetition and analyze how accurately start and end times of repetitions can be detected by our algorithm. Our evaluations are based on 3,598 repetitions performed by ten volunteers exercising in two distinct scenarios, a gym and a natural environment. The results show an overall repetition miscount rate of about 1 % and overall temporal detection error of about 11 % of individual repetition duration
This document provides guidelines for planning speed training for team sports. It discusses understanding the speed requirements of different sports, acknowledging the physical stimulus of games while knowing games are not optimal for fitness. It outlines considering individual factors when planning. The document recommends annual planning while writing plans in pencil, emphasizing recovery. It discusses applying the correct training tools and following best practice session guidelines. It also notes the importance of logistics, balance, and considering speed training as an ongoing process with windows of opportunity.
Course Overview:
This course offers a comprehensive exploration of recommender systems, focusing on both theoretical foundations and practical applications. Through a combination of lectures, hands-on exercises, and real-world case studies, you will gain a deep understanding of the key principles, methodologies, and evaluation techniques that drive effective recommendation algorithms.
Course Objectives:
Acquire a solid understanding of recommender systems, including their significance and impact in various domains.
Explore different types of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches.
Study cutting-edge techniques, including deep learning, matrix factorization, and graph-based methods, for enhanced recommendation accuracy.
Gain hands-on experience with popular recommendation frameworks and libraries, and learn how to implement and evaluate recommendation models.
Investigate advanced topics in recommender systems, such as fairness, diversity, and explainability, and their ethical implications.
Analyze and discuss real-world case studies and research papers to gain insights into the challenges and future directions of recommender systems.
Course Structure:
Introduction to Recommender Systems
Collaborative Filtering Techniques
Content-Based Filtering and Hybrid Approaches
Matrix Factorization Methods
Deep Learning for Recommender Systems
Graph-Based Recommendation Approaches
Evaluation Metrics and Experimental Design
Ethical Considerations in Recommender Systems
Fairness, Diversity, and Explainability in Recommendations
Case Studies and Research Trends
Course Delivery:
The course will be delivered through a combination of lectures, interactive discussions, hands-on coding exercises, and group projects. You will have access to state-of-the-art resources, including relevant research papers, datasets, and software tools, to enhance your learning experience.
Course Overview:
This course offers a comprehensive exploration of recommender systems, focusing on both theoretical foundations and practical applications. Through a combination of lectures, hands-on exercises, and real-world case studies, you will gain a deep understanding of the key principles, methodologies, and evaluation techniques that drive effective recommendation algorithms.
Course Objectives:
Acquire a solid understanding of recommender systems, including their significance and impact in various domains.
Explore different types of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches.
Study cutting-edge techniques, including deep learning, matrix factorization, and graph-based methods, for enhanced recommendation accuracy.
Gain hands-on experience with popular recommendation frameworks and libraries, and learn how to implement and evaluate recommendation models.
Investigate advanced topics in recommender systems, such as fairness, diversity, and explainability, and their ethical implications.
Analyze and discuss real-world case studies and research papers to gain insights into the challenges and future directions of recommender systems.
Course Structure:
Introduction to Recommender Systems
Collaborative Filtering Techniques
Content-Based Filtering and Hybrid Approaches
Matrix Factorization Methods
Deep Learning for Recommender Systems
Graph-Based Recommendation Approaches
Evaluation Metrics and Experimental Design
Ethical Considerations in Recommender Systems
Fairness, Diversity, and Explainability in Recommendations
Case Studies and Research Trends
Course Delivery:
The course will be delivered through a combination of lectures, interactive discussions, hands-on coding exercises, and group projects. You will have access to state-of-the-art resources, including relevant research papers, datasets, and software tools, to enhance your learning experience.
[1002 Lab meeting] Using Annotations in Online Group Chats yoonjungwon
油
Comparing static Gantt and mosaic charts for visualization of task schedules
1. Comparing static Gantt
and mosaic charts for
visualization of task
schedules
+ Information Visualisation
2011
-Saturnino Luz et al.
/れ
x 2016 Spring
2. Comparing static Gantt and mosaic charts for visualization of task schedules
Information Visualisation (IV), 2011 15th International Conference
Saturnino Luz
Department of Computer Science Trinity College Dublin, Dublin, Ireland
Masood Masoodian Department of Computer Science
The University of Waikato, Hamilton, New Zealand
Author
Published
11. Evaluation
TYPE Sample Question
e
No more than 3 tasks should be scheduled for the same day.
Is this a problem with the current plan?
i
How many days are free in the first two weeks?
c
All the windows are installed by the same person and should
happen in the same days. Is this possible in the current
schedule?
d How many days does the bathroom plumbing take in total?
o
Painting a room should finish before its carpet can be installed.
Is this a problem for the bedroom schedule?
Sample Question
exclusion
Inactivity
Concurrence
duration
ordering
![Evaluation
3) Results
- 炎骸 螳 讌覓 豌 覿 蟆郁骸, 螳/覈危 谿 螳 譯 谿企 覦蟆
蠏 れ姶 蟇一
: 覈危:15.1%, / 螳:15%
讌覓語 螳 覩誤讌 (t[436]=0.5, p = 0.5)
: 覈危: 蠏 20.4豐 / 螳: 蠏 21.2豐
讌覓語 企 螳 覦 觜訣
: 覈危: 2.3 / 螳: 2.4
牛蠍 る 蟇碁Π 讌覓語殊襦 螳 覦 伎 企糾 螳 蟆渚レ](https://image.slidesharecdn.com/160315-160317024242/85/Comparing-static-Gantt-and-mosaic-charts-for-visualization-of-task-schedules-16-320.jpg)
![[IMR2014]WEEK08](https://cdn.slidesharecdn.com/ss_thumbnails/imrweek08-140427233359-phpapp01-thumbnail.jpg?width=560&fit=bounds)
![[Imr]week3](https://cdn.slidesharecdn.com/ss_thumbnails/imrweek3-120321012835-phpapp02-thumbnail.jpg?width=560&fit=bounds)
![[IMR2014]WEEK05](https://cdn.slidesharecdn.com/ss_thumbnails/imrweek05-140403222222-phpapp01-thumbnail.jpg?width=560&fit=bounds)
![[1002 Lab meeting] Using Annotations in Online Group Chats](https://cdn.slidesharecdn.com/ss_thumbnails/random-141013012341-conversion-gate02-thumbnail.jpg?width=560&fit=bounds)