The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose

The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks. In the context of understanding human activities, existing public datasets, while large in size, are often limited to a single RGB camera and provide only per-frame or per-clip action annotations. To enable richer analysis and understanding of human activities, we introduce IKEA ASM---a three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human pose. Additionally, we benchmark prominent methods for video action recognition, object segmentation and human pose estimation tasks on this challenging dataset. The dataset enables the development of holistic methods, which integrate multi-modal and multi-view data to better perform on these tasks

Triggering context-appropriate reminders dependent on user activities

Users often set reminders to be alerted for performing specific tasks. Such reminder functionality often deals with tasks connected to specific times and/or places. However, some tasks, such as those that require planning based on dependencies between multiple actions, require a more nuanced understanding of the context. This disclosure presents techniques to trigger a reminder for a given action that is dependent on another action that will be performed by the user. The techniques infer a user’s future actions with permission from the user. Alternatively, or in addition, if the user permits, the user’s likely future activities can be inferred from other relevant contextual information or specified manually by the user. Subsequently, the inferred and/or user-specified future user activities with a sufficiently high likelihood of occurring are used to trigger reminders for actions that are connected to and/or dependent on the inferred future activities

Adapting the 23Things programme for health librarian professional education

In the UK, as elsewhere, continuing professional development is seen to be a necessary professional activity. However resourcing is problematic and funding varies between employing agencies. The 23Things programme has evolved to address these constraints. In particular it reduces the time for staff to be absent from core functions and makes maximum use of peer support. Within a common structure the programme delivers development to personnel at all professional levels. Activities are learner defined and relevant to their personal context. The pace of progress is learner determined. Cambridge University Medical Library (CUML) has designed its own 23Things programme. While conforming to the general model of 23Things, CUML adapted the programme in three ways. 1. to meet the specific learning needs of the library staff 2. to select topics and tasks related to the subject focus of the library 3. to include a new strand of professional development called Follow That.. Follow That... was designed to foster better understanding of each team members role in delivering core services in the library, and to improve the integration and awareness of expertise within a relatively large team This paper will describe the way in which the programme was set up, the selection of tasks and activities, and the pitfalls encountered. Also included will be an overall evaluation of the programme from the perspective of the participants and the programme designers

Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment and were randomly assigned into four groups: (G1/G2) Batch email interruptions with/without exogenous stress. (G3/G4) Continual email interruptions with/without exogenous stress. To provide context, the experiment's email treatments were surrounded by typical office tasks. The captured variables include physiological indicators of stress, measures of report writing quality and keystroke dynamics, as well as psychometric scores and biographic information detailing participants' profiles. Investigations powered by this dataset are expected to lead to personalized recommendations for handling email interruptions and a deeper understanding of synergistic and antagonistic office activities. Given the centrality of email in the modern office, and the importance of office work to people's lives and the economy, the present data have a valuable role to play

Understanding Blind Users\u27 Accessibility and Usability Problems in an Online Task

We believe that Web lacks accessibility and usability, creating problems for blind user’s in online activities. Literature recognizes this problem exists, but does not explain its nature. This understanding is needed to determine accessibility and usability requirements of the Web for blind users. We examine the question: What is the nature of accessibility and usability problems blind users face in completing online tasks? Adopting a task-oriented approach, we investigate this question in the context of online assessment. Employing verbal protocol analysis, we capture evidence of problems 6 blind participants observe and experience completing online assessment. Analysis reveals two aspects of Web design that present accessibility and usability problems for blind users in performing online tasks. Our study contributes with a deep understanding about blind user’s problems due to lack of Web accessibility and usability. Future research may use this understanding to create blind user profile for online assessment applications

Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet

Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems

Conceptualizing routines of practice that support algebraic reasoning in elementary schools: a constructivist grounded theory

There is ample literature documenting that, for many decades, high school students view algebra as difficult and do not demonstrate understanding of algebraic concepts. Algebraic reasoning in elementary school aims at meaningfully introducing algebra to elementary school students in preparation for higher-level mathematics. While there is research on elementary school students' algebraic reasoning, there is a scarcity of research on how elementary school teachers implement algebraic reasoning curriculum and how their practices support algebraic reasoning. The purpose of this study therefore was to discover practices that promote algebraic reasoning in elementary classrooms by studying elementary school teachers' practices and algebraic reasoning that the practices co-constructed. Specifically, the questions that guided the study included (a) what were the teachers' routines of practice, and (b) in what ways did the routines of practice support algebraic reasoning. I sampled On Track Learn Math project and worked with six teachers to explore their routines of practice and students' algebraic reasoning. As a participant observer, I analyzed video data of the classroom activities, memos, field notes, students' written transcripts and interview data using constructivist grounded theory approach and descriptive statistics. Member checking, data triangulation, and data coding by multiple raters ensured consistency and trustworthiness of the results. Descriptive analysis of students' written generalizations showed that about 74% of the generalizations were explicit and about 55% of the generalizations included names of variables indicating that students were learning how to reason algebraically. Data analysis also revealed five routines of practice. These routines are; (a) maintaining open-endedness of the tasks, (b) nurturing co-construction of ideas, (c) fostering understanding of variable, (d) creating a context for mathematical connections and (e) promoting understanding of generalizations. Teachers maintained open-endedness by giving minimal instructions when launching the tasks and providing students with workspaces. They nurtured co-construction of ideas by creating opportunities for students to collaborate, fostering collaboration, and balancing the support of discourse and content. They fostered understanding of variable as a changing quantity and as a relationship. Teachers created a context for mathematical connections between On Track tasks and students' everyday experiences, between student strategies, between different tasks, between On Track tasks and other curriculum ideas, and between different representations. Teachers promoted understanding of generalizations by encouraging students to justify their conjectures, to apply and evaluate peers' generalizations among other practices. These practices were dependent and informed each other