Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

The student evaluation of teaching and the competence of students as evaluators

When the college student satisfaction survey is considered in the promotion and recognition of instructors, a usual complaint is related to the impact that biased ratings have on the arithmetic mean (used as a measure of teaching effectiveness). This is especially significant when the number of students responding to the survey is small. In this work a new methodology, considering student to student perceptions, is presented. Two different estimators of student rating credibility, based on centrality properties of the student social network, are proposed. This method is established on the idea that in the case of on-site higher education, students often know which others are competent in rating the teaching and learning process.Comment: 20 pages, 2 table

A Review On Table Recognition Based On Deep Learning

Table recognition is using the computer to automatically understand the table, to detect the position of the table from the document or picture, and to correctly extract and identify the internal structure and content of the table. After earlier mainstream approaches based on heuristic rules and machine learning, the development of deep learning techniques has brought a new paradigm to this field. This review mainly discusses the table recognition problem from five aspects. The first part introduces data sets, benchmarks, and commonly used evaluation indicators. This section selects representative data sets, benchmarks, and evaluation indicators that are frequently used by researchers. The second part introduces the table recognition model. This survey introduces the development of the table recognition model, especially the table recognition model based on deep learning. It is generally accepted that table recognition is divided into two stages: table detection and table structure recognition. This section introduces the models that follow this paradigm (TD and TSR). The third part is the End-to-End method, this section introduces some scholars' attempts to use an end-to-end approach to solve the table recognition problem once and for all and the part are Data-centric methods, such as data augmentation, aligning benchmarks, and other methods. The fourth part is the data-centric approach, such as data enhancement, alignment benchmark, and so on. The fifth part summarizes and compares the experimental data in the field of form recognition, and analyzes the mainstream and more advantageous methods. Finally, this paper also discusses the possible development direction and trend of form processing in the future, to provide some ideas for researchers in the field of table recognition. (Resource will be released at https://github.com/Wa1den-jy/Topic-on-Table-Recognition .)Comment: 12 figures,6 tables, in Chinese languag

The Hollins Alumnae Quarterly, vol. 10, no. 1 (1935 Summer)

Table of Contents: In Memoriam A Survey of Hollins Graduates Recognition of the College Alumnae Day Commencement Resume of the Minutes of the Annual Meeting of the Alumnae Association Report of the Executive Secretary of the Alumnae Association A Resume of the Annual Meeting of the Hollins College Board of Trustees, Friday, June 21, 1935 Gifts to the College Class Notes Alumnae Clubs Second Session of the Alumnae Institutehttps://digitalcommons.hollins.edu/alummagazines/1036/thumbnail.jp

Undergraduate students do not understand some library jargon typically used in library instruction

A review of: Hutcherson, Norman B. “Library Jargon: Student Recognition of Terms and Concepts Commonly Used by Librarians in the Classroom.” College and Research Libraries 65.4 (July 2004): 349-54. Objective – To determine students’ level of recognition for 28 commonly used terms in library instruction. Design – Survey, multiple-choice questionnaire. Setting – Large state university library in the United States (this is assumed from the author’s current affiliation). Subjects – 300 first- and second-year university students enrolled in a library skills course between September 2000 and June 2003. Methods – Two 15-question multiple-choice questionnaires were created to verify students’ understanding of 28 terms commonly used in library instruction, or “library jargon”. Each questionnaire included 12 unique terms and, in order to ensure consistency between questionnaire results, three common terms. For each question, a definition was provided and four terms, including the correct one, were offered as possible answers. Four variants of each survey were developed with varied question and answer order. Students who completed a seven-week library skills lab received one of the two questionnaires. Lab instructors explained the objective of the survey and the students completed them in 10 to 15 minutes during class time. Of the 300 students enrolled in the lab between September 2000 and June 2003, 297 returned completed questionnaires. The researcher used Microsoft Excel to calculate descriptive statistics, including the mean, median, and standard deviation for individual questionnaires as well as combined results. No demographic data were collected. Main results – The mean score for both questionnaires was 62.31% (n=297). That is, on average, students answered 9.35 out of 15 questions correctly, with a standard deviation of +-4.12. Students were able to recognize library-related terms to varying degrees. Terms identified correctly most often included: plagiarism (100%), reference services (94.60%), research (94.00%), copyright (91.58%), and table of contents (90.50%). Terms identified correctly the least often included: Boolean logic (8.10%), bibliography (14.90%), controlled vocabulary (18.10%), truncation (27.70%), and precision (31.80%). For the three terms used in both questionnaires, results were similar. Conclusion – The results of this study demonstrate that terms used more widely (e.g. plagiarism, copyright) are more often recognized by students compared with terms used less frequently (e.g. Boolean logic, truncation). Also, terms whose meanings are well-understood in everyday language, such as citation and authority, may be misunderstood in the context of library instruction. For this reason, it can be assumed that students may be confused when faced with this unfamiliar terminology. The study makes recommendations for librarians to take measures to prevent misunderstandings during library instruction such as defining terms used and reducing the use of library jargon

Team Plan Recognition: A Review of the State of the Art

There is an increasing need to develop artificial intelligence systems that assist groups of humans working on coordinated tasks. These systems must recognize and understand the plans and relationships between actions for a team of humans working toward a common objective. This article reviews the literature on team plan recognition and surveys the most recent logic-based approaches for implementing it. First, we provide some background knowledge, including a general definition of plan recognition in a team setting and a discussion of implementation challenges. Next, we explain our reasoning for focusing on logic-based methods. Finally, we survey recent approaches from two primary classes of logic-based methods (plan library-based and domain theory-based). We aim to bring more attention to this sparse but vital topic and inspire new directions for implementing team plan recognition.Comment: 10 pages, 1 figure, 1 table. Abstract accepted, paper submitted to 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023

Hollins Columns (1996 Apr 15)

Table of Contents: Institutional Effectiveness: Hollins gears up for phase 2 or reaccreditation Executive producer of \u27Larry King Live\u27 to speak at 1996 commencement Leadership: an opportunity and an adventure Ritalin: A Wonder Drug? Theatre deserves recognition Freya thanks the Hollins community for support Student frustrated with SGA elections Freshman characteristics: Results of national survey \u27New look\u27 for Paris program Two years of Hollins Take Care Work continues on the Habitat for Humanity House Scholarships available in D.C. Arkansas Bear delights, inspires audiences Roanoke provides alternatives Riding team wins top showing at ODAC meet Lacrosse team wins bighttps://digitalcommons.hollins.edu/newspapers/2257/thumbnail.jp