Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos

The task of video grounding, which temporally localizes a natural language description in a video, plays an important role in understanding videos. Existing studies have adopted strategies of sliding window over the entire video or exhaustively ranking all possible clip-sentence pairs in a pre-segmented video, which inevitably suffer from exhaustively enumerated candidates. To alleviate this problem, we formulate this task as a problem of sequential decision making by learning an agent which regulates the temporal grounding boundaries progressively based on its policy. Specifically, we propose a reinforcement learning based framework improved by multi-task learning and it shows steady performance gains by considering additional supervised boundary information during training. Our proposed framework achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201

Pure Exploration with Multiple Correct Answers

We determine the sample complexity of pure exploration bandit problems with multiple good answers. We derive a lower bound using a new game equilibrium argument. We show how continuity and convexity properties of single-answer problems ensures that the Track-and-Stop algorithm has asymptotically optimal sample complexity. However, that convexity is lost when going to the multiple-answer setting. We present a new algorithm which extends Track-and-Stop to the multiple-answer case and has asymptotic sample complexity matching the lower bound

Transactional leadership as a moderator between self-leadership strategies and innovative behavior

Leadership in organizations is important in shaping workers’ perception and increase employee work performance. There are several types of leadership style that are important in affecting employee work performance and one of it is transactional leadership. Transactional leadership, in contrast to transformational leadership, is based more on reinforcement and exchanges approach. Previous studies that explored about the transactional leadership effect in terms of direct effect or moderating effect relating to organizational behavior such as innovative behavior are scare. Innovation is a complex process and not happened in a vacuum situation, interaction between each of the organizational members is very important. Addressing this issue, this study proposes that transactional leadership moderates the relationship of self-leadership strategies (behavior-focused, constructive thought pattern, natural reward and physical vitality) with innovative behavior. In a field study with 485 engineers from Electrical and Electronics (E&E) manufacturing in Malaysia, this study showed that behavior-focused strategies, constructive thought pattern strategies, natural reward strategies and physical vitality strategies of self-leadership positively related to innovative behavior when transactional leadership is high. Transactional leadership positively and significantly moderates the relationship between each of self-leadership strategies with innovative behavior. The finding contributes to the enrichment of innovative behavior concept by including the transactional leadership as moderator in helping the researcher to explore on how leadership differences contribute to difference research outcomes

Teaching an Active Learner with Contrastive Examples

We study the problem of active learning with the added twist that the learner is assisted by a helpful teacher. We consider the following natural interaction protocol: At each round, the learner proposes a query asking for the label of an instance $x^q$, the teacher provides the requested label $\{x^q, y^q\}$ along with explanatory information to guide the learning process. In this paper, we view this information in the form of an additional contrastive example ($\{x^c, y^c\}$) where $x^c$ is picked from a set constrained by $x^q$ (e.g., dissimilar instances with the same label). Our focus is to design a teaching algorithm that can provide an informative sequence of contrastive examples to the learner to speed up the learning process. We show that this leads to a challenging sequence optimization problem where the algorithm's choices at a given round depend on the history of interactions. We investigate an efficient teaching algorithm that adaptively picks these contrastive examples. We derive strong performance guarantees for our algorithm based on two problem-dependent parameters and further show that for specific types of active learners (e.g., a generalized binary search learner), the proposed teaching algorithm exhibits strong approximation guarantees. Finally, we illustrate our bounds and demonstrate the effectiveness of our teaching framework via two numerical case studies.Comment: Fix the illustrative exampl

Technical and Applied Features of Functional Assessments and Behavioral Intervention Plans

ABSTRACT TECHNICAL AND APPLIED FEATURES OF FUNCTIONAL BEHAVIORAL ASSESSMENTS AND BEHAVIOR INTERVENTION PLANS by Shannon M. Hawkins When conducted correctly, functional behavior assessments (FBAs) can help professionals intervene with problem behavior using function-based interventions. Despite the fact that researchers have shown that effective interventions are based on function, recent investigators have found that most behavioral intervention plans (BIPs) are written without regard to the function of students’ problem behaviors as documented in their FBAs. This study was conducted to examine the overall technical adequacy of FBAs and BIPs within one educational system to evaluate reliance on the outcomes of FBAs in the development of BIPs. The technical and applied features of a randomly selected sample of 134 FBA/BIPs of students with disabilities, ages 3-21 years, who were receiving services due to their severe emotional and behavioral disorders (SEBD) or autism spectrum disorders (ASD) within the Georgia Network of Educational and Therapeutic Services (GNETS) were analyzed. In addition, similarities and differences between function-based strategies specified in BIPs were examined. Logistic regression was used to reveal the probability that a given behavioral function can predict which intervention(s) might be chosen. A series of chi-square tests of independence and a multinomial logistic regression model were used to examine how BIP component variables, demographic variables, behavioral function variables, and behavioral intervention variables related to each other statistically. Components described as critical in research literature for conducting FBAs and developing BIPs were absent from a significant number of the student files. Results suggest few of the prescribed interventions were likely to be related to function. The findings extend research on FBAs and BIPs, particularly as they are used with students with SEBD and autism, documenting that a significant number of BIPs are developed without regard of the function of the problem behavior