NTCIR Lifelog: The First Test Collection for Lifelog Research

Test collections have a long history of supporting repeatable and comparable evaluation in Information Retrieval (IR). However, thus far, no shared test collection exists for IR systems that are designed to index and retrieve multimodal lifelog data. In this paper we introduce the first test col- lection for personal lifelog data. The requirements for such a test collection are motivated, the process of creating the test collection is described, along with an overview of the test collection and finally suggestions are given for possible applications of the test collection, which has been employed for the NTCIR12-Lifelog task

Loss Modification Incentives for Insurers Under Expected Utility and Loss Aversion

We investigate whether a profit-maximizing insurer with the opportunity to modify the loss probability will engage in loss prevention or instead spend effort to increase the loss probability. First we study this question within a traditional expected utility framework; then we apply Koszegi and Rabin's (2006, 2007) loss aversion model to account for reference-dependence in consumer preferences. Largely independent of the adopted framework, we find that the profit-maximizing loss probability for many commonly used parameterizations is close to 1/2. So in cases where the initial loss probability is low, insurers will have an incentive to increase it. This qualifies appeals to grant insurers market power to incentivize them to engage in loss prevention

Causality inspired retrieval of human-object interactions from video

Notwithstanding recent advances in machine vision, video activity recognition from multiple cameras still remains a challenging task as many real-world interactions cannot be automatically recognised for many reasons, such as partial occlusion or coverage black-spots. In this paper we propose a new technique that infers the unseen relationship between two individuals captured by different cameras and use it to retrieve relevant video clips if there is a likely interaction between the two individuals. We introduce a human object interaction (HOI) model integrating the causal relationship between the humans and the objects. For this we first extract the key frames and generate the labels or annotations using the state-of-the-art image captioning models. Next, we extract SVO (subject, verb, object) triples and encode the descriptions into a vector form for HOI inference using the Stanford CoreNLP parser. In order to calculate the HOI co-existence and the possible causality score we use transfer entropy. From our experimentation, we found that integrating casual relations into the content indexing process and using transfer entropy to calculate the causality score leads to improvement in retrieval performance

Overview of NTCIR-12 Lifelog Task

In this paper we review the NTCIR12-Lifelog pilot task, which ran at NTCIR-12. We outline the test collection employed, along with the tasks, the eight submissions and the findings from this pilot task. We finish by suggesting future plans for the task

QAScore -- An Unsupervised Unreferenced Metric for the Question Generation Evaluation

Question Generation (QG) aims to automate the task of composing questions for a passage with a set of chosen answers found within the passage. In recent years, the introduction of neural generation models has resulted in substantial improvements of automatically generated questions in terms of quality, especially compared to traditional approaches that employ manually crafted heuristics. However, the metrics commonly applied in QG evaluations have been criticized for their low agreement with human judgement. We therefore propose a new reference-free evaluation metric that has the potential to provide a better mechanism for evaluating QG systems, called QAScore. Instead of fine-tuning a language model to maximize its correlation with human judgements, QAScore evaluates a question by computing the cross entropy according to the probability that the language model can correctly generate the masked words in the answer to that question. Furthermore, we conduct a new crowd-sourcing human evaluation experiment for the QG evaluation to investigate how QAScore and other metrics can correlate with human judgements. Experiments show that QAScore obtains a stronger correlation with the results of our proposed human evaluation method compared to existing traditional word-overlap-based metrics such as BLEU and ROUGE, as well as the existing pretrained-model-based metric BERTScore.Comment: 19 pages, 5 figures, 7 table

DCU at the NTCIR-13 Lifelog-2 Task

In this work, we outline the submissions of Dublin City University (DCU) team, the organisers, to the NTCIR-13 Lifelog-2 Task. We submitted runs to the Lifelog Semantics Access (LSAT) and the Lifelog Insight (LIT) sub-tasks

Examining the Factors Influencing Tourists’ Destination: A Case of Nanhai Movie Theme Park in China

The present study used a stimulus-organism-response (S-O-R) theoretical framework to examine the relationship between theme park tourists’ experience, brand identity, brand satisfaction, and brand loyalty in China. By using the structural equation model (CB-SEM), this paper illustrates the process of forming destination brand loyalty for sustainable tourism on theme parks. The results suggested a second-order structure of tourism experience. The first-order four factors have different impacts on the second-order tourism experience. Activity experience is the most important factor influencing tourism experience, followed by environment experience, then facility experience, and finally interaction experience. In terms of tourism experience, individual brand identity-brand satisfaction-brand loyalty is the most important path of a theme park on tourists’ behavioral intention, among which brand satisfaction plays the most significant partial mediation effect in the relationship between individual identity and destination loyalty. It is expected that the results of this study provide a reference for improving tourists’ brand loyalty to achieve sustainable development of theme parks

LIFER 2.0: discovering personal lifelog insights using an interactive lifelog retrieval system

This paper describes the participation of the Organiser Team in the ImageCLEFlifelog 2019 Solve My Life Puzzle (Puzzle) and Lifelog Moment Retrieval (LMRT) tasks. We proposed to use LIFER 2.0, an enhanced version of LIFER, which was an interactive retrieval system for personal lifelog data. We utilised LIFER 2.0 with some additional visual features, obtained by using traditional visual bag-of-words, to solve the Puzzle task, while with the LMRT, we applied LIFER 2.0 only with the provided information. The results on both tasks confirmed that by using faceted filter and context browsing, a user can gain insights from their personal lifelog by employing very simple interactions. These results also serve as baselines for other approaches in the ImageCLEFlifelog 2019 challenge to compare with

Organizer team at ImageCLEFlifelog 2017: baseline approaches for lifelog retrieval and summarization

This paper describes the participation of Organizer Team in the ImageCLEFlifelog 2017 Retrieval and Summarization subtasks. In this paper, we propose some baseline approaches, using only the provided information, which require different involvement levels from the users. With these baselines we target at providing references for other approaches that aim to solve the problems of lifelog retrieval and summarization

Overview of ImageCLEF lifelog 2017: lifelog retrieval and summarization

Despite the increasing number of successful related work- shops and panels, lifelogging has rarely been the subject of a rigorous comparative benchmarking exercise. Following the success of the new lifelog evaluation task at NTCIR-12, the first ImageCLEF 2017 LifeLog task aims to bring the attention of lifelogging to a wide audience and to promote research into some of the key challenges of the coming years. The ImageCLEF 2017 LifeLog task aims to be a comparative evaluation framework for information access and retrieval systems operating over personal lifelog data. Two subtasks were available to participants; all tasks use a single mixed modality data source from three lifeloggers for a period of about one month each. The data contains a large collection of wearable camera images, an XML description of the semantic locations, as well as the physical activities of the lifeloggers. Additional visual concept information was also provided by exploiting the Caffe CNN-based visual concept detector. For the two sub-tasks, 51 topics were chosen based on the real interests of the lifeloggers. In this first year three groups participated in the task, submitting 19 runs across all subtasks, and all participants also provided working notes papers. In general, the groups performance is very good across the tasks, and there are interesting insights into these very relevant challenges