Conservative Likelihood Ratio Estimator for Infrequent Data Slightly above a Frequency Threshold

A naive likelihood ratio (LR) estimation using the observed frequencies of events can overestimate LRs for infrequent data. One approach to avoid this problem is to use a frequency threshold and set the estimates to zero for frequencies below the threshold. This approach eliminates the computation of some estimates, thereby making practical tasks using LRs more efficient. However, it still overestimates LRs for low frequencies near the threshold. This study proposes a conservative estimator for low frequencies, slightly above the threshold. Our experiment used LRs to predict the occurrence contexts of named entities from a corpus. The experimental results demonstrate that our estimator improves the prediction accuracy while maintaining efficiency in the context prediction task.Comment: The 9th International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA 2022

A Computational Model of Tonality Cognition Based on Prime Factor Representation of Frequency Ratios and Its Application

(Abstract to follow

Who Was Wrong? An Object Detection Based Responsibility Assessment System for Crossroad Vehicle Collisions

Car crashes, known also as vehicle collisions, are recurrent events that occur every day. As long as vehicles exist, vehicle collisions will, unfortunately, continue to occur. When a car crash occurs, it is important to investigate and determine the actors’ responsibilities. The police in charge of that task, as well as claims adjusters, usually process manually by going to the crash spot, collecting data on the field, drafting the crash, and assessing responsibilities based on road rules. This later task of assessing responsibilities usually takes time and requires enough knowledge of road rules. With the aim to support the police and claims adjusters and simplify the process of responsibility determination, we built a system that can automatically assess actors’ responsibilities within a crossroad crash. The system is mainly based on image detection and uses a rule-based knowledge system to assess responsibilities within driving recorders’ videos. It uses the crash video recorded by one of the involved vehicles’ driving recorders as the input data source and outputs the evaluation of each actor’s responsibility within the crash. The rule-based knowledge system was implemented to make the reasoning about responsibility assessment clear and allow users to easily understand the reasons for the results. The system consists of three modules: (a) a crash time detection module, (b) a traffic sign detection module, and (c) a responsibility assessment module. To detect a crash within a video, we discovered that the simple application of existing vehicle detection models would result in wrong detections with many false positives. To solve the issue, we made our proposed model take into account only the collided vehicle, its shape, and its position within the video. Moreover, with the biggest challenge being finding data to train the system’s detection models, we built our own dataset from scratch with more than 1500 images of head-on car crashes within the context of crossroad accidents taken by the driving recorder of one of the vehicles involved in the crash. The experiment’s results show how the system performs in (1) detecting the crash time, (2) detecting traffic signs, and (3) assessing each party’s responsibility. The system performs well when light conditions and the visibility of collided objects are good and traffic lights’ view distances are close

Towards Continuous Collaboration on Civic Tech Projects: Use Cases of a Goal Sharing System Based on Linked Open Data

Part 2: Deliberation and ConsultationInternational audienceCivic hackathon is a participatory event for prototyping of innovative services through collaboration between citizens and engineers towards addressing social issues. Although continuous contributions are needed for improving the prototypes and for applying them to social issues, participants frequently stop contributions after the hackathon due to their day job. To address this problem, we applied our Web system, called GoalShare, which gathers linked open data (LOD) of hierarchical goals to address social issues, to civic hackathons held in the city of Nagoya in Japan. We compared goal structures between two situations. The results showed that goal structures input by team members themselves with enough instruction time were relatively detailed but varied widely among teams, and those input by a single GoalShare user with limited time remained at a simple overview level but had uniform level of detail. A more user-friendly interface usable without instruction is required for real-world situations

A Goal Matching Service for Facilitating Public Collaboration Using Linked Open Data

Part 4: Software Platforms and EvaluationInternational audienceInter-organizational collaboration in the public sphere is essentially important to address sustainability problems in contemporary regional societies. To facilitate public collaboration, we are developing a Web application for sharing public issues and their solutions as public goals. Since participating in abstract or general goals is more difficult than concrete or specific ones, our system provides a functionality to break down individual public goals into concrete subgoals. Our Web application, GoalShare, is based on a linked open dataset of public goals that are linked with titles, participants, subgoals, related issues, related articles, and related geographic regions. GoalShare recommends public goals and users on the basis of similarity calculations taking into account not only surficial and semantic features but also contextual features extracted from subgoals and supergoals. We conducted experiments to investigate the effects of contextual features in subgoals and supergoals. Moreover, we conducted a trial workshop with GoalShare in Ogaki city to improve system design through actual use