Time-offset interaction applications (TOIA) allow simulating conversations with people who have previously recorded relevant video utterances, which are played in response to their interacting user. TOIAs have great potential for preserving cross-gen
erational and cross-cultural histories, online teaching, simulated interviews, etc. Current TOIAs exist in niche contexts involving high production costs. Democratizing TOIA presents different challenges when creating appropriate pre-recordings, designing different user stories, and creating simple online interfaces for experimentation. We open-source TOIA 2.0, a user-centered time-offset interaction application, and make it available for everyone who wants to interact with people's pre-recordings, or create their pre-recordings.
Dialogue systems like chatbots, and tasks like question-answering (QA) have gained traction in recent years; yet evaluating such systems remains difficult. Reasons include the great variety in contexts and use cases for these systems as well as the h
igh cost of human evaluation. In this paper, we focus on a specific type of dialogue systems: Time-Offset Interaction Applications (TOIAs) are intelligent, conversational software that simulates face-to-face conversations between humans and pre-recorded human avatars. Under the constraint that a TOIA is a single output system interacting with users with different expectations, we identify two challenges: first, how do we define a good' answer? and second, what's an appropriate metric to use? We explore both challenges through the creation of a novel dataset that identifies multiple good answers to specific TOIA questions through the help of Amazon Mechanical Turk workers. This view from the crowd' allows us to study the variations of how TOIA interrogators perceive its answers. Our contributions include the annotated dataset that we make publicly available and the proposal of Success Rate @k as an evaluation metric that is more appropriate than the traditional QA's and information retrieval's metrics.
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Background & Objective: Interimplant distance is one of the factors influencing interimplant crestal bone resorption. In addition, Several studies have shown the ability of microthreads and platform switching to minimize crestal bone resorption aroun
d implants. This study aimed at evaluating the effect of interimplant distances of 2 mm and 3 mm on the vertical resorption of interimplant bone peak.
Methods & Materials: 39 implants were inserted in 13 patients ( 6 males and 7 females) whose ages ranged between 30 -55 years old with an average of 43.7 years. Every patient received 3 implants in the posterior mandible. Thus, we had tow study groups: First group where the interimplant distance was 2mm, and the second group where the interimplant distance was 3mm.The amount of vertical resorption of interimplant bone peak was measured radiographically at implant exposure, prosthesis delivery, 3 months after loading, and 6 months after loading. Statistical comparisons were performed using T student test ,and the P-value was set at 0.05.
Results: No statistically significant differences were found between the 2 groups regarding the amount of vertical resorption of interimplant bone peak . The mean vertical resorption of interimplant bone peak was 0.48 mm in the first group and 0.23 mm in the second group at the end of study.
Conclusion: Within the limits of this study, there are no statistically significant differences in the influence of interimplant distance of 2mm and 3mm on interimplant crestal bone resorption.
