Event Transformation for Browser Based Web Devices

Today a smartphone or tablet supports seven to eight ways by which user can interact with it. These interaction methods are touch, mouse, keyboard, voice, gestures, hover & stylus. Future is going towards IoE (Internet of everything) but if we really want to realize this vision then we need someone who can deal with these various existing and upcoming device interaction methods. This paper talks about a custom JavaScript library, which is accountable for registering native events coming from different event sources and maps it with the user defined key map to form a proper gesture. It is not a plain mapping because it takes care of many parameters like event state, occurrence, time interval of key press etc. If the events are coming from touch screen device then complexity increases many folds because forming a touch gesture involves all mathematical steps related to identification of swipe direction. Also in order to support the acceleration, its required to know till how long key was pressed and when it was released else no gesture will be formed and all events will be discarded. Based on device capability supported events could be discarded to completely knock off a device interaction method. It could be touch, mouse or key anything. This paper investigates heterogeneity of device interaction method events to form uniform gestures so that application developer need not to write code for each and every device interaction method

Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.Comment: Findings of EMNLP 202

Study of omega-meson production in pp collisions at ANKE

The production of omega-mesons in the pp->pp omega reaction has been investigated with the COSY-ANKE spectrometer for excess energies of 60 and 92MeV by detecting the two final protons and reconstructing their missing mass. The large multipion background was subtracted using an event-by-event transformation of the proton momenta between the two energies. Differential distributions and total cross sections were obtained after careful studies of possible systematic uncertainties in the overall ANKE acceptance. The results are compared with the predictions of theoretical models. Combined with data on the phi-meson, a more refined estimate is made of the Okubo-Zweig-Iizuka rule violation in the phi/omega production ratio.Comment: 10 pages, 9 figures, version 1, submitted to EPJ-

Event-centric question answering via contrastive learning and invertible event transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR

HLA-CSPIF panel on commercial off-the-shelf distributed simulation

Commercial-off-the-shelf (COTS) simulation packages are widely used in many areas of industry. Several research groups are attempting to integrate distributed simulation principles and techniques with these packages to potentially give us COTS distributed simulation. The High Level Architecture-COTS Simulation Package Interoperation Forum (HLA-CSPIF) is a group of researchers and practitioners that are studying methodological and technological issues in this area. This panel paper presents the views of four members of this forum on the technical problems that must be overcome for this emerging field to be realized

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art