Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Cloud-based User-Centered Time-Offset Interaction Application
تطبيق تفاعل الزمن المتمركز في الوقت المحدد من قبل المستخدم
337
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تسمح تطبيقات التفاعل في إزاحة الوقت (TOIA) بمحاكاة المحادثات مع الأشخاص الذين سجلوا كلمات الفيديو ذات الصلة مسبقا، والتي يتم لعبها استجابة لمستخدمها التفاعل.لدى Toias إمكانات كبيرة للحفاظ على التواريخ عبر الأجيال والثقافية، والتدريس عبر الإنترنت، والمقابلات المحاكاة، وما إلى ذلك. توجد Toias الحالية في السياقات المتخصصة التي تنطوي على تكاليف إنتاج عالية.تقدم الديمقراطية TOIA تحديات مختلفة عند إنشاء التسجيلات السابقة المناسبة، وتصميم قصص مستخدم مختلفة، وإنشاء واجهات بسيطة عبر الإنترنت للتجريب.نقوم بفتح المصدر TOIA 2.0، وهو تطبيق تفاعل متوفع للوقت الذي تركز على المستخدم، وجعله متاحا لكل من يريد التفاعل مع التسجيلات السابقة للأشخاص، أو إنشاء تسجيلات مسبقة الدفع.
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-generational 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.
References used
https://aclanthology.org/
rate research
Read More
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.
Morphological analysis (MA) and lexical normalization (LN) are both important tasks for Japanese user-generated text (UGT). To evaluate and compare different MA/LN systems, we have constructed a publicly available Japanese UGT corpus. Our corpus comp
rises 929 sentences annotated with morphological and normalization information, along with category information we classified for frequent UGT-specific phenomena. Experiments on the corpus demonstrated the low performance of existing MA/LN methods for non-general words and non-standard forms, indicating that the corpus would be a challenging benchmark for further research on UGT.
This research deals with time psychology in stories of the Holy Quran. It begins with
defining time psychology and shows its kinds and their reasons, from a personal and
internal time and the time of ego. Then the research moves on to reveal the ti
me
psychology in Arabic Literature and it begins with poetry. It displays some lines of verse
that illustrate the sense of time, and then it clarifies the meaning of time psychology in the
new narrative studies.
After that, the research deals with time psychology in the story of the Holy Quran
and the wrong estimate of time due to not realizing it because of the loss of loss of life, or
unconsciousness. Then it exposes some human moments of different characters from the
Holy Quran, like incidents of drowning, giving birth, moments of fear and worry, moments
of departure and meeting and the moments of determining and triumph.
The research demonstrates that the story in the Holy Quran can transmit human
feelings, explain emotions and express psychological senses and the inner depths of the
characters, using words with high level of transparency and subtlety. The words are
meaningul and carefully chosen.
So, all of that what has probably given the miraculous story of the Holy Quran it's
superiority in both it's linguistics and semantics. Also, it exceeds high above the limited
level of the human capacity.
We present an algorithm based on multi-layer transformers for identifying Adverse Drug Reactions (ADR) in social media data. Our model relies on the properties of the problem and the characteristics of contextual word embeddings to extract two views
from documents. Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view. Finally, the initialized classifier in each view is further trained using the initial training examples. We evaluated our model in the largest publicly available ADR dataset. The experiments testify that our model significantly outperforms the transformer-based models pretrained on domain-specific data.
Multimodal pre-training has propelled great advancement in vision-and-language research. These large-scale pre-trained models, although successful, fatefully suffer from slow inference speed due to enormous computational cost mainly from cross-modal
attention in Transformer architecture. When applied to real-life applications, such latency and computation demand severely deter the practical use of pre-trained models. In this paper, we study Image-text retrieval (ITR), the most mature scenario of V+L application, which has been widely studied even prior to the emergence of recent pre-trained models. We propose a simple yet highly effective approach, LightningDOT that accelerates the inference time of ITR by thousands of times, without sacrificing accuracy. LightningDOT removes the time-consuming cross-modal attention by extracting pre-cached feature indexes offline, and employing instant dot-product matching online, which significantly speeds up retrieval process. In fact, our LightningDOT achieves superior performance across mainstream ITR benchmarks such as Flickr30k and COCO datasets, outperforming existing pre-trained models that consume 1000 times magnitude of computational hours using the same features.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
