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Register a new userWhyAct: Identifying Action Reasons in Lifestyle Vlogs
Whessact: تحديد أسباب العمل في Lifestyle Vlogs
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نحن نهدف إلى تحديد أسباب العمل البشري تلقائيا في مقاطع الفيديو عبر الإنترنت.نحن نركز على النوع الواسع من Lifestyle Vlogs، حيث يقوم الأشخاص بإجراء أعمال بينما يصفهم لفظيا.نقدم وجعلها متاحة للجمهور DataSet Whyact، والتي تتكون من 1،077 إجراء بصري مشروح يدويا مع أسبابها.نحن تصف نموذج متعدد الوسائط يرفع المعلومات المرئية والنصية إلى الاستفادة تلقائيا الأسباب المقابلة للعمل المقدم في الفيديو.
We aim to automatically identify human action reasons in online videos. We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describing them. We introduce and make publicly available the WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons. We describe a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.
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If it is not cracked then it is not working. This statement is the actual result of difference
between tension strength of concrete and tension strength of steel in concrete structures
elements that are economically designed.
In spite of that, eng
ineers rarely use this statement to explain cracks in buildings, but
sometimes they use it to calm down the owner of some cracked building. That's why we should
define acceptable cracks and their degrees.
Cracks in concrete and reinforced concrete structures are a bad sign, although some of
these cracks have no negative effect on the utilization of the structure.
Reasons of cracks are various and different, some of them might occur in different types
of cracking while others may lead to a single type of crack.
Cracks formation in concrete varies due to the different factors causing them, which also
may be structural or related to the utilization of building.
It is not easy to put a useful list for probable reasons of cracking as cracks have many
shapes and any one of them might increase the danger of another one or even exceeds it. To
explain the relationship between these types, we proposed the cracking tree, which may help
the designer to take into consideration the probable reasons for cracking.
The main specification of cracks is that its three nodes define the three periods in the life
of a structure: design, construction and service. These are also divided into lists including
reasons causing cracking and their effects on each other.
Cracking tree branches into tension roots, taking into consideration that the first branches
determine the physical properties of cracks (width, depth, length,…). Then these branches
take their way throughout the three phases,(restriction of movement is the most important
branch). When these branches stop growing, they can tell the severity of cracking, in respect
to durability and beauty. This definitely still needs accurate study and repair.
This research aims to study the direct reasons of cracking, factors that increase cracks,
and types of cracking growing in structural members (Columns, Beams, Plates,..). Add to that
the procedures needed to prevent or stop cracks growing. We also offer an attempt to point the
best methods and materials to be used for restoration.
This paper is intended to investigate and scrutinize the reasons
behind the weaknesses of English major students in Philadelphia
University. One thing I would like to make clear before hand is that my
observations and conclusions are based on my p
ersonal experience gained
during my work as General Education Specialist ( English) UNRWA –
UNESCO through classroom visits to some community colleges and
Education Science Faculties in the five fields of UNRWA operations,
namely, Jordan, Syria, Lebanon, West Bank and Gaza on the one hand,
and as a teacher of English at both Philadelphia University and the
University of Jordan on the other hand. I have discovered that the reasons
behind our students' weaknesses may refer to the learner, the curriculum
or the study plan with its three major components, namely, language,
linguistics and literature, and methods and teaching strategies.
In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about
products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of reviews for a given product. The selected sentence should meet two conditions: first, it should be helpful for a purchase decision and second, the opinion it expresses should be supported by multiple reviewers. This task is closely related to the task of Multi Document Summarization in the product reviews domain but differs in its objective and its level of conciseness. We collect a dataset in English of sentence helpfulness scores via crowd-sourcing and demonstrate its reliability despite the inherent subjectivity involved. Next, we describe a complete model that extracts representative helpful sentences with positive and negative sentiment towards the product and demonstrate that it outperforms several baselines.
Natural language inference (NLI) is the task of determining whether a piece of text is entailed, contradicted by or unrelated to another piece of text. In this paper, we investigate how to tease systematic inferences (i.e., items for which people agr
ee on the NLI label) apart from disagreement items (i.e., items which lead to different annotations), which most prior work has overlooked. To distinguish systematic inferences from disagreement items, we propose Artificial Annotators (AAs) to simulate the uncertainty in the annotation process by capturing the modes in annotations. Results on the CommitmentBank, a corpus of naturally occurring discourses in English, confirm that our approach performs statistically significantly better than all baselines. We further show that AAs learn linguistic patterns and context-dependent reasoning.
Discrepancies exist among different cultures or languages. A lack of mutual understanding among different colingual groups about the perspectives on specific values or events may lead to uninformed decisions or biased opinions. Thus, automatically un
derstanding the group perspectives can provide essential back-ground for many natural language processing tasks. In this paper, we study colingual groups and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. Ona held out set of diverse topics, including marriage, corruption, democracy, etc., our model achieves high correlation with human judgements regarding intra-group values and inter-group differences
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