The question for anyone who has a statement that claims to be the first statement, that is, a statement that opens the way to wisdom and true knowledge, is this: How can the speaker -he be reassured about the truth. of his statement? So, when he claims this truth, how can he affirm with fullness and certainty that what he expresses through the words of the declaration is the substance of the truth.
The purpose of this paper is to extract roads from satellite images, based on developing the performance of the deep convolutional neural network model (Deeplabv3+) for roads segmentation, and to evaluate and test the performance of this mode
l after training on our data.This experimental study was applied at Google Colab cloud platform, by software instructions and advanced libraries in the Python.We conducted data pre -processing to prepare ground truth masks,then we trained the model.The training and validation process required (Epochs=4), by(Patch Size=4images).The Loss function decreased to its minimum value (0.025). Training time was three hours and ten minutes, aided by the advanced Graphics Processing Unit (GPU) and additional RAM.We achieved good results in evaluating the accuracy of the predictions of the trained model (IoU = 0.953). It was tested on two different areas, one of which is residential and the other agricultural in Lattakia city. The results showed that the trained model (DeepLabv3+) in our research can extract the road network accurately and effectively.But its performance is poor in some areas which includes tree shadows on the edges of the road, and where the spectral characteristics are similar to the road, such as the roofs of some buildings, and it is invalid for extracting side and unpaved roads. The research presented several recommendations to improve the performance of the (Deeplabv3+) in extracting roads from high-resolution satellite images, which is useful for updating road maps and urban planning works.
Tables provide valuable knowledge that can be used to verify textual statements. While a number of works have considered table-based fact verification, direct alignments of tabular data with tokens in textual statements are rarely available. Moreover
, training a generalized fact verification model requires abundant labeled training data. In this paper, we propose a novel system to address these problems. Inspired by counterfactual causality, our system identifies token-level salience in the statement with probing-based salience estimation. Salience estimation allows enhanced learning of fact verification from two perspectives. From one perspective, our system conducts masked salient token prediction to enhance the model for alignment and reasoning between the table and the statement. From the other perspective, our system applies salience-aware data augmentation to generate a more diverse set of training instances by replacing non-salient terms. Experimental results on TabFact show the effective improvement by the proposed salience-aware learning techniques, leading to the new SOTA performance on the benchmark.
Understanding tables is an important and relevant task that involves understanding table structure as well as being able to compare and contrast information within cells. In this paper, we address this challenge by presenting a new dataset and tasks
that addresses this goal in a shared task in SemEval 2020 Task 9: Fact Verification and Evidence Finding for Tabular Data in Scientific Documents (SEM-TAB-FACTS). Our dataset contains 981 manually-generated tables and an auto-generated dataset of 1980 tables providing over 180K statement and over 16M evidence annotations. SEM-TAB-FACTS featured two sub-tasks. In sub-task A, the goal was to determine if a statement is supported, refuted or unknown in relation to a table. In sub-task B, the focus was on identifying the specific cells of a table that provide evidence for the statement. 69 teams signed up to participate in the task with 19 successful submissions to subtask A and 12 successful submissions to subtask B. We present our results and main findings from the competition.
Crowdsourcing has been ubiquitously used for annotating enormous collections of data. However, the major obstacles to using crowd-sourced labels are noise and errors from non-expert annotations. In this work, two approaches dealing with the noise and
errors in crowd-sourced labels are proposed. The first approach uses Sharpness-Aware Minimization (SAM), an optimization technique robust to noisy labels. The other approach leverages a neural network layer called softmax-Crowdlayer specifically designed to learn from crowd-sourced annotations. According to the results, the proposed approaches can improve the performance of the Wide Residual Network model and Multi-layer Perception model applied on crowd-sourced datasets in the image processing domain. It also has similar and comparable results with the majority voting technique when applied to the sequential data domain whereby the Bidirectional Encoder Representations from Transformers (BERT) is used as the base model in both instances.
Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to tra
nsplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion. Different from previous work which ignores the continuity of states of TKG in time evolution, we treat the sequence of graphs as a Markov chain, which transitions from the previous state to the next state. RTFE takes the SKGE to initialize the embeddings of TKG. Then it recursively tracks the state transition of TKG by passing updated parameters/features between timestamps. Specifically, at each timestamp, we approximate the state transition as the gradient update process. Since RTFE learns each timestamp recursively, it can naturally transit to future timestamps. Experiments on five TKG datasets show the effectiveness of RTFE.
The metaphor is considered a deviation from the truth and this deviation does
not mean breaking out of it, it is the conjunction of the sign with the signifier in
a route that deviates from the truth and exceeds eloquence on it.
The entire Arabic
language moves within the framework of truth and
metaphor, thus, gaining new flexibility and relations which could not be
acceptable in the door of the truth without the metaphor. The metaphor
approaches the language using the brevity approach and keep it away from the
stuffing and undesirable prolonging.The semantic transformation acquired by
language by metaphorical relationships is considered a reduced energy
confirming the principle of customary union between the sign and the signifier.
The metaphor combines words that cannot be collected, as it takes us from
the meaning of the word on the meaning to the meaning of meaning on a
broader meaning, then we move from small meaning spaces to large areas, and
the advantage of metaphor in the language does not depend on that only, as his
relationship also revolves in the orbit of brevity and contribute to maintain the
integrity of poetic weight and legislate thecollection in places that require
individual and vice versa,as well as other characteristics and features shown
through discussion in the core of the research.
The reverse Stylistically event received considerable attention from the ancient and
modern linguists because of generated from the energies of linguistic expression and
creativity of thought beyond the mainstream and fashionable, and forms a metap
hor based
on the bypass typical literary expression, so it came this research to show the impact of the
metaphor in the formation of the phenomenon of reverse dependent on the views of some
linguists, and the search result that metaphor reverse based on breach uncommon in Sunan
standard speech, which raises the recipient impressive including achieve characteristic
aesthetic rhetorical, but it does not come out at all a departure from the grammar, but
preferred creative pattern expressive on the other, as if the use of metaphor does not make
it relevant germ of truth, but remain connected by an invisible bond of flour; because of
which branch, and the branch does not understand only through the origin, but does not
know it.
