Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userComments on the new Syrian rent Law
تعليقات على قانون الإيجار السوري الجديد
688
0 0
0
(
0
)
Authors
مؤيد زيدان( باحث )
Created by
Shamra Editor
Ask ChatGPT about the research
يعد القانون ظاهرة اجتماعية تخضع لقانون الغاية و هي ضمان شروط حياة أفضل للمجتمع، فحياة القانون تكمن في تطبيقه في الواقع العملي، و تعتبر قواعد قانون الإيجار وثيقة الصلة بواقع المجتمع. لهذا تضمن البحث تعليقاً على أهم مواد قانون الإيجار الجديد رقم 20 الصادر بتاريخ 2012/11/11 لإلقاء الضوء على مدى توافقها مع مبادئ و غايات القانون و بالتالي مناسبتها للمجتمع.
Law is a social phenomenon that is subject to the law of the purpose of ensuring better conditions of life for the community. The life of the law are found in its application in practice, and the rules of the rent law considered relevant by the community. For this research included comments on the most important new rent law No. 20 dated 11/11/2015 materials to shed light on their compatibility with the principles and objectives of the law and therefore suitability for the community.
References used
مجلة المحامون.
أحمد رمضان _ نظرية الغلط و أثره في العقود في الفقه الإسلامي.
علي الخفيف_ أحكام المعاملات الشرعية _ دار الفكر العربي _القاهرة 2005
rate research
Read More
In view of the weak economic position of the shipper vis-à-vis the carrier and his inability to discuss the terms of the maritime transport contract and what resulted from the principle of freedom of contract from severe damage to shippers, insurance companies, banks and consignees, the general rules of liability have become invalid to regulate the responsibility of the carrier.
This study aims to specify the states that people prevented from
witness in The Syrian Evidence Law, and to clarify the states that
include the reason of prevention but they are not mentioned in law.
Besides jurisprudence in this field.
It is clear that the
jurisdiction of juvenile court is stated in any crime committed by
the juvenile whether it is a traditional crime or a new crime such as
informatics crimes. Thus, a question arises about the importance of
the Syrian juvenile
Law No. 18 of 1974 which was amended by
Law 51 of 1979, to be applied on the informatics juvenile
delinquency in the absence of specialized judicial bodies concerned
with juvenile cases.
Cross-language authorship attribution is the challenging task of classifying documents by bilingual authors where the training documents are written in a different language than the evaluation documents. Traditional solutions rely on either translati
on to enable the use of single-language features, or language-independent feature extraction methods. More recently, transformer-based language models like BERT can also be pre-trained on multiple languages, making them intuitive candidates for cross-language classifiers which have not been used for this task yet. We perform extensive experiments to benchmark the performance of three different approaches to a smallscale cross-language authorship attribution experiment: (1) using language-independent features with traditional classification models, (2) using multilingual pre-trained language models, and (3) using machine translation to allow single-language classification. For the language-independent features, we utilize universal syntactic features like part-of-speech tags and dependency graphs, and multilingual BERT as a pre-trained language model. We use a small-scale social media comments dataset, closely reflecting practical scenarios. We show that applying machine translation drastically increases the performance of almost all approaches, and that the syntactic features in combination with the translation step achieve the best overall classification performance. In particular, we demonstrate that pre-trained language models are outperformed by traditional models in small scale authorship attribution problems for every language combination analyzed in this paper.
We present the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments. This shared task comprises three binary classification subtasks with the goal to identify: toxic comments, engaging comments, and comments
that include indications of a need for fact-checking, here referred to as fact-claiming comments. Building on the two previous GermEval shared tasks on the identification of offensive language in 2018 and 2019, we extend this year's task definition to meet the demand of moderators and community managers to also highlight comments that foster respectful communication, encourage in-depth discussions, and check facts that lines of arguments rely on. The dataset comprises 4,188 posts extracted from the Facebook page of a German political talk show of a national public television broadcaster. A theoretical framework and additional reliability tests during the data annotation process ensure particularly high data quality. The shared task had 15 participating teams submitting 31 runs for the subtask on toxic comments, 25 runs for the subtask on engaging comments, and 31 for the subtask on fact-claiming comments. The shared task website can be found at https://germeval2021toxic.github.io/SharedTask/.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
