تأثر الأدب العربي من تعليمات النبي ﷺ دراسة و تحقيقا

It is estimated by studying the history that the imagination of life was limited before the appearance of Islam. A new era started after the arrival of Islam. Revolution came in thoughts and ideas. Every department was effected even poetry, literature and language pleasantly effected. A revolution created in the Arabic literature after the revelation of the Holy Quran even it taught the rituals of representation of emotions along with facial and spiritual beauty to the Arabic literature. Arabic language is full of knowledge and thoughts of whole world today and the axis of Arabic language and literature is the Holy Quran. The resources of ignorant literature which we get today was collected to save and understand the language of the Holy Quran. For example to eliminate the linguistic flaws, grammar science came into being and rhetoric science came into being to prove Quranic miracle and language and literature came into being to explain the poor words, and Hadith, tafseer, fiqah and other sciences came into being for religious laws. The Holy Quran changed the direction of literature towards justice, service to humanity and support of right and truth and chastity and modesty and God-worship. It gave appropriate dignified styles to explain every topic and invited to work by using reasons and thoughts. Arabic language is effected by the Holy Quran in such a way that it softened the hard and ruthless hearts of Arabs and made the surface wisdom heavy and solid by entering in it.  Could not get effected by Holy Quran as the level which prose got benefit. The prose got more shine in the time of Khulafa-e-rashidin when victories increased, boundaries of Islamic state expanded and political and developmental issues increased. It is a fact that Arabic prose got too high as compared to the Arabic poetry due to the Holy Quran.  

Temporal Human Action Detection in Long and Untrimmed Videos

With the advancement in information and communication technologies, sensing devices have now become pervasive. The pervasiveness of camera devices has enabled recording of video data at anytime and anywhere. It gives rise to a massive amount of untrimmed video data being produced, which consist of several human-related activities and actions including some background activities as well. It is important to detect the actions of interest in such long and untrimmed videos so that it can be further used in numerous applications i.e., video analysis, video summarization, surveillance, retrieval and captioning etc. This thesis targets temporal human action detection in long and untrimmed videos. Given a long and untrimmed video, the task of the temporal action detection is to detect starting and ending time of all occurrences of actions of interest and to predict action label of the detected intervals. Detecting human actions in long untrimmed videos is important but a challenging problem because of the unconstrained nature of long untrimmed videos in both space and time. In this work we solve the temporal action detection problem using two di erent paradigms: \proposal + classi cation" and \end-to-end temporal action detection". In proposal + classi cation approach, the regions which likely to contain human actions, known as proposals, arerst generated from untrimmed videos which are then classi ed into the targeted actions. To this end, we propose two di erent methods to generate action proposals: (1) un-supervised and (2) supervised temporal action proposal methods. In therst method, we propose unsupervised proposal generation method named as Proposals from Motion History Images (PMHI). PMHI discriminates actions from non-action regions by clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. PMHI outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)- uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets.PMHI depends upon precise silhouettes extraction which is challenging for realistic videos and for moving cameras. To solve aforementioned problem, we propose a supervised temporal action proposal method named as Temporally Aggregated Bag-of-Discriminant-Words (TAB) which work directly on RGB videos. TAB is based on the observation that there are many overlapping frames in action and background temporal regions of untrimmed videos, which cause di culties in segmenting actions from non-action regions. TAB solve this issue by extracting class-speci c codewords from the action and background videos and extracting the discriminative weights of these codewords based on their ability to discriminate between these two classes. We integrate these discriminative weights with Bag of Word encoding, which we then call Bag-of-Discriminant-Words (BoDW). We sample the untrimmed videos into non-overlapping snippets and temporally aggregate the BoDW representation of multiple snippets into action proposals. We present the e ectiveness of TAB proposal method on two challenging temporal action detection datasets: MSR-II and Thumos14, where it improves upon state-ofthe- art methods. \Proposal + classi cation", requires multiple passes through testing data for these two stages, therefore, it is di cult to use these methods in an end-to-end manner. To solve this problem, we propose an end-to-end temporal action detection method known as Bag of Discriminant Snippets (BoDS). BoDS is based on the observation that multiple actions and the background classes have similar snippets, which cause incorrect classi cation of action regions and imprecise boundaries. We solve this issue bynding the key-snippets from the training data of each class and compute their discriminative power which is used in BoDS encoding. During testing of an untrimmed video, wend the BoDS representation for multiple candidate regions andnd their class label based on a majority voting scheme. We test BoDS on the Thumos14 and ActivityNet datasets and obtain state-of-the-art results.