Search

Due to the rapid developments in technology and the sudden expansion of social media use, Dialect Arabic has become an important source of data that needs to be addressed when building Arabic corpora. In this paper, thirty-three Arabic corpora are surveyed to show that despite all of the developments in the literature, Saudi dialect (SD) corpora still need further expansion. This paper contributes to the literature on SD corpora by creating the largest Saudi corpus – the King Saud University Saudi Corpus (KSUSC) – with +1B total words, including +119M SD words. The KSUSC not only is the newest and largest SD corpus but is also diverse, covering 26 domains in text collected from five different sources. This paper also contributes to the literature by developing a new incremental preprocessing system that is used to create relevant lexicons that are then used to clean and normalize the collected data. This incremental system is scalable and can be adapted for different resources and dialects. Moreover, the collection process for building the KSUSC is discussed in detail, and the challenges in collecting SD text with respect to each platform are highlighted. By the end of this paper, different design criteria are proposed and used with the KSUSC to conclude that the resulting corpus can be of great benefit to researchers who are interested in integrating the corpus with their own work or using its resulting lexicons with Saudi-based NLP tasks.

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.

In recent years, and with the presence of many efficient image processing tools, digital image forgery has become a serious social issue. Copy-move forgery is one of the most widely used methods for image forgeries in which a part of the image is copied and then pasted to another location in the same image. This procedure is usually used to add or cover a critical part of the image. In this paper, we propose a new fast and accurate algorithm for copy-move forgery detection in digital images. In the proposed algorithm, the image to analyze is segmented into overlapping square blocks with a predefined side length, each one of the blocks is split into equally spaced k sub-blocks. The sum of pixel intensities of each sub-block is used to form a k-dimensional vector with the help of sliding window and such vector is used as a feature for each block. The resulting features of all blocks are stored in a KD-tree. The block corresponding to each node in the KD-tree is checked with the block corresponding to the nearest neighbor of this node. If the correlation between such blocks is above a prespecified threshold, the two blocks are considered as clones. Experimental results and comparisons with a state of the art method show that the proposed algorithm is fast and accurate.

In this paper, we consider the square jigsaw puzzle problem in which one is required to reassemble the complete image from a number of unordered square puzzle pieces. Here we focus on the special case where both location and orientation of each piece are unknown. We propose a new automatic solver for such problem without assuming prior knowledge about the original image or its dimensions. We use an accelerated edge matching based greedy method with combined compatibility measures to provide fast performance while maintaining robust results. Complexity analysis and experimental results reveal that the new solver is fast and efficient.

Template matching is one of the most basic techniques in computer vision, where the algorithm should search for a template image T in an image to analyze I. This paper considers the rotation, scale, brightness and contrast invariant grayscale template matching problem. The proposed algorithm uses a sufficient condition for distinguishing between candidate matching positions and other positions that cannot provide a better degree of match with respect to the current best candidate. Such condition is used to significantly accelerate the search process by skipping unsuitable search locations without sacrificing exhaustive accuracy. Our proposed algorithm is compared with eight existing state-of-the-art techniques. Theoretical analysis and experiments on eight image datasets show that the proposed simple algorithm can maintain exhaustive accuracy while providing a significant speedup.