G. M. Mostafa

Robust video steganography algorithm using adaptive skin-tone detection

Human skin regions have recently drawn attention in the literature of data hiding due to its promising robustness characteristics. In this paper, we propose a blind adaptive data hiding algorithm for video files where human skin regions are regarded as the Regions Of Interest (ROI) hosting the embedding process. A skin map is created for each frame using an adaptive skin detection algorithm with reduced number of false positives. Then the skin map is converted to a skin-block-map in order to eliminate the error-prone skin pixels that can result in inefficient retrieval of the hidden data. Moreover, the embedding process is done using a wavelet quantization technique over the red and blue channels of the host frames for increased robustness. Experimental results showed the high imperceptibility of the proposed method as well as its robustness against MPEG-4 compression.

A novel social network mining approach for customer segmentation and viral marketing

Emergence of social networks facilitates individuals to communicate, share opinions and form communities. Organizations benefit from social networks in monitoring customers’ behavior. Social networks mining and analysis aims to segment customers and determine the most influential actors for viral marketing. In this article, we propose a novel social network mining approach for influential analysis and community detection. The community detection task benefits from the most influential users in the network. The proposed approach identifies the most influential users by using a direct mining leaders discovery algorithm and uses these leaders as core points to expand communities around them. This is based on the observation that communities tend to be formed around users of great influence. Extensive experiments have been completed on a real dataset and results show that our approach can contribute in identifying communities of high quality. WIREs Data Mining Knowl Discov 2016, 6:177–189. doi: 10.1002/widm.1183

English-Arabic Statistical Machine Translation: State of the Art

This paper presents state of the art of the statistical methods that enhance English to Arabic (En-Ar) Machine Translation (MT). First, the paper introduces a brief history of the machine translation by clarifying the obstacles it faced; as exploring the history shows that research can develop new ideas. Second, the paper discusses the Statistical Machine Translation (SMT) method as an effective state of the art in the MT field. Moreover, it presents the SMT pipeline in brief and explores the En-Ar MT enhancements that have been applied by processing both sides of the parallel corpus before, after and within the pipeline. The paper explores Arabic linguistic challenges in MT such as: orthographic, morphological and syntactical issues. The purpose of surveying only En-Ar translation direction in the SMT is to help transferring the knowledge and science to the Arabic language and spreading the information to all who are interested in the Arabic language.

Securing the Digital Script of the Holy Quran on the Internet

With the explosion of the world wide web (WWW), the overwhelming number of web pages that contain electronic versions of the Holy Quran script is rapidly increasing with a very fast pace. This leads to the vulnerability of the electronic version of Holy Quran script to deliberate or accidental mistakes or changes. None of the sites on the internet that presents the Holy Quran script shows any verification of the displayed version from a well-known authentication agency. In this paper, a computer system based on the public key infrastructure (PKI) and the digital signature is presented to secure and verify the content of Holy Quran script on the web. The implementation of the proposed system shows its applicability.

An unsupervised method for face photo-sketch synthesis and recognition

Face recognition is considered one of the most essential applications of Biometrics for personal identification. Face sketch recognition is a special case of face recognition, and it is very important for forensic applications. In this paper, we propose an unsupervised method for face photo-sketch recognition by synthesizing a pseudo-sketch from a single photo. The proposed method is the first unsupervised method that deals with face sketch recognition. The proposed photo-sketch synthesis step consists of two main steps, namely: edge detection and hair detection, which are applied on the grayscale image of the photo image. In the recognition step, the artist sketch is compared with the generated pseudo-sketch. PCA and LDA are used to extract features from the sketch images. The k-nearest neighbor classifier with Euclidean distance is used in the classification step. We use the CUHK database to test the performance of the proposed Method. Results for the synthesized sketches are compared with state-of-the-art methods, e.g., Local Linear Embedding (LLE) and Eigen transformation. The experimental results show that the proposed method generates a clear synthesis sketch and it defines persons more accurate than other methods. Moreover, in the recognition step, the proposed method achieves a recognition rate at the 1-nearest neighbor (rank1: first-match) range from 82% with PCA to 94% with LDA. The highest recognition rate is obtained at the 5-nearest neighbor (rank 5) is 98% that is better than some of the state-of-the-art methods.

An efficient framework based on usage and semantic data for next page prediction

The World Wide Web is becoming the most important source to search for information or products. But the size and the unstructured nature of the available information makes the location of the right information a challenging task. Recommender systems and web usage mining techniques are two of the main methods used to overcome information overload. In this paper, we present a framework for the next page prediction that exploits users’ access history combined with his semantic interests to generate personalized and accurate recommendations. We are suggesting two different approaches for decision fusion between usage and semantic data. The two proposed techniques offered a 47.3% and 54.3% improvement in prediction accuracy over conventional methods for next page prediction. The suggested framework also employs user clustering to focus the search which reduced the prediction time by an average of 68.7% and 63.4%.

A modified cutoff scanning matrix protein representation for enhancing protein function prediction

Protein function prediction is an active research area in bioinformatics. Protein functions are highly related to their structures. Therefore, effective structure based protein representations are required. Pires et al. [BMC Genomics, 12, S12 (2011)] proposed a cutoff scanning matrix (CSM) method for protein representation that utilizes distance patterns between protein residues and a maximum cutoff. This paper proposes a modified cutoff scanning matrix (MCSM) representation for enhancing protein function prediction. The proposed representation considers the whole protein instead of using cutoff. A comparative analysis was done to evaluate the proposed MCSM method and the original CSM method. Two different classification algorithms, Random Forest and K-nearest neighbor (KNN), were used in the analysis. The aspect of protein function considered is based on enzyme activity. The results show that the proposed MCSM representation outperforms the CSM representation with a prediction accuracy of 90.12% and 80.27% for superfamily and family level, respectively, with accuracy improvement of about 5 % on average.

A new protein structure representation for efficient protein function prediction.

One of the challenging problems in bioinformatics is the prediction of protein function. Protein function is the main key that can be used to classify different proteins. Protein function can be inferred experimentally with very small throughput or computationally with very high throughput. Computational methods are sequence based or structure based. Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient protein function prediction. The representation is based on three-dimensional patterns of protein residues. In the analysis, we used protein function based on enzyme activity through six mechanistically diverse enzyme superfamilies: amidohydrolase, crotonase, haloacid dehalogenase, isoprenoid synthase type I, and vicinal oxygen chelate. We applied three different classification methods, naïve Bayes, k-nearest neighbors, and random forest, to predict the enzyme superfamily of a given protein. The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns. The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average.

A Generic, Scalable and Fine-Grained Data Access System for Sharing Digital Objects in Honest but Curious Cloud Environments

This paper presents a generic, scalable and fine-grained data access system that realizes the main challenges which hinder the growth of using storage-as-a-service for sharing digital objects offered by honest but curious cloud environments. These main challenges are maintaining data confidentiality, enforcing fine-grained data access control, applying efficient user revocation mechanism, preventing the collusion between users to access unauthorized digital objects, achieving scalability and possessing generic construction desirable feature. In addition, the proposed system avails digital passport which is presented by the user to be granted access to any digital object in the cloud environment. The usage of digital passport minimizes the number of transactions needed to authenticate the specified user. Moreover, the digital passport simplifies the data management for users since the user has to keep his passport only to use it to access the cloud. Furthermore, the digital passport prevents a rejoined user who possesses different attributes to access his previously authorized data. Additionally, the digital passport prohibits the collusion between an authorized user and a revoked one to own the access privileges once assigned to the revoked user. The proposed system exploits public key infrastructure (PKI) to capitalize the usage of offline operations to enhance system performance and to secure the transmission of private data as well as defending man in the middle attack. It should be noted that the implementation of the proposed system has showed the system computational validity.

Leader-based community detection algorithm for social networks

Community detection has become a crucial task in social network mining. Detecting communities summarizes interactions between members for gaining deep understanding of interesting characteristics shared between members of the same community. In this research, we propose a novel community detection algorithm for the purpose of revealing and analyzing hidden similar behavior of online users. The proposed algorithm is based mainly on similar members’ actions rather than the structure similarity only for the aim of detecting communities that are closely mapped to the underlying behavioral communities in real social networks. First, leaders of the social network are discovered, then, communities are detected based on those leaders. The idea is grounded on the assumption that communities could be formed around people with great influence. Extensive experiments and analysis show the ability of the proposed algorithm to successfully detect real‐world communities with improved accuracy. WIREs Data Mining Knowl Discov 2017, 7:e1213. doi: 10.1002/widm.1213