Abstract—Early detection of malignant melanoma skin cancer is crucial for treating the disease and saving lives. Many computerized techniques have been reported in the literature to diagnose and classify the disease with satisfactory skin cancer detection performance. However, reducing the false detection rate is still challenging and preoccupying because false positives trigger the alarm and require intervention by an expert pathologist for further examination and screening. In this paper, an automatic skin cancer diagno- sis system that combines different textural and color features is proposed. New textural and color features are used in a bag-of-features approach for efficient and accurate detection. We particularly claim that the Histogram of Gradients (HG) and the Histogram of Lines (HL) are more suitable for the analysis and clas- sification of dermoscopic and standard skin images than the conventional Histogram of Oriented Gradient (HOG) and the Histogram of Oriented Lines (HOL), respectively. The HG and HL are bagged separately us- ing a codebook for each and then combined with other bagged color vector angles and Zernike moments to exploit the color information. The overall system has been assessed through intensive experiments using different classifiers on a dermoscopic image dataset and another standard dataset. Experimental results have shown the superiority of the proposed system over state-of-the-art techniques.

Optimistic concurrency control is widely studied in the literature due to the properties of non-blocking and deadlock free execution especially in the domain of real-time systems. In this paper we review the substantial research of optimistic concurrency control protocols. We characterize them into four categories and explore their properties. Then we investigate the general concepts and properties related to Optimistic concurrency control Finally, we demonstrate a comparison table between the varieties of these

protocols.

The current paper proposes a novel scheme for non-blind watermarking of images, making use of discrete wavelet transform (DWT), discrete time Fourier transform (DTFT), as well as singular value decomposition, or SVD. During the process of embedding, 1-level DWT is used to decompose the host image into its various frequency sub-bands. After this, the high-frequency sub band receives an application of DTFT. This is followed then by SVD, after which the watermark becomes embedded into the now-transformed host image's singular matrix. Then, the inverses of 1-level DWT, DTFT and SVD are applied in order to obtain a watermarked final image. This paper evaluates the performance of the proposed method of watermarking against a number of attacks, including sharpening, salt and pepper noise, AWGN, gamma correction, histogram equalisation, flipping and cropping. Results obtained during experiments have found that the scheme as proposed does provide high levels of robustness and imperceptibility against various signal processing attacks.

This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

Abstract—With a rapidly increasing incidence of melanoma

skin cancer, there is a need for decision support systems to

detect it in its early stages, which would lead to better decisions

in treating it successfully. However, developing such systems is

still a challenging task for researchers. Several Computer Aided-

Diagnosis (CAD) systems have been proposed in the last two

decades to increase the accuracy of melanoma detection. Image

feature extraction is a critical step in differentiating between

melanoma and normal skin lesions. In this paper, we propose

to improve a bag-of-words approach by combining features

consisting of the color histogram and first order moments with the

Histogram of Oriented Gradients (HOG). Experimental results

show that the proposed technique significantly improves the

detection accuracy, with an average sensitivity of 91% and

specificity of 85%. The proposed system was validated on a

dataset of 200 medically annotated images (40 melanomas and

160 non-melanomas) obtained from the database of the Hospital

Pedro Hispano. 

This paper presents a novel secure and robust steganographic technique in the compressed video domain namely embedding-based byte differencing (EBBD). Unlike most of the current video steganographic techniques which take into account only the intra frames for data embedding, the proposed EBBD technique aims to hide information in both intra and inter frames. The information is embedded into a compressed video by simultaneously manipulating the quantized AC coefficients (AC-QTCs) of luminance components of the frames during MPEG-2 encoding process. Later, during the decoding process, the embedded information can be detected and extracted completely. Furthermore, the EBBD basically deals with two security concepts: data encryption and data concealing. Hence, during the embedding process, secret data is encrypted using the simplified data encryption standard (S-DES) algorithm to provide better security to the implemented system. The security of the method lies in selecting candidate AC-QTCs within each non-overlapping 8 × 8 sub-block using a pseudo random key. Basic performance of this steganographic technique verified through experiments on various existing MPEG-2 encoded videos over a wide range of embedded payload rates. Overall, the experimental results verify the excellent performance of the proposed EBBD with a better trade-off in terms of imperceptibility and payload, as compared with previous techniques while at the same time ensuring minimal bitrate increase and negligible degradation of PSNR values.

Recent developments in both information and communication security have heightened interest in enhancing the embedding capacity for data handling techniques. Although many steganographic techniques, in the literature, have been developed for this purpose, most of them distort the quality of the host-signal during data embedding and the changes will be become visible to the human eye especially for those signals distributed via the Internet which must be processed by a low bit rate compression due to bandwidth limitations. Therefore, the challenge is to create a steganographic technique that is able to hide acceptable amount of data without altering the quality of the host-signal. In this paper, pixel-value differencing (PVD) steganographic scheme and its two modified versions, namely, enhanced pixel-value differencing (EPVD) and tri-way pixel-value differencing (TPVD) were implemented, analyzed and compared in terms of invisibility, fidelity and impact of data hiding on the compression efficiency. Experimental results indicate that the EPVD scheme is capable of providing better performance than other compared schemes.

Abstract Mel Frequency Cepstral Coefficients (MFCCs) are the most widely used features in the majority of the speaker and speech recognition applications. Since 1980s, remarkable efforts have been undertaken for the development of these features. Issues such as use suitable spectral estimation methods, design of effective filter banks, and the number of chosen features all play an important role in the performance and robustness of the speech recognition systems. This paper provides an overview of MFCC's enhancement techniques that are applied in speech recognition systems. The details such as accuracy, types of environments, the nature of data, and the number of features are investigated and summarized in the table combined with the corresponding key references. Benefits and drawbacks of these MFCC's enhancement techniques have been discussed. This study will hopefully contribute to raising …

Abstract— Diagnosing skin cancer in its early stages is a challenging task for dermatologists given the fact that the chance for a patient’s survival is higher and hence the process of analyzing skin images and making decisions should be time efficient. Therefore, diagnosing the disease using automated and computerized systems has nowadays become essential. This paper proposes an efficient system for skin cancer detection on dermoscopic images. It has been shown that the statistical characteristics of the pigment network, extracted from the dermoscopic image, could be used as efficient discriminating features for cancer detection. The proposed system has been assessed on a dataset of 200 dermoscopic images of the ‘Hospital Pedro Hispano’ [1] and the results of cross-validation have shown high detection accuracy.

With the rapid development of the Internet and communication networks, the confidentiality of digital

images transmitted over public networks must be preserved by using encryption techniques. Advanced

Encryption Standard (AES) is one of the most commonly used encryption algorithms at present. Although

AES has several advantages, such as security in data encryption, it also has a number of drawbacks, such as

high computations, pattern problems when used for encrypting images, and fixed S-box weak points. This

paper proposes a method that overcomes the fixed S-box weak points and improves the performance of

AES when used for encrypting images, particularly when the image data are large. In addition, the

MixColumn stage is replaced by chaotic mapping and XOR operation to reduce the high computations in

MixColumn transform. The proposed method is tested on several images, and the results show that the

proposed method efficiently generated cipher images with very low correlation coefficients of adjacent

pixels and provided better encryption speed and high security as a result of the dependence of the S-box on

the key characteristics of the chaotic system