Abstract Domain name generation algorithm (DGA) classification is an essential but challenging problem. Both feature-extracting machine learning (ML) methods and deep learning (DL) models such as convolutional neural networks and long short-term memory have been developed. However, the performance of these approaches varies with different types of DGAs. Most features in the ML methods can characterize random-looking DGAs better than word-looking DGAs. To improve the classification performance on word-looking DGAs, subword tokenization is employed for the DL models. Our experimental results proved that the subword tokenization can provide excellent classification performance on the word-looking DGAs. We then propose an integrated scheme that chooses an appropriate method for DGA classification depending on the nature of the DGAs. Results show that the integrated scheme outperformed existing ML and DL methods, and also the subword DL methods.

Video cameras have been widely deployed for surveillance and smart city. But the authenticity of a video scene faces a great challenge due to the ease of tampering with video data without leaving visible traces. Unfortunately, existing authentication schemes are not efficient for emerging high-resolution videos. In this article, we propose stateful correlation coefficient sampling-based hash (Stateful-CCSH), which adopts correlation coefficient sampling in image hash and learns from previous frames to sample those blocks with more dynamic contents and thus, more likely to be tampered in a video forgery. To decrease the impact of false detection introduced by compression and sampling, we also propose a group smoothing-based authentication scheme. The experimental results show that Stateful-CCSH not only achieves excellent performance in forgery detection, particularly, the detection of moving object removal, but also saves significant computation and communication costs even when the video resolutions are high.

Due to the advancement of high‐throughput sequencing technologies, it is now feasible for sequencing individual genomes in a fast and affordable manner. With the significant increase in the number of individual genomes, compression methods are needed to reduce pressure on data storage as well as enable effective data distribution and management. The compression methods can generally be divided into two classes, namely reference‐free methods and reference‐based methods. In reference‐free methods, redundancies within the target DNA sequence to be compressed are explored. In reference‐based methods, redundancies between the target DNA sequence and other reference sequences are identified to achieve compression. This type of method is applicable to population sequences which are highly similar to each other and have a small number of mismatches. Some of the methods can also be applied to partially similar sequences such as chromosome sequences or sequences having evolutionary relationship. The authors highlight recent developments in these methods. In the comparative study, the authors’ simulation results reveal that the selection of a reference sequence is a crucial factor affecting the compression performance. Use of multiple number of reference sequences and enhancement strategies such as reference rewriting are important to achieve a large compression gain.

Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based Compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, k-means clustering is employed to partition sequences into clusters for better compression. A reference sequence is then constructed for each cluster so that sequences in that cluster can be compressed by referring to this reference sequence. The reference sequence of each cluster is also compressed with reference to a sequence which is derived from all the reference sequences. Experiments show that RCC can further reduce the compressed size by up to 91.0 percent when compared with state-of-the-art compression approaches. There is a compromise between compressed size and processing time. The current implementation in Matlab has time complexity in a factor of thousands higher than the existing algorithms implemented in C/C++. Further investigation is required to improve processing time in future.

Traditionally, intra-sequence similarity is exploited for compressing a single DNA sequence. Recently, remarkable compression performance of individual DNA sequence from the same population is achieved by encoding its difference with a nearly identical reference sequence. Nevertheless, there is lack of general algorithms that also allow less similar reference sequences. In this work, we extend the intra-sequence to the inter-sequence similarity in that approximate matches of subsequences are found between the DNA sequence and a set of reference sequences. Hence, a set of nearly identical DNA sequences from the same population or a set of partially similar DNA sequences like chromosome sequences and DNA sequences of related species can be compressed together. For practical compressors, the compressed size is usually influenced by the compression order of sequences. Fast search algorithms for the optimal compression order are thus developed for multiple sequences compression. Experimental results on artificial and real datasets demonstrate that our proposed multiple sequences compression methods with fast compression order search are able to achieve good compression performance under different levels of similarity in the multiple DNA sequences.

In this paper, we propose a two-dimensional digital camera identification method based on the photo-response non-uniformity (PRNU). The traditional identification method is based on a correlation estimator which calculates the correlation between the reference PRNU and the PRNU extracted from the testing image. However, the correlation calculated greatly depends on the image content. To reduce the image content effect in classification, a correlation predictor is trained based on different types of image features. By using the predicted correlation and the actual correlation, a 2D classifier using support vector machine is proposed in this paper. Experimental results show that the proposed method can have a more flexible threshold setting which gives a better identification results as compared to the traditional identification method.

Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techniques and how they utilize local or global information from within the data, or their use of domain knowledge during imputation. In addition, we describe how the imputation results can be validated and the different ways to assess the performance of different imputation algorithms, as well as a discussion on some possible future research directions. It is hoped that this review will give the readers a good understanding of the current development in this field and inspire them to come up with the next generation of imputation algorithms.

In this paper, a robust background extraction and novel object detection are proposed, which comprise of filtering operations to detect non background objects in a monitoring scene. Conventionally, a statistical background model is extracted by using a training sequence without foreground objects and the background model parameters are being updated continuously to adapt changes in the scene. However, it is not possible to require a monitoring scene to be static. Furthermore, static objects in the scene could be adapted into the background. Problems arise when static objects start to move again. The convention method would produce false alarms in the detection process. In our proposed algorithm, two background models are constructed by using N-bins histogram method to indicate short term and long term changes of the monitoring scene. We then apply background subtractions to the current frame to obtain two error frames, which are combined for objects detection and classification. Extensive experimental work has been done, results of which show that the present approach provides a better solution compared with the conventional approach, including to resolve the problem of re-active objects.

In this paper, we propose an efficient and low complexity image coding algorithm based on the lifting wavelet transform and listless modified SPIHT (LWT-LMSPIHT). LWT-LMSPIHT jointly considers the advantages of progressive transmission and spatial scalability that were not fully provided by the SPIHT algorithm, thus it outperforms the SPIHT at low bit rates coding. The coding efficiency of LWT-LMSPIHT comes from three aspects. The lifting scheme lowers the number of arithmetic operations of the wavelet transform. Moreover, a significance reordering of the modified SPIHT ensures that it codes more significant information earlier in the bit stream belonging to the lower frequency bands than SPIHT to better exploit the energy compaction of the wavelet coefficients. Finally, a listless structure further reduces the amount of memory and improves the speed of compression by more than 47% for a 512×512 image, as compared with the SPIHT algorithm.

BACKGROUND: The DNA microarray technology allows the measurement of expression levels of thousands of genes under tens/hundreds of different conditions. In microarray data, genes with similar functions usually co-express under certain conditions only 1. Thus, biclustering which clusters genes and conditions simultaneously is preferred over the traditional clustering technique in discovering these coherent genes. Various biclustering algorithms have been developed using different bicluster formulations. Unfortunately, many useful formulations result in NP-complete problems. In this article, we investigate an efficient method for identifying a popular type of biclusters called additive model. Furthermore, parallel coordinate (PC) plots are used for bicluster visualization and analysis. RESULTS: We develop a novel and efficient biclustering algorithm which can be regarded as a greedy version of an existing algorithm known as pCluster algorithm. By relaxing the constraint in homogeneity, the proposed algorithm has polynomial-time complexity in the worst case instead of exponential-time complexity as in the pCluster algorithm. Experiments on artificial datasets verify that our algorithm can identify both additive-related and multiplicative-related biclusters in the presence of overlap and noise. Biologically significant biclusters have been validated on the yeast cell-cycle expression dataset using Gene Ontology annotations. Comparative study shows that the proposed approach outperforms several existing biclustering algorithms. We also provide an interactive exploratory tool based on PC plot visualization for determining the parameters of our biclustering algorithm. CONCLUSION: We have proposed a novel biclustering algorithm which works with PC plots for an interactive exploratory analysis of gene expression data. Experiments show that the biclustering algorithm is efficient and is capable of detecting co-regulated genes. The interactive analysis enables an optimum parameter determination in the biclustering algorithm so as to achieve the best result. In future, we will modify the proposed algorithm for other bicluster models such as the coherent evolution model.

UNLABELLED: BiVisu is an open-source software tool for detecting and visualizing biclusters embedded in a gene expression matrix. Through the use of appropriate coherence relations, BiVisu can detect constant, constant-row, constant-column, additive-related as well as multiplicative-related biclusters. The biclustering results are then visualized under a 2D setting for easy inspection. In particular, parallel coordinate (PC) plots for each bicluster are displayed, from which objective and subjective cluster quality evaluation can be performed. AVAILABILITY: BiVisu has been developed in Matlab and is available at http://www.eie.polyu.edu.hk/~nflaw/Biclustering/.

The multiscale directional filter bank (MDFB) improves the radial frequency resolution of the contourlet transform by introducing an additional decomposition in the high-frequency band. The increase in frequency resolution is particularly useful for texture description because of the quasi-periodic property of textures. However, the MDFB needs an extra set of scale and directional decomposition, which is performed on the full image size. The rise in computational complexity is, thus, prominent. In this paper, we develop an efficient implementation framework for the MDFB. In the new framework, directional decomposition on the first two scales is performed prior to the scale decomposition. This allows sharing of directional decomposition among the two scales and, hence, reduces the computational complexity significantly. Based on this framework, two fast implementations of the MDFB are proposed. The first one can maintain the same flexibility in directional selectivity in the first two scales while the other has the same redundancy ratio as the contourlet transform. Experimental results show that the first and the second schemes can reduce the computational time by 33.3%-34.6% and 37.1%-37.5%, respectively, compared to the original MDFB algorithm. Meanwhile, the texture retrieval performance of the proposed algorithms is more or less the same as the original MDFB approach which outperforms the steerable pyramid and the contourlet transform approaches.

In this paper, we propose to use co-occurrence features computed from multi-scale directional filter bank (MDFB) for texture characterization. As the filter band coefficients are localized frequency components, features from co-occurrence matrices of filter bands can characterize structures of textures by describing correlation among coefficients. Our experiments show that the co-occurrence features outperform energy features considerably in texture retrieval. In particular, they significantly improve the retrieval rate for textures with weak directionality and periodicity while still maintains a high retrieval rate for regular textures as the energy features

Images are often compressed using JPEG or JPEG2000. Many retrieval systems operated in either uncompressed or compressed domains have been proposed. However, retrieving in multiple domains typically involves full decompression for feature analysis in spatial domain. Common features in different domains are thus worth of investigation for direct image indexing. By employing a common subband filtering model, outputs from JPEG and JPEG2000 can be compared directly without having a full decompression. Despite of high compression, similar translation and rotation invariant features can be extracted from these two domains. Simulation results reveal that JPEG and JPEG2000 compressed images can be searched from one another irrespective of the compression ratio. Our experimental studies confirm that retrieval in multiple domains is possible without a full decompression.

It is well known that low bit rate block-based discrete cosine transform coded image exhibits visually annoying coding artifacts. In this paper, we proposed a projection onto convex sets (PCOS)-based deblocking algorithm using a novel region smoothness constraint set for graphic images containing objects with smooth regions. The smoothness constraint set is obtained by an explicit modeling of smooth regions in the image using a spatially adaptive thin-plate spline. In contrast to most deblocking algorithms which enforce smoothness just around the 8/spl times/8 block boundaries, our algorithm enforces smoothness in regions which could possibly span several blocks. We showed that convergence of our algorithm could be reached within one iteration. The performance of the proposed algorithm is evaluated visually and quantitatively in term of peak signal-to-noise ratios and the mean squared difference of slope metric, which measures the impact of the blocking effects, for several graphic images. The results show that our algorithm can effectively suppress blockiness in smooth regions while still preserving the sharpness of object edges.

An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.

We studied the computational complexity of the over-complete wavelet representation for the commonly used Spline wavelet family with an arbitrary order. By deriving a general expression for the complexity, it is shown that the inverse transform is significantly more costly in computation than the forward transform. In order to reduce the computational complexity, a new spatial implementation is proposed. This new implementation exploits the redundancy between the lowpass and the bandpass outputs that is inherent to the over-complete wavelet scheme. It is shown that the new implementation can greatly simplify the computations, give an efficient inverse structure and allow the use of an arbitrary boundary extension method without affecting the ease of the inverse transform.

A new architecture of video combiner for multipoint video conferencing is proposed. The proposed video combiner is wavelet-based which extracts the motion activities information from the video bitstreams produced by a wavelet-based video coder. Using the progressive properties of wavelet transform, the encoded bitstream become scalable. Hence, the video quality of inactive sub-sequences can be easily adjusted in the video combiner by discarding the fine detail information bitstreams. In other words, more bits can be reallocated to the active sub-sequences to achieve a good visual quality with smooth motion. In addition, the video coder is region-based so that different wavelet kernels can be used for the foreground and the background. This setting can on one hand reduce the computational complexity significantly. On the other hand, by considering the unequal importance of various regions, a high video quality in foreground can always be guaranteed and an acceptable quality in background can be maintained even under low bitrate environments. Since the video combiner only needs to rearrange the video quality level according to their motion activities, no re-encoding process is required. Therefore, a significant computational complexity saving can be achieved as compared to the conventional video combiner using a transcoding approach. The new video combiner is then used to realize a multipoint video conferencing and some results are presented to show the improvement in performance due to our proposed architecture.

We have studied the computational complexity of the over-complete wavelet representation for the commonly used spline wavelet family with an arbitrary order. By deriving a general expression for the complexity, it is shown that the inverse transform is nearly three times more costly in computation than the forward transform. In order to reduce the computational complexity, a new spatial implementation is proposed. This new implementation is based on the exploitation of redundancy between the lowpass and the bandpass outputs that is inherent to the over-complete wavelet scheme. It is shown that the new implementation can greatly simplify computation and give an efficient inverse structure.

Wavelet decomposition has recently been generalized to the binary field in which the arithmetic is performed wholly in GF(2). In order to maintain an invertible binary wavelet transform with desirable multiresolution properties, the bandwidth, the perfect reconstruction and the vanishing moment constraints are placed on the binary filters. While they guarantee an invertible transform, the transform becomes non-orthogonal and non-biorthogonal in which the inverse filters could be signal-length dependent. We propose to apply the perpendicular constraint on the binary filters to make them length-independent. A filter design strategy is outlined in which a filter design for a length of eight is given. We also propose an efficient implementation structure for the binary filters that saves memory space and reduces the computational complexity.

The lifting scheme for the real field wavelet transform has provided a new insight into its practical implementation. This paper shows that a similar scheme can be developed for the binary field wavelet transform. In particular, by using the Euclidean algorithm, the binary filters can be decomposed into a finite sequence of simple lifting steps over the binary field. This provides an alternative method for the implementations of the binary field wavelet transform for image processing applications. It is found that the new implementation can reduce the number of arithmetic operations involved in the transform and allow an efficient in-place implementation structure.

A novel scalable coding scheme is proposed in which wavelet transform is used to induce a multiple resolution framework for progressive coding. The proposed algorithm corresponds well to human visual characteristics. In particular, our proposed algorithm would display visually important information, such as edges first with visually not-so-important information displayed progressively. A common problem with edge-based coding is the high bit rare. We propose a way to make a compromise between the high bit rate and the skeleton coding idea. As simulation results show, the shape of an image can be recognized with only one or two decoded skeleton frames, i.e., bit rates under 0.05. This is important for image browsing application as images can be recognized with a low bit rate. Also, the accurate coding of edges facilitates the motion estimation in video coding. Therefore, the proposed algorithm has great potential in both image and video coding.

A non-iterative wavelet-based algorithm was proposed to reduce the ringing artifacts associated with a lossy compressed image. The proposed algorithm is based on the fact that an increase in the magnitude of the quantized wavelet coefficients leads to a decrease in visual smoothness. Thus a shrinkage algorithm is applied to maintain visual smoothness. The proposed algorithm is, however, adaptive in nature and the amount of shrinkage depends on edge strength, region activity, and compression ratio. Experimental results have confirmed that the adaptive algorithm could suppress the ringing artifacts and improve visual smoothness, especially around edges where ringing is severe.