Search

Numerous image authentication techniques have been devised to address the potential security issue of malicious tampering with image content since digital images can be easily duplicated, modified, transformed and diffused via the Internet transmission. However, the existing works still remain many shortcomings in terms of the recovery incapability and detection accuracy with extensive tampering. To improve the performance of tamper detection and image recovery, we present a block mapping and dual-matrix-based watermarking scheme for image authentication with self-recovery capability in this paper. The to-be-embedded watermark information is composed of the authentication data and recovery data. The Authentication Feature Composition Calculation algorithm is proposed to generate the authentication data for image tamper detection and localization. Furthermore, the recovery data for tampered region recovery is comprised of self-recovery bits and mapped-recovery bits. The Set Partition in Hierarchical Trees encoding algorithm is applied to obtain the self-recovery bits, whereas the Rehashing Model-based Block Mapping algorithm is proposed to obtain the mapped-recovery bits for retrieving the damaged codes caused by tampering. Subsequently, the watermark information is embedded into the original image as digital watermarking with the guidance of a dual-matrix. The experimental results demonstrate that comparing with other state-of-the-art works, our proposed scheme not only improves the performance in recovery, but also extends the limitation of tampering rate up to 90%. Furthermore, it obtains a desirable image quality above 40 dB, large watermark payload up to 3.169 bpp, and the effective resistance to malicious attack, such as copy-move and collage attacks., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A key problem in understanding transcriptional regulatory networks is deciphering what cis regulatory logic is encoded in gene promoter sequences and how this sequence information maps to expression. A typical computational approach to this problem involves clustering genes by their expression profiles and then searching for overrepresented motifs in the promoter sequences of genes in a cluster. However, genes with similar expression profiles may be controlled by distinct regulatory programs. Moreover, if many gene expression profiles in a data set are highly correlated, as in the case of whole organism developmental time series, it may be difficult to resolve fine-grained clusters in the first place. We present a predictive framework for modeling the natural flow of information, from promoter sequence to expression, to learn cis regulatory motifs and characterize gene expression patterns in developmental time courses. We introduce a cluster-free algorithm based on a graph-regularized version of partial least squares (PLS) regression to learn sequence patterns--represented by graphs of k-mers, or "graph-mers"--that predict gene expression trajectories. Applying the approach to wildtype germline development in Caenorhabditis elegans, we found that the first and second latent PLS factors mapped to expression profiles for oocyte and sperm genes, respectively. We extracted both known and novel motifs from the graph-mers associated to these germline-specific patterns, including novel CG-rich motifs specific to oocyte genes. We found evidence supporting the functional relevance of these putative regulatory elements through analysis of positional bias, motif conservation and in situ gene expression. This study demonstrates that our regression model can learn biologically meaningful latent structure and identify potentially functional motifs from subtle developmental time course expression data.

Inferring gene regulatory networks from high-throughput genomic data is one of the central problems in computational biology. In this paper, we describe a predictive modeling approach for studying regulatory networks, based on a machine learning algorithm called MEDUSA. MEDUSA integrates promoter sequence, mRNA expression, and transcription factor occupancy data to learn gene regulatory programs that predict the differential expression of target genes. Instead of using clustering or correlation of expression profiles to infer regulatory relationships, MEDUSA determines condition-specific regulators and discovers regulatory motifs that mediate the regulation of target genes. In this way, MEDUSA meaningfully models biological mechanisms of transcriptional regulation. MEDUSA solves the problem of predicting the differential (up/down) expression of target genes by using boosting, a technique from statistical learning, which helps to avoid overfitting as the algorithm searches through the high-dimensional space of potential regulators and sequence motifs. Experimental results demonstrate that MEDUSA achieves high prediction accuracy on held-out experiments (test data), that is, data not seen in training. We also present context-specific analysis of MEDUSA regulatory programs for DNA damage and hypoxia, demonstrating that MEDUSA identifies key regulators and motifs in these processes. A central challenge in the field is the difficulty of validating reverse-engineered networks in the absence of a gold standard. Our approach of learning regulatory programs provides at least a partial solution for the problem: MEDUSA's prediction accuracy on held-out data gives a concrete and statistically sound way to validate how well the algorithm performs. With MEDUSA, statistical validation becomes a prerequisite for hypothesis generation and network building rather than a secondary consideration.

An accurate map matching is an essential but difficult step in mapping raw float car trajectories onto a digital road network. This task is challenging because of the unavoidable positioning errors of GPS devices and the complexity of the road network structure. Aiming to address these problems, in this study, we focus on three improvements over the existing hidden Markov model: (i) The direction feature between the current and historical points is used for calculating the observation probability; (ii) With regard to the reachable cost between the current road section and the destination, we overcome the shortcoming of feature rarefaction when calculating the transition probability with low sampling rates; (iii) The directional similarity shows a good performance in complex intersection environments. The experimental results verify that the proposed algorithm can reduce the error rate in intersection matching and is suitable for GPS devices with low sampling rates. [ABSTRACT FROM AUTHOR]

Big trajectory data feature analysis for mobile networks is a popular big data analysis task. Due to the large coverage and complexity of the mobile networks, it is difficult to define and detect anomalies in urban motion behavior. Some existing methods are not suitable for the detection of abnormal urban vehicle trajectories because they use the limited single detection techniques, such as determining the common patterns. In this study, we propose a framework for urban trajectory modeling and anomaly detection. Our framework takes into account the fact that anomalous behavior manifests the overall shape of unusual locations and trajectories in the spatial domain as well as the way these locations appear. Therefore, this study determines the peripheral features required for anomaly detection, including spatial location, sequence, and behavioral features. Then, we explore sports behaviors from the three types of features and build a taxi trajectory model for anomaly detection. Anomaly detection, including sports behaviors, are (i) detour behavior detection using an algorithm for global router anomaly detection of trajectories having a pair of same starting and ending points; this method is based on the isolation forest algorithm; (ii) local speed anomaly detection based on the DBSCAN algorithm; and (iii) local shape anomaly detection based on the local outlier factor algorithm. Using a real-life dataset, we demonstrate the effectiveness of our methods in detecting outliers. Furthermore, experiments show that the proposed algorithms perform better than the classical algorithm in terms of high accuracy and recall rate; thus, the proposed methods can accurately detect drivers' abnormal behavior. [ABSTRACT FROM AUTHOR]

With the increasing popularity of terminals and applications, the corresponding requirements of services have been growing significantly. In order to improve the quality of services in resource restrained user devices and reduce the large latency of service migration caused by long distance in cloud computing, mobile fog computing (MFC) is presented to provide supplementary resources by adding a fog layer with several servers near user devices. Focusing on cloud-aware MFC networks with multiple servers, we formulate a problem with the optimization objective to improve the quality of service, relieve the restrained resource of user device, and balance the workload of participant server. In consideration of the data size of remaining task, the power consumption of user device, and the appended workload of participant server, this paper designs a machine learning-based algorithm which aims to generate intelligent adaptive strategies related with load balancing of collaborative servers and dynamic scheduling of sequential tasks. Based on the proposed algorithm and software-defined networking technology, the tasks can be executed cooperatively by the user device and the servers in the MFC network. Besides, we conducted some experiments to verify the algorithm effectiveness under different numerical parameters including task arrival rate, avaliable server workload, and wireless channel condition. The simulation results show that the proposed intelligent adaptive algorithm achieves a superior performance in terms of latency and power consumption compared to candidate algorithms. [ABSTRACT FROM AUTHOR]

The state-of-the-art objects discovery approaches can be categorized into two: deep learning methods based on a convolutional neural network with region proposals, and conventional machine learning methods based on topic models, low-rank matrix factorization, or image processing. The deep learning methods for objects discovery are based on sacrificing computational complexity to achieve precision, and the training time can be long without GPU platforms, whereas the conventional methods usually lack detection accuracy. To effectively address the problems of the training complexity and the detection speed, we present a new objects discovery approach by proposing a two-stage (training and verification) method. In the training stage, a topic model with words co-occurrence prior is proposed on the basis of Latent Dirichlet Allocation (LDA) model, in which the co-occurrence information among the features is sufficiently utilized. In the verification stage, we propose an anchor-box polishing algorithm that fine-tunes the detection results corresponding to the pre-trained topic model from some conventional algorithms with fast detection time. The experiments on various datasets demonstrate that the proposed approach can improve the detection performance in terms of efficiency and computing costs. It is also robust to objects different in colors, lightings, scales, and so on. Interestingly, the proposed method can be combined with many fast but inaccurate detection algorithms, in which it enhances the model’s flexibility. [ABSTRACT FROM AUTHOR]

As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency. [ABSTRACT FROM AUTHOR]