Reviewerly/exHarmony huggingface dataset:
Defining Cost Functions for Adaptive JPEG Steganography at the Microscale

Minimal distortion steganography is the most successful model for adaptive steganography, in which the cost function determines the security. Texture complexity is the major factor in defining cost function in images. In this paper, we proposed a method to improve the cost function of JPEG steganography by exploiting the texture in microscale. The proposed scheme is designed by using a “microscope” to highlight details in an image, so that distortion definition can be more refined. Linear unsharp masking acts as the microscope, because it can accentuate the texture region as well as maintain the original characteristics of images. Inter-block spreading rule is proposed to further strengthen the security. We improve the state-of-the-art schemes, J-UNIWARD and UERD, as J-UNIWARD has outstanding performance on resisting detection while UERD has significant lower computational complexity. In order to keep high efficiency of UERD, filtering in the DCT domain is introduced. Extending experiments show that in most cases the proposed methods (J-MSUNIWARD and MSUERD) can achieve a higher level of security than the original methods.
Reversible Data Hiding With Automatic Brightness Preserving Contrast Enhancement

Reversible data hiding with automatic contrast enhancement methods provide an interoperable way to reduce the storage requirement for automatic image enhancement applications: original image can be recovered from the enhanced image without any additional information. Unlike the previous work, where the goal was to maximize the contrast, the proposed method increases the contrast to an appropriate level using an idea called brightness preservation. This is achieved by using an adaptive bin selection process based on the original brightness. Extensive experimental results verify that the enhanced images produced using the proposed method are visually and quantitatively superior than the existing work.
Selection-based resampling ensemble algorithm for nonstationary imbalanced stream data learning

Although the issues of concept drift and class imbalance have been studied separately, the joint problem is underexplored even though it has received increasing attention. Concept drift is further complicated when the dataset is class imbalanced. Meanwhile, most of the existing techniques have ignored the influence of complex data distribution on learning imbalanced data streams. To overcome these issues, we propose an ensemble-based model for learning concept drift from imbalanced data streams with complex data distribution, called selection-based resampling ensemble (SRE). SRE combines the operators of resampling and periodical update to handle the joint issue. In the chunk-based framework, a selection-based resampling mechanism, which focuses on drifting and unsafe examples, is first employed to re-balance the class distribution of the latest block. Then, previous ensemble members are periodically updated using the latest examples, where update weights are determined to emphasize costly misclassification examples and minority examples. Meanwhile, SRE can quickly react to new conditions. Empirical studies demonstrate the effectiveness of SRE in learning nonstationary imbalanced data streams.
Lossless medical image watermarking method based on significant difference of cellular automata transform coefficient

Conventional medical image watermarking techniques focus on improving invisibility and robustness of the watermarking mechanism to prevent medical disputes. This paper proposes a medical image watermarking algorithm based on the significant difference of cellular automata transform (CAT) for copyright protection. The medical image is firstly subsampled into four subimages, and two images are randomly chosen to obtain two low-frequency bandwidths using CAT. Coefficients within a low-frequency bandwidth are important information in an image. Hence, the difference between two low-frequency bandwidths is used as an important feature in the medical image. From these important features, watermarks and cover images can be used to generate an ownership share image (OSI) used for verifying the medical image. Besides appearing like cover images, the OSI will also be able to register with a third party. When a suspected medical image requires verification, the important features from the suspected medical image are first extracted. The master share image (MSI) can be generated from the important features from the suspected medical image. Lastly, the OSI and MSI can be combined to extract the watermark to verify the suspected medical image. The advantage of our proposed method is that the medical image does not require alteration to protect the copyright of the medical image. This means that while the image is protected, medical disputes will be unlikely and the appearance of the registered OSI will carry significant data to make management more convenient. Lastly, the proposed method has the features of having better security, invisibility, and robustness. Moreover, experimental results have demonstrated that our method results in good performance.
Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams

Designing efficient algorithms for mining massive high-speed data streams has become one of the contemporary challenges for the machine learning community. Such models must display highest possible accuracy and ability to swiftly adapt to any kind of changes, while at the same time being characterized by low time and memory complexities. However, little attention has been paid to designing learning systems that will allow us to gain a better understanding of incoming data. There are few proposals on how to design interpretable classifiers for drifting data streams, yet most of them are characterized by a significant trade-off between accuracy and interpretability. In this paper, we show that it is possible to have all of these desirable properties in one model. We introduce ERulesD2S: evolving rule-based classifier for drifting data Streams. By using grammar-guided genetic programming, we are able to obtain accurate sets of rules per class that are able to adapt to changes in the stream without a need for an explicit drift detector. Additionally, we augment our learning model with new proposals for rule propagation and data stream sampling, in order to maintain a balance between learning and forgetting of concepts. To improve efficiency of mining massive and non-stationary data, we implement ERulesD2S parallelized on GPUs. A thorough experimental study on 30 datasets proves that ERulesD2S is able to efficiently adapt to any type of concept drift and outperform state-of-the-art rule-based classifiers, while using small number of rules. At the same time ERulesD2S is highly competitive to other single and ensemble learners in terms of accuracy and computational complexity, while offering fully interpretable classification rules. Additionally, we show that ERulesD2S can scale-up efficiently to high-dimensional data streams, while offering very fast update and classification times. Finally, we present the learning capabilities of ERulesD2S for sparsely labeled data streams.
Failure Management for Reliable Cloud Computing: A Taxonomy, Model, and Future Directions

The next generation of cloud computing must be reliable to fulfil the end-user requirements, which are changing dynamically. Presently, cloud providers are facing challenges to ensure the reliability of their services. In this paper, we propose a comprehensive taxonomy of failure management in cloud computing. The taxonomy is used to investigate the existing techniques for reliability that need careful attention and investigation, as proposed by several academic and industry groups. Further, the existing techniques have been compared based on the common characteristics and properties of failure management as implemented in commercial and open-source solutions. A conceptual model for reliable cloud computing has been proposed, along with a discussion on future research directions. Moreover, a case study of astronomy workflow is presented for reliable execution in the cloud environment.
Speed up genetic algorithms in the cloud using software containers

Scalability issues might prevent Genetic Algorithms from being applied to real world problems. Exploiting parallelisation in the cloud might be an affordable approach to getting time efficient solutions that benefit of the appealing features of scalability, resource discovery, reliability, fault-tolerance and cost-effectiveness. Nevertheless, parallel computation is very prone to cause considerable overhead for communication. Also, making Genetic Algorithms distributed in an on-demand fashion is not trivial. Aiming at keeping under control the communication cost and, at the same time, supporting developers in the construction and deployment of parallel Genetic Algorithms, we designed and implemented a novel approach to distribute Genetic Algorithms in the form of a cloud-based application. It is based on the master/slave model, exploiting software containers, their cloud orchestration and message queues. We also devised a conceptual workflow covering each cloud Genetic Algorithms distribution phase, from resources allocation to actual deployment and execution, in an engineered fashion. Then, the application performance has been evaluated using a benchmark problem. According to the performance and setup times results, it emerged that the cloud can be considered a compelling way of scaling Genetic Algorithms and an excellent alternative to other technologies strictly related to the physically owned hardware.
Learning Local-Global Multi-Graph Descriptors for RGB-T Object Tracking

RGB-thermal (RGB-T) object tracking, which has attracted much recent attention, uses thermal infrared information to assist object tracking with visible light information. However, it still faces many challenging problems, especially the background inclusion in the target bounding box which easily results in model drifting. To handle this problem, we propose a novel and general approach to learn a local-global multi-graph descriptor to suppress background effects for RGB-T tracking. Our approach relies on a novel graph learning algorithm. First, the object is represented with multiple graphs, with a set of multi-modal image patches as nodes, for the robustness to prevent deformation and partial occlusion. Second, we dynamically learn a joint graph over time with both local and global considerations using spatial smoothness and low-rank representation. In particular, we design a single unified alternating direction method of multipliers-based optimization framework to learn graph structure, edge weights, and node weights simultaneously. Third, we combine multi-graph information with corresponding graph node weights to form a robust object descriptor, and tracking is finally carried out by adopting the structured support vector machine. Extensive experiments conducted on the tracking benchmark data sets demonstrate the effectiveness of the proposed approach against the state-of-the-art RGB-T trackers.
Diversity-Based Cooperative Multivehicle Path Planning for Risk Management in Costmap Environments

This paper focuses on developing new navigation algorithms for cooperative unmanned vehicles in costmap environments. The vehicles need to plan optimal paths subject to mutual diversity. We first propose three algorithms that can find optimal paths in costmap environments with improved computational efficiency. The proposed algorithms include 1) the scaled A*; 2) the A* plus-plus; and 3) the A* plus-plus with reconstruction. The heuristics of the three algorithms are proved admissible. Then, a diversity-based path planning algorithm is proposed based on the new A star variant algorithms for multiple vehicles to plan diverse paths for risk management. In particular, we propose an iterative segment planner, which generates a set of spatially diverse paths by iteratively planning segments of all paths in a sequential manner. To prevent the generation of spatially close paths, the iterative segment planner utilizes a penalty function to increase the cost of paths that lie near the route(s) assigned to other vehicles. By varying the penalty gains, different degrees of path diversity can be achieved. Some illustrative examples are provided to compare the performance of the proposed algorithms with the standard A* and Dijkstra's algorithms, and show the effectiveness of the proposed diversity-based path planning algorithm.
Time Protection

Timing channels enable data leakage that threatens the security of computer systems, from cloud platforms to smartphones and browsers executing untrusted third-party code. Preventing unauthorised information flow is a core duty of the operating system, however, present OSes are unable to prevent timing channels. We argue that OSes must provide time protection, the temporal equivalent of the established memory protection, for isolating security domains. We examine the requirements of time protection, present a design and its implementation in the seL4 microkernel, and evaluate efficacy and cost on x86 and Arm processors.
Persistent standoff tracking guidance using constrained particle filter for multiple UAVs

This paper presents a new standoff tracking framework of a moving ground target using UAVs with limited sensing capabilities and motion constraints. To maintain persistent track of the target even in case of target loss for a certain period, this study predicts the target existence area using the particle filter and produces control commands that ensure that all predicted particles can stay within the field-of-view of the UAV sensor at all times. To improve target position prediction and estimation accuracy, the road information is incorporated into the constrained particle filter where the road boundaries are modelled as inequality constraints. Both Lyapunov vector field guidance and nonlinear model predictive control-based methods are applied, and the characteristics of them are compared using numerical simulations.
Network Community Detection on Small Quantum Computers

Abstract In recent years, a number of quantum computing devices with small numbers of qubits have become available. A hybrid quantum local search (QLS) approach that combines a classical machine and a small quantum device to solve problems of practical size is presented. The proposed approach is applied to the network community detection problem. QLS is hardware‐agnostic and easily extendable to new quantum computing devices as they become available. It is demonstrated to solve the 2‐community detection problem on graphs of sizes of up to 410 vertices using the 16‐qubit IBM quantum computer and D‐Wave 2000Q, and compare their performance with the optimal solutions. The results herein demonstrate that QLS performs similarly in terms of quality of the solution and the number of iterations to convergence on both types of quantum computers and it is capable of achieving results comparable to state‐of‐the‐art solvers in terms of quality of the solution including reaching the optimal solutions.
Internet of image things-discrete wavelet transform and Gabor wavelet transform based image enhancement resolution technique for IoT satellite applications

In image processing, image enhancement is a vital processing chore. The image enhancement can improve the image quality by removing either blur or any kind of noise in the image. Image enhancement technique is utilized in many applications, such as medical, satellite, agriculture, oceanography and so on. This paper focuses on the IoT satellite applications. Most of the satellite images are essential to have high resolution satellite images, low resolution images are majorly affected by absorption, scattering, spatial resolution and spectral resolution issues. For better resolution of these kinds of issued images, Discrete Wavelet Transform (DWT) based interpolation method, combination of DWT and stationary wavelet transform (SWT) methods, bicubic interpolation methods are utilized. However, DWT with SWT method is failed avoid distorted in the resultant images, the bicubic interpolation method is quite complex and cannot give a clear image. DWT based interpolation method lose linear features and unwanted oscillations are occurred and edges data is lost. Therefore, DWT and Gabor technique is proposed to overcome existing method issues. DWT is decomposed into multiple sub-bands; GWT is employed to minimize the loss of information in wavelet domain. The advantages of the GWT are less complexity, remove the noise, and also sharp the image. The proposed method of the PSNR, MSE is compared with existing methods by using the different satellite images.
The cloud computing adoption in higher learning institutions in Kenya: Hindering factors and recommendations for the way forward

Cloud computing technology has revolutionized the delivery of computing services to the consumers as customized services on demand making them cost effective and resource efficient. However, various challenges hinder the adoption of this technology, especially in educational institutions. This paper explores the factors that information technology (IT) leaders consider to be impeding the adoption of cloud-computing in institutions of higher learning in Kenya. A quantitative investigation using an online questionnaire was used to conduct this study in the 69 accredited universities and colleges in Kenya. The study findings were statistically analyzed to rank the level of influence of the factors that under investigation. The key factors identified as hindering the adoption of cloud computing include concerns about the reliability of Cloud Service providers and lack of skills in cloud computing. Also, there are concerns about inadequate support and training from Cloud Service providers and government policies on cloud computing, data security and confidentiality. Role based training for Cloud services users, proactive management of Cloud services and implementation of information systems governance policies are recommended to address these concerns. These recommendations have been discussed and consolidated into a framework for adoption of cloud computing that can also be applied by educational institutions in other developing countries to assist the key stakeholders in guiding their decision-making process on adoption of cloud computing. Although the study is limited to accredited universities and colleges in Kenya, it comes at a convenient time when the Kenyan government is launching e-learning in all public primary schools. There is however not much research that had been done especially on cloud computing in Kenyan educational institutions. This study, therefore, adds new insights on what needs to be considered and addressed to facilitate the adoption of cloud computing in institutions of higher education.
Multilingual POS tagging by a composite deep architecture based on character-level features and on-the-fly enriched Word Embeddings

Natural Language Processing (NLP) field is taking great advantage from adopting models and methodologies from Artificial Intelligence. In particular, Part-Of-Speech (POS) tagging is a building block for many NLP applications. In this paper, a POS tagging system based on a deep neural network is proposed. It is made of a static and task-independent pre-trained model for representing words semantics enriched by morphological information, by approximating the Word Embedding representation learned from an unlabelled corpus by the fastText model, so as to handle consistently common and known words as well as rare and Out-of-Vocabulary words. A character-level representation of words is dynamically learned according to the POS tagging task, and is concatenated to the previous one. This joint representation is fed to the main network, comprising a Bi-LSTM layer, trained to associate a sequence of tags to a sequence of words. The effectiveness of the contributions of the proposed system with respect to the state-of-the-art is proven by an extensive experimental campaign, which provides evidence that improvements are gained in POS tagging accuracy by using Word Embeddings enriched with morphological information, by estimating embeddings for both known and unknown words, and by concatenating Word Embeddings with character-level information of the same size. Similar trends are obtained for two languages of different characteristics, namely English and Italian: in both cases, the overall accuracy on the POS tagging test set was increased with respect to the most advanced existing systems, with particular improvements on the accuracy of Out-of-Vocabulary words. Finally, the method has a general basis, and could be proficiently used for all languages, particularly for those showing a wide morphological richness.
A multimodal fusion approach for image captioning

Deep convolution neural networks connected with the recurrent neural networks are potent models that have achieved excellent performance on image caption task. Although many methods based on the neural network can generate fluent and complete sentences, the image feature vectors extracted by the convolution neural network can only retain a few significant features of the original image, which will lose a lot of useful image information. Moreover, RNNs have a gradient vanishing problem with the growth of RNNs time step, and the generation of sentences will lack the guidance of previous information. In this paper, we introduce a multimodal fusion method for generating descriptions to explain the content of images. Our model consists of four sub-networks: a convolutional neural network for image feature extraction, a ATTssd model for image attributes extraction, a language CNN model CNNm for sentence modeling and a recurrent network (e.g., GRU, LSTM, etc.) for word prediction. Compared with existing methods which predict next word based on one previous word and hidden state, our model uses image attributes information to enhance the image representation and handles all the previous words to modeling the long-term dependencies of history words. The methods are evaluated on the Flickr8k, Flickr30k and MSCOCO datasets. We prove that our model combined with ATTssd and CNNm can significantly enhance the performance, and achieve the competitive results.
Benchmarking a large-scale FIR dataset for on-road pedestrian detection

Far infrared (FIR) pedestrian detection is an essential module of advanced driver assistance systems (ADAS) at nighttime. Although recent deep learning-based detectors have achieved excellent results on visible images in the daytime, their performance on nighttime FIR images is still unidentified, due to the existing nighttime FIR data set is not sufficient to fully train a deep learning detector. To this end, a nighttime FIR pedestrian dataset with the largest scale at present is introduced in this paper, which is called SCUT (South China University of Technology) dataset. The dataset contains fine-grained annotated videos recorded from variable road scenes. In addition, a detailed statistical analysis of the dataset is provided and four representative pedestrian detection methods are evaluated. Benefit from the volume and diversity of training data, the experiment results show that convolutional neural networks (CNN) based detectors obtained good performance on FIR image. To further explore the performance of pedestrian detection on FIR images, four important modifications on Faster R-CNN were studied and a strong baseline for SCUT dataset was proposed, which achieves the best detection result by reducing log-average miss rate from 36.78% to 17.73%. The dataset will be public online (SCUT dataset will be published on: https://github.com/SCUT-CV/SCUT_FIR_Pedestrian_Dataset).