Reviewerly/exHarmony huggingface dataset:
Brokering in interconnected cloud computing environments: A survey

Cloud computing provides computing platforms and facilitates to optimize utilization of infrastructure resources, reduces deployment time and increases flexibility. The popularity of cloud computing led to development of interconnected cloud computing environments (ICCE) such as hybrid cloud, inter-cloud, multi-cloud, and federated cloud, enabling the possibilities to share resources among individual clouds. However, individual proprietary technologies and access interfaces employed by cloud service providers made it difficult to share resources. Interoperability and portability are two of the major challenges to be addressed to ensure seamless access and sharing of resources and services. Many cloud service providers have similar service offerings but different access patterns. It is difficult and time consuming for a cloud user to select an appropriate cloud service as per the application’s requirement. Cloud user has to gather information from various cloud service providers and analyze them. Cloud broker has been proposed to address the challenge of cloud users to get best out of cloud provider. Cloud broker is an entity which works as an independent third party between cloud users and cloud providers. Cloud broker negotiates with several cloud providers as per user’s requirements and tries to select the best services. Cloud broker coordinates the sharing of resources and provides interoperability and portability with other cloud providers. In this paper, a comprehensive survey of cloud brokering in interconnected cloud computing environments has been provided. The need and importance of cloud broker has been discussed. The existing architectures and frameworks of Cloud Brokering are reviewed. A comprehensive literature survey of various Cloud Brokering techniques is presented. A taxonomy of Cloud Brokering techniques has been presented and analyzed on the basis of their strengths and weaknesses/limitations. The taxonomy includes pricing, multi-criteria, quality of services, optimization and trust techniques. The techniques are analyzed on various performance metrics. Research challenges and open problems are identified from reviewed techniques. A model for cloud broker is proposed to address identified challenges. We hope that our work will enable researchers to launch and dive deep into Cloud Brokering challenges in interconnected cloud computing environments.
Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph Learning

Microblog sentiment prediction has attracted extensive research focus with wide application prospects. With the increasing proportion of multimodal tweets consisting of images, texts, and emoticons, new challenges have been raised to the existing sentiment prediction schemes. More crucially, it remains an open problem to model the dependency among multiple modalities, where one or more modalities may be missing. In this paper, we present a novel Bi-layer Multimodal Hypergraph learning (Bi-MHG) toward robust sentiment prediction of multimodal tweets to tackle the above challenges. In particular, we design a two-layer structure for the proposed Bi-MHG model: The first layer, that is, a tweet-level hypergraph, learns the tweet-feature correlation and the tweet relevance to predict the sentiments of unlabeled tweets. The second layer, that is, a feature-level hypergraph learns the relevance among different feature modalities (including the midlevel visual features in Sentibank [1]) by leveraging prior multimodal sentiment dictionaries. These two layers are connected by sharing the relevance of multimodal features in a unified bilayer learning scheme. In such a way, Bi-MHG explicitly models the modality relevance rather than implicitly weighting multimodal features adopted in the existing Multimodal Hypergraph learning [2]. Finally, a nested alternating optimization is further proposed for Bi-MHG parameter learning. We have carried out extensive evaluations on a real-world microblog dataset crawled from Sina Weibo. For the task of multimodal sentiment prediction, superior performance is reported over several state-of-the-art and alternative approaches, which demonstrates the merits of the proposed scheme.
Enhancing comprehensive learning particle swarm optimization with local optima topology

Recently, particle swarm optimization (PSO) has been employed in many studies for solving numerous real-world problems. However, PSO may suffer from premature convergence when dealing with multimodal problems. Thus, we propose a local optima topology (LOT) structure based on the comprehensive learning particle swarm optimizer (CLPSO) called CLPSO-LOT. The local optima are found in the iterative process and a new topology space is composed. A random element from the space can serve as the next exemplar that the particle uses for learning. This topology structure comprises the local optima that enlarge the particle’s search space and increase the convergence speed with a certain probability. We conducted numerical experiments based on various functions from CEC2005 and CEC2014, where the results demonstrated good performance of this algorithm. Furthermore, we applied the algorithm to the optimization of four-bar linkages, where the results indicated that the CLPSO-LOT performed better than other algorithms, and that the performance of the CLPSO was improved.
Complete Random Forest Based Class Noise Filtering Learning for Improving the Generalizability of Classifiers

The existing noise detection methods required the classifiers or distance measurements or data overall distribution, and `curse of dimensionality' and other restrictions made them insufficiently effective in complex data, e.g., different attribute weights, high-dimensionality, containing feature noise, nonlinearity, etc. This is also the main reason that the existing noise filtering methods were not widely applied and formed an effective learning framework. To address this problem, we propose here a complete and efficient random forest method (CRF) specifically for the class noise detection by simulating the grid generation and expansion. The CRF is not based on distance measures or overall distribution or classifiers; besides, the voting mechanism makes it able to effectively process datasets containing feature noise. Furthermore, we introduce CRF based class noise filtering learning framework (CRF-NFL) and derive its mathematical model. The framework is then applied to many widely used classifiers including some state-of-the-art algorithms, e.g., k-means tree, GBDT, and XGBoost. Moreover, its parallelized is designed for large-scale data. The CRF-NFL show much better generalizability than the conventional classifiers and the relative density-based method, which is the most effective noise filtering method as far as we know. All research has formed an open source library, called CRF-NFL: http://www.cquptshuyinxia.com/CRF-NFL.html.
Nonlinear gradient neural network for solving system of linear equations

For purpose of solving system of linear equations (SoLE) more efficiently, a fast convergent gradient neural network (FCGNN) model is designed and discussed in this paper. Different from the design of the conventional gradient neural network (CGNN), the design of the FCGNN model is based on a nonlinear activation function, and thus the better convergence speed can be reached. In addition, the convergence upper bound of the FCGNN model is estimated and provided in details. Simulative results validate the superiority of the FCGNN model, as compared to the CGNN model for finding SoLE.
Single-Sensor RGB-NIR Imaging: High-Quality System Design and Prototype Implementation

In recent years, many applications using a set of red-green-blue (RGB) and near-infrared (NIR) images, also called an RGB-NIR image, have been proposed. However, RGB-NIR imaging, i.e., simultaneous acquisition of RGB and NIR images, is still a laborious task because existing acquisition systems typically require two sensors or shots. In contrast, single-sensor RGB-NIR imaging using an RGB-NIR sensor, which is composed of a mosaic of RGB and NIR pixels, provides a practical and low-cost way of one-shot RGB-NIR image acquisition. In this paper, we investigate high-quality system designs for single-sensor RGB-NIR imaging. We first present a system evaluation framework using a new hyperspectral image data set we constructed. Different from existing work, our framework takes both the RGB-NIR sensor characteristics and the RGB-NIR imaging pipeline into account. Based on the evaluation framework, we then design each imaging factor that affects the RGB-NIR imaging quality and propose the best-performed system design. We finally present the configuration of our developed prototype RGB-NIR camera, which was implemented based on the best system design, and demonstrate several potential applications using the prototype.
Optimistic virtual machine placement in cloud data centers using queuing approach

Cloud computing gives many beneficial services to share large scale of information, storage resources, computing resources, and provide knowledge for research. Cloud users deploy their own applications and related data on a pay-as-you-go basis. Virtual machines (VMs) usually host these data-intensive applications. The performance of these applications often depends on workload types I/O data-intensive or I/O computation, workload volume, CPU attributes on computing nodes, Virtual machines and the network. Therefore, the application jobs in the workload have different completion times based on the VM placement decision and large data retrieval. The main contribution of this thesis to gain high performance for the applications executed on the cloud by minimizing the completion time, minimizing the production cost and maximizing the throughput of cloud links. To provide a solution for minimizing the overall jobs’ completion time (computing time as well as data transferring time) in both static and dynamic workloads, we propose VMs placement algorithm that considers computation resources, Quality of Service (QoS) metrics and virtual machine status and I/O data with priority based probability queuing model. The results obtained by the proposed methodology shows that the proposed optimal VM placement algorithm has a reduced processing cost and completion time compared with the traditional algorithms such as FCFS and priority scheduling.
Edge-Aware Convolution Neural Network Based Salient Object Detection

Salient object detection has received great amount of attention in recent years. In this letter, we propose a novel salient object detection algorithm, which combines the global contextual information along with the low-level edge features. First, we train an edge detection stream based on the state-of-the-art holistically-nested edge detection (HED) model and extract hierarchical boundary information from each VGG block. Then, the edge contours are served as the complementary edge-aware information and integrated with the saliency detection stream to depict continuous boundary for salient objects. Finally, we combine pyramid pooling modules with auxiliary side output supervision to form the multi-scale pyramid-based supervision module, providing multi-scale global contextual information for the saliency detection network. Compared with the previous methods, the proposed network contains more explicit edge-aware features and exploit the multi-scale global information more effectively. Experiments demonstrate the effectiveness of the proposed method, which achieves the state-of-the-art performance on five popular benchmarks.
An effective Decision Support System for social media listening based on cross-source sentiment analysis models

Nowadays, companies and enterprises are more and more incline to exploit the pervasive action of on-line social media, such as Facebook, Twitter and Instagram. Indeed, several promotional and marketing campaigns are carried out by concurrently adopting several social medial channels. These campaigns reach very quickly a wide range of different categories of users, since many people spend most of their time on on-line social media during the day. In this work, a Decision Support System (DSS) is presented, which is able to efficiently support companies and enterprises in managing promotional and marketing campaigns on multiple social media channels. The proposed DSS continuously monitors multiple social channels, by collecting social media users’ comments on promotions, products, and services. Then, through the analysis of these data, the DSS estimates the reputation of brands related to specific companies and provides feedbacks about a digital marketing campaign. The core of the proposed DSS is a Sentiment Analysis Engine (SAE), which is able to estimate the users’ sentiment in terms of positive, negative or neutral polarity, expressed in a comment. The SAE is based on a machine learning text classification model, which is initially trained by using real data streams coming from different social media platforms specialized in user reviews (e.g., TripAdvisor). Then, the monitoring and the sentiment classification are carried out on the comments continuously extracted from a set of public pages and channels of publicly available social networking platforms (e.g., Facebook, Twitter, and Instagram). This approach is labeled as cross-source sentiment analysis. After some discussions on the design and the implementation of the proposed DSS, some results are shown about the experimentation of the proposed DSS on two scenarios, namely restaurants and consumer electronics online shops. Specifically, first the application of effective sentiment analysis models, created relying on user reviews is discussed: the models achieve accuracies higher than 90%. Then, such models are embedded into the proposed DSS. Finally, the results of a social listening campaign are presented. The campaign was carried out by fusing data streams coming from real social channels of popular companies belonging to the selected scenarios.
A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine

The examination and classification of Electrocardiogram (ECG) records have become particularly significant for diagnosing heart diseases. Machine learning methods are widely used in classifying ECG signals. In this study, Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTBDB) from Physionet Database was used to classify ECG signals. Pan-Tompkins algorithm and Discrete Wavelet Transform (DWT) methods were used for extracting critical points such as QRS complex, PR, ST and QT of ECG signal. Afterwards, Traditional Extreme Learning Machine (ELM) was implemented to the ECG signal. Finally, Genetic Algorithm on software Genetic Algorithm Wavelet Kernel Extreme Learning Machine was improved for the determination of the coefficients, which were used in the Wavelet Kernel Extreme Learning Machine algorithm, in which wavelet function was accomplished. In this scope, it was observed that best classification performance values were reached to Acc 95%, Se 100% and Spe 80% with the implementation developed with the Genetic algorithm.
MCRMR: Maximum coverage and relevancy with minimal redundancy based multi-document summarization

In this paper, we propose a novel extraction based method for multi-document summarization that covers three important features of a good summary: coverage, non-redundancy, and relevancy. The coverage and non-redundancy features are modeled to generate a single document from the multiple documents. These features are explored by the weighted combination of word embedding and Google based similarity methods. To accommodate the relevancy feature in the system generated summaries, the text summarization task is modeled as an optimization problem, where various text features with their optimized weights are used to score the sentences to find the relevant sentences. For features’ weight optimization, we use the meta-heuristic approach, Shark Smell Optimization (SSO). The experiments are performed on six benchmark datasets (DUC04, DUC06, DUC07, TAC08, TAC11, and MultiLing13) with the co-selection and content based performance parameters. The experimental results show that the proposed approach is viable and effective for multi-document summarization.
Comparison of techniques for radiometric identification based on deep convolutional neural networks

The authors investigate the application of deep convolutional neural networks (CNNs) to the problem of radiometric identification, i.e. the task of authenticating wireless devices on the basis of their radio frequency (RF) emissions, which contain features directly related to the physical properties of the wireless devices. They collected digitised RF from 12 wireless devices, and used various techniques to transform the time series derived from the RF to images. A deep CNN is then applied to the images. The authors' results show that the identification performance of the combination of deep CNN with an image representation significantly outperforms conventional methods based on dissimilarity on the original time series. Moreover, a specific comparison among RF-to-image techniques show that on their datasets the wavelet-based approach outperforms other approaches, also in the presence of white Gaussian noise.
Style and Content Disentanglement in Generative Adversarial Networks

Disentangling factors of variation within data has become a very challenging problem for image generation tasks. Current frameworks for training a Generative Adversarial Network (GAN), learn to disentangle the representations of the data in an unsupervised fashion and capture the most significant factors of the data variations. However, these approaches ignore the principle of content and style disentanglement in image generation, which means their learned latent code may alter the content and style of the generated images at the same time. This paper describes the Style and Content Disentangled GAN (SC-GAN), a new unsupervised algorithm for training GANs that learns disentangled style and content representations of the data. We assume that the representation of an image can be decomposed into a content code that represents the geometrical information of the data, and a style code that captures textural properties. Consequently, by fixing the style portion of the latent representation, we can generate diverse images in a particular style. Reversely, we can set the content code and generate a specific scene in a variety of styles. The proposed SC-GAN has two components: a content code which is the input to the generator, and a style code which modifies the scene style through modification of the Adaptive Instance Normalization (AdaIN) layers' parameters. We evaluate the proposed SC-GAN framework on a set of baseline datasets.
Can I touch this? Using Natural Interaction in a Museum Augmented Reality System

A general problem in museums is the limited accessibility to artifacts by museum visitors, due to their fragile nature, their uniqueness, or just the lack of physical space for exhibiting the objects. Moreover, only in extreme cases, visitors are allowed to touch the exhibits. Our work integrates Natural Interaction and Augmented Reality applied in a cultural heritage museum, to overcome the problems of inaccessibility and non-interaction with the museum artifacts. We use off the shelf digital components to provide a novel approach of interacting with 3D replicas of museum objects. Users are able to easily interact with digital content in a virtual environment mimicking physical environments. We evaluated the visitors’ experience using our system and report on its acceptance.
Chaos-based image encryption algorithm with orbit perturbation and dynamic state variable selection mechanisms

In this paper, we propose a novel chaos-based image encryption algorithm with the orbit perturbation and dynamic state variable selection mechanisms. The two-dimensional logistic-adjusted-sine map is employed to generate pseudo-random numbers. Using an auxiliary variable, the orbit of the chaotic map is perturbed continuously by the previously processed pixel and one of two state variables is selected to generate the keystream. Consequently, the keystream depends on not only the initial condition and control parameter of the chaotic map, but also on the information in the plain and cipher images, and thus, the algorithm is highly sensitive to both the images. A number of simulations were conducted to verify the effectiveness and security of the algorithm. Moreover, the proposed algorithm provides excellent encryption performance in only one round.
Neural networks versus Logistic regression for 30 days all-cause readmission prediction

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the healthcare system. Consequently, the identification of patients at risk for readmission is a key step in improving disease management and patient outcome. In this work, we used a large administrative claims dataset to (1) explore the systematic application of neural network-based models versus logistic regression for predicting 30 days all-cause readmission after discharge from a HF admission, and (2) to examine the additive value of patients' hospitalization timelines on prediction performance. Based on data from 272,778 (49% female) patients with a mean (SD) age of 73 years (14) and 343,328 HF admissions (67% of total admissions), we trained and tested our predictive readmission models following a stratified 5-fold cross-validation scheme. Among the deep learning approaches, a recurrent neural network (RNN) combined with conditional random fields (CRF) model (RNNCRF) achieved the best performance in readmission prediction with 0.642 AUC (95% CI, 0.640-0.645). Other models, such as those based on RNN, convolutional neural networks and CRF alone had lower performance, with a non-timeline based model (MLP) performing worst. A competitive model based on logistic regression with LASSO achieved a performance of 0.643 AUC (95% CI, 0.640-0.646). We conclude that data from patient timelines improve 30 day readmission prediction, that a logistic regression with LASSO has equal performance to the best neural network model and that the use of administrative data result in competitive performance compared to published approaches based on richer clinical datasets.
A new greedy Kaczmarz algorithm for the solution of very large linear systems

We propose a new greedy Kaczmarz algorithm for the solution of very large systems of linear equations. In our proposed algorithm, control sequence is determined by a greedy rule, and relaxation parameters are determined adaptively. Convergence of our proposed algorithm is proved. Numerical results show that the proposed algorithm is feasible and has faster convergence rate than the greedy randomized Kaczmarz algorithm.
Learning spatiotemporal representations for human fall detection in surveillance video

In this paper, a computer vision based framework is proposed that detects falls from surveillance videos. Firstly, we employ background subtraction and rank pooling to model spatial and temporal representations in videos, respectively. We then introduce a novel three-stream Convolutional Neural Networks as an event classifier. Silhouettes and their motion history images serve as input to the first two streams, while dynamic images whose temporal duration is equal to motion history images, are used as input to the third stream. Finally, we apply voting on the results of event classification to perform multi-camera fall detection. The main novelty of our method against the conventional ones is that high-quality spatiotemporal representations in different levels are learned to take full advantage of the appearance and motion information. Extensive experiments have been conducted on two widely used fall datasets. The results have shown to demonstrate the effectiveness of the proposed method.