Reviewerly/exHarmony huggingface dataset:
Scheduling Workflows With Composite Tasks: A Nested Particle Swarm Optimization Approach

Scientific cloud workflows enable the access to distributed computing resources in cloud environments for executing scientific computing applications. In the literature, most workflow scheduling models assume that each workflow task is mapped to only one service instance. But in computation and data-intensive applications, it is common that the computation resources provided by a single service instance are insufficient for some complicated tasks which contain several closely correlated sub-tasks. To manage such complicated workflows, this article devises a novel workflow model with composite tasks (cWFS). The model views a complicated task as a composite task and allows mapping multiple service instances to a composite task. The data transmission among sub-tasks of a composite task can also be addressed by the proposed model. To solve cWFS problem, we devise a nested particle swarm optimization (N-PSO) that utilizes two kinds of populations, i.e., the outer population and inner population. Since N-PSO is a bit time-consuming, we further devise a Fast version of N-PSO (FN-PSO), which can save more than 60 percent of running time compared with N-PSO. The proposed approaches are evaluated on five real-world workflow types. The experimental results verify that the proposed approaches can solve the new workflow model effectively.
Predicting the number of defects in a new software version

Predicting the number of defects in software at the method level is important. However, little or no research has focused on method-level defect prediction. Therefore, considerable efforts are still required to demonstrate how method-level defect prediction can be achieved for a new software version. In the current study, we present an analysis of the relevant information obtained from the current version of a software product to construct regression models to predict the estimated number of defects in a new version using the variables of defect density, defect velocity and defect introduction time, which show considerable correlation with the number of method-level defects. These variables also show a mathematical relationship between defect density and defect acceleration at the method level, further indicating that the increase in the number of defects and the defect density are functions of the defect acceleration. We report an experiment conducted on the Finding Faults Using Ensemble Learners (ELFF) open-source Java projects, which contain 289,132 methods. The results show correlation coefficients of 60% for the defect density, -4% for the defect introduction time, and 93% for the defect velocity. These findings indicate that the average defect velocity shows a firm and considerable correlation with the number of defects at the method level. The proposed approach also motivates an investigation and comparison of the average performances of classifiers before and after method-level data preprocessing and of the level of entropy in the datasets.
A survey: the role of the internet of things in the development of education

There is a demand to change the contents and activities, and adapt the methods for higher education institutions, especially, universities to let researchers and educational to act more efficiently in a digital context. A well-designed campus, finally combining technology, is basic for developing digital university by facilities for learning, teaching, and research, enhancing the student trials, and supplying the convenient settings. Within digital universities, technology can improve security, reduce costs, and offer devices for faculty, scholars, academics, and students. These advantages give more attention to university processes and evolutions, the experience of researchers, and students. In this research, we have done a study on the Internet of things and what its role in the development of education through the review for a group of previous research. In addition, we have studied the smart class and its components and the difference from the traditional class, and then we have displayed the smart laboratories and its applications. At the end of the research, the great importance of Internet things in universities and its importance to the teacher and the student was concluded by learning faster and developing and improving the educational process.
AES-Secured Bit-Cycling Steganography in Sliced 3D Images

This paper proposes 2 double-layer message security schemes in 3D cover images. For each of the schemes, the first layer involves the use of cryptography, namely the advanced encryption standard (AES). While for the second layer, both schemes employ a variant of LSB 3D image steganography. In this second layer of security, each of the schemes allows the bit embedding to take place according to a mathematical sequence. The first scheme makes use of an arithmetic sequence, while the second scheme makes use of a geometric sequence. Various performance evaluation metrics are utilized in this work, to check for the quality of steganography in the stego 3D images used. Those include computations of the means squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean absolute error (MAE) as well as the entropy.
Deep learning for survival outcomes

Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. We develop a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the absence of censoring is replaced by a censoring unbiased transformation. The resulting class of algorithms can be used to estimate both survival probabilities and restricted mean survival. We show how the deep learning algorithms can be implemented by adapting software for uncensored data by using a form of response transformation. We provide comparisons of the proposed deep learning algorithms to existing risk prediction algorithms for predicting survival probabilities and restricted mean survival through both simulated datasets and analysis of data from breast cancer patients.
Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.
BTNC: A blockchain based trusted network connection protocol in IoT

Along with the rapid growth of the size and complexity of Internet of Things (IoT), the security of terminal devices has increasingly become a focus. In order to ensure the security of terminals, the trusted network connect (TNC) could realize not only the user authentication but also the platform attestation during the network access process. However, the existing TNC infrastructure is based on a centralized architecture, which is not suitable for distributed services. To address this problem, we present a blockchain-based TNC protocol named BTNC to ensure the reliability of terminals in IoT. Due to the decentralization, trustlessness, trackability, and immutability features of blockchain, BTNC is able to verify the security of terminal devices in IoT networks. First, we come up with some threats, including unauthorized user, illegal platform and platform replacement attack, then correspondingly define the security goals of our scheme. Second, combining key exchange protocol based on blockchain and D–H PN protocol included in TNC specification, we propose a blockchain-based trusted network connection protocol, which realizes mutual user authentication, platform attestation and trust network access by cryptography among terminals in IoT. Third, we make a security analysis in the PCL mode and conclude that our protocol can resist the attacks above. Finally, the performance overheads caused by our scheme are evaluated and the experiments show that it is efficient and feasible for different kinds of terminals in IoT.
On the Interpretability of Machine Learning Models and Experimental Feature Selection in Case of Multicollinear Data

In the field of machine learning, a considerable amount of research is involved in the interpretability of models and their decisions. The interpretability contradicts the model quality. Random Forests are among the best quality technologies of machine learning, but their operation is of “black box” character. Among the quantifiable approaches to the model interpretation, there are measures of association of predictors and response. In case of the Random Forests, this approach usually consists of calculating the model’s feature importances. Known methods, including the built-in one, are less suitable in settings with strong multicollinearity of features. Therefore, we propose an experimental approach to the feature selection task, a greedy forward feature selection method with least-trees-used criterion. It yields a set of most informative features that can be used in a machine learning (ML) training process with similar prediction quality as the original feature set. We verify the results of the proposed method on two known datasets, one with small feature multicollinearity and another with large feature multicollinearity. The proposed method also allows for a domain expert help with selecting among equally important features, which is known as the human-in-the-loop approach.
Using Mobile Devices for Educational Purposes in Compulsory Secondary Education to Improve Student’s Learning Achievements

The number of mobiles surpassed the population of the planet in 2018. Without a doubt, they have become the tool of reference in our social life, as well as in our work and leisure, but in educational centers, they continue to be used inefficiently or are declared off limits to some classrooms. This study aims to establish the relationship that the use of mobile phones in secondary education schools may have in regard to the student’s academic performance. For this purpose, we will carry out a secondary exploitation of the Ministry of Education database for 2017 with the data of 1,887,027 students from 7381 compulsory secondary education schools in Spain. As a method of analysis, a multilevel correlation study is carried out. The results show a strong correlation between centers and territories that allow the usage of mobile devices in education and academic performance. Although the methodology that accompanies the use of the smartphone as an educational tool is very important, the data obtained leads us to think that using mobile phones in schools is a proposal that somehow influences the achievement of better academic results, with almost any methodology used.
EvoU-Net

Developing a Deep Convolutional Neural Network (DCNN) for image segmentation is challenging research topic which needs extensive experiments to find an appropriate network structure and a precise set of hyper-parameters. The limited number of available labelled images and the required computational infrastructure make this task even more challenging. Evolutionary strategy is an optimisation technique that is applicable to alleviate the above difficulties. This paper proposes an evolutionary-based method to find a precise and small network for medical image segmentation. To the best of our knowledge, EvoU-Net is the first evolutionary method to develop an U-Net-based deep network topology with for medical image segmentation. In the proposed model, a Genetic Algorithm (GA) is applied to design an optimal network structure, along with its parameters, for MRI image segmentation. EvoU-Net outperformed U-Net and AdaResU-Net while using less than 10% and 50% of trainable parameters respectively, for segmentation of a publicly available prostate MRI dataset.
Deep Tensor Capsule Network

Capsule network is a promising model in computer vision. It has achieved excellent results on simple datasets such as MNIST, but the performance deteriorates as data becomes complicated. In order to address this issue, we propose a deep capsule network in this paper. To deepen the capsule network, we present a new tensor capsule based routing algorithm and the corresponding convolution operation. Compared to vector capsules, tensor capsules can capture more instance-level information. Together, the relevant convolution operation is beneficial for reducing the amount of parameters in the routing process. Furthermore, we propose a dropout mechanism for vectors and tensors in order to alleviate the potential overfitting problem. Finally, we also inject the multi-scale capsules of the middle layers into a multi-scale decoder to pursue more details of an image and more clear reconstructed image. Experimental results on CIFAR10, Fashion-MNIST, and SVHN demonstrate that the proposed deep tensor network can achieve very competitive performance compared to other state-of-the-art capsule networks.
Overlap-Based Undersampling Method for Classification of Imbalanced Medical Datasets

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques.
Boolean algebras of conditionals, probability and logic

This paper presents an investigation on the structure of conditional events and on the probability measures which arise naturally in that context. In particular we introduce a construction which defines a (finite) Boolean algebra of conditionals from any (finite) Boolean algebra of events. By doing so we distinguish the properties of conditional events which depend on probability and those which are intrinsic to the logico-algebraic structure of conditionals. Our main result provides a way to regard standard two-place conditional probabilities as one-place probability functions on conditional events. We also consider a logical counterpart of our Boolean algebras of conditionals with links to preferential consequence relations for non-monotonic reasoning. The overall framework of this paper provides a novel perspective on the rich interplay between logic and probability in the representation of conditional knowledge.
Global and Local Attention-Based Free-Form Image Inpainting

Deep-learning-based image inpainting methods have shown significant promise in both rectangular and irregular holes. However, the inpainting of irregular holes presents numerous challenges owing to uncertainties in their shapes and locations. When depending solely on convolutional neural network (CNN) or adversarial supervision, plausible inpainting results cannot be guaranteed because irregular holes need attention-based guidance for retrieving information for content generation. In this paper, we propose two new attention mechanisms, namely a mask pruning-based global attention module and a global and local attention module to obtain global dependency information and the local similarity information among the features for refined results. The proposed method is evaluated using state-of-the-art methods, and the experimental results show that our method outperforms the existing methods in both quantitative and qualitative measures.
Multimodal Neural Graph Memory Networks for Visual Question Answering

We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for visual question answering.The MN-GMN uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in an image.The input module of the MN-GMN generates a set of visual features plus a set of encoded region-grounded captions (RGCs) for the image.The RGCs capture object attributes and their relationships.Two GNs are constructed from the input module using the visual features and encoded RGCs.Each node of the GNs iteratively computes a questionguided contextualized representation of the visual/textual information assigned to it.Then, to combine the information from both GNs, the nodes write the updated representations to an external spatial memory.The final states of the memory cells are fed into an answer module to predict an answer.Experiments show MN-GMN rivals the state-of-the-art models on Visual7W, VQA-v2.0, and CLEVR datasets.
Multimedia in Education: What do the Students Think?

Multimedia is considered a cost effective and practical learning medium. This study aimed to investigate the attitudes of medical and allied healthcare students towards the use of various multimedia learning aids and explored students’ attitudes towards these learning aids. An online questionnaire was used to collect data. Students were invited to partake in the study via email and data was collected over a six-month period. A total of 153 students responded to the survey. The data shows that students have had a positive experience using devices like smartphones (88.88%, n = 136) and laptops (89.19%, n = 132). Students are confident searching for multimedia resources (76.31%, n = 116), and have some familiarity or exposure to multimedia as part of their learning experience. Students preferred traditional teaching methods to learning with multimedia (58.59%, n = 75), but regarded multimedia as an effective and efficient tool for practical learning (73.02%, n = 111). The participants reported using e-learning tools and 2D animations more frequently than other multimedia tools and reported being least familiar with Augmented Reality and 3D websites. Finally, students expressed an interest in the development of 3D animations (66.66%, n = 80), interactive 3D teaching tools (65.45%, n = 72), and simulators (64.07%, n = 66) to complement their future studies. The results of the study show that participants acknowledged the important role of multimedia as a practical learning tool that can greatly complement and enhance the traditional teaching methods but cannot replace them. Medical and healthcare students expressed a particular interest in the development of interactive tools including simulators to supplement their studies and enhance the learning process.
Intelligent Virtual Machine Provisioning in Cloud Computing

Virtual machine (VM) provisioning is a common and critical problem in cloud computing. In industrial cloud platforms, there are a huge number of VMs provisioned per day. Due to the complexity and resource constraints, it needs to be carefully optimized to make cloud platforms effectively utilize the resources. Moreover, in practice, provisioning a VM from scratch requires fairly long time, which would degrade the customer experience. Hence, it is advisable to provision VMs ahead for upcoming demands. In this work, we formulate the practical scenario as the predictive VM provisioning (PreVMP) problem, where upcoming demands are unknown and need to be predicted in advance, and then the VM provisioning plan is optimized based on the predicted demands. Further, we propose Uncertainty-Aware Heuristic Search (UAHS) for solving the PreVMP problem. UAHS first models the prediction uncertainty, and then utilizes the prediction uncertainty in optimization. Moreover, UAHS leverages Bayesian optimization to interact prediction and optimization to improve its practical performance. Extensive experiments show that UAHS performs much better than state-of-the-art competitors on two public datasets and an industrial dataset. UAHS has been successfully applied in Microsoft Azure and brought practical benefits in real-world applications.
Image Inpainting Detection Based on Multi-task Deep Learning Network

Image inpainting can effectively repair damaged areas, but it can also be a way of image tampering when it is used to remove meaningful content from an image. Therefore, this paper focuses on the research of inpainting forensics, and proposes a multi-task deep learning method. In order to enhance the learning of texture features, the corresponding local binary pattern channels are added to the input of the network. Furthermore, considering that the multi-task object detection network Mask R-CNN cannot fully utilize the features of all scale feature information during the FPN feature extraction phase, the network in this paper combines Feature Pyramid Networks and back connections to extract more features. This network model can detect not only the images tampered by traditional inpainting methods, but also the images inpainted by deep learning methods. Experimental results on two large public data sets demonstrate the superior performance of the proposed method.