Reviewerly/exHarmony huggingface dataset:
DAE-GP

Estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of the royal tree problem that DAE-GP outperforms standard GP and that performance differences increase with higher problem complexity. Furthermore, DAE-GP is able to create offspring with higher fitness from a learned model in comparison to standard GP. We believe that the key reason for the high performance of DAE-GP is that we do not impose any assumptions about the relationships between learned variables which is different to previous EDA-GP models. Instead, DAE-GP flexibly identifies and models relevant dependencies of promising candidate solutions.
Blockchain based Secure Data Storage and Access Control System using Cloud

Cloud storage today depends entirely on large storage providers. Such storage providers function as untrusted third parties that process data for storing, sending and receiving data from an entity. This style of system has many problems, such as high operating costs, software quality and data security. In this paper, we present a model of a multi-user access control system for databases that use blockchain technology to provide stable, distributed data processing. The system allows the data owner to upload the data via a web portal. So, the user who has the secret key to the particular data that has been uploaded to Cloud in encrypted form can only access the folder. Eventually, the system promotes data privacy by maintaining the immutability of the blockchain by processing it in the cloud. We have proposed a secure, blockchain-based data storage and access control system to increase the security of cloud storage.
Optimal subset selection for causal inference using machine learning ensembles and particle swarm optimization

Abstract We suggest and evaluate a method for optimal construction of synthetic treatment and control samples for the purpose of drawing causal inference. The balance optimization subset selection problem, which formulates minimization of aggregate imbalance in covariate distributions to reduce bias in data, is a new area of study in operations research. We investigate a novel metric, cross-validated area under the receiver operating characteristic curve (AUC) as a measure of balance between treatment and control groups. The proposed approach provides direct and automatic balancing of covariate distributions. In addition, the AUC-based approach is able to detect subtler distributional differences than existing measures, such as simple empirical mean/variance and count-based metrics. Thus, optimizing AUCs achieves a greater balance than the existing methods. Using 5 widely used real data sets and 7 synthetic data sets, we show that optimization of samples using existing methods (Chi-square, mean variance differences, Kolmogorov–Smirnov, and Mahalanobis) results in samples containing imbalance that is detectable using machine learning ensembles. We minimize covariate imbalance by minimizing the absolute value of the distance of the maximum cross-validated AUC on $$ M $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>M</mml:mi></mml:math> folds from 0.50, using evolutionary optimization. We demonstrate that particle swarm optimization (PSO) outperforms modified cuckoo swarm (MCS) for a gradient-free, non-linear noisy cost function. To compute AUCs, we use supervised binary classification approaches from the machine learning and credit scoring literature. Using superscore ensembles adds to the classifier-based two-sample testing literature. If the mean cross-validated AUC based on machine learning is 0.50, the two groups are indistinguishable and suitable for causal inference.
Quasi-Bayes Properties of a Procedure for Sequential Learning in Mixture Models

Summary Bayesian methods are often optimal, yet increasing pressure for fast computations, especially with streaming data, brings renewed interest in faster, possibly suboptimal, solutions. The extent to which these algorithms approximate Bayesian solutions is a question of interest, but often unanswered. We propose a methodology to address this question in predictive settings, when the algorithm can be reinterpreted as a probabilistic predictive rule. We specifically develop the proposed methodology for a recursive procedure for on-line learning in non-parametric mixture models, which is often referred to as Newton’s algorithm. This algorithm is simple and fast; however, its approximation properties are unclear. By reinterpreting it as a predictive rule, we can show that it underlies a statistical model which is, asymptotically, a Bayesian, exchangeable mixture model. In this sense, the recursive rule provides a quasi-Bayes solution. Although the algorithm offers only a point estimate, our clean statistical formulation enables us to provide the asymptotic posterior distribution and asymptotic credible intervals for the mixing distribution. Moreover, it gives insights for tuning the parameters, as we illustrate in simulation studies, and paves the way to extensions in various directions. Beyond mixture models, our approach can be applied to other predictive algorithms.
StoCast: Stochastic Disease Forecasting With Progression Uncertainty

Forecasting patients' disease progressions with rich longitudinal clinical data has drawn much attention in recent years due to its impactful application in healthcare and the medical field. Researchers have tackled this problem by leveraging traditional machine learning, statistical techniques and deep learning based models. However, existing methods suffer from either deterministic internal structures or over-simplified stochastic components, failing to deal with complex uncertain scenarios such as progression uncertainty (i.e., multiple possible trajectories) and data uncertainty (i.e., imprecise observations and misdiagnosis). To overcome these major uncertainty issues, we propose a novel deep generative model, Stochastic Disease Forecasting Model (StoCast), along with an associated neural network architecture StoCastNet, that can be trained efficiently via stochastic optimization techniques. Our StoCast model uses internal stochastic components to deal with departures of observed data from patients' true health states, and more importantly, is able to produce a comprehensive estimate of future disease progression trajectories. Based on two public datasets related to Alzheimer's disease and Parkinson's disease, we demonstrate how our StoCast model achieves robust and superior performance than deterministic baseline approaches, and conveys richer information that can potentially assist doctors to make decisions with greater confidence in a complex uncertain scenario.
Evaluating integrated use of information technologies in secondary schools of Ethiopia using <scp>design‐reality</scp> gap analysis: A school‐level study

Abstract The purpose of this study was to examine the level of readiness of Ethiopian secondary education systems in terms of access to technologies and preparedness in skills and motivation for the integrated use of information communication technologies (ICT) for quality education. To serve this end, a Design‐Reality Gap Analysis approach was employed. Relevant data were collected from teachers, students, and school leaders through a questionnaire survey, and the result was cross‐examined against the national goals and strategies. The Design‐Reality Gap Analysis of integrated use of ICT in classroom teaching in Ethiopian secondary schools thus far revealed fewer successes and widespread challenges in the country's discourse to achieve the 2016‐2020 national targets. The study showed that students have better access to mobile phones and other technologies at home and outside of schools. Major school‐level factors for the gap observed were delay in implementation of a nationwide e‐cloud based ICT infrastructure, lack of coordination for the pedagogical use of ICT, and insufficient capacity building training for teachers and school leaders. This suggests the importance of a strategy that integrates inside‐ and outside of schools ICT resources and services for improved use of ICT in student learning.
COCO Dataset Stuff Segmentation Challenge

In computer vision, image segmentation is a method in which a digital image is divided/partitioned into multiple set of pixels which are called super-pixels, stuff segmentation challenge is a newly introduced task in which we have to segment out stuff out of the digital image. This job is to promote the state of the art in semantical category segmentation. Semantic segmentation defines the method by which each image pixel with the category is combined. Semantic classes can be well-defined stuff such as a vehicle, an individual or things like huge backdrop areas such as anarea covered with grass. stuff classes are crucial, as they explain aspects of an image such as type of scene and thing classes represent things, objects, there are various factors to it such as location, physical attributes, type of the material and properties of the scene. Only courses like lawn, ceiling, are focused on this problem. The method in this paper consists of a convolutional neural network and provides a superior framework pixel-level task, the dataset used in this research is the COCO dataset [1], which is used in a worldwide challenge on Codalab. It is a huge dataset with a total of 164k images in which around 118k images in the training dataset and around 5k images in the validation dataset, 20K images in test-dev , 20K images in test-challenge, we use this dataset to analyze the importance of thing and stuff classes in terms of their cover range and to check how many times they appear in image captions, this research is also important to find out whether stuff segmentation is easier than thing segmentation or not.
A framework for the recognition of horse gaits through wearable devices

The wearable devices market has been growing exponentially in the last few years and it is expected to count up to 930 million devices by the end of 2021. A common application of wearable devices is Human Activity Recognition (HAR), i.e., the ability of using the sensing capabilities of these devices to monitor and infer human activities. However, Animal Activity Recognition (AAR) has received significantly less attention, and most works on AAR are generally based on invasive specialized devices carried by or implanted on animals. Conversely, in this work we exploit the potential of portable and unobtrusive devices, namely smartwatches, for AAR and specifically for horse gaits recognition. This application finds natural use in horse riding, to improve the structure and balance of the horse work and training. We develop a framework that can be used in a fog computing system composed by a smartphone and a smartwatch for the recognition of horse gaits. The framework performs classification by means of a machine learning approach trained on ad-hoc features based on accelerometer data. The framework allows an offline and an online modes of operation. In the offline mode, the smartwatch is used to collect the accelerometer data and transfer it to the smartphone at the end of the riding session. The feature extraction and classification can be processed directly on the smartphone or offloaded to the cloud. Conversely, in the online mode, the smartwatch is responsible to collect and process the data, thus being able to provide real-time feedback to the rider. This modality also allows to reduce computation, storage, and energy burden on the smartwatch through an adaptive setting of the sampling frequency. We implement our approach on a system composed by a Fitbit Ionic smartwatch and a Samsung Galaxy S10. We use two horses to evaluate the performance, versus recently proposed AAR approaches. Results show that our framework achieves significantly higher classification accuracy. Furthermore, the online scheme enables flexible real-time feedback, at the expense of a small loss in the classification accuracy.
DeepVix

Machine learning automates the process of analytical model building by means of the computing power of machines. Visual analytics couples interactive visual representations and underlying analysis, putting the human at the center of the analytics and decisionmaking process. This paper aims to combine the strengths of both data science fields into a unified system, called DeepVix, which focuses on the visual explainability of the multivariate time-series predictions using neural networks. Within our DeepVix system, a visual presentation of the neural network explains the intermediate steps, as well as the temporal weights of various gates of the entire learning process. The relationships between input variables and the target variable can also be inferred automatically from the trained model. Interactive operations allow users to explore the neural network, to gain understandings of the model and essential features with layers and nodes, and finally to customize the neural network configurations to fit their needs. We demonstrate our approach with Recurrent Deep Learning on various real-world time series datasets, including the multivariate measurements of a medium-size High-Performance Computing Center, the S&P500 stock data over the past 39 years, and the US employment data retrieved from the Bureau of Labor and Statistics.
Intelligence Graphs for Threat Intelligence and Security Policy Validation of Cyber Systems

While the recent advances in data science and machine learning attract lots of attention in cyber security because of their promise for effective security analytics, vulnerability analysis, risk assessment, and security policy validation remain slightly aside. This is mainly due to the relatively slow progress in the theoretical formulation and the technological foundation of the cyber security concepts such as logical vulnerability, threats, and risks. In this article, we are proposing a framework for logical analysis, threat intelligence, and validation of security policies in cyber systems. It is based on multi-level model, consisting of ontology of situations and actions under security threats, security policies governing the security-related activities, and graph of the transactions. The framework is validated using a set of scenarios describing the most common security threats in digital banking, and a prototype of an event-driven engine for navigation through the intelligence graphs has been implemented. Although the framework was developed specifically for application in digital banking, the authors believe that it has much wider applicability to security policy analysis, threat intelligence, and security by design of cyber systems for financial, commercial, and business operations.
Recommendations for Effective Security Assurance of Software-Dependent Systems

Assuring the security of software-dependent systems in the face of cyber-attacks and failures is now among the top priorities for governments and providers of electric, financial, communication, and other essential services. Practical and foundational solutions for systematic, secure, and trustworthy system development are needed to support developers, regulators, and certification bodies in providing assurance that security threats faced by the software systems used in these environments have been adequately mitigated. Using recent experiences reported in the literature as a basis, we discuss the challenges of providing security assurance for software-dependent systems. We also explore the barriers to adoption of existing approaches and techniques which can play an important role in security assurance efforts. Ultimately, we present a set of recommendations which outline a collection of follow-on research directions that can advance the state-of-the-art and support the development of more effective security assurance solutions for critical software-dependent systems.
Underwater image enhancement using improved generative adversarial network

Summary The generative adversarial network is widely used in image generation, and the generation of images with different styles is applied to underwater image enhancement. The existing underwater image generative adversarial network does not realize color correction when processing underwater images Therefore, we propose an improved generative adversarial network for image color restoration. Firstly, the loss function in the network is improved to train the dataset. Then the improved network is used to detect the underwater image. After network testing, the underwater image is more satisfactory than the traditional image. Numerical results show that this method has a good color restoration and sharpening effects.
Understanding the Workload Characteristics in Alibaba: A View from Directed Acyclic Graph Analysis

Cloud computing technology is widely used in data centers due to the benefits such as high scalability, high availability, on-demand provision, and so on. Understanding the characteristics of complex workloads is crucial for job scheduling, capacity planning, and performance optimization, which provide guarantees for the normal operation of cloud-native applications. Co-location of online services and batch jobs is a common way to improve resource utilization in today's cloud data centers. It is very important for the resource scheduler to adapt to the characteristics of complex and varied workloads. In this paper, we perform an in-depth analysis on the latest released trace dataset by Alibaba in December 2018, consists of 4195049 batch jobs and 71476 containers co-locating on about 4000 machines. Different from Alibaba trace 2017, the information of directed acyclic graph (DAG) is added in the trace, which explains the relationship between the jobs and tasks. We first show the basic statics of the cluster. Then, we conducted an in-depth analysis of the workload types and DAG information. The results reveal several new insights: i) The resource utilization in the cluster shows an obvious periodic phenomenon (day and week). ii) There is a resource (e.g. CPU, memory usage) overprovisioning phenomenon in the cluster. iii) The workloads' characteristics of different workload types are very different. iv) The distribution of instance startup time and termination time is unbalanced. v) 90% of the job's DAG length does not exceed 30 while the maximum length is 54.
Effects of planning and action control on smartphone security behavior

Smartphone users are often said to be the weakest link in information security. They often install apps from unknown sources, connect to open WIFI in public places, and check and open dangerous email attachments on their phones. The current study aims to uncover psychological mechanisms that may be responsible for individual differences in smartphone security behavior, theoretically based on the health action process approach (HAPA). In a longitudinal survey, 192 students in the age range from 18 to 47 years (141 of them women) completed psychometric assessments at baseline and two weeks later. Intention, planning, action control, and smartphone security behaviors were measured along with socio-demographic information. The path analysis revealed that planning and action control mediated sequentially the relationship between intention and smartphone security behavior. The model accounted for 52.30% of the variance in smartphone security behavior. The result supported the theoretical assumptions of the HAPA, with a focus on the mediation by planning and action control. Interventions that focus on such factors might help improve compliance with recommendations for smartphone security behavior.
Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.
Globalized Multiple Balanced Subsets With Collaborative Learning for Imbalanced Data

The skewed distribution of data brings difficulties to classify minority and majority samples in the imbalanced problem. The balanced bagging randomly undersampes majority samples several times and combines the selected majority samples with minority samples to form several balanced subsets, in which the numbers of minority and majority samples are roughly equal. However, the balanced bagging is the lack of a unified learning framework. Moreover, it fails to concern the connection of all subsets and the global information of the entire data distribution. To this end, this article puts several balanced subsets into an effective learning framework with a criterion function. In the learning framework, one regularization term called RS establishes the connection and realizes the collaborative learning of all subsets by requiring the consistent outputs of the minority samples in different subsets. Besides, another regularization term called RW provides the global information to each basic classifier by reducing the difference between the direction of the solution vector in each subset and that in the entire dataset. The proposed learning framework is called globalized multiple balanced subsets with collaborative learning (GMBSCL). The experimental results validate the effectiveness of the proposed GMBSCL.