Reviewerly/exHarmony huggingface dataset:
K voprosu o tsifrovoj transformatsii predmetnogo obucheniya [About digital transformation of subject learning]

Digital education is carried out due to a deep transformation of the educational process and widespread use of digital educational technologies. Within the framework of subject learning, digital technologies are radically changing the content of the disciplines taught and the form of their presentation. The experience of creating a digital educational environment (DEE) in the framework of subject training is considered on the example of basic geometric and graphic training (GGT) of technical university students. Currently, the main factors shaping GGT are the paradigm of engineering education and methodology of design and development activities, which have undergone dramatic changes with the development of information technology. The basic concept of graphic education should initially and entirely be based on a digital 3D (volumetric) model that combines information about a product at all stages of its life cycle. At the same time, applied geometric aspects should prevail over abstract geometry and graphics, not underestimating the role of theoretical foundations in the basic GGP. The architecture of building DEE of geometric-graphic training is presented, which integrates all types of educational activities and is based on the principle of “continuity” of the learning process in a digital environment at all stages of the educational process. The functional use of digital technologies in various forms of training sessions, which allows students to carry out independent activities, taking into account personalization, is noted. Practical examples of graphic tasks, design and test tasks are given. When organizing monitoring in DEE conditions, existing methodological developments can be used, adapted and integrated in terms of the DEE system and the paradigmatic concept of GGT. Monitoring of learning outcomes in assessing digital competence, along with the constant improvement of test control, implies its completion with other forms of assessing the achievements of students.
Time-constrained and network-aware containers scheduling in GPU era

The recent advances on data center management and applications development are reflected by lightweight containers technology and critical Quality-of-Service (QoS) requirements. Tenants encapsulate applications in containers abstracting away details on the infrastructure, and entrust its management framework with the provisioning of network and time QOS requirements. In this paper, we addressed this NP-hard scheduling problem proposing a GPU Accelerated Containers Scheduler (GPUACS). We model the joint allocation of network and containers with QoS requirements as a graph embedding problem. GPUACS innovates by refactoring two Multicriteria Decision Makings (MCDMs) to GPU model, as well as by defining an efficient data structure to speed up the comparison of time-evolving QoS requirements. GPUACS follows a modular and configurable architecture, and the scheduling objective function can be adjusted by selecting the MCDM method and setting the appropriated weights to guide the comparisons. An experimental analysis demonstrated the sensitivity that GPU-tailored MCDM methods have to schedule container requests considering critical time, network, and processing criteria, as well as multiple queueing policies.
Towards Efficient Machine Learning Methods for Penguin Counting in Unmanned Aerial System Imagery

Mapping ecological change in the Antarctic Peninsula is an important problem in the context of global climate change. Penguin populations are an important indicator of the health of the oceans and their associated ecosystems. Unfortunately, most species of penguins live and nest far from human settlements and are, thus, difficult to monitor. Systematic population surveys of penguin colonies require researchers to land on remote Antarctic islands and count the penguins and their nests manually. A recent alternative has been to use Unmanned Aerial Vehicles (UAVs) to map out colonies in detail and use the overlapping imagery to construct orthomosaics that have enough resolution to allow researchers to count entire populations within large areas that encompass multiple colonies. However, the sheer volume of data collected with UAVs acts as a major impediment to a manual census of the penguin population. Thus, our efforts have focused on an image-based penguin counting pipeline that is efficient, automated and non-intrusive. Our approach includes using an UAV to perform aerial imagery surveys on-site, stitching the UAV images into orthomosaics and counting penguins automatically with a deep learning model. We applied two different criteria in the deep learning model: counting penguins laying on their nests during incubation and counting penguin with their chicks on the nests after incubation. Our pipeline has shown promising results on data collected in our 2015 Danger Island campaign and our 2020 Elephant Island and Low Island campaign. This work broadens and expands our own previous efforts using UAV and deep convolutional networks in 2015 on the Danger Islands.
Scene Recognition Based on Recurrent Memorized Attention Network

We propose a new end-to-end scene recognition framework, called a Recurrent Memorized Attention Network (RMAN) model, which performs object-based scene classification by recurrently locating and memorizing objects in the image. Based on the proposed framework, we introduce a multi-task mechanism that contiguously attends on the different essential objects in a scene image and recurrently performs memory fusion of the features of object focused by an attention model to improve the scene recognition accuracy. The experimental results show that the RMAN model has achieved better classification performance on the constructed dataset and two public scene datasets, surpassing state-of-the-art image scene recognition approaches.
Semantic Segmentation of MOOC Lecture Videos by Analyzing Concept Change in Domain Knowledge Graph

Long lecture video metadata needs to have topic wise annotation information for quick topic searching and video browsing. In this work we perform topical segmentation of long MOOC lecture videos to obtain start-time and end-time of different topics taught by the instructor. During teaching instructor uses different concepts to explain a topic. So instructor has his own way of selecting and binding these concepts to represent a topic. Additionally knowledge graph of a subject domain contains inherent domain knowledge. In this work we analyze how the instructor changes concepts during topic change, the inherent knowledge available in a domain knowledge graph, semantic similarity and contextual relationship between different concepts to perform topical segmentation of long lecture videos. As output, we get semantically coherent topics taught by the instructor along with their interval (start-time and end-time). We tested our approach on 61 long NPTEL [1] videos delivered on software engineering domain. Experimentally we find that the topic intervals generated by our system has $$\sim $$ 83% similarity with the intervals present in the ground truth. Holistic evaluation shows that our approach performs better than the other approaches in the literature.
Probability of default estimation, with a reject option

Many companies, such as credit granting companies, have to decide on granting or denying customer or invoice loans on a daily basis.Increasingly, machine learning is used to learn probability-of-default models from previously granted cases and, thus, whether the outcome was positive or negative for the company, i.e. whether the client paid back or defaulted.However, as the outcome can only be observed for the granted cases, the data inherently has sample selection bias and caution should be taken when applying the probability-of-default model to the full through-the-door population.In reject inference, this problem is studied with respect to whether using the unlabeled rejected instances can help improve a classifier that is only trained on granted instances, e.g. using semi-supervised learning.In contrast, we investigate under what circumstances a model trained on the granted instances, with known outcome, can be used on all possible instances.For this, we believe a model should indicate when it cannot reliably predict the outcome.That is, it should refrain from making predictions on instances unlike those on which it was trained.If not, the credit granting company would expose itself to great risk, and experts could lose their trust in the predictions.We discuss similarities and differences of this problem compared to novelty detection, classification with a reject option and reject inference.We compare a number of methods that combine novelty detection with classification, with decent results even for two-stage methods and especially when using data of existing instances with unknown outcome.
Towards Privacy-Preserving Deep Learning: Opportunities and Challenges

During the past decade, deep learning has achieved excellent results in many classic machine learning problems, such as face recognition, spam detection, and financial prediction, etc. The success of deep learning mainly benefits from training on a large amount of data gathered from different sources. However, the training data may include highly sensitive information that may lead to serious threats to personal privacy. In this paper, we review privacy threats in deep learning and present effective privacy-preserving techniques in the literature against these threats. We give a detailed comparison of different techniques and summarize the performance of the existing solutions. Furthermore, we discuss some open problems and challenges in this research area. In the end, we conclude this paper and point out possible research directions.
Sentiment Analysis using Topic-Document Embeddings

Sentiment analysis plays an important role in automatically finding the polarity and insights of users with regards to a specific subject, events, and entity. In this article, we propose a new topic-document embedding (TOPICDOC2VEC) for detecting the polarity of a text. The TOPICDOC2VEC is constructed as the concatenation between a document embedding (DOC2VEC) and a topic embedding (TOPIC2VEC). A DOC2VEC is computed using word embeddings weighted by TF-IDF, while a TOPIC2VEC is computed using the word embeddings for the relevant terms belonging to the topic. To determine the quality of classification using the new proposed topic-document embeddings, we apply two algorithms, i.e., Logistic Regression and Long Short-Term Memory, for the task of sentiment analysis. We build the TOPICDOC2VEC using five word embedding models, i.e., Word2Vec with CBOW and SkipGram, FastText with CBOW and Skip-Gram, and GloVe. The classification task is evaluated using accuracy, precision, and recall. For our empirical validation, we use a large labeled corpus containing game reviews. The experimental results prove that the TOPICDOC2VEC embeddings outperform the DOC2VEC embeddings for detecting the polarity of a document.
An epistemic interpretation of the posterior likelihood ratio distribution

Abstract Often the expression of a likelihood ratio involves model parameters θ. This fact prompted many researchers to argue that a likelihood ratio should be accompanied by a confidence interval, as one would do when estimating θ itself. We first argue against this, based on our view of the likelihood ratio as a function of our knowledge of the model parameters, rather than being a function of the parameters themselves. There is, however, another interval that can be constructed, and which has been introduced in the literature. This is the interval obtained upon sampling from the so-called ‘posterior likelihood ratio distribution’, after removing, say, the most extreme 5% of a sample from this distribution. Although this construction appears in the literature, its interpretation remained unclear, as explicitly acknowledged in the literature. In this article we provide an interpretation: the posterior likelihood ratio distribution tells us which likelihood ratios we can expect if we were to obtain more information. As such, it can play a role in decision making procedures, for instance about the question whether or not it is worthwhile to try to obtain more data. The posterior likelihood ratio distribution has no relevance for the evidential value of the current data with our current knowledge. We illustrate all this with a number of examples.
Generalization performance of Lagrangian support vector machine based on Markov sampling

In this paper, we first establish the generalization bounds of Lagrangian Support Vector Machines (LSVM) based on uniformly ergodic Markov chain (u.e.M.c.) samples. As an application, we also obtain the generalization bounds of LSVM based on strongly mixing sequence, independent and identically distributed (i.i.d.) samples, respectively. The fast learning rates of LSVM for u.e.M.c., strongly mixing sequence and i.i.d. samples are established. We also propose a new LSVM algorithm based on Markov sampling (LSVM̲MS) and show the learning performance of LSVM̲MS for UCI datasets. The experimental results show that the LSVM̲MS can improve obviously the learning performance of the classical LSVM algorithm. If the sampling and training total time is a main concern, the LSVM̲MS algorithm is the preferred method compared the known SVM algorithm based on Markov sampling.
Single-Image Camera Response Function Using Prediction Consistency and Gradual Refinement

A few methods have been proposed to estimate the CRF from a single image, however most of them tend to fail in handling general real images. For instance, EdgeCRF based on patches extracted from colour edges works effectively only when the presence of noise is insignificant, which is not the case for many real images; and, CRFNet, a recent method based on fully supervised deep learning works only for the CRFs that are in the training data, and hence fail to deal with other possible CRFs beyond the training data. To address these problems, we introduce a non-deep-learning method using prediction consistency and gradual refinement. First, we rely more on the patches of the input image that provide more consistent predictions. If the predictions from a patch are more consistent, it means that the patch is likely to be less affected by noise or any inferior colour combinations, and hence, it can be more reliable for CRF estimation. Second, we employ a gradual refinement scheme in which we start from a simple CRF model to generate a result which is more robust to noise but less accurate, and then we gradually increase the model's complexity to improve the estimation. This is because a simple model, while being less accurate, overfits less to noise than a complex model does. Our experiments confirm that our method outperforms the existing single-image methods for both daytime and nighttime real images.
Clustering Based Undersampling for Handling Class Imbalance in C4.5 Classification Algorithm

Abstract Machine Learning is very difficult to make an effective learning model if the distribution of classes in the training data set that is used is not balanced. The problem of class imbalance is mostly found during classifications in the real world where one class is very small in number (minority class) while the other classes are very numerous (majority in class). Building a learning algorithm model without considering the problem of class imbalance causes the learning model to be flooded by majority class instances so that it ignores minority class predictions. Random undersampling and oversampling techniques have been widely used in various studies to overcome class imbalances. In this study using the undersampling strategy with clustering techniques while the classification model uses C4.5. Clustering is used to group data and the undersampling process is performed on each data group. The goal is that sample samples that are useful are not eliminated. Statistical test results from experiments using 10 imbalance datasets from KEEL-repository dan Kaggle dataset with various sample sizes indicate that clustering-based undersampling produces satisfactory performance. Improved performance can be seen from the sensitivity and AUC values that increased significantly.
An adaptive noise removal tool for IoT image processing under influence of weather conditions

As the foundation of intelligent algorithms and applications, data collection from the real world faces the problem that there is serious data degradation under various complex environments. As a typical situation, the visual degradation of images under different weather conditions only can be utilized after arduous image noise removal by application developers previously. To overcome the challenges, previous approaches cannot handle with comprehensive situations. In this paper, we will briefly describe an adaptive image noise removal tool, which can classify multiple weather conditions and enhance image quality with optimized algorithms. Further, we constructed a recognition application using YOLO-3, and validated the effect of our tool through recognition results of real-world images.
An Optimized Structure of State Channel Network to Improve Scalability of Blockchain Algorithms

Nowadays, blockchain is very common and widely used in various fields. The properties of blockchain-based algorithms such as being decentralized and uncontrolled by institutions and governments, are the main reasons that has attracted many applications. The security and the scalability limitations are the main challenges for the development of these systems. Using second layer network is one of the various methods proposed to improve the scalability of these systems. This network can increase the total number of transactions per second by creating extra channels between the nodes that operate in a different layer not obligated to be on consensus ledger. In this paper, the optimal structure for the second layer network has been presented. In the proposed structure we try to distribute the parameters of the second layer network as symmetrically as possible. To prove the optimality of this structure we first introduce the maximum scalability bound, and then calculate it for the proposed structure. This paper will show how the second layer method can improve the scalability without any information about the rate of transactions between nodes.
A flexible image cipher based on orthogonal arrays

A flexible image cipher based on orthogonal arrays is proposed in this paper. Orthogonal array is a typical combinatorial configuration. It has discreteness and uniformity, which makes orthogonal array a natural candidate for cryptography. In particular, an orthogonal array can provide multiple substitution sequences and permutation maps for image cipher. To control the selection of substitution sequences and permutation maps, two public parameters are introduced in the proposed algorithm. If the parameter values vary, the substitution sequences and permutation maps will be different, then the same plaintext image corresponds to different cipher images. By the utilization of orthogonal arrays, the proposed algorithm is flexible and secure, so it is quite applicable to the complex network environment.
Virtual Playground: An Augmented Reality Application to Improve English Learning

Augmented Reality applications for education change the classroom experience for both teachers and students. However, the use of these tools are not widespread yet. The applications still have some limitations, such as: hard to use interfaces, lack of freedom to create content, lack of support for a shared experience and many others. To tackle these problems, this work presents the process of ideation, implementation and testing of Virtual Playground, an application collaboratively created with language teachers, designers and engineers. This work presents results regarding what are the main teachers' needs that developers and researchers have to take into account: intuitive interface, freedom to create and reuse virtual content, an offline version to avoid infrastructure limitations, among others. It also presents the concept validation by teachers and the confirmation that the AR application proposed could be used in different stages of a lesson to enhance learning.
Anisotropic total variation model for removing oblique stripe noise in remote sensing image

Destriping is one of typical problems in remote sensing image processing which is crucial in subsequent applications. The orientation of the stripe in remote sensing image plays a key role in the destriping methods. However, the direction of the oblique stripe noises is uncertain which makes the destriping methods for vertical/horizontal stripes no longer applicable for the oblique ones. To address the issue, we propose an anisotropic total variation (TV) model for oblique stripe noise removal. The model is constructed by combining L1 norm regularization and anisotropic total variation regularization, where the L1 norm regularizations represent the global characteristics and orientation of stripe noise, anisotropic total variation regularizations describe the different effects of oblique stripe noise on the smoothness of the original clear image along the vertical and horizontal directions, which can preserve the strong edges and geometric features while suppressing stripes. In order to solve this model, we design an effective alternating direction method of multipliers (ADMM) algorithm with guaranteed convergence. The experimental results over simulated and real datasets demonstrate that the proposed approach can not only effectively remove oblique stripe noise, but also for vertical/horizontal ones, and outperform the related state-of-the-art methods.
Research on Cybersecurity Risk Assessment in SCADA Networks Based on AHP-RSR

Due to the increase in demand and use of Supervisory Control and Data Acquisition (SCADA) systems, it is crucial to provide a risk assessment for ensuring the normal operation of these industrial systems. In this paper, a novel approach based on Analytic Hierarchy Process (AHP) and Rank-Sum Ratio (RSR) is proposed to reduce the subjectivity and uncertainty of risk assessment, quantitatively and comprehensively assess the security risk level in SCADA networks. Furthermore, this method is able to shorten the time of identifying risks after the occurrence of cyber threats. The simulation results illustrate that the proposed approach is effective for relative risk evaluation for SCADA systems.
Design of a service computing system platform for monitoring plant nutrient deficiencies based on the SCSE framework

In the industrial era 4.0, conventional agriculture is shifting towards smart agriculture that utilizes the internet of things technology to help the development of business services to meet the challenges of competitiveness and demand. The gap between the world of agricultural business and information technology needs to be bridged with the discipline of service computing using a service-oriented architecture (SOA) approach therefore the business processes contained in agriculture can be automated using information technology services to support agricultural management. In this paper, the service computing system engineering (SCSE) framework is used to design a service computing system platform for monitoring plant nutrient deficiencies. We also evaluate the results of service computing system platform designs on four SOA principles, namely the coupling factor, cohesion factor, reusability, and complexity factor. The design of the service computing system platform has met the four SOA principles.