Improving Multi-Label Learning by Correlation Embedding

In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such as high time and memory costs. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. Specifically, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we construct a multi-label classifier from the low-dimensional latent feature space to the label space, where the embedding matrix is utilized as the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE. The comparison experiment with the state-of-the-art approaches shows that the proposed method MLLCE has a competitive performance in multi-label learning.

Preprocedural fasting for contrast-enhanced CT: when experience meets evidence

Traditional preparatory fasting policy prior to iodinated contrast media (ICM) assisted contrast-enhanced CT (CECT) examinations lacks methodologically acceptable evidence. Considering the possible negative effects of preprocedural fasting, the latest European Society of Urogenital Radiology guidelines V10.0 and American Committee of Radiology 2021 guidelines clearly state that preprocedural fasting is not recommended prior to routine intravenous ICM administration. This comprehensive and detailed Review presents the current global dietary preparation policies, potential harm of excessive fasting, and a systematical and well-bedded description of practice advancements of dietary preparation. The evidences revealed that there has been no single instance of vomiting-associated aspiration pneumonia due to the undemanding implementation of preparatory fasting prior to CECT yet. Non-fasting would not increase the incidence of emetic symptoms and the risk of aspiration pneumonia. Not every patient should undergo all CECT examinations without preparatory fasting. There is still much more refinement to be done on the preparatory fasting policy. Changes in traditional preparatory fasting policy will make positive and significant implications on clinical practice. This Review aims to provide operational guidance and suggestions for practitioners and policymakers, motivate efficient, reasonable, safe and normative ICM usage, and achieve optimal patient clinical benefits and high-quality radiological care practices.

Client side and Server Side Load Balancing

In the microservice architecture it is vital to distribute loads across replicated instances of microservices. Load distribution such that no single instance is overloaded is called as load balancing. Often the instances of microservices are replicated across different racks, different data centers or even different geographies. Modern cloud based platforms offer deployment of microservices across different server instances which are geographically disperse. Having a system that will balance the load across service instances becomes a key success criteria for accurate functioning of distributed software architecture Keywords: Load Balancing, Microservices, Distributed software system

Feature-Based Interpretation of the Deep Neural Network

The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as influential factor to predict their academic achievement. This paper aims to explore the real importance of assignment information for solving students’ performance prediction in distance learning and evaluate the beneficial effect of including this information. We investigate and compare this factor and its potential from two information representation approaches: the traditional representation based on single instances and a more flexible representation based on Multiple Instance Learning (MIL), focus on handle weakly labeled data. A comparative study is carried out using the Open University Learning Analytics dataset, one of the most important public datasets in education provided by one of the greatest online universities of United Kingdom. The study includes a wide set of different types of machine learning algorithms addressed from the two data representation commented, showing that algorithms using only information about assignments with a representation based on MIL can outperform more than 20% the accuracy with respect to a representation based on single instance learning. Thus, it is concluded that applying an appropriate representation that eliminates the sparseness of data allows to show the relevance of a factor, such as the assignments submitted, not widely used to date to predict students’ academic performance. Moreover, a comparison with previous works on the same dataset and problem shows that predictive models based on MIL using only assignments information obtain competitive results compared to previous studies that include other factors to predict students performance.

THE STATE OF THE HERBACEOUS VEGETATION OF THE ROSSOSHANSKY DISTRICT OF THE VORONEZH REGION

This article presents information about the state of the herbaceous vegetation of the Rossoshansky district of the Voronezh Region. The studies were conducted on 2 trial sites. For this purpose, the eye — measuring method of ground cover accounting was used- accounting for the abundance of individual representatives of the ground cover on the Drude scale and on the Zh. Brown-Blanquet scale. Thanks to this, it was possible to identify plant species that predominate in this area, and species that occur only in a single instance.

On cognitive tensionsm.

Aim. The foundation of symbolization is a substitution: a mediation between a Representamen and Object. The paper leverages this core mechanic to examine the substitutions within the conscious and unconscious parts of the mind, which compose every act of thinking. Recognizing it is a single instance: the Ego, which regulates this parallel mediation, the paper focuses on the exploration of dichotomies that result from the necessity to perform two symbolizations simultaneously. Concepts. The study’s theoretical framework is determined by Charles S. Peirce’s (1998) concept of sign and Melanie Klein’s (1948) psychoanalytic theory. From semiotic and psychoanalytic angles, this paper explores possible comprehensions of the object in the quasi-mind (Interpretant in infinite semiosis) and actual realization of code in the act of individual thinking (Ego mediating between conscious and unconscious symbolization). Results and conclusion. The main result of the study is the exposure of dichotomies that structure the shared ground for the conscious and the unconscious symbolization. This, in turn, highlights tangible constraints that the mind is subjected to in the act of thinking. Cognitive value. The study’s main contribution is the high-level scheme of dynamics that hold the Ego in reality through the means of unconscious and conscious symbolization. The study also incorporates into coherent model unexamined aspects of individual sign usage: it deploys psychic continuity into the conscious symbolization process (by basing the model on the instance of Ego), which allows addressing the issues arising at the border of conscious and unconscious symbolization.

ON THE USE OF MULTIPLE INSTANCE LEARNING FOR DATA CLASSIFICATION

In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag. Whilst in a single instance learning each instance is connected with the label that contains a single feature vector. This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework. In single-instance learning, two datasets are applied (students’ dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning. Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results. A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning. The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75%, whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of 99.33%.

Real-Time Air Quality Feedback in Children’s Bedrooms Increases Compliance with Indoor Air Guidelines

Air particle monitors were placed in the children's bedrooms in 298 homes. We examined whether outfitting these monitors with immediate auditory and visual stimuli plus weekly coaching increased the probability of establishing a smoke-free home, operationalized by at least single instance of 30 consecutive days below the WHO 25 ug/m3 guideline.

MULTI LABEL CLASSIFICATION FOR AN IMAGE USING CONVOLUTIONAL NEURAL NETWORKS

The machine learning has many capabilities one of them is classification. Classification employed in many contexts like telling hotel reviews good / bad, or inferring the image consists of dog, cat etc. As the data increases there is a need to organize it, for that purpose classification can be helpful. Modern classification problems involve the prediction of multiple labels simultaneously associated with a single instance known as "Multi Label Classification". In multi-label classification, each of the input data samples belongs to one or more than one classes or labels. The traditional binary and multi-class classification problems are the subset of the multi-label classification problem. In this paper we are implementing the multi label classification using CNN framework with keras libraries. Classification can be applied to different domain such as text, audio etc. In this paper we are applying classification for an image dataset.