Numerical Modeling of Inclusion Removal in Electromagnetic Stirred Steel Billets

To investigate the inclusion removal in billets cast under electromagnetic stirring (EMS) influence, a numerical model has been developed to compute the magnetic field, the Lorentz force, the steel flow velocities, and the particle transport within the liquid pool. The electromagnetic field was described by the Maxwell equations and the finite element method was applied using a commercial package. The turbulent fluid flow was described by the Navier‐Stokes equation and by the Reynolds Stress model and the finite volume method was applied using another numerical package. The time average of the Lorentz force was calculated in each element center and this value was applied as a body force in the Navier‐Stokes equation. The magnetic flux density profile was compared with the data obtained in the stirrer of the steel plant. The particle transport model includes the drag force, the buoyancy force and the random walk model, to include the turbulence effects on the particle trajectory. The inclusion removal was calculated and analysed in function of casting speed and stirring current for one size section of mold. The inclusions considered in the calculations have a fixed density and four values of diameter. The numerical results of the electromagnetic model are in agreement with the experimental measurements. A good relationship between the electromagnetic model and the fluid flow model could be shown. An interesting effect is the break of the rotation motion due to the EMS by the jet from the nozzle. The fraction of inclusions removed by the top surface of the mold was improved due to the EMS.     

Neural network solution for a real-time no-reference video quality assessment of H.264/AVC video bitstreams

The ever-growing video streaming services require accurate quality assessment with often no reference to the original media. One primary challenge in developing no-reference (NR) video quality metrics is achieving real-timeliness while retaining the accuracy. A real-time no-reference video quality assessment (VQA) method is proposed for videos encoded by H.264/AVC codec. Temporal and spatial features are extracted from the encoded bit-stream and pixel values to train and validate a fully connected neural network. The hand-crafted features and network dynamics are designed in a manner to ensure a high correlation with human judgment of quality as well as minimizing the computational complexities. Proof-of-concept experiments are conducted via comparison with: 1) video sequences rated by a full-reference quality metric, and 2) H.264-encoded sequences from the LIVE video dataset which are subjectively evaluated through differential mean opinion scores (DMOS). The performance of the proposed method is verified by correlation measurements with the aforementioned objective and subjective scores. The framework achieves real-time execution while outperforming state-of-art full-reference and no-reference video quality assessment methods.

     

A Timing Assumption and Two t-Resilient Protocols for Implementing an Eventual Leader Service in Asynchronous Shared Memory Systems

This paper considers the problem of electing an eventual leader in an asynchronous shared memory system. While this problem has received a lot of attention in message-passing systems, very few solutions have been proposed for shared memory systems. As an eventual leader cannot be elected in a pure asynchronous system prone to process crashes, the paper first proposes to enrich the asynchronous system model with an additional assumption. That assumption (denoted AWB) is particularly weak. It is made up of two complementary parts. More precisely, it requires that, after some time, (1) there is a process whose write accesses to some shared variables be timely, and (2) the timers of (t−f) other processes be asymptotically well-behaved (t denotes the maximal number of processes that may crash, and f the actual number of process crashes in a run). The asymptotically well-behaved timer notion is a new notion that generalizes and weakens the traditional notion of timers whose durations are required to monotonically increase when the values they are set to increase (a timer works incorrectly when it expires at arbitrary times, i.e., independently of the value it has been set to). The paper then focuses on the design of t-resilient AWB-based eventual leader protocols. “t-resilient” means that each protocol can cope with up to t process crashes (taking t=n−1 provides wait-free protocols, i.e., protocols that can cope with any number of process failures). Two protocols are presented. The first enjoys the following noteworthy properties: after some time only the elected leader has to write the shared memory, and all but one shared variables have a bounded domain, be the execution finite or infinite. This protocol is consequently optimal with respect to the number of processes that have to write the shared memory. The second protocol guarantees that all the shared variables have a bounded domain. This is obtained at the following additional price: t+1 processes are required to forever write the shared memory. A theorem is proved which states that this price has to be paid by any protocol that elects an eventual leader in a bounded shared memory model. This second protocol is consequently optimal with respect to the number of processes that have to write in such a constrained memory model. In a very interesting way, these protocols show an inherent tradeoff relating the number of processes that have to write the shared memory and the bounded/unbounded attribute of that memory.     

Drug Usage Safety from Drug Reviews with Hybrid Machine Learning Approach

With the increasing usage of drugs to remedy different diseases, drug safety has become crucial over the past few years. Often medicine from several companies is offered for a single disease that involves the same/similar substances with slightly different formulae. Such diversification is both helpful and dangerous as such medicine proves to be more effective or shows side effects to different patients. Despite clinical trials, side effects are reported when the medicine is used by the mass public, of which several such experiences are shared on social media platforms. A system capable of analyzing such reviews could be very helpful to assist healthcare professionals and companies for evaluating the safety of drugs after it has been marketed. Sentiment analysis of drug reviews has a large potential for providing valuable insights into these cases. Therefore, this study proposes an approach to perform analysis on the drug safety reviews using lexicon-based and deep learning techniques. A dataset acquired from the ‘Drugs.Com’ containing reviews of drug-related side effects and reactions, is used for experiments. A lexicon-based approach, Textblob is used to extract the positive, negative or neutral sentiment from the review text. Review classification is achieved using a novel hybrid deep learning model of convolutional neural networks and long short-term memory (CNN-LSTM) network. The CNN is used at the first level to extract the appropriate features while LSTM is used at the second level. Several well-known machine learning models including logistic regression, random forest, decision tree, and AdaBoost are evaluated using term frequency-inverse document frequency (TF-IDF), a bag of words (BoW), feature union of (TF-IDF + BoW), and lexicon-based methods. Performance analysis with machine learning models, long short term memory and convolutional neural network models, and state-of-the-art approaches indicate that the proposed CNN-LSTM model shows superior performance with an 0.96 accuracy. We also performed a statistical significance T-test to show the significance of the proposed CNN-LSTM model in comparison with other approaches.     

O-Buffer based IFT watershed from markers for large medical datasets

The watershed transform from markers is a very popular image segmentation operator. The image foresting transform (IFT) watershed is a common method to compute the watershed transform from markers using a priority queue, but which can consume too much memory when applied to three-dimensional medical datasets. This is a considerable limitation on the applicability of the IFT watershed, as the size of medical datasets keeps increasing at a faster pace than physical memory technologies develop. This paper presents the O-IFT watershed, a new type of IFT watershed based on the O-Buffer framework, and introduces an efficient data representation which considerably reduces the memory consumption of the algorithm. In addition, this paper introduces the O-Buckets, a new implementation of the priority queue which further reduces the memory consumption of the algorithm. The new O-IFT watershed with O-Buckets allows the application of the watershed transform from markers to large medical datasets.     

Volumetric Curved Planar Reformation for virtual endoscopy

Curved Planar Reformation (CPR) has proved to be a practical and widely used tool for the visualization of curved tubular structures within the human body. It has been useful in medical procedures involving the examination of blood vessels and the spine. However, it is more difficult to use it for large, tubular, structures such as the trachea and the colon because abnormalities may be smaller relative to the size of the structure and may not have such distinct density and shape characteristics. Our new approach improves on this situation by using volume rendering for hollow regions and standard CPR for the surrounding tissue. This effectively combines gray scale contextual information with detailed color information from the area of interest. The approach is successfully used with each of the standard CPR types and the resulting images are promising as an alternative to virtual endoscopy. Because the CPR and the volume rendering are tightly coupled, the projection method used has a significant effect on properties of the volume renderer, such as distortion and isometry. We describe and compare the different CPR projection methods and how they affect the volume rendering process. A version of the algorithm is also presented which makes use of importance driven techniques; this ensures the users attention is always focused on the area of interest and also improves the speed of the algorithm.     

Compared Analysis of Metro Networks Supported by Graph Theory

This paper brings into focus the topological and geographical evaluation of metro networks through the definition of a methodological approach based on a set of indicators, a lot of which are defined in the sector literature. Once the methodology was stated, the results of an application on the metro networks of 13 big metropolitan areas were illustrated. Statistical comparative analyses are proposed to classify networks.     

OWL2Vec*: embedding of OWL ontologies

Machine Learning - Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing...