Neural Arabic Text Diacritization: State-of-the-Art Results and a Novel Approach for Arabic NLP Downstream Tasks

In this work, we present several deep learning models for the automatic diacritization of Arabic text. Our models are built using two main approaches, viz. Feed-Forward Neural Network (FFNN) and Recurrent Neural Network (RNN), with several enhancements such as 100-hot encoding, embeddings, Conditional Random Field (CRF), and Block-Normalized Gradient (BNG). The models are tested on the only freely available benchmark dataset and the results show that our models are either better or on par with other models even those requiring human-crafted language-dependent post-processing steps, unlike ours. Moreover, we show how diacritics in Arabic can be used to enhance the models of downstream NLP tasks such as Machine Translation (MT) and Sentiment Analysis (SA) by proposing novel Translation over Diacritization (ToD) and Sentiment over Diacritization (SoD) approaches.

Satellite Image Processing Systems: An Architectural Perspective

Satellite image processing systems include satellite image classification, long ranged data processing, yield prediction systems, etc. All of these systems require a large quantity of images for effective processing, and thus they are directed towards big-data applications. All these applications require a series of highly complex image processing and signal processing steps, which include but are not limited to image acquisition, image pre-processing, segmentation, feature extraction & selection, classification and post processing. Numerous researchers globally have proposed a large variety of algorithms, protocols and techniques in order to effectively process satellite images. This makes it very difficult for any satellite image system designer to develop a highly effective and application-oriented processing system. In this paper, we aim to categorize these large number of researches w.r.t. their effectiveness and further perform statistical analysis on the same. This study will assist researchers in selecting the best and most optimally performing algorithmic combinations in order to design a highly accurate satellite image processing system.

End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach

Background The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes. Results We present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. Conclusion We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy.

High Speed Visual Insect Swarm Tracker (Hi-VISTA) used to identify the effects of confinement on individual insect flight

Individual insects flying in crowded assemblies perform complex aerial maneuvers by sensing and feeding back neighbor measurements to small changes in their wing motions. To understand the individual feedback rules that permit these fast, adaptive behaviors in group flight, a high-speed tracking system is needed capable of tracking both body motions and more subtle wing motion changes for multiple insects in simultaneous flight. This capability extends tracking beyond the previous focus on individual insects to multiple insects. This paper presents Hi-VISTA, which provides a capability to track wing and body motions of multiple insects using high speed cameras (9000 fps). Processing steps consist of automatic background identification, data association, hull reconstruction, segmentation, and feature measurement. To improve the biological relevance of laboratory experiments and develop a platform for interaction studies, this paper applies the Hi-VISTA measurement system to Apis mellifera foragers habituated to transit flights through a transparent tunnel. Binary statistical analysis (Welch's t-test, Cohen's d effect size) of 95 flight trajectories is presented, quantifying the differences between flights in an unobstructed tunnel and in a confined tunnel volume. The results indicate that body pitch angle, heading rate, flapping frequency, and vertical speed (heave) are all affected by confinement, and other flight variables show minor or statistically insignificant changes. These results form a baseline as swarm tracking and analysis begins to isolate the effects of neighbors from environment.

Physicochemical features, functional characteristics, and health benefits of cottonseed oil: a review

Vegetable oils have their specific physicochemical properties due to which they are playing vital role in human nutritional diet for health benefits. Cottonseed oil is obtained from various species of cotton seeds that are famous to be grown mainly for their fiber quality. The most prominently used specie is Gossypium hirsutum. It is obvious that the seeds of different variety of cotton vary as grown in diverse agroclimatic conditions with respect to oil, fats and protein contents. Cottonseed oil is routinely used for cooking and food manufacturing products. Cottonseed oil obtained after proper extraction/processing steps from crude state to refined oil in a variety of ways. Cotton crop is considered for their dual-use purpose, for fiber quality and oil production to promote health benefits in the world. Keeping in view the above facts, this review clearly demonstrated an overview about physicochemical and functional properties of cottonseed oil to promote health benefits associated with the use of this oil. The overall characteristics and all concerned health benefits of CSO will further improve their usefulness is a compact way. We have summarized a brief multi-dimensional features of CSO in all aspects up to the best of our knowledge for the end researchers who can further research in the respective aspect.

Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN

ECG feature extraction has an important role in identifying a number of cardiac diseases. Lots of work has been done in this field but the most important challenges faced in previous work are the selection of proper R-peaks and R-R intervals due to the lack of appropriate pre-processing steps like decomposition, smoothing, filtering, etc., and the optimization of the features for proper classification. In this article, DWT-based pre-processing and ABC is used for optimization of features which helps to achieve better classification accuracy. It is utilized for initial diagnosis of abnormalities. The signals are taken from MIT-BIH arrhythmia database for the analysis. The aim of the research is to classification of six diseases; Normal, Atrial, Paced, PVC, LBBB, RBBB with an ABC optimization algorithm and an ANN classification algorithm on the basis of the extracted features. Various parameters, like, FAR, FRR, and accuracy are measured for the execution. Comparative analysis is shown of the proposed and the existing work to depict the effectiveness of the work.

TXM-Sandbox: an open-source software for transmission X-ray microscopy data analysis

A transmission X-ray microscope (TXM) can investigate morphological and chemical information of a tens to hundred micrometre-thick specimen on a length scale of tens to hundreds of nanometres. It has broad applications in material sciences and battery research. TXM data processing is composed of multiple steps. A workflow software has been developed that integrates all the tools required for general TXM data processing and visualization. The software is written in Python and has a graphic user interface in Jupyter Notebook. Users have access to the intermediate analysis results within Jupyter Notebook and have options to insert extra data processing steps in addition to those that are integrated in the software. The software seamlessly integrates ImageJ as its primary image viewer, providing rich image visualization and processing routines. As a guide for users, several TXM specific data analysis issues and examples are also presented.

Super-sparse marine 3D: A game changer for seismic exploration

TotalEnergies SE and partners Shell and PetroSA recently completed the acquisition and processing of a large (10,000 km2) ultra-sparse (200 m between streamers) marine seismic acquisition survey off the west coast of South Africa in block 5/6/7 using a large PGS Titan Class Ramform vessel. The sparse design enabled fast acquisition and low survey cost and health, safety, and environment exposure. Advances in sparse processing enabled high-quality final seismic data consistent with the exploration objectives. In addition, DUG optimized the 4D regularization/interpolation parameters to approach the near offsets differently than the offsets with more complete coverage to help several processing steps. The survey was designed to be upgradable to a higher-resolution survey if needed via the addition of a custom regular infill pattern, either in its entirety or over targeted areas.

Plant disease prediction using convolutional neural network

Every year India losses the significant amount of annual crop yield due to unidentified plant diseases. The traditional method of disease detection is manual examination by either farmers or experts, which may be time-consuming and inaccurate. It is proving infeasible for many small and medium-sized farms around the world. To mitigate this issue, computer aided disease recognition model is proposed. It uses leaf image classification with the help of deep convolutional networks. In this paper, VGG16 and Resnet34 CNN was proposed to detect the plant disease. It has three processing steps namely feature extraction, downsizing image and classification. In CNN, the convolutional layer extracts the feature from plant image. The pooling layer downsizing the image. The disease classification was done in dense layer. The proposed model can recognize 38 differing types of plant diseases out of 14 different plants with the power to differentiate plant leaves from their surroundings. The performance of VGG16 and Resnet34 was compared. The accuracy, sensitivity and specificity was taken as performance Metrix. It helps to give personalized recommendations to the farmers based on soil features, temperature and humidity

Aroma Profiles of Dry-Hopped Ciders Produced with Citra, Galaxy, and Mosaic Hops

Cider quality and consumer acceptance are greatly influenced by its aroma. With the continued expansion of the craft cider industry, cider producers are employing techniques such as dry hopping to develop unique flavor profiles. Few studies, however, have explored the VOCs of dry-hopped cider. Herein, we monitor the development of VOCs from pressed apple juice, through fermentation and dry hopping by HS–SPME–GC–MS, to elucidate when and how aroma compounds arise in cider production. In all, 89 VOCs were detected, spanning eight classes of organic compounds. Racking events decreased ester concentrations by 10 ± 1%, but resting on the lees allowed these pleasant, fruity aromas to be reestablished. Dry hopping was conducted with three types of hops (Citra, Galaxy, and Mosaic). The varied development of terpenes and esters between hop varieties supports the use of this technique to diversify the aroma profiles of ciders. Herein, we report that both the variety of hops and the timing of key processing steps including racking and hop addition significantly alter the identity and concentration of aroma-important VOCs in dry-hopped cider.