The recently proposed mask-predict decoding algorithm has narrowed the performance gap between semi-autoregressive machine translation models and the traditional left-to-right approach. We introduce a new training method for conditional masked language models, SMART, which mimics the semi-autoregressive behavior of mask-predict, producing training examples that contain model predictions as part of their inputs. Models trained with SMART produce higher-quality translations when using mask-predict decoding, effectively closing the remaining performance gap with fully autoregressive models.

School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK; a. mosavi@brookes.ac.uk Abstract: Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods . The traditional road inspecti on systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, t he proposed models utilize surface deflection data from falling weight deflectometer (FWD) test s to predict the PC I. Machine learning methods are the single multi - layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., L eve nberg - M arquardt (MLP - LM), scaled conjugate gradient (MLP - SCG), imperialist competitive (RBF - ICA), and g enetic algorithms (RBF - GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accur acy of the modeling. The results of the analysis have been verified through using four criteria of aver age percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS mode l outperforms other models with the promising results of APRE 2.3303, AAPRE 11.6768, RMSE 12.0056, and SD 0.0210. Introduction In road transportation, pavement plays a vital role as th e part of the road that is in direct contact with vehicles . U sers' judgment about the quality of road service is primarily predicated upon pavement conditions. The Maintena nce, Rehabilitation, and Reconstruction (MR&R) program of pavement network is a multidimensional decision - making process that takes into account several consideration s.

The second generation of Google's smallest smart speaker gets a new name, more eco-friendly, a little smarter and more bass. The £49 Nest Mini replaces the Google Home Mini as part of a revamped and renamed line of Google smart home products under the Nest brand, pushing its predecessor to a clearance price of only £19. From the outside you would be hard pushed to see what has changed. The Nest Mini sticks with the same pincushion design with a fabric top and nonslip rubber pad on the bottom. The top contains three far-field microphones and is touch sensitive.

It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier. The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.

The AIOps Catalyst team's work has resulted in a new collaborative workstream focused around the topic within TM Forum. Artificial intelligence (AI) offers huge opportunities for communications service providers (CSPs) to do things better, faster and cheaper. In fact, they have no choice but to introduce AI into operations and business processes due to growing complexity and the sheer volume of data and transactions. However, as well as delivering huge benefits, the introduction of AI also creates new challenges relating to the management of services and processes. A TM Forum Catalyst team is taking a two-pronged approach, tackling both these areas simultaneously to ensure CSPs – and their customers – reap the rewards of AI.

The front end of recycling is familiar to the point of invisibility: Blue bins, clear bags, and barely comprehensible signs designating which material goes where. Once the right plastic or paper is put in the right place, most people forget all about it. For the actual recycled material, though, that's not the end of the journey but rather the beginning. Most of it gets trucked to a special recycling facility, where it is unceremoniously dumped on a concrete floor. Front-end loaders scoop bottles, papers, and myriad other materials onto conveyors, which zoom off in various directions, often climbing to different levels like staircases.

NIRSpectroscopy NIRS Sensors NearInfrared Analyzers DigitalTransformation QualityControl foodtech machinelearning AI datascience LINK SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK Do you develop NIR / NIRS calibrations by yourself? Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared "Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution" LINK "Advances in Near Infrared Spectroscopy and Related Computational Methods" MDPI Books – Pages: 496 OpenAccess NIRSpectroscopy NIRS NIR LINK " Ampliación de una librería espectral de mezclas unifeed analizadas en un instrumento NIRS de laboratorio" LINK "Applied Sciences, Vol. 9, Pages 5058: Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy" LINK " Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK "Non-invasive measurements of'Yunhe'pears by vis-NIRS technology coupled with deviation fusion modeling approach" LINK "Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties." LINK "Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis" LINK "Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "NIR spectroscopic determination of urine components in spot urine: preliminary investigation towards optical point-of-care test." LINK "O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression."

New technologies like artificial intelligence and machine learning are changing the way work gets done all over the world. We believe that 2020 is the year that companies will embrace these powerful technologies and apply them to revolutionize their business processes. Here's how process-minded leaders can capture the opportunity. In the past few years, Process Mining grew faster than any other technology in the BPM and process excellence space -- even faster than RPA, according to theInternational Data Corporation, IDC. In 2020, the rapid growth will continue.

We've written a lot about how new technologies are building a better supply chain. But technologies such as AI, IoT and advanced analytics can only achieve their true potential if all parties within the supply chain network are working together. Even the smallest, most well-intentioned decisions made by individual stakeholders can cause catastrophic failures in the manufacturing and delivery of goods. Modern collaborative business planning lets partners on a supply chain work off shared data unspoiled by human misconceptions and misestimations. Automated integrated planning removes bias from supply chain data and creates a single, transparent source that engenders collaboration.

The paper announces the new long-term challenge for improving the performance of automatic speech recognition systems. The goal of the challenge is to investigate methods of correcting the recognition results on the basis of previously made errors by the speech processing system. The dataset prepared for the task is described and evaluation criteria are presented.