Identifying Leading Indicators for Tactical Truck Parts’ Sales Predictions Using LASSO

This paper aimed to identify leading indicators for a case company that supplies truck parts to the European truck aftersales market. We used LASSO to extract relevant information from a collected pool of business, economic, and market indicators. We propose the efficient one-standard error rule, as an alternative to the default one-standard error rule, to reduce the influence of sampling variation on the LASSO tuning parameter value. We found that applying the efficient one-standard error rule over the default one, improved forecasting performance with an average of 0.73%. Next to that, we found that for our case study, applying forecast combination yielded the best forecasting performance, outperforming all other considered models, with an average improvement of 2.38%. Thus, including leading context information did lead to more accurate parts sales predictions for the case company. Also, due to the transparency of LASSO, using LASSO provided business intelligence about relevant predictors and lead effects. Finally, from a pool of 34 indicators, 7 indicators appeared to have clear lead effects for the case company.</p

A New Open Information Extraction System Using Sentence Difficulty Estimation

The World Wide Web has a considerable amount of information expressed using natural language. While unstructured text is often difficult for machines to understand, Open Information Extraction (OIE) is a relation-independent extraction paradigm designed to extract assertions directly from massive and heterogeneous corpora. Allocation of low-cost computational resources is a main demand for Open Relation Extraction (ORE) systems. A large number of ORE methods have been proposed recently, covering a wide range of NLP tools, from ``shallow'' (e.g., part-of-speech tagging) to ``deep'' (e.g., semantic role labeling). There is a trade-off between NLP tools depth versus efficiency (computational cost) of ORE systems. This paper describes a novel approach called Sentence Difficulty Estimator for Open Information Extraction (SDE-OIE) for automatic estimation of relation extraction difficulty by developing some difficulty classifiers. These classifiers dedicate the input sentence to an appropriate OIE extractor in order to decrease the overall computational cost. Our evaluations show that an intelligent selection of a proper depth of ORE systems has a significant improvement on the effectiveness and scalability of SDE-OIE. It avoids wasting resources and achieves almost the same performance as its constituent deep extractor in a more reasonable time

A Deep Learning-Based Approach for Train Arrival Time Prediction

Level crossings have a function to let the traffic cross the railroad from one side to the other. In the Netherlands, 2300 level crossings are spread out over the country, playing a significant role in daily traffic. Currently, there isn’t an accurate estimation of the arrival time of trains at level crossings while it plays an important role in traffic flow management in intelligent transport systems. This paper presents a state-of-the-art deep learning model for predicting the arrival time of trains at level crossings using spatial and temporal aspects, external attributes, and multi-task learning. The spatial and temporal aspects incorporate geographical and historical travel data and the attributes provide specific information about a train route. Using multi-task learning all the information is combined and an arrival time prediction is made both for the entire route as for sub-parts of that route. Experimental results show that on average, the error is only 281 s with an average trip time of one hour. The model is able to accurately predict the arrival time at level crossings for various time steps in advance. The source code is available at https://github.com/basbuijse/train-arrival-time-estimator.</p

SCRE:special cargo relation extraction using representation learning

The airfreight industry of shipping goods with special handling needs, also known as special cargo, often deals with non-transparent data and outdated technology, resulting in significant inefficiency. A special cargo ontology is a means of extracting, structuring, and storing domain knowledge and representing the concepts and relationships that can be processed by computers. This ontology can be used as the base of semantic data retrieval in many artificial intelligence applications, such as planning for special cargo shipments. Domain information extraction is an essential task in implementing and maintaining special cargo ontology. However, the absence of domain information makes instantiating the cargo ontology challenging. We propose a relation representation learning approach based on a hierarchical attention-based multi-task model and leverage it in the special cargo domain. The proposed relation representation learning architecture is applied for identifying and categorizing samples of various relation types in the special cargo ontology. The model is trained with domain-specific documents on a number of semantic tasks that vary from lightweight tasks in the bottom layers to the heavyweight tasks in the top layers of the model in a hierarchical setting. Therefore, it conveys complementary input features and learns a rich representation. We also train a domain-specific relation representation model that relies only on an entity-linked corpus of cargo shipment domain. These two relation representation models are then employed in a supervised multi-class classifier called Special Cargo Relation Extractor (SCRE). The results of the experiments show that the proposed relation representation models can represent the complex semantic information of the special cargo domain efficiently.</p

Improving the Performance of Automated Optical Inspection (AOI) Using Machine Learning Classifiers

Automated Optical Inspection (AOI) machines inspect the Printed Circuit Board (PCB) manufacturing visually using a camera autonomously scans the device under test for both catastrophic failure (e.g. missing component) and quality defects (e.g. fillet size, shape or component skew). High false call rate is a fundamental concern of AOI machines that occurs when a component is considered as a ‘fail’ incorrectly that then have to be verified manually. In order to alleviate this problem, we train and compare different machine learning models (Decision Tree, Random Forest, K-Nearest Neighbors and Artificial Neural Network) and thresholds using logged fail data and extracting the efficient categorical and numerical features. The results show that the trained classifiers are able to identify the false calls well and increase the accuracy without increasing the error slip much. The K-Nearest Neighbor model, with a low threshold achieves the best result

Identifying Leading Indicators for Tactical Truck Parts’ Sales Predictions Using LASSO

This paper aimed to identify leading indicators for a case company that supplies truck parts to the European truck aftersales market. We used LASSO to extract relevant information from a collected pool of business, economic, and market indicators. We propose the efficient one-standard error rule, as an alternative to the default one-standard error rule, to reduce the influence of sampling variation on the LASSO tuning parameter value. We found that applying the efficient one-standard error rule over the default one, improved forecasting performance with an average of 0.73%. Next to that, we found that for our case study, applying forecast combination yielded the best forecasting performance, outperforming all other considered models, with an average improvement of 2.38%. Thus, including leading context information did lead to more accurate parts sales predictions for the case company. Also, due to the transparency of LASSO, using LASSO provided business intelligence about relevant predictors and lead effects. Finally, from a pool of 34 indicators, 7 indicators appeared to have clear lead effects for the case company

Relation Representation Learning for Special Cargo Ontology

Non-transparent shipping processes of transporting goods with special handling needs (special cargoes) have resulted in inefficiency in the airfreight industry. Special cargo ontology elicits, structures, and stores domain knowledge and represents the domain concepts and relationship between them in a machine-readable format. In this paper, we proposed an ontology population pipeline for the special cargo domain, and as part of the ontology population task, we investigated how to build an efficient information extraction model from low-resource domains based on available domain data for industry use cases. For this purpose, a model is designed for extracting and classifying instances of different relation types between each concept pair. The model is based on a relation representation learning approach built upon a Hierarchical Attention-based Multi-task architecture in the special cargo domain. The results of experiments show that the model could represent the complex semantic information of the domain, and tasks initialized with these representations achieve promising results