Miracle-dz/UniFAR-FTUs huggingface dataset:
Variance-Minimization Iterative Matching Method for Free-Form Surfaces—Part I: Theory and Method

Free-form surface matching that aligns measured points with a design model is a common problem in manufacturing automation. In this paper, an iterative variance-minimization matching (VMM) method is proposed to address measured points that have measuring defects, such as uneven/open point distributions and measuring noise. The basic idea is that the objective function is defined as the variance of the closest distance from each measured point to the design model, and the measuring defects are considered by incorporating an average distance item into the objective function. Using the defined average distance item, a strategy for analyzing the effect of measuring defects on VMM and existing methods is presented. It is shown that the VMM method does not easily become trapped in a local optimum when measuring defects exist. To consider convergence speed and convergence stability, a new distance based on the first-order point-to-point distance and point-to-tangent distance is developed and used in the objective function. To demonstrate the availability of the proposed method, quadratic convergence and positive definiteness are theoretically analyzed. The proposed method is efficient and insensitive to measuring defects and is useful for shape matching tasks involving free-form surface features. Note to Practitioners—This paper is motivated by the problem of matching measured points with a design model to automate manufacturing processes such as geometric inspection, workpiece localization, and allowance distribution. Measured points are obtained by applying a scanning device where measuring defects usually appear. Existing matching methods suffer from the drawback that the measured points may incline toward dense data and become trapped in a local optimum, due to measuring defects. To address this practical issue, this paper proposes a new method called variance-minimization matching (VMM), in which the objective function is optimized to weaken the effect of measuring defects. By examining the differences between VMM and existing methods, it is found that VMM can achieve quadratic convergence speed. Most importantly, the method is insensitive to uneven/open point distributions. In summary: 1) this method allows us to improve the matching accuracy in the presence of measuring defects; 2) there is no need to obtain a high-quality scan of the entire workpiece, potentially reducing scanning difficulty and improving scanning efficiency; and 3) the requirement of uniform sampling for measured points is reduced.
What to expect from Neural Machine Translation: a practical in-class translation evaluation exercise

ABSTRACT Machine translation is currently undergoing a paradigm shift from statistical to neural network models. Neural machine translation (NMT) is difficult to conceptualise for translation students, especially without context. This article describes a short in-class evaluation exercise to compare statistical and neural MT, including details of student results and follow-on discussions. As part of this exercise, students carry out evaluations of two types of MT output using three translation quality assurance (TQA) metrics: adequacy, post-editing productivity, and a simple error taxonomy. In this way, the exercise introduces NMT, TQA, and post-editing. In our module, a more detailed explanation of NMT followed the evaluation. The rise of NMT has been accompanied by a good deal of media hyperbole about neural networks and machine learning, some of which has suggested that several professions, including translation, may be under threat. This evaluation exercise is intended to empower the students, and help them understand the strengths and weaknesses of this new technology. Students’ findings using several language pairs mirror those from published research, such as improved fluency and word order in NMT output, with some unpredictable problems of omission and mistranslation.
Deep Features Extraction for Robust Fingerprint Spoofing Attack Detection

Abstract Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection.
Large Matchings in Large Markets with Flexible Supply

The size of a matching is defined as the expected number of agents who are matched to some objects. It is an important design objective in many applications, including refugee resettlement, daycare assignment, and mass vaccination campaigns. This paper studies the matching size achieved by strategy-proof mechanisms in a general model of matching with constraints. Our model generalizes a number of constraint structures considered in the extant literature. We show that naive extensions of classical mechanisms may generate an arbitrarily small matching when we have such generalized constraints. Assuming a large market (in that the variety of objects is fixed but the capacities are large), we develop a technique to reshape a given resource constraint to a more structured one, called a polymatroidal constraint, with which a classical mechanism performs well. We propose a mechanism that (i) satisfies strategy-proofness, (ii) always achieves at least 1-1/e = 63.2% of the maximum size (which is the best approximation ratio a strategy-proof mechanism can achieve), (iii) respects agents' preferences, and (iv) is computationally efficient, for general instances.
VALIDATION OF THE GLOBAL HIGH-RESOLUTION GLOBELAND30 LAND COVER MAP IN EUROPE USING LAND COVER FIELD SURVEY DATABASE - LUCAS

Abstract. High-resolution land cover maps are one of the technological innovations driving improvements in many fields influenced by Geographic Information Systems (GIS) and Remote Sensing. In particular, the GlobeLand30 (GL30), global LC map with spatial resolution of 30 m, is thought to be one of the highest quality high-resolution products. However, these LC maps require validation to determine their suitability for a particular purpose. One of the best ways to provide useful validation reference data is to do a high-level accuracy field survey, but this is time consuming and expensive. Another option is to exploit already available datasets. This study assesses thematic accuracy of GL30 in Europe using LUCAS as a validation reference, because it is a free and open field survey database. The results were generally not good, and very bad for some classes. Analysis was then restricted to a small region (Lombardy, Italy) where LC data of higher resolution than those of GL30 were available. LUCAS was also found to be incoherent with this product. Further comparisons of LUCAS with other independent sources confirmed that the LC attributes of LUCAS are inconsistent with expectations. Although these findings may not be generalized to other regions, the results warn against the suitability of LUCAS as ground truth for LC validation. The paper discusses the process of thematic accuracy assessment of the GL30 and the applicability of LUCAS for high-resolution global LC validation.
Enabling ‘Ba’ by using the photovoice technique to realize expansive learning: An ICT4D research case

The introduction and implementation of ICT depends not only on technological issues but also on social and institutional factors. To respond to these challenges, in this article we describe our engagement with an ICT4D research case conducted in collaboration with the Community Empowerment Programme (CEP) of the non-governmental organization (NGO) Bangladesh Rural Advancement Committee (BRAC). The project applies the expansive learning approach in a developing country context, with the aim of achieving ICT skills building in the broadest sense. In this article, we look more closely at one intervention within this research: digital literacy workshops for a sample of CEP field facilitators using ‘photovoice’ techniques. We apply Engestrom’s expansive learning framework for ICT intervention on the ground level, showing how intermediary actions and tools can be used in order to create active learning spaces for the community development program’s field facilitators, and we explain how the photovoice technique can be an effective intermediary tool for expansive learning. To reach this conclusion, Nonaka and Takeuchi’s knowledge-creation framework serves as a lens through which to examine the dynamic nature of the knowledge conversion context and its influence on the progression of knowledge.
A text-based learning model for college General English

The fact that an English learning program is provided to equip college students with English skills poses several problems since every university, even every non-English department, likely has its own system. One solution is to provide an acceptable material that is arranged based on the language that needs to be acquired. In order to solve the problem of supporting the English learning process in college, this research aims to produce a learning model for the novice level, i.e. college general English. The general English course is needed to provide the students with college English before academic English. Thus, the problem discussed in this study is what is the acceptable product of the text-based college general English? The researcher employed ADDIE model for the instructional design. The researcher also adopted Borg’s and Gall’s R&D model as the research methodology. The final result was a course book named College Just Ahead. This book consists of five units, which has two or three sub-units in every unit. The result showed that the product was acceptable by the mean score above 4.00 out of 5. Therefore, when it is completed, the course is more transferable. The students and instructors more focus on the process of acquiring an English competence, so that the product is more valid and reliable.
Tensorial Change Analysis Using Probabilistic Tensor Regression

This paper proposes a new method for change detection and analysis using tensor regression. Change detection in our setting is to detect changes in the relationship between the input tensor and the output scalar while change analysis is to compute the responsibility score of individual tensor modes and dimensions for the change detected. We develop a new probabilistic tensor regression method, which can be viewed as a probabilistic generalization of the alternating least squares algorithm. Thanks to the probabilistic formulation, the derived change scores have a clear information-theoretic interpretation. We apply our method to semiconductor manufacturing to demonstrate the utility. To the best of our knowledge, this is the first work of change analysis based on probabilistic tensor regression.
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

Learning with noisy labels, which aims to reduce expensive labors on accurate annotations, has become imperative in the Big Data era. Previous noise transition based method has achieved promising results and presented a theoretical guarantee on performance in the case of class-conditional noise. However, this type of approaches critically depend on an accurate pre-estimation of the noise transition, which is usually impractical. Subsequent improvement adapts the pre-estimation along with the training progress via a Softmax layer. However, the parameters in the Softmax layer are highly tweaked for the fragile performance due to the ill-posed stochastic approximation. To address these issues, we propose a Latent Class-Conditional Noise model (LCCN) that naturally embeds the noise transition under a Bayesian framework. By projecting the noise transition into a Dirichlet-distributed space, the learning is constrained on a simplex based on the whole dataset, instead of some ad-hoc parametric space. We then deduce a dynamic label regression method for LCCN to iteratively infer the latent labels, to stochastically train the classifier and to model the noise. Our approach safeguards the bounded update of the noise transition, which avoids previous arbitrarily tuning via a batch of samples. We further generalize LCCN for open-set noisy labels and the semi-supervised setting. We perform extensive experiments with the controllable noise data sets, CIFAR-10 and CIFAR-100, and the agnostic noise data sets, Clothing1M and WebVision17. The experimental results have demonstrated that the proposed model outperforms several state-of-the-art methods.
CANet: Class-Agnostic Segmentation Networks With Iterative Refinement and Attentive Few-Shot Learning

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.
A Synchronized Multi-Modal Attention-Caption Dataset and Analysis

In this work, we present a novel multi-modal dataset consisting of eye movements and verbal descriptions recorded synchronously over images. Using this data, we study the differences between human attention in free-viewing and image captioning tasks. We look into the relationship between human attention and language constructs during perception and sentence articulation. We also compare human and machine attention, in particular the top-down soft attention approach that is argued to mimick human attention, in captioning tasks. Our study reveals that, (1) human attention behaviour in free-viewing is different than image description as humans tend to fixate on a greater variety of regions under the latter task; (2) there is a strong relationship between the described objects and the objects attended by subjects ($97\%$ of described objects are being attended); (3) a convolutional neural network as feature encoder captures regions that human attend under image captioning to a great extent (around $78\%$); (4) the soft-attention as the top-down mechanism does not agree with human attention behaviour neither spatially nor temporally; and (5) soft-attention does not add strong beneficial human-like attention behaviour for the task of captioning as it has low correlation between caption scores and attention consistency scores, indicating a large gap between human and machine in regard to top-down attention.
Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks

Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.
Fast Graph Representation Learning with PyTorch Geometric

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch. In addition to general graph data structures and processing methods, it contains a variety of recently published methods from the domains of relational learning and 3D data processing. PyTorch Geometric achieves high data throughput by leveraging sparse GPU acceleration, by providing dedicated CUDA kernels and by introducing efficient mini-batch handling for input examples of different size. In this work, we present the library in detail and perform a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios.
Understanding and Visualizing Deep Visual Saliency Models

Recently, data-driven deep saliency models have achieved high performance and have outperformed classical saliency models, as demonstrated by results on datasets such as the MIT300 and SALICON. Yet, there remains a large gap between the performance of these models and the inter-human baseline. Some outstanding questions include what have these models learned, how and where they fail, and how they can be improved. This article attempts to answer these questions by analyzing the representations learned by individual neurons located at the intermediate layers of deep saliency models. To this end, we follow the steps of existing deep saliency models, that is borrowing a pre-trained model of object recognition to encode the visual features and learning a decoder to infer the saliency. We consider two cases when the encoder is used as a fixed feature extractor and when it is fine-tuned, and compare the inner representations of the network. To study how the learned representations depend on the task, we fine-tune the same network using the same image set but for two different tasks: saliency prediction versus scene classification. Our analyses reveal that: 1) some visual regions (e.g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e.g. head) over some others (e.g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e.g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data
Evolutionary Cell Aided Design for Neural Network Architectures

Mathematical theory shows us that multilayer feedforward Artificial Neural Networks(ANNs) are universal function approximators, capable of approximating any measurable function to any desired degree of accuracy. In practice designing practical and efficient neural network architectures require significant effort and expertise. We present a novel software framework called Evolutionary Cell Aided Design(ECAD) meant to aid in the exploration and design of efficient Neural Network Architectures(NNAs) for reconfigurable hardware. Given a general neural network structure and a set of constraints and fitness functions, the framework will explore both the space of possible NNA and the space of possible hardware designs, using evolutionary algorithms, and attempt to find the fittest co-design solutions according to a predefined set of goals. We test the framework on an image classification task and use the MNIST data set of hand written digits with an Intel Arria 10 GX 1150 device as our target platform. We design and implement a modular and scalable 2D systolic array with enhancements for machine learning that can be used by the framework for the hardware search space. Our results demonstrate the ability to pair neural network design and hardware development together using an evolutionary algorithm and removing traditional human-in-the-loop development tasks. By running various experiments of the fittest solutions for neural network and hardware searches, we demonstrate the full end-to-end capabilities of the ECAD framework.