Reviewerly/exHarmony huggingface dataset:
The Scent of Deep Learning Code

Deep learning practitioners are often interested in improving their model accuracy rather than the interpretability of their models. As a result, deep learning applications are inherently complex in their structures. They also need to continuously evolve in terms of code changes and model updates. Given these confounding factors, there is a great chance of violating the recommended programming practices by the developers in their deep learning applications. In particular, the code quality might be negatively affected due to their drive for the higher model performance. Unfortunately, the code quality of deep learning applications has rarely been studied to date. In this paper, we conduct an empirical study to investigate the distribution of code smells in deep learning applications. To this end, we perform a comparative analysis between deep learning and traditional open-source applications collected from GitHub. We have several major findings. First, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, deep learning code involves more complex or longer expressions than the traditional code does. Second, the number of code smells increases across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the studied deep learning code, which confirms our conjecture on the degraded code quality of deep learning applications.
Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images

The aim of deformable brain image registration is to align anatomical structures, which can potentially vary with large and complex deformations. Anatomical structures vary in size and shape, requiring the registration algorithm to estimate deformation fields at various degrees of complexity. Here, we present a difficulty-aware model based on an attention mechanism to automatically identify hard-to-register regions, allowing better estimation of large complex deformations. The difficulty-aware model is incorporated into a cascaded neural network consisting of three sub-networks to fully leverage both global and local contextual information for effective registration. The first sub-network is trained at the image level to predict a coarse-scale deformation field, which is then used for initializing the subsequent sub-network. The next two sub-networks progressively optimize at the patch level with different resolutions to predict a fine-scale deformation field. Embedding difficulty-aware learning into the hierarchical neural network allows harder patches to be identified in the deeper sub-networks at higher resolutions for refining the deformation field. Experiments conducted on four public datasets validate that our method achieves promising registration accuracy with better preservation of topology, compared with state-of-the-art registration methods.
Transfer Learning For Videos: From Action Recognition To Sign Language Recognition

In this paper, we propose using Inflated 3D (I3D) Convolutional Neural Networks for large-scale signer-independent sign language recognition (SLR). Unlike other recent methods, our method relies only on RGB video data and does not require other modalities such as depth. This is beneficial for many applications in which depth data is not available. We show that transferring spatiotemporal features from a large-scale action recognition dataset is highly valuable to the training for SLR. Based on an architecture for action recognition [1], we use two-stream I3D ConvNets operating on RGB and optical flow images. Our method is evaluated on the ChaLearn249 Isolated Gesture Recognition dataset and clearly outperforms other state-of-the-art RGB-based methods.
Optimization of Convolutional Neural Networks for Imbalanced Set Classification

Various research works demonstrate that the distribution of training data has a significant impact on the performance of Convolutional Neural Networks (CNNs). The CNN classifiers using balanced datasets achieve very accurate performance when compared to shallow classifiers. However, considering the CNNs originally are not designed to handle imbalanced datasets, such cases have a severely negative impact on their overall performance. In this research we focus on examining the effect of imbalanced data sets on performance outcomes of various CNN-based classifier models. The work also discusses implementation of some newly suggested optimizers as well as traditional ones for Deep Learning (DL), along with different approaches of learning rate adaptation including a method based on cyclic increase and decrease during training. We compare the impact of using two different methods of ensembling in order to further increase the classifier performance. Additionally, we experimentally evaluate the proposed approaches on two imbalanced image datasets.
Iterative Local-Global Collaboration Learning Towards One-Shot Video Person Re-Identification

Video person re-identification (video Re-ID) plays an important role in surveillance video analysis and has gained increasing attention recently. However, existing supervised methods require vast labeled identities across cameras, resulting in poor scalability in practical applications. Although some unsupervised approaches have been exploited for video Re-ID, they are still in their infancy due to the complex nature of learning discriminative features on unlabelled data. In this paper, we focus on one-shot video Re-ID and present an iterative local-global collaboration learning approach to learning robust and discriminative person representations. Specifically, it jointly considers the global video information and local frame sequence information to better capture the diverse appearance of the person for feature learning and pseudo-label estimation. Moreover, as the cross-entropy loss may induce the model to focus on identity-irrelevant factors, we introduce the variational information bottleneck as a regularization term to train the model together. It can help filter undesirable information and characterize subtle differences among persons. Since accuracy cannot always be guaranteed for pseudo-labels, we adopt a dynamic selection strategy to select part of pseudo-labeled data with higher confidence to update the training set and re-train the learning model. During training, our method iteratively executes the feature learning, pseudo-label estimation, and dynamic sample selection until all the unlabeled data have been seen. Extensive experiments on two public datasets, i.e., DukeMTMC-VideoReID and MARS, have verified the superiority of our model to several cutting-edge competitors.
Designing learning experiences for outdoor hybrid learning spaces

Abstract With the support of various personal and institutional mobile technologies, numerous physical and virtual spaces can be turned into interesting and motivating hybrid learning settings. By its nature, outdoor mobile learning happens on the move is connected to specific locations and settings inviting learners to study their surroundings, inquire about natural phenomena and solve complex real‐life problems. This poses challenges for teachers to design meaningful, contextualized and comprehensive learning experiences. The paper aims to explore what kind of mobile learning scenarios K‐12 teachers create and what are the limitations and development perspectives of these scenarios. Content analysis of 25 location‐based outdoor learning tracks with 465 questions and tasks was carried out. The results demonstrate that designing and implementing learning activities outside the safe, familiar classroom environment in new hybrid learning spaces is a challenging task for the teachers on many different levels. Taking Bloom’s revised taxonomy, types of contextualization and integrated learning models as frameworks for content analysis of the learning scenarios, the study shows that the teachers do not perceive the potential hybrid spaces and mobile technologies offer in order to design consistent learning experiences that emphasize higher order thinking levels, encompass contextual information and integrate knowledge from multiple disciplinary sources. The paper concludes that there is a need for more teacher training and systemic interventions into current teaching practices that encourage teachers to step outside of their existing teaching paradigms to acknowledge the pedagogical potential of hybrid learning spaces as well as the technological affordances.
An Exposure Fusion-Based Structured Light Approach for the 3D Measurement of a Specular Surface

Reflection happening on a specular surface severely degrades the performance of structured light-based 3D measurements. Traditional structured light techniques remain limited to ideal scenes. Inspired by the multiple exposure fusion technique in photography, which fuses a number of exposure sequences into a well-exposed image, we propose an exposure fusion (EF)-based structured light method in this work. With well-exposed images, the brightness difference of the specular surface can be reduced. Binary gray code is adopted as the primary structured light pattern. With the help of the EF technique, the generated well-exposed structured light images are used for 3D reconstruction of the specular surface. To evaluate the performance of our method, the defective batteries with reflective properties are tested in this experiment. The high dynamic range (HDR)-based structured light method, auto-HDR and ABFDP methods are introduced for comparison. In these experiments, an average error of 0.088 mm and a standard deviation of 0.1921 mm are obtained. These results show that the EF method performs better than other methods for the reconstruction of specular surfaces and can be used to distinguish defects on the surface of a battery.
Exploring askers’ switching from free to paid social Q&amp;A services: A perspective on the push-pull-mooring framework

The purpose of this study is to explore the factors that prompt askers to switch from free to paid social question-and-answer (SQA) services. Prior studies have investigated users’ motivations and participation in free and paid SQA services; however, little attention has been paid to askers’ switching behavior. We empirically analyzed the content of qualitative interviews from 64 askers on a well-known SQA platform in China. Based on the push-pull-mooring framework, we identified and classified factors that influenced askers’ to switch from free to paid Q&A services, using the critical incident technique, after which we calculated the entropy weights of the 16 subcategories before and after the switch, using the entropy weight method. The findings suggest that askers’ switching behavior was influenced by push factors (i.e., dissatisfaction with the free SQA service), pull factors (i.e., satisfaction with the paid SQA service), and mooring factors (i.e., social factors, personal factors, situational factors). Moreover, the findings show that the effects of these factors vary significantly before and after a switch. Dissatisfaction with the quality of information from the free SQA service would influence users before a switch, whereas satisfaction with the quality of information from the paid SQA service would influence them after a switch. In terms of mooring factors, the effects of social and personal factors on askers’ switching behavior, especially subjective norms and cognitive lock-in, turn out to be less significant after a switch, whereas the effect of trust is more significant. Besides, the effects of situational factors are more or less the same before and after a switch. To the best of our knowledge, this paper is one of the first attempts to explore factors that affect askers’ switching behavior and to shed light on the managerial strategies of paid SQA services.
A T1OWA and aspect-based model for customizing recommendations on eCommerce

Online reviews have a significant impact on the decisions of consumers, providing valuable information which must be managed from two different perspectives: that of the user who reads the review and the people who gave those opinions. These two perspectives are the basis of the novel fuzzy aspect-based sentiment analysis approach described in this paper to recommend the most suitable products for a specific user. This approach consists of a T1OWA-based mechanism to characterize the user profile, which is able to model whether the user can be more influenced by negative opinions or positive opinions, a mechanism for determining their preferences, and a variation coefficient method for weighting the importance of the aspects of the product reviews. Combining these ideas, our model outperforms other well-known methods for ranking products, while also having the advantage of being adaptable to the preferences and characteristics of a specific user.
SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI

Recently, it has been shown that CycleGANs are masters of steganography. They cannot only learn reliable mappings between two distributions without calling for paired training data, but can effectively hide information unseen during training in mapping results from which input data can be recovered almost perfectly. When preventing this during training, CycleGANs actually map samples much closer to the training distribution. Here, we propose to leverage this effect in the context of trending unsupervised anomaly detection, which primarily relies on modeling healthy anatomy with generative models. Here, we embed anomaly detection into a CycleGAN-based style-transfer framework, which is trained to translate healthy brain MR images to a simulated distribution with lower entropy and vice versa. By filtering high frequency, low amplitude signals from lower entropy samples during training, the resulting model suppresses anomalies in reconstructions of the input data at test time. Similar to Autoencoder and GAN-based anomaly detection methods, this allows us to delineate pathologies directly from residuals between input and reconstruction. Various ablative studies and comparisons to state-of-the-art methods highlight the potential of our method.
Novelty Detection Through Model-Based Characterization of Neural Networks

In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs that can significantly degrade the performance of machine learning algorithms. Majority of existing studies have focused on activation-based representations to detect abnormal inputs, which limits the characterization of abnormality from a data perspective. However, a model perspective can also be informative in terms of the novelties and abnormalities. To articulate the significance of the model perspective in novelty detection, we utilize back-propagated gradients. We conduct a comprehensive analysis to compare the representation capability of gradients with that of activation and show that the gradients outperform the activation in novel class and condition detection. We validate our approach using four image recognition datasets including MNIST, Fashion-MNIST, CIFAR10, and CURE-TSR. We achieve a significant improvement on all four datasets with an average AUROC of 0.953, 0.918, 0.582, and 0.746, respectively.
Fast training of deep LSTM networks with guaranteed stability for nonlinear system modeling

Deep recurrent neural networks (RNN), such as LSTM, have many advantages over forward networks for nonlinear system modeling. However, the most used training method, backward propagation through time (BPTT), is very slow. In this paper, by separating the LSTM cell into forward and recurrent models, we give a faster training method than BPTT. The deep LSTM is modified by combining the deep RNN with the multilayer perceptrons (MLP). The backpropagation-like training methods are proposed for the deep RNN and MLP trainings. The stability of these algorithms are demonstrated. The simulation results show that our fast training methods for LSTM are better than the conventional approaches.
Patch Learning

There have been different strategies to improve the performance of a machine learning model, e.g., increasing the depth, width, and/or nonlinearity of the model, and using ensemble learning to aggregate multiple base/weak learners in parallel or in series. This article proposes a novel strategy called patch learning (PL) for this problem. It consists of three steps: first, train an initial global model using all training data; second, identify from the initial global model the patches that contribute the most to the learning error, and train a (local) patch model for each such patch; and, third, update the global model using training data that do not fall into any patch. To use a PL model, we first determine if the input falls into any patch. If yes, then the corresponding patch model is used to compute the output. Otherwise, the global model is used. We explain in detail how PL can be implemented using fuzzy systems. Five regression problems on one-dimensional (1-D)/2-D/3-D curve fitting, nonlinear system identification, and chaotic time-series prediction, verified its effectiveness. To our knowledge, the PL idea has not appeared in the literature before, and it opens up a promising new line of research in machine learning.
Weakly-Supervised Nucleus Segmentation Based on Point Annotations: A Coarse-to-Fine Self-Stimulated Learning Strategy

Nucleus segmentation is a fundamental task in digital pathology analysis. However, it is labor-expensive and time-consuming to manually annotate the pixel-level full nucleus masks, while it is easier to make point annotations. In this paper, we propose a coarse-to-fine weakly-supervised framework to train the segmentation model from only point annotations to reduce the labor cost of generating pixel-level masks. Our coarse-to-fine strategy can improve segmentation performance progressively in a self-stimulated learning manner. Specifically, to generate coarse segmentation masks, we employ a self-supervision strategy using clustering to perform the binary classification. To avoid trivial solutions, our model is sparsely supervised by annotated positive points and geometric-constrained negative boundaries, via point-to-region spatial expansion and Voronoi partition, respectively. Then, to generate fine segmentation masks, the prior knowledge of edges in the unadorned image is additionally utilized by our proposed contour-sensitive constraint to further tune the nucleus contours. Experimental results on two public datasets show that our model trained with weakly-supervised data (i.e., point annotations) achieves competitive performance compared with the model trained with fully supervised data (i.e., full nucleus masks). The code is made publicly available at https://github.com/tiankuan93/C2FNet.
Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network

Over 56,000 images of 268 mammograms from 94 patients were labeled to 3 classes according to the BI-RADS standard: "no microcalcifications" (BI-RADS 1), "probably benign microcalcifications" (BI-RADS 2/3), and "suspicious microcalcifications" (BI-RADS 4/5). Using the preprocessed images, a dCNN was trained and validated, generating 3 types of models: BI-RADS 4 cohort, BI-RADS 5 cohort, and BI-RADS 4 + 5 cohort. For the final validation of the trained dCNN models, a test data set consisting of 141 images of 51 mammograms from 26 patients labeled according to the corresponding BI-RADS classification from the radiological reports was applied. The performances of the dCNN models were evaluated, classifying each of the mammograms and computing the accuracy in comparison to the classification from the radiological reports. For visualization, probability maps of the classification were generated.The accuracy on the validation set after 130 epochs was 99.5% for the BI-RADS 4 cohort, 99.6% for the BI-RADS 5 cohort, and 98.1% for the BI-RADS 4 + 5 cohort. Confusion matrices of the "real-world" test data set for the 3 cohorts were generated where the radiological reports served as ground truth. The resulting accuracy was 39.0% for the BI-RADS 4 cohort, 80.9% for BI-RADS 5 cohort, and 76.6% for BI-RADS 4 + 5 cohort. The probability maps exhibited excellent image quality with correct classification of microcalcification distribution.The dCNNs can be trained to successfully classify microcalcifications on mammograms according to the BI-RADS classification system in order to act as a standardized quality control tool providing the expertise of a team of radiologists.
Systems Thinking Approach to Implementing Kanban: A case study

Abstract Against the backdrop of the growing adoption of Kanban in a variety of software development domains, this paper reports on how a Systems Thinking approach was used to implement Kanban within a business unit of a government organization. Based on an in‐depth case study in the development organization, we describe how Kanban practices such as visualization, manage flow, classes of service, and board design were implemented. Stages in the double diamond design thinking process of exploring an issue more widely (divergent thinking) and then taking focused action (convergent thinking) were incorporated to adapt Kanban practices such as board design. Positive experiences included improved visibility, improved collaboration, and improvements in lead time. Challenges included lack of team buy‐in, setting work‐in‐progress limits, and short‐term Kanban coach. We present the key lessons learned and offer practical recommendations on how teams can better produce knowledge work in today's fast‐paced dynamic business environment.
Purely Attention Based Local Feature Integration for Video Classification

Recently, substantial research effort has focused on how to apply CNNs or RNNs to better capture temporal patterns in videos, so as to improve the accuracy of video classification. In this paper, we investigate the potential of a purely attention based local feature integration. Accounting for the characteristics of such features in video classification, we first propose Basic Attention Clusters (BAC), which concatenates the output of multiple attention units applied in parallel, and introduce a shifting operation to capture more diverse signals. Experiments show that BAC can achieve excellent results on multiple datasets. However, BAC treats all feature channels as an indivisible whole, which is suboptimal for achieving a finer-grained local feature integration over the channel dimension. Additionally, it treats the entire local feature sequence as an unordered set, thus ignoring the sequential relationships. To improve over BAC, we further propose the channel pyramid attention schema by splitting features into sub-features at multiple scales for coarse-to-fine sub-feature interaction modeling, and propose the temporal pyramid attention schema by dividing the feature sequences into ordered sub-sequences of multiple lengths to account for the sequential order. Our final model pyramid×pyramid attention clusters (PPAC) combines both channel pyramid attention and temporal pyramid attention to focus on the most important sub-features, while also preserving the temporal information of the video. We demonstrate the effectiveness of PPAC on seven real-world video classification datasets. Our model achieves competitive results across all of these, showing that our proposed framework can consistently outperform the existing local feature integration methods across a range of different scenarios.