Reviewerly/exHarmony huggingface dataset:
3D face imaging with the spatial-temporal correlation method using a rotary speckle projector

In this paper, a compact, cost-effective, and fast rotary speckle projector (RSP) is designed and manufactured for high-precision three-dimensional (3D) face data acquisition. Compared with the common speckle projectors, RSP uses a simple speckle pattern design method and has a good performance in high-speed projection and compact structure, which allows a flexible balance between measurement accuracy and time cost in a real acquisition task. Using a carefully designed rotation angle of the speckle mask, temporally and spatially non-correlative speckle patterns in the measurement volume can be generated. The rotation angle of the speckle mask is carefully checked and optimally selected via detailed theoretical analysis, simulation, and experiments to ensure 3D reconstruction accuracy across the reconstruction area. Subsequently, a binocular 3D face imaging system composed of the RSP and two cameras is constructed. With captured stereo speckle image pairs, we adopted our previously well-established spatial-temporal correlation method to determine the disparity. The accuracy of the 3D face imaging system was verified by using a real face mask, which is standardized by a certified, high-precision industrial 3D scanner. The real face data collection under various expressions has demonstrated that the proposed system also has a good performance for 3D face imaging in dynamic scenes.
Boundary graph convolutional network for temporal action detection

Temporal action proposal generation is a fundamental yet challenging to locate the temporal action in untrimmed videos. Although current proposal generation methods can generate the precise boundary of actions, few focus on considering the relation of proposals. In this paper, we propose a unified framework to generate the temporal boundary proposals with a graph convolution network based on the boundary proposals' feature named Boundary Graph Convolutional Network (BGCN). BGCN draws inspiration from boundary methods and uses edge graph convolution relay on the boundary proposals' feature. First, we use a base layer to fusion the two-stream video features to get two-branches of base features. Then the two-branches of base features enter into the same structure of Proposal Features Graph Convolutional Network (PFGCN): Action PFGCN to extract the action classification score and Boundary PFGCN to extract the ending score and staring score. In proposal features graph convolutional network, we first densely sampled the proposals' feature from the video features. We construct a proposal feature graph, where each proposal feature as a node and their relations between proposals' features as an edge with edge convolution for graph convolution. After that, map the relations into a 2D map score. Experiments on popular benchmarks THUMOS14 demonstrate the superiority of BGCN over (44.8% versus 42.8% at tIoU 0.5) the state-of-the-art proposal generator (e.g., G-TAD, TAL-Net, and BMN) at any of tIoU thresholds from 0.3 to 0.7. On ActivityNet1.3, BGCN also got better results. Moreover, BGCN has high efficiency for action detection with less than 2 MB model size and fast inference time. GCN based on boundary generation for densely produce the action proposals Efficient and novel BGCN model has a great capability to learn the proposal features Has a lower model size for temporal action proposals generation Has fast inference time for temporal action proposals generation.
Optimized FPGA-based Deep Learning Accelerator for Sparse CNN using High Bandwidth Memory

Large Convolutional Neural Networks (CNNs) are often pruned and compressed to reduce the amount of parameters and memory requirement. However, the resulting irregularity in the sparse data makes it difficult for FPGA accelerators that contains systolic arrays of Multiply-and-Accumulate (MAC) units, such as Intel's FPGA-based Deep Learning Accelerator (DLA), to achieve their maximum potential. Moreover, FPGAs with low-bandwidth off-chip memory could not satisfy the memory bandwidth requirement for sparse matrix computation. In this paper, we present 1) a sparse matrix packing technique that condenses sparse inputs and filters before feeding them into the systolic array of MAC units in the Intel DLA, and 2) a customization of the Intel DLA which allows the FPGA to efficiently utilize a high bandwidth memory (HBM2) integrated in the same package. For end-to-end inference with randomly pruned ResNet-50/MobileNet CNN models, our experiments demonstrate 2.7x/3x performance improvement compared to an FPGA with DDR4, 2.2x/2.1x speedup against a server-class Intel SkyLake CPU, and comparable performance with 1.7x/2x power efficiency gain as compared to an NVidia V100 GPU.
Zero-Shot Learning via Structure-Aligned Generative Adversarial Network

In this article, we propose a structure-aligned generative adversarial network framework to improve zero-shot learning (ZSL) by mitigating the semantic gap, domain shift, and hubness problem. The proposed framework contains two parts, i.e., a generative adversarial network with a softmax classifier part, and a structure-aligned part. In the first part, the generative adversarial network aims at generating pseudovisual features through the guiding generator and discriminator play the minimax two-player game together. At the same time, the softmax classifier is committed to increasing the interclass distance and reducing intraclass distance. Then, the harmful effect of domain shift and hubness problems can be mitigated. In another part, we introduce a structure-aligned module where the structural consistency between visual space and semantic space is learned. By aligning the structure between visual space and semantic space, the semantic gap between them can be bridged. The performance of classification is improved when the structure-aligned visual-semantic embedding space is transferred to the unseen classes. Our framework reformulates the ZSL as a standard fully supervised classification task using the pseudovisual features of unseen classes. Extensive experiments conducted on five benchmark data sets demonstrate that the proposed framework significantly outperforms state-of-the-art methods in both conventional and generalized settings.
Research on sports aided teaching and training decision system oriented to deep convolutional neural network

Aiming at the construction of the decision-making system for sports-assisted teaching and training, this article first gives a deep convolutional neural network model for sports-assisted teaching and training decision-making. Subsequently, In order to meet the needs of athletes to assist in physical exercise, a squat training robot is built using a self-developed modular flexible cable drive unit, and its control system is designed to assist athletes in squatting training in sports. First, the human squat training mechanism is analyzed, and the overall structure of the robot is determined; second, the robot force servo control strategy is designed, including the flexible cable traction force planning link, the lateral force compensation link and the establishment of a single flexible cable passive force controller; In order to verify the effect of robot training, a single flexible cable force control experiment and a man-machine squat training experiment were carried out. In the single flexible cable force control experiment, the suppression effect of excess force reached more than 50%. In the squat experiment under 200 N, the standard deviation of the system loading force is 7.52 N, and the dynamic accuracy is above 90.2%. Experimental results show that the robot has a reasonable configuration, small footprint, stable control system, high loading accuracy, and can assist in squat training in physical education.
A Large-scale Study on API Misuses in the Wild

API misuses are prevalent and extremely harmful. Despite various techniques have been proposed for API-misuse detection, it is not even clear how different types of API misuses distribute and whether existing techniques have covered all major types of API misuses. Therefore, in this paper, we conduct the first large-scale empirical study on API misuses based on 528,546 historical bug-fixing commits from GitHub (from 2011 to 2018). By leveraging a state-of-the-art fine-grained AST differencing tool, GumTree, we extract more than one million bug-fixing edit operations, 51.7% of which are API misuses. We further systematically classify API misuses into nine different categories according to the edit operations and context. We also extract various frequent API-misuse patterns based on the categories and corresponding operations, which can be complementary to existing API-misuse detection tools. Our study reveals various practical guidelines regarding the importance of different types of API misuses. Furthermore, based on our dataset, we perform a user study to manually analyze the usage constraints of 10 patterns to explore whether the mined patterns can guide the design of future API-misuse detection tools. Specifically, we find that 7,541 potential misuses still exist in latest Apache projects and 149 of them have been reported to developers. To date, 57 have already been confirmed and fixed (with 15 rejected misuses correspondingly). The results indicate the importance of studying historical API misuses and the promising future of employing our mined patterns for detecting unknown API misuses.
Implementation of Machine Learning Algorithms For Human Activity Recognition

Human Activity Recognition (HAR) is technically the problem of forecasting an individual's actions based on evidence of their gesture using sensors functioning as accelerometer and gyroscope. It plays a major role in contrasting sectors such as personal biometric signature, daily life monitoring, anti-terrorists along with anti-crime securities, medical-related applications, and so on. These days, smart phones are well-resourced with leading processors and built-in sensors. This comes up with the possibility to unfold a new arena of data mining. This paper signifies the analysis of HAR focused on data composed via accelerometer sensors of smart phones. Further, it illustrates the use of time-domain features which are acquired with the help of a windowing approach termed as overlapping. It is accompanied by a window size of 250ms along with overlapping of 25%. Numerous machine learning classifiers such as k-nearest neighbors, linear discriminant analysis, bagging classifier, gradient boosting classifier, decision tree, random forest, and support vector machine using three different kernels were practiced. The outcomes exhibit that random forest with 5-fold cross-validation imparts the highest accuracy (92.71%) in recognition of human activities.
Partitioning of Metal Contaminants between Bulk and Fine-Grained Fraction in Freshwater Sediments: A Critical Appraisal

The distribution of six common metal contaminants (Cd, Cr, Cu, Ni, Pb, Zn) in the bulk (&lt;2 mm) and fine fractions (&lt;63 µm) of freshwater sediments was compared to conclude on the long-existing dilemma which fraction should be used in the investigation of the metal contamination. The environments included in the study (24 rivers, 8 lakes) were very different with respect to sediments origin and composition and they provided a good review of the possible scenarios. For the river sediments, particularly those having &gt;40% of sand fraction, metal concentrations were up to seven times higher in the fine fraction, implying the necessity for considering sand dilution effect in compositional data analysis. The same samples were also characterized with higher organic matter content (OM) in the fine fraction. Lake environments were characterized by fine-grained sedimentation and the difference between metal concentrations in the bulk and fine fraction was not so expressed. The preparation of samples for the geochemical and compositional data mining should be carried out in accordance with the sedimentological characteristics of the investigated environment. It implies that the insight into geological setting and determination of sedimentological characteristics should be an obligatory part of monitoring/investigating metal contamination in freshwater sediments. For river sediments, the analysis of the fine sediment fraction or correction for sediment lithology are advisable.
Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Porphyry copper (Cu) systems occur along magmatic belts derived in subduction zones. Our current understanding of their formation is restricted to observations from the overriding plate, resulting in a knowledge gap in terms of processes occurring in convergence zones through time. An association between key tectonic processes and the timing and location of porphyry Cu systems requires linking geological observations to plate tectonic subduction models. We connect the evolution of subduction zones and downgoing slab properties with the history of porphyry ore deposition across the Americas by using a spatio-temporal machine learning approach. We use these spatio-temporal properties to apply a wide range of prominent machine learning methods and show the results in terms of accuracy of predictions on the test dataset. We then provide spatial visualisations in deep-time showing highly prospective areas for porphyry Cu mineralisation along areas not related with known mineral occurrences along the subduction margins of North and South America. We show that a high orthogonal plate convergence speed is the most important feature related to the formation of porphyry Cu systems. This parameter is linked with the thickening of continental arcs which has been shown to enhance Cu transport within plutons. Convergence speed also controls the volume of plate material subducted into the upper mantle, including carbonate phases in the upper volcanic portion of the ocean crust and water-rich pelagic deep sea sediments, which have been demonstrated to enhance sulphur and metal transport, respectively, boosting metasomatic enrichment of the mantle wedge in volatiles, sulphur, and fluid-mobile large ion lithophile elements (LILE), and promoting the formation of porphyry Cu systems in the overriding plate. We incorporate plate tectonic subduction models with machine learning models for the generation of copper mineralization prediction maps. We demonstrate the potential of this application in global mineral exploration along Cordilleran subduction zones and provide open source code and data with our results.
Image inpainting using directional wavelet packets originating from polynomial splines

The paper presents a new algorithm for the image inpainting problem. The algorithm uses a recently designed versatile library of quasi-analytic complex-valued wavelet packets (qWPs) which originate from polynomial splines of arbitrary orders. Tensor products of 1D qWPs provide a diversity of 2D qWPs oriented in multiple directions. For example, a set of the fourth-level qWPs comprises 62 different directions. The properties of these qWPs such as refined frequency resolution, directionality of waveforms with unlimited number of orientations, (anti-)symmetry of waveforms and windowed oscillating structure of waveforms with a variety of frequencies, make them efficient in image processing applications, in particular, in dealing with the inpainting problem addressed in the paper. The obtained results for this problem are quite competitive with the best state-of-the-art algorithms. The inpainting is implemented by an iterative scheme, which expands the Split Bregman Iteration (SBI) procedure by supplying it with an adaptive variable soft thresholding based on the Bivariate Shrinkage algorithm. In the inpainting experiments, performance comparison between the qWP-based methods and the state-of-the-art algorithms is presented.
Subset Node Representation Learning over Large Dynamic Graphs

Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks to social networks. Graphs of this type are usually large-scale but only a small subset of vertices are related in downstream tasks. Current methods are too expensive to this setting as the complexity is at best linear-dependent on both the number of nodes and edges.
A framework for generating agile methods for product development

Increasingly, companies are integrating agile approaches in the processes of physical product development, although they may have different reasons for doing so (e.g. shorter production times, better product quality, etc.). The number of different agile approaches has also increased over time. Despite the various agile approaches, it has been shown that companies in product development have difficulties in integrating these approaches and a change process is extremely complex. This results in the need for suitable methodological support that generates situation-dependent and company-specific process solutions and thus addresses and achieves different and individual goals. For this, the complex and company-specific adaptation of an organizational unit needs to be tailored to the needs and situation of the affected organization. This is made possible by an individual agile-structuring process solution, which is generated on the basis of a methodical support in the form of a framework, which is developed in this article on basis of current research. Based on this framework, process authors are enabled to generate an agile structuring process solution for the organizational unit concerned, which enables a suitable, company-specific combination of agile and plan-driven approaches and elements. By applying the framework, a specific organizational unit is given the ability to implement an appropriate degree of agility in its development process through the generated process solution. This reduces the expected difficulties of agile approaches in product development and enables the organization to react more flexibly and adaptively to unpredictable and changing situations.
Investigating training effectiveness of public and private banks employees in this digital age: an empirical study

Purpose The purpose of this study is to examine the pre-training and post-training variables influencing employee training effectiveness in the banking industry in this era of the digital age. Design/methodology/approach This study adopted a cross-sectional research design and a structured questionnaire to survey the participants (i.e., bank employees) ( n = 702) for data collection. The data gathered from the surveyed respondents were analyzed using the Partial Least Square to Structural Equation Modeling (PLS-SEM) approach to test the proposed hypotheses. Findings The research findings suggest a significant influence of pre-training (i.e., training environment, trainer quality) and post-training (i.e., trainee motivation, trainee self-efficacy, and authentic leadership practices) factors towards the bank's staff training effectiveness in this era of digital age. Originality/value The research is one of the pioneer attempts on training effectiveness that significantly open opportunity for financial institutions such as banks to meet the challenge of the fourth industrial revolution from a developing country perspective. The current study also contributes to the extension of the theoretical and managerial doctrine in terms of the relationship among the pre-and post-training factors to enhance training effectiveness under the scope of the financial sector's employees to manage human resources and their development in the digital age.
Creativity and diversification: What digital systems teach

Creativity is everywhere. Diversity is everywhere. Digitization is everywhere. This present article gives a new perspective on my and my laboratory's work over the last decade, explaining why creativity, diversity, and digitization go everywhere together.
Structured learning of rigid‐body dynamics: A survey and unified view from a robotics perspective

Abstract Accurate models of mechanical system dynamics are often critical for model‐based control and reinforcement learning. Fully data‐driven dynamics models promise to ease the process of modeling and analysis, but require considerable amounts of data for training and often do not generalize well to unseen parts of the state space. Combining data‐driven modeling with prior analytical knowledge is an attractive alternative as the inclusion of structural knowledge into a regression model improves the model's data efficiency and physical integrity. In this article, we survey supervised regression models that combine rigid‐body mechanics with data‐driven modeling techniques. We analyze the different latent functions (such as kinetic energy or dissipative forces) and operators (such as differential operators and projection matrices) underlying common descriptions of rigid‐body mechanics. Based on this analysis, we provide a unified view on the combination of data‐driven regression models, such as neural networks and Gaussian processes, with analytical model priors. Furthermore, we review and discuss key techniques for designing structured models such as automatic differentiation.
A Visual Analytics Approach for the Diagnosis of Heterogeneous and Multidimensional Machine Maintenance Data

Analysis of large, high-dimensional, and heterogeneous datasets is challenging as no one technique is suitable for visualizing and clustering such data in order to make sense of the underlying information. For instance, heterogeneous logs detailing machine repair and maintenance in an organization often need to be analyzed to diagnose errors and identify abnormal patterns, formalize root-cause analyses, and plan preventive maintenance. Such real-world datasets are also beset by issues such as inconsistent and/or missing entries. To conduct an effective diagnosis, it is important to extract and understand patterns from the data with support from analytic algorithms (e.g., finding that certain kinds of machine complaints occur more in the summer) while involving the human-in-the-loop. To address these challenges, we adopt existing techniques for dimensionality reduction (DR) and clustering of numerical, categorical, and text data dimensions, and introduce a visual analytics approach that uses multiple coordinated views to connect DR + clustering results across each kind of the data dimension stated. To help analysts label the clusters, each clustering view is supplemented with techniques and visualizations that contrast a cluster of interest with the rest of the dataset. Our approach assists analysts to make sense of machine maintenance logs and their errors. Then the gained insights help them carry out preventive maintenance. We illustrate and evaluate our approach through use cases and expert studies respectively, and discuss generalization of the approach to other heterogeneous data.
FaceCaps for facial expression recognition

Abstract Facial expression recognition (FER) is a significant research task in the computer vision field. In this paper, we present a novel network FaceCaps for facial expression recognition with the following novel characteristics: an embedding structure based on a Capsule network which encodes relative spatial relationships between features; incorporates the feature polymerization property of FaceNet, thus offering a more efficient approach to discriminate complex facial expressions; a target reconstruction loss as a better regularization term for Capsule networks. Experimental results on both lab‐controlled datasets (CK+) and real‐world databases (RAF‐DB and SFEW 2.0) demonstrate that the method significantly outperforms the state‐of‐the‐art.