Reviewerly/exHarmony huggingface dataset:
An Approach to Training Decision Trees with the Relearning of Nodes

Decision tree learning algorithms are quite old but very popular because of their efficiency. Decision trees are built sequentially from node to node, resulting in a "near-optimal" tree. This article discusses an approach to training a decision tree based on relearning already built nodes, i.e. a tree is not built sequentially, but with a return to the previous nodes. Such algorithms as Separation Measure and Differential Evolution are applied for this approach. Separation Measure selects the attributes to optimize the thresholds, and Differential Evolution performs optimization. The considered approach to learning a decision tree is compared with standard algorithms. Statistical analysis is performed to show the reliability of the results, with Student’s t-test being applied in this case. On average, the efficiency of the standard and modified decision tree learning algorithms are at the same level. However, a modified decision tree learning algorithm can find a tree that is more efficient than the trees built by the standard algorithm.
Time-Delayed Reservoir Computing Based on a Two-Element Phased Laser Array for Image Identification

We report on a simple approach of time-delayed reservoir computing (RC) based on a two-element phased laser array for image identification. Here the phased laser array with optical feedback and injection is trained according to the representative characteristics extracted through histograms of oriented gradients. These characteristic vectors are multiplied by a random mask signal to form input data, which are subsequently trained in the reservoir. By optimizing the parameters of the RC, we achieve an identification accuracy of 97.44% on the MNIST dataset and 85.46% on the Fashion-MNIST dataset. These results indicate that our proposed RC indeed allows accurate classification of handwritten digit and fashion production. Moreover, we further forecast an RC scheme based on a larger-scale phased laser array, which is expected to tackle more complex tasks at a high speed. Our work offers a possibility for advanced image processing using highly integrated neuromorphic photonic systems.
FashionMirror: Co-attention Feature-remapping Virtual Try-on with Sequential Template Poses

Virtual try-on tasks have drawn increased attention. Prior arts focus on tackling this task via warping clothes and fusing the information at the pixel level with the help of semantic segmentation. However, conducting semantic segmentation is time-consuming and easily causes error accumulation over time. Besides, warping the information at the pixel level instead of the feature level limits the performance (e.g., unable to generate different views) and is unstable since it directly demonstrates the results even with a misalignment. In contrast, fusing information at the feature level can be further refined by the convolution to obtain the final results. Based on these assumptions, we propose a co-attention feature-remapping framework, namely FashionMirror, that generates the try-on results according to the driven-pose sequence in two stages. In the first stage, we consider the source human image and the target try-on clothes to predict the removed mask and the try-on clothing mask, which replaces the pre-processed semantic segmentation and reduces the inference time. In the second stage, we first remove the clothes on the source human via the removed mask and warp the clothing features conditioning on the try-on clothing mask to fit the next frame human. Meanwhile, we predict the optical flows from the consecutive 2D poses and warp the source human to the next frame at the feature level. Then, we enhance the clothing features and source human features in every frame to generate realistic try-on results with spatiotemporal smoothness. Both qualitative and quantitative results show that FashionMirror outperforms the state-of-the-art virtual try-on approaches.
Skeleton-Based Action Recognition With Focusing-Diffusion Graph Convolutional Networks

Graph Convolutional Networks have been successfully applied in skeleton-based action recognition. The key is fully exploring the spatial-temporal context. This letter proposes a Focusing-Diffusion Graph Convolutional Network (FDGCN) to address this issue. Each skeleton frame is first decomposed into two opposite-direction graphs for subsequent focusing and diffusion processes. Next, the focusing process generates a spatial-level representation for each frame individually by an attention module. This representation is regarded as a supernode to aggregate the feature from each joint node in each frame for spatial context extraction. After generating supernodes for the entire sequence, a transformer encoder layer is proposed to capture the temporal context further. Finally, these supernodes pass the embedded spatial-temporal context back to the spatial joints through the diffusion graph in the diffusing process. Extensive experiments on the NTU RGB+D and Skeleton-Kinetics benchmarks demonstrate the effectiveness of our approach.
The impact of project team characteristics and client collaboration on project agility and project success: An empirical study

Although many companies pursue agile projects, extant literature reveals a lack of research on project agility determinants. This study examines the project team characteristics' impact on project agility and success using cross-sectional survey data from 292 agile projects. Using agile principles and complex adaptive systems theory, we find that project team autonomy, team diversity, and client collaboration have significant positive relationships with project agility. Project agility, in turn, has a significant positive relationship to project success. We measure project success by on-time completion, on-budget completion, specifications' attainment, and success rating by the project sponsor, client, and project team members. We find that project team members' adaptive performance partially mediates the relationship between project agility and success. These results guide agile project managers while facilitating team members to independently schedule their work, determine effective work methods, and develop innovative solutions. Moreover, they help agile managers recruit team members with relevant, diverse skill sets, domain knowledge, and expertise. Agile project managers must emphasize client collaboration in requirements gathering, designing, testing, and project reviews.
Hybrid quantum investment optimization with minimal holding period

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a 1 year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.
SafetyNet: Safe Planning for Real-World Self-Driving Vehicles Using Machine-Learned Policies

In this paper we present the first safe system for full control of self-driving vehicles trained from human demonstrations and deployed in challenging, real-world, urban environments. Current industry-standard solutions use rule-based systems for planning. Although they perform reasonably well in common scenarios, the engineering complexity renders this approach incompatible with human-level performance. On the other hand, the performance of machine-learned (ML) planning solutions can be improved by simply adding more exemplar data. However, ML methods cannot offer safety guarantees and sometimes behave unpredictably. To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e.g. avoiding collision, assuring physical feasibility). This allows us to leverage ML to handle complex situations while still assuring the safety, reducing ML planner-only collisions by 95%. We train our ML planner on 300 hours of expert driving demonstrations using imitation learning and deploy it along with the fallback layer in downtown San Francisco, where it takes complete control of a real vehicle and navigates a wide variety of challenging urban driving scenarios.
Self-supervised Generative Adversarial Network for Depth Estimation in Laparoscopic Images

Dense depth estimation and 3D reconstruction of a surgical scene are crucial steps in computer assisted surgery. Recent work has shown that depth estimation from a stereo image pair could be solved with convolutional neural networks. However, most recent depth estimation models were trained on datasets with per-pixel ground truth. Such data is especially rare for laparoscopic imaging, making it hard to apply supervised depth estimation to real surgical applications. To overcome this limitation, we propose SADepth, a new self-supervised depth estimation method based on Generative Adversarial Networks. It consists of an encoder-decoder generator and a discriminator to incorporate geometry constraints during training. Multi-scale outputs from the generator help to solve the local minima caused by the photometric reprojection loss, while the adversarial learning improves the framework generation quality. Extensive experiments on two public datasets show that SADepth outperforms recent state-of-the-art unsupervised methods by a large margin, and reduces the gap between supervised and unsupervised depth estimation in laparoscopic images.
Pedagogical Strategies Used by English Teacher Educators to Overcome the Challenges Posed by Emergency Remote Teaching During the COVID-19 Pandemic

The crisis in the education system generated by the appearance of covid-19 revealed the multiple gaps that exist due to the diversity of contexts to which students and teachers belong. This article reports on a case study which explored the challenges English teacher educators (tes) at a public university in Neiva, Colombia faced during the pandemic due to the imposition of emergency remote teaching (ert), the pedagogical strategies they used to respond to these challenges, and the ones that remain. To do this, data were collected from a questionnaire and a focus group with tes and pre-service teachers (psts) from the language teacher education program. The main findings show that the transition from classroom teaching to ert brought some challenges for tes and their trainees related to the social realities existing in the context of the latter. To respond to these challenges, tes had to adjust their teaching strategies and learn to use some technologies such as videoconferencing software and educational apps. Still some challenges remain for the future, such as enhancing pst’s motivation, autonomy and classroom interaction. The study suggests the need to continue training language tes on the use of ert technologies and to find more and better ways to promote autonomous learning processes to adapt teaching practices to current times.
MRDet: A Multihead Network for Accurate Rotated Object Detection in Aerial Images

Objects in aerial images usually have arbitrary orientations and are densely located over the ground, making them extremely challenge to be detected. Many of the recent developed methods attempt to solve these issues by estimating an extra orientation parameter and placing dense anchors, which will result in high model complexity and computational costs. In this article, we propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. The AO-RPN is very efficient with only a few amounts of parameters increase than the original RPN. Furthermore, to obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multihead network to accomplish them. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately. We name it multihead rotated object detector (MRDet). We evaluate the performance of the proposed MRDet on two challenging benchmarks, i.e., DOTA and HRSC2016, and compare it with several state-of-the-art methods. Our method achieves very promising results, which clearly demonstrates its effectiveness. Code has been available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qinr/MRDet</uri> .
Datafication research: Mapping the field for a future agenda

A growing body of research centers around the concept of “datafication” suggesting a buzz around data studies and, perhaps, the emergence of a research field. This article analyzes and discusses the current state of datafication research. Our dataset comprises 463 publications on datafication identified through a systematic literature search in Web of Science and Scopus, an explorative network analysis of keyword co-occurrences and a content analysis of these publications. We map datafication research interests in various research fields, find that the majority of studies are theoretically oriented, whereas empirical analyses largely apply qualitative approaches and rarely make use of data-driven methods. We suggest studies on datafication can be devised into categories reflecting research interests in either user understandings and practices or in infrastructure and technological processes of datafication. The latter strand is particularly sparse in empirical anchoring, and needs empirical and methodological attention. We conclude by outlining three paths for future datafication research to cross-pollinate infrastructural and user perspectives, highlighting the bridging role of communication research in such an endeavor.
Meningioma Segmentation in T1-Weighted MRI Leveraging Global Context and Attention Mechanisms

Purpose: Meningiomas are the most common type of primary brain tumor, accounting for ~30% of all brain tumors. A substantial number of these tumors are never surgically removed but rather monitored over time. Automatic and precise meningioma segmentation is, therefore, beneficial to enable reliable growth estimation and patient-specific treatment planning. Methods: In this study, we propose the inclusion of attention mechanisms on top of a U-Net architecture used as backbone: (i) Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a three-dimensional (3D) magnetic resonance imaging (MRI) volume as input. Attention has the potential to leverage the global context and identify features' relationships across the entire volume. To limit spatial resolution degradation and loss of detail inherent to encoder-decoder architectures, we studied the impact of multi-scale input and deep supervision components. The proposed architectures are trainable end-to-end and each concept can be seamlessly disabled for ablation studies. Results: The validation studies were performed using a five-fold cross-validation over 600 T1-weighted MRI volumes from St. Olavs Hospital, Trondheim University Hospital, Norway. Models were evaluated based on segmentation, detection, and speed performances, and results are reported patient-wise after averaging across all folds. For the best-performing architecture, an average Dice score of 81.6% was reached for an F1-score of 95.6%. With an almost perfect precision of 98%, meningiomas smaller than 3 ml were occasionally missed hence reaching an overall recall of 93%. Conclusion: Leveraging global context from a 3D MRI volume provided the best performances, even if the native volume resolution could not be processed directly due to current GPU memory limitations. Overall, near-perfect detection was achieved for meningiomas larger than 3 ml, which is relevant for clinical use. In the future, the use of multi-scale designs and refinement networks should be further investigated. A larger number of cases with meningiomas below 3 ml might also be needed to improve the performance for the smallest tumors.
Nonfungible Tokens: Constructing Value Systems in Parallel Societies

Welcome to the fifth issue of Ieee Transactions on Computational Social Systems (TCSS) of 2021. As usual, we would like to share some great news first. Since April 2021, IEEE TCSS has been added to the Science Citation Index Expanded (SCIE) database in Clarivate Web of Science. We are excited to report that all TCSS articles published since 2018 have been backtracked and indexed by SCIE.
Temporal Convolutional Attention Neural Networks for Time Series Forecasting

Temporal Convolutional Neural Networks (TCNNs) have been applied for various sequence modelling tasks including time series forecasting. However, TCNNs may require many convolutional layers if the input sequence is long and are not able to provide interpretable results. In this paper, we present TCAN, a novel deep learning approach that employs attention mechanism with temporal convolutions for probabilistic forecasting, and demonstrate its performance in a case study for solar power forecasting. TCAN uses the hierarchical convolutional structure of TCNN to extract temporal dependencies and then uses sparse attention to focus on the important timesteps. The sparse attention layer of TCAN enables an extended receptive field without requiring a deeper architecture and allows for interpretability of the forecasting results. An evaluation using three large solar power data sets demonstrates that TCAN outperforms several state-of-the-art deep learning forecasting models including TCNN in terms of accuracy. TCAN requires less number of convolutional layers than TCNN for an extended receptive field, is faster to train and is able to visualize the most important timesteps for the prediction.
Knowledge-Driven Generative Adversarial Network for Text-to-Image Synthesis

Text-to-Image (T2I) synthesis is a challenging task that aims to convert natural language descriptions to real images. It remains an open problem mainly due to the diversity of text descriptions, which poses a huge obstacle in generating vivid and relevant images. Moreover, the existing evaluation metrics in T2I synthesis are mainly used to evaluate the visual quality of the generated images, while the semantic consistency between the two modalities is often ignored. To address these issues, we present a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knowledge-Driven Generative Adversarial Network</i> , termed KD-GAN, and a new evaluation system, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pseudo Turing Test</i> (PTT for short). Concretely, KD-GAN takes a further step in imitating the behavior of human painting, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , drawing an image according to reference knowledge. The introduction of reference knowledge in KD-GAN not only improves the quality of the generated images but also enhances the semantic consistency between them and the input texts. In addition, KD-GAN can also greatly avoid some flaws against common sense during image generation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , skiing in the blue sky. The proposed PTT is an important supplement to the existing evaluation system of T2I synthesis. It includes a set of pseudo-experts of different multimedia tasks to evaluate the semantic consistency between the given texts and the generated images. To validate the proposed KD-GAN, we conducted extensive experiments on two benchmark datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , Caltech-UCSD Birds (CUB), and MS-COCO (COCO). The experimental results demonstrate that KD-GAN outperforms state-of-the-art methods on IS, FID, and the proposed PTT metrics. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> <fn id="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><label><sup>1</sup></label> The codes of KD-GAN are at [Online]. Available: <uri>https://github.com/pengjunn/KD-GAN</uri> and the codes and models of PTT are at [Online]. Available: <uri>https://github.com/pengjunn/PTT</uri>. </fn>
Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data

Continual learning is the problem of learning and retaining knowledge through time over multiple tasks and environments. Research has primarily focused on the incremental classification setting, where new tasks/classes are added at discrete time intervals. Such an "offline" setting does not evaluate the ability of agents to learn effectively and efficiently, since an agent can perform multiple learning epochs without any time limitation when a task is added. We argue that "online" continual learning, where data is a single continuous stream without task boundaries, enables evaluating both information retention and online learning efficacy. In online continual learning, each incoming small batch of data is first used for testing and then added to the training set, making the problem truly online. Trained models are later evaluated on historical data to assess information retention. We introduce a new benchmark for online continual visual learning that exhibits large scale and natural distribution shifts. Through a large-scale analysis, we identify critical and previously unobserved phenomena of gradient-based optimization in continual learning, and propose effective strategies for improving gradient-based online continual learning with real data. The source code and dataset are available in: https://github.com/IntelLabs/continuallearning.
Dynamically Corrected Nonadiabatic Holonomic Quantum Gates

The key for realizing fault-tolerant quantum computation lies in maintaining the coherence of all qubits so that high-fidelity and robust quantum manipulations on them can be achieved. One of the promising approaches is to use geometric phases in the construction of universal quantum gates, due to their intrinsic robustness against certain types of local noises. However, due to limitations in previous implementations, the noise-resilience feature of nonadiabatic holonomic quantum computation (NHQC) still needs to be improved. Here, combining with the dynamical correction technique, we propose a general protocol of universal NHQC with simplified control, which can greatly suppress the effect of the accompanied X errors, retaining the main merit of geometric quantum operations. Numerical simulation shows that the performance of our gate can be much better than previous protocols. Remarkably, when incorporating a decoherence-free subspace encoding for the collective dephasing noise, our scheme can also be robust against the involved Z errors. In addition, we also outline the physical implementation of the protocol that is insensitive to both X and Z errors. Therefore, our protocol provides a promising strategy for scalable fault-tolerant quantum computation.