Reviewerly/exHarmony huggingface dataset:
A neural approach for detecting inline mathematical expressions from scientific documents

Abstract Scientific documents generally contain multiple mathematical expressions in them. Detecting inline mathematical expressions are one of the most important and challenging tasks in scientific text mining. Recent works that detect inline mathematical expressions in scientific documents have looked at the problem from an image processing perspective. There is little work that has targeted the problem from NLP perspective. Towards this, we define a few features and applied Conditional Random Fields (CRF) to detect inline mathematical expressions in scientific documents. Apart from this feature based approach, we also propose a hybrid algorithm that combines Bidirectional Long Short Term Memory networks (Bi‐LSTM) and feature‐based approach for this task. Experimental results suggest that this proposed hybrid method outperforms several baselines in the literature and also individual methods in the hybrid approach.
Enhanced Image Registration With a Network Paradigm and Incorporation of a Deformation Representation Model

In conventional registration methods, regularization functionals and balancing hyper-parameters need to be designed and tuned. Even so, heterogeneous tissue property and balance requirement remain challenging. In this study, we propose a registration network with a novel deformation representation model to achieve spatially variant conditioning on the deformation vector field (DVF). In the form of a convolutional auto-encoder, the proposed representation model is trained with a rich set of DVFs as a feasibility descriptor. Then the auto-encoding discrepancy is combined with fidelity in training the overall registration network in an unsupervised learning paradigm. The trained network generates DVF estimates from paired images with a single forward inference evaluation run. Experiments with synthetic images and 3D cardiac MRIs demonstrate that the method can accomplish registration with physically and physiologically more feasible DVFs, sub-pixel registration errors and millisecond execution time, and incorporation of the representation model improved the registration network performance significantly.
SUNNet: A novel framework for simultaneous human parsing and pose estimation

This paper presents a novel Separation-and-UnioN Network (SUNNet) for simultaneous human parsing and pose estimation. Our SUNNet consists of two stages: a feature separation stage and a feature union stage. In feature separation stage, we leverage a common feature extractor to implicitly encode the correlation between human parsing and pose estimation, meanwhile, two task-specific feature extractors are designed to extract the features for both tasks. By combining the task-specific features and common features with a feature consolidation module in a coarse-to-fine manner, we can get an initial prediction for parsing and pose estimation; In feature union stage, we refine the initial prediction by explicitly leveraging the features from parallel task to predict the kernels’ receptive fields in a convolutional neural network. We further propose to leverage a 3D human body reconstructed from the image to facilitate these tasks, and a novel Gated Feature Fusion (GFF) block is designed to automatically decide whether to use or skip the priors from the reconstructed 3D human body. Extensive experiments demonstrate the effectiveness of our SUNNet model for human body configuration analysis.
Scratch-DKG: A Framework for Constructing Scratch Domain Knowledge Graph

With the rapid development of programming platforms, how to utilize the tremendous amount of data produced by the platforms, such as Scatch, has been a big challenge to researchers. The growing data is not only huge, but also heterogeneous and diverse, leading that the existing tools cannot effectively extract valuable information. In this article, considering particular features of Scratch data, we propose an effective framework about constructing a Scratch Domain Knowledge Graph (Scratch-DKG). Our framework includes four modules which are designed to process the semi-structured data, users profile data, projects data and programming knowledge points, respectively. For webpages, we design a template-based wrapper method to extract triples from the semi-structured data. As for users profile data, we improve DeepDive, which is a useful tool to extract information but with the problem of wrong labeling, to extract knowledge triples by the proposed Secondary Labeling Algorithm. For projects data, we propose an advanced keywords extraction method (S-TextRank) to extract keywords triples. For programming knowledge points, we develop a frequently contiguous block combinations mining algorithm to extract the potential domain information of Scratch. Finally, extensive experiments are carried out to evaluate the performance of our proposed methods. The experimental results show that, compared to other competing methods, our proposal can extract more correct and comprehensive Scratch triples.
Rethinking Blockchains in the Internet of Things Era from a Wireless Communication Perspective

Due to the rapid development of Internet of Things (IoT), a massive number of devices are connected to the Internet. For these distributed devices in IoT networks, how to ensure their security and privacy becomes a significant challenge. The blockchain technology provides a promising solution to protect the data integrity, provenance, privacy, and consistency for IoT networks. In blockchains, communication is a prerequisite for participants, which are distributed in the system, to reach consensus. However, in IoT networks, most of the devices communicate through wireless links, which are not always reliable. Hence, the communication reliability of IoT devices influences the system security. In this article, we rethink the roles of communication and computing in blockchains by accounting for communication reliability. We analyze the tradeoff between communication reliability and computing power in blockchain security, and present a lower bound to the computing power that is needed to conduct an attack with a given communication reliability. Simulation results show that adversarial nodes can succeed in tampering a block with less computing power by hindering the propagation of blocks from other nodes.
Quantum enhanced metrology of Hamiltonian parameters beyond the Cramèr–Rao bound

This is a tutorial aimed at illustrating some recent developments in quantum parameter estimation beyond the Cramèr–Rao bound, as well as their applications in quantum metrology. Our starting point is the observation that there are situations in classical and quantum metrology where the unknown parameter of interest, besides determining the state of the probe, is also influencing the operation of the measuring devices, e.g. the range of possible outcomes. In those cases, nonregular statistical models may appear, for which the Cramèr–Rao theorem does not hold. In turn, the achievable precision may exceed the Cramèr–Rao bound, opening new avenues for enhanced metrology. We focus on quantum estimation of Hamiltonian parameters and show that an achievable bound to precision (beyond the Cramèr–Rao) may be obtained in a closed form for the class of so-called controlled energy measurements. Examples of applications of the new bound to various estimation problems in quantum metrology are worked out in some details.
E-Commerce Comment Sentiment Classification Based on Deep Learning

With the development of e-commerce, comments on e-commerce platform has emerged as an important source of information for understanding customers' attitude. Sentiment classification is a direction of NLP (Natural Language Processing), which focuses on classify the comments into positive class, neutral class, negative class according to the polarity of sentiment. Deep learning-based method for sentiment classification has been a mainstream due to its outstanding performance. Sentiment classification of e-commerce comments can help merchants and e-commerce platforms understand customer preferences and needs to improve service quality and customer's satisfaction. In this paper, in order to annotate the comment dataset efficiently, we propose a method for annotation based on artificial and emotional lexicon. This paper consists of two parts of works. First of all, we adopt the artificial plus emotional lexicon method to annotate the original comment dataset, which can remarkably improve the efficiency and accuracy of the annotation. And then we enter the labeled comment set into the deep learning model and the SVM model. The results show the accuracy of sentiment classification model based on deep learning is significantly higher than that of SVM model.
Overcoming Data Scarcity in Speaker Identification: Dataset Augmentation with Synthetic MFCCs via Character-level RNN

Autonomous speaker identification suffers issues of data scarcity due to it being unrealistic to gather hours of speaker audio to form a dataset, which inevitably leads to class imbalance in comparison to the large data availability from non-speakers since large-scale speech datasets are available online. In this study, we explore the possibility of improving speaker recognition by augmenting the dataset with synthetic data produced by training a Character-level Recurrent Neural Network on a short clip of five spoken sentences. A deep neural network is trained on a selection of the Flickr8k dataset as well as the real and synthetic speaker data (all in the form of MFCCs) as a binary classification problem in order to discern the speaker from the Flickr speakers. Ranging from 2,500 to 10,000 synthetic data objects, the network weights are then transferred to the original dataset of only Flickr8k and the real speaker data, in order to discern whether useful rules can be learnt from the synthetic data. Results for all three subjects show that fine-tune learning from datasets augmented with synthetic speech improve the classification accuracy, F1 score, precision, and the recall when applied to the scarce real data vs non-speaker data. We conclude that even with just five spoken short sentences, data augmentation via synthetic speech data generated by a Char- RNN can improve the speaker classification process. Accuracy and related metrics are shown to improve from around 93% to 99% for three subjects classified from thousands of others when fine-tuning from exposure to 2500-1000 synthetic data points. High F1 scores, precision and recall also show that issues due to class imbalance are also solved.
Augmentation of Segmented Motion Capture Data for Improving Generalization of Deep Neural Networks

This paper presents a method for augmenting the motion capture trajectories to improve generalization performance of recurrent long short-term memory (LSTM) neural networks. The presented algorithm is based on the interpolation of existing time series and can be applied only to segmented or easy-to-segment data due to the possibility of blending similar motion trajectories that are not significantly time-shifted. The paper shows the results of the classification efficiency with and without augmentation for two publicly available databases: Multimodal Kinect-IMU Dataset and National Chiao Tung University Multisensor Fitness Dataset. The former contains the data representing separate human computer interaction gestures, while the latter comprises the data of unsegmented series of body exercises. As a result of using the presented algorithm, the classification accuracy increased by approximately 11% points for the first dataset and 8% points for the second one.
Experiences on Teaching Alloy with an Automated Assessment Platform

This paper presents Alloy4Fun, a web application that enables online editing and sharing of Alloy models and instances (including dynamic ones developed with the Electrum extension), to be used mainly in an educational context. By introducing secret paragraphs and commands in the models, Alloy4Fun allows the distribution and automated assessment of simple specification challenges, a mechanism that enables students to learn the language at their own pace. Alloy4Fun stores all versions of shared and analyzed models, as well as derivation trees that depict how they evolved over time: this wealth of information can be mined by researchers or tutors to identify, for example, learning breakdowns in the class or typical mistakes made by Alloy users. Alloy4Fun has been used in formal methods graduate courses for two years and for the latest edition we present results regarding its adoption by the students, as well as preliminary insights regarding the most common bottlenecks when learning Alloy (and Electrum).
Improving knowledge about Indonesian culture with augmented reality gamification

Abstract Digital technology cannot be dammed or avoided, moreover, the current generation of children is a native digital generation is those who are born very close to the world of digital technology such as smartphones, laptops, digital applications, internet access, and others. This may affect the attitudes and behaviors of children in both positive and negative impacts. However, we can act wisely, not by alienating or avoiding children from technology but by providing education about technology (digital literacy) and directing video game access to positive video games that are beneficial. The method used in this research is Research and Development with a modified SDLC (Software Development Life Cycle) development model in the development of Augmented Reality (AR) educational games. This study also uses a pre-test-post-test group matching design to analyze the increase of elementary school students’ knowledge in learning about Indonesian culture. The measurement results of student learning outcomes are carried out by giving tests in the form of multiple choice. The statistical analysis used in this study is the validity test and Wilcoxon Signed Rank test. Validity testing shows 93.33% of the questionnaire items are valid. The results of the Wilcoxon Signed Rank test analysis obtained a significant value of 0,00 &lt;0.05, then it was stated that there were differences in the average student learning outcomes for pre-test with posttest (increased). This research concludes that after the use of AR Edugame learning media, it has an influence compared to before learning with the media. This shows there is an increase in student learning outcomes.
A Context Based Deep Learning Approach for Unbalanced Medical Image Segmentation

Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation tasks, with U - Net and generative adversarial nets (GANs) being some of the commonly used ones. Foreground-background class imbalance is a common occurrence in medical images, and U-Net has difficulty in handling class imbalance because of its cross entropy (CE) objective function. Similarly, GAN also suffers from class imbalance because the discriminator looks at the entire image to classify it as real or fake. Since the discriminator is essentially a deep learning classifier, it is incapable of correctly identifying minor changes in small structures. To address these issues, we propose a novel context based CE loss function for U-Net, and a novel architecture Seg-GLGAN. The context based CE is a linear combination of CE obtained over the entire image and its region of interest (ROI). In Seg-GLGAN, we introduce a novel context discriminator to which the entire image and its ROI are fed as input, thus enforcing local context. We conduct extensive experiments using two challenging unbalanced datasets: PROMISE12 and ACDC. We observe that segmentation results obtained from our methods give better segmentation metrics as compared to various baseline methods.
Adversarial-Prediction Guided Multi-Task Adaptation for Semantic Segmentation of Electron Microscopy Images

Semantic segmentation is an essential step for electron microscopy (EM) image analysis. Although supervised models have achieved significant progress, the need for labor intensive pixel-wise annotation is a major limitation. To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift. In this study, we introduce an adversarial-prediction guided multitask network to learn the adaptation of a well-trained model for use on a novel unlabeled target domain. Since no label is available on target domain, we learn an encoding representation not only for the supervised segmentation on source domain but also for unsupervised reconstruction of the target data. To improve the discriminative ability of the supervised learned model on unlabeled target domain, we further guide the representation learning by multi-level adversarial learning in semantic prediction space. Comparisons and ablation study on public benchmark demonstrated state-of-the-art performance and effectiveness of our approach.
The KDD Cup 2019 Report

The KDD Cup has been a data science competition affiliated with the ACM SIGKDD conference with more than 20 years' tradition. In 2019, we organized the KDD Cup by hosting 3 parallel tracks, each with tremendous innovation. The regular machine learning (ML) track was a context-aware travel mode recommendation problem, sponsored by Baidu.com. The automatic machine learning (AutoML) track, sponsored by 4Paradigm, was about nding cost-e ective and transferable solutions for temporal relational data represented as multiple related tables. The humanity reinforcement learning (RL) track was sponsored by IBM Africa and Hexagon-ML to determine the best policy in distribution of control measures to eradicate Malaria. In 3 competitions collectively, we had more than 2,800 registered teams from over 39 countries and 230 academic and research institutions. Among the 1,200 most actively participating teams, over 5,000 individuals participated, and more than 17,000 submissions were made. A total exceeding 100 thousand U.S. dollars were awarded to the winning teams.
Full-body motion capture for multiple closely interacting persons

Human shape and pose estimation is a popular but challenging problem, especially when asked to capture the body, hands, feet and face jointly for multiple persons with close interaction. Existing methods can only have a total motion capture of a single person or multiple persons without close interaction. In this paper, we present a fully automatic and effective method to capture full-body human performance including body poses, face poses, hand gestures, and feet orientations for closely interacting multiple persons. We predict 2D keypoints corresponding to the poses of body, face, hands and feet for each person, and associate the same person in multi-view videos by computing personalized appearance descriptors to reduce ambiguities and uncertainties. To deal with occlusions and obtain temporally coherent human shapes, we estimate shape and pose for each person with the spatio-temporal tracking and constraints. Experimental results demonstrate that our method achieves better performance than state-of-the-art methods.
Challenges, Strategies and Adaptations on Interactive Dashboards

Interactive dashboards enable viewing and interacting with complex underlying data using visualisations such as charts, tables, maps, or even text typically on a single display. By bringing the most important information in a single place, dashboards enable performance monitoring and support decision making. Although nowadays dashboards are widely adopted in many domains, they involve challenges that prevent users from utilising them as they were intended. For example, having a dashboard with too much data can negatively affect decision making and lead to misleading interpretation. Through this research, we identify and investigate the challenges associated with dashboards, what users do in response to those challenges, and what adaptations can be applied to mitigate these challenges. Consequently, we aim to examine and evaluate a set of adaptation techniques that can improve the experience of users interacting with dashboards.
A Generic Ensemble Based Deep Convolutional Neural Network for Semi-Supervised Medical Image Segmentation

Deep learning based image segmentation has achieved the state-of-the-art performance in many medical applications such as lesion quantification, organ detection, etc. However, most of the methods rely on supervised learning, which require a large set of high-quality labeled data. Data annotation is generally an extremely time-consuming process. To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN). An encoder-decoder based DCNN is initially trained using a few annotated training samples. This initially trained model is then copied into sub-models and improved iteratively using random subsets of unlabeled data with pseudo labels generated from models trained in the previous iteration. The number of sub-models is gradually decreased to one in the final iteration. We evaluate the proposed method on a public grand-challenge dataset for skin lesion segmentation. Our method is able to significantly improve beyond fully supervised model learning by incorporating unlabeled data. The code is available on Github <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/ruizhe-1/semi-segmentation.
Dynamic redirection of real-time data streams for elastic stream computing

An elastic stream computing system needs elastic adjustment of computing resource allocation and vertex parallelism to improve latency and throughput, which includes continuously or periodically scaling in/out the workload of computing nodes at runtime. Dynamic redirection can help with this elasticity issue by dynamically redirecting real-time data streams to computing resources. Due to the time-varying and unpredictable nature of real-time data streams, implementing redirection of data streams is challenging. Currently, the requirements of data streams redirection are not fully fulfilled, which directly affects the latency and throughput of stream computing systems. To bridge this gap, we proposed a dynamic redirection framework (called Dr-Stream) for elastic stream computing systems. This paper discussed the following aspects: (1) Investigating the dynamic redirection of real-time data streams, providing a general stream application model, a data stream model and a data stream grouping model, as well as formalizing the problem of load balancing optimization and data stream redirection. (2) Redirecting data streams among multiple instances of an operator at runtime by a lightweight load balancing strategy to improve the load balancing of a data center at the vertex level. Managing system states, especially the states of stateful operators by a logical ring-based strategy to improve accuracy. (3) Determining the number of instances for each operator, and deploying the instance(s) to computing nodes by a modified first-fit strategy at runtime. (4) Evaluating the fulfillment of low latency, high throughput, and load balancing objectives in a real-world distributed stream computing environment. Experimental results showed that Dr-Stream reduced the average system latency and load balancing of the data center by more than 20% and 15%, respectively. It also improved the average system stability by more than 15% and avoided over-utilization of computing nodes, as compared to the existing strategies in Storm.