Reviewerly/exHarmony huggingface dataset:
Controlling Interactions between Quantum Emitters Using Atom Arrays

We investigate the potential for two-dimensional atom arrays to modify the radiation and interaction of individual quantum emitters. Specifically, we demonstrate that control over the emission linewidths, resonant frequency shifts, and local driving field enhancement in impurity atoms is possible due to strong dipole-dipole interactions within ordered, subwavelength atom array configurations. We demonstrate that these effects can be used to dramatically enhance coherent dipole-dipole interactions between distant impurity atoms within an atom array. Possible experimental realizations and potential applications are discussed.
Generative adversarial classifier for handwriting characters super-resolution

Generative Adversarial Networks (GAN) receive great attention recently due to its excellent performance in image generation, transformation, and super-resolution. However, less emphasis or study has been put on GAN for classification with super-resolution. Moreover, though GANs may fabricate images which perceptually looks realistic, they usually fabricate some fake details especially in character data; this would impose further difficulties when they are input for classification. In this paper, we propose a novel Generative Adversarial Classifier (GAC) for low-resolution handwriting character recognition. Specifically, we design an additional classifier component in GAC, leading to a novel three-player GAN model which is not only able to generate high-quality super-resolved images, but also favorable for classification. Experimental results show that our proposed method can obtain remarkable performance in handwriting characters with 8 × super-resolution, achieving new state-of-the-art on benchmark dataset CASIA-HWDB1.1, and MNIST.
Perceptions about the Use of Educational Robotics in the Initial Training of Future Teachers: A Study on STEAM Sustainability among Female Teachers

In these moments of future uncertainty and change, teachers must be trained to respond to the challenges posed by today’s society, and the challenges that are closely related to the economy. We are going through the first steps of the Fourth Industrial Revolution, and changes are already taking place in our daily lives, in our way of learning, working, and interacting with each other. According to the data of the World Economic Forum (WEF), the future of teacher professional development is disfigured—most technological profiles play a strong role, and this affects the skills and abilities of teachers, especially in the fields of Science, Technology, Engineering, Arts, and Mathematics (STEAM). The goal is to achieve the Millennium Goal number three proposed by the United Nations: All countries must promote gender equality and the empowerment of women. This objective aims to eliminate gender disparity in primary and secondary education, and the promotion of ICT (Information and Communication Technology) to improve the competences of women and vulnerable groups to ensure that no one is left behind. These are priority areas to consider regarding SDG4 (Sustainable Development Goal 4) and Education 2030.
Rapidly-exploring Random Trees multi-robot map exploration under optimization framework

Rapidly-exploring Randomized Trees (RRT) is a kind of probabilistically complete exploration algorithm based on the tree structure. It has been widely used in the robotic navigation since it guarantees the complete discovery and the exploration of environment maps through robots. In the present study, the RRT algorithm is extended to propose an optimization-based map exploration strategy for multiple robots to actively explore and build environment maps. The present study adopts a market-based task allocation strategy, which to maximize the profit, for the coordination between robots. In the extension of the RRT, the cost function consists the unknown region and the passed unknown region. The unknown region is explored for a given frontier point, while the passed unknown region is the area, where the robot moves towards the target frontier point. When the robot moves from the start position to the target frontier point, the trajectory length is defined as a constraint for the optimization. The main contributions of the present study can be summarized in optimizing the frontier points, defining a new task allocation strategy and applying different evaluation rules, including the running time and the trajectory length. These rules are applied to explore the multi-robot map in simulated and practical environments. Then the Robot Operating System (ROS) is utilized to evaluate the application of the proposed exploration strategy on Turtlebots in a 270 m2 room. Obtained results from the simulation and the experiment demonstrate that the proposed method outperforms the Umari’s approach from both the running time and the trajectory length aspects.
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model

Recently, generating adversarial examples has become an important means of measuring robustness of a deep learning model. Adversarial examples help us identify the susceptibilities of the model and further counter those vulnerabilities by applying adversarial training techniques. In natural language domain, small perturbations in the form of misspellings or paraphrases can drastically change the semantics of the text. We propose a reinforcement learning based approach towards generating adversarial examples in black-box settings. We demonstrate that our method is able to fool well-trained models for (a) IMDB sentiment classification task and (b) AG's news corpus news categorization task with significantly high success rates. We find that the adversarial examples generated are semantics-preserving perturbations to the original text.
Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans

The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions. The overlapping area among the intensity distribution of healthy, enhancing, non-enhancing, and edema regions makes the automatic segmentation a challenging task. Here, we show that a convolutional neural network trained on high-contrast images can transform the intensity distribution of brain lesions in its internal subregions. Specifically, a generative adversarial network (GAN) is extended to synthesize high-contrast images. A comparison of these synthetic images and real images of brain tumor tissue in MR scans showed significant segmentation improvement and decreased the number of real channels for segmentation. The synthetic images are used as a substitute for real channels and can bypass real modalities in the multimodal brain tumor segmentation framework. Segmentation results on BraTS 2019 dataset demonstrate that our proposed approach can efficiently segment the tumor areas. In the end, we predict patient survival time based on volumetric features of the tumor subregions as well as the age of each case through several regression models.
DBPal: A Fully Pluggable NL2SQL Training Pipeline

Natural language is a promising alternative interface to DBMSs because it enables non-technical users to formulate complex questions in a more concise manner than SQL. Recently, deep learning has gained traction for translating natural language to SQL, since similar ideas have been successful in the related domain of machine translation. However, the core problem with existing deep learning approaches is that they require an enormous amount of training data in order to provide accurate translations. This training data is extremely expensive to curate, since it generally requires humans to manually annotate natural language examples with the corresponding SQL queries (or vice versa). Based on these observations, we propose DBPal, a new approach that augments existing deep learning techniques in order to improve the performance of models for natural language to SQL translation. More specifically, we present a novel training pipeline that automatically generates synthetic training data in order to (1) improve overall translation accuracy, (2) increase robustness to linguistic variation, and (3) specialize the model for the target database. As we show, our DBPal training pipeline is able to improve both the accuracy and linguistic robustness of state-of-the-art natural language to SQL translation models.
Development and validation of a deep learning system for ascites cytopathology interpretation

Early diagnosis of Peritoneal metastasis (PM) is clinically significant regarding optimal treatment selection and avoidance of unnecessary surgical procedures. Cytopathology plays an important role in early screening of PM. We aimed to develop a deep learning (DL) system to achieve intelligent cytopathology interpretation, especially in ascites cytopathology.The original ascites cytopathology image dataset consists of 139 patients' original hematoxylin-eosin (HE) and Papanicolaou (PAP) Staining images. DL system was developed using transfer learning (TL) to achieve cell detection and classification. Pre-trained alexnet, vgg16, goolenet, resnet18 and resnet50 models were studied. Cell detection dataset consists of 176 cropped images with 6573 annotated cell bounding boxes. Cell classification data set consists of 487 cropped images with 18,558 and 6089 annotated malignant and benign cells in total, respectively.We established a novel ascites cytopathology image dataset and achieved automatically cell detection and classification. DetectionNet based on Faster R-CNN using pre-trained resnet18 achieved cell detection with 87.22% of cells' Intersection of Union (IoU) bigger than the threshold of 0.5. The mean average precision (mAP) was 0.8316. The ClassificationNet based on resnet50 achieved the greatest performance in cell classification with AUC = 0.8851, Precision = 96.80%, FNR = 4.73%. The DL system integrating the separately trained DetectionNet and Classificationnet showed great performance in the cytopathology image interpretation.We demonstrate that the integration of DL can improve the efficiency of healthcare. The DL system we developed using TL techniques achieved accurate cytopathology interpretation, and had great potential to be integrated into clinician workflow.
Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout

Observing, classifying and assessing human movements is important in many applied fields, including human-computer interface, clinical assessment, activity monitoring and sports performance. The redundancy of options in planning and implementing motor programmes, the inter- and intra-individual variability in movement execution, and the time-continuous, high-dimensional nature of motion data make segmenting sequential movements into a smaller set of discrete classes of actions non-trivial. We aimed to develop and validate a method for the automatic classification of four popular functional fitness drills, which are commonly performed in current circuit training routines. Five inertial measurement units were located on the upper and lower limb, and on the trunk of fourteen participants. Positions were chosen by keeping into account the dynamics of the movement and the positions where commercially-available smart technologies are typically secured. Accelerations and angular velocities were acquired continuously from the units and used to train and test different supervised learning models, including k-Nearest Neighbours (kNN) and support-vector machine (SVM) algorithms. The use of different kernel functions, as well as different strategies to segment continuous inertial data were explored. Classification performance was assessed from both the training dataset (k-fold cross-validation), and a test dataset (leave-one-subject-out validation). Classification from different subsets of the measurement units was also evaluated (1-sensor and 2-sensor data). SVM with a cubic kernel and fed with data from 600 ms windows with a 10% overlap gave the best classification performances, yielding to an overall accuracy of 97.8%. This approach did not misclassify any functional fitness movement for another, but confused relatively frequently (2.8-18.9%) a fitness movement phase with the transition between subsequent repetitions of the same task or different drills. Among 1-sensor configurations, the upper arm achieved the best classification performance (96.4% accuracy), whereas combining the upper arm and the thigh sensors obtained the highest level of accuracy (97.6%) from 2-sensors movement tracking. We found that supervised learning can successfully classify complex sequential movements such as those of functional fitness workouts. Our approach, which could exploit technologies currently available in the consumer market, demonstrated exciting potential for future on-field applications including unstructured training.
Is Cybersecurity a Team Sport? A Multilevel Examination of Workgroup Information Security Effectiveness

Organizations maintain their desired level of cybersecurity by evaluating the information security (ISec) effectiveness of their organizational units. Numerous ISec studies have delved into examining individual compliance, yet studies that develop an un
Filter Grafting for Deep Neural Networks

This paper proposes a new learning paradigm called filter grafting, which aims to improve the representation capability of Deep Neural Networks (DNNs). The motivation is that DNNs have unimportant (invalid) filters (e.g., l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm close to 0). These filters limit the potential of DNNs since they are identified as having little effect on the network. While filter pruning removes these invalid filters for efficiency consideration, filter grafting re-activates them from an accuracy boosting perspective. The activation is processed by grafting external information (weights) into invalid filters. To better perform the grafting process, we develop an entropy-based criterion to measure the information of filters and an adaptive weighting strategy for balancing the grafted information among networks. After the grafting operation, the network has very few invalid filters compared with its untouched state, empowering the model with more representation capacity. We also perform extensive experiments on the classification and recognition tasks to show the superiority of our method. For example, the grafted MobileNetV2 outperforms the non-grafted MobileNetV2 by about 7 percent on CIFAR-100 dataset.
Structured Tuning for Semantic Role Labeling

Recent neural network-driven semantic role labeling (SRL) systems have shown impressive improvements in F1 scores.These improvements are due to expressive input representations, which, at least at the surface, are orthogonal to knowledge-rich constrained decoding mechanisms that helped linear SRL models.Introducing the benefits of structure to inform neural models presents a methodological challenge.In this paper, we present a structured tuning framework to improve models using softened constraints only at training time.Our framework leverages the expressiveness of neural networks and provides supervision with structured loss components.We start with a strong baseline (RoBERTa) to validate the impact of our approach, and show that our framework outperforms the baseline by learning to comply with declarative constraints.Additionally, our experiments with smaller training sizes show that we can achieve consistent improvements under low-resource scenarios.
Coloring Graph Neural Networks for Node Disambiguation

In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks (MPNNs). More specifically, we introduce a graph neural network called Colored Local Iterative Procedure (CLIP) that uses colors to disambiguate identical node attributes, and show that this representation is a universal approximator of continuous functions on graphs with node attributes. Our method relies on separability, a key topological characteristic that allows to extend well-chosen neural networks into universal representations. Finally, we show experimentally that CLIP is capable of capturing structural characteristics that traditional MPNNs fail to distinguish, while being state-of-the-art on benchmark graph classification datasets.
Multitrace first-break picking using an integrated seismic and machine learning method

Picking the first breaks from seismic data is often a challenging problem and still requires significant human effort. We have developed an iterative process that applies a traditional seismic automated picking method to obtain preliminary first breaks and then uses a machine learning (ML) method to identify, remove, and fix poor picks based on a multitrace analysis. The ML method involves constructing a convolutional neural network architecture to help identify poor picks across multiple traces and eliminate them. We then further refill the picks on empty traces with the help of the trained model. To allow training samples applicable to various regions and different data sets, we apply moveout correction with preliminary picks and address the picks in the flattened input. We collect 11,239,800 labeled seismic traces. During the training process, the model’s classification accuracy on the training and validation data sets reaches 98.2% and 97.3%, respectively. We also evaluate the precision and recall rate, both of which exceed 94%. For prediction, the results of 2D and 3D data sets that differ from the training data sets are used to demonstrate the feasibility of our method.
An Online Semantic-enhanced Dirichlet Model for Short Text Stream Clustering

Clustering short text streams is a challenging task due to its unique properties: infinite length, sparse data representation and cluster evolution.Existing approaches often exploit short text streams in a batch way.However, determine the optimal batch size is usually a difficult task since we have no prior knowledge when the topics evolve.In addition, traditional independent word representation in the graphical model tends to cause "term ambiguity" problem in short text clustering.Therefore, in this paper, we propose an Online Semantic-enhanced Dirichlet Model for short text stream clustering, called OSDM, which integrates the word-occurrence semantic information (i.e., context) into a new graphical model and clusters for each arriving short text automatically in an online way.Extensive results have demonstrated that OSDM gives better performance compared to many state-ofthe-art algorithms on both synthetic and realworld data sets.
Don’t Eclipse Your Arts Due to Small Discrepancies: Boundary Repositioning with a Pointer Network for Aspect Extraction

The current aspect extraction methods suffer from boundary errors.These errors lead to a relatively minor difference between the extracted aspects and the ground-truth.However, they hurt the performance severely.In this paper, we propose to utilize a pointer network for repositioning the boundaries.Recycling mechanism is used which enables the training data to be collected without manual intervention.We conduct the experiments on the benchmark datasets SE14 of laptop and SE14-16 of restaurant.Experimental results show that our method achieves substantial improvements over the baseline, and outperforms stateof-the-art methods.
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems

A Dialogue State Tracker (DST) is a core component of a modular task-oriented dialogue system.Tremendous progress has been made in recent years.However, the major challenges remain.The state-of-the-art accuracy for DST is below 50% for a multi-domain dialogue task.A learnable DST for any new domain requires a large amount of labeled indomain data and training from scratch.In this paper, we propose a Meta-Reinforced Multi-Domain State Generator (MERET).Our first contribution is to improve the DST accuracy.We enhance a neural model based DST generator with a reward manager, which is built on policy gradient reinforcement learning (R-L) to fine-tune the generator.With this change, we are able to improve the joint accuracy of DST from 48.79% to 50.91% on the Multi-WOZ corpus.Second, we explore to train a DST meta-learning model with a few domains as source domains and a new domain as target domain.We apply the model-agnostic metalearning (MAML) algorithm to DST and the obtained meta-learning model is used for new domain adaptation.Our experimental results show this solution is able to outperform the traditional training approach with extremely less training data in target domain.
Probability Weighted Compact Feature for Domain Adaptive Retrieval

Domain adaptive image retrieval includes single-domain retrieval and cross-domain retrieval. Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar. However, in practical application, the discrepancies between retrieval databases often taken in ideal illumination/pose/background/camera conditions and queries usually obtained in uncontrolled conditions are very large. In this paper, considering the practical application, we focus on challenging cross-domain retrieval. To address the problem, we propose an effective method named Probability Weighted Compact Feature Learning (PWCF), which provides inter-domain correlation guidance to promote cross-domain retrieval accuracy and learns a series of compact binary codes to improve the retrieval speed. First, we derive our loss function through the Maximum A Posteriori Estimation (MAP): Bayesian Perspective (BP) induced focal-triplet loss, BP induced quantization loss and BP induced classification loss. Second, we propose a common manifold structure between domains to explore the potential correlation across domains. Considering the original feature representation is biased due to the inter-domain discrepancy, the manifold structure is difficult to be constructed. Therefore, we propose a new feature named Histogram Feature of Neighbors (HFON) from the sample statistics perspective. Extensive experiments on various benchmark databases validate that our method outperforms many state-of-the-art image retrieval methods for domain adaptive image retrieval. The source code is available at {https://github.com/fuxianghuang1/PWCF}.
An Analysis of BERT in Document Ranking

Although BERT has shown its effectiveness in a number of IR-related tasks, especially document ranking, the understanding of its internal mechanism remains insufficient. To increase the explainability of the ranking process performed by BERT, we investigate a state-of-the-art BERT-based ranking model with focus on its attention mechanism and interaction behavior. Firstly, we look into the evolving of the attention distribution. It shows that in each step, BERT dumps redundant attention weights on tokens with high document frequency (such as periods). This may lead to a potential threat to the model robustness and should be considered in future studies. Secondly, we study how BERT models interactions between query and document and find that BERT aggregates document information to query token representations through their interactions, but extracts query-independent representations for document tokens. It indicates that it is possible to transform BERT into a more efficient representation-focused model. These findings help us better understand the ranking process by BERT and may inspire future improvement.