Reviewerly/exHarmony huggingface dataset:
CenterMask: Single Shot Instance Segmentation With Point Representation

In this paper, we propose a single-shot instance segmentation method, which is simple, fast and accurate. There are two main challenges for one-stage instance segmentation: object instances differentiation and pixel-wise feature alignment. Accordingly, we decompose the instance segmentation into two parallel subtasks: Local Shape prediction that separates instances even in overlapping conditions, and Global Saliency generation that segments the whole image in a pixel-to-pixel manner. The outputs of the two branches are assembled to form the final instance masks. To realize that, the local shape information is adopted from the representation of object center points. Totally trained from scratch and without any bells and whistles, the proposed CenterMask achieves 34.5 mask AP with a speed of 12.3 fps, using a single-model with single-scale training/testing on the challenging COCO dataset. The accuracy is higher than all other one-stage instance segmentation methods except the 5 times slower TensorMask, which shows the effectiveness of CenterMask. Besides, our method can be easily embedded to other one-stage object detectors such as FCOS and performs well, showing the generation of CenterMask.
Fast Sparse ConvNets

Historically, the pursuit of efficient inference has been one of the driving forces behind the research into new deep learning architectures and building blocks. Some of the recent examples include: the squeeze-and-excitation module, depthwise separable convolutions in Xception, and the inverted bottleneck in MobileNet v2. Notably, in all of these cases, the resulting building blocks enabled not only higher efficiency, but also higher accuracy, and found wide adoption in the field. In this work, we further expand the arsenal of efficient building blocks for neural network architectures; but instead of combining standard primitives (such as convolution), we advocate for the replacement of these dense primitives with their sparse counterparts. While the idea of using sparsity to decrease the parameter count is not new, the conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains. We aim to correct this misconception by introducing a family of efficient sparse kernels for several hardware platforms, which we plan to open source for the benefit of the community. Equipped with our efficient implementation of sparse primitives, we show that sparse versions of MobileNet v1 and MobileNet v2 architectures substantially outperform strong dense baselines on the efficiency-accuracy curve. On Snapdragon 835 our sparse networks outperform their dense equivalents by 1.3 - 2.4× - equivalent to approximately one entire generation of improvement. We hope that our findings will facilitate wider adoption of sparsity as a tool for creating efficient and accurate deep learning architectures.
Learning Physics-Guided Face Relighting Under Directional Light

Relighting is an essential step in realistically transferring objects from a captured image into another environment. For example, authentic telepresence in Augmented Reality requires faces to be displayed and relit consistent with the observer's scene lighting. We investigate end-to-end deep learning architectures that both de-light and relight an image of a human face. Our model decomposes the input image into intrinsic components according to a diffuse physics-based image formation model. We enable non-diffuse effects including cast shadows and specular highlights by predicting a residual correction to the diffuse render. To train and evaluate our model, we collected a portrait database of 21 subjects with various expressions and poses. Each sample is captured in a controlled light stage setup with 32 individual light sources. Our method creates precise and believable relighting results and generalizes to complex illumination conditions and challenging poses, including when the subject is not looking straight at the camera.
Data-Driven Optimal Power Flow: A Physics-Informed Machine Learning Approach

This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework. SELM has a fast training speed and does not require the time-consuming parameter tuning process compared with the deep learning algorithms. However, the direct application of SELM for OPF is not tractable due to the complicated relationship between the system operating status and the OPF solutions. To this end, a data-driven OPF regression framework is developed that decomposes the OPF model features into three stages. This not only reduces the learning complexity but also helps correct the learning bias. A sample pre-classification strategy based on active constraint identification is also developed to achieve enhanced feature attractions. Numerical results carried out on IEEE and Polish benchmark systems demonstrate that the proposed method outperforms other alternatives. It is also shown that the proposed method can be easily extended to address different test systems by adjusting only a few hyperparameters.
Learning From Noisy Anchors for One-Stage Object Detection

State-of-the-art object detectors rely on regressing and classifying an extensive list of possible anchors, which are divided into positive and negative samples based on their intersection-over-union (IoU) with corresponding ground-truth objects. Such a harsh split conditioned on IoU results in binary labels that are potentially noisy and challenging for training. In this paper, we propose to mitigate noise incurred by imperfect label assignment such that the contributions of anchors are dynamically determined by a carefully constructed cleanliness score associated with each anchor. Exploring outputs from both regression and classification branches, the cleanliness scores, estimated without incurring any additional computational overhead, are used not only as soft labels to supervise the training of the classification branch but also sample re-weighting factors for improved localization and classification accuracy. We conduct extensive experiments on COCO, and demonstrate, among other things, the proposed approach steadily improves RetinaNet by ~2% with various backbones.
On Translation Invariance in CNNs: Convolutional Layers Can Exploit Absolute Spatial Location

In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial location by learning filters that respond exclusively to particular absolute locations by exploiting image boundary effects. Because modern CNNs filters have a huge receptive field, these boundary effects operate even far from the image boundary, allowing the network to exploit absolute spatial location all over the image. We give a simple solution to remove spatial location encoding which improves translation invariance and thus gives a stronger visual inductive bias which particularly benefits small data sets. We broadly demonstrate these benefits on several architectures and various applications such as image classification, patch matching, and two video classification datasets.
Human action recognition using convolutional LSTM and fully-connected LSTM with different attentions

This paper aims to address the human action recognition issue by using convolutional long short-term memory networks (Conv-LSTM) and fully-connected LSTM (FC-LSTM) with different attentions. To this end, the spatial-temporal dual-attention network (STDAN), which is mainly composed of feature extraction, attention and fusion modules, is designed. Different from the features of high-level fully-connected layer mostly used in previous work, the features of convolution and fully-connected layers of convolutional neural network (CNN) are both extracted in STDAN, which can enrich the initial level of video representation. Besides, the Conv-LSTM and FC-LSTM are employed to handle the long-duration sequential features with different temporal context information. To reinforce the spatial-temporal attention ability, a temporal attention module (TAM) and a joint spatial-temporal attention module (JSTAM) are implemented. Through the principle components analysis (PCA) and features fusion, the potential of STDAN is effectively explored and weighted. Finally, the experimental results show that the proposed STDAN has better recognition performance than existing state-of-the-art methods.
Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations. These include having to deal with limited, low-quality data, which inevitably compromises AI performance. We suggest that these problems can be mitigated by taking steps that allow rapid trust calibration so that decision makers understand the AI system's limitations and likely failures and can calibrate their trust in its outputs appropriately. We propose that AI services can achieve this by being both interpretable and uncertainty-aware. Creating such AI systems poses various technical and human factors challenges. We review these challenges and recommend directions for future research.
Heavy metal contamination prediction using ensemble model: Case study of Bay sedimentation, Australia

Lead (Pb) is a primary toxic heavy metal (HM) which present throughout the entire ecosystem. Some commonly observed challenges in HM (Pb) prediction using artificial intelligence (AI) models include overfitting, normalization, validation against classical AI models, and lack in learning/technology transfer. This study explores the extreme gradient boosting (XGBoost) model as a superior SuperLearning (SL) algorithms for Pb prediction. The proposed model was examined using historical data at the Bramble and Deception Bay (BB and DB) stations, Australia. The model was trained at one of the stations and transferred to a cross-station and vice versa. XGBoost showed higher reliability with less declination in (R2: coefficient of determination), i.e., 0.97 % over the testing phase, among others models at BB. At the cross-station (DB), the performance of the XGBoost model was decreased by 2.74 % (R2) against random forests (RF). The mean absolute error (MAE) observed 40 % (XGBoost) and 47 % (RF) less than artificial neural network (ANN). The XGBoost model performance declined by 3.44 % (R2) over testing (DB), which is minor among validated models. At the cross-station (BB), the XGBoost model showed the least decrement in terms of R2, i.e., 7.99 % against the ANN (8.31 %), RF (10.26 %), and support vector machine (SVM, 36.19 %).
Finding flows of a Navier–Stokes fluid through quantum computing

Abstract There is great interest in using quantum computers to efficiently simulate a quantum system’s dynamics as existing classical computers cannot do this. Little attention, however, has been given to quantum simulation of a classical nonlinear continuum system such as a viscous fluid even though this too is hard for classical computers. Such fluids obey the Navier–Stokes nonlinear partial differential equations, whose solution is essential to the aerospace industry, weather forecasting, plasma magneto-hydrodynamics, and astrophysics. Here we present a quantum algorithm for solving the Navier–Stokes equations. We test the algorithm by using it to find the steady-state inviscid, compressible flow through a convergent-divergent nozzle when a shockwave is (is not) present. We find excellent agreement between numerical simulation results and the exact solution, including shockwave capture when present. Finally, we compare the algorithm’s computational cost to deterministic and random classical algorithms and show that a significant speed-up is possible. Our work points to a large new application area for quantum computing with substantial economic impact, including the trillion-dollar aerospace industry, weather-forecasting, and engineered-plasma technologies.
The Longer‐Term Impact of COVID‐19 on K–12 Student Learning and Assessment

Abstract Due to the precipitous onset of the coronavirus disease, teachers and students across the nation were thrust into a new environment, and the impact of this new experience will be felt both shorter and longer term. This academic year saw “test pollution” with the switch to online instruction, and student learning was significantly impacted by stress, anxiety, illness, being forced to learn in a vastly different method than previously experienced, and the increased potential to fall behind due to lack of access to materials. Classroom assessment, teaching and learning, and measurement and interpretation of student growth are among the numerous areas that have been affected by the sudden switch of schools to online instruction that will require much thought in order to examine the impact of the significant deviation from the classroom norms on which much of previous research has been based. Educators, educational researchers, and policymakers have been presented with a challenge that does not have a definitive answer. There are many unknowns that remain as schools plan to move forward with instruction. However, through collaboration, the knowledge that each of these professionals can contribute ensures that adequate decisions will be made that will benefit all students equitably.
Aggression detection through deep neural model on Twitter

Social interaction is being facilitated by every online environment that results in rise of anti-social behavior. Incidents like cyberbullying, trolling and hate speech have been grown significantly across the globe. Hate and aggression detection had become a crucial part of cyberbullying and cyberharassment. Cyberbullying refers to aggressive behavior with rude, offensive, insulting, hateful and teasing comments to harm other individuals on social media. Human moderation is slow and expensive and even not feasible in speedily growing data, only automatic detection can lead to put a stop on trolling. In this paper we address the challenge of automatic identification of aggression detection on tweets of cyber-troll dataset. We deploy Multilayer Perceptron by feeding important manually engineered features and also experimented on state-of-the-art combination of CNN-LSTM and CNN-BiLSTM in deep neural network, both perform well. Statistical results proved that our proposed model performed best and detects aggressive behavior with 92% accuracy.
DistBlockBuilding: A Distributed Blockchain-Based SDN-IoT Network for Smart Building Management

Security, privacy, and transparency aspects of the Internet of Things (IoT) sensors have recently raised significant concerns among the public and policymakers. The distributed ledger and Blockchain technologies had brought solutions through transparently bridging two or multiple untrusted parties. In this research, a novel architecture for a smart building system, including a control system and automatic approaches, is proposed. An efficient cluster head selection algorithm is proposed to select the desired cluster head with the consideration of low energy consumption and fast head selection. An enhanced combination of IoT forwarding devices and Software-Defined Networking (SDN) technology is further advanced. Furthermore, the proposed “DistBlockBuilding” architecture is implemented for managing a safe and secure data transfer from one surface to another surface. Besides, Blockchain technology is performed for transferring data within the smart building. Finally, the performance of IoT-SDN based secured networks is evaluated.
Towards Blockchain-Based Reputation-Aware Federated Learning

Federated learning (FL) is the collaborative machine learning (ML) technique whereby the devices collectively train and update a shared ML model while preserving their personal datasets. FL systems solve the problems of communication-efficiency, bandwidth-optimization, and privacy-preservation. Despite the potential benefits of FL, one centralized shared ML model across all the devices produce coarse-grained predictions which, in essence, are not required in many application areas involving personalized prediction services. In this paper, we present a novel concept of fine-grained FL to decentralize the shared ML models on the edge servers. We then present a formal extended definition of fine-grained FL process in mobile edge computing systems. In addition, we define the core requirements of fine-grained FL systems including personalization, decentralization, fine-grained FL, incentive mechanisms, trust, activity monitoring, heterogeneity and context-awareness, model synchronization, and communication and bandwidth-efficiency. Moreover, we present the concept of blockchain-based reputation-aware fine-grained FL in order to ensure trustworthy collaborative training in mobile edge computing systems. Finally, we perform the qualitative comparison of proposed approach with state-of-the-art related work and found some promising initial results.
KBot: A Knowledge Graph Based ChatBot for Natural Language Understanding Over Linked Data

With the rapid progress of the semantic web, a huge amount of structured data has become available on the web in the form of knowledge bases (KBs).Making these data accessible and useful for end-users is one of the main objectives of chatbots over linked data.Building a chatbot over linked data raises different challenges, including user queries understanding, multiple knowledge base support, and multilingual aspect.To address these challenges, we first design and develop an architecture to provide an interactive user interface.Secondly, we propose a machine learning approach based on intent classification and natural language understanding to understand user intents and generate SPARQL queries.We especially process a new social network dataset (i.e., myPersonality) and add it to the existing knowledge bases to extend the chatbot capabilities by understanding analytical queries.The system can be extended with a new domain on-demand, flexible, multiple knowledge base, multilingual, and allows intuitive creation and execution of different tasks for an extensive range of topics.Furthermore, evaluation and application cases in the chatbot are provided to show how it facilitates interactive semantic data towards different real application scenarios and showcase the proposed approach for a knowledge graph and data-driven chatbot.
Effects of learning physics using Augmented Reality on students’ self‐efficacy and conceptions of learning

Abstract The Augmented Reality (AR)‐based learning environment not only provides educators with novel ways to present learning materials but also give learners the opportunity to spontaneously interact with the material. Previous studies have shown that AR has many advantages in education; however, few focuses on the mechanisms behind promoting inquiry motivation, such as the effect of AR on learners’ self‐efficacy and conceptions of learning. This study developed an AR‐based wave‐particle duality learning application, “AROSE,” to explore the effect of AR technology on students’ self‐efficacy and conceptions of learning physics. A quasi‐experimental study method was used, and 98 high school students aged between 16 and 18 were randomly assigned to experimental and control group. After a 4‐week intervention, it was found that integrating AR technology into physics classrooms can (1) significantly enhance students’ physics learning self‐efficacy, as indicated by understanding of concepts, higher‐level cognitive skills, practice and communication; (2) guide students to be more inclined to higher‐level conceptions of learning physics rather than lower ones; and (3) stimulates students’ motivation to learn more deeply.
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks

In unsupervised domain adaptation (UDA), a classifier for a target domain is trained with labeled source data and unlabeled target data. Existing UDA methods assume that the source data come from the same source domain (i.e., single-source scenario) or from multiple source domains, whose feature spaces have the same dimension ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homogeneous</i> ) but different distributions (i.e., multihomogeneous-source scenario). However, in the real world, for a specific target domain, we probably have multiple different-dimension ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> ) source domains, which do not satisfy the assumption of existing UDA methods. To remove this assumption and move forward to a realistic UDA problem, this article presents a shared-fuzzy-equivalence-relation neural network (SFERNN) for addressing the multisource heterogeneous UDA problem. The SFERNN is a five-layer neural network containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> source branches and one target branch. The network structure of the SFERNN is first confirmed by a novel fuzzy relation called multisource shared fuzzy equivalence relation. Then, we optimize parameters of the SFERNN via minimizing cross-entropy loss on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$c$</tex-math></inline-formula> source branches and the distributional discrepancy between each source branch and the target branch. Experiments distributed across eight real-world datasets are conducted to validate the SFERNN. This testing regime demonstrates that the SFERNN outperforms the existing single-source heterogeneous UDA methods, especially when the target domain contains few data.
Bibliometric mapping of mobile learning

This study aims to reveal the tendency towards research in the field of mobile learning with the analysis of co-authorship, bibliographic coupling, co-occurrence, and citation by taking into consideration of author, publication, keyword, journal, country, university and citation variables. As of September 2019, the meta-data of a total of 5167 studies in the Web of Science database constituted the scope of this study. VOSviewer and sciMAT were used for the bibliometric analysis while Harzing’s Publish and Perish software was used for the h-index. As a result of the bibliometric analysis, it was concluded that the most effective countries in mobile learning are Taiwan, USA, China, and England. According to the keyword co-occurrence analysis, mobile devices, higher education, mobile technologies, tablet, and smartphone keywords stand out in the field of mobile learning. Within the period of 2015–2019, trending topics were broadly educational technologies and, more specifically, tablets, mobile phones, MOOCs and learning strategies. It was found that G. J. Hwang is the most influential researcher and National Taiwan University of Science and Technology is the most influential university. It can be concluded that the most effective researches are augmented reality, higher education, and smartphone oriented mobile learning researches. According to the analyses conducted in the context of journals, Computers & Education, British Journal of Educational Technology and Educational Technology & Society were the most contributing journals.