Reviewerly/exHarmony huggingface dataset:
Online Localization and Prediction of Actions and Interactions

This paper proposes a person-centric and online approach to the challenging problem of localization and prediction of actions and interactions in videos. Typically, localization or recognition is performed in an offline manner where all the frames in the video are processed together. This prevents timely localization and prediction of actions and interactions - an important consideration for many tasks including surveillance and human-machine interaction. In our approach, we estimate human poses at each frame and train discriminative appearance models using the superpixels inside the pose bounding boxes. Since the pose estimation per frame is inherently noisy, the conditional probability of pose hypotheses at current time-step (frame) is computed using pose estimations in the current frame and their consistency with poses in the previous frames. Next, both the superpixel and pose-based foreground likelihoods are used to infer the location of actors at each time through a Conditional Random Field enforcing spatio-temporal smoothness in color, optical flow, motion boundaries and edges among superpixels. The issue of visual drift is handled by updating the appearance models, and refining poses using motion smoothness on joint locations, in an online manner. For online prediction of action/interaction confidences, we propose an approach based on Structural SVM that operates on short video segments, and is trained with the objective that confidence of an action or interaction increases as time passes in a positive training clip. Lastly, we quantify the performance of both detection and prediction together, and analyze how the prediction accuracy varies as a time function of observed action/interaction at different levels of detection performance. Our experiments on several datasets suggest that despite using only a few frames to localize actions/interactions at each time instant, we are able to obtain competitive results to state-of-the-art offline methods.
SDNet: Semantically Guided Depth Estimation Network

Autonomous vehicles and robots require a full scene understanding of the environment to interact with it. Such a perception typically incorporates pixel-wise knowledge of the depths and semantic labels for each image from a video sensor. Recent learning-based methods estimate both types of information independently using two separate CNNs. In this paper, we propose a model that is able to predict both outputs simultaneously, which leads to improved results and even reduced computational costs compared to independent estimation of depth and semantics. We also empirically prove that the CNN is capable of learning more meaningful and semantically richer features. Furthermore, our SDNet estimates the depth based on ordinal classification. On the basis of these two enhancements, our proposed method achieves state-of-the-art results in semantic segmentation and depth estimation from single monocular input images on two challenging datasets.
Sequence to Sequence Model Performance for Education Chatbot

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelli-gence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, in-depth research and analysis of op-timal settings of the various components of Seq2Seq model for natural an-swer generation problem is very limited. Additionally, there has been no ex-periments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experi-ments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a cu-rated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model.
Uncertainty-guided Continual Learning with Bayesian Neural Networks

Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters' \textit{importance}. In contrast, we propose Uncertainty-guided Continual Bayesian Neural Networks (UCB) where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. Uncertainty is a natural way to identify \textit{what to remember} and \textit{what to change} as we continually learn, and thus mitigate catastrophic forgetting. We also show a variant of our model, which uses uncertainty for weight pruning and retains task performance after pruning by saving binary masks per tasks. We evaluate our UCB approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally, we show that our model does not necessarily need task information at test time, i.e.~it does not presume knowledge of which task a sample belongs to.
Towards a blockchain-based certificate authentication system in Vietnam

Anti-forgery information, transaction verification, and smart contract are functionalities of blockchain technology that can change the traditional business processes of IT applications. These functionalities increase the data transparency, and trust of users in the new application models, thus resolving many different social problems today. In this work, we take all the advantages of this technology to build a blockchain-based authentication system (called the Vietnamese Educational Certification blockchain, which stands for VECefblock) to deal with the delimitation of fake certificate issues in Vietnam. In this direction, firstly, we categorize and analyze blockchain research and application trends to make out our contributions in this domain. Our motivating factor is to curb fake certificates in Vietnam by applying the suitability of blockchain technology to the problem domain. This study proposed some blockchain-based application development principles in order to build a step by step VECefblock with the following procedures: designing overall architecture along with business processes, data mapping structure and implementing the decentralized application that can meet the specific Vietnamese requirements. To test system functionalities, we used Hyperledger Fabric as a blockchain platform that is deployed on the Amazon EC2 cloud. Through performance evaluations, we proved the operability of VECefblock in the practical deployment environment. This experiment also shows the feasibility of our proposal, thus promoting the application of blockchain technology to deal with social problems in general as well as certificate management in Vietnam.
GAN-Based Anomaly Detection and Localization of Multivariate Time Series Data for Power Plant

Recently, as real-time sensor data collection increases in various fields such as power plants, smart factories, and health care systems, anomaly detection for multivariate time series data analysis becomes more important. However, extracting significant features from multivariate time series data is still challenging because it simultaneously takes into account the correlation between the pair of sensors and temporal information of each time series. Meanwhile, in the field of image based anomaly detection, Generative Adversarial Networks(GANs) is developed due to its ability to model the complex high-dimensional distribution of images. In this paper, we propose a novel GAN-based anomaly detection and localization framework along with a transformation method for time series imaging, called distance image. Our goal is to learn a mapping a series of distance image to the next distance image. The transforming multivariate time series into 2D image allows us to exploit encoder and decoder structure. Especially, the encoder with pointwise convolution in a series of images ensures to encode temporal information of each time series data as well the correlation between each variable. As a result, an anomaly can be detected and localized by conducting a residual image and an anomaly score function. We empirically demonstrate the effectiveness of our approach for anomaly detection tasks on a real-world power plant data.
Structure learning with similarity preserving

Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications. However, in many applications the data can display structures beyond simply being low-rank or sparse. Fully extracting and exploiting hidden structure information in the data is always desirable and favorable. To reveal more underlying effective manifold structure, in this paper, we explicitly model the data relation. Specifically, we propose a structure learning framework that retains the pairwise similarities between the data points. Rather than just trying to reconstruct the original data based on self-expression, we also manage to reconstruct the kernel matrix, which functions as similarity preserving. Consequently, this technique is particularly suitable for the class of learning problems that are sensitive to sample similarity, e.g., clustering and semisupervised classification. To take advantage of representation power of deep neural network, a deep auto-encoder architecture is further designed to implement our model. Extensive experiments on benchmark data sets demonstrate that our proposed framework can consistently and significantly improve performance on both evaluation tasks. We conclude that the quality of structure learning can be enhanced if similarity information is incorporated.
Segmenting areas of potential contamination for adaptive robotic disinfection in built environments

Mass-gathering built environments such as hospitals, schools, and airports can become hot spots for pathogen transmission and exposure. Disinfection is critical for reducing infection risks and preventing outbreaks of infectious diseases. However, cleaning and disinfection are labor-intensive, time-consuming, and health-undermining, particularly during the pandemic of the coronavirus disease in 2019. To address the challenge, a novel framework is proposed in this study to enable robotic disinfection in built environments to reduce pathogen transmission and exposure. First, a simultaneous localization and mapping technique is exploited for robot navigation in built environments. Second, a deep-learning method is developed to segment and map areas of potential contamination in three dimensions based on the object affordance concept. Third, with short-wavelength ultraviolet light, the trajectories of robotic disinfection are generated to adapt to the geometries of areas of potential contamination to ensure complete and safe disinfection. Both simulations and physical experiments were conducted to validate the proposed methods, which demonstrated the feasibility of intelligent robotic disinfection and highlighted the applicability in mass-gathering built environments.
AGL

Machine learning over graphs has been emerging as powerful learning tools for graph data. However, it is challenging for industrial communities to leverage the techniques, such as graph neural networks (GNNs), and solve real-world problems at scale because of inherent data dependency in the graphs. As such, we cannot simply train a GNN with classic learning systems, for instance, parameter server that assumes data parallelism. Existing systems store the graph data in-memory for fast accesses either in a single machine or graph stores from remote. The major drawbacks are three-fold. First, they cannot scale because of the limitations on the volume of the memories, or the bandwidth between graph stores and workers. Second, they require extra development of graph stores without well exploiting mature infrastructures such as MapReduce that guarantee good system properties. Third, they focus on training but ignore optimizing the performance of inference over graphs, thus makes them an unintegrated system. In this paper, we design AGL, a scalable and integrated system, with fully-functional training and inference for GNNs. Our system design follows the message passing scheme underlying the computations of GNNs. We design to generate the K -hop neighborhood, an information-complete subgraph for each node, as well as do the inference simply by merging values from in-edge neighbors and propagating values to out-edge neighbors via MapReduce. In addition, the K -hop neighborhood contains information-complete subgraphs for each node, thus we simply do the training on parameter servers due to data independence. Our system AGL, implemented on mature infrastructures, can finish the training of a 2-layer GNN on a graph with billions of nodes and hundred billions of edges in 14 hours, and complete the inference in 1.2 hours.
An End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA

One of the methods for stratifying different molecular classes of breast cancer is the Nottingham Prognostic Index Plus (NPI+) which uses breast cancer relevant biomarkers to stain tumour tissues prepared on tissue microarray (TMA). To determine the molecular class of the tumour, pathologists will have to manually mark the nuclei activity biomarkers through a microscope and use a semi-quantitative assessment method to assign a histochemical score (H-Score) to each TMA core. Manually marking positively stained nuclei is a time consuming, imprecise and subjective process which will lead to inter-observer and intra-observer discrepancies. In this paper, we present an end-to-end deep learning system which directly predicts the H-Score automatically. Our system imitates the pathologists' decision process and uses one fully convolutional network (FCN) to extract all nuclei region (tumour and non-tumour), a second FCN to extract tumour nuclei region, and a multi-column convolutional neural network which takes the outputs of the first two FCNs and the stain intensity description image as input and acts as the high-level decision making mechanism to directly output the H-Score of the input TMA image. To the best of our knowledge, this is the first end-to-end system that takes a TMA image as input and directly outputs a clinical score. We will present experimental results which demonstrate that the H-Scores predicted by our model have very high and statistically significant correlation with experienced pathologists' scores and that the H-Score discrepancy between our algorithm and the pathologists is on par with the inter-subject discrepancy between the pathologists.
An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction

Decisions of complex models for language understanding can be explained by limiting the inputs they are provided to a relevant subsequence of the original text — a rationale. Models that condition predictions on a concise rationale, while being more interpretable, tend to be less accurate than models that are able to use the entire context. In this paper, we show that it is possible to better manage the trade-off between concise explanations and high task accuracy by optimizing a bound on the Information Bottleneck (IB) objective. Our approach jointly learns an explainer that predicts sparse binary masks over input sentences without explicit supervision, and an end-task predictor that considers only the residual sentences. Using IB, we derive a learning objective that allows direct control of mask sparsity levels through a tunable sparse prior. Experiments on the ERASER benchmark demonstrate significant gains over previous work for both task performance and agreement with human rationales. Furthermore, we find that in the semi-supervised setting, a modest amount of gold rationales (25% of training examples with gold masks) can close the performance gap with a model that uses the full input.
Potentially harmful metals, and health risk evaluation in groundwater of Mardan, Pakistan: Application of geostatistical approach and geographic information system

This study investigates the values of pH, total dissolved solids (TDS), elevation, oxidative reduction potential (ORP), temperature, and depth, while the concentrations of Br, and potentially harmful metals (PHMs) such as Cr, Ni, Cd, Mn, Cu, Pb, Co, Zn, and Fe in the groundwater samples. Moreover, geographic information system (GIS), XLSTAT, and IBM SPSS Statistics 20 software were used for spatial distribution modeling, principal component analysis (PCA), cluster analysis (CA), and Quantile-Quantile (Q-Q) plotting to determine groundwater pollution sources, similarity index, and normal distribution reference line for the selected parameters. The mean values of pH, TDS, elevation, ORP, temperature, depth, and Br were 7.2, 322 mg/L, 364 m, 188 mV, 29.6 °C, 70 m, 0.20 mg/L, and PHMs like Cr, Ni, Cd, Mn, Cu, Pb, Co, Zn, and Fe were 0.38, 0.26, 0.08, 0.27, 0.36, 0.22, 0.04, 0.43 and 0.86 mg/L, respectively. PHMs including Cr (89%), Cd (43%), Mn (23%), Pb (79%), Co (20%), and Fe (91%) exceeded the guideline values set by the world health organization (WHO). The significant R2 values of PCA for selected parameters were also determined (0.62, 0.67, 0.78, 0.73, 0.60, 0.87, −0.50, 0.69, 0.70, 0.74, −0.50, 0.70, 0.67, 0.79, 0.59, and −0.55, respectively). PCA revealed three geochemical processes such as geogenic, anthropogenic, and reducing conditions. The mineral phases of Cd(OH)2, Fe(OH)3, FeOOH, Mn3O4, Fe2O3, MnOOH, Pb(OH)2, Mn(OH)2, MnO2, and Zn(OH)2 (−3.7, 3.75, 9.7, −5.8, 8.9, −3.6, 2.2, −4.6, −7.7, −0.9, and 0.003, respectively) showed super-saturation and under-saturation conditions. Health risk assessment (HRA) values for PHMs were also calculated and the values of hazard quotient (HQ), and hazard indices (HI) for the entire study area were increased in the following order: Cd>Ni>Cu>Pb>Mn>Zn>Cr. Relatively higher HQ and HI values of Ni, Cd, Pb, and Cu were greater than one showing unsuitability of groundwater for domestic, agriculture, and drinking purposes. The long-term ingestion of groundwater could also cause severe health concerns such as kidney, brain dysfunction, liver, stomach problems, and even cancer.
Testing by Betting: A Strategy for Statistical and Scientific Communication

Abstract The most widely used concept of statistical inference—the p-value—is too complicated for effective communication to a wide audience. This paper introduces a simpler way of reporting statistical evidence: report the outcome of a bet against the null hypothesis. This leads to a new role for likelihood, to alternatives to power and confidence, and to a framework for meta-analysis that accommodates both planned and opportunistic testing of statistical hypotheses and probabilistic forecasts. This framework builds on the foundation for mathematical probability developed in previous work by Vladimir Vovk and myself.
Covid-19 controversies and critical research in digital education

If financial markets are taken as the main measure of the impact of the Covid-19 pandemic on education since early 2020, then education technology (‘edtech’) has finally arrived with disruptive and...
Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine

Blasting is a common method of breaking rock in surface mines. Although the fragmentation with proper size is the main purpose, other undesirable effects such as flyrock are inevitable. This study is carried out to evaluate the capability of a novel kernel-based extreme learning machine algorithm, called kernel extreme learning machine (KELM), by which the flyrock distance (FRD) is predicted. Furthermore, the other three data-driven models including local weighted linear regression (LWLR), response surface methodology (RSM) and boosted regression tree (BRT) are also developed to validate the main model. A database gathered from three quarry sites in Malaysia is employed to construct the proposed models using 73 sets of spacing, burden, stemming length and powder factor data as inputs and FRD as target. Afterwards, the validity of the models is evaluated by comparing the corresponding values of some statistical metrics and validation tools. Finally, the results verify that the proposed KELM model on account of highest correlation coefficient (R) and lowest root mean square error (RMSE) is more computationally efficient, leading to better predictive capability compared to LWLR, RSM and BRT models for all data sets.
A novel DeepMaskNet model for face mask detection and masked facial recognition

Coronavirus disease (COVID-19) has significantly affected the daily life activities of people globally. To prevent the spread of COVID-19, the World Health Organization has recommended the people to wear face mask in public places. Manual inspection of people for wearing face masks in public places is a challenging task. Moreover, the use of face masks makes the traditional face recognition techniques ineffective, which are typically designed for unveiled faces. Thus, introduces an urgent need to develop a robust system capable of detecting the people not wearing the face masks and recognizing different persons while wearing the face mask. In this paper, we propose a novel DeepMasknet framework capable of both the face mask detection and masked facial recognition. Moreover, presently there is an absence of a unified and diverse dataset that can be used to evaluate both the face mask detection and masked facial recognition. For this purpose, we also developed a largescale and diverse unified mask detection and masked facial recognition (MDMFR) dataset to measure the performance of both the face mask detection and masked facial recognition methods. Experimental results on multiple datasets including the cross-dataset setting show the superiority of our DeepMasknet framework over the contemporary models.
Multi-Fold Gabor, PCA, and ICA Filter Convolution Descriptor for Face Recognition

This paper devises a new means of filter diversification, dubbed multi-fold filter convolution (M-FFC), for face recognition. On the assumption that M-FFC receives single-scale Gabor filters of varying orientations as input, these filters are selfcross convolved by M-fold to instantiate a filter offspring set. The M-FFC flexibility also permits cross convolution amongst Gabor filters and other filter banks of profoundly dissimilar traits, e.g., principal component analysis (PCA) filters and independent component analysis (ICA) filters. The 2-FFC of Gabor, PCA, and ICA filters thus yields three offspring sets: 1) Gabor filters solely; 2) Gabor-PCA filters; and 3) Gabor-ICA filters, to render the learning-free and the learning-based 2-FFC descriptors. To facilitate a sensible Gabor filter selection for M-FFC, the 40 multi-scale, multi-orientation Gabor filters are condensed into eight elementary filters. Aside from that, an average histogram pooling operator is employed to leverage the 2-FFC histogram features, prior to the final whitening PCA compression. The empirical results substantiate that the 2-FFC descriptors prevail over, or on par with, other face descriptors on both identification and verification tasks.