Reviewerly/exHarmony huggingface dataset:
What's Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities

Computational notebooks - such as Azure, Databricks, and Jupyter - are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all within a single document. Nevertheless, as data scientists incorporate more of their activities into notebooks, they encounter unexpected difficulties, or pain points, that impact their productivity and disrupt their workflow. Through a systematic, mixed-methods study using semi-structured interviews (n=20) and survey (n=156) with data scientists, we catalog nine pain points when working with notebooks. Our findings suggest that data scientists face numerous pain points throughout the entire workflow - from setting up notebooks to deploying to production - across many notebook environments. Our data scientists report essential notebook requirements, such as supporting data exploration and visualization. The results of our study inform and inspire the design of computational notebooks.
A Data Verification System for CCTV Surveillance Cameras Using Blockchain Technology in Smart Cities

The video created by a surveillance cameras plays a crucial role in crime prevention and examinations in smart cities. The closed-circuit television camera (CCTV) is essential for a range of public uses in a smart city; combined with Internet of Things (IoT) technologies they can turn into smart sensors that help to ensure safety and security. However, the authenticity of the camera itself raises issues of building up integrity and suitability of data. In this paper, we present a blockchain-based system to guarantee the trustworthiness of the stored recordings, allowing authorities to validate whether or not a video has been altered. It helps to discriminate fake videos from original ones and to make sure that surveillance cameras are authentic. Since the distributed ledger of the blockchain records the metadata of the CCTV video as well, it is obstructing the chance of forgery of the data. This immutable ledger diminishes the risk of copyright encroachment for law enforcement agencies and clients users by securing possession and identity.
Anomaly Detection of Time Series With Smoothness-Inducing Sequential Variational Auto-Encoder

Deep generative models have demonstrated their effectiveness in learning latent representation and modeling complex dependencies of time series. In this article, we present a smoothness-inducing sequential variational auto-encoder (VAE) (SISVAE) model for the robust estimation and anomaly detection of multidimensional time series. Our model is based on VAE, and its backbone is fulfilled by a recurrent neural network to capture latent temporal structures of time series for both the generative model and the inference model. Specifically, our model parameterizes mean and variance for each time-stamp with flexible neural networks, resulting in a nonstationary model that can work without the assumption of constant noise as commonly made by existing Markov models. However, such flexibility may cause the model fragile to anomalies. To achieve robust density estimation which can also benefit detection tasks, we propose a smoothness-inducing prior over possible estimations. The proposed prior works as a regularizer that places penalty at nonsmooth reconstructions. Our model is learned efficiently with a novel stochastic gradient variational Bayes estimator. In particular, we study two decision criteria for anomaly detection: reconstruction probability and reconstruction error. We show the effectiveness of our model on both synthetic data sets and public real-world benchmarks.
Advantages and Constraints of a Hybrid Model K-12 E-Learning Assistant Chatbot

E-Learning has become more and more popular in recent years with the advance of new technologies. Using their mobile devices, people can expand their knowledge anytime and anywhere. E-Learning also makes it possible for people to manage their learning progression freely and follow their own learning style. However, studies show that E-Learning can cause the user to experience feelings of isolation and detachment due to the lack of human-like interactions in most E-Learning platforms. These feelings could reduce the user's motivation to learn. In this paper, we explore and evaluate how well current chatbot technologies assist users' learning on E-Learning platforms and how these technologies could possibly reduce problems such as feelings of isolation and detachment. For evaluation, we specifically designed a chatbot to be an E-Learning assistant. The NLP core of our chatbot is based on two different models: a retrieval-based model and a QANet model. We designed this two-model hybrid chatbot to be used alongside an E-Learning platform. The core response context of our chatbot is not only designed with course materials in mind but also everyday conversation and chitchat, which make it feel more like a human companion. Experiment and questionnaire evaluation results show that chatbots could be helpful in learning and could potentially reduce E-Learning users' feelings of isolation and detachment. Our chatbot also performed better than the teacher counselling service in the E-Learning platform on which the chatbot is based.
A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

Human Activity Recognition (HAR) using wearable and mobile sensors has gained momentum in last few years, in various fields, such as, healthcare, surveillance, education, entertainment. Nowadays, Edge Computing has emerged to reduce communication latency and network traffic. Edge devices are resource constrained devices and cannot support high computation. In literature, various models have been developed for HAR. In recent years, deep learning algorithms have shown high performance in HAR, but these algorithms require lot of computation making them inefficient to be deployed on edge devices. This paper, proposes a Lightweight Deep Learning Model for HAR requiring less computational power, making it suitable to be deployed on edge devices. The performance of proposed model is tested on the participant’s six daily activities data. Results show that the proposed model outperforms many of the existing machine learning and deep learning techniques.
Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages.
Solar irradiance resource and forecasting: a comprehensive review

With the increase in demand for energy, penetration of alternative sources of energy in the power grid has increased. Photovoltaic (PV) energy is the most common and popular form of energy sources which is widely integrated into the existing grid. As solar energy is intermittent in nature, to ensure uninterrupted and reliable power supply to the prosumers, it is essential to forecast the solar irradiance. Accurate solar forecasting is necessary to facilitate large-scale modelling and deployment of PV plants without disrupting the quality and reliability of the power grid as well as to manage the power demand and supply. There are various methods to predict the solar irradiance such as numerical weather prediction methods, satellite-based approaches, cloud-image based methodologies, data-driven methods, and sensor-network based approaches. This study gives an overall review of the different resources and methods used for forecasting solar irradiance in different time horizons and also gives an extensive review of the sensor networks that are used for determining solar irradiance. The various error metrics and accessible data sets available for the sensor networks are also discussed that can be used for validation purposes.
Impulsive synchronization of coupled delayed neural networks with actuator saturation and its application to image encryption

The actuator of any physical control systems is constrained by amplitude and energy, which causes the control systems to be inevitably affected by actuator saturation. In this paper, impulsive synchronization of coupled delayed neural networks with actuator saturation is presented. A new controller is designed to introduce actuator saturation term into impulsive controller. Based on sector nonlinearity model approach, impulsive controls with actuator saturation and with partial actuator saturation are studied, respectively, and some effective sufficient conditions are obtained. Numerical simulation is presented to verify the validity of the theoretical analysis results. Finally, the impulsive synchronization is applied to image encryption. The experimental results show that the proposed image encryption system has high security properties.
Blockchain based secured information sharing protocol in supply chain management system with key distribution mechanism

The concept of Supply Chain Management (SCM) is very imperative while moving sensitive products from one entity to the next entity until it reaches to the end-users to avoid damage(s) in the product. In the traditional supply chain management system, several serious problems such as tampering of products, delay, and fraud, etc. exist. It also lacks proper authentication among the participants, data management as well as the integrity of the data. The blockchain mechanism is capable of solving the above-mentioned issues due to its important features such as decentralization, transparency, trust-less environment, anonymity, and immutability. This paper describes how the blockchain mechanism combines with the traditional pharmaceutical supply chain system and to achieve a better SCM system, we present a blockchain-based scheme for information sharing securely in the pharmaceutical supply chain system with smart contracts and consensus mechanism. The proposed scheme also provides a mechanism to distribute required cryptographic keys to all the participants securely using the smart contract technique. Further, transaction and block validation protocols have been designed in our protocol. The security analysis ensures that our protocol is robust and also achieves reasonable performance in terms of computation and communication overheads.
Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks

3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lack the fidelity as exhibited in the input images. Recent works demonstrate high-quality facial texture recovering with generative networks trained from a large-scale database of high-resolution UV maps of face textures, which is hard to prepare and not publicly available. In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in the wild, without the need to capture a large-scale face texture database. The main idea is to refine the initial texture generated by a 3DMM based method with facial details from the input image. To this end, we propose to use graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map. Experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.
Self-Supervised Monocular Scene Flow Estimation

Scene flow estimation has been receiving increasing attention for 3D environment perception. Monocular scene flow estimation - obtaining 3D structure and 3D motion from two temporally consecutive images - is a highly ill-posed problem, and practical solutions are lacking to date. We propose a novel monocular scene flow method that yields competitive accuracy and real-time performance. By taking an inverse problem view, we design a single convolutional neural network (CNN) that successfully estimates depth and 3D motion simultaneously from a classical optical flow cost volume. We adopt self-supervised learning with 3D loss functions and occlusion reasoning to leverage unlabeled data. We validate our design choices, including the proxy loss and augmentation setup. Our model achieves state-of-the-art accuracy among unsupervised/self-supervised learning approaches to monocular scene flow, and yields competitive results for the optical flow and monocular depth estimation sub-tasks. Semi-supervised fine-tuning further improves the accuracy and yields promising results in real-time.
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End

The focus in deep learning research has been mostly to push the limits of prediction accuracy. However, this was often achieved at the cost of increased complexity, raising concerns about the interpretability and the reliability of deep networks. Recently, an increasing attention has been given to untangling the complexity of deep networks and quantifying their uncertainty for different computer vision tasks. Differently, the task of depth completion has not received enough attention despite the inherent noisy nature of depth sensors. In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction. We propose a novel approach to identify disturbed measurements in the input by learning an input confidence estimator in a self-supervised manner based on the normalized convolutional neural networks (NCNNs). Further, we propose a probabilistic version of NCNNs that produces a statistically meaningful uncertainty measure for the final prediction. When we evaluate our approach on the KITTI dataset for depth completion, we outperform all the existing Bayesian Deep Learning approaches in terms of prediction accuracy, quality of the uncertainty measure, and the computational efficiency. Moreover, our small network with 670k parameters performs on-par with conventional approaches with millions of parameters. These results give strong evidence that separating the network into parallel uncertainty and prediction streams leads to state-of-the-art performance with accurate uncertainty estimates.
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences. To deal with the dynamic user preference and optimize accumulative utilities, researchers have introduced reinforcement learning (RL) into IRS. However, RL methods share a common issue of sample efficiency, i.e., huge amount of interaction data is required to train an effective recommendation policy, which is caused by the sparse user responses and the large action space consisting of a large number of candidate items. Moreover, it is infeasible to collect much data with explorative policies in online environments, which will probably harm user experience. In this work, we investigate the potential of leveraging knowledge graph (KG) in dealing with these issues of RL methods for IRS, which provides rich side information for recommendation decision making. Instead of learning RL policies from scratch, we make use of the prior knowledge of the item correlation learned from KG to (i) guide the candidate selection for better candidate item retrieval, (ii) enrich the representation of items and user states, and (iii) propagate user preferences among the correlated items over KG to deal with the sparsity of user feedback. Comprehensive experiments have been conducted on two real-world datasets, which demonstrate the superiority of our approach with significant improvements against state-of-the-arts.
Accurate, data-efficient, unconstrained text recognition with convolutional neural networks

Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose a data-efficient, end-to-end neural network model for generic, unconstrained text recognition. In our proposed architecture we strive for simplicity and efficiency without sacrificing recognition accuracy. Our proposed architecture is a fully convolutional network without any recurrent connections trained with the CTC loss function. Thus it operates on arbitrary input sizes and produces strings of arbitrary length in a very efficient and parallelizable manner. We show the generality and superiority of our proposed text recognition architecture by achieving state-of-the-art results on seven public benchmark datasets, covering a wide spectrum of text recognition tasks, namely: Handwriting Recognition, CAPTCHA recognition, OCR, License Plate Recognition, and Scene Text Recognition. Our proposed architecture has won the ICFHR2018 Competition on Automated Text Recognition on a READ Dataset.
Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series

Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios.Reservoir Computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers.Despite their unrivaled training speed, MTS classifiers based on a standard RC architecture fail to achieve the same accuracy of fully trainable neural networks.In this paper we introduce the reservoir model space, an unsupervised approach based on RC to learn vectorial representations of MTS.Each MTS is encoded within the parameters of a linear model trained to predict a lowdimensional embedding of the reservoir dynamics.Compared to other RC methods, our model space yields better representations and attains comparable computational performance, thanks to an intermediate dimensionality reduction procedure.As a second contribution we propose a modular RC framework for MTS classification, with an associated open-source Python library.The framework provides different modules to seamlessly implement advanced RC architectures.The architectures are compared to other MTS classifiers, including deep learning models and time series kernels.Results obtained on benchmark and real-world MTS datasets show that RC classifiers are dramatically faster and, when implemented using our proposed representation, also achieve superior classification accuracy.
RCSMOTE: Range-Controlled synthetic minority over-sampling technique for handling the class imbalance problem

The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well known methods to solve the unequal class distribution problem in imbalanced datasets. However, it has three shortcomings: 1) it may cause the over-generalization problem due to over-sampling of noisy samples, 2) over-sampling of uninformative samples, and 3) increasing the overlaps between different classes around the class boundaries. In this research, an improved SMOTE-based method, namely Range-Controlled SMOTE (RCSMOTE), which targets all three problems simultaneously, is proposed. In order to cope with the two first problems, a sample categorization scheme is applied to identify the minor samples that are proper for over-sampling. In order to mitigate the third problem, an improved sample generation process is proposed which generates the synthetic samples considering an accurately calculated safe range. This range is calculated based on the characteristics of the input data in order to provide us a safe over-sampling region for each dimension in the feature space. The extracted range is used to control the location of the new synthetic samples in data space and prevents the penetration of them into the majority class regions. Experiments conducted on various datasets, confirm that the RCSMOTE overcomes the above-mentioned problems of SMOTE.
Housing Price Prediction via Improved Machine Learning Techniques

House Price Index (HPI) is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from HPI to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.
Research trends in text mining: Semantic network and main path analysis of selected journals

In this study, network and main path analyses were conducted on 1856 studies related to text mining, by extracting keywords and citation information from the text of each paper. Our findings indicate that research papers on text mining have been published in 45 academic disciplines in the 1980s and 1990s, 105 disciplines in the 2000s, and 171 disciplines in the 2010s. The results show that using text mining as a research topic and method has rapidly increased. We also demonstrate that the main theme of text mining research is discourse and content analysis in the 1980s and 1990s, biology and data mining in the 2000s, and medicine and advanced text mining in the 2010s. Moreover, we examined the main citation path for text mining studies and suggest that the main focus of text mining studies has evolved from information science to information systems and technology management. Additionally, influential papers have been recently published in fields such as architecture and social ecology revealing the wide scope of text mining. This article presents an understanding of previously unexplored research trends in text mining and how these trends shed light on the most influential academic papers in the field.
COVID-19 apps in Singapore and Australia: reimagining healthy nations with digital technology

Widely and intensively used digital technologies have been an important feature of international responses to the COVID-19 pandemic. One especially interesting class of such technologies are dedicated contact and tracing apps collecting proximity data via the Bluetooth technology. In this article, I consider the development, deployment and imagined uses of apps in two countries: Singapore, a pioneer in the field, with its TraceTogether app, and Australia, a country that adapted Singapore’s app, devising its own COVIDSafe, as key to its national public health strategy early in the crisis. What is especially interesting about these cases is the privacy concerns the apps raised, and how these are dealt with in each country, also the ways in which each nation reimagines its immediate social future and health approach via such an app.