Reviewerly/exHarmony huggingface dataset:
Knowledge acquisition from chemical accident databases using an ontology-based method and natural language processing

Accident databases are used to learn from past accidents and avoid future accidents in the chemical process industry. Classical accident databases can be tedious to use because the database entries were written over the years by various persons in different styles. Thus, accident case entries must be interpreted to identify cause-effect relationships and recognize lessons learned. Semantically enriched accident databases can make information retrieval more efficient. In this research approach Natural Language Processing methods are used to extract information from a chemical accident database. Additionally, substance information is enriched using web scraping techniques. Afterwards, a predefined ontology structure is automatically populated with the extracted information. The ontology-based chemical accident database provides additional accident exploration capabilities that can be used by human experts or computer systems. The results indicate that the proposed extraction method is well suited to extract accident information. The ontology is useful to discover causal accident relations due to the semantically described context of accident information. The method described can be adapted to other databases with minor adaptations and refinements. By combining various ontology-based accident databases a human and machine-processable, and sharable knowledge structure can be provided to reuse knowledge across companies and countries.
Correlations among cryptocurrencies: Evidence from multivariate factor stochastic volatility model

This paper is the first study to apply the multivariate factor stochastic volatility model (MFSVM) for analyzing the correlations among six cryptocurrencies. We use MFSVM with the Bayesian estimation procedure for the period from August 8, 2015, to January 1, 2020. According to the findings, there is a significant positive correlation between price volatility values of Bitcoin and Litecoin. Besides, the volatility values of Ethereum have a positive correlation with both Ripple and Stellar. There is also a positive correlation between the volatility values of Ripple and Dash. These findings are robust to consider different correlation networks. The evidence implies that Bitcoin is mainly related to Litecoin, but Ethereum is associated with other cryptocurrencies.
Smart Contract-Based Spectrum Sharing Transactions for Multi-Operators Wireless Communication Networks

Multiple-operators (multi-OPs) spectrum sharing mechanism can effectively improve the spectrum utilization in fifth-generation (5G) wireless communication networks. The secondary users are introduced to opportunistically access the licensed spectrum of idle operators (OPs). However, the identity privacy and data security issues raise great concerns about the secure spectrum sharing among multi-OPs. To address these challenges, a permissioned blockchain trust framework is proposed for the spectrum sharing in multi-OPs wireless communication networks in this paper. The Multi-OPs Spectrum Sharing (MOSS) smart contract is designed on the constructed permissioned blockchain to implement the spectrum trading among multi-OPs. Without the need of trustless spectrum broker, the MOSS smart contract enforces multi-OPs to share the spectrum truthfully and designs a punishment mechanism to punish malicious OPs. The MOSS smart contract is tested on the Remix integrated development environment (IDE) and the gas costs of MOSS smart contract is estimated. The performance analysis of the proposed permissioned blockchain trust framework demonstrates that the privacy, openness and fairness of the proposed solution are better than traditional spectrum allocation solutions.
Crypto‐economy and new sustainable business models: Reflections and projections using a case study analysis

Abstract The purpose of this article is to shed light on how the blockchain technology can lead to the creation of new sustainable business models (SBMs). The literature highlights the need to merge the sustainability perspective into business models, creating new SBMs. Using a case study methodology, the paper investigates how the blockchain can foster the creation of SBMs, thus exploring the following Research Question (RQ): How can blockchain facilitate the development of new SBM? The case represents an example of an SBM merging more theoretical definitions. The unique features of the blockchain foster inclusivity even in traditional sectors, allowing widespread distribution of the benefits for the community with a long‐term orientation. Consequently, our study provides systematized evidence of how the blockchain can foster financial and social sustainability. This manuscript adds to the state‐of‐the‐art literature on the application of emerging technologies to organizational sustainability by shedding light on real business case evidence.
Sentiment Analysis on Movie Review Data Using Machine Learning Approach

At present Sentiment analysis is the most discussed topic which is purposed to assist one to get important information from a large dataset. It centers on the investigation and comprehension of the feelings from the text patterns. It automatically characterizes the expression of feelings, e.g., negative, positive or neutral about the existence of anything. Various sources like medical, social media, newspaper, and movie review can be used in data analysis. Here, we have collected movie review data as well as used five kinds of machine learning classifiers to analyze these data. Hence, the considered classifiers are Bernoulli Naïve Bayes (BNB), Decision Tree (DE), Support Vector Machine (SVM), Maximum Entropy (ME), as well as Multinomial Naïve Bayes (MNB). Our analysis outlines that MNB achieves better accuracy, precision and F-score while SVM shows higher recall compared to others. Besides it also show that BNB Classifier achieves better accuracy than previous experiment over this classifier.
OntoQuest: An Ontological Strategy for Automatic Question Generation for e-assessment using Static and Dynamic Knowledge

In modern times, e-learning has become the most convenient environment to attain knowledge about and across various domains. While there are plenty of e-learning resources available across the ever-expanding web, imbibing knowledge is not the only factor that contributes to the edification of an individual. The assessment also must be given a quintessential role because both learning and assessment are two sides of a coin. In this paper, OntoQuest, which is a scheme for generation of multiple-choice questions is done based on the domain or subject of choice from the user. An equivalence set based summarization scheme has been proposed. Synonymization and Anonymization are used for the generation of best key and distractors respectively. Domain and Granular Ontologies are crawled for relevant sub-topics and auxiliary topics. WordNet integrates dynamic knowledge for improving the overall accuracy. Jaccard similarity is encompassed for the generation of the correct key. OntoQuest is a reliable framework that yields an overall accuracy of 95.46% and 95.05% for key and distractor generation respectively, which is higher and consistent compared to the existing models available.
Comparative Study on Deep Convolution Neural Networks DCNN-Based Offline Arabic Handwriting Recognition

Recently, deep learning techniques demonstrated efficiency in building better performing machine learning models which are required in the field of offline Arabic handwriting recognition. Our ancient civilizations presented valuable handwritten manuscripts that need to be documented digitally. If we compared between Latin and the isolated Arabic character recognition, the latter is much more challenging due to the similarity between characters, and the variability of the writing styles. This paper proposes a multi-stage cascading system to serve the field of offline Arabic handwriting recognition. The approach starts with applying the Hierarchical Agglomerative Clustering (HAC) technique to split the database into partially inter-related clusters. The inter-relations between the constructed clusters support representing the database as a big search tree model and help to attain a reduced complexity in matching each test image with a cluster. Cluster members are then ranked based on our new proposed ranking algorithm. This ranking algorithm starts with computing Pyramid Histogram of Oriented Gradients (PHoG), and is followed by measuring divergence by Kullback-Leibler method. Eventually, the classification process is applied only to the highly ranked matching classes. A comparative study is made to assess the effect of six different deep Convolution Neural Networks (DCNNs) on the final recognition rates of the proposed system. Experiments are done using the IFN/ENIT Arabic database. The proposed clustering and ranking stages lead to using only 11% of the whole database in classifying test images. Accordingly, more reduced computation complexity and more enhanced classification results are achieved compared to recent existing systems.
Fast Video Multi-Style Transfer

Recent progress in video style transfer has shown promising results which contain less flickering effects. However, existing algorithms mainly trade off generality for efficiency, i.e., constructing one network per style example, and often work for short video clips only. In this work, we propose a video multi-style transfer (VMST) framework which enables fast and multi-style video transfer within one single network. Specifically, we design a multi-instance normalization block (MIN-Block) to learn different style examples and two ConvLSTM modules to encourage the temporal consistency. The proposed algorithm is demonstrated to be able to generate temporally-consistent video transfer results in different styles while keeping each stylized frame visually pleasing. Extensive experimental results show that the proposed method performs favorably against single-style models and some post-processing techniques that alleviate the flickering issue. We achieve as many as 120 stylization effects in a single model and show results on long-term videos that consist of thousands of frames.
Active Learning for ML Enhanced Database Systems

Recent research has shown promising results by using machine learning (ML) techniques to improve the performance of database systems, e.g., in query optimization or index recommendation. However, in many production deployments, the ML models' performance degrades significantly when the test data diverges from the data used to train these models. In this paper, we address this performance degradation by using B-instances to collect additional data during deployment. We propose an active data collection platform, ADCP, that employs active learning (AL) to gather relevant data cost-effectively. We develop a novel AL technique, Holistic Active Learner (HAL), that robustly combines multiple noisy signals for data gathering in the context of database applications. HAL applies to various ML tasks, budget sizes, cost types, and budgeting interfaces for database applications. We evaluate ADCP on both industry-standard benchmarks and real customer workloads. Our evaluation shows that, compared with other baselines, our technique improves ML models' prediction performance by up to 2x with the same cost budget. In particular, on production workloads, our technique reduces the prediction error of ML models by 75% using about 100 additionally collected queries.
Self-Tuning Unbiased Finite Impulse Response Filtering Algorithm for Processes With Unknown Measurement Noise Covariance

An unbiased finite impulse response (UFIR) filtering algorithm is designed in the discrete-time state-space for industrial processes with unknown measurement data covariance. By assuming an inverse-Wishart distribution, the data noise covariance is recursively estimated using the variational Bayesian (VB) approach. The optimal averaging horizon length N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> is estimated in real time by incorporating the estimated data noise covariance into the full-horizon UFIR filter and specifying N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> at a point, where the estimation error covariance reaches a minimum. The proposed VB-UFIR algorithm is applied to a quadrupled water tank system and moving target tracking. It is demonstrated that the VB-UFIR filter self-estimates N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> more accurately than known solutions. Furthermore, the VB-UFIR filter is not prone to divergence and produces more stable and more reliable estimates than the VB-Kalman filter.
Toward scalable simulations of lattice gauge theories on quantum computers

The simulation of real-time dynamics in lattice gauge theories is particularly hard for classical computing due to the exponential scaling of the required resources. On the other hand, quantum algorithms can potentially perform the same calculation with a polynomial dependence on the number of degrees of freedom. A precise estimation is however particularly challenging for the simulation of lattice gauge theories in arbitrary dimensions, where gauge fields are dynamical variables, in addition to the particle fields. Moreover, there exist several choices for discretizing particles and gauge fields on a lattice, each of them coming at different prices in terms of qubit register size and circuit depth. Here we provide a resource counting for real-time evolution of $U(1)$ gauge theories, such as quantum electrodynamics, on arbitrary dimension using the Wilson fermion representation for the particles, and the quantum link model approach for the gauge fields. We study the phenomena of flux-string breaking up to a genuine bidimensional model using classical simulations of the quantum circuits and discuss the advantages of our discretization choice in simulation of more challenging $SU(N)$ gauge theories such as quantum chromodynamics.
Characterizing Smart Home IoT Traffic in the Wild

As the smart home IoT ecosystem flourishes, it is imperative to gain a better understanding of the unique challenges it poses in terms of management, security, and privacy. Prior studies are limited because they examine smart home IoT devices in testbed environments or at a small scale. To address this gap, we present a measurement study of smart home IoT devices in the wild by instrumenting home gateways and passively collecting real-world network traffic logs from more than 200 homes across a large metropolitan area in the United States. We characterize smart home IoT traffic in terms of its volume, temporal patterns, and external endpoints along with focusing on certain security and privacy concerns. We first show that traffic characteristics reflect the functionality of smart home IoT devices such as smart TVs generating high volume traffic to content streaming services following diurnal patterns associated with human activity. While the smart home IoT ecosystem seems fragmented, our analysis reveals that it is mostly centralized due to its reliance on a few popular cloud and DNS services. Our findings also highlight several interesting security and privacy concerns in smart home IoT ecosystem such as the need to improve policy-based access control for IoT traffic, lack of use of application layer encryption, and prevalence of third-party advertising and tracking services. Our findings have important implications for future research on improving management, security, and privacy of the smart home IoT ecosystem.
Multitask LSTM Model for Human Activity Recognition and Intensity Estimation Using Wearable Sensor Data

Human activity recognition (HAR) and measuring the intensity of activity are increasingly important for healthcare applications, such as fitness tracking and patient monitoring. However, these two tasks have been performed separately, leading to delays and expensive implementations. In this article, we propose a holistic approach to achieve both HAR and intensity estimation simultaneously. We introduce a new data set with both activity types and activity intensities, and a multitask long short-term memory (LSTM) model to accurately classify the activity types and estimate the intensity of each activity. In addition, we evaluate our proposed neural network with other publicly available data sets and show that including activity intensities in the data set help multitask models perform comparably to two separate single-task models.
Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis

Digitizing whole-slide imaging in digital pathology has led to the advancement of computer-aided tissue examination using machine learning techniques, especially convolutional neural networks. A number of convolutional neural network-based methodologies have been proposed to accurately analyze histopathological images for cancer detection, risk prediction, and cancer subtype classification. Most existing methods have conducted patch-based examinations, due to the extremely large size of histopathological images. However, patches of a small window often do not contain sufficient information or patterns for the tasks of interest. It corresponds that pathologists also examine tissues at various magnification levels, while checking complex morphological patterns in a microscope. We propose a novel multi-task based deep learning model for HIstoPatholOgy (named Deep-Hipo) that takes multi-scale patches simultaneously for accurate histopathological image analysis. Deep-Hipo extracts two patches of the same size in both high and low magnification levels, and captures complex morphological patterns in both large and small receptive fields of a whole-slide image. Deep-Hipo has outperformed the current state-of-the-art deep learning methods. We assessed the proposed method in various types of whole-slide images of the stomach: well-differentiated, moderately-differentiated, and poorly-differentiated adenocarcinoma; poorly cohesive carcinoma, including signet-ring cell features; and normal gastric mucosa. The optimally trained model was also applied to histopathological images of The Cancer Genome Atlas (TCGA), Stomach Adenocarcinoma (TCGA-STAD) and TCGA Colon Adenocarcinoma (TCGA-COAD), which show similar pathological patterns with gastric carcinoma, and the experimental results were clinically verified by a pathologist. The source code of Deep-Hipo is publicly available athttp://dataxlab.org/deep-hipo.
Efficient Human Activity Recognition Using a Single Wearable Sensor

A reliable recognition of human activities using IoT devices (e.g., on-body wearable sensors) enables various applications, such as fitness tracking, bad habit detecting, healthcare support, and eldercare support. However, inaccurate results may cause an adverse effect on users or even an unpredictable accident. In order to improve the accuracy in the daily life activities classification, in this article, we propose countable and uncountable activities to better facilitate the understanding of the nature of daily life activities. We design global and local features and their integrated feature set for classifying countable and uncountable activities. The key idea is to examine human daily life activities from different perspectives and attempt to give a comprehensive description of the characteristics of each activity through leveraging the global and local features. By using only one simple accelerometer, our approach is evaluated to be able to recognize daily life activities with higher accuracy than the state of the art, based on one self-collected and another public available data set.
Model Compression Using Progressive Channel Pruning

In this work, we propose a simple but effective channel pruning framework called Progressive Channel Pruning (PCP) to accelerate Convolutional Neural Networks (CNNs). In contrast to the existing channel pruning methods that prune the channels only once per layer in a layer-by-layer fashion, our new progressive framework iteratively prunes a small number of channels from several selected layers, which consists of a three-step attempting-selecting-pruning pipeline in each iteration. In the attempting step, we attempt to prune a pre-defined number of channels from one layer by using any existing channel pruning methods and estimate the accuracy drop for this layer based on the labelled samples in the validation set. In the selecting step, based on the estimated accuracy drops for all layers, we propose a greedy strategy to automatically select a set of layers that will lead to less overall accuracy drop after pruning these layers. In the pruning step, we prune a small number of channels from these selected layers. We further extend our PCP framework to prune channels for the deep transfer learning methods like Domain Adversarial Neural Network (DANN), in which we effectively reduce the data distribution mismatch in the channel pruning process by using both labelled samples from the source domain and pseudo-labelled samples from the target domain. Our comprehensive experiments on two benchmark datasets demonstrate that our PCP framework outperforms the existing channel pruning approaches under both supervised learning and transfer learning settings.
Starling: A Scalable Query Engine on Cloud Functions

Much like on-premises systems, the natural choice for running database analytics workloads in the cloud is to provision a cluster of nodes to run a database instance. However, analytics workloads are often bursty or low volume, leaving clusters idle much of the time, meaning customers pay for compute resources even when underutilized. The ability of cloud function services, such as AWS Lambda or Azure Functions, to run small, fine granularity tasks make them appear to be a natural choice for query processing in such settings. But implementing an analytics system on cloud functions comes with its own set of challenges. These include managing hundreds of tiny stateless resource-constrained workers, handling stragglers, and shuffling data through opaque cloud services. In this paper we present Starling, a query execution engine built on cloud function services that employs a number of techniques to mitigate these challenges, providing interactive query latency at a lower total cost than provisioned systems with low-to-moderate utilization. In particular, on a 1TB TPC-H dataset in cloud storage, Starling is less expensive than the best provisioned systems for workloads when queries arrive 1 minute apart or more. Starling also has lower latency than competing systems reading from cloud object stores and can scale to larger datasets.
Utilizing VR and Gaze Tracking to Develop AR Solutions for Industrial Maintenance

Augmented reality (AR) presents a variety of possibilities for industrial maintenance. However, the development of real-world AR solutions has been limited due to the technological capabilities and uncertainty with respect to safety at deployment. We introduce the approach of using AR simulation in virtual reality (VR) coupled with gaze tracking to enable resource-efficient AR development. We tested in-field AR guidance and safety awareness features in an iterative development-evaluation process with experts from the elevator maintenance industry. We further conducted a survey, utilizing actual gaze data from the evaluation to elicit comments from industry experts on the usefulness of AR simulation and gaze tracking. Our results show the potential of AR within VR approach combined with gaze tracking. With this framework, AR solutions can be iteratively and safely tested without actual implementation, while gaze data provide advanced objective means to evaluate the designed AR content, documentation usage, and safety awareness.