Reviewerly/exHarmony huggingface dataset:
Risk assessment for long-distance gas pipelines in coal mine gobs based on structure entropy weight method and multi-step backward cloud transformation algorithm based on sampling with replacement

Destruction of pipelines because of coal mine gob disasters may result in enormous financial loss and significantly affect public safety. Hence, risk assessment of gob pipelines is of immense significance for field personnel. Given the lack of statistical data and the limitations of expert experience, a prior single risk value is often insufficient to reflect the actual situation and cannot meet the needs of the site. The backward cloud transformation (BCT) algorithm is a method that can fully mine the local information contained in expert experience to restore the overall information. However, the existing BCT algorithm has no solution under certain conditions, which considerably limits its application. This study proposes a comprehensive risk assessment method by combining the structural entropy method with a multi-step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR). First, a simplified model for rapid identification is used to determine whether it is worth calculating risk values. Second, a fault tree that fits the actual situation is established, and the weights of the indexes are determined by the structural entropy weight method. Third, the interval scores of the indexes are transformed into numerical features of the cloud model, which are then logically operated using virtual cloud algorithms. Finally, the risk values of the pipeline can be obtained, the cloud droplet diagram of the risk values is clearly shown by the forward cloud transformation (FCT) algorithm, and the risk level can be obtained by the probability that the cloud droplet falls into each risk level interval. To validate the utility of the proposed method, a case study of a coal mine gob around a long-distance gas pipeline was investigated.
An Improved Random Forest Algorithm for Predicting Employee Turnover

Employee turnover is considered a major problem for many organizations and enterprises. The problem is critical because it affects not only the sustainability of work but also the continuity of enterprise planning and culture. Therefore, human resource departments are paying greater attention to employee turnover seeking to improve their understanding of the underlying reasons and main factors. To address this need, this study aims to enhance the ability to forecast employee turnover and introduce a new method based on an improved random forest algorithm. The proposed weighted quadratic random forest algorithm is applied to employee turnover data with high-dimensional unbalanced characteristics. First, the random forest algorithm is used to order feature importance and reduce dimensions. Second, the selected features are used with the random forest algorithm and the F-measure values are calculated for each decision tree as weights to build the prediction model for employee turnover. In the area of employee turnover forecasting, compared with the random forest, C4.5, Logistic, BP, and other algorithms, the proposed algorithm shows significant improvement in terms of various performance indicators, specifically recall and F-measure. In the experiment using the employee dataset of a branch of a communications company in China, the key factors influencing employee turnover were identified as monthly income, overtime, age, distance from home, years at the company, and percent of salary increase. Among them, monthly income and overtime were the two most important factors. The study offers a new analytic method that can help human resource departments predict employee turnover more accurately and its experimental results provide further insights to reduce employee turnover intention.
Moonstone: a novel natural language processing system for inferring social risk from clinical narratives

Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present Moonstone, a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information - in this case, housing situation, living alone, and social support - from clinical notes. Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation.An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest (housing situation, living alone, and social support). The system achieved positive predictive value (i.e. precision) scores ranging from 0.66 (homeless/marginally housed) to 0.98 (lives at home/not homeless), accuracy scores ranging from 0.63 (lives in facility) to 0.95 (lives alone), and sensitivity (i.e. recall) scores ranging from 0.75 (lives in facility) to 0.97 (lives alone).The Moonstone system is - to the best of our knowledge - the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good (lives in facility) to excellent (lives alone) performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.
Interferi

Interferi is an on-body gesture sensing technique using acoustic interferometry. We use ultrasonic transducers resting on the skin to create acoustic interference patterns inside the wearer's body, which interact with anatomical features in complex, yet characteristic ways. We focus on two areas of the body with great expressive power: the hands and face. For each, we built and tested a series of worn sensor configurations, which we used to identify useful transducer arrangements and machine learning fea-tures. We created final prototypes for the hand and face, which our study results show can support eleven- and nine-class gestures sets at 93.4% and 89.0% accuracy, re-spectively. We also evaluated our system in four continu-ous tracking tasks, including smile intensity and weight estimation, which never exceed 9.5% error. We believe these results show great promise and illuminate an inter-esting sensing technique for HCI applications.
Wearable technologies for active living and rehabilitation: Current research challenges and future opportunities

This paper presents some recent developments in the field of wearable sensors and systems that are relevant to rehabilitation and provides examples of systems with evidence supporting their effectiveness for rehabilitation. A discussion of current challenges and future developments for selected systems is followed by suggestions for future directions needed to advance towards wider deployment of wearable sensors and systems for rehabilitation.
The extended marine underwater environment database and baseline evaluations

Images captured in underwater environments usually exhibit complex illuminations, severe turbidity of water, and often display objects with large varieties in pose and spatial location, etc., which cause challenges to underwater vision research. In this paper, an extended underwater image database for salient-object detection or saliency detection is introduced. This database is called the Marine Underwater Environment Database (MUED), which contains 8600 underwater images of 430 individual groups of conspicuous objects with complex backgrounds, multiple salient objects, and complicated variations in pose, spatial location, illumination, turbidity of water, etc. The publicly available MUED provides researchers in relevant industrial and academic fields with underwater images under different types of variations. Manually labeled ground-truth information is also included in the database, so as to facilitate the research on more applicable and robust methods for both underwater image processing and underwater computer vision. The scale, accuracy, diversity, and background structure of MUED cannot only be widely used to assess and evaluate the performance of the state-of-the-art salient-object detection and saliency-detection algorithms for general images, but also particularly benefit the development of underwater vision technology and offer unparalleled opportunities to researchers in the underwater vision community and beyond.
Cybersecurity Insurance: Modeling and Pricing

Cybersecurity risk has attracted considerable attention in recent decades. However, the modeling of cybersecurity risk is still in its infancy, mainly because of its unique characteristics. In this study, we develop a framework for modeling and pricing cybersecurity risk. The proposed model consists of three components: the epidemic model, loss function, and premium strategy. We study the dynamic upper bounds for the infection probabilities based on both Markov and non-Markov models. A simulation approach is proposed to compute the premium for cybersecurity risk for practical use. The effects of different infection distributions and dependence among infection processes on the losses are also studied.
‘It’s made me a lot more aware’: a new materialist analysis of health self-tracking

With the advent of apps, other software and wearable devices designed to enable people to easily monitor and measure details about their bodies, much attention has been paid to the phenomenon of health self-tracking. In this article, findings from a study involving interviews with 40 Australian self-trackers are discussed and analysed from a feminist new materialist perspective, focusing on relational dimensions, affective forces and agential capacities. Analysis of their accounts identified several major agential capacities generated by self-tracking health and illness, including achieving knowledge, awareness and problem-solving; taking control; and feeling better. Affective forces were strongly evident in the ways the participants talked about their practices and rationales for health self-tracking, including the pleasure and satisfaction they experienced, as well as the demoralising or burdensome elements they described. Relational dimensions included interpersonal and biographical contexts as well as enactments of embodied and technological sensing and recording.
A Design Space for Gaze Interaction on Head-mounted Displays

Augmented and virtual reality (AR/VR) has entered the mass market and, with it, will soon eye tracking as a core technology for next generation head-mounted displays (HMDs). In contrast to existing gaze interfaces, the 3D nature of AR and VR requires estimating a user's gaze in 3D. While first applications, such as foveated rendering, hint at the compelling potential of combining HMDs and gaze, a systematic analysis is missing. To fill this gap, we present the first design space for gaze interaction on HMDs. Our design space covers human depth perception and technical requirements in two dimensions aiming to identify challenges and opportunities for interaction design. As such, our design space provides a comprehensive overview and serves as an important guideline for researchers and practitioners working on gaze interaction on HMDs. We further demonstrate how our design space is used in practice by presenting two interactive applications: EyeHealth and XRay-Vision.
Understanding Affective Experiences with Conversational Agents

While previous studies of Conversational Agents (e.g. Siri, Google Assistant, Alexa and Cortana) have focused on evaluating usability and exploring capabilities of these systems, little work has examined users' affective experiences. In this paper we present a survey study with 171 participants to examine CA users' affective experiences. Specifically, we present four major usage scenarios, users' affective responses in these scenarios, and the factors which influenced the affective responses. We found that users' overall experience was positive with interest being the most salient positive emotion. Affective responses differed depending on the scenarios. Both pragmatic and hedonic qualities influenced affect. The factors underlying pragmatic quality are: helpfulness, proactivity, fluidity, seamlessness and responsiveness. The factors underlying hedonic quality are: comfort in human-machine conversation, pride of using cutting-edge technology, fun during use, perception of having a human-like assistant, concern about privacy and fear of causing distraction.
Deep Proximal Unrolling: Algorithmic Framework, Convergence Analysis and Applications

Deep learning models have gained great success in many real-world applications. However, most existing networks are typically designed in heuristic manners, thus these approaches lack rigorous mathematical derivations and clear interpretations. Several recent studies try to build deep models by unrolling a particular optimization model that involves task information. Unfortunately, due to the dynamic nature of network parameters, their resultant deep propagations do not possess the nice convergence property as the original optimization scheme does. In this work, we develop a generic paradigm to unroll nonconvex optimization for deep model design. Different from most existing frameworks, which just replace the iterations by network architectures, we prove in theory that the propagation generated by our proximally unrolled deep model can globally converge to the critical-point of the original optimization model. Moreover, even if the task information is only partially available (e.g., no prior regularization), we can still train convergent deep propagations. We also extend these theoretical investigations on the more general multi-block models and thus a lot of real-world applications can be successfully handled by the proposed framework. Finally, we conduct experiments on various low-level vision tasks (i.e., non-blind deconvolution, dehazing, and low-light image enhancement) and demonstrate the superiority of our proposed framework, compared with existing state-of-the-art approaches.
The digital frontier: Envisioning future technologies impact on the classroom

Global advances in technology and information are purportedly propelling transformations and disruptions across many sectors, including education. However, historical reviews of technology integration in education mainly reveal weak or ineffectual impacts on learning, and only minor reforms to date within the education system. This study adopted a futures studies methodological approach to explore how K-12 educational spaces and experiences might be shaped by emerging and emergent technologies. In this regard, a series of vignettes are presented which critically examine the potential of augmented reality technologies, artificial intelligence, and smart materials technologies to transform future learning experiences and learning environments across K-12 education contexts, while also challenging assumptions about, and considering influences on, these futures. The focus of the study was not to predict a single or desired future for education, but rather to critically consider a range of possible education futures informed by the articulation of these three vignettes. The paper concludes with discourse on an emergent pedagogic approach that has the potential to prepare teachers and learners to interact and flourish within radically re-configured learning spaces that lean on the aforementioned technologies to support transitions within and beyond the school and its connected communities.
Ecological Data Should Not Be So Hard to Find and Reuse

Drawing upon the data deposited in publicly shared archives has the potential to transform the way we conduct ecological research. For this transformation to happen, we argue that data need to be more interoperable and easier to discover. One way to achieve these goals is to adopt domain-specific data representations.
Novel trust consensus protocol and blockchain-based trust evaluation system for M2M application services

The increasing number of intelligent Machine-to-Machine Communication (M2M) devices in the end-user domain provide good resources for creating and sharing M2M application services. Therefore, transferring the role of a traditional centralized service provider to decentralized peers (end-users) acting as service providers is very promising. However, the future of decentralized M2M application services which are independently provided or consumed by several end-users in the M2M community depends on trust. Untrustworthy peers trying to deploy malfunctioning services for others mitigate the benefits of decentralized systems. Nowadays, the concept of distributed ledger and blockchain has an increased popularity regarding trustless computing among communities operating without centralized authorities. This research publication provides a comprehensive analysis and approach merging the concepts of M2M application services, trust and distributed ledger technologies. Moreover, this publication presents an optimized Trust Evaluation System which is used to ensure trustworthiness among peers in a M2M community. To improve the Trust Evaluation System the integration of blockchain for data storage is introduced. Additionally, blockchain technology is used to extend the existing trust model of the Trust Evaluation System to enable tamper-proof data and detection of untrustworthy peers. This publication reviews several existing approaches in the academic and industry sector to highlight the limitations of the blockchain regarding the consensus and proposes a novel Trust Consensus Protocol. This research publication also provides a practical evaluation of the proposed protocol.
Applying Deep Learning to Audit Procedures: An Illustrative Framework

SYNOPSIS This paper aims to promote the application of deep learning to audit procedures by illustrating how the capabilities of deep learning for text understanding, speech recognition, visual recognition, and structured data analysis fit into the audit environment. Based on these four capabilities, deep learning serves two major functions in supporting audit decision making: information identification and judgment support. The paper proposes a framework for applying these two deep learning functions to a variety of audit procedures in different audit phases. An audit data warehouse of historical data can be used to construct prediction models, providing suggested actions for various audit procedures. The data warehouse will be updated and enriched with new data instances through the application of deep learning and a human auditor's corrections. Finally, the paper discusses the challenges faced by the accounting profession, regulators, and educators when it comes to applying deep learning.
Extractive summarization using supervised and unsupervised learning

In this paper, three methods of extracting single document summary by combining supervised learning with unsupervised learning are proposed. The purpose of these three methods is to measure the importance of sentences by combining the statistical features of sentences and the relationship between sentences at the same time. The first method uses supervised model and graph model to score sentences separately, and then linear combination of scores is used as the final score of sentences. In the second method, the graph model is used as an independent feature of the supervised model to evaluate the importance of sentences. The third method is to score the importance of sentences by supervised model, then as a priori value of nodes in the graph model, and finally use biased graph model to score sentences. On the data sets of DUC2001 and DUC2002, the ROUGE method is used as the evaluation criterion, which shows that the three methods have achieved good results, and are superior to the methods of extracting summary only using supervised learning or unsupervised learning. We also validate that priori knowledge can improve the accuracy of key sentence selection in graph model.
Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review

Steganography consists of hiding messages inside some object known as a carrier in order to establish a covert communication channel so that the act of communication itself goes unnoticed by observers who have access to that channel. The steganalysis is dedicated to the detection of hidden messages using steganography; these messages can be implicit in different types of media, such as digital images, video files, audio files or plain text. Traditionally, steganalysis has been divided into two separate stages, the first stage consists of manual extraction of sophisticated features and the second stage is classification using Ensemble Classifiers or Support Vector Machines. In recent years, the development of Deep Learning has made it possible to unify and automate the two traditional stages into an end to end approach with promising results. This paper shows the evolution of steganalysis in recent years using the Deep Learning techniques. The results of these techniques have surpassed those obtained with conventional methods - Rich Models with Ensemble Classifiers - both in the spatial and frequency (JPEG) domains. Since 2014, researchers have used The Convolutional Neural Networks to solve this problem generating diverse architectures and strategies to improve the detection percentages of steganographic images on the last generation algorithms (WOW, S-UNIWARD, HUGO, J-UNIWARD, among others). The Deep Learning, being applied to steganalysis, is now in the process of construction and results so far are encouraging for researchers that are interested in the topic.
Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features

A smart city connects physical, information technology, social, and business infrastructures together to leverage their collective intelligence. Feedback drives improvements in service, city development, and quality of life in the city. Therefore, sentiment analysis in real-time of opinions expressed in text form by residents in the city is absolutely necessary. Nowadays, machine learning is widely applied to sentiment analysis of decisions in business, especially deep learning. In this experiment, we evaluated and compared the performances of several conventional deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM), in sentiment analysis of Thai children tales. In several previous studies, many features have been used in all of the models mentioned, features such as word embedding that helps a model to understand the semantics of each word, POS-tag that helps a model to understand the grammatical function of words, and sentic that helps a model to understand the emotion of words. Some combinations of these features have also been used. The results of this experiment show that the CNN model that used all three features gave the best result of 0.817 F1-score at p < 0.01, which was significantly better than all other models.