Reviewerly/exHarmony huggingface dataset:
Factors impacting digital transformations of the food industry by adoption of blockchain technology

Purpose This study aims to blockchain facilitate information sharing among different players in the food industry, such as farmers, food suppliers and investors, enabling an effective decision-making process where the information about goods is traceable without any inherent risk. Although blockchain technology is emerging, its use in the food industry needs to be explored from different angles, such as organizations’ policy, adoption strategies and potential technology innovations which could positively impact business processes. Design/methodology/approach For this purpose, the authors survey 84 organizations in the food industry. Further, the authors conducted in-depth- interviews with three organizations that applied blockchain technology in the production phase to address the pros and cons of this adaption and discuss how the technology could be improved based on the challenges they faced. Moreover, an overview of current and potential digital transformation uses cases of blockchain technology in the food industry has been provided. Findings The results suggested that perceived efficiency, transparency, standardization and platform development and traceability factors positively influenced intention to use (ITU). Practical implications The adoption of the blockchain cloud solution is neither time-consuming nor expensive; organizations may attempt to predict how regulations or standards will be developed to create a blockchain solution compatible with them. Originality/value This study is among the primary studies investigating the role of blockchain technology in the food sector from the organizations’ viewpoint, highlighting the factors influencing the ITU.
Virtual Reality for the Treatment of Anxiety Disorders: A Scoping Review

Virtual reality in head-mounted displays (HMD-VR) may be a valuable tool in occupational therapy to address anxiety. Findings from the virtual reality exposure therapy (VRET) literature may facilitate translation of HMD-VR to occupational therapy psychosocial practice.To explore how HMD-VR has been used to treat anxiety through VRET and could be translated to occupational therapy.We searched seven electronic databases for articles published between 2000 and 2020: CINAHL, Cochrane Library, Embase, ERIC, Ovid MEDLINE, PsycINFO, and Web of Science. Search terms included HMD-VR constructs, products, and therapy concepts. Study Selection and Data Collection: We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to report studies implementing VRET to treat anxiety. At least two reviewers assessed each citation, and a third resolved disagreements. Articles were included if they were in English, reported experimental data, and used HMD-VR. Letters, commentaries, book chapters, technical descriptions, theoretical papers, conference proceedings (≤4 pages), and reviews were excluded.Twenty-eight studies used HMD-VR to treat posttraumatic stress disorder (n = 3), specific phobias (n = 19), and performance-based social anxiety (n = 6); protocols and levels of evidence varied (randomized controlled trials, n = 11; controlled trials without randomization, n = 6; case-control or cohort studies, n = 11). Qualitative examination indicates HMD-VR is an effective treatment tool.HMD-VR can be a valuable tool for occupational therapy to simulate environments where clients with anxiety disorders participate. Eliciting presence through multisensory features and body representation may enhance outcomes. What This Article Adds: Drawing from the VRET literature, this scoping review suggests that HMD-VR can be used by occupational therapy practitioners to simulate ecologically valid environments, evaluate client responses to fearful stimuli, and remediate anxiety though immersion in virtual tasks when participation in natural contexts is unfeasible. Having ecologically valid environments is particularly important for people with anxiety disorders because they need support to cope when they encounter triggers in everyday life environments.
An end-to-end approach to segmentation in medical images with CNN and posterior-CRF

Conditional Random Fields (CRFs) are often used to improve the output of an initial segmentation model, such as a convolutional neural network (CNN). Conventional CRF approaches in medical imaging use manually defined features, such as intensity to improve appearance similarity or location to improve spatial coherence. These features work well for some tasks, but can fail for others. For example, in medical image segmentation applications where different anatomical structures can have similar intensity values, an intensity-based CRF may produce incorrect results. As an alternative, we propose Posterior-CRF, an end-to-end segmentation method that uses CNN-learned features in a CRF and optimizes the CRF and CNN parameters concurrently. We validate our method on three medical image segmentation tasks: aorta and pulmonary artery segmentation in non-contrast CT, white matter hyperintensities segmentation in multi-modal MRI, and ischemic stroke lesion segmentation in multi-modal MRI. We compare this with the state-of-the-art CNN-CRF methods. In all applications, our proposed method outperforms the existing methods in terms of Dice coefficient, average volume difference, and lesion-wise F1 score.
E<sup> 3 </sup>MoP: Efficient Motion Planning Based on Heuristic-Guided Motion Primitives Pruning and Path Optimization With Sparse-Banded Structure

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21% in the global planning stage and the motion efficiency of the robot is improved by 22.87% compared with the recent quintic Bézier curve-based state space sampling approach. We name the proposed motion planning framework E <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {^{3}} $ </tex-math></inline-formula> MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages. Note to Practitioners—This paper is motivated by the challenges of motion planning problems of mobile robots. A three-layer motion planning framework is proposed by combining global path planning, local path optimization, and time-optimal velocity planning. For mobile robot navigation applications in semi-structured environments, optimization-based local planners are recommended. Extensive simulation and experimental results show the effectiveness of the proposed motion planning framework. However, due to the non-convexity of the path optimization formulation, the proposed local planner may get stuck in local optima. In future research, we will concentrate on extending the proposed local path optimization approach with the theory of homology classes to maintain several homotopically distinct local paths and seek global optima.
Information Extraction and Named Entity Recognition Supported Social Media Sentiment Analysis during the COVID-19 Pandemic

Social media platforms are increasingly being used to communicate information, something which has only intensified during the pandemic. News portals and governments are also increasing attention to digital communications, announcements and response or reaction monitoring. Twitter, as one of the largest social networking sites, which has become even more important in the communication of information during the pandemic, provides space for a lot of different opinions and news, with many discussions as well. In this paper, we look at the sentiments of people and we use tweets to determine how people have related to COVID-19 over a given period of time. These sentiment analyses are augmented with information extraction and named entity recognition to get an even more comprehensive picture. The sentiment analysis is based on the ’Bidirectional encoder representations from transformers’ (BERT) model, which is the basic measurement model for the comparisons. We consider BERT as the baseline and compare the results with the RNN, NLTK and TextBlob sentiment analyses. The RNN results are significantly closer to the benchmark results given by BERT, both models are able to categorize all tweets without a single tweet fall into the neutral category. Then, via a deeper analysis of these results, we can get an even more concise picture of people’s emotional state in the given period of time. The data from these analyses further support the emotional categories, and provide a deeper understanding that can provide a solid starting point for other disciplines as well, such as linguistics or psychology. Thus, the sentiment analysis, supplemented with information extraction and named entity recognition analyses, can provide a supported and deeply explored picture of specific sentiment categories and user attitudes.
Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization

Preconditioning and Regularization Enable Faster Reinforcement Learning Natural policy gradient (NPG) methods, in conjunction with entropy regularization to encourage exploration, are among the most popular policy optimization algorithms in contemporary reinforcement learning. Despite the empirical success, the theoretical underpinnings for NPG methods remain severely limited. In “Fast Global Convergence of Natural Policy Gradient Methods with Entropy Regularization”, Cen, Cheng, Chen, Wei, and Chi develop nonasymptotic convergence guarantees for entropy-regularized NPG methods under softmax parameterization, focusing on tabular discounted Markov decision processes. Assuming access to exact policy evaluation, the authors demonstrate that the algorithm converges linearly at an astonishing rate that is independent of the dimension of the state-action space. Moreover, the algorithm is provably stable vis-à-vis inexactness of policy evaluation. Accommodating a wide range of learning rates, this convergence result highlights the role of preconditioning and regularization in enabling fast convergence.
Unsupervised time series outlier detection with diversity-driven convolutional ensembles

With the sweeping digitalization of societal, medical, industrial, and scientific processes, sensing technologies are being deployed that produce increasing volumes of time series data, thus fueling a plethora of new or improved applications. In this setting, outlier detection is frequently important, and while solutions based on neural networks exist, they leave room for improvement in terms of both accuracy and efficiency. With the objective of achieving such improvements, we propose a diversity-driven, convolutional ensemble. To improve accuracy, the ensemble employs multiple basic outlier detection models built on convolutional sequence-to-sequence autoencoders that can capture temporal dependencies in time series. Further, a novel diversity-driven training method maintains diversity among the basic models, with the aim of improving the ensemble's accuracy. To improve efficiency, the approach enables a high degree of parallelism during training. In addition, it is able to transfer some model parameters from one basic model to another, which reduces training time. We report on extensive experiments using real-world multivariate time series that offer insight into the design choices underlying the new approach and offer evidence that it is capable of improved accuracy and efficiency. This is an extended version of "Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles", to appear in PVLDB 2022.
Insights into emergency remote teaching in EFL

Abstract The transition to emergency remote teaching (ERT) in response to the COVID-19 pandemic has spawned many research studies. However, studies exploring EFL instructors’ emotions, in-class experiences, and relationships with their students and their colleagues during the pandemic are scarce. This mixed study captured nineteen EFL instructors’ emotions on ERT, their transformation of classroom practices, and how they related to students and colleagues during this period in North Cyprus. Data were gathered through a qualitative survey, supplemented with quantitative data from a technological pedagogical content knowledge (TPACK) scale. Our findings revealed that the instructors gradually adapted to ERT despite the challenges that hindered their classroom practice transformation. The findings further suggest that if the instructors develop their TPACK and relatedness with their students and colleagues with the support of their institutions, teaching in a remote environment in the ‘new pedagogy’ contributes to establishing a possible way forward in EFL.
Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU

Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods.
COVID-19 Contact Tracing Outcomes in Washington State, August and October 2020

Introduction: Case investigation and contact tracing are important tools to limit the spread of SARS-CoV-2, particularly when implemented efficiently. Our objective was to evaluate participation in and timeliness of COVID-19 contact tracing and whether these measures changed over time. Methods: We retrospectively assessed COVID-19 case investigation and contact tracing surveillance data from the Washington State centralized program for August 1-31, 2020 and October 1-31, 2020. We combined SARS-CoV-2 testing reports with contact tracing data to compare completeness, reporting of contacts, and program timeliness. Results: For August and October respectively, 4,600 (of 12,521) and 2,166 (of 16,269) individuals with COVID-19 were referred to the state program for case investigation. Investigators called 100% of referred individuals; 65% (August) and 76% (October) were interviewed. Of individuals interviewed, 33% reported contacts in August and 45% in October, with only mild variation by age, sex, race/ethnicity, and urbanicity. In August, 992 individuals with COVID-19 reported a total of 2,584 contacts (mean, 2.6), and in October, 739 individuals reported 2,218 contacts (mean, 3.0). Among contacts, 86% and 78% participated in interviews for August and October. The median time elapsed from specimen collection to contact interview was 4 days in August and 3 days in October, and from symptom onset to contact interview was 7 days in August and 6 days in October. Conclusions: While contact tracing improved with time, the proportion of individuals disclosing contacts remained below 50% and differed minimally by demographic characteristics. The longest time interval occurred between symptom onset and test result notification. Improving elicitation of contacts and timeliness of contact tracing may further decrease SARS-CoV-2 transmission.
Semantic clustering based deduction learning for image recognition and classification

The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains. Human beings can make judgments based on experience and cognition, and as a result, no one would recognize an unknown animal as a car. Inspired by this observation, we propose to train deep learning models using the clustering prior that can guide the models to learn with the ability of semantic deducing and summarizing from classification attributes, such as a cat belonging to animals while a car pertaining to vehicles. The proposed approach realizes the high-level clustering in the semantic space, enabling the model to deduce the relations among various classes during the learning process. In addition, the paper introduces a semantic prior based random search for the opposite labels to ensure the smooth distribution of the clustering and the robustness of the classifiers. The proposed approach is supported theoretically and empirically through extensive experiments. We compare the performance across state-of-the-art classifiers on popular benchmarks, and the generalization ability is verified by adding noisy labeling to the datasets. Experimental results demonstrate the superiority of the proposed approach.
Feature selection based on non-negative spectral feature learning and adaptive rank constraint

Unsupervised feature selection plays a significant role in data classification and clustering. General regression models cannot directly exploit the information on the feature space and fail to accurately describe the local geometric structure of data during feature selection. To address these problems, this paper proposes the feature selection algorithm, which is based on non-negative spectral feature learning and adaptive rank constraint (NNSAFS). First, the algorithm utilizes the residual term in sparse regression to ensure that the learned low-dimensional subspaces have greater fault tolerance and introduces a feature graph on the sparse transformation matrix to reveal the manifold information on the feature space. This sparse transformation matrix is the projection matrix because the introduction of feature graphs on this matrix can connect manifold learning to feature selection. In addition, traditional spectral clustering algorithms usually construct fixed similarity graphs for clustering analysis. The NNSAFS algorithm imposes a rank constraint on the clustering indicator matrix, which is equivalent to the graph regularization that can recover accurate local structure information. Moreover, the similarity matrix in the regularization term is constructed by using the maximum entropy theory, which can increase the adaptability of manifold learning. Finally, the algorithm imposes the l1-norm constraint on the projection matrix, which makes the selected features more conducive to clustering performance. The clustering performance of the NNSAFS algorithm is evaluated against seven other unsupervised feature selection algorithms on nine benchmark datasets. The experimental results show that the features selected by the proposed algorithm are more discriminative and outperform other algorithms in the clustering task.1
A blockchain-based models for student information systems

Blockchain stores a series of transactions in form of a sequence of linked blocks. Hence, the concept of a single decentralized ledger is easily maintained. Transactions and interactions that take place among the participants accessing the distributed and decentralized but cooperative blockchain network are held through a single ledger. A student information system (SIS) can make use of a decentralized, reliable, and highly trusted ledger that stores vital information. Traditional education systems encounter problems such as centralized record keeping where fault tolerance depends on a single cloud provider; not to mention locally hosted databases. The implementation of blockchain in the education sector provides a new horizon for set of non-functional requirements including but not limited to: security, immutability, independence from the institution, immutability of official records and certificates. In addition, total trust in the accuracy and infallibility are all gathered in the decentralized ledgers of blockchain. The proposed models emphasize on the data availability; represented in students' ability to access all of their data at any time. This paper proposes three models for using blockchains to implement fully functional SIS that maintains transactions such as students’ and faculty members’ records, course registration records and student marks. In addition, avoiding the role of a super administrator or a centralized exposed store where data integrity is vulnerable. Using the proposed models pushes towards an electronic community where genuine certificates can be easily issued and published to the interested parties without the need for involving a centralized administration.