Reviewerly/exHarmony huggingface dataset:
Application of a Domain-specific BERT for Detection of Speech Recognition Errors in Radiology Reports

Purpose To develop radiology domain–specific bidirectional encoder representations from transformers (BERT) models that can identify speech recognition (SR) errors and suggest corrections in radiology reports. Materials and Methods A pretrained BERT model, Clinical BioBERT, was further pretrained on a corpus of 114 008 radiology reports between April 2016 and August 2019 that were retrospectively collected from two hospitals. Next, the model was fine-tuned on a training dataset of generated insertion, deletion, and substitution errors, creating Radiology BERT. This model was retrospectively evaluated on an independent dataset of radiology reports with generated errors (n = 18 885) and on unaltered report sentences (n = 2000) and prospectively evaluated on true clinical SR errors (n = 92). Correction Radiology BERT was separately trained to suggest corrections for detected deletion and substitution errors. Area under the receiver operating characteristic curve (AUC) and bootstrapped 95% CIs were calculated for each evaluation dataset. Results Radiology-specific BERT had AUC values of >.99 (95% CI: >0.99, >0.99), 0.94 (95% CI: 0.93, 0.94), 0.98 (95% CI: 0.98, 0.98), and 0.97 (95% CI: 0.97, 0.97) for detecting insertion, deletion, substitution, and all errors, respectively, on the independently generated test set. Testing on unaltered report impressions revealed a sensitivity of 82% (28 of 34; 95% CI: 70%, 93%) and specificity of 88% (1521 of 1728; 95% CI: 87%, 90%). Testing on prospective SR errors showed an accuracy of 75% (69 of 92; 95% CI: 65%, 83%). Finally, the correct word was the top suggestion for 45.6% (475 of 1041; 95% CI: 42.5%, 49.3%) of errors. Conclusion Radiology-specific BERT models fine-tuned on generated errors were able to identify SR errors in radiology reports and suggest corrections. Keywords: Computer Applications, Technology Assessment Supplemental material is available for this article. © RSNA, 2022 See also the commentary by Abajian and Cheung in this issue.
The Moderating Effect of Technology Readiness Towards Open and Distance Learning (ODL) Technology Acceptance During COVID-19 Pandemic

: The COVID-19 pandemic has impacted higher education in Malaysia that requires the academics to transform their teaching style to online teaching. Hence, it is essential for them to be skilful in using new technology in teaching and learning. In Open and Distance Learning (ODL), the academic staff must learn a new environment of learning technology in terms of giving lectures and managing all related ODL documents. In this perspective, technology readiness of the ODL technology plays an important role to enhance the acceptance of using the latest technology in ODL among them. This study was conducted to test the moderating effect of the Technology Readiness of the academic staff towards their acceptance of ODL technology to manage teaching and learning process such as UFUTURE and Google Classrooms. The research model was developed based on Technology Acceptance Model (TAM) and Technology Readiness concept. The online survey was created and then emailed to the academic staff in UiTM Selangor, resulting in 321 responses were received subsequently. The results show that the Technology Readiness factors (Optimism and Innovativeness) strengthen the relationship between Technology Acceptance factors (Perceived Ease of Use and Perceived Usefulness) and intention behaviour to use the ODL technology. Additionally, the direct effect testing has also shown that the related factors influence the intention of the academic staff to use the ODL technology except Insecurity and Discomfort. Technology readiness does play an important role; therefore, it is essential for the university to train academic staff on new ODL technology and it should be planned accordingly.
Determination of Suitable Hyperparameters of Artificial Neural Network for the Best Prediction of Geotechnical Properties of Soil

Abstract: The artificial neural network is robust in predicting soil properties. The present study aims to determine the suitable hyperparameters such as number of hidden layers, neurons, and backpropagation algorithms for the best prediction of geotechnical properties of soil. The supervised learning category-based multilayer perceptron artificial neural network approach is used, and models are developed in MATLAB R2020a. The ANN models are configured with neurons (5, 10 &amp; 15), hidden layers (one to five), and a backpropagation algorithm (LM, BFG, SCG, GDA, GD &amp; GDA). Fifteen ANN models are developed for each algorithm. The study shows that the LM, BFG, and SCG algorithm-based ANN models require strongly (0.61-0.8) to very strongly (0.81-1) correlated datasets. On the other hand, the GDM, GD, and GDA algorithm-based ANN models require only strongly correlated datasets to achieve a performance of more than 0.9. In most cases, it is also found that the GDM, GD, and GDA algorithm-based ANN models achieve high performance with three hidden layers interconnected with ten neurons. Still, LM algorithm-based ANN model achieves high performance with a single hidden layer interconnected with 5/15 neurons. The present work draws a relationship between the correlation coefficient and the number of hidden layers &amp; neurons. It also helps to study the effect of hidden layers and neurons on the performance of ANN models. Formulas are derived from the performance of ANN models to calculate the required number of hidden layers and neurons for a particular backpropagation algorithm to achieve a testing performance of more than 0.9. Keywords: Artificial Neural Network, Consistency Limits, Compaction Parameters, Hidden Layers, Backpropagation Algorithms
Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis

Abstract This research constructs a disruptive technology identification framework based on attributes and functions and selects intelligent logistics robot technology for empirical analysis in the artificial intelligence &amp; Internet of Things (AIoT) industry. We take the three attributes of technological novelty, breakthrough, and influence as measurement indicators to identify disruptive technologies and perform functional analysis of the technology to find potential markets for disruptive technologies. The research demonstrates that two‐dimensional code technology, sensing technology, intelligent control technology, artificial intelligence technology, and wireless communication technology are disruptive technologies in intelligent logistics robots. The future development directions of intelligent logistics robots include efficient, intelligent control operations, good human‐computer interaction, accurate safety obstacle avoidance, and accurate and efficient position detection. This research has put forward the future development direction of disruptive technology in AIoT and provided a reference for the R&amp;D and manufacture of intelligent logistics robots.
Diagnosing Ensemble Few-Shot Classifiers

The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually difficult to understand the underlying causes and make improvements. To tackle this issue, we propose a visual analysis method, FSLDiagnotor. Given a set of base learners and a collection of samples with a few shots, we consider two problems: 1) finding a subset of base learners that well predict the sample collections; and 2) replacing the low-quality shots with more representative ones to adequately represent the sample collections. We formulate both problems as sparse subset selection and develop two selection algorithms to recommend appropriate learners and shots, respectively. A matrix visualization and a scatterplot are combined to explain the recommended learners and shots in context and facilitate users in adjusting them. Based on the adjustment, the algorithm updates the recommendation results for another round of improvement. Two case studies are conducted to demonstrate that FSLDiagnotor helps build a few-shot classifier efficiently and increases the accuracy by 12% and 21%, respectively.
FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics

We present FlexPatch, a novel mobile system to enable accurate and real-time object detection over high-resolution video streams. A widely-used approach for real-time video analysis is detection-based tracking (DBT), i.e., running the heavy-but-accurate detector every few frames and applying a lightweight tracker for in-between frames. However, the approach is limited for real-time processing of high-resolution videos in that i) a lightweight tracker fails to handle occlusion, object appearance changes, and occurrences of new objects, and ii) the detection results do not effectively offset tracking errors due to the high detection latency. We propose tracking-aware patching technique to address such limitations of the DBT frameworks. It effectively identifies a set of subareas where the tracker likely fails and tightly packs them into a small-sized rectangular area where the detection can be efficiently performed at low latency. This prevents the accumulation of tracking errors and offsets the tracking errors with frequent fresh detection results. Our extensive evaluation shows that FlexPatch not only enables real-time and power-efficient analysis of high-resolution frames on mobile devices but also improves the overall accuracy by 146% compared to baseline DBT frameworks.
A real 3D scene rendering optimization method based on region of interest and viewing frustum prediction in virtual reality

As an important technology of digital construction, real 3D models can improve the immersion and realism of virtual reality (VR) scenes. The large amount of data for real 3D scenes requires more effective rendering methods, but the current rendering optimization methods have some defects and cannot render real 3D scenes in virtual reality. In this study, the location of the viewing frustum is predicted by a Kalman filter, and eye-tracking equipment is used to recognize the region of interest (ROI) in the scene. Finally, the real 3D model of interest in the predicted frustum is rendered first. The experimental results show that the method of this study can predict the frustrum location approximately 200 ms in advance, the prediction accuracy is approximately 87%, the scene rendering efficiency is improved by 8.3%, and the motion sickness is reduced by approximately 54.5%. These studies help promote the use of real 3D models in virtual reality and ROI recognition methods. In future work, we will further improve the prediction accuracy of viewing frustums in virtual reality and the application of eye tracking in virtual geographic scenes.
Secure IIoT Information Reinforcement Model Based on IIoT Information Platform Using Blockchain

Data created at industrial sites through industrial internet of things devices are now being processed automatically or in real-time in the industrial structure, due to the application of artificial intelligence technology to industrial sites. However, the expenses of autonomous or real-time data processing and steady data processing (analysis, prediction, prescription, and implementation) necessitate a new processing method. We propose a blockchain-based industrial internet of things information reinforcement model in this work that may reliably ensure the integrity of industrial internet of things data produced at industrial locations. The proposed model processes industrial internet of things data that may occur at endpoints at industrial sites into the blockchain by processing data generated by the same industrial internet of things device independently. As a result, the IIoT data sent to the industrial internet of things server can be evaluated more readily, and production accuracy may be enhanced. The proposed model optimizes industrial internet of things information linkage by stochastically reflecting the information based on attribute value frequency. By dynamically aggregating the related data of industrial internet of things information acquired as a seed through hierarchical subnets, the proposed model increases stability and accuracy. Furthermore, the proposed model may be used to enhance an organizations’ operational efficiency (consulting and training, for example) and strategic decision-making by utilizing fundamental knowledge about items produced at industrial locations. Furthermore, the proposed model allows for information sharing and system connectivity between industrial locations, allowing for close collaboration between industrial internet of things features. As a result of the performance evaluation, the proposed model included an industrial internet of things sensor to the blockchain, eliminating the need for an extra function in the manufacturing process and reducing the time required to validate the integrity of industrial internet of things data. In addition, as a result of analyzing industrial internet of things data by an algorithm according to the number of simulated clouds, the accuracy of industrial internet of things information was improved by 2.5% to 3%, on average.
Deep Image Steganography Using Transformer and Recursive Permutation

Image steganography, which usually hides a small image (hidden image or secret image) in a large image (carrier) so that the crackers cannot feel the existence of the hidden image in the carrier, has become a hot topic in the community of image security. Recent deep-learning techniques have promoted image steganography to a new stage. To improve the performance of steganography, this paper proposes a novel scheme that uses the Transformer for feature extraction in steganography. In addition, an image encryption algorithm using recursive permutation is proposed to further enhance the security of secret images. We conduct extensive experiments to demonstrate the effectiveness of the proposed scheme. We reveal that the Transformer is superior to the compared state-of-the-art deep-learning models in feature extraction for steganography. In addition, the proposed image encryption algorithm has good attributes for image security, which further enhances the performance of the proposed scheme of steganography.
New uncertainty principles for the $(k,a)$-generalized wavelet transform

We present the basic (k, a)-generalized wavelet theory and prove several Heisenberg-type inequalities for this transform.After reviewing Pitt's and Beckner's inequalities for the (k, a)-generalized Fourier transform, we connect both inequalities to show a generalization of uncertainty principles for the (k, a)-generalized wavelet transform.We also present two concentration uncertainty principles, namely the Benedicks-Amrein-Berthier's uncertainty principle and local uncertainty principles.Finally, we connect these inequalities to show a generalization of the uncertainty principle of Heisenberg type and we prove the Faris-Price uncertainty principle for the (k, a)-generalized wavelet transform.
Analyzing Encoded Concepts in Transformer Language Models

We propose a novel framework ConceptX, to analyze how latent concepts are encoded in representations learned within pre-trained language models.It uses clustering to discover the encoded concepts and explains them by aligning with a large set of human-defined concepts.Our analysis on seven transformer language models reveal interesting insights: i) the latent space within the learned representations overlap with different linguistic concepts to a varying degree, ii) the lower layers in the model are dominated by lexical concepts (e.g., affixation), whereas the core-linguistic concepts (e.g., morphological or syntactic relations) are better represented in the middle and higher layers, iii) some encoded concepts are multi-faceted and cannot be adequately explained using the existing human-defined concepts. 1
Hierarchical reinforcement learning for automatic disease diagnosis

Abstract Motivation Disease diagnosis-oriented dialog system models the interactive consultation procedure as the Markov decision process, and reinforcement learning algorithms are used to solve the problem. Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making. This strategy works well in a simple scenario when the action space is small; however, its efficiency will be challenged in the real environment. Inspired by the offline consultation process, we propose to integrate a hierarchical policy structure of two levels into the dialog system for policy learning. The high-level policy consists of a master model that is responsible for triggering a low-level model, the low-level policy consists of several symptom checkers and a disease classifier. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms. Results Experimental results on three real-world datasets and a synthetic dataset demonstrate that our hierarchical framework achieves higher accuracy and symptom recall in disease diagnosis compared with existing systems. We construct a benchmark including datasets and implementation of existing algorithms to encourage follow-up researches. Availability and implementation The code and data are available from https://github.com/FudanDISC/DISCOpen-MedBox-DialoDiagnosis Supplementary information Supplementary data are available at Bioinformatics online.
CF-DETR: Coarse-to-Fine Transformers for End-to-End Object Detection

The recently proposed DEtection TRansformer (DETR) achieves promising performance for end-to-end object detection. However, it has relatively lower detection performance on small objects and suffers from slow convergence. This paper observed that DETR performs surprisingly well even on small objects when measuring Average Precision (AP) at decreased Intersection-over-Union (IoU) thresholds. Motivated by this observation, we propose a simple way to improve DETR by refining the coarse features and predicted locations. Specifically, we propose a novel Coarse-to-Fine (CF) decoder layer constituted of a coarse layer and a carefully designed fine layer. Within each CF decoder layer, the extracted local information (region of interest feature) is introduced into the flow of global context information from the coarse layer to refine and enrich the object query features via the fine layer. In the fine layer, the multi-scale information can be fully explored and exploited via the Adaptive Scale Fusion(ASF) module and Local Cross-Attention (LCA) module. The multi-scale information can also be enhanced by another proposed Transformer Enhanced FPN (TEF) module to further improve the performance. With our proposed framework (named CF-DETR), the localization accuracy of objects (especially for small objects) can be largely improved. As a byproduct, the slow convergence issue of DETR can also be addressed. The effectiveness of CF-DETR is validated via extensive experiments on the coco benchmark. CF-DETR achieves state-of-the-art performance among end-to-end detectors, e.g., achieving 47.8 AP using ResNet-50 with 36 epochs in the standard 3x training schedule.
Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory

Recently, automated surveillance cameras can change a visible sensor and a thermal sensor for all-day operation. However, existing single-modal pedestrian detectors mainly focus on detecting pedestrians in only one specific modality (i.e., visible or thermal), so they cannot cope with other modal inputs. In addition, recent multispectral pedestrian detectors have shown remarkable performance by adopting multispectral modalities, but they also have limitations in practical applications (e.g., different Field-of-View (FoV) and frame rate). In this paper, we introduce a versatile pedestrian detector that shows robust detection performance in any single modality. We propose a multisensory-matching contrastive loss to reduce the difference between the visual representation of pedestrians in the visible and thermal modalities. Moreover, for the robust detection on a single modality, we design a Multispectral Recalling (MSR) Memory. The MSR Memory enhances the visual representation of the single modal features by recalling that of the multispectral modalities. To guide the MSR Memory to store the multispectral modal contexts, we introduce a multispectral recalling loss. It enables the pedestrian detector to encode more discriminative features with a single input modality. We believe our method is a step forward detector that can be applied to a variety of real-world applications. The comprehensive experimental results verify the effectiveness of the proposed method.
Early Peak Ground Acceleration Prediction for On-Site Earthquake Early Warning Using LSTM Neural Network

On-site earthquake early warning techniques, which issue alerts based on seismic waves measured at a single station, are promising, and have performed quite successfully during some damaging earthquakes. Conventionally, most existing techniques extract several P-wave features from the first few seconds of seismic waves after the trigger to predict the intensity or destructiveness of an incoming earthquake. This type of technique neglects the behavior of temporal varying features within P waves. In other words, the characteristics of data sequences are not considered. In this study, a long short-term memory (LSTM) neural network, which is capable of learning order dependence in seismic waves, is employed to predict the peak ground acceleration (PGA) of the coming earthquake. A dense LSTM architecture is proposed and a large data set of earthquakes is used to train the LSTM model. The general performance of the LSTM model indicated that the predicted PGA values are quite promising but are generally overestimated. However, the predicted PGA of the Chi-Chi earthquake data set, whose fault rupture is complex and long, using the proposed LSTM model is more accurate than the PGA predicted in a previous study using a support vector regression approach. In addition, an alternative alert criterion, which issues alerts when the predicted PGA exceeds the threshold in successive time windows, is presented, and the performance of the proposed LSTM model when different PGA thresholds are considered is also discussed.
The Impact of Trust, Knowledge Sharing, and Affective Commitment on SME Innovation Performance

Small and medium-sized enterprise (SME) performance can enhance SME sustainability and competitiveness. This study aims to examine the impacts of trust, knowledge sharing, and affective commitment on SME innovation performance. The present study will contribute to current knowledge by providing factors affecting SME innovation performance in the context of Indonesia and pandemic crises. The present study uses regression analysis by surveying 56 small business owners or managers in West Sumatra, Indonesia. It was found that trust, knowledge sharing, and affective commitment are related to SME innovation performance. Therefore, it is a moment to renew commitment to innovate by promoting trust and collaboration even during difficult situations.