Reviewerly/exHarmony huggingface dataset:
Density Peak Clustering with connectivity estimation

In 2014, a novel clustering algorithm called Density Peak Clustering (DPC) was proposed in journal Science, which has received great attention in many fields due to its simplicity and effectiveness. However, empirical studies have demonstrated that DPC has two main deficiencies: 1. It is very hard to identify the true cluster centers in the decision graph provided by DPC, especially when handling clusters with non-spherical shapes and non-uniform densities; 2. The performance of DPC is significantly affected by the ‘chain reaction’, i.e., an incorrect assignment of the point with the highest density of a region will lead all points in this region to the same wrong cluster. To address these two deficiencies, a density peak clustering with connectivity estimation (DPC”–CE) is presented. In the improved algorithm, points with higher relative distance are chosen as local centers for further calculation. Then a graph-based strategy is proposed to estimate the connectivity information between local centers. With the estimated information, a distance punishment which considers both Euclidean distance and connectivity information is further applied to reassess the similarity between local centers. By adding connectivity information into distance calculation, DPC-CE can not only ensure the true cluster centers can stand out in the decision graph, but also assign all local centers correctly, even on clusters with arbitrary shapes and non-uniform densities. And because of the ‘chain reaction’ we discussed above, those local centers will further lead all points around them to the right cluster. Experimental results on 14 synthetic datasets and 10 read-world datasets demonstrate the effectiveness and robustness of DPC”–CE in terms of three evaluation metrics.
MIMO-OFDM Modulation Classification Using Three-Dimensional Convolutional Network

Automatic modulation classification (AMC) plays a vital role in cognitive radio to improve spectrum utilization efficiency, however, most of the existing works have focused on single-carrier communications in single-input single-output systems. In this paper, we propose an efficient AMC method for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) communication systems with the assumption of unknown frequency-selective fading channels and signal-to-noise ratio. At the receiver, the complex envelope samples of a burst signal acquired by multiple antennas are decomposed into in-phase and quadrature samples, which are then structured into a high-dimensional data array. To learn the modulation patterns from received signals, we develop a deep network, namely three-dimensional MIMO-OFDM convolutional neural network (MONet). With cuboidal convolution filters, the proposed MONet allows the network to capture underlying features as intra- and inter-antenna correlations at multi-scale signal representations. Relying on simulations, MONet achieves the classification accuracy of over 95% at 0 dB SNR under various channel impairments and shows the robustness with different MIMO antenna configurations.
Half-UNet: A Simplified U-Net Architecture for Medical Image Segmentation

Medical image segmentation plays a vital role in computer-aided diagnosis procedures. Recently, U-Net is widely used in medical image segmentation. Many variants of U-Net have been proposed, which attempt to improve the network performance while keeping the U-shaped structure unchanged. However, this U-shaped structure is not necessarily optimal. In this article, the effects of different parts of the U-Net on the segmentation ability are experimentally analyzed. Then a more efficient architecture, Half-UNet, is proposed. The proposed architecture is essentially an encoder-decoder network based on the U-Net structure, in which both the encoder and decoder are simplified. The re-designed architecture takes advantage of the unification of channel numbers, full-scale feature fusion, and Ghost modules. We compared Half-UNet with U-Net and its variants across multiple medical image segmentation tasks: mammography segmentation, lung nodule segmentation in the CT images, and left ventricular MRI image segmentation. Experiments demonstrate that Half-UNet has similar segmentation accuracy compared U-Net and its variants, while the parameters and floating-point operations are reduced by 98.6 and 81.8%, respectively, compared with U-Net.
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression

Night images suffer not only from low light, but also from uneven distributions of light. Most existing night visibility enhancement methods focus mainly on enhancing low-light regions. This inevitably leads to over enhancement and saturation in bright regions, such as those regions affected by light effects (glare, floodlight, etc.). To address this problem, we need to suppress the light effects in bright regions while, at the same time, boosting the intensity of dark regions. With this idea in mind, we introduce an unsupervised method that integrates a layer decomposition network and a light-effects suppression network. Given a single night image as input, our decomposition network learns to decompose shading, reflectance and light-effects layers, guided by unsupervised layer-specific prior losses. Our light-effects suppression network further suppresses the light effects and, at the same time, enhances the illumination in dark regions. This light-effects suppression network exploits the estimated light-effects layer as the guidance to focus on the light-effects regions. To recover the background details and reduce hallucination/artefacts, we propose structure and high-frequency consistency losses. Our quantitative and qualitative evaluations on real images show that our method outperforms state-of-the-art methods in suppressing night light effects and boosting the intensity of dark regions.
Evaluating GPT4 on Impressions Generation in Radiology Reports

HomeRadiologyVol. 307, No. 5 PreviousNext Original ResearchComputer ApplicationsEvaluating GPT-4 on Impressions Generation in Radiology ReportsZhaoyi Sun*, Hanley Ong*, Patrick Kennedy, Liyan Tang, Shirley Chen, Jonathan Elias, Eugene Lucas, George Shih, Yifan Peng Zhaoyi Sun*, Hanley Ong*, Patrick Kennedy, Liyan Tang, Shirley Chen, Jonathan Elias, Eugene Lucas, George Shih, Yifan Peng Author AffiliationsFrom the Departments of Population Health Sciences (Z.S., Y.P.), Radiology (H.O., P.K., S.C., G.S.), and Primary Care (J.E.), Weill Cornell Medicine, 425 E 61st St, Suite 301, New York, NY 10065; School of Information, The University of Texas at Austin, Austin, Tex (L.T.); and Comprehensive Weight Control Center, New York–Presbyterian/Weill Cornell Medical Center, New York, NY (E.L.).Address correspondence to Y.P. (email: [email protected]).Zhaoyi Sun*Hanley Ong*Patrick KennedyLiyan TangShirley ChenJonathan EliasEugene LucasGeorge ShihYifan Peng Published Online:Jun 27 2023https://doi.org/10.1148/radiol.231259MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In * Z.S. and H.O. contributed equally to this work.AbstractRadiologist-generated Impressions from radiology report Findings outperformed GPT-4-generated ones in coherence, comprehensiveness, and factual consistency, highlighting areas for future artificial intelligence improvements in radiology report generation.Download as PowerPointReferences1. Hartung MP, Bickle IC, Gaillard F, Kanne JP. How to Create a Great Radiology Report. RadioGraphics 2020;40(6):1658–1670. Link, Google Scholar2. Gundogdu B, Pamuksuz U, Chung JH, et al. Customized Impression Prediction from Radiology Reports Using BERT and LSTMs. IEEE Transactions on Artificial Intelligence. 2021; 1–1. Crossref, Google Scholar3. GPT-4. OpenAI. https://openai.com/gpt-4. Accessed May 25, 2023. Google Scholar4. Tang L, Sun Z, Idnay B, et al. Evaluating Large Language Models on Medical Evidence Summarization. medRxiv 2023.04.22.23288967. Posted April 24, 2023. Accessed May 15, 2023. Google Scholar5. Bhayana R, Krishna S, Bleakney RR. Performance of ChatGPT on a Radiology Board-style Examination: Insights into Current Strengths and Limitations. Radiology 2023;307(5):e230582. Link, Google Scholar6. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017; 3462–3471. Google ScholarArticle HistoryReceived: May 16 2023Revision requested: May 17 2023Revision received: May 31 2023Accepted: June 12 2023Published online: June 27 2023 FiguresReferencesRelatedDetailsCited ByThe Need to Re-evaluate the Role of GPT-4 in Generating Radiology ReportsPartha Pratim Ray, 1 August 2023 | Radiology, Vol. 308, No. 2Recommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 307, No. 5 Supplemental MaterialMetrics Altmetric Score PDF download
Extracting Medical Knowledge from Crowdsourced Question Answering Website

The medical crowdsourced question answering (Q&A) websites are booming in recent years, and an increasingly large amount of patients and doctors are involved. The valuable information from these medical crowdsourced Q&A websites can benefit patients, doctors and the society. One key to unleash the power of these Q&A websites is to extract medical knowledge from the noisy question-answer pairs and filter out unrelated or even incorrect information. Facing the daunting scale of information generated on medical Q&A websites everyday, it is unrealistic to fulfill this task via supervised method due to the expensive annotation cost. In this paper, we propose a Medical Knowledge Extraction (MKE) system that can automatically provide high-quality knowledge triples extracted from the noisy question-answer pairs, and at the same time, estimate expertise for the doctors who give answers on these Q&A websites. The MKE system is built upon a truth discovery framework, where we jointly estimate trustworthiness of answers and doctor expertise from the data without any supervision. We further tackle three unique challenges in the medical knowledge extraction task, namely representation of noisy input, multiple linked truths, and the long-tail phenomenon in the data. The MKE system is applied to real-world datasets crawled from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xywy.com</uri> , one of the most popular medical crowdsourced Q&A websites. Both quantitative evaluation and case studies demonstrate that the proposed MKE system can successfully provide useful medical knowledge and accurate doctor expertise. We further demonstrate a real-world application, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ask A Doctor</i> , which can automatically give patients suggestions to their questions.
A Utility-Optimized Framework for Personalized Private Histogram Estimation

Recently, local differential privacy (LDP), as a strong and practical notion, has been applied to deal with privacy issues in data collection. However, existing LDP-based strategies mainly focus on utility optimization at a single privacy level while ignoring various privacy preferences of data providers and multilevel privacy demands for statistics. In this paper, we for the first time propose a framework to optimize the utility of histogram estimation with these two privacy requirements. To clarify the goal of privacy protection, we personalize the traditional definition of LDP. We design two independent approaches to minimize the utility loss: Advanced Combination, which composes multilevel results for utility optimization, and Data Recycle with Personalized Privacy, which enlarges the sample size for an estimation. We demonstrate their effectiveness on privacy and utility, respectively. Moreover, we embed these approaches within a Recycle and Combination Framework and prove that the framework stably achieves the optimal utility by quantifying its error bounds. On real-world datasets, our approaches are experimentally validated and remarkably outperform baseline methods.
Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images

Sparse-representation-based classification (SRC) has been showing a good performance for face recognition in recent years. But SRC is not good at face recognition with low quality images (e.g., disguised, corrupted, occluded, and so on) which often appear in practical applications. To solve the problem, in this paper, we propose a novel SRC-based method for face recognition with low quality images named sparse low-rank component-based representation (SLCR). In SLCR, we utilize low-rank matrix recovery on the training data set to obtain low-rank components and non-low-rank components, which are used to construct the dictionary. The new dictionary is capable of describing facial features better, especially for low quality face samples. Furthermore, the minimum class-wise reconstruction residual is used as the recognition rule, leading to a substantial improvement on the proposed SLCR's performance. Extensive experiments on benchmark face databases demonstrate that the proposed method is consistently superior to other sparse-representation-based approaches for face recognition with low quality images.
When Crowd Meets Big Video Data: Cloud-Edge Collaborative Transcoding for Personal Livecast

Deep penetration of personal computing devices and high-speed Internet has enabled everyone to be a broadcaster. In this crowdsourced live streaming service, numerous amateur broadcasters lively stream their video contents to viewers around the world. Consequently, these broadcasters generate a massive amount of video data. The set of video sources and recipients are big as well, so are demand for the storage and computational resources. Transcoding becomes a must to better service these viewers with different network and device configurations. However, the massive amount of video data contributed by countless channels even makes cloud significantly expensive for providing transcoding services to the whole community. In this paper, inspired by the paradigm of Edge Computing, we propose a Cloud-edge collaborative system which combines the idle end-viewers' resources with the cloud to transcode the massive amount of videos at scale. Specifically, we put forward tailored viewer selection algorithms after empirically analyses the viewer behavior data. In the meantime, we propose auction-based payment schemes to motivate these viewers participating in the transcoding. Large-scale trace-driven simulations demonstrate the superiority of our approach in cost reduction and service stability. We further implement a prototype in PlanetLab to prove the feasibility of our design.
Complex Zhang neural networks for complex-variable dynamic quadratic programming

In this paper, a neurodynamic approach concerning complex Zhang neural networks (ZNNs) is presented to solve a complex-variable dynamic quadratic programming (QP). The proposed complex ZNNs are activated by various complex activation functions respectively, such as linear function, sign function and Li function. It is proved that ZNNs with different activation functions have different convergence rates involving super exponential convergency and finite time convergency. Furthermore, by introducing an integral term, a noise-tolerated ZNN is proposed to ensure the convergency under constant noise. Compared with existing works, the presented complex ZNNs in this paper have less amount of neurons and superior convergence rate. Some numerical examples are provided to illustrate the effectiveness and validity of the results. In addition, the complex ZNNs also apply well to LCMP beamforming problem.
Rich Feature Combination for Cost-Based Broad Learning System

Broad learning system (BLS) is an emerging learning algorithm for the connectionist models, which have enjoyed much popularity on many applications. As an alternative approach of learning in deep structure, the BLS develops an incremental learning neural network that can be modeled in a flexible way, and becomes a promising technique in the field of knowledge discovery and data engineering. To further improve the performance of BLS, our focus is to investigate these algorithms which can enhance the BLS. On one hand, from the viewpoint of feature engineering, unsupervised group-wise encoding is conducted for feature extraction, and broadly fused feature representation is used to improve the ability of BLS, in terms of the learning and reusing multi-level features. On the other hand, for imbalanced learning from disproportionate size of categories instances, a cost-sensitive BLS framework is proposed in this paper, which aims to minimize the total misclassifying cost in classification learning. Finally, we conduct extensive experiments on a wide range of datasets (e.g., computer vision and bug reports) to demonstrate the effectiveness of the proposed BLS framework.
On Kalman filtering with linear state equality constraints

This article is concerned with the state estimation problem for linear systems with linear state equality constraints. We re-examine constrained Kalman filter variations and propose an alternative derivation of the optimal constrained Kalman filter for time variant systems. This results in an oblique state projection that gives the smallest error covariance. A simple example illustrates the performance of the different Kalman filters.
Creating ‘automatic subjects’: Corporate wellness and self-tracking

The use of self-tracking devices has increased dramatically in recent years with enthusiasm from the public as well as public health officers, healthcare providers and workplaces seeking to instigate behaviour change in populations. Analysis of the ontological principles informing the design and implementation of the Apple Watch and corporate wellness programmes using self-tracking technologies shows that their primary focus is on the capture and control of attention rather than material health outcomes. Health, wellness and happiness have been conflated with productivity, which is now deemed to be dependent on the harnessing of libidinal as well as physical energy. In this context, self-tracking technologies and related corporate wellness interventions have been informed by ‘emotional design’, neuroscientific and behavioural principles which target the ‘pre subjective’ consciousness of individuals through manipulating their habits and neurological functioning. This article draws on the work of Bernard Stiegler to suggest framing self-tracking as ‘industrial temporal objects’, which capture and ‘short circuit’ attention. It is proposed that a central aim is to ‘accumulate the consciousnesses’ of subjects consistent with the methods of a contemporary ‘attention economy’. This new logic of accumulation informs the behaviour change strategies of designers of self-tracking devices, and corporate wellness initiatives, taking the form of ‘psychotechnologies’ which attempt to reconstruct active subjects as automatic and reactive ‘nodes’ as part of managed networks.
Toward a Smarter Data Science

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A study on reinforcing of subject description as a result of English records

This study aimed to find out the direction of reinforcing the subject special description as English records. The study analyzed the English subject description data published in 2017 and 2020 by the Ministry of Education. Focus group interviews were conducted with a total of 18 teachers, 6 each for elementary, middle, and high school. Analysis results of the 2017 data showed that the elementary data had a weak link with the curriculum, middle school data focused on functional areas rather than knowledge or attitudes, and high school data only presented examples of students with high achievement levels. On the other hand, the 2020 data includes not only subject special description but also teachers' content of teaching and evaluation materials according to achievement standards. Based on the results, the introduction of Likert scales and the creation of vocabulary groups by achievement standards are suggested.
The coaching process and contributions to personal knowledge management development: an integrative review

The objective of this article is to verify how the coaching process can contribute to the development of Personal Knowledge Management (PKM). Despite being extensively discussed in the business environment, especially in the development of executives and leadership, and in the sports field, the debate about coaching in other areas of personal development is still in its early stages, as is personal knowledge management. Although in many works, PKM is treated as a subarea of knowledge management, in this paper, we consider it as a specific area of study. The method used was integrative review, which allows for the synthesis of previous studies, thus generating new knowledge on the studied topic. The search focused on works published in the CAPES journals portal, in the period between 2009 and 2019. The results demonstrated that coaching can contribute to personal knowledge management, as it allows individuals participating in the process to identify, categorize, store, organize, and share personal knowledge, and consequently, self-development and a greater perception of self-awareness for individuals.
Corrigendum: An exploratory analysis of divergent patterns in reading progression during a tablet-based literacy program

CORRECTION article Front. Educ., 21 September 2023Sec. Educational Psychology Volume 8 - 2023 | https://doi.org/10.3389/feduc.2023.1278214
Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing

Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.