Reviewerly/exHarmony huggingface dataset:
Lung Cancer Detection and Classification using CT Scan Image Processing

Lung cancer is the stereotypical cancer after breast cancer in this era. The survival rate in this cancer is less than other cancers as well. Lung cancer screening can help find cancer at an early stage. If the disease is found and treated at an early stage, the chances of recovery are more. Computed Tomography (CT) is the most preferred and effective way of lung cancer screening. However, visual interpretation of CT scan images is quite difficult, time consuming and may lead to wrong interpretation of the malignancy. Therefore, computer aided techniques are required for proper and accurate detection of the lung diseases. There are several techniques available in the literature. In this paper, we propose a novel approach of lung cancer detection and classification by image processing of the CT scan. We apply different preprocessing techniques for smoothing and image enhancement. Then we apply thresholding and edge detection for segmentation of the region of interest (ROI) of the lung tumor. Finally, we compute several geometrical features of the extracted ROI and classify them into severity levels as Benign and Malignant using support vector machine (SVM) classifier. We find significant accuracy in detection of lung cancer nodules and estimation of the severity level using our proposed method.
Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset

Reasoning over commonsense knowledge bases (CSKBs) whose elements are in the form of free-text is an important yet hard task in NLP.While CSKB completion only fills the missing links within the domain of the CSKB, CSKB population is alternatively proposed with the goal of reasoning unseen assertions from external resources.In this task, CSKBs are grounded to a large-scale eventuality (activity, state, and event) graph to discriminate whether novel triples from the eventuality graph are plausible or not.However, existing evaluations on the population task are either not accurate (automatic evaluation with randomly sampled negative examples) or of small scale (human annotation).In this paper, we benchmark the CSKB population task with a new large-scale dataset by first aligning four popular CSKBs, and then presenting a highquality human-annotated evaluation set to probe neural models' commonsense reasoning ability.We also propose a novel inductive commonsense reasoning model that reasons over graphs.Experimental results show that generalizing commonsense reasoning on unseen assertions is inherently a hard task.Models achieving high accuracy during training perform poorly on the evaluation set, with a large gap between human performance.Codes and
Coarse2Fine: Fine-grained Text Classification on Coarsely-grained Annotated Data

Existing text classification methods mainly focus on a fixed label set, whereas many realworld applications require extending to new fine-grained classes as the number of samples per label increases.To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data.Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pretrained generative language models into the iterative weak supervision strategy.Specifically, we first propose a label-conditioned finetuning formulation to attune these generators for our task.Furthermore, we devise a regularization objective based on the coarse-fine label constraints derived from our problem setting, giving us even further improvements over the prior formulation.Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement.Extensive experiments and case studies on two real-world datasets demonstrate superior performance over SOTA zeroshot classification baselines.
A Novel Binary Slime Mould Algorithm with AU Strategy for Cognitive Radio Spectrum Allocation

Abstract Spectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.
Improving deep learning performance by using Explainable Artificial Intelligence (XAI) approaches

Abstract In this work we propose a workflow to deal with overlaid images—images with superimposed text and company logos—, which is very common in underwater monitoring videos and surveillance camera footage. It is demonstrated that it is possible to use Explaining Artificial Intelligence to improve deep learning models performance for image classification tasks in general. A deep learning model trained to classify metal surface defect, which previously had a low performance, is then evaluated with Layer-wise relevance propagation—an Explaining Artificial Intelligence technique—to identify problems in a dataset that hinder the training of deep learning models in a wide range of applications. Thereafter, it is possible to remove this unwanted information from the dataset—using different approaches: from cutting part of the images to training a Generative Inpainting neural network model—and retrain the model with the new preprocessed images. This proposed methodology improved F1 score in 20% when compared to the original trained dataset, validating the proposed workflow.
Remote mentoring in laparotomic and laparoscopic cancer surgery during Covid-19 pandemic: an experimental setup based on mixed reality

In this paper, Mixed Reality (MR) has been exploited in the operating rooms to perform laparoscopic and open surgery with the aim of providing remote mentoring to the medical doctors under training during the Covid-19 pandemic. The employed architecture, which has put together MR smartglasses, a Digital Imaging Player, and a Mixed Reality Toolkit, has been used for cancer surgery at the IRCCS Hospital 'Giovanni Paolo II' in southern Italy. The feasibility of using the conceived platform for real-time remote mentoring has been assessed on the basis of surveys distributed to the trainees after each surgery.
Detecting and Learning Against Unknown Opponents for Automated Negotiations

Learning in automated negotiations, while successful for many tasks in recent years, is still hard when coping with different types of opponents with unknown strategies. It is critically essential to learn about the opponents from observations and then find the best response in order to achieve efficient agreements. In this paper, we propose a novel framework named Deep BPR+ (DBPR+) negotiating agent framework, which includes two key components: a learning module to learn a new coping policy when encountering an opponent using a previously unseen strategy, and a policy reuse mechanism to efficiently detect the strategy of an opponent and select the optimal response policy from the policy library. The performance of the proposed DBPR+ agent is evaluated against winning agents of ANAC competitions under varied negotiation scenarios. The experimental results show that DBPR+ agent outperforms existing state-of-the-art agents, and is able to make efficient detection and optimal response against unknown opponents.
Automatic layer selection for transfer learning and quantitative evaluation of layer effectiveness

The performance of transfer learning in convolutional neural networks depends on the selection of which layer to update and fix. Because the number of layers is increasing, it is becoming increasingly difficult for humans to select layers. Therefore, in this study, we propose a method to automatically select effective update layers for transfer learning using a genetic algorithm. In our experiments, we conducted transfer learning from InceptionV3 pretrained with ImageNet to Canadian Institute for Advanced Research-100 dataset, The Street View House Numbers dataset and Food-101 dataset. We found that the test accuracy obtained by an ensemble of models selected by the genetic algorithm was greater than that obtained by from-scratch and fine-tuning for all target dataset. The distribution of the layers selected by the genetic algorithm as effective update layers was spread over the entire network. We also employed the optimal transport distance to evaluate whether each convolutional layer is an effective update layer for transfer learning. In our experiments, we compared the layer importance values and the accuracy of transfer learning. The layer importance was then correlated with the test accuracy of transfer learning, and the results demonstrate that the proposed method can quantitatively evaluate how well each network layer can detect general features in the target datasets.
GeDi: Generative Discriminator Guided Sequence Generation

While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of the distribution they generate. This is especially problematic because datasets used for training large LMs usually contain significant toxicity, hate, bias, and negativity. We propose GeDi as an efficient method for using smaller LMs as generative discriminators to guide generation from large LMs to make them safer and more controllable. GeDi guides generation at each step by computing classification probabilities for all possible next tokens via Bayes rule by normalizing over two class-conditional distributions; one conditioned on the desired attribute, or control code, and another conditioned on the undesired attribute, or anti control code. We find that GeDi gives controllability on par with or better than the state of the art method in a variety of settings, while also achieving generation speeds more than 30 times faster. Additionally, training GeDi on only three topics allows us to controllably generate new topics zero-shot from just a keyword. Lastly, we show that GeDi can make GPT-2 and GPT-3 significantly less toxic without sacrificing on linguistic fluency, making it by far the most practical existing method for detoxifying large language models while maintaining a fast generation speed.
A Lightweight CNN Architecture for Automatic Modulation Classification

Automatic modulation classification (AMC) algorithms based on deep learning (DL) have been widely studied in the past decade, showing significant performance advantage compared to traditional ones. However, the existing DL methods generally behave worse in computational complexity. For this, this paper proposes a lightweight convolutional neural network (CNN) for AMC task, where we design a depthwise separable convolution (DSC) residual architecture for feature extraction to prevent the vanishing gradient problem and lighten the computational burden. Besides that, in order to further reduce model complexity, global depthwise convolution (GDWConv) is adopted for feature reconstruction after the last (non-global) convolutional layer. Compared to recent works, the experimental results show that the proposed network can save approximately 70~98% model parameters and 30~99% inference time on two well-known benchmarks.
Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds

LiDAR is an important method for autonomous driving systems to sense the environment. The point clouds obtained by LiDAR typically exhibit sparse and irregular distribution, thus posing great challenges to the detection of 3D objects, especially those that are small and distant. To tackle this difficulty, we propose Reconfigurable Voxels, a new approach to constructing representations from 3D point clouds. Specifically, we devise a biased random walk scheme, which adaptively covers each neighborhood with a fixed number of voxels based on the local spatial distribution and produces a representation by integrating the points in the chosen neighbors. We found empirically that this approach effectively improves the stability of voxel features, especially for sparse regions. Experimental results on multiple benchmarks, including nuScenes, Lyft, and KITTI, show that this new representation can remarkably improve the detection performance for small and distant objects, without incurring noticeable overhead costs.
Uniform error estimates for nonequispaced fast Fourier transforms

Abstract In this paper, we study the error behavior of the nonequispaced fast Fourier transform (NFFT). This approximate algorithm is mainly based on the convenient choice of a compactly supported window function. So far, various window functions have been used and new window functions have recently been proposed. We present novel error estimates for NFFT with compactly supported, continuous window functions and derive rules for convenient choice from the parameters involved in NFFT. The error constant of a window function depends mainly on the oversampling factor and the truncation parameter.
Sentiment Analysis for Necessary Preview of 30-Day Mortality in Sepsis Patients and the Control Strategies

This study was to preview the risk of 30-day mortality in sepsis patients using sentiment analysis. The clinical data of patients and nursing notes were collected from the Medical Information Mart for Intensive Care (MIMIC-III) database. The factors influencing 30-day mortality were analyzed using the Cox regression model. And, the prognostic index (PI) was estimated. The receiver operating characteristic (ROC) curve was used to determine the PI cut-off point and assess the prediction ability of the model. In total, 1844 of 3560 patients were eligible for the study, with a 30-day mortality of 37.58%. Multivariate Cox analysis showed that sentiment polarity scores, sentiment subjectivity scores, simplified acute physiology score (SAPS)-II, age, and intensive care unit (ICU) types were all associated with the risk of 30-day mortality ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ). In the preview of 30-day mortality, the area under the curve (AUC) of ROC was 0.78 (95%CI: 0.74–0.81, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>P</mi> <mo>&lt;</mo> <mn>0.001</mn> </math> ) when the cut-off point of PI was 0.467. The documented notes from nurses were described for the first time. Sentiment scores measured in nursing notes are associated with the risk of 30-day mortality in sepsis patients and may improve the preview of 30-day mortality.
Research on the Application of Gradient Descent Algorithm in Machine Learning

The gradient descent algorithm is a type of optimization algorithm that is widely used to solve machine learning algorithm model parameters. Through continuous iteration, it obtains the gradient of the objective function, gradually approaches the optimal solution of the objective function, and finally obtains the minimum loss function and related parameters. The gradient descent algorithm is frequently used in the solution process of logical regression, which is a common binary classification approach. This paper compares and analyzes the differences between batch gradient descent and its derivative algorithms — stochastic gradient descent algorithm and mini- batch gradient descent algorithm in terms of iteration number, loss function through experiments, and provides some suggestions on how to pick the best algorithm for the logistic regression binary task in machine learning.
Marker-free stitching deflectometry for three-dimensional measurement of the specular surface

Due to the ‘invisible’ property of the specular surface, it is difficult for the stitching deflectometry to identify the overlapping area. Previously, markers were used on the unit under test with a roughly known shape to find the overlapping area. We propose a marker-free stitching deflectometry that utilizes the stereo-iterative algorithm to calculate the sub-aperture point cloud without height-slope ambiguity, and the overlapping area is identified with the point cloud datum. The measured area is significantly enlarged. The simulation and experiments are conducted to verify the proposal and evaluate the accuracy. We test a high-quality flat with 190mm diameter, the measurement error is below 100nm RMS with comparison to the interferometer.
Neural Video Compression Using GANs for Detail Synthesis and Propagation

We present the first neural video compression method based on generative adversarial networks (GANs). Our approach significantly outperforms previous neural and non-neural video compression methods in a user study, setting a new state-of-the-art in visual quality for neural methods. We show that the GAN loss is crucial to obtain this high visual quality. Two components make the GAN loss effective: we i) synthesize detail by conditioning the generator on a latent extracted from the warped previous reconstruction to then ii) propagate this detail with high-quality flow. We find that user studies are required to compare methods, i.e., none of our quantitative metrics were able to predict all studies. We present the network design choices in detail, and ablate them with user studies.
Gamification in otolaryngology: A narrative review

Abstract Introduction The medical field has incorporated gamification elements into education platforms over the past decade. The standard definition for gamification that has been adopted by most research studies is the addition of game elements and game mechanics within a platform to enhance user engagement. In this review, seven established, consolidated components, as well as an additional new or novel component, will be evaluated: a point system/leaderboards, question banks or gradable content, social interaction with other participants, leaderboards, progress or levels, immediate feedback, badges/icons or a reward system, and the novel component, a story line. Methods Two reviewers searched MEDLINE, Cochrane, PsycINFO, Web of Knowledge, and the Nursing Registry. This review compares the one identified otolaryngology study with current residency education gamification practices within the medical field. The authors searched “residency AND gamification”, “residency AND video games”, and “residency AND games”. After applying exclusion criteria, the 13 remaining studies included a procedure, questions/scenarios, and at least three gamification elements. Results Across the 13 studies, the average number of included gamification elements was higher than the minimum threshold of three (3.84). Ten of the studies incorporated leaderboards, feedback, and social interaction; eight incorporated a question bank; and four incorporated progress bars, rewards, and story lines. The otolaryngology study incorporated four of the gamification components: a point system, instant feedback/solution after a question was answered, player‐to‐player communication, and a leaderboard. Conclusion Review of the current literature found that the medical field has limited research regarding the use of gamification in educational platforms. Despite many simulation studies and attempts at gamification, the medical community has not fully embraced gamification within residency education. In closing, the medical education community should establish a definition of “gamification” and survey residency programs to identify desired gamification elements.
Scaling the Virtual Fitness Buddy Ecosystem as a School-Based Physical Activity Intervention for Children

Childhood obesity is a growing concern as it can lead to lifelong health problems that carry over into adulthood. A substantial contributing factor to obesity is the physical activity (PA) habits that are formed in early childhood, as these habits tend to sustain throughout adulthood. To aid children in forming healthy PA habits, we designed a mixed reality system called the Virtual Fitness Buddy ecosystem, in which children can interact with a virtual pet agent. As a child exercises, their pet becomes slimmer, faster, and able to play more games with them. Our initial deployment of this project showed promise but was only designed for a short-term intervention lasting three days. More recently, we have scaled it from a pilot grade study to a 9-month intervention comprised of 422 children. Ultimately, our goal is to scale this project to be a nationwide primary prevention program to encourage moderate to vigorous PA in children. This article explores the challenges and lessons learned during the design and deployment of this system at scale in the field.
Key performance indicators of Russian universities for 2015–2018: Dataset and Benchmarking Data

This article presents a performance dataset of 93 Russian universities, collected from 2015 to 2018 and evaluated according to 24 indicators. These data were gathered from materials, published in the process of monitoring the effectiveness of higher education institutions by the Ministry of Science and Higher Education of the Russian Federation, Web of Science (citation-based research analytics tool InCites) and Scopus (citation-based research analytics tool SciVal) databases, and information from international ranking agencies QS, THE, ARWU. The dataset comprises the assessments of university performances according to the most important indicators used in socio-economic studies of comparative analysis of higher education system development levels in different countries: educational, scientific and research, international, financial and economic performance and international public recognition (university positions in leading international rankings). Evaluated universities are grouped pursuant to their missions: Federal Universities (FU), National Research Universities (NRU), Flagship Universities (FlU) and university-participants of the Russian Academic Excellence Project (Project 5-100). The indicators for the comparative analysis are aggregated by the type of activities and analyzed based on the calculation of median values and Displaced Ideal Method. The dataset can be helpful to researchers, university administration, specialists of higher education system, etc. Data processing can be executed using data mining methods, machine learning, and pattern analysis for the development of intellectual structures, applicable for university performance assessment in different educational systems. Presented data allows us to assert that the implementation of targeted support for leading Russian universities has a positive impact on the development of Russian higher education increasing its role on the international academic arena. Leading national research university-participants of the Project 5-100 had the greatest influence on increasing the competitiveness of Russian education in the world.