Reviewerly/exHarmony huggingface dataset:
Information-Centric Blockchain Technology for the Smart Grid

This paper proposes an application of blockchain technology for securing the infrastructure of the modern power grid - an Information-Centric design for the blockchain network. In this design, all the transactions in the blockchain network are classified into different groups, and each group has a group number. A sender’s identity is encrypted by the control centre’s public key; energy data is encrypted by the subscriber’s public key, and by a receiver’s public key if this transaction is for a specific receiver; a valid signature is created via a group message and the group publisher’s private key. Our implementation of the design demonstrated the proposal is applicable, publisher’s identities are protected, data sources are hidden, data privacy is maintained, and data consistency is preserved.
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent

In the vanishing learning rate regime, stochastic gradient descent (SGD) is now relatively well understood. In this work, we propose to study the basic properties of SGD and its variants in the non-vanishing learning rate regime. The focus is on deriving exactly solvable results and discussing their implications. The main contributions of this work are to derive the stationary distribution for discrete-time SGD in a quadratic loss function with and without momentum; in particular, one implication of our result is that the fluctuation caused by discrete-time dynamics takes a distorted shape and is dramatically larger than a continuous-time theory could predict. Examples of applications of the proposed theory considered in this work include the approximation error of variants of SGD, the effect of minibatch noise, the optimal Bayesian inference, the escape rate from a sharp minimum, and the stationary covariance of a few second-order methods including damped Newton's method, natural gradient descent, and Adam.
Efficient Clustering of Unlabeled Brain DICOM Images based on similarity

Abstract Clustering has proven to be an effective method in the medical field for finding patterns in labelled and unlabelled datasets. This work is implemented over whole body CT scans (∼1TB) of 3500 patients in form of unlabelled DICOM images. The whole-body CT images have been anonymized for 30 attributes based on DICOM regulations and the Brain images alone are segmented using the DICOM tag element called ‘Protocol stack’. The segmented Brain images are efficiently grouped based on visual similarity using K-means clustering after performing feature extraction and dimensionality reduction. The results of the clustering can be furtherutilized by radiologists to perform labelling or find patterns in Brain CT scans of patients that are difficult where each scan consists of a varying number of slices during detection of Internal Bleeding. The efficiency of K-means is analyzed by performing computation over a different number of clusters (K) by applying silhouette scores to find optimal cluster.
On Explainability of Graph Neural Networks via Subgraph Explorations

We consider the problem of explaining the predictions of graph neural networks (GNNs), which otherwise are considered as black boxes. Existing methods invariably focus on explaining the importance of graph nodes or edges but ignore the substructures of graphs, which are more intuitive and human-intelligible. In this work, we propose a novel method, known as SubgraphX, to explain GNNs by identifying important subgraphs. Given a trained GNN model and an input graph, our SubgraphX explains its predictions by efficiently exploring different subgraphs with Monte Carlo tree search. To make the tree search more effective, we propose to use Shapley values as a measure of subgraph importance, which can also capture the interactions among different subgraphs. To expedite computations, we propose efficient approximation schemes to compute Shapley values for graph data. Our work represents the first attempt to explain GNNs via identifying subgraphs explicitly and directly. Experimental results show that our SubgraphX achieves significantly improved explanations, while keeping computations at a reasonable level.
Feature Attention Network for Simultaneous Nuclei Instance Segmentation and Classification in Histology Images

Segmentation and classification of various types of nuclei in tumor tissue histology images is a crucial step in development of computer aided diagnostic systems. Existing techniques for digital profiling of tumor micro environment have common limitations; they require a lot of training data, are computationally costly and don't perform well in challenging scenarios where nuclei exhibit varying inter and intra class characteristics. Hence, to address the challenges of segmenting and classifying nuclei given their vast morphometric properties, we propose a deep learning based model where we use pixel distances from their respective nuclei center points to separate touching and overlapping nuclei. We incorporate attention mechanism to learn complex features of nuclei and refine representation for high accuracy classification. The proposed methodology is assessed on two publicly accessible H&E stained multi-organ histology datasets. We demonstrate higher performance of our model by comparing with recently published algorithms.
Text Encryption Method Using multi Hyper-chaotic systems

With rapid evaluation of technologies, huge amount of critical information can exchange over unsecure network every day, secure and fast encryption schemes become more and more urgent need to provide the desirable confidentiality and thwart unauthorized access, lately and for many reasons, chaotic based encryption algorithms become more and more popular technique due to exceptionally superior characteristics such as simplicity, resistance attacks and high speed, in this paper the advantages of two nonlinear hyper-chaotic systems will utilized to strength the &nbsp;proposed encryption algorithm, the random sequences generated from systems will used to position scramble and value change of the plaintext, the main features of such method are efficiency, large key space, low computational overhead, and simple design as well as the experimental results demonstrate that the proposed method have more ability to thwart attacks.&nbsp;&nbsp
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes

We derive the optimal approximate posterior over the top-layer weights in a Bayesian neural network for regression, and show that it exhibits strong dependencies on the lower-layer weights. We adapt this result to develop a correlated approximate posterior over the weights at all layers in a Bayesian neural network. We extend this approach to deep Gaussian processes, unifying inference in the two model classes. Our approximate posterior uses learned global inducing points, which are defined only at the input layer and propagated through the network to obtain inducing inputs at subsequent layers. By contrast, standard, local, inducing point methods from the deep Gaussian process literature optimise a separate set of inducing inputs at every layer, and thus do not model correlations across layers. Our method gives state-of-the-art performance for a variational Bayesian method, without data augmentation or tempering, on CIFAR-10 of 86.7%.
Efficacy of training a deep learning model for mitotic count in breast carcinoma using opensource software

Mitotic count is a semiquantitative part of the routine histological grading of breast cancers and introduces significant interobserver reliability issues. One potential systematic solution is artificial intelligence (AI) and computer vision algorithms. Previous literature has shown proprietary AI models to be effective at counting breast cancer mitoses.1 Aim: To evaluate the efficacy of training a deep learning computer vision model using only opensource software, to improve the democratisation of AI in this application. Method: 778 images of breast carcinoma slides from Sunshine Coast University Hospital were split into training, validation and test sets and labelled by a trained non-pathologist. A ScaledYOLOv4 computer vision architecture was trained on 659 images, and the model was evaluated on the validation set (68 images), unseen by the model during training. Results: The model demonstrated a sensitivity of 0.92 and a positive predictive value (PPV) of 0.64. Discussion: The model's main weakness is its frequent misidentification of pyknotic cells. The model is most effective in tumours with high mitotic counts, as the ratio of mitotic figures to pyknotic cells is high. Next steps include creating opensource databases for improved image quantity, and standardisation of data to encourage further, higher quality opensource projects. 1.Pantanowitz L, Hartman D, Qi Y, et al. Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses. Diagn Pathol 2020; 15: 80.
Adaptive coding unit size convolutional neural network for fast 3D-HEVC depth map intracoding

The advanced three-dimensional extension of high-efficiency video coding (3D-HEVC) is the latest coding standard for 3D video. The coding of the depth map for 3D-HEVC is very time-consuming. With the development of deep learning, it has become feasible to employ convolutional neural networks (CNNs) to predict the coding unit (CU) division of the depth map. However, there are three types of CU sizes: 64, 32, and 16, which makes it difficult to unify the model. The features of the depth map are very different from the texture map. In view of the aforementioned problems, we propose an adaptive CU size CNNs for fast 3D-HEVC depth map intracoding. We first employ spatial pyramid pooling to fully extract the features of the three types of CUs. Then, we apply the nonlocal self-attention mechanism to make it suitable for depth maps. Compared with the 3D-HEVC reference algorithm, the proposed network reduces the coding time by an average of 35.7%, while the quality degradation of the synthesized virtual view is negligible.
Problems of mass distance learning in a pandemic

In 2020, the coronavirus pandemic affected the whole world, including Russia. The Russian government had to introduce emergency measures to combat the pandemic, including the transfer to distance learning of schoolchildren and students. In the context of a pandemic, it became necessary to urgently consider issues and problems of distance learning in educational organizations, primarily in secondary education organizations. Previously, none of the concepts of informatization of education even considered the massive transition to distance learning for schoolchildren and students. The article examines the readiness of educational organizations, parents and children for the massive use of distance learning, analyzes the problems that arise in the implementation of this form of education, including: the problem of choosing software and platforms for distance learning; problems associated with underdeveloped infrastructure; problems of the availability of electronic educational resources corresponding to the new learning conditions; problems of lack of online work experience for both teachers and schoolchildren; as well as other issues related to distance learning. The need is noted: switching the attention of researchers and developers of software and online platforms to research in the field of mass transition to distance learning at all age levels; the development of new distance online platforms and the processing of many electronic educational resources for new platforms; linking multimedia educational content to exemplary basic educational programs of primary, basic and secondary general education.
Evaluation of educational product by students as criterion of real Image of University

Analyzed are behavior of applicants before and after entering the university, as well as forms of image of the university based on real perception in the context of the educational process. Modern universities are focused on attracting as many applicants as possible, since this has become a criterion for evaluating universities against the background of competitors. The positioning and promotion of the university is based on the rather ephemeral concept of “image”. The image at the stage of admission is reinforced by the constants set by the university, which after the applicant's admission become phantom and have no direct relation to the educational process, the prestige of the university, or the prospective employment in the labor market. The research process was provided by sending a personal questionnaire, compiled in Google, to the contacts of groups of students and teachers of the Rostov State University of Economics (Rostov-on-Don). The final conclusions are based on the generalization of the participants ' responses, their collective discussion, and the development of an algorithm for structural analysis of the real and virtual advantages of the university that affect the formation of a favorable image of the university in the minds of its audience.
Image Transmission Based on Spiking Dynamics of Electrically Controlled VCSEL-SA Neuron

Based on the spiking dynamics of the electrically controlled vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA), we propose an image transmission system using two unidirectionally coupled VCSEL-SAs and numerically investigate the binary-to-spike (BTS) conversation characteristics and the image transmission performance. The simulation results show that, through electrically injecting the binary data to VCSEL-SA, the BTS conversation can be realized and the conversion rate of BTS highly depends on the injection strength and bias current. Thus, the image transmission can be realized in the proposed system. Moreover, the parameter mismatches between these two VCSEL-SAs have some effects on the image transmission performance, but the encoded images are still successfully decoded even under certain parameter mismatches. In addition, spiking patterns can be also stored and transmitted in the cascaded system with optoelectronic feedback loop.
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations

To help individuals express themselves better, quotation recommendation is receiving growing attention.Nevertheless, most prior efforts focus on modeling quotations and queries separately and ignore the relationship between the quotations and the queries.In this work, we introduce a transformation matrix that directly maps the query representations to quotation representations.To better learn the mapping relationship, we employ a mapping loss that minimizes the distance of two semantic spaces (one for quotation and another for mappedquery).Furthermore, we explore using the words in history queries to interpret the figurative language of quotations, where quotationaware attention is applied on top of history queries to highlight the indicator words.Experiments on two datasets in English and Chinese show that our model outperforms previous state-of-the-art models.
Blockchain-Based Network Architecture for Accountable Auctions and to Overcome Resource Problems

The world is changing undoubtedly, the change is digitalization. From home to defense and hospitals, we see new technology emerging and helping humans as we rise with the technology itself. Researchers believe this change is affecting every facet of our society. This paper helps in discussing the revolution by a remarkably emerging Blockchain technology. This paper creatively provides the solution to limited hardware resource problems and also throws light upon the features, advantages, and practical use case of Blockchain as Economic model. We also aim to focus to mention a way to use intelligent devices as a part of network architecture which will help us greatly in the collection of data which can be further utilized for targeted marketing and refine the overall efficiency and as a practical application. We believe Blockchain can be highly suited for a management system such as auction management, for which we have provided a way to decrease the dishonesty between parties involving finance while auctioning by the use of hybrid Blockchain technology and as a countermeasure penalize the dishonest member. Therefore, actively keeping track, verifying the details, and taking appropriate measures when needed.
Would You Rather: A Focus Group Method for Eliciting and Discussing Formative Design Insights with Children

Would you rather go 1000 days without the Internet or five days where anyone can read your mind? We present “Would You Rather” (WYR), a technique for generating formative design insights (inspired by the conversational game of the same name) that combines design provocations with forced-choice scaffolding. Here, we describe the components of a WYR session, which include scenario generation, voting, and group discussion. As children disproportionately benefit from scaffolding during the co-design process, we also report on an evaluation of the technique with 16 children, conducted across seven sessions and spanning the course of one year. We find that WYR fulfills recommendations for focus groups (e.g. eliciting mental models and values, producing focused yet animated discussion) and leverages playfulness, humor, structure, and forced choice to overcome known common challenges of designing with children.
Sentiment Analysis of Online Food Reviews using Big Data Analytics

Nowadays sentiment analysis has become very important, mostly used for huge datasets and helpful for researchers for applying methods and techniques. Amazons food data is growing exponentially and traditional systems are unable to process it, so we used Big Data to overcome this problem. In this paper, we explore different methods and techniques of sentiment analysis using apache spark data processing system for big datasets of Amazon Fine Food reviews. Three mechanisms are applied that have more than 80% accuracy named as Linear SVC, Logistic Regression, and Naive Bayes by using MLlib which is Apache Sparks library for ML. When applied these methods we realize that Linear SVC performs efficiently than NB and logistic regression.
Frequency Consistent Adaptation for Real World Super Resolution

Recent deep-learning based Super-Resolution (SR) methods have achieved remarkable performance on images with known degradation. However, these methods always fail in real-world scene, since the Low-Resolution (LR) images after the ideal degradation (e.g., bicubic down-sampling) deviate from real source domain. The domain gap between the LR images and the real-world images can be observed clearly on frequency density, which inspires us to explicitly narrow the undesired gap caused by incorrect degradation. From this point of view, we design a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying existing SR methods to the real scene. We estimate degradation kernels from unsupervised images and generate the corresponding LR images. To provide useful gradient information for kernel estimation, we propose Frequency Density Comparator (FDC) by distinguishing the frequency density of images on different scales. Based on the domain-consistent LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR models. Extensive experiments show that the proposed FCA improves the performance of the SR model under real-world setting achieving state-of-the-art results with high fidelity and plausible perception, thus providing a novel effective framework for real-world SR application.
PC-SuperPoint: interest point detection and descriptor extraction using pyramid convolution and circle loss

Nowadays, deep learning is widely used to detect interest points and extract the corresponding descriptors and achieved suitable results for many applications of computer vision, such as image matching, three-dimensional reconstruction, simultaneous localization, and mapping. We propose an approach for interest point detection and descriptor extraction using pyramid convolution and circle loss, which is named as PC-SuperPoint. We utilize pyramid convolutions in the backbone network, which includes convolution kernels of different scales for multiscale feature extraction. The following well-designed networks are able to capture the local and global information from the obtained backbone feature maps. In addition, circle loss, which enhances weight attributes for each pair of descriptors, is also applied to improve the convergence speed in the training phase. Experiments on the HPatches dataset and KITTI dataset achieve promising results, which reveal the effectiveness of the proposed method.
Selective deployment of dynamic telecytology for rapid evaluation of cytology smears: assessment of workflow processes and role of cytopathology fellows as on-site operators

The deployment of telecytology (TC) requires a substantial investment of financial and human resources. To offset the high demand for rapid on-site evaluation, we performed a limited deployment of dynamic TC and have detailed the workflow processes and the role of trainees.TC systems were installed in radiology suites with a high volume of cases. Validation was performed using retrospective and prospective cases. Cytotechnologists and cytopathology fellows were the operators of the instrument. TC malignant and benign diagnoses were correlated with the final sign-out diagnoses.Of the 120 cases, 50 (41.6%) were fine needle aspirations and 70 (58.3%) were touch imprint smears of core biopsy specimens. The cytotechnologists were the operators for 34 cases (28.3%) and cytology fellows for 86 cases (71.6%). Adequacy concordance with the final diagnosis was 100% and 98.5% in the retrospective and prospective cases, respectively. In the prospective cases, concordance of TC with the final diagnosis of malignancy was 42 of 45 (93.3%), with 2 of 45 (4.4%) discordant and a downgrade rate of 2.7%. For the benign diagnoses, the concordance was 90%. For the malignant diagnoses, the sensitivity of TC was 97.67% (95% confidence interval [CI], 87.71 to 99.94%; specificity, 81.82%; 95% CI, 48.22% to 97.72%). The positive predictive value was 95.45% (95% CI, 85.69% to 98.66%), the negative predictive value was 90.00% (95% CI, 55.98% to 98.45%), and the accuracy was 94.44% (95% CI, 84.61% to 98.84%).TC can be deployed in a limited fashion as an option for cytopathologists to offset the high demand for rapid on-site evaluations. Trainee participation in TC service is important for building confidence and honing their cytology skills.
Pain-o-vision, effortless pain management

Chronic pain is often an ongoing challenge for patients to track and collect data. Pain-O-Vision is a smartwatch enabled pain management system that uses computer vision to capture the details of painful events from the user. A natural reaction to pain is to clench ones fist. The embedded camera is used to capture different types of fist clenching, to represent different levels of pain. An initial prototype was built on an Android smartwatch that uses a cloud-based classification service to detect the fist clench gestures. Our results show that it is possible to map a fist clench to different levels of pain which allows the patient to record the intensity of a painful event without carrying a specialized pain management device.