Reviewerly/exHarmony huggingface dataset:
An anisotropically diffusible fuzzy clustering based image segmentation methodology for intracranial cyst detection

An Intracranial cyst is an abnormal growth of mass in the brain that affects functioning of the nervous system and so an early detection of the lesion enables to avoid adverse effects. The processing unit in the Magnetic Resonance Imaging (MRI) system performs reading the images followed by primary image enhancement to suppress distortions thereby enhancing the feature quality in terms of its intensity, augmenting the resolution by image segmentation, post-processing by thresholding based on grayscale values and performing several morphological operations. With the existing methodologies, extracting the Region Of Interest (ROI) with the overlapping intensity values lead to inaccurate results. A novel method in which the input image that is anisotropically diffused and blurred is converted into a sharp image. Further, fuzzy partitioning of pixels deployed on Global Thresholding –Clustering Methodology (GT-CM) based segmentation takes 4 clusters into account hence forth seperating the exterior portion of the skull, the border region of the skull, the ventricles which may include the lesion and the noise. Statistical results based on several metrics such as sensitivity, specificity, F measure, Jaccord Index, Dice Coefficient and precision show that the proposed method is far more effective. An accuracy of 99.26% is obtained in exactly locating and extracting the lesion along with its attributes.
ECG-NETS – A novel integration of capsule networks and extreme gated recurrent neural network for IoT based human activity recognition

Human Activity Recognition (HAR) has reached its new dimension with the support of Internet of Things (IoT) and Artificial Intelligence (AI). To observe human activities, motion sensors like accelerometer or gyroscope can be integrated with microcontrollers to collect all the inputs and send to the cloud with the help of IoT transceivers. These inputs give the characteristics such as, angular velocity of movements, acceleration and apply them for an effective HAR. But reaching high recognition rate with less complicated computational overhead still represents a problem in the research. To solve this aforementioned issue, this work proposes a novel ensembling of Capsule Networks (CN) and modified Gated Recurrent Units (MGRU) with Extreme Learning Machine (ELM) for an effective HAR classification based on data collected using IoT systems called Ensemble Capsule Gated (ECG)-Networks (NETS). The proposed system uses Capsule networks for spatio-feature extraction and modified (Gated Recurrent Unit) GRU for temporal feature extraction. The powerful feed forward training networks are then employed to train these features for human activity recognition. The proposed model is validated on real time IoT data and WISDM datasets. Experimental results demonstrates that proposed model achieves better results comparatively higher than existing (Deep Learning) DL models.
End-to-End LU Factorization of Large Matrices on GPUs

LU factorization for sparse matrices is an important computing step for many engineering and scientific problems such as circuit simulation. There have been many efforts toward parallelizing and scaling this algorithm, which include the recent efforts targeting the GPUs. However, it is still challenging to deploy a complete sparse LU factorization workflow on a GPU due to high memory requirements and data dependencies. In this paper, we propose the first complete GPU solution for sparse LU factorization. To achieve this goal, we propose an out-of-core implementation of the symbolic execution phase, thus removing the bottleneck due to large intermediate data structures. Next, we propose a dynamic parallelism implementation of Kahn's algorithm for topological sort on the GPUs. Finally, for the numeric factorization phase, we increase the parallelism degree by removing the memory limits for large matrices as compared to the existing implementation approaches. Experimental results show that compared with an implementation modified from GLU 3.0, our out-of-core version achieves speedups of 1.13--32.65X. Further, our out-of-core implementation achieves a speedup of 1.2--2.2 over an optimized unified memory implementation on the GPU. Finally, we show that the optimizations we introduce for numeric factorization turn out to be effective.
GAF-RCNN: Grid attention fusion 3D object detection from point cloud

<ns4:p><ns4:bold>Background: </ns4:bold>Due to the refinement of region of the interests (RoIs), two-stage 3D detection algorithms can usually obtain better performance compared with most single-stage detectors. However, most two-stage methods adopt feature connection, to aggregate the grid point features using multi-scale RoI pooling in the second stage. This connection mode does not consider the correlation between multi-scale grid features.</ns4:p><ns4:p> <ns4:bold>Methods: </ns4:bold>In the first stage, we employ 3D sparse convolution and 2D convolution to fully extract rich semantic features. Then, a small number of coarse RoIs are predicted based region proposal network (RPN) on generated bird’s eye view (BEV) map. After that, we adopt voxel RoI-pooling strategy to aggregate the neighborhood nonempty voxel features of each grid point in RoI in the last two layers of 3D sparse convolution. In this way, we obtain two aggregated features from 3D sparse voxel space for each grid point. Next, we design an attention feature fusion module. This module includes a local and a global attention layer, which can fully integrate the grid point features from different voxel layers.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>We carry out<ns4:bold> </ns4:bold>relevant experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset. The average precisions of our proposed method are 88.21%, 81.51%, 77.07% on three difficulty levels (easy, moderate, and hard, respectively) for 3D detection, and 92.30%, 90.19%, 86.00% on three difficulty levels (easy, moderate, and hard, respectively) for BEV detection.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>In this paper, we propose a novel two-stage 3D detection algorithm named Grid Attention Fusion Region-based Convolutional Neural Network (GAF-RCNN) from point cloud. Because we integrate multi-scale RoI grid features with attention mechanism in the refinement stage, different multi-scale features can be better correlated, achieving a competitive level compared with other well tested detection algorithms. This 3D object detection has important implications for robot and cobot technology.</ns4:p>
Swift

As the size of deep learning models gets larger and larger, training takes longer time and more resources, making fault tolerance critical. Existing state-of-the-art methods like Check-Freq and Elastic Horovod need to back up a copy of the model state in memory, which is costly for large models and leads to non-trivial overhead. This paper presents Swift, a novel failure recovery design for distributed deep neural network training that significantly reduces the failure recovery overhead without affecting training throughput and model accuracy. Instead of making an additional copy of the model state, Swift resolves the inconsistencies of the model state caused by the failure and exploits replicas of the model state in data parallelism for failure recovery. We propose a logging-based approach when replicas are unavailable, which records intermediate data and replays the computation to recover the lost state upon a failure. Evaluations show that Swift significantly reduces the failure recovery time and achieves similar or better training throughput during failure-free execution compared to state-of-the-art methods without degrading final model accuracy.
OptiMD-3D DCNN: A Framework for Restoring the Haze-Free Images by Image Dehazing Techniques Using Heuristic Approach of Adaptive Lifting Wavelet Transform

AbstractAbstractThe recently implemented illustrative techniques are focused to restore haze-free images by using learning approaches and also physical models. Managing finer details of the images when completely removing the haze is a complicated task especially in performing the hazing on single images. Generally, hazy images comprise distorted colors, poor contrast, and lower visibility, which lead to lower efficiency in the recognition, detection of objects, and classification of images. The main aspect of the proposed work is to restore haze-free images without any degradation in image quality. Initially, the required raw hazy images are acquired from the benchmark data sources. Further, these images undergo the decomposing stage, which is accomplished by adopting the Adaptive Lifting Wavelet Transform (ALWT), where the wavelet levels are optimized by using the improved hybrid algorithm of the Modified Tunicate Galactic Swarm Optimization (MTGSO). Once the decomposed images are obtained, Optimal Multiscale Dilation assisted 3D Deep Convolutional Neural Network (OptiMD-3D DCNN) is developed for the image dehazing task, in which the hyperparameters are optimally tuned using a hybrid MTGSO algorithm. At last, the effectiveness is examined and guaranteed that the recommended scheme shows higher performance in estimating with the classical methods.Keywords: Adaptive lifting wavelet transformimage dehazingmodified tunicate galactic swarm optimizationmultiscale dilation assisted 3D deep convolutional neural network
dsBinVal: Conducting distributed ROC analysis using DataSHIELD

Our R (R Core Team, 2021) package dsBinVal implements the methodology explained by Schalk et al. (2022).It extends the ROC-GLM (Pepe, 2000) to distributed data by using techniques of differential privacy (Dwork et al., 2006) and the idea of sharing highly aggregated values only.The package also exports functionality to calculate distributed calibration curves and assess the calibration.Using the package allows us to evaluate a prognostic model based on a binary outcome using the DataSHIELD (Gaye et al., 2014) framework.Therefore, the main functionality makes it able to 1) compute the receiver operating characteristic (ROC) curve using the ROC-GLM from which 2) the area under the curve (AUC) and confidence intervals (CI) are derived to conduct hypothesis testing according to DeLong et al. (1988).Furthermore, 3) the calibration can be assessed distributively via calibration curves and the Brier score.Visualizing the approximated ROC curve, the AUC with confidence intervals, and the calibration curves using ggplot2 is also supported.Examples can be found in the README file of the repository.
Connect, Collaborate, and Succeed: The Ninth Annual Meeting of the Southeastern Association of Shared Resources (SEASR) Nashville, TN, USA, June 8–10, 2022

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Inventory Management in Supply Chains Based on a Linear Discrete System with a Quadratic Quality Criterion

Purpose of research . To analyze theoretical studies of models and methods used in the theory of inventory management and directions of their practical implementation. To present the problem of optimal inventory management as a discrete control problem. Tasks of this type are formulated in cases where there is a need to form strategies of management of material and production inventories in supply chains, taking into account their organization at a certain time interval. To propose a method for solving the corresponding management problem. Methods . As a basic model, we consider the process of inventory management in the warehouse, in which a certain balance between the products released from the warehouse into production, released for sale, as well as its residues, which form the inventory of the next period must be fulfilled. The process of inventory management in supply chains during the planning period is based on the procedure of finding the appropriate control action for a linear discrete controlled system with a quadratic quality index. Implementation of control uses the principle of feedback. The computer algebra system Wolfram Mathematica is chosen as a tool for implementing the control. Results. Within the framework of studying the methods of finding the optimal control, two solution methods were singled out - the method of finding the optimal program control and the method of determining the optimal control according to the feedback principle. In the process of verification of the obtained solution, several model examples with different initial conditions were considered. Conclusion. The results of testing the proposed discrete model showed that the feedback method makes it possible to quite efficiently solve the inventory management problem with different initial conditions, which is described by a system of difference equations.
PENGGUNAAN SELEKSI FITUR CHI-SQUARE DAN ALGORITMA MULTINOMIAL NAÏVE BAYES UNTUK ANALISIS SENTIMEN PELANGGGAN TOKOPEDIA

E-commerce is a medium for online shopping that is popular among the public. Ease of access for all internet users and the completeness of products offered by e-commerce are new alternatives in meeting the needs of the community. This causes stiff competition in the e-commerce, so e-commerce need to carry out the right marketing strategy in order to compete in obtaining, retaining, and partnering with customers, one of which is by reviewing aspects of customer satisfaction. Tokopedia is an e-commerce buying and selling online that connects sellers and buyers throughout Indonesia for free. In this study, an analysis of Tokopedia's customer sentiment was carried out with the Multinomial Naïve Bayes classification. Algorithm Multinomial Nave Bayes is a model development of the Nave Bayes. The difference lies in the selection of data, if Naïve Bayes uses a Gaussian that is suitable for continue, while Multinomial Naïve Bayes is suitable for discrete data such as the number of words in a document. Multinomial Naïve Bayes is the simplest method of probability classification but is sensitive to feature selection, so the amount of data is determined by the results of Chi-Square.Multinomial Naïve Bayes is used to classify customer opinions that are positive and negative so that they can form customer satisfaction factors Tokopedia, while the Chi-Square used to measure the level of feature dependence with class (positive and negative) so as to eliminate disturbing features in the classification process. Classification performance results using Multinomial Naïve Bayes without Chi-Square obtained accuracy and kappa statistics of 88% and 75.95%, while using Chi-Square obtained accuracy and kappa statistics of 95% and 89.99%, respectively. This means that Multinomial Naïve Bayes has quite effective performance and results in analyzing Tokopedia customer satisfaction sentiment and the use of Chi-Square for feature selection can improve the accuracy of the classification process.
Introduction

As the storage and computation progressively migrate to the cloud, the data center (DC) as the core infrastructure of cloud computing provides vital technical and platform support for enterprise and cloud services. However, with the rapid rise of the data center scale, the network optimization, resource management, operation and maintenance, and data center security have become more and more complicated and challenging.
Insights, Challenges and Opportunities

Through systematic research and analysis, we found that ML has been gradually introduced and applied to various fields of data center network, and has made certain achievements. However, the current researches are still in its infancy and need to be further improved in various areas.
Microgrid trading mechanism enhancement for smart contract considering reputation values

ABSTRACTABSTRACTIn order to reduce the system instability caused by credit risk in microgrid transactions in the blockchain, we propose a smart contract microgrid transaction model considering reputation value. Considering the instability caused by credit risk, the reputation factor is introduced to ensure the secure and stable operation of the microgrid energy trading system. The effectiveness of the scheme is verified by comparing traditional electricity trading and the trading with the introduced credit value scheme through simulation experiments.KEYWORDS: Reputation valueblockchainsmart contractmicrogrid Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the NSFC under grant nos. 61803279.
Fisheye-based visual aligned regulation for wheeled mobile robots

In this paper, a fisheye-based visual servo regulation method is proposed for wheeled mobile robots, which utilizes motion planning algorithms to provide trajectories for driving the robot to desired poses in an aligned pattern. To satisfy the wide field-of-view (FOV) requirement in robotic systems, the image projection model of fisheye is analyzed, and derivation and estimation of homography for fisheye vision are implemented to facilitate robotic pose calculation. Compared with traditional visual regulation schemes, motion planning and trajectory tracking techniques are combined to make mobile robots reach given poses in the frontal aligned style, which guarantees the robot to move smoothly and is extendable such as for car-like robots. Desired image trajectories are calculated reversely by the fisheye model with respect to the planned geometric path, and then a tracking controller is employed for maneuvering the mobile robot. Simulation and comparative experimental results are collected to verify effectiveness of the proposed strategy.
Perception of WhatsApp-based Learning among Medical Students in Delhi, India

Sample Size and Study DesignThere was a total of 100 students (n = 100), of which 46 were girls and 54 were boys.A group named "group I" was made.The relevant faculty members were added to the group.A group of the first 50 students was taught a session using case-based learning (CBL) as the teaching method, while the next 50 students were taught the same session using WhatsApp as the teaching method.The rules related to this particular activity were explained.During this interactive M-learning session, some questions pertaining to the topic were posted.They were answered by students either as videos, attachments, typed answers, etc.Any doubts on the topic were cleared after the session.After the session, the two groups of students' perceptions related to the different teaching methods, that is, CBL and WhatsApp-assisted learning was assessed using a questionnaire. Data Collection and AnalysisStudents' responses were assessed and their performances, as well as their views, were documented through a preset questionnaire.The questionnaire consists of a set of 10 questions to be answered ReseaRch QuestionTo find out the perception of students toward a newer approach to learning. aimTo determine the perception of WhatsApp-based learning as a M-learning tool. ObjectiveThis study was done to evaluate:• The edge of WhatsApp-assisted learning as an addition to traditional teaching from student's perspective.• The evaluation of the suitability, feasibility, and possibility of WhatsApp-based learning in the community medicine curriculum.
On bias and fairness in deep learning-based facial analysis

Facial analysis systems are used in a variety of scenarios such as law enforcement, military, and daily life, which impact important aspects of our lives. With the onset of the deep learning era, neural networks are being widely used for the development of facial analysis systems. However, existing systems have been shown to yield disparate performance across different demographic subgroups. This has led to unfair outcomes for certain members of society. With an aim to provide fair treatment in the face of diversity, it has become imperative to study the biased behavior of systems. It is crucial that these systems do not discriminate based on the gender, identity, skin tone, or ethnicity of individuals. In recent years, a section of the research community has started to focus on the fairness of such deep learning systems. In this work, we survey the research that has been done in the direction of analyzing fairness and the techniques used to mitigate bias. A taxonomy for the bias mitigation techniques is provided. We also discuss the databases proposed in the research community for studying bias and the relevant evaluation metrics. Lastly, we discuss the open challenges in the field of biased facial analysis.
SUBCONSCIOUS INSIGHTS AS A SOURCE OF CREATIVE INSPIRATION OF R. BRADBURY

The peculiarities of literary principles of American Writer R. Bradbury represented by the author’s self-reflective views on the techniques of stimulating the subconscious, the peculiarities of memory and imagination activity in the process of writing are analyzed in the given article. It is revealed that the word-association process introduced by R. Bradbury echoes the method of free association of Z. Freud. The application of the word-association method led R. Bradbury to the idea that the subconscious for the literary person is a source of unique ideas and a stimulant of creative process. In addition, Bradbury used special techniques (lists of words, self-revelation) in order to activate his subconscious, to make it supply the level of conscious with thoughts, ideas and memories that are usually hidden in the depths of the subconscious. These elements of the creative process stimulate the writer’s imagination by activating the work of the subconscious and memory by making specific connections between these elements aiming to create new literary meanings.
UAV Path Planning in Multi-Task Environments with Risks through Natural Language Understanding

Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of labeled words, from which the semantics are extracted to select the waypoints and trajectory patterns accordingly. Finally, paths between the waypoints are generated using rapidly exploring random trees (RRT) or Dubins curves based on the task requirements. We demonstrate the effectiveness of the proposed method using a simulated quadrotor UAV that follows sequential commands in four typical tasks with potential risks.
Discovering and Ranking Relevant Comment for Chinese Automatic Question-Answering System

Intelligent customer service system is timely, efficient, and accurate, which is more and more popular in grid electric power companies, and the amount of customer consultation is increasing day by day. It is infeasible for human customer service to answer these questions on time, so an automatic question-answering system is of great help to the grid electric power company. The customer queries from the grid electric power company customer service is very different from open-domain questions: the problems questioned by customer tend to be for a specific device or system within the enterprise operation problem. Most grid electric companies provide customers with a communication platform where customers can get guidance on using equipment and the business process. The comments from communication platforms are valuable resources for answering customer questions. In our work, we use three neural network models which excavate potential answers to customer queries from comments. One of the key challenges, however, is the difficulty of matching customer questions with comments. To solve this problem, we propose a method based on deep learning to find the comments related to customer questions to generate more accurate and reliable answers. Experiments can prove that our method performed well in the customer service of grid electric power company.