Reviewerly/exHarmony huggingface dataset:
Scheduling Algorithms of Cloud Computing: State of the Art

Cloud computing comes in center advancement of network guring, virtualization and web advances. The cloud computing is a blend of advancements where countless frameworks are associated in private or public organizations. This innovation oers powerfully adaptable framework for information, record stockpiling, and application.Scheduling is a primary task in a cloud computing climate. In cloud computing environment datacenters deal with this undertaking. The determination of a scheduling algorithm calculation relies on diffierent components like the parameters to be upgraded (cost or time), nature of administration to be given and data accessible with respect to diffierent parts of work. Work ow applications are the applications which need diffierent sub-taks to be executed in a specic manner so as to nish the entire undertaking. Diffierent scheduling algorithms are studied in this paper. The objective of cloud task scheduling is to accomplish high framework throughput and to appoint diffierent processing assets to applications. The Complexness of scheduling inconvenience increments with the size of the task and turns out to be exceptionally hard to fathom viably. Min-Min scheduling is utilized to decrease the make span of tasks by assuming the undertaking task length. Remembering this, cloud suppliers ought toaccomplish client fulllment.
FRaGenLP: A Generator of Random Linear Programming Problems for Cluster Computing Systems

The article presents and evaluates a scalable FRaGenLP algorithm for generating random linear programming problems of large dimension $n$ on cluster computing systems. To ensure the consistency of the problem and the boundedness of the feasible region, the constraint system includes $2n+1$ standard inequalities, called support inequalities. New random inequalities are generated and added to the system in a manner that ensures the consistency of the constraints. Furthermore, the algorithm uses two likeness metrics to prevent the addition of a new random inequality that is similar to one already present in the constraint system. The algorithm also rejects random inequalities that cannot affect the solution of the linear programming problem bounded by the support inequalities. The parallel implementation of the FRaGenLP algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the FRaGenLP algorithm.
Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness

Sparse rewards are double-edged training signals in reinforcement learning: easy to design but hard to optimize. Intrinsic motivation guidances have thus been developed toward alleviating the resulting exploration problem. They usually incentivize agents to look for new states through novelty signals. Yet, such methods encourage exhaustive exploration of the state space rather than focusing on the environment's salient interaction opportunities. We propose a new exploration method, called Don't Do What Doesn't Matter (DoWhaM), shifting the emphasis from state novelty to state with relevant actions. While most actions consistently change the state when used, \textit{e.g.} moving the agent, some actions are only effective in specific states, \textit{e.g.}, \emph{opening} a door, \emph{grabbing} an object. DoWhaM detects and rewards actions that seldom affect the environment. We evaluate DoWhaM on the procedurally-generated environment MiniGrid, against state-of-the-art methods and show that DoWhaM greatly reduces sample complexity.
RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection

Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models are unable to learn the shapes of curved texts and often require complex label assignments or repeated feature aggregations for more accurate detection. In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic Text-Spine Labeling, which model local spatial transformation and simplify label assignments separately. Based on these strategies, RSCA achieves state-of-the-art performance in both speed and accuracy, without complex label assignments or repeated feature aggregations. We conduct extensive experiments on multiple benchmarks to validate the effectiveness of our method. RSCA-640 reaches 83.9% F-measure at 48.3 FPS on CTW1500 dataset.
Visual saliency detection based on visual center shift

The saliency areas extracted by traditional visual saliency detection methods are not clear enough. This paper presents a visual saliency detection method based on visual center offset. On the basis of pre-segmentation of the image, the significant areas of the image are extracted by combining the color contrast, color distribution and location characteristics. Using visual center transfer to simulate the visual transfer process of human observation, the image is analyzed at multiple scales. The results indicate that this approach is efficient because ROC curve and Precision-Recall performed well.
The Effect of the Interaction between the Discovery Type across Mobile Applications and the Cognitive Styles in Development of Environmental Awareness among Students of Primary School: أثر التفاعل بين نمط الاكتشاف عبر تطبيقات الجوال والأساليب المعرفية في تنمية الوعي البيئي لدى طالبات المرحلة الابتدائية

The study aimed to identify The Effect of the Interaction between the Discovery Type across Mobile Applications, and the Cognitive Styles in Development of Environmental Awareness among female Students of Primary School in Jeddah, through the experimental treatment of the content of the unit "My environment" in the fifth grade curriculum the second semester in the course of family education. The semi-experimental approach was used, the researcher designed the mobile application And preparing environmental awareness scale. The sample consisted of (106) female primary school students, divided into four groups according to cognitive style (Impulsive/ Reflective), and the Discovery Type used (free/guided). After analyzing data by appropriate statistical methods, The study found multiple results: There is also a statistically significant difference at (α ≥ 0.05) between the mean scores of the primary stage students in the post-application of the environmental awareness scale due to the fundamental effect of the Interaction between the pattern of discovery through mobile (free/ guided) and cognitive style (Impulsivity/Reflectivity). The researcher recommended to train female teachers to employ a discovery type in mobile learning applications, , and in the teaching of various courses.
Blockchain-Based Approach to Foster Student Engagement on Campus

On-campus activities like positions of responsibility in campus amenities and participation in research, benefit the students as well as the university, while also making students financially self-sufficient to a certain extent. However, this student participation is stymied by lack of awareness and motivation. Significant impetus to innovation and student participation can be provided by incentivization of these activities. In this paper, we propose a system to create a blockchain-based economy, to incentivize students with empirical benefits or monetary awards calculated using objective algorithms. The incentivization algorithms have been designed for three promising use cases: research work, positions of responsibility in universities, and crowdfunding. The demonstrated implementation of this system utilises VJTI Chain, an already established Proof of Authority blockchain in VJTI Mumbai, India. This creates a circular economy within the university which encourages students to earn more rewards by reinforcing positive feedback.
RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspirational data distributions: data generated to reflect an ideally fair, FEO-satisfying society. FEO states that everyone with the same talent and willingness to use it should have the same chance of achieving advantageous social positions (e.g., employment), regardless of their background circumstances (e.g., socioeconomic status). Satisfying FEO requires alterations to social structures such as school assignments. Our paper describes RAWLSNET, a method which takes as input a BN representation of an FEO application and alters the BN's parameters so as to satisfy FEO when possible, and minimize deviation from FEO otherwise. We also offer guidance for applying RAWLSNET, including on recognizing proper applications of FEO. We demonstrate the use of our system with publicly available data sets. RAWLSNET's altered BNs offer the novel capability of generating aspirational data for FEO-relevant tasks. Aspirational data are free from the biases of real-world data, and thus are useful for recognizing and detecting sources of unfairness in machine learning algorithms besides biased data.
Utilization of Human Resources Management Information System (SIMPEG) Application to Support E-Government in the BKPP at Palangka Raya Municipality

Dynamic city development produces opportunities for innovation in managing the government sector. Technological innovation in the governmental sector is implemented in the form of e-government. In the process of e-government, the government of Palangka Raya Municipality through Personnel, Education, and Training Agency (BKPP) creates and implements Human Resources Management Information System (SIMPEG). The purposes of this research are to find out the application of SIMPEG in the local government and to identify the driving factors and inhibitors of its application. This research uses a qualitative descriptive method, where data is obtained from the results of in-depth interviews with employees responsible for implementing SIMPEG on BKPP. The research results show that the application of SIMPEG in the e-government of Palangka Raya Municipality could help in assessing the performance of the State Civil Apparatus (ASN), and also able to present information on each ASN staff in detail.
Briefly dry before rain returns for tomorrow

A pleasant spring day ahead for most of us with a dry day and some sun breaking through the clouds before the clouds win out by the evening. Temps will top our close to 70 around Puget Sound.
SISTEM INFORMASI PENGELOLAAN PENGUNJUNG DI KANTOR BPN (BADAN PERTANAHAN NASIONAL) KOTA BATAM DENGAN MENGGUNAKAN PHP DAN MYSQL

Data management of the BPN is one important component in realizing an information system in BPN. Suppose a visitor registration system, visitor data or the like, the BPN itself still uses a data management manually, of course, still has many weaknesses, for example, the accuracy was unacceptable because of the possibility of error is quite large and take a long time in the process. This study aims to analyze and design a Visitor Management Information Systems at the Land Office (BPN) Batam Using PHP and MySQL. The systems used today are still using manual systems by way of the visitors filling the biodata themselves. Currently the system used is still not support data recording in Web-Based guest So if you want to know the data troublesome guests who had been some time ago, is also often schedule a clash with the other guests. Thus the researchers developed a system where the system can later be used to resolve the existing temporary system, the system will be used, namely the Management Information System Visitors In the Land Office (BPN) Batam Using PHP and MySQL. It is useful to keep the authenticity of the data visitor.The media created a web-based in design appropriate problem analysis collected from the beginning and developement into a website with a dynamic display.
Two-step skeletization of binary images based on the Zhang-Suen model and the producing mask

The aim of the work is to limit excessive thinning and increase the resistance to contour noise of skeletons resulted from arbitrary binary image shape while maintaining a high skeletonization rate. The skeleton is a set of thin lines, the relative position, the size and shape, which conveys information of size, shape and orientation in space of the corresponding homogeneous region of the image. To ensure resistance to contour noise, skeletonization algorithms are built on the basis of several steps. Zhang-Suen algorithm is widely known by high-quality skeletons and average performance, which disadvantages are the blurring of diagonal lines with a thickness of 2 pixels and the totally disappear patterns of 2x2 pixels. To overcome them, a mathematical model that compensates the Zhang-Suen algorithm has proposed in this paper, along with a producing mask and two logical conditions for evaluating its elements.
BoundarySqueeze: Image Segmentation as Boundary Squeezing

This paper proposes a novel method for high-quality image segmentation of both objects and scenes. Inspired by the dilation and erosion operations in morphological image processing techniques, the pixel-level image segmentation problems are treated as squeezing object boundaries. From this perspective, a novel and efficient \textbf{Boundary Squeeze} module is proposed. This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation. A bi-directionally flow-based warping process is proposed to generate such squeezed feature representation, and two specific loss signals are designed to supervise the squeezing process. The Boundary Squeeze module can be easily applied to both instance and semantic segmentation tasks as a plug-and-play module by building on top of some existing methods. Moreover, the proposed module is light-weighted, and thus has potential for practical usage. Experiment results show that our simple yet effective design can produce high-quality results on several different datasets. Besides, several other metrics on the boundary are used to prove the effectiveness of our method over previous work. Our approach yields significant improvement on challenging COCO and Cityscapes datasets for both instance and semantic segmentation, and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting. Codes and models will be published at \url{https://github.com/lxtGH/BSSeg}.
A STUDY ON REGION BASED ANALYSIS TO DETECT THE ORIGINALITY OF THE WEBSITES USING MACHINE LEARNING ALGORITHM

To determine whether the websites are original or fake with the help of https anddomainreglen. To avoid the phishing website by analyzing and the count for original & fakewebsite is deducted from the dataset. Classifying the data into training set and test set in theNaive Bayes Classifier Algorithm and training the model. Fitting the training model intoprediction and then evaluating the prediction model. Accuracy is deducted from the Bayestheorem that isin percentageform.
Feature Space Targeted Attacks by Statistic Alignment

By adding human-imperceptible perturbations to images, DNNs can be easily fooled. As one of the mainstream methods, feature space targeted attacks perturb images by modulating their intermediate feature maps, for the discrepancy between the intermediate source and target features is minimized. However, the current choice of pixel-wise Euclidean Distance to measure the discrepancy is questionable because it unreasonably imposes a spatial-consistency constraint on the source and target features. Intuitively, an image can be categorized as "cat" no matter the cat is on the left or right of the image. To address this issue, we propose to measure this discrepancy using statistic alignment. Specifically, we design two novel approaches called Pair-wise Alignment Attack and Global-wise Alignment Attack, which attempt to measure similarities between feature maps by high-order statistics with translation invariance. Furthermore, we systematically analyze the layer-wise transferability with varied difficulties to obtain highly reliable attacks. Extensive experiments verify the effectiveness of our proposed method, and it outperforms the state-of-the-art algorithms by a large margin. Our code is publicly available at https://github.com/yaya-cheng/PAA-GAA.
PENINGKATAN KEMAMPUAN KOGNITIF ANAK USIA DINI DALAM MENGENAL ANGKA 1-10 MELALUI MEDIA BAHAN ALAM DI RAUDHATUL ATHFAL AL GHAZALI MEDAN TP. 2020-2021

ABSTRAK:  In the world of education today, especially in early childhood education institutions, there is rapid progress and competition in improving the quality of early childhood education institutions. Therefore, each educational institution implements the learning process in various ways in using the right teaching methods and learning media that are appropriate and easily understood by students so that the material taught is achieved. Not a few educational institutions delivered by their educators did not understand the appropriate learning media on the material they were teaching. Therefore, the purpose of this study is to determine the appropriate media in recognizing numbers, especially numbers 1 to 10. The method of this research is classroom action research. The results of the study are the learning media used in recognizing numbers 1 to 10 using media from natural materials and have increased according to the development of the results of the cycle, namely in the first cycle the criteria developed according to expectations and in the second cycle the results of the criteria developed very well. Keyword:  Cognitive, Early Childhood.
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

Non-maximum Suppression (NMS) is an essential postprocessing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere.
Longitudinal Pooling &amp; Consistency Regularization to Model Disease Progression from MRIs

Many neurological diseases are characterized by gradual deterioration of brain structure and function. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in line with disease progression. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimer's Disease Neuroimaging Initiative (ADNI, N=404), a dataset composed of 274 normal controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). In all three experiments our method is superior to other widely used approaches for longitudinal classification thus making a unique contribution towards more accurate tracking of the impact of conditions on the brain. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.
Management Tool for Processing Network Element Information

The Network Element Information Processing Management Tool provides an interface for Network Element information maintenance. Every network element's information is still managed manually by the user, and there can be hundreds of them, which is time-consuming. The proposed system solves the problem by automating the information gathering and information processing related to Network Elements. All network elements must be known, maintained, monitored, and allocated using the management tool, which will aid in automating the process of allocation, software version, fetching IPs, availability and reachability. The management tool shows correct and up-to-date information.
Classification of Binary Self-Dual [76, 38, 14] Codes With an Automorphism of Order 9

Using the method for constructing binary self-dual codes with an automorphism of order square of a prime number we have classified all binary self-dual codes with length 76 having minimum distance $d=14$ and automorphism of order 9. Up to equivalence, there are six self-dual $[76, 38, 14]$ codes with an automorphism of type $9$-$(8,0,4)$. All codes obtained have new values of the parameter in their weight enumerator thus more than doubling the number of known values.
Quantum codes obtained through (1+(p−2)ν)-constacyclic codes over Zp+νZp

This paper is concerned with, structural properties and construction of quantum codes over Zp by using (1+(p−2)ν)-Constacyclic codes over the finite commutative non-chain ring ℜ = Zp+νZp where ν2 = ν and Zp is field having p elements with characteristic p where p is prime. A Gray map is defined between ℜ and Zp2. The parameters of quantum codes over Zp are obtained by decomposing (1+(p−2)ν)-constacyclic codes into cyclic and negacyclic codes over Zp. As an application, some examples of quantum codes of arbitrary length, are also obtained.