Reviewerly/exHarmony huggingface dataset:
Cost Effective and Energy Efficient VM Migration for Scheduling in Cloud Computing

Abstract In cloud computing, to achieve proper load balancing, the cloud data center dynamically migrates and deploys VM to meet user’s requirements without degrading the service being delivered to the user. Several migration techniques are available for migrating VM from one host to another. But they fail to consider the migration cost while determining the energy consumption during migration. One of the options to reduce the power consumption of data centers is to reduce the number of idle servers, or to switch idle servers into low-power sleep states. However, the servers cannot process the requests immediately when transiting to an active state. In this paper, a cost effective and energy efficient VM migration for scheduling in cloud computing is developed. In this technique, VMs having minimum cost and minimized energy consumption are selected for migration. During load balancing, the tasks are migrated to the underutilized server if the needed current server is overloaded. Similarly, the tasks are migrated from the under-loaded server to the current server. In energy efficient scheduling, given the arrival of user requests, the VMs are scheduled such that the total energy consumption of the data center is minimized. The proposed CEEM-VM-MS technique is implemented in Cloudusim tool. In experiments, the upper threshold value for CPU size is increased and the performance metrics power consumption, number of VM migrations, response delay, and CPU utilization are measured. Results indicate that the proposed CEEM-VM-MS technique minimizes the power consumption, reduces the number of VM migrations, reduced the response delay and increases the CPU utilization.
Eye-referenced dynamic bounding box for face recognition using light convolutional neural network

Face recognition systems have imprinted its presence in many applications from offices to security to daily use as in personal digital gadgets. With many Face recognition systems in use, still there is scope for its performance improvement. The performance of such systems suffers due to presence of covariates like non-uniform illumination, pose deviations, occlusions, low resolution etc. Extracting the region of interest from input face images is very crucial steps in face recognition system. We proposed methodology to create a bounding box for every sample face image. This bounding box dynamically tries to fit the face image to cover the relevant face features useful for recognition purpose. We used a light CNN structure to perform the experiment on MUCT face dataset using three different methodologies for extracting region of interests from sample face images using bounding box. These are 1) min-max based ROI 2) OpenCV face alignment and 3) eye-reference based ROI. It is observed that the proposed eye-reference based face alignment method works better than conventional methods of min-max based ROI and OpenCV face alignment with considerable amount of improvement in the recognition accuracy. Our future work includes use of other structured datasets with various covariates for face recognition.
Real time obstacle motion prediction using neural network based extended Kalman filter for robot path planning

Navigation in dynamic environments for mobile robots is a difficult problem as it involves estimating the path of moving obstacles. The measured data usually contains a bias and noise in addition to its true value. Based on a stacked denoising autoencoder (SDAE), the enhanced Kalman filter developed in this paper can estimate the obstacle position from any type of noisy input. The extended Kalman filter's ability to predict an error-free path is impacted by the measurement noise covariance matrix employed. The SDAE is a neural network topology based on deep learning that can be used to determine the optimum covariance matrix. Both Adam and stochastic gradient learning algorithms are used to train the neural network. The robot's path is re-planned based on the predicted obstacle path to ensure safe navigation. MATLAB-based numerical simulations are used to demonstrate the utility and superiority of the proposed method over the traditional Kalman filter and Particle filter methodologies. The simulation results show that in the presence of any sort of noise, the proposed technique is exceptionally durable and reliable. The simulation findings also reveal that when it comes to denoising the measured data, the stacked denoising autoencoder with Adam optimizer is more efficient than the stochastic approach. The performance of the developed algorithm is validated in MATLAB simulated environments, and it can be extended for navigation tasks. In terms of computation time and robustness in closely spaced obstacles, simulation experiments demonstrated that the path planning using the proposed algorithm outperforms the hybrid A star, artificial potential field, and decision algorithms.
ANALYSIS OF ACCOUNTING INFORMATION SYSTEMS SALES AND CASH RECEIPTS TO IMPROVE INTERNAL CONTROL

Sales accounting information system and cash receipts is one of the sub-systems in accounting information that describes the correct procedures for sales and cash receipts activities. The problem in this study is how to implement a sales information system and cash receipts at PT Sumber Alfaria Trijaya. The type of research used by researchers in the preparation of this study is a qualitative method. The data used in this study is primary data. Data collection procedures are observation, documentation and direct interviews to the object of research. The conclusion of this study is the application of accounting information systems in cash sales and cash receipts at PT Sumber Alfaria Trijaya is still not good, seen from the dual function, namely the delivery function which doubles as a warehouse function and the lack of data and completeness. The analysis of the accounting information system for cash sales and cash receipts on internal control at PT Sumber Alfaria Trijaya is still not good and weak because there are several problems including the existence of multiple functions by the Assistant Head of the store, cash is not directly deposited to the Bank. In the cash function bill, and the amount of cash receipts from cash sales deposited in the bank on the third day not on the day of the transaction. Then there is also no periodic or sudden checking of cash balances by internal control.
Sistem Pendukung Keputusan Penentuan Dosen Pembimbing Tugas Akhir Menggunakan Fuzzy Dan Simple Additive Weighting Berbasis Android: Studi Kasus IKADO Surabaya

Final Project is a scientific work carried out by conducting research based on a problem and supported by data obtained from research activities. In the process of completing the final project, students are usually guided by a supervisor. Currently, students at IKADO Surabaya choose their supervisor only by subjective assessment and only through intuition. This causes the supervisors chosen by students to be less relevant to the topic of the final project and also in terms of experience in guiding with the topic of the final project is still small so that it affects the results/ quality of the final project itself. Therefore, we need an application that can provide recommendations to students regarding their final assignment supervisor in accordance with certain criteria. In this study, the application developed will use the fuzzy method and simple additive weighting to get the supervisor's recommendation according to the topic of the student's final project. The results show the choice of the names of the first and second supervisors, along with the top five ranking lecturers' names that can be used as alternative supervisors. The development method used is extreme programming, and uses testing in the form of Blackbox testing, while testing the ease and usability of the application uses usability testing. By making a decision support application called SIPEKA, it can help students and final project coordinators in determining alternative final project supervisors, by utilizing the features contained in the application.
EXTRACURRICULAR WORK WITH YOUTH AS A MEANS ADAPTATIONS TO TEACHING TEACHING DISCIPLINE IN PROFESSIONAL CONDITIONS TEACHING

The article reveals the problems of students' after-school activities and adjusting of first-grade students to the process of education in higher school. The leading role of the core chairs and the forms of after-school activities on psychology and pedagogy in the higher educational establishment of the economy have been confirmed. The schedule of tutorial work for the first-grade students are proposed.
Self-supervised Neural Articulated Shape and Appearance Models

Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches has focused on static objects, dynamic objects, especially with controllable articulation, are less explored. We propose a novel approach for learning a representation of the geometry, appearance, and motion of a class of articulated objects given only a set of color images as input. In a self-supervised manner, our novel representation learns shape, appearance, and articulation codes that enable independent control of these semantic dimensions. Our model is trained end-to-end without requiring any articulation annotations. Experiments show that our approach performs well for different joint types, such as revolute and prismatic joints, as well as different combinations of these joints. Compared to state of the art that uses direct 3D supervision and does not output appearance, we recover more faithful geometry and appearance from 2D observations only. In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis.
Subdivisions and Crossroads: Identifying Hidden Community Structures in  a Data Archive's Citation Network

Data archives are an important source of high quality data in many fields, making them ideal sites to study data reuse. By studying data reuse through citation networks, we are able to learn how hidden research communities - those that use the same scientific datasets - are organized. This paper analyzes the community structure of an authoritative network of datasets cited in academic publications, which have been collected by a large, social science data archive: the Interuniversity Consortium for Political and Social Research (ICPSR). Through network analysis, we identified communities of social science datasets and fields of research connected through shared data use. We argue that communities of exclusive data reuse form subdivisions that contain valuable disciplinary resources, while datasets at a "crossroads" broadly connect research communities. Our research reveals the hidden structure of data reuse and demonstrates how interdisciplinary research communities organize around datasets as shared scientific inputs. These findings contribute new ways of describing scientific communities in order to understand the impacts of research data reuse.
The Relation of Indonesian Language Learning to the Environment

Learning Indonesian is familiar with the surrounding environmental conditions. The environment is one of the main factors that can affect a child's language development. Formally, it can be found in Indonesian language learning. Learning Indonesian cannot be separated from language skills, one of which is writingskills. Writing is the most important skill in education, but writing is a difficult skill, comparedto other language skills.
Browser-based Hyperbolic Visualization of Graphs

Hyperbolic geometry offers a natural focus + context for data visualization and has been shown to underlie real-world complex networks. However, current hyperbolic network visualization approaches are limited to special types of networks and do not scale to large datasets. With this in mind, we designed, implemented, and analyzed three methods for hyperbolic visualization of networks in the browser based on inverse projections, generalized force-directed algorithms, and hyperbolic multi-dimensional scaling (H-MDS). A comparison with Euclidean MDS shows that H-MDS produces embeddings with lower distortion for several types of networks. All three methods can handle node-link representations and are available in fully functional web-based systems.
MULTI-CRITERIA OPTIMIZATION FOR KNOWLEDGE EXTENSIONISTS IN INTERNAL KNOWLEDGE TRANSFER OF HIGHER EDUCATION IN NIGERIA

The demand for high education in an environment of an insufficient number of experts requires efficient knowledge management. The introduction of knowledge extensionists in higher learning institutions remains relevant to bridge the gap created by this imbalance. In this work, we developed a new multi-objective mathematical model for optimizing the knowledge transfer capability of extensionists and knowledge management in higher learning institutions in Nigeria. The Model provides the information needed for a detailed analysis of the design trade-off between conflicting objectives that are most interesting to a decision-maker. The binary Integer MultiObjective Model is a suitable model for a population of potential solutions. The Model focused on practical factors for effective knowledge transfer. Several indices were considered, such as number of task types, number of clusters (groups), number of employees, etc., as the knowledge transfer (KT) assessment factors. Some parameters are adequate to recognize the KT potential in an employee, such as whether task type can be handled by an employee, whether the employee is interested in cooperating with another employee, used the minimum size of the cluster in terms of the number of employees. The concept of “interest to work with each other” is a novel aspect considered in the recommended algorithm. Therefore, for illustration, an intelligent algorithm based on matrix and clustering concepts to evaluate data collected via a questionnaire from Waziri Umaru Federal Polytechnic Birnin Kebbi, Nigeria was developed. The model is suitable for any given set of data to be applied elsewhere
Learning Non-target Knowledge for Few-shot Semantic Segmentation

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5i and COCO-20i datasets show that our approach is effective despite its simplicity.
The security strength of Blockchain technology : A Survey Report

The advent of blockchain technology by the Nakamoto group in 2008 has created a new trend on how to deal with various security issues and vulnerabilities. Blockchain systems have gained momentum in various spheres of technology deployment in business organizations. This paper presents a critical literature survey on the security strength of blockchains and security issues associated with blockchain technology deployment. Numerous studies have experimented with the various technical features of blockchain systems across various transaction domains. Findings obtained from the literature survey and thematic content analysis of the existing research studies indicate that blockchain systems provide unique capabilities that support processes and transactions across various sectors with a high level of integrity, transparency, confidentiality, and privacy. However, some loopholes and limitations associated with the deployment and use of blockchains have been highlighted in various studies. The present study cross-examined the security issues of the underlying scientific research evidence.
Application of blockchain to underground utilities

Blockchain technology enables encrypted and secure logging and transactions for information and data. This technology is expected to revolutionize computing in several areas, mainly where centralization is unnatural, and privacy is important. We are driven by circumstance and the complexity of underground utilities, which need measures to record information, secure data, and utilize new technologies with recorded information and store it into a secure digital platform. The platform must be able to deal with different types of files and documents with high efficiency and workability. Current problems due to uncertain information regarding the location of existing underground utilities often leads to striking of such utilities. This study describes blockchain technology and a proposed application to manage infrastructure utilities. The paper compares blockchain with Underground Infrastructure Management (UIM) and One Call System 811, and the potential advantage of this technology to manage underground utilities. Potential integration with blockchain and UIM is described to maximize infrastructure sustainability in underground utilities with respect to security and management. The research presented is focused on exploring the possibilities that blockchain has to offer with regard to protection of underground utilities.
Improving colorization through denoiser with MLP

This paper addresses the problem of generating a plausible colored photograph given a grayscale image.Previous approaches to solve this problem have either relied on significant human input or resulted in desaturated colorizations.Inspired by [1], we present a fully automatic approach that uses deep neural networks to color images.We explore several architectures/models, approaches and loss functions to understand the best practices to obtain the most aesthetically pleasing colored images.We train a convolutional neural network, and observe that certain loss functions perform better than others due to their inherent properties.In particular, we find that our classificationbased model that uses cross-entropy loss seems to perform the best.We also discuss other state-of-the-art approaches to solve this problem including conditional generative adversarial networks [2].Finally, we provide a qualitative and quantitative analysis of our results, concluding with avenues for future research.
Improving the Red-Black tree delete algorithm

Abstract Today, Red-Black trees are becoming a popular data structure typically used to implement dictionaries, associative arrays, symbol tables within some compilers (C++, Java …) and many other systems. In this paper, we present an improvement of the delete algorithm of this kind of binary search tree. The proposed algorithm is very promising since it colors differently the tree while reducing color changes by a factor of about 29%. Moreover, the maintenance operations re-establishing Red-Black tree balance properties are reduced by a factor of about 11%. As a consequence, the proposed algorithm saves about 4% on running time when insert and delete operations are used together while conserving search performance of the standard algorithm.
Cross-domain Few-shot Meta-learning Using Stacking

Cross-domain few-shot meta-learning (CDFSML) addresses learning problems where knowledge needs to be transferred from several source domains into an instance-scarce target domain with an explicitly different input distribution. Recently published CDFSML methods generally construct a "universal model" that combines knowledge of multiple source domains into one backbone feature extractor. This enables efficient inference but necessitates re-computation of the backbone whenever a new source domain is added. Moreover, state-of-the-art methods derive their universal model from a collection of backbones -- normally one for each source domain -- and the backbones may be constrained to have the same architecture as the universal model. We propose a CDFSML method that is inspired by the classic stacking approach to meta learning. It imposes no constraints on the backbones' architecture or feature shape and does not incur the computational overhead of (re-)computing a universal model. Given a target-domain task, it fine-tunes each backbone independently, uses cross-validation to extract meta training data from the task's instance-scarce support set, and learns a simple linear meta classifier from this data. We evaluate our stacking approach on the well-known Meta-Dataset benchmark, targeting image classification with convolutional neural networks, and show that it often yields substantially higher accuracy than competing methods.
Turning grain maps into diagrams

The present paper studies mathematical models for representing, imaging, and analyzing polycrystalline materials. We introduce various techniques for converting grain maps into diagram or tessellation representations that rely on constrained clustering. In particular, we show how to significantly accelerate the generalized balanced power diagram method from [1] and how to extend it to allow for optimization over all relevant parameters. A comparison of the accuracies of the proposed approaches is given based on a 3D real-world data set of $339\times 339 \times 599$ voxels.
Korelasi Sikap Belajar Dengan Hasil Belajar Siswa

The mindset of learning outcomes that most ordinary people know is only limited to the values ​​of the tests that have been carried out. So it still happens that teachers only focus on learning outcomes in the cognitive domain. Whereas the distribution of learning outcomes has 3 domains, namely cognitive, affective, and psychomotor. This research is a quantitative research with correlation technique. The population in this study were all grade VIII students of SMP Negeri 01 Kotabumi for the academic year 2020/2021, totaling 317 students. The sample of this study amounted to 79 students. The sampling technique used a proportionate stratified random sampling technique. The learning attitude instrument uses a questionnaire/questionnaire. Meanwhile, the learning outcomes instrument used a multiple choice test. Hypothesis test using Product Moment Correlation. For the correlation analysis of learning attitudes and learning outcomes, it produces a correlation coefficient (r_xy) of 0.2341 and r_(table(79)(0.05)) of 0.2210. This shows that there is a correlation (relationship) of learning attitudes (affective learning outcomes) and mathematics learning outcomes (cognitive domains).
A Cryptographic Iterative Hash Function Scheme for Wireless Sensor Network (WSNs) Security Enhancement for Sensor Data Transmission in Blockchain

&lt;p&gt;The study will shed light on the proposed system of WSN security for the sensor data transmission using the blockchain. The system will be presented according to the research and practical implications of the blockchain systems. The paper proposes a method for enhancing the data security of wireless sensor networks based on blockchain technology which makes the transfer data of the system challenging to be tamped. The aimed system utilizes a hash value representation calculated by the Merkel-tree algorithm. We offer a particular method of constructing a hash function from a compression function. This construction has the property that the resulting hash function satisfies desirable security properties, such as collision resistance, given that the compression function does. Moreover, the proposed system will implement the most secure algorithms that help to ensure better security for the WSN systems to transmit data.&lt;/p&gt;