Reviewerly/exHarmony huggingface dataset:
DIGWO: Hybridization of Dragonfly Algorithm with Improved Grey Wolf Optimization Algorithm for Data Clustering

Data present in great quantity raises the difficulty of managing them that affects the effectual decision-making procedure.Therefore, data clustering achieves notable significance in knowledge extraction and a well-organized clustering algorithm endorses the effectual decision making.For that reason, an algorithm for data clustering by exploiting the DIGWO method is presented in this paper, which decides the optimal centroid to perform the clustering procedure.The developed DIGWO technique exploits the calculation steps of the Dragonfly Algorithm (DA) with the incorporation of the Improved Grey Wolf Optimization (IGWO) with a novel formulated fitness model.Moreover, the proposed method exploits the least fitness measure to position the optimal centroid and the fitness measure based upon three constraints, such as intra-cluster distance, intercluster distance, and cluster density.The optimal centroid ensuing to the minimum value of the fitness is exploited for clustering the data.Simulation is performed by exploiting three datasets and the comparative evaluation is performed that shows that the performance of the developed method is better than the conventional algorithms such as Grey Wolf Optimization (GWO), Dragonfly and Particle Swarm Optimization (PSO).
Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms (HIOAs). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms (HIOAs) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.
Systematic review of nowcasting approaches for solar energy production based upon ground-based cloud imaging

Nowcasting of solar energy considering clouds is important for photovoltaic solar plants and distributed systems. Clouds present a challenge for modeling, due to constant changes in shape and size, and are dependent on local atmospheric conditions. Several methods are being used for the automatic assessment of clouds from the surface to predict solar power generation, assisted by camera, side sensors, etc. During our research we did not find a Systematic Literature Review on this topic. This review is intended to search the related scientific articles to find the state of the art in the area from the period of 2011-2020. We found 65 articles to review after the meta-analysis. We look for the main short-term forecasting methods used. The majority of articles rely on classical statistics approaches based on historical data. Yet recent articles show that this trend might be shifting towards Machine Learning approaches. Our analysis shows that most articles found are based on images captured by fish-eye lenses using a single camera. The most common forecasting techniques are Artificial Neural Networks and Convolutional Neural Networks, with the root mean squared error being the most predominant error metric used for model validation among both classical and Machine Learning approaches.
Glioma Brain Tumor Segmentation in Four MRI Modalities Using a Convolutional Neural Network and Based on a Transfer Learning Method

Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) images is still a challenging task for many applications in the medical domain. To gain a better segmentation outcome, it is vital to employ more information from brain tissue, which is available in different modalities. So, in this study, to segment brain tumors in MRI images, a new four different feature extraction Convolutional Neural Network (CNN) architecture is utilized. Also, we used four image modalities, including T1, T2, T1-c, and FLAIR. Weights and biases from a ResNet-50 network were imported to our structure to increase the segmentation accuracy. We designed two main building blocks to extract additional features from each modality. Our model uses both local and global features by defining two different sizes of patches. The proposed framework was evaluated on the BRATS 2018 dataset and showed that our architecture has a competitive dice score (0.9223, 0.8993, and 0.9211 for Enhanced, Whole, and Core tumor areas, respectively).
Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5

With the development of science and technology, the traditional industrial structures are constantly being upgraded. As far as drones are concerned, an increasing number of researchers are using reinforcement learning or deep learning to make drones more intelligent. At present, there are many algorithms for object detection. Although many models have a high accuracy of detection, these models have many parameters and high complexity, making them unable to perform real-time detection. Therefore, it is particularly important to design a lightweight object detection algorithm that is able to meet the needs of real-time detection using UAVs. In response to the above problems, this paper establishes a dataset of six animals in grassland from different angles and during different time periods on the basis of the remote sensing images of drones. In addition, on the basis of the Yolov5s network model, a lightweight object detector is designed. First, Squeeze-and-Excitation Networks are introduced to improve the expressiveness of the network model. Secondly, the convolutional layer of branch 2 in the BottleNeckCSP structure is deleted, and 3/4 of its input channels are directly merged with the results of branch 1 processing, which reduces the number of model parameters. Next, in the SPP module of the network model, a 3 × 3 maximum pooling layer is added to improve the receptive field of the model. Finally, the trained model is applied to NVIDIA-TX2 processor for real-time object detection. After testing, the optimized YOLOv5 grassland animal detection model was able to effectively identify six different forms of grassland animal. Compared with the YOLOv3, EfficientDet-D0, YOLOv4 and YOLOv5s network models, the mAP_0.5 value was improved by 0.186, 0.03, 0.007 and 0.011, respectively, and the mAP_0.5:0.95 value was improved by 0.216, 0.066, 0.034 and 0.051, respectively, with an average detection speed of 26 fps. The experimental results show that the grassland animal detection model based on the YOLOv5 network has high detection accuracy, good robustness, and faster calculation speed in different time periods and at different viewing angles.
Cross-platform comparison of arbitrary quantum states

As we approach the era of quantum advantage, when quantum computers (QCs) can outperform any classical computer on particular tasks, there remains the difficult challenge of how to validate their performance. While algorithmic success can be easily verified in some instances such as number factoring or oracular algorithms, these approaches only provide pass/fail information of executing specific tasks for a single QC. On the other hand, a comparison between different QCs preparing nominally the same arbitrary circuit provides an insight for generic validation: a quantum computation is only as valid as the agreement between the results produced on different QCs. Such an approach is also at the heart of evaluating metrological standards such as disparate atomic clocks. In this paper, we report a cross-platform QC comparison using randomized and correlated measurements that results in a wealth of information on the QC systems. We execute several quantum circuits on widely different physical QC platforms and analyze the cross-platform state fidelities.
GraphCode2Vec

Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program syntax and semantics in a way that is generic. To this end, we propose the first self-supervised pre-training approach (called GraphCode2Vec) which produces task-agnostic embedding of lexical and program dependence features. GraphCode2Vec achieves this via a synergistic combination of code analysis and Graph Neural Networks. GraphCode2Vec is generic, it allows pre-training, and it is applicable to several SE downstream tasks. We evaluate the effectiveness of GraphCode2Vec on four (4) tasks (method name prediction, solution classification, mutation testing and overfitted patch classification), and compare it with four (4) similarly generic code embedding baselines (Code2Seq, Code2Vec, CodeBERT, Graph-CodeBERT) and seven (7) task-specific, learning-based methods. In particular, GraphCode2Vec is more effective than both generic and task-specific learning-based baselines. It is also complementary and comparable to GraphCodeBERT (a larger and more complex model). We also demonstrate through a probing and ablation study that GraphCode2Vec learns lexical and program dependence features and that self-supervised pre-training improves effectiveness.
An Attention-based AI-assisted Segmentation System for Osteosarcoma MRI Images

Osteosarcoma is one of the common malignant bone tumors, which is great harm to human health. Magnetic resonance imaging (MRI) is a common method for osteosarcoma detection because it can more clearly see the extent of lesions and the degree of invasion, with minimal damage to the human body. However, in developing countries or areas with less developed medical technology, due to the high cost of hardware equipment and the problem of image detection accuracy, the existing MRI image-assisted detection technology still has not achieved the expected application. Based on this, this study proposes an attention-based MRI image-assisted segmentation system for osteosarcoma (AIMSost). The results of the system segmentation can assist doctors to determine the tumor area and identify the tumor location. Among them, we design a segmentation method, which is based on the idea of AttendSeg. We design a new module to replace the original convolutional layer, and use residual network structure, LayerNorm, and Depth-Wise to reduce the number of parameters and operational complexity of the model. Experiments show that the DSC and IOU values of AIMSost are improved by more than 2.79% and 2.03%, respectively, compared with other models in the experiment. Our proposed method has not only higher segmentation accuracy, but also higher efficiency and a more lightweight model structure. The code for AIMSost is available online at https://github.com/gnafuog/AIMSost.git.
Experimental free-space continuous-variable quantum key distribution with thermal source

Passive-state-preparation (PSP) continuous-variable quantum key distribution (CVQKD) protocol explores the intrinsic field fluctuations of a thermal source. Compared with traditional Gaussian-modulated coherent-state CVQKD, it does not need active modulations and has promising applications in chip integration and portable free-space quantum key distribution. In this Letter, we propose and experimentally realize a PSP CVQKD scheme with transmitted local oscillator (LO) through fluctuating transmittance free-space channel using an off-the-shelf amplified spontaneous emission source for the first time. By proposing thermal-state polarization multiplexing transmitted LO, synchronized channel transmittance monitoring and fine-grained phase compensation techniques, secure keys within −15 dB transmittance of simulated free-space channel with turbulence are generated, with a final average secure key rate of 1.015 Mbps asymptotically. Equivalent atmospheric turbulence model analysis shows that the free-space PSP CVQKD scheme provides a promising outlook for high-speed and chip-based CVQKD for kilometer-level atmospheric channel networks.
APLIKASI ARTEFAK MUSEUM BANK INDONESIA BERBASIS MODEL 3D

Bank Indonesia Museum stores important and historical objects. Unfortunately, the impact of Covid-19 hascaused the number of museum visitors to decline sharply. Evenwithout a pandemic, the congested conditions of Jakarta makeit increasingly difficult for visitors who are far away from themuseum location. In this study, we build a mobile-basedmuseum artifact application using the prototyping method.This application is able to display museum artifacts in the formof 3D models. The application is built using Unity which workson the Android operating system. The 3D models of themuseum artifacts created using Blender and then stored in theFirebase Realtime Database. To evaluate the usability of theapplication, we used the Post-Study System UsabilityQuestionnaire (PSSUQ). The PSSUQ’s overall average scorereaches 5.54 which indicates that user satisfaction with theapplication is acceptable. Meanwhile, Sysuse, Infoqual, andInterqual scores reached 5.40, 5.55, and 5.80 respectively. Thethree metrics show the application’s usability, informationquality, and the UI (interface) quality is acceptable. Therefore,this application may help visitors who find it difficult to cometo the Bank Indonesia museum, especially during the pandemicand allow them to still be able to learn the history of museumartifacts.
Correction to: Sentiment Analysis on Substance Use Disorder (SUD) Tweets Before and During COVID-19 Pandemic

Acknowledgment section of the originally published version of chapter 77 was not complete. Additional acknowledgment and grant number were added in respective section.
Study Of Energy Efficient And Secure Routing Mechanisms In Wireless Sensor Network

Wireless Sensor Network (WSN) is a group of Sensor Nodes (SN) correspond with each other and sharing the information between the networks. SN are low power and low cost devices are used to construct the network. Sensor based networks are used in various integrated applications. One of the most benefited sensor based application is Healthcare applications. In Healthcare applications, the physical components are data sources and collected information’s are transmitted through the network in the mode of intermediate node access. All the physical components or sensors are battery powered in nature, so the energy was play a vital role in Healthcare-WSN (HWSN) applications. Nodes energy will decide the utilisation of the network and accurate data transmission between the nodes. The future of WSN is purely dependent on the Energy saving mechanism and now it is create a new dimension of research constraint. Now, the researchers focusing on the Energy efficient and secure techniques for improve the lifetime of the network and safest data transmission between the nodes and the server. In this paper, various energy efficient mechanisms are analyzed for prolong the network and also review the various secure mechanisms to improve the security of the HWSN applications.
ВЫЯВЛЕНИЕ РОЛИ И ФУНКЦИЙ ИНФРАСТРУКТУРЫ В СОЦИАЛЬНО-ЭКОНОМИЧЕСКОМ И КУЛЬТУРНОМ ПРОСТРАНСТВЕ УНИВЕРСИТЕТСКОГО ГОРОДА

Article is devoted to studying of functions of the university city and the providing infrastructure. The relevance of article is that university campuses as the centers of development of the urban environment, promote formation of special infrastructure of the university city. It is connected, on the one hand, with the new mission of the university 3.0. On the other hand, student's and teaching communities show special demand for the urban environment and modern infrastructure. Interference of the universities and economy continuously increases in the developed countries. Interaction «city university» is considered. The role of the university environment in development of infrastructure of the city is revealed. Traditional and non-material factors of placement of the university cities are allocated. On the example of two Siberian university cities the condition of infrastructure facilities is fragmentary described and conclusions are drawn.
Web Search Personalization in E-Commerce Sites Using Fuzzy Logic System

In recent times, web mining is a process to obtain knowledge from the documents across the web. The web personalization system usually overcome various difficulties that includes entity interest, their inclination and requirements, and offers personalized knowledge to the users. Considering such limitations we develop a web page recommendation system that works in accordance with conventional collaborative filtering process. In this paper, we model a Fuzzy C-means that predicts the behavior of user in an e-commerce platform. The prediction of user behavior using Fuzzy C-means enables the model to provide web page recommendations based on the demands in the times like pandemic. The categorization of potential items enables easier association of web page recommendation using Fuzzy C-means clustering. The simulation is conducted to validate the efficacy of the model with conventional web page recommendation systems.
LibGuides: Computer Classes: About Classes

Dickinson Public Library offer free computer classes for patron in our community. Learn about what classes are offer at your library.
METODE ANALYTICAL HIERARCHY PROCESS (AHP) UNTUK REKOMENDASI PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN (PKH)

In this research, the process of determining PKH beneficiaries in the Duri Kosambi Village has no system that could directly recommend a list of beneficiaries. So from this problem, the system design was made to recommend recipients of PKH assistance using the Analytical Hierarchy Process (AHP) method which has 7 different criterias. Using this method could produce a list of PKH beneficiary recommendations that have a weighting of ranking criteria. Ranking weights are used as a basis for recipient recommendations by the criteria. From the results of testing the accuracy of the criteria obtained weighting criteria for the status of residential buildings 0.26 error difference 0.4712%, widest floor type 0.11 error difference 0.3210%, widest wall type 0.11 error difference 0.1722%, drinking water sources 0.03 error difference 0.0244%, lighting source 0.14 difference error 0.2153%, installed electrical power 0.20 error difference 1.6519, and chapter facilities 0.14 error difference 0.4478%. These results indicate that the criteria weights could be used in the process of determining recommendations for PKH recipients by the priority criteria.
A New Approach to Development of Basic Education Informatization towards Intelligent Education

Due to the rapid development of information technology, basic education informatization has developed from audio-visual education, information technology education to intelligent education supported by artificial Intelligence. This paper discusses in detail the development history of basic education informatization, comprehensively analyzes the deep integration of artificial intelligence and basic education,and discusses the new features of basic education informatization in the stage of intelligent education, such as Intelligent teaching environment, precision teaching by teachers, personalized learning by students,diversified educational evaluation, scientific management of education. Based on this, this paper puts forward a new approach to the development of information technology in basic education towards intelligent education: focusing on deeper learning to develop higher order thinking in primary and secondary school students; strengthening moral education to promote the all-round development of primary and secondary school students; improving the quality of education and realizing fair and high-quality basic education;adhering to education governance, optimizing education management, and opening a new journey of basic education informatization in the age of intelligence.
Doctoral studies - Current situation a challenges

The scientific conference focused on the current situation in doctoral studies at the faculties of education. Recent trends in Research, publication strategy leading to excellent outcomes and international cooperation were mentioned. A partial topic was the accreditation processes of doctoral studies. The conference was virtual, with participation from more than 20 universities.
Survey On Secured Health Care Data Sharing On Cloud Using Revocable Attribute Based Encryption Schemes

The applications on generating, collecting and processing of a vast amount of sensitive or personal information have made easier by the latest advancements in the information technologies. In recent period, a problem arises in privacy module and storage module over the health care systems in storing and retrieving the large amount of data bases regarding to the medical history of every patient over a public cloud environment day by day. An additional security and privacy protection concern solutions are introduced by the cloud computing environment when it comes to data being outsourced to the cloud. Revocable Attribute Based Encryption (ABE) with data deduplication is the best way to avoid the privacy problems. In this paper, survey on secured health care data sharing on cloud using Revocable Attribute Based Encryption (ABE) schemes is studied that provides measures to meet any specific application requirements also for strong security to the system. In the results all different studies are evaluated and compared with each other to obtain a best out of it in terms security and performance.
LSC-Tomball: Library Research Guides: Electrical Technology: Course Reserves

Electrical Technology provides practical training to students who want to learn about construction or maintenance electrical work, electrical interfaces with wiring and telecommunications systems, or who desire training to passthe Texas State Licensing