Reviewerly/exHarmony huggingface dataset:
Multi authority Ciphertext-Policy Attribute-Based encryption for security enhancement in cloud storage unit

Multi Authority Ciphertext-Policy Attribute-Based Encryption (MCP-ABE) can be developed aimed at enabling efficient secure operation in this research. The proposed methodology is developed to achieve two main characteristics such as to avoid multiuser illegal share their private key and provide data owner to access the data and flexibility to change their access policy. The proposed methodology is providing high security of multi authority with different traceable and dynamic policy updating procedure. The proposed method is implemented in Java and analyzed with performance metrices such as key size, computation time and functionality. Compared to traditional cryptographic methods, attribute-based encryption has a number of benefits, including its adaptable and fine-grained control of access. Additionally, it is independent of key sharing and management methods that may protect against security attacks, a method of undermining encryption. The proposed methodology is protecting the users with different conditions such as data protection, fraud prevention, message detection and general attack resistance. To validate the performance of the proposed methodology, it can be contrasted with the conventional methods such as Ciphertext-Policy Attribute-Based Signcryption with Outsourced Designcryption (CP-OABSC) and revocable signature (CP-ABSC) with Ciphertext-Policy Attribute-Based Signcryption With Accountable and Verifiable Outsourced Designcryption (CP-ABSc-AVODs) respectively.
Multiview clustering via nonnegative matrix factorization based on graph agreement

With the rapid development of information technology, one object can usually be described by multiple views. Intuitively, the diversity and complementarity of different views provide a more comprehensive data description, which can lead to better performance on multiview clustering tasks. Motivated by the fact that how to fully discover diversity and complementary information across views is the key point to deal with multiview clustering, we propose a multiview learning method, termed as graph-agreement nonnegative matrix factorization (GANMF). GANMF attempts to implement this goal based on matrix factorization technology while exploiting the rich multiview information based on the intra- and interview aspects. Specifically, the graph agreement between representation space and raw space is maximized to preserve the intrinsic geometric property in individual view for the intraview case. Similarly, the graph structures in different views are expected to keep consistent with each other by minimizing the divergence between pairwise views. To this end, the intrinsic information and geometric structure information in the intraview case and complementary and compatibility information in the interview case can be simultaneously formulated into one framework. To solve the proposed GANMF, we correspondingly develop an effective algorithm based on iterative alternating strategy. Extensive experimental results on seven multiview datasets demonstrate the superiority and effectiveness of our proposed method.
PENDEKATAN REGRESI ROBUST DENGAN FUNGSI PEMBOBOT BISQUARE TUKEY PADA ESTIMASI-M DAN ESTIMASI-S

Least Square Method is one of methods for estimating of parameters of regression model. Model of least square methods is not valid if there are some disobeydiance in classical assumptions, for example, there are outliers. To resolve the problem, robust regression method is usually used. The method is used because it can detect the outliers and give stable results. In this research, data used is data for human development index of districts in Central Java from 2019 to 2020. Estimation for robust regression method chosen is estimation-M and estimation-s with Tukey Bisquare as a weight function. Criterions for choosing the best model are based on adjusted R-Squared value and mean square error (MSE). The result shows that robust regression model with estimation-M is a better model since it has adjusted R-Squared value tending to one and the least MSE.
Multi-level Optimization of Shared Cloud Architecture for Computer-Supported Sports Collaborative Learning Experiment Scenario

Multi-level optimization of the shared cloud architecture for the computer-supported sports collaborative learning experiment scenario is studied in the paper. Incremental checkpointing can also reduce the size of the checkpoint file, thereby reducing the overhead of saving checkpoints. The implementation of incremental checkpoints relies on a runtime monitor. If the monitor finds that a certain storage area has not been modified since the last checkpoint during a checkpoint, it will be ignored in the current checkpoint. This memory area can reduce the size of the current checkpoint file. IaaS cloud computing platform mainly provides users with basic resource services in three aspects: computer, storage, and network. Hence, with this model, the designed optimization of shared cloud architecture for the computer-supported sports collaborative learning experiment scenario is demonstrated. Through the experimental results, the performance is validated.
Solar Power Generation Prediction for Better Energy Efficiency using Machine Learning

The solar energy plant works by converting solar energy into electricity. The utilization of sunlight energy has escalated in recent years. Not only the electricity is conserved through the medium of a PV power plant but it also contributes towards the environment. The solar energy gets converted into electricity directly using photovoltaics. Nowadays, machine learning model is used in many everyday applications. The main necessity of Artificial intelligence is data. The past dataset is collected and that dataset is used to build a machine learning model. The necessary pre-processing techniques are applied like univariate analysis and bivariate analysis are implemented. The data is visualized for better under-standing of the features and based on that a classification model is built by using machine learning algorithm and comparison of algorithms are done based on their performance metrics like accuracy MAE, MSE, R2 etc.
AutoCAD fundamentals—Part II

This chapter concludes the introduction of AutoCAD basics by discussing the concept of grips, units, scale, snap, and grid. The Cartesian coordinate system is reviewed, followed by a discussion of various geometric data entry techniques including dynamic and manual input. Then, the chapter covers Inquiry commands (quick, area, distance, list, ID, radius, and angle), followed by additional drafting commands and concepts (explode, polygon, ellipse, chamfer, templates, limits, and save). The chapter concludes with a discussion of the Help files, the Tangent OSNAP and a drawing challenge.
Advanced output—Paper Space

This chapter describes the concept of paper space. Covered are the primary reasons for using paper space, how to get in/out of paper space, and how to set up title blocks (layouts), viewports and their properties, viewport scale assignments, controlling what is visible in viewports, setting up text and dim styles in viewports, and the annotative scaling of annotation concept.
Analyzing the Impact of Ensemble Techniques and Resampling Techniques Over Multi Class Skewed Datasets

Machine Learning is having great importance in this era, since of its board spectrum of applications and its capability to adjust and give solutions to complex problems reliably, rapidly, and productively. Machine learning models trained with the data from past experiences and based on the learned data it produces outcomes. The data used for training with these machine learning models should be in balanced manner otherwise the model gives incorrect results. Data is having an important role in this scenario, and it is evident that most of the data are skewed towards some classes and this kind of skewness can be found in all sectors of data in real world. Multimajority datasets and multiminority datasets are the different types of imbalances viewed in multiclass datasets. In this study three different datasets from multimajority domain and three different datasets from multiminority domain are analysed. Six different resampling procedure were applied out of which three belongs to undersampling and three belongs to oversampling. Four different classifiers K-NN, SVM, Random Forest and XGBoost were used to create the various models and their performance were analysed in this study.
Taxa and super taxa of schema evolution and their relationship to activity, heartbeat and duration

In this paper, we present the findings of a large study of the evolution of the schema of 195 Free Open Source Software projects. We identify families of evolutionary behaviors, or taxa, in FOSS projects. A large percentage of the projects demonstrate very few, if any, actions of schema evolution. Two other taxa involve the evolution via focused actions, with either a single focused maintenance action, or a large percentage of evolution activity grouped in no more than a couple interventions. Schema evolution also involves moderate, and active evolution, with very different volumes of updates to the schema. We also investigate how the different taxa relate to measurable properties of schema evolution, specifically, duration of schema and project updates, activity volume, and heartbeat. We show that although different taxa have practically very similar duration, the evolutionary characteristics differ in analogy to the “active” character of each taxon. Moreover, by observing certain similarities in the measurable properties of the taxa, we take the opportunity to introduce super taxa, which complement the previous taxonomy with the groupings of the aforementioned taxa in terms of overall profile similarity, resulting in a more concise and intuitive taxonomy, providing a cleaner separation of evolution measures. Finally, we show that schema evolution is frequently, a time-concentrated activity.
Klasifikasi Status Keaktifan Siswa SMA di Jawa Barat Menggunakan Random Forest dengan SMOTE

The dropout rate in Indonesia has a higher percentage as education levels grow. The high school dropout rate in Indonesia is at 0.67%. West Java is the province with the highest high school dropout rate in the academic year 2017/2018. In the next academic year, the high school dropout rate in West Java decreased. The student who drop out of school was caused by various factors. This study examines important variables and classification performance that are generated by random forest. The number of dropout students is very small compared to the number of active students. The imbalance data is handled using SMOTE. Random forest with SMOTE is considered able to predict data classes better because it can increase sensitivity values and reduce errors in classifying dropout students as active students. Father's income, number of siblings, class, father's education level, and father's type of work are important variables that have a major influence in determining the active status of high school students in West Java.
Library Markers Space in Academic and Public Libraries

This chapter examines the library markers space in academic and public libraries. These spaces are specifically designed to meet the unexpressed needs of library users and to influence them to profitably engage in the library by creating physical or digital objects, which are open for the free use of all library users. Library makers space enables graphic arts, web design, and animation, and assists students in their projects that involve digital images. Nevertheless, factors such as lack of technical skills, inadequate power supply, and lack of trained manpower distort the application of library markers space in academic and public libraries.
Automatic Hand Gesture Recognition with Semantic Segmentation and Deep Learning

Automatic Hand Gesture Recognition is a key requirement for variety of applications, including translation of Sign Language, Human-Computer Interaction (HCI) and, ubiquitous vision-based systems. Due to the lighting variance and complicated background in the input image set of gestures, meeting this criterion remains a challenge. This paper introduces semantic segmentation to deep learning-based hand gesture recognition system for sign language translation. Building on the U - Net architecture, the proposed model obtains the semantically segmented mask of the input image, which is then fed to convolutional neural networks (CNNs) for multiclass classification. The proposed model is trained and tested for four different depths of the CNN architectures followed by the comparison with some pre-trained CNN architectures such as Inception V3, VGG16, VGG19, ResNet50. The proposed model is evaluated on National University of Singapore (NUS) hand posture dataset II (subset A), which contains 2000 images in 10 classes. A significant recognition rate of 97.15 &#x0025; is achieved for the proposed model outperforming a set of prominent models and demonstrating its promises for sign language translation.
An Engaging User Experience Framework for Mobile Augmented Reality

This research proposes an Engaging User Experience (ENUX) framework which caters for the influential User Experience (UX) and User Engagement (UE) dimensions for a MAR system. The ENUX framework extends on the UX and UE theoretical groundings, the multimodal and multisensory techniques and performance metrics to effectively enhance the UX and UE in MAR systems. Designers and developers in the MAR field have focused on providing a usable system rather than concentrating on a user-centric system that may lead to an enhanced UX and UE. The recommendations from the ENUX framework adopt a user-centered perspective to ensure a high-quality MAR experience in various application areas, and therefore can demonstrate the following: point of engagement, period of engagement, maximum engagement and re-engagement. Previous works in this field have focussed on building a framework which revolves particularly on fixing specific UX issues and does not take into account the MAR experience in its whole encapsulation. This framework is a three-tier structure which engages in providing an enhanced MAR experience by leveraging on (1) MAR policies which binds on influential UX and UE constructs, (2) a multidimensional sensory and interaction technique, (3) an assessment model which recursively measure the UX and UE performance. An initial MAR prototype for a cultural heritage site has been developed using the instrumental, cognitive and sensory dimensions from the ENUX framework. A field study of sixty-six users has evaluated the MAR prototype to carry out an impact analysis of the contribution of the ENUX framework. The findings have confirmed that the partial development of the ENUX framework have significantly improved the UX and UE in the MAR prototype and is a promising start to implement the overall ENUX framework. The framework lays a solid foundation for the assessment of the UX and UE factors in MAR systems, which has the potential to assist the MAR system developers to identify and improve the most UX and UE influential factors in their systems.
Text Sentiment Analysis Using the Bald Eagle-Based Bidirectional Long Short-Term Memory

Extraction of emotions that are conveyed through text is termed as sentimental analysis, which is also called as opinion mining. The sentimental analysis is enabled to extract the information about particular objects or an event or various subjects, which helps to analyze and detect the problems associated with them. The detection of this opinion helps in resolving the problems and it is embedded with the growth of the organization. In this research, a bald eagle based BiLSTM classifier is proposed to extricate the sentiment in the text. Initially, the data are collected and preprocessing is performed using the techniques tokenization, stopword removal, stemming and POS tagging. Features are extracted from the preprocessed data and classification is performed using the bald eagle optimization enabled BiLSTM classifier. The experiment shows that the proposed method works more efficient with a precision of 92.65%, and recall of 93.82%.
A Data Obfuscation Method Using Ant-Lion-Rider Optimization for Privacy Preservation in the Cloud

In this paper, a obfuscation-based technique namely, AROA based BMCG method is developed for secure data transmission in cloud. Initially, the input data with the mixed attributes is provided to the privacy preservation process directly, where the data matrix and bilinear map coefficient generation co-efficient is multiplied through Hilbert space-based tensor product. Here, bilinear map co-efficient is the new co-efficient proposed to multiply with original data matrix and the OB-MECC Encryption is utilized in the privacy preservation phase to maintain the security of the data. The derivation of bilinear map co-efficient is used to handle both the utility and the sensitive information. The new algorithm called, AROA is developed by integrating the ALO with ROA. The performance and the comparative analysis of the proposed AROA based BMCG method is done using the metrics, such as accuracy and information loss. The proposed AROA based BMCG method obtained a maximal accuracy of 94% and minimal information loss of 6% respectively.
CrDrcnn: Design and Development of Crow Optimization-Based Deep Recurrent Neural Network for Software Defect Prediction

Software defect prediction (SDP) is the emerging research direction, which attains significant consideration among the advanced research community. The SDP is utilized to evaluate the reliability and quality of the software together with better allocation of limited testing resources. Numerous data mining methods are available in SDP for analyzing the source code, and developmental process and also for extracting critical metrics. The Deep Recurrent Neural Network (DNN) is utilized in this research for the prediction purpose, in which the hyperparameters of the classifier are effectively tuned by the crow search optimization. Further, to minimize the computational time most relevant features are selected with the help of the wrapper selection technique. The analysis is done using the PROMISE data set, based on the performance metrics such as specificity, sensitivity, and accuracy. The specificity, sensitivity, and accuracy of the proposed method are found to be 98.6248%, 93.5694%, and 92.0506% in accordance with the training percentage.
AC/DC: Adaptive Cutoffs and Disputable Cutoffs for Robust Critical Transactions in Smart-Contracts

To guarantee delivery of their intended functionalities in the presence of unresponsive parties, current smart-contracts cut users off from being able to commit their responses after a fixed period of time has elapsed. However, current blockchains have limited transaction processing capacities, so a fixed amount of time will not always be sufficient to receive every critical transaction (C-TX). This paper presents a mechanism for adaptive cutoffs (ACs) which ensures that users retain the opportunity to commit C-TXs despite blockchain congestion, and enables early cutoffs when the number of required C-TXs is low. A non-interactive argument system for setting adaptive cutoffs under the current Ethereum Virtual Machine is described. Additionally, disputable cutoffs (DCs) are presented, which are a more efficient approach used in parallel to ACs based on a bisection-based dispute. Furthermore, it’s empirically demonstrated that an AC/DC-enabled smart-contract can receive a larger number of C-TXs than its non-adaptive counterparts when user responsiveness is slowed due to denial of service or congestion.
Incentive-based probability sensitivity suppression in the behavioral security games

This paper investigates the behavioral security game under the influence of subjective irrationality that is described by probability sensitivity. Firstly, based on the classical attack graph, the behavioral security game is established, in which the attackers' probability sensitivity is modeled by incorporating the probability function into a nonlinear weighting function. Secondly, the existence and uniqueness of the optimal resource allocation are rigorously analyzed for the established behavioral security game. Moreover, from the side of attacker, the adverse effect caused by probability sensitivity is deeply analyzed, based on which, two incentive mechanisms, namely the reward mechanism and the punishment mechanism, are designed to suppress probability sensitivity. Finally, a numerical example is given to show the effectiveness of the proposed method.
Graph Neural Networks Via Contrast between Separation and Aggregation for Self and Neighborhood

Graph Neural Networks (GNNs) have demonstrated state-of-the-art performance in various analytical tasks. Current GNN approaches focus on learning node representations based on the proximity similarity principle. However, they may fail to generalize to complex relations such as disassortative structure, because irrelevant information may be aggregated and generate unreasonable representations. To this end, we introduce a novel approach – Graph neural networks via co ntrast between s eparation and a ggregation for self and neighborhood (COSA), to effectively learn the node representations in the disassortative structure. To search friendly neighbor space with mitigating the unreasonable of connections, COSA synthesizes the feature semantic space and the structure semantic space. To learn reasonable representations for disassortative graphs, COSA stacks multiple GCNs and learns the intrinsic interrelationship between the optimal friendly aggregated information and the separated information originating from self and neighborhood by contrastive learning. At the same time, COSA integrates the variational EM to train the whole framework for exploring the approximated posterior. Extensive experimental results on eight real-world graph datasets show that COSA outperforms eight state-of-the-art GNN models in diverse graph mining tasks, including node classification and link prediction, demonstrating COSA’s superior graph representation ability.
Dynamic Pruning for Parsimonious CNN Inference on Embedded Systems

As a consequence of the current edge-processing trend, Convolutional Neural Networks (CNNs) deployment has spread to a rich landscape of devices, highlighting the need to reduce the algorithm's complexity and exploit hardware-aided computing, as two prospective ways to improve performance on resource-constrained embedded systems. In this work, we refer to a compression method reducing a CNN computational workload based on the complexity of the data to be processed, by pruning unnecessary connections at runtime. To evaluate its efficiency when applied on edge processing platforms, we consider a keyword spotting (KWS) task executing on SensorTile, a low-power microcontroller platform by ST, and an image recognition task running on NEURAghe, an FPGA-based inference accelerator. In the first case, we obtained a 51% average reduction of the computing workload, resulting in up to 44% inference speedup, and 15% energy-saving, while in the latter, a 36% speedup is achieved, thanks to a 44% workload reduction.