Reviewerly/exHarmony huggingface dataset:
Computational Theory

All calculations presented in this thesis were performed using ab initio methods. These describe chemical systems by working up from fundamental physics, in contrast to empirical or semi-empirical approaches which rely on fitting to experimental measurements.
Blockchain-based Big Data Analytics Approach for Smart Cities

The wireless communication is the fastest-growing field of studies to enable humans to communicate with each other. Introducing a new blockchain architecture with big data analytics can be an enticing platform towards increasing connectivity performance throughout the smart cities. ong the physical objects.
Allocation of Cloudlets using Solution of Job Assignment Problem

Resource allocation policies play a key role in determining the performance of cloud. Service providers in cloud computing have to provide services to many users simultaneously. So the job of allocating cloudlets to appropriate virtual machines is becoming one of the challenging issues of cloud computing. Many algorithms have been proposed to allocate cloudlets to the virtual machines. Here in our paper, we have represented cloudlet allocation problem as job assignment problem and we have proposed Hungarian algorithm based solution for allocating cloudlets to virtual machines. The main objective is to minimize total execution time of cloudlets. Proposed algorithm is implemented in Cloudsim-3.03 simulator. We have done comparative analysis of the simulation results of proposed algorithm with the existing First Come First Serve (FCFS) scheduling policy and Min-Min scheduling algorithm. Proposed algorithm performs better than the above mentioned algorithms in terms of total execution time and makespan time (finishing time of last cloudlet)
Secured Processing o f Data Over Cloud u sing Disjoint Multi Attribute Authority Scheme f or Key Generation

Cloud computing being the extensive technology used across globe for data sharing. The data may vary from small file to a highly confidential file consisting of various sensitive information stored in it. Since the cloud services are provided by the third party vendors, users are very much concerned about the security and privacy of the data and data access details. The users wants their traceability to be hidden by the cloud vendors. The biggest challenge is to share the data in a most secured way by encrypting and also preserving the anonymity of the users in cloud from the vendors. This paper addresses the issue by proposing a multi attribute authority in key generations of users, where the few sub sets of attributes will be used by multiple attribute authorities randomly and hence masking of the selection of attributes from various authorities and providing a mechanism for efficient data distribution in cloud by preserving the anonymity of the users.
Visual Analytics for Network Events Classification in LAN With Deep Convolutional Neural Network

Abstract This article illustrates a method of visualizing network traffic in LAN based on the Hilbert Curve structure and the array exchange and projection, with nine types of protocols’ communication frequency information as the discriminators, the results of which we call them feature maps of network events. Several known scan cases are simulated in LANs and network traffic is collected for generating feature maps under each case. In order to solve this multi-label classification task, we adopt and train a deep convolutional neural network (DCNN), in two different network environments with feature maps as the input data, and different scan cases as the labels. We separate datasets with a ratio of 4:1 into the training dataset and the validation dataset. Then, based on the micro scores and the macro scores of the validation, we evaluate performance of the scheme, achieving macro-F-measure scores of 0.982 and 0.975, and micro-F-measure scores of 0.976 and 0.965 separately in these two LANs.
Forecasting the Number of Tuberculosis (TBC) Using Fuzzy Prey Predator

Tuberculosis (TB or TB) is an infectious disease caused by the bacteriumMycobacterium tuberculosis. Tuberculosis (TB) is still a major health problem in the world, so from this it is necessary to forecast to determine the increase in the number of TB diseases, and later can be taken in prevention. This study uses the Fuzzy preypredator method. The data source used is data on TB patients at Batumarmar Health Center. From the results of this study, it was found that the population of Prey and Predator was sought for stability, so that after applying the fuzzy prey predator model, convergent results were obtained. Prey population and Predator population decreasedfor one month, afterwards covergen.
Research on Joint Cultivation Mechanism of Adult Education under Collaborative Innovation Environment in China

Under the initial formation of collaborative innovation environment in China, adult education is also quietly playing a role in the cooperation among various innovative subjects. Among universities, between universities and scientific research institutions, and between universities and local innovation forces, through the joint training of high-level talents, to achieve adult education cooperation, the mode of repeated innovation, the quality advantage of joint training of talents is increasingly obvious. Based on previous studies, this paper analyzes new characteristics of the adult education in the era of collaborative innovation, and proposes countermeasures against challenges to adult education of collaborative innovation from four aspects.
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH THE JIGSAW II LEARNING METHOD AT THE SECOND-GRADE OF MAS NIDA’UL KHAIRAAT POMBEWE

This research aimes to investigate the significant effect of applying the jigsaw II learning method at the second-grade of MAS Nida’ul Khairaat Pombewe. The research employed quasi and need pre-test and post-test. The population of this research was all students of the second grade of MAS Nida’ul Khairaat Pombewe. The sample was selected by using quota sampling technique. The samples were 20 students of IPS class as the controlled class and Agama class as the experimental class. The researcher analyzed the data statistically. The mean score of the experimental class was (67.05), it was higher than the mean score of the control class (45). The result of t-counted (2.327) which was higher than the t-table (2.153). The researcher used the 0.05 level of significance and the degree of freedom (df= 10 + 10 – 2). This research has two hypotheses, which were : there is no improvement of the students’ reading comprehension through the Jigsaw II learning method at the second grade of MAS Nida’ul Khairaat Pombewe and : there is improvement of the students’ reading comprehension through the Jigsaw II learning method at the second grade of MAS Nida’ul Khairaat Pombewe.Based on the resuls of the data above it proven that was accepted and was rejected. In conclusion, the use of the Jigsaw II learning method can improve the students’ reading comprehension at the second grade of MAS Nida’ul Khairaat Pombewe.
Enduring Meaning

This chapter provides a deeper discussion of the theories of meaning and communication that were employed by the experts involved in developing plans for nuclear waste repository marker systems. These linguistic, semiotic, and psychological models were more important to planners than archaeological models for the material form. These models emphasized the intentions of people creating messages with the intention of having them be understood by receivers. The models used are contrasted with the kind of semiotic approaches, especially those rooted in the work of pragmatic philosopher Charles Sanders Peirce, that contemporary archaeologists employ, in which rather than having preformed meanings conveyed by a vehicle, meanings are constantly emerging in practice.
A Brief Concept of Natural Language Processing: The Chomsky Hierarchy and Hidden Markov Models

Nowadays Natural Language Processing (NLP) has been popularly applied in the real world, and a lot of NLP applications can be seen in our daily life.Basically, NLP plays an important role in promoting an advanced development to Artificial Intelligence, and the primary methods contributing a highperformance NLP comprise Chomsky Hierarchy and Hidden Markov Models.Furthermore, these methods become an increasing issue to carry out a more advanced application of NLP.
NIGER: Bazoum’s Win Confirmed

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 58, Issue 3 p. 23087B-23088A Internal Developments NIGER: Bazoum’s Win Confirmed First published: 16 April 2021 https://doi.org/10.1111/j.1467-825X.2021.09914.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume58, Issue3April 2021Pages 23087B-23088A RelatedInformation
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time

Abstract Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs." To implement RC in edge computing, it is highly important to reduce the amount of computational resources that RC requires. In this study, we propose methods that reduce the size of the reservoir by inputting the past or drifting states of the reservoir to the output layer at the current time step. To elucidate the mechanism of model-size reduction, the proposed methods are analyzed based on information processing capacity proposed by Dambre et al. (2012). In addition, we evaluate the effectiveness of the proposed methods on time-series prediction tasks: the generalized Hénon-map and NARMA. On these tasks, we found that the proposed methods were able to reduce the size of the reservoir up to one tenth without a substantial increase in regression error.
Support Vector Machine Based Feature Extraction for Gender Recognition From Objects Using LASSO Classifier

Abstract Object detection and gender recognition are the two different categories to be classified in a single section is a complicated task and needs to support the blind people. In this paper, our method to the better sensation of blind persons by conversion of visualized data to audio data. Therefore the artificial intelligence model requires to detect the objects as well as human face recognition with gender classification algorithms. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale-invariant feature transform ( MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification, three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognizing human actions using image processing approach. This approach can be applied for both offline and online modes. But in this scenario, an offline mode was implemented and was tested with a combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression(LR) and LASSO regression were implemented. The final classification results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and proposed approach(LRGS) with 98.4% accurate detection rate with prediction name of classes.
5. Administrasi Pendidik dan Tenaga Kependidikan (PTK) 2

The purpose of writing this article is to find out the guidance and development of educators and education personnel, promotion of educators and education staff, dismissal and retirement of educators and education personnel, evaluation of educators and education staff and the role of teachers in the administration of educators and education staff. Educators and education personnel have certain term of office and will retire with the conditions that will be explained in this article. In the evaluation of educators and educational staff aims to evaluate evaluating the level of performance of teachers individually in order to achieve maximum school performance that has an impact on improving student achievement.
Object Detection In Cluttered Background using Color Clusters

Object detection in presence of complex background and illumination variation is important image analysis problem with many applications. Most of the object detection algorithms use local image descriptors which are computed from interest points based on luminance information and neglect precious color information of an object. If appearances of the object to be detected contain multiple colors in non-homogeneous distributions then it makes it difficult to detect these objects using shape features. In this context, we propose a robust algorithm designed to detect a class of objects using a descriptor which is computed from color information of an object. Clusters are formed in Hue and Saturation (HS) color space of an object using k-means clustering and cluster analysis based on number of pixels belong to each cluster, object detection is performed. Use of clustering algorithm in color space of an object to form descriptor reduces the large dimensionality of the histogram bins in the computation. The performance of the algorithm is demonstrated by experimentation carried out on standard dataset GroZi-120. Experimental results shows that the proposed algorithm is insensitive to scaling, object rotation, illumination variations and capable of handling cluttered background effectively. Finally results shows that proposed algorithm outperforms closely related algorithm by a decisive margin.
Investigating the impact of preprocessing on document embedding: an empirical comparison

Digital representation of text documents is a crucial task in machine learning and natural language processing (NLP). It aims to transform unstructured text documents into mathematically-computable elements. In recent years, several methods have been proposed and implemented to encode text documents into fixed-length feature vectors. This operation is known as document embedding and it has become an interesting and open area of research. Paragraph vector (Doc2vec) is one of the most used document embedding methods. It has gained a good reputation thanks to its good results. To overcome its limits, Doc2vec, was extended by proposing the document through corruption (Doc2vecC) technique. To get a deep view of these two methods, this work presents a study on the impact of morphosyntactic text preprocessing on these two document embedding methods. We have done this analysis by applying the most-used text preprocessing techniques, such as cleaning, stemming and lemmatisation, and their different combinations. The experimental analysis on the Microsoft Research Paraphrase dataset (MSRP), reveals that the preprocessing techniques serve to improve the classifier accuracy; and that the stemming method outperforms the other techniques.
Position Identification using Dihedral Function for Data Hiding

Data hiding techniques can be used for protecting data from unauthorized users. Here, data hiding technique called steganography is used to prevent illegal access of data. We introduced a new method of hiding data in images by randomly selecting pixel position using dihedral function. We have used different window size to hide data and applied variable dihedral function based on the size of variable. The security of this approach lies in the knowledge of window size and the function. The performance of our method was analyzed using Chi-square, RS steganalysis attack and histogram attack. The quality of the image was also preserved and found to be 52 dB.
Antecedents of Rape Cases Exposure Over Social Media: A Comparative Study of Urban and Rural Areas of Lahore District

The goal of this study is to determine the effects of rape case's exposure over social media.The researcher has observed the changes among users who are going through the exposure of rape cases in this research.The present study is based on the quantitative approach in which the researcher has selected the survey method as a tool to explore and investigate the consequences of rape cases exposure among users.The data was collected through a questionnaire.The questionnaires were distributed in ive different tehsils of Lahore (Lahore Cantt, Raiwind, and Lahore City, Shalimar, and Model town) including both urban and rural areas using conveniencesampling technique.The data were analyzed through SPSS using regression and chi-square techniques.One hundred participants from each tehsil were assigned to complete several surveys regarding rape cases exposure.This research focused on the selected effects of rape case exposure like anxiety, phobia, fear, depression, frustration, and mental illness problems.Results indicate that any person who has more exposure to rape cases has more effect on the user's mental state.Upon mining such data on Facebook, Instagram, and Twitter, some privacies and securities to control the negative exposure over social media and the government should develop a strict law and order situations and check the calculations highlighted in the thesis that the subject matter has become serious.The researcher will suggest that the punishments from an Islamic point of view are the perfect solution to the problem of raping young girls and children.
Research of NOSQL and SQL and Designing a Better Database Schema for Databases

The amount of data storage and querying is increasing day by day. The requirement of data-storage, security, scalability and management is an alarming issue. Structured Query Language (SQL) databases was designed to overcome most of the trouble faced over tradition file storage system. But the alarming rate of data storage leads to Not only Structured Query Language (NoSQL) databases. in this paper, various aspects of SQL and NoSQL are compared with respect to their data management and to develop a better schema for data management is attempted.
Efficient Comparator Design for Motion Estimation on FPGA

Motion Estimation(ME) operationinvolves predicting the frames and identifying motion vectors sothat redundancy can be exploited by eliminating the transfer ofsimilar information between successive frames.The most efficientand simple technique to estimate the motion vectors is Summation ofAbsolute Difference(SAD) where comparator forms one of an elemental component in SAD computation.This paper proposes two different comparator designs where propoundcircuit I is based on efficient look ahead comparator andpropound circuit II uses alteredone’s complement and conditional sum adder method. Results shows that propound circuit I reduces delay by 23%but with 16% increase in number of slice LUTs whereas the propoundcircuit II reduces delay by 11% and gives 33% reduction in number of slice LUTsas compared to traditional circuit. The propound hardwarecircuits are implemented on Virtex 7 FPGA and synthesized using Verilog as HDL language on Xilinx ISE 14.2.
A single photonic perceptron based on a soliton crystal microcomb for scalable, high speed, optical neural networks

&lt;p&gt;&lt;b&gt;Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning — have significant potential for ultra-high computing speed and energy efficiency. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking. &lt;/b&gt;&lt;/p&gt;
Scale-invariant histogram of oriented gradients: novel approach for pedestrian detection in multiresolution image dataset

This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection.Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM).Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection.However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM.The proposed method forms a scale-space pyramid wherein the histogram bin is calculated.Thus, the gradient information from all the scales is encapsulated in a single fixed-length feature vector.The proposed method is also combined with color and texture features.The proposed approach is tested on three established benchmark pedestrian datasets: INRIA, NICTA, and Daimler.An improvement of ≥ 4.5% in the miss rate is achieved for all the three datasets considered.We also show that the SI-HOG can be applied to multiresolution datasets for which the HOG feature cannot be applied.Additionally, the MapReduce model is used to obtain the same outcome.The results indicate that the proposed approach outperforms the pedestrian-detection methods considered in this work.
STARTING A CYBER SECURITY PROGRAM AT A UNIVERSITY: A CASE STUDY

Many institutions today are interested in starting cybersecurity programs mainly because the job market demand is very high students want to take advantage of the employment opportunities.This paper describes the journey that one university went through to build a successful cybersecurity program.There are essential components that must exist to build a successful program and this paper goes into what those are and describes specific examples from an existing successful program.