Reviewerly/exHarmony huggingface dataset:
A Prototype Model for Image Encryption using ZigZig Blocks in Inter-Pixel Displacement of RGB Value

Amount of information which can be stored within the images, and the movement of images over open network takes to the problem of image theft and breach of confidentiality.To prevent the loss of information from images, it leads the idea of image encryption in which various researchers are trying to encrypt image in such a way that un-authorized users should not be able to access the information kept within image, and for the same purpose researchers have proposed different image encryption techniques with confusing features to keep the images secure when they are moving over open networks.So, in the chain of existing work, we are purposing an image encryption method which is using block formation within the image, and each block is then used in zigzag manner followed by interpixel displacement of RGB values within that zigzag area of the block.The block sizes can vary from image to image and this is part of the key.
Using a Pursuit Tracking Task to Compare Continuous Control of Various NIME Sensors

This study investigates how accurately users can continuously control a variety of one degree of freedom sensors commonly used in electronic music interfaces.Analysis within an information-theoretic model yields channel capacities of maximum information throughput in bits/sec that can support a unified comparison.The results may inform the design of digital musical instruments and the design of systems with similarly demanding control tasks.
Extreme Learning Machine for Thyroid Nodule Classification with Graph Cluster Ant Colony Optimization Based Feature Selection

Thyroid nodule is defined as an endocrine malignancy that occurs in humans due to abnormal growth of cells. Recently, an increasing level of thyroid incidence has been identified worldwide. Thus, it is necessary to detect the nodules at an early stage. Ultrasonography is an important tool that is utilized for the detection as well as differentiation of malignant thyroid nodules from benign nodules. The nodules in ultrasound appear in different heterogenic forms, which are difficult to differentiate by the physicians. Further, large number of features available in US characteristics increases the computation time as well as complexity of classification. In this paper, Graph-Clustering Ant Colony Optimization based Extreme Learning Machine approach is proposed to achieve efficient diagnosis of thyroid nodules. It will enhance thyroid nodule classification by selecting only the optimal features and further using it for improving the function of classifier. The main goal of this technique is to differentiate the malignant nodules from the benign nodules. The performance of both feature selection and classification are evaluated through parameters such as accuracy, AUC, sensitivity and specificity. From the experimental results, it is revealed that the proposed method is significantly better than the existing methods. Thus, it is considered to be an effective tool for diagnosing the thyroid nodules with less complexity and reduced computation time.
Musical Computer

A new page in the manifestation and demonstration of expressive means of musical sound has been opened by the computer as a musical instrument, or the “musical computer.” Contemporary musicians have endeavored at the attempts at connecting composition with the theory of information, the unification of musical and acoustic parameters, having realized the idea of a regulated poly-dimensional sound space, a multilevel compositional model applied for sound generation with the use of concisely differentiated modulations of parameters of the level of frequencies, durations and dynamics. In the present lecture the author turns to the main stages of the evolution of the concept of “musical computer” reflecting the changes in the sound and the musical material during the course of the development of the practice of composition and music-making; questions are examined in connection with the particularities of development of the computer synthesis of sound; a retrospective analysis of technical and technological experiments made by musicians in their interactions with computer technologists, who have enhanced the development of the musical computer as a polyfunctional musical instrument, including the capabilities of its use as a means of providing the symphonic organization of music by means of stereophonic panorama setting of musical space; a broad range of issues is touched upon in connection with the peculiarities of musicians’ thinking and perception, including the impediments to a consistent acquisition of possibilities of the programmed musical instrument existent in the sphere.
The Impact of ICDL Training on Developing Digital Literacy of Young People

The purpose of this study is to examine how teaching ICDL courses could improve the IT skills of young people.A sample of 27 young participants was selected from Mosul city.A questionnaire and interviews regarding digital skills and knowledge were the data collection methods.The data were then analyzed to determine the current level of participants and to fulfill the objective of this study.At the end of the training course, the students were evaluated to analyze the learning outcomes of the course.According to the study findings, most of the participants improved their online and spreadsheets skills by an increase of 90 percent from their previously recorded results and around 50 percent increase for the computer and word processing essentials.The gender gap was not entirely evident in the conducted test as the participation rate of females was high and reached an approximation of 50% in total, however, female, and male counterparts of both age groups, proved competent during the tests, with males proving to be more successful in both age groups.This leads to the conclusion that more efforts are needed to rally females into being involved in the computer world and to challenge their male counterparts in computer literacy and in taking their future path in computer science majors.Additional ICDL training is also recommended to be given to IDPs and war-torn areas where a large percentage of youth lack the basic IT skills, as it is deemed useful to the recovery of their community and in turn, provide an adequate future to shape the return framework.
Hubungan Antara Kecerdasan Emosi Dan Keterampilan Sosial Dengan Penyesuaian Diri Pada Remaja Karang Taruna Bina Muda Dusun Ploso Desa Ngrawan Kec.Getasan Kab. Semarang

As long as humans live, they need interaction with other people both individually and in groups, as social beings humans will go through stages of life that influence one another. One of them is the adolescent stage which has a big influence on his life. This study aims to determine the significance of the relationship between emotional intelligence and social skills with self- adjustment in youth youth organizations of Bina Muda Desa Ngrawan. The population in this study amounted to 30 adolescents. Data collection techniques used in this study were emotional intelligence questionnaires, social skills questionnaires, and self-adjustment questionnaires that have been developed by researchers. The data analysis technique used in this research is the Kendall-tau technique which is processed with the help of the SPSS program. 26.0 for windows. Based on the research results obtained r = 0.560 and p = 0.000 &lt;0.05, which indicates that there is a significant relationship between emotional intelligence and social skills with self-adjustment in youth youth organizations at Ploso Hamlet, Ngrawan Village, Getasan, District Semarang. From these results it can be concluded that the higher the score for emotional intelligence and social skills, the higher the score for self-adjustment. Conversely, the lower the emotional intelligence and social skills scores, the lower the adjustment scores will be.
Safe Light Weight Cipher using Ethernet and Pentatop Number

Current essential factor in this world to send a sensi-tive information over the unsecured network like the internet is security. Protection of sensitive data is becoming a major raising problem due to rising technologies. A recent attack on Electron-ic Mail of CBI shows that attacker’s efficiency rate. Standard cryptographic algorithms can be exploited by the attackers fre-quently and unable to apply for standard devices because of their energy consumption due to high computation with slow processing. Lightweight cryptography based algorithms can reduce these problems. This paper deals with symmetric key cryptography technique to encrypt the data where the sender and receiver share a common key which can also be called a secret key cryptography. To encrypt and decrypt the data, randomly generated Pentatope Number has used as a key. Next level of security will be provided using EHA (Ethernet Hardware Ad-dress or MAC Address) which is globally unique, to provide secure data transmission. The increasing of attacks on related key attacks motivates this. In particular, we investigate the effi-ciency impact comparatively other standard algorithms and observed that applications do not always use cryptographic algorithms without their intended use.
Blockchain-based Smart Waste Management System

Blockchain generation and the Internet of Things are two of the most famous technology these days. IoT is an interconnection of devices which have the usefulness to detect, degree, a strategy the country of natural markers just as themselves and incite dependent on the enter outfitted. It can help make astute arrangements which could enhance the best of ways of life of individuals. In like manner, blockchain is dispensed database structures that guarantee an extreme dimension of wellbeing and accessibility of actualities with least exchange overhead. In this proposal, we endeavor to convey together that two innovation to grow a Smart Waste Management System (SWMS). The SWMS is weight-based for example Clients need to pay for the utilization of administrations as indicated by the measure of waste they produce. Installments are made the use of a custom digital currency controlled by utilizing Smart Contracts and the total SWMS can be supported with the guide of a DAO through a totally computerized, observably secure strategy. Blockchain can help bring down the infiltration and supplier esteem which might be particularly valuable to developing countries in which governments are not exceptionally inventive. This paper tries to set up an evidence of concept thru size of performance and evaluation of the applicability of this type of device.
Statistics of Numerals in Authorial Texts and Stylometry

Two approaches to the statistical analysis of texts are suggested, both based on the study of numerals occurring in coherent literary texts.The first approach is related to the study of the frequency distribution of various leading digits of numerals occurring in the text.These frequencies are unequal: the digit 1 is strongly dominating; usually, the incidence of subsequent digits is monotonically decreasing.The frequencies of occurrence of the digit 1, as well as, to a lesser extent, the digits 2 and 3, are usually a characteristic author's style feature, manifested in all (sufficiently long) literary texts of any author.This approach is convenient for testing whether a group of texts has common authorship: the latter is dubious if the frequency distributions are sufficiently different.The second approach is the extension of the first one and requires the study of the frequency distribution of numerals themselves (not their leading digits).The approach yields non-trivial information about the author, stylistic and genre peculiarities of the texts and is suited for the advanced discourse analysis.A. Platonov, I. Ilf and E. Petrov, V. Kataev, and M. Bulgakov.Frequency distributions of the leading digits of numerals in Dobychin's and Platonov's texts are shown, which differ markedly in appearance and confirm the significant stylistic originality of the texts of the two authors.For all of the above authors, we obtained the frequency distributions of the numerals found in the texts.The hypothesis that Ilf and Petrov are fake authors of the novels "The Twelve Chairs" and "The Little Golden Calf", and they were ghosted by Bulgakov, is investigated.The frequency distribution of numerals, as well as its cluster analysis, do not confirm this hypothesis. The proposed approaches are illustrated by examples of computer analysis of the literary texts by L. Dobychin,
Software Defect Prediction Using Machine Learning

Software defect prediction analysis is an important problem in the software engineering community. Software defect prediction can directly affect the quality and has achieved significant popularity in the last few years. This software prediction analysis helps in delivering the best development and makes the maintenance of software more reliable. This is because predicting the software faults in the earlier phase improves the software quality,efficiency, reliability and the overall cost in SDLC. Developing and improving the software defect prediction model is a challenging task and many techniques are introducing for better performance. Supervised ML algorithms have been used to predict future software faults based on historical data[1]. These classifiers are Naïve Bayes (NB), Support Vector Machine(SVM) and Artificial neural network(ANN). The evaluation process showed that ML algorithms can be used effectively with a high accuracy rate. The comparison is made with other machine learning algorithms to finds the algorithms which gives more accuracy. And the results show that machine learning algorithms gives the best performance. The existence of software defects affects dramatically on software reliability, quality, and maintenance cost. Achieving reliable software also is hard work, even the software applied carefully because most time there is hidden errors. In addition, developing a software defect prediction model which could predict the faulty modules in the early phase is a real challenge in software engineering. Software defect prediction analysis is an essential activity in software development. This is because predicting the bugs prior to software deployment achieves user satisfaction, and helps in increasing the overall performance of the software. Moreover, predicting software defects early improves software adaptation to different environments and increases resource utilization.
Domain Specific Model Driven Approach for Adaptive Systems

An adaptive system is any system that can self-conform according to changes that occur inhis environment. Self-adaptation includes self-reconfiguration, self-restructuring, self-repair, self-optimization or allat the same time. The realization of this kind of systems, in spite of the efforts made, suffers from a deficiency of engineering approaches. One of the most promising techniques in this quest is model-driven engineering. In the model-driven engineering paradigm, the model is the backbone of the systems engineering process. In this paper, we outline a model-based approach that offers a way to explicitly design self-adapting standard systems. We define it based on the UML profiling technique which allows to specify models for the most application domain frameworks. Through this profile we clearly define the components involved in the management of adaptation of systems, as well as the relationships between them. We present, for practical validation, an example application based on the approach.
Museums of the Future: Heritage Experiences in the Reality-Virtuality Continuum

In this paper we reflect on the interplay and the disconnects between real and virtual heritage experiences, and the fragmented nature of digital experiences.We consider the important engagement potential that virtual interactions bring to small less visible artefacts, like clay cuneiform tablets, and, with case study examples, we imagine museums of the future where engagements unite, blend and reinforce rich heritage experiences.
Characterization of Upper Cretaceous Sediments in Parts of Afikpo Area, Afikpo Syncline, Southeastern Nigeria

Lithostratigraphic study in parts of Afikpo area, was undertaken to reappraise and characterize sedimentary packages of the Upper Campanian -Maastrichtian periods using descriptive lithostratigraphic, sedimentologic, petrographic and paleontologic methods.The study was undertaken to give insights into the petroleum prospectivity of the area by reconstructing the paleogeographic conditions of the Afikpo sedimentary basin.Six lithofacies were analyzed to establish dominant depositional processes.The sediments of the Nkporo Group in the area, with dominant coarsening up and aggradational patterns were deposited in shallow shelf environments of deposition (EODs).The sediments of Mamu Formation have heterolithic carbonaceous mudstone and sandstone intervals suggesting brackish backshore marsh EOD.The identified gastropod (Turritella) in the sediments of Nkporo Formation indicated shallow marine conditions of about 22 m paleobathymetry where Turritella was able to burrow spires downwards into muddy sediments.The foraminiferal assemblage in the studied sediments was mainly coarsely agglutinated benthonic taxa suggesting deposition in estuarine-nearshore shallow marine EOD.Palynofacies recovered in the studied sediments are indicative of swampy conditions within a predominantly fluvial setting.The age range of the studied sediments diagnosed using palynofacies indicated Late Campanian to Late Maastrichtian age.Petrographic examination of sandstone samples showed a mineralogy dominated by quartz with minor feldspars and rock fragments and a Maturity Index in the range of 5-16, which is indicative of mineralogically mature sandstones.Provenance of the sandstones indicated sources from recycled orogen.A 3-D depositional model for the study area showed pro-delta to delta front deposits for the Nkporo Group as the sediments were deposited in shallow marine conditions during the waning periods of the Late Campanian transgression and became increasingly arenaceous and shallower during the deposition of Afikpo Sandstone deposits.An estuarine tidal delta to shallow marine conditions with intermittent paludal paleodepositional model was proposed for the Mamu Formation.The lithostratigraphic units in the study area thus have petroleum system elements deposited under conditions that could favor generation and accumulation of hydrocarbons.
Designing Educational Board Games For Children On Snake Handling Using Competitive Mechanisms

Education with material and only listening to explanations make some people bored so that the information they want to convey is not well received. In addition, there is a lack of media that can educate snake handling and facilitate children in learning in a fun way and can be played outside of study hours. This study aims to analyze an educational board game for snake handling using competitive mechanisms in children. This research uses a descriptive method. The techniques used in collecting data are interviews, observations, and questionnaires. The instrument used to collect data is a questionnaire. Sources of data obtained from previous studies, online articles, and interviews will be analyzed descriptively. Based on the results of data analysis, the board game as an educational media about the basic handling of snakes has a positive impact on the target. 80% of the targets who do not understand handling snakes and other types of snakes understand and enjoy the education implemented in the board game media. It can be concluded that educational board games can educate children about snake handling using competitive mechanisms.
Abnormal network data mining model based on deep training learning

Aiming at the problems of low detection efficiency and poor clustering effect in traditional abnormal network data mining process, an abnormal data mining model based on kernel extreme learning machine and particle swarm optimisation is proposed. The enhanced local linear embedding algorithm is used to extract the features of abnormal network data, and the required feature dimensions are extracted repeatedly to obtain the corresponding features of target network data. K-means algorithm is introduced to cluster the target network data to increase the identification of data mining. By improving the particle swarm optimisation algorithm to optimise the parameters of the kernel limit learning machine, the final abnormal data mining results are the best. The experimental results show that the proposed method has high detection efficiency and good clustering effect, which fully proves the superiority of the proposed method and lays a foundation for the progress of abnormal network data mining technology.
Literature Circle: A Strategy in Improving Critical Thinking Skills

Reading is the prime foundation of one’s academic and social status. However, this vital skill is what most students lack attention and interest. As a result, it deteriorates their critical thinking skills. To address the problem, Harvey Daniel introduced his literature circle strategy. It showcases different features that likely aid student’s struggles in the aspect of reading. This study, therefore, aims to determine the effectiveness of the literature circle strategy in improving critical thinking skills in reading among students at the University of Cebu-Banilad Campus. The study utilized a quasi-experimental method where the 48 ABM Grade 12 research subjects of the study were divided into two groups, namely: control and experimental. Strategies employed in these groups were different. The traditional method was practiced in the control group, while the literature circle strategy was implemented in the experimental group. For data gathering, a researcher-made test about the short story “Dead Stars” by Paz Marquez Benitez was used as the main instrument. Another short story entitled “The Small Key” by Paz Latorena was used as a springboard in the implementation of strategies for both groups. Statistical treatments used for data analysis were simple percent, t-test for Correlational Samples, and t-test for Two Independent Samples. Findings revealed that in measuring subjects’ critical thinking skills in reading, there is a significant difference between the posttest performances of the groups. The research subjects under experimental group performed higher than the control group. The study concludes that literature circle strategy is effective in enhancing student’s critical thinking skills in reading.
Antiphishing Model Based on Similarity Index and Neural Networks

Phishing is a negative technique that is used to steel private and confidential information over the web. In the present work author proposed a hybrid similarity of Cosine and Soft Cosine to calculate the similarity between the user query and repository as an anti-phishing approach. The proposed work model uses a multiclass learning method called Feed Forward Back Propagation Neural Network. The model evaluation results with 100 to 3000 test files shows that the hybrid model is able to detect the phishing attack with an average precision of 71% and is highly effective.
Signify: Signature Verification Technique using Convolutional Neural Network

Signature is one of the biometric traits that are being used in person authentication and due to its dominant usage; it became one of the top subjects of forgery. In this study, a signature verification using Convolutional Neural Network (CNN) is proposed. With the use of transfer learning, inception-v3 is mainly used for the feature extraction of data samples and for classification of signatures. The proposed method is assessed on dataset of handwritten signatures gathered from 4 people with 100 signatures each. The testing results determine the threshold value which is 96.43%. Factors that affect the accuracy of the result were also identified.