Reviewerly/exHarmony huggingface dataset:
Enhanced PROBCONS for Multiple Sequence Alignment in Cloud Computing

Multiple protein sequence alignment (MPSA) intend to realize the similarity between multiple protein sequences and increasing accuracy.MPSA turns into a critical bottleneck for large scale protein sequence data sets.It is vital for existing MPSA tools to be kept running in a parallelized design.Joining MPSA tools with cloud computing will improve the speed and accuracy in case of large scale data sets.PROBCONS is probabilistic consistency for progressive MPSA based on hidden Markov models.PROBCONS is an MPSA tool that achieves the maximum expected accuracy, but it has a time-consuming problem.In this paper firstly, the proposed approach is to cluster the large multiple protein sequences into structurally similar protein sequences.This classification is done based on secondary structure, LCS, and amino acids features.Then PROBCONS MPSA tool will be performed in parallel to clusters.The last step is to merge the final PROBCONS of clusters.The proposed algorithm is in the Amazon Elastic Cloud (EC2).The proposed algorithm achieved the highest alignment accuracy.Feature classification understands protein sequence, structure and function, and all these features affect accuracy strongly and reduce the running time of searching to produce the final alignment result.
Sequential Inverse Covariance Intersection Fusion Estimation for Multi-sensor Systems with Multiple Delays and Correlated Noises

In order to deal with the fusion estimation problem for the multi-sensor system with multiple time delays and correlated noises, the state space model is converted into a new system with uncorrelated white noises first. Applying the optimal Kalman filtering method, based on the Inverse Covariance Intersection (ICI) method, the Sequential Inverse Covariance Intersection (SICI) fusion Kalman Estimators are presented, which avoids the computation of the cross-covariances. Compared with the traditional Covariance Intersection (CI) algorithm, the ICI algorithm has less conservativeness, and the SICI structure can handle a wider class of fusion problems. The accuracy relations among the local estimators and the fused estimators are proved. The simulation example shows the effectiveness and the consistence of the proposed SICI fusers, and gives the geometric interpretation of the accuracy relations.
Pengaruh Kecerdasan Emosional terhadap Prestasi Belajar Matematika pada Siswa SMP Kelas VIII

This study aims to determine the effect of emotional intelligence on the learning achievement of class VIII junior high school. This type of research is quantitative research. The population in this study is all students of class VIII SMP N 2 Kupang and the research sample is class VIII D students with the sampling technique is simple ramdom sampling. Data collection techniques using questionnaires and mathematics learning achievement tests. Data analysis was carried out in two ways, namely the SPSS program. From the results of the analysis, based on the tests of normality, linearity, significance, emotional intelligence has an influence on mathematics learning achievement and was obtained by equation of regression Y = 0,642X and t-count = 4.272 &gt; 2, 052 = t table then, a push H0, it means that there is a significant effect of emotional intelligence on learning achievement.&#x0D; Based on the results of the study, the researcher concluded that there was a significant effect of emotional intelligence on mathematics learning achievement of class VIII students of SMPN 2 Kota Kupang.
Dual paths cryptosystem based on tilt Fresnel diffraction using non-spherical mirror and phase modulation in expanded fractional Fourier transform domain

Abstract In this paper, a dual optics paths optical image cryptosystem based on tilt Fresnel diffraction and a phase modulation in extend fractional Fourier transform (eFrFT) domain is presented. The tilt Fresnel is designed by using a non-spherical mirror. A part of data from the original image is modulated by the mirror, while the other part is encoded by an expanded fractional Fourier transform. Besides, the random data of the dual channels is combined for forming the encrypted image. The structure parameters in designing the optical hardware system and the random phase can be regarded as decryption keys. Various potential attack experiments are implemented to check the validity of the proposed cryptographic system.
Readiness of workers for a new economy in condition of systemic changes (man technologies – economy)

The article analyses and systematizes requirements set to workers in the conditions of a new economy. It has been proven that a gap exists between those requirements and real skills that young people develop under the influence of information technologies and of modern society culture. A conceptual authors’ vision of the educational system development is proposed. Principles of providing a lifetime economic support for the educational system have been singled out.
Imputing Missing Data Analysis in Web Service Quality Dataset for Improving QoS Prediction

Abstract The web services at present have countless options for similar tasks. This wide range in web services induce challenge to choose the best service among all available. QoS prediction is a key of the selection but it is very time-consuming affair. Any prediction strategy relies on accuracy and completeness of available data, especially in case of QOS Prediction. Feedback, throughput and response time are the major attribute that should not be missed and incorrect. So, it's important to identify the missing value in the web service datasets. Therefore, a study of three missing value prediction approaches was undertaken to investigate their performance for missing values in datasets for web service. Benchmarked WS Dream dataset include response time and throughput matrices of web services is selected to analyze the performance of selected approaches. An extensive experiment is performed, and results are collected, which conclude the superiority of MICE approach over other approaches. Keywords: WS Dream, MICE, kNN, missForest, MAE, MSE, RMSE, QoS Cite this Article Gaurav Raj, Manish Mahajan, Dheerendra Singh, Anjali Singh. Imputing Missing Data Analysis in Web Service Quality Dataset for Improving QoS Prediction. Recent Trends in Programming languages . 2019; 6(2): 9–23p.
An Audio Steganography based on Twi-DWT and Audio-extremum Features

Audio steganography, hiding the secret audio into a host audio, is one of the research difficulties in Computer Network Information Security. It is employed to hide the secret spreading, protect security and prevent illegal tampering. In the paper, a novel steganography based on twice discrete wavelet transform (Twi-DWT) and extremum features is proposed. Firstly, in order to improve the embedding capacity, we designed a second-order difference to extract the extremum sequence of the secret signal as the covert information. Next, the host signal was decomposed twice by DWT to extract a security sequence, which achieves a balance between transparency and robustness. Finally, the security signal is replaced by the covert information. The content and waveform of the stego audio are similar to that of the original host audio, which indicates our method has a good transparency. Through the simulations, it is verified that our algorithm is superior to classical ways on robustness and embedding capacity.
Modeling the Long-Term Post History for Personalized Hashtag Recommendation

Hashtag recommendation aims to recommend hashtags when social media users show the intention to insert a hashtag by typing in the hashtag symbol "#" while writing a microblog. Previous methods usually considered the textual information of the post itself or only fixed-length short-term post history. In this paper, we propose to model the long-term post histories of user with a novel neural memory network called the Adaptive neural MEmory Network (AMEN). Compared with existing memory networks, AMEN was specially designed to combine both content and hashtag information from historical posts. In addition, AMEN contains a mechanism to deal with out-of-memory situations. Experimental results on a dataset of Twitter demonstrated that the proposed method significantly outperforms the state-of-the-art methods.
A Similar Distribution Discriminant Analysis with Orthogonal and Nearly Statistically Uncorrelated Characteristics

It is very difficult to process and analyze high-dimensional data directly. Therefore, it is necessary to learn a potential subspace of high-dimensional data through excellent dimensionality reduction algorithms to preserve the intrinsic structure of high-dimensional data and abandon the less useful information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are two popular dimensionality reduction methods for high-dimensional sensor data preprocessing. LDA contains two basic methods, namely, classic linear discriminant analysis and FS linear discriminant analysis. In this paper, a new method, called similar distribution discriminant analysis (SDDA), is proposed based on the similarity of samples’ distribution. Furthermore, the method of solving the optimal discriminant vector is given. These discriminant vectors are orthogonal and nearly statistically uncorrelated. The disadvantages of PCA and LDA are overcome, and the extracted features are more effective by using SDDA. The recognition performance of SDDA exceeds PCA and LDA largely. Some experiments on the Yale face database, FERET face database, and UCI multiple features dataset demonstrate that the proposed method is effective. The results reveal that SDDA obtains better performance than comparison dimensionality reduction methods.
Aplikasi Interaktif Pengenalan Huruf Hijaiyah Sebagai Media Pembelajaran Anak

Learning to Read the Koran is important for Muslims, learning the Koran is given as early as possible to children with the aim to more quickly study the Koran with tartil as well as to instill the values of Islamic religious education in the child. Before going to the Koran reading stage, the first thing to learn is the introduction of hijaiyah letters. Starting from the pronunciation and how to distinguish the forms, amounting to 30 letters. As the development of technology today, the interest in learning by the book method often makes children bored. So that researchers realize how important it is to introduce hijaiyah letters to children with a fun interactive application. This interactive application is expected to make it easier for children to learn to recognize hijaiyah letters. Its introduction uses audio animation, visuals, and games which are expected to be more effective and efficient than learning methods using books. It is hoped that children will be even more motivated in learning to recognize hijaiyah letters. Based on the results of interactive application testing the introduction of hijaiyah letters for children states that this application can run well on computers and many children love it
Spam Classification on 2019 Indonesian President Election Youtube Comments Using Multinomial Naïve-Bayes

Text classification are used in many aspect of technologies such as spam classification, news categorization, Auto-correct texting. One of the most popular algorithm for text classification nowadays is Multinomial Naïve-Bayes. This paper explained how Naïve-Bayes assumption method works to classify 2019 Indonesian Election Youtube comments. The output prediction of this algorithm is spam or not spam. Spam messages are defined as racist comments, advertising comments, and unsolicited comments. The algorithms text representation method used bag-of-words method. Bag-of-words method defined a text as the multiset of its words. The algorithm then calculate the probability of a word given the class of spam or not spam. The main difference between normal Naïve-Bayes algorithm and Multinomial Naïve-Bayes is the way the algorithm treats the data itself. Multinomial Naïve-Bayes treats data as a frequency data hence it is suitable for text classification task.
INVESTIGATION OF SPECTRAL CHARACTERISTICS OF CARBONATE ROCKS – A CASE STUDY ON POSHT MOLEH MOUNT IN IRAN

Abstract. Recent developments in the image processing approaches and the availability of multi and/or hyper spectral remote sensing data with high spectral, spatial and temporal resolutions have made remote sensing technique of great interest in investigations of geological sciences. One of the biggest advantage of the application of remote sensing in geology is recognizing the type of unknown rocks and minerals. In this study, an investigation on spectral features of carbonate rocks (i.e. calcite, dolomite, and dolomitized calcite) were done in terms of main absorptions, the reasons of those absorptions and comparison of these absorption with Johns Hopkins University (JHU) spectral library and laboratory spectra of Analytical Spectral Devices (ASD) instrument. For this purpose, we used the VNIR and SWIR bands of ASTER and OLI datasets. Finally, we applied the Spectral Analyst Algorithm in order to comparison between the obtained spectra from ASTER dataset and carbonate spectra of JHU spectral library.
A Novel Block Image Encryption Algorithm Based on DNA Dynamic Encoding and Chaotic System

a novel block image encryption algorithm based on Deoxyribonucleic Acid (DNA) dynamic encoding and chaotic system is proposed. First, the image is converted into a DNA sequence by random encoding rules, and the global scrambling at DNA level is performed on it. The change of the DNA base position causes the image pixel value and the pixel position to change, so the scrambling is performed at the same time the effect of diffusion. Then the image is split into blocks, and the image is diffused by the combination of intra-block DNA sequence operation and inter-block pixel operation. In the scrambling and diffusion process, the quantification related to plaintext and ciphertext feedback mechanisms are introduced respectively, so that the algorithm can effectively resist the choice of plaintext attacks. Theoretical analysis and simulation experiments show that the algorithm can resist various typical attacks, has high security and is easy to implement.
Reframing Writing Instruction in Physical Learning Environments: Making Connections Between Digital and Nondigital Technologies

Active Learning Classrooms provide several advantages for teaching and learning by offering many physical and technological affordances that one can choose from when designing instruction. For courses where student writing is central activity to course learning outcomes, a challenge exists in that the innovative digital technologies may hide the opportunity to incorporate non-digital tools, such as paper-based student writing. We argue that treating student writing as a technology can increase opportunities for active learning within technology-enhanced learning environments. In this article, we describe an approach to writing instruction that builds intentional connections between paper-based texts and digital technologies, describing the rationale for the design decisions in an introductory composition course through a design case model. Classroom applications are discussed for physical learning spaces where student writing is incorporated into overall course learning activities.
Theoretic Analysis of Implicatures in Pride and Prejudice and Its Pedagogical Implications

This study was aimed at finding out the implicatures, the types of the implicatures, and the intended meaning of the implicatures employed by the characters in Jane Austenâ€™s Pride and Prejudice. This study was a descriptive qualitative study with corpus-based approach. The data were collected through some procedures, namely reading the novel repeatedly and thoroughly, determining the utterances containing implicatures in dialogues based on the relevance theory, and selecting and organizing the data to be analyzed. To analyze the data, the procedures were presenting and describing the data, Â interpreting the data by referring to the relevance theory, determining the implicated premises and implicated conclusions, determining the strongly implicated conclusions and weakly implicated conclusions, and interpreting and describing the intended meanings of the implicatures. From the results, there were 60 implicatures found in the novel and the two types of them were strong and weak implicatures. As the follow-up, a dissemination to the second semester students of Graduate Program of Sriwijaya University was conducted which yielded to the result that there was no significant association between the respondentsâ€™ knowledge of implicatures and relevance theory and their performance in analyzing implicatures using the theory with the p-value of 0.406 (p-value &gt; 0.05).
E-Learning Sebagai Media Pembelajaran Berbasis Edmodo

Community demands on the quality of education services from time to time are increasingly high. Along with the development of increasingly rapid science and technology, educational institutions in various levels and levels of education can no longer stand by to preserve the cultural and performance capabilities of a school, but must strive to innovate changes in various aspects so that they are not left behind by people who live in in the era of globalization. Computer-based Information Technology is one medium that is quite effective in managing school academic information systems. The use of e-learning in schools as learning media can open up broad insights for students how to communicate in teaching and learning activities using computers. With the construction of e-learning systems as edmodo-based learning media can improve the quality of students in using computers. In addition, it is expected to facilitate students in taking a computer-based national examination.
Multi-word Units in Machine Translation: why the Tip of the Iceberg Remains Problematic – and a Tentative Corpus-driven Solution

Neural machine translation (NMT) has recently made considerable progress in the improvement of the quality of the produced texts.Among the new features of NMT are the fluidity of the translations and their successful treatment of multi-word units.In this paper we first report the results of a global and automated evaluation of the percentage of phraseology in the translations produced by Google Translate and DeepL.A corpus-based approach makes it possible to estimate that both NMT systems succeed in producing an average percentage of phraseology that is quite natural and may sometimes even be higher than in natural language production by native speakers.Taking a closer look at some problematic cases, however, reveals that the phraseological value of NMT systems may be deceptive, as they are often inable to cope with contextual complexity and low-frequency idioms.
C-ARM POSE ESTIMATION AND NAVIGATION IN SURGERIES FOR AUGMENTED REALITY APPLICATION

Abstract. C-arm X-ray imaging systems are widely applied in surgeries. Overlaying X-ray with optical images during the surgery has been shown to be an efficient approach. Moreover, overlaying needed data from different modalities in an augmented reality (AR) manner can improve the accuracy of surgical procedures, decrease the variability of surgical outcomes, reduce trauma to the critical structures, increase the reproducibility of surgeons’ performance, and reduce radiation exposure. C-Arm geometric calibration and recovering the C-arm pose are essential for surgical navigation and AR applications in operating rooms. Therefore, in this paper, existing researches for calibration and pose estimation of C-arm devices in surgical AR applications are evaluated from photogrammetric point of view. Then, a proposed marker-based method for C-arm pose estimation is introduced. For this purpose, a marker is designed to facilitate tracking and pose estimation in mixed reality based on golden section principle, and perspective invariants such as cross-ratios, collinearity, and intersection. Moreover, a procedure is also proposed for fast determination of these fiducial markers. The experiments show benefits of such a structure which has a limited occlusion with consistency to different conditions such as narrow field of view and at the same time, even in images with high projectivity. It also results that the distortion correction step is important and the effect of distortion of X-ray images can cause inconsistency in the perspective invariants.
An Intelligent Internet-of- Things(IoT) Door Bell System for Smart Notification Alert

This paper presents an innovative redesign of a doorbell system in order to eliminate unnecessary ringing noise from users' daily life.Employing artificial intelligence for face recognition, the IoT doorbell system define the visitors as complete strangers or someone who is expected.The next step operates based on this result; the doorbell system will either ring or send out notification to the users' phone depending on the familiarity of the visitor and the user.
Consensus Based Mechanism Using Blockchain for Intensive Data of Vehicles

The explosive development of Intelligent Vehicles (IVs) has led to a complex network, which is difficult to manage due to the extensive communication of vehicles and storage of vehicles’ data. Due to increase in number of vehicles, IVs come up with large difficulties. Huge data generated by IVs is very difficult to be handled due to limited storage and lack of intelligent management. Many security and privacy problems are also related to the IV networks. Traditional centralized approaches are used to deal with limited storage and security issues. Increasing number of vehicles expand the number of links in network and also leads to the intensive data. Lack of coordination of vehicles, reliability of the network and traffic among vehicles are some of the major issues. These issues hinder the performance of the vehicle industry. We propose a consensus based mechanism using blockchain technology to manage the intensive data and authenticate the data of vehicles in the EV industry. This mechanism also ensures data privacy, security and also promotes data immutability. The transactions are stored in distributed ledger to provide facility of transparency. In a nutshell, blockchain technology is incorporated in EVs sector to revolutionize the World.
Deep CNN with Residual Connections and Range Normalization for Clinical Text Classification

Deep learning has achieved remarkable performance in many classification tasks such as image processing and computer vision.Due to its impressive performance, deep learning techniques have found their way into natural language processing tasks as well.Deep learning methods are based on neural network architectures such as CNN (Convolutional Neural Networks) with many layers.Deep learning methods have shown state of-the-art performance on many classification tasks through several research works.It has shown great promise in many NLP (Natural language processing) tasks such as learning text representations.In this paper, we study the possibility of using deep learning methods and techniques in clinical documents classification.We review various deep learning-based techniques and their applications in classifying clinical documents.Further, we identify research challenges and describe our proposed convolutional neural network with residual connections and range normalization.Our proposed model automatically learns and classifies clinical sentences into multi-faceted clinical classes, which can help physicians to navigate patients' medical histories easily.Our propose technique uses sentence embedding and Convolutional Neural Network with residual connections and range normalization.To the best of our knowledge, this is the first time that sentence embedding and deep convolutional neural networks with residual connections and range normalization have been simultaneously applied to text processing.Lastly, this work follows a generalized conclusion on clinical documents classification and references.